biopython-1.63/0000755000175000017500000000000012250104774013056 5ustar tratra00000000000000biopython-1.63/NEWS0000644000175000017500000017743712250104543013572 0ustar tratra00000000000000News for the Biopython Project ============================== This file contains release notes and general news about the Biopython project. See also the DEPRECATED file which tracks the removal of obsolete modules or functions, and online http://biopython.org/wiki/News and http://news.open-bio.org/news/category/obf-projects/biopython/ The latest news is at the top of this file. =================================================================== 4 December 2013: Biopython 1.63 released. This release supports Python 3.3 onwards without conversion via the 2to3 library. See the Biopython 1.63 beta release notes below for details. Since the beta release we have made some minor bug fixes and test improvements. The restriction enzyme list in Bio.Restriction has been updated to the December 2013 release of REBASE. Additional contributors since the beta: Gokcen Eraslan (first contribution) =================================================================== 12 November 2013: Biopython 1.63 beta released. This is a beta release for testing purposes, the main reason for a beta version is the large amount of changes imposed by the removal of the 2to3 library previously required for the support of Python 3.X. This was made possible by dropping Python 2.5 (and Jython 2.5). This release of Biopython supports Python 2.6 and 2.7, and also Python 3.3. The Biopython Tutorial & Cookbook, and the docstring examples in the source code, now use the Python 3 style print function in place of the Python 2 style print statement. This language feature is available under Python 2.6 and 2.7 via: from __future__ import print_function Similarly we now use the Python 3 style built-in next function in place of the Python 2 style iterators' .next() method. This language feature is also available under Python 2.6 and 2.7. Many thanks to the Biopython developers and community for making this release possible, especially the following contributors: Chris Mitchell (first contribution) Christian Brueffer Eric Talevich Josha Inglis (first contribution) Konstantin Tretyakov (first contribution) Lenna Peterson Martin Mokrejs Nigel Delaney (first contribution) Peter Cock Sergei Lebedev (first contribution) Tiago Antao Wayne Decatur (first contribution) Wibowo 'Bow' Arindrarto =================================================================== 28 August 2013: Biopython 1.62 released. This is our first release to officially support Python 3, however it is also our final release supporting Python 2.5. Specifically this release is supported and tested on standard Python 2.5, 2.6, 2.7 and 3.3. It was also tested under Jython 2.5, 2.7 and PyPy 1.9, 2.0. See the Biopython 1.62 beta release notes below for most changes. Since the beta release we have added several minor bug fixes and test improvements. Additional contributors since the beta: Bertrand Néron (first contribution) Lenna Peterson Martin Mokrejs Matsuyuki Shirota (first contribution) =================================================================== 15 July 2013: Biopython 1.62 beta released. This is a beta release for testing purposes, both for new features added, and changes to location parsing, but more importantly Biopython 1.62 will be our first release to officially support Python 3. Specifically we intend Biopython 1.62 to support standard Python 2.5, 2.6, 2.7 and 3.3, but the release will also be tested under Jython 2.5, 2.7 and PyPy 1.9, 2.0 as well. It will be our final release supporting Python 2.5. The translation functions will give a warning on any partial codons (and this will probably become an error in a future release). If you know you are dealing with partial sequences, either pad with N to extend the sequence length to a multiple of three, or explicitly trim the sequence. The handling of joins and related complex features in Genbank/EMBL files has been changed with the introduction of a CompoundLocation object. Previously a SeqFeature for something like a multi-exon CDS would have a child SeqFeature (under the sub_features attribute) for each exon. The sub_features property will still be populated for now, but is deprecated and will in future be removed. Please consult the examples in the help (docstrings) and Tutorial. Thanks to the efforts of Ben Morris, the Phylo module now supports the file formats NeXML and CDAO. The Newick parser is also significantly faster, and can now optionally extract bootstrap values from the Newick comment field (like Molphy and Archaeopteryx do). Nate Sutton added a wrapper for FastTree to Bio.Phylo.Applications. New module Bio.UniProt adds parsers for the GAF, GPA and GPI formats from UniProt-GOA. The BioSQL module is now supported in Jython. MySQL and PostgreSQL databases can be used. The relevant JDBC driver should be available in the CLASSPATH. Feature labels on circular GenomeDiagram figures now support the label_position argument (start, middle or end) in addition to the current default placement, and in a change to prior releases these labels are outside the features which is now consistent with the linear diagrams. The code for parsing 3D structures in mmCIF files was updated to use the Python standard library's shlex module instead of C code using flex. The Bio.Sequencing.Applications module now includes a BWA command line wrapper. Bio.motifs supports JASPAR format files with multiple position-frequence matrices. Additionally there have been other minor bug fixes and more unit tests. Many thanks to the Biopython developers and community for making this release possible, especially the following contributors: Alexander Campbell (first contribution) Andrea Rizzi (first contribution) Anthony Mathelier (first contribution) Ben Morris (first contribution) Brad Chapman Christian Brueffer David Arenillas (first contribution) David Martin (first contribution) Eric Talevich Iddo Friedberg Jian-Long Huang (first contribution) Joao Rodrigues Kai Blin Michiel de Hoon Nate Sutton (first contribution) Peter Cock Petra Kubincová (first contribution) Phillip Garland Saket Choudhary (first contribution) Tiago Antao Wibowo 'Bow' Arindrarto Xabier Bello (first contribution) =================================================================== 5 February 2013: Biopython 1.61 released. GenomeDiagram has three new sigils (shapes to illustrate features). OCTO shows an octagonal shape, like the existing BOX sigil but with the corners cut off. JAGGY shows a box with jagged edges at the start and end, intended for things like NNNNN regions in draft genomes. Finally BIGARROW is like the existing ARROW sigil but is drawn straddling the axis. This is useful for drawing vertically compact figures where you do not have overlapping genes. New module Bio.Graphics.ColorSpiral can generate colors along a spiral path through HSV color space. This can be used to make arbitrary 'rainbow' scales, for example to color features or cross-links on a GenomeDiagram figure. The Bio.SeqIO module now supports reading sequences from PDB files in two different ways. The "pdb-atom" format determines the sequence as it appears in the structure based on the atom coordinate section of the file (via Bio.PDB, so NumPy is currently required for this). Alternatively, you can use the "pdb-seqres" format to read the complete protein sequence as it is listed in the PDB header, if available. The Bio.SeqUtils module how has a seq1 function to turn a sequence using three letter amino acid codes into one using the more common one letter codes. This acts as the inverse of the existing seq3 function. The multiple-sequence-alignment object used by Bio.AlignIO etc now supports an annotation dictionary. Additional support for per-column annotation is planned, with addition and splicing to work like that for the SeqRecord per-letter annotation. A new warning, Bio.BiopythonExperimentalWarning, has been introduced. This marks any experimental code included in the otherwise stable release. Such 'beta' level code is ready for wider testing, but still likely to change and should only be tried by early adopters to give feedback via the biopython-dev mailing list. We'd expect such experimental code to reach stable status in one or two releases time, at which point our normal policies about trying to preserve backwards compatibility would apply. See also the README file. This release also includes Bow's Google Summer of Code work writing a unified parsing framework for NCBI BLAST (assorted formats including tabular and XML), HMMER, BLAT, and other sequence searching tools. This is currently available with the new BiopythonExperimentalWarning to indicate that this is still somewhat experimental. We're bundling it with the main release to get more public feedback, but with the big warning that the API is likely to change. In fact, even the current name of Bio.SearchIO may change since unless you are familiar with BioPerl its purpose isn't immediately clear. The Bio.Motif module has been updated and reorganized. To allow for a clean deprecation of the old code, the new motif code is stored in a new module Bio.motifs, and a PendingDeprecationWarning was added to Bio.Motif. Additionally there have been other minor bug fixes and more unit tests. Finally, we are phasing out support for Python 2.5. We will continue support for at least one further release (Biopython 1.62). This could be extended given feedback from our users (or if the Jython 2.7 release is delayed, since the current stable release Jython 2.5 implemented Python 2.5 only). Focusing on Python 2.6 and 2.7 only will make writing Python 3 compatible code easier. Many thanks to the Biopython developers and community for making this release possible, especially the following contributors: Brandon Invergo Bryan Lunt (first contribution) Christian Brueffer (first contribution) David Cain Eric Talevich Grace Yeo (first contribution) Jeffrey Chang Jingping Li (first contribution) Kai Blin (first contribution) Leighton Pritchard Lenna Peterson Lucas Sinclair (first contribution) Michiel de Hoon Nick Semenkovich (first contribution) Peter Cock Robert Ernst (first contribution) Tiago Antao Wibowo 'Bow' Arindrarto =================================================================== 25 June 2012: Biopython 1.60 released. New module Bio.bgzf supports reading and writing BGZF files (Blocked GNU Zip Format), a variant of GZIP with efficient random access, most commonly used as part of the BAM file format. This uses Python's zlib library internally, and provides a simple interface like Python's gzip library. Using this the Bio.SeqIO indexing functions now support BGZF compressed sequence files. The GenBank/EMBL parser will now give a warning on unrecognised feature locations and continue parsing (leaving the feature's location as None). Previously it would abort with an exception, which was often unhelpful. The Bio.PDB.MMCIFParser is now compiled by default (but is still not available under Jython, PyPy or Python 3). The SFF parser in Bio.SeqIO now decodes Roche 454 'universal accession number' 14 character read names, which encode the timestamp of the run, the region the read came from, and the location of the well. In the Phylo module, the "draw" function for plotting tree objects has become much more flexible, with improved support for matplotlib conventions and new parameters for specifying branch and taxon labels. Writing in the PhyloXML format has been updated to more closely match the output of other programs. A wrapper for the program RAxML has been added under Bio.Phylo.Applications, alongside the existing wrapper for PhyML. Additionally there have been other minor bug fixes and more unit tests. Many thanks to the Biopython developers and community for making this release possible, especially the following contributors: Brandon Invergo Eric Talevich Jeff Hussmann (first contribution) John Comeau (first contribution) Kamil Slowikowski (first contribution) Kevin Jacobs Lenna Peterson (first contribution) Matt Fenwick (first contribution) Peter Cock Paul T. Bathen Wibowo Arindrarto =================================================================== 24 February 2012: Biopython 1.59 released. Please note that this release will *not* work on Python 2.4 (while the recent releases have worked despite us not officially supporting this). The position objects used in Bio.SeqFeature now act almost like integers, making dealing with fuzzy locations in EMBL/GenBank files much easier. Note as part of this work, the arguments to create fuzzy positions OneOfPosition and WithinPosition have changed in a non-backwards compatible way. The SeqFeature's strand and any database reference are now properties of the FeatureLocation object (a more logical placement), with proxy methods for backwards compatibility. As part of this change, if you print a location object it will now display any strand and database reference information. The installation setup.py now supports 'install_requires' when setuptools is installed. This avoids the manual dialog when installing Biopython via easy_install or pip and numpy is not installed. It also allows user libraries that require Biopython to include it in their install_requires and get automatical installation of dependencies. Bio.Graphics.BasicChromosome has been extended to allow simple sub-features to be drawn on chromosome segments, suitable to show the position of genes, SNPs or other loci. Note Bio.Graphics requires the ReportLab library. Bio.Graphics.GenomeDiagram has been extended to allow cross-links between tracks, and track specific start/end positions for showing regions. This can be used to imitate the output from the Artemis Comparison Tool (ACT). Also, a new attribute circle_core makes it easier to have an empty space in the middle of a circular diagram (see tutorial). Bio.Align.Applications now includes a wrapper for command line tool Clustal Omega for protein multiple sequence alignment. Bio.AlignIO now supports sequential PHYLIP files (as well as interlaced PHYLIP files) as a separate format variant. New module Bio.TogoWS offers a wrapper for the TogoWS REST API, a web service based in Japan offering access to KEGG, DDBJ, PDBj, CBRC plus access to some NCBI, EBI resources including PubMed, GenBank and UniProt. This is much easier to use than the NCBI Entrez API, but should be especially useful for Biopython users based in Asia. Bio.Entrez function efetch has been updated to handle the NCBI's stricter handling of multiple ID arguments in EFetch 2.0, however the NCBI have also changed the retmode default argument so you may need to make this explicit. e.g. retmode="text" Additionally there have been other minor bug fixes and more unit tests. Many thanks to the Biopython developers and community for making this release possible, especially the following contributors: Andreas Wilm (first contribution) Alessio Papini (first contribution) Brad Chapman Brandon Invergo Connor McCoy Eric Talevich João Rodrigues Konrad Förstner (first contribution) Michiel de Hoon Matej Repič (first contribution) Leighton Pritchard Peter Cock =================================================================== 18 August 2011: Biopython 1.58 released. A new interface and parsers for the PAML (Phylogenetic Analysis by Maximum Likelihood) package of programs, supporting codeml, baseml and yn00 as well as a Python re-implementation of chi2 was added as the Bio.Phylo.PAML module. Bio.SeqIO now includes read and write support for the SeqXML, a simple XML format offering basic annotation support. See Schmitt et al (2011) in Briefings in Bioinformatics, http://dx.doi.org/10.1093/bib/bbr025 Bio.SeqIO now includes read support for ABI files ("Sanger" capillary sequencing trace files, containing called sequence with PHRED qualities). The Bio.AlignIO "fasta-m10" parser was updated to cope with the >>><<< lines as used in Bill Pearson's FASTA version 3.36, without this fix the parser would only return alignments for the first query sequence. The Bio.AlignIO "phylip" parser and writer now treat a dot/period in the sequence as an error, in line with the official PHYLIP specification. Older versions of our code didn't do anything special with this character. Also, support for "phylip-relaxed" has been added which allows longer record names as used in RAxML and PHYML. Of potential interest to anyone subclassing Biopython objects, any remaining "old style" Python classes have been switched to "new style" classes. This allows things like defining properties. Bio.HMM's Viterbi algorithm now expects the initial probabilities explicitly. Many thanks to the Biopython developers and community for making this release possible, especially the following contributors: Aaron Gallagher (first contribution) Bartek Wilczynski Bogdan T. (first contribution) Brandon Invergo (first contribution) Connor McCoy (first contribution) David Cain (first contribution) Eric Talevich Fábio Madeira (first contribution) Hongbo Zhu Joao Rodrigues Michiel de Hoon Peter Cock Thomas Schmitt (first contribution) Tiago Antao Walter Gillett Wibowo Arindrarto (first contribution) =================================================================== 2 April 2011: Biopython 1.57 released. Bio.SeqIO now includes an index_db() function which extends the existing indexing functionality to allow indexing many files, and more importantly this keeps the index on disk in a simple SQLite3 database rather than in memory in a Python dictionary. Bio.Blast.Applications now includes a wrapper for the BLAST+ blast_formatter tool from NCBI BLAST 2.2.24+ or later. This release of BLAST+ added the ability to run the BLAST tools and save the output as ASN.1 format, and then convert this to any other supported BLAST output format (plain text, tabular, XML, or HTML) with the blast_formatter tool. The wrappers were also updated to include new arguments added in BLAST 2.2.25+ such as -db_hard_mask. The SeqRecord object now has a reverse_complement method (similar to that of the Seq object). This is most useful to reversing per-letter-annotation (such as quality scores from FASTQ) or features (such as annotation from GenBank). Bio.SeqIO.write's QUAL output has been sped up, and Bio.SeqIO.convert now uses an optimised routine for FASTQ to QUAL making this much faster. Biopython can now be installed with pip. Thanks to David Koppstein and James Casbon for reporting the problem. Bio.SeqIO.write now uses lower case for the sequence for GenBank, EMBL and IMGT output. The Bio.PDB module received several fixes and improvements, including starting to merge João's work from GSoC 2010; consequently Atom objects now know their element type and IUPAC mass. (The new features that use these attributes won't be included in Biopython until the next release, though, so stay tuned.) The nodetype hierarchy in the Bio.SCOP.Cla.Record class is now a dictionary (previously it was a list of key,value tuples) to better match the standard. Many thanks to the Biopython developers and community for making this release possible, especially the following contributors: Brad Chapman Eric Talevich Erick Matsen (first contribution) Hongbo Zhu Jeffrey Finkelstein (first contribution) Joanna & Dominik Kasprzak (first contribution) Joao Rodrigues Kristian Rother Leighton Pritchard Michiel de Hoon Peter Cock Peter Thorpe (first contribution) Phillip Garland Walter Gillett (first contribution) =================================================================== 26 November 2010: Biopython 1.56 released. This is planned to be our last release to support Python 2.4, however this could be delayed given immediate feedback from our users (e.g. if this proves to be a problem in combination with other libraries or a popular Linux distribution). Bio.SeqIO can now read and index UniProt XML files (under format name "uniprot-xml", which was agreed with EMBOSS and BioPerl for when/if they support it too). Bio.SeqIO can now read, write and index IMGT files. These are a variant of the EMBL sequence text file format with longer feature indentation. Bio.SeqIO now supports protein EMBL files (used in the EMBL patents database file epo_prt.dat) - previously we only expected nucleotide EMBL files. The Bio.Seq translation methods and function will now accept an arbitrary CodonTable object (for those of you working on very unusual organisms). The SeqFeature object now supports len(feature) giving the length consistent with the existing extract method. Also, it now supports iteration giving the coordinate (with respect to the parent sequence) of each letter within the feature (in frame aware order), and "in" which allows you to check if a (parent based) coordinate is within the feature location. Bio.Entrez will now try to download any missing NCBI DTD files and cache them in the user's home directory. The provisional database schema for BioSQL support on SQLite which Biopython has been using since Release 1.53 has now been added to BioSQL, and updated slightly. Bio.PopGen.FDist now supports the DFDist command line tool as well as FDist2. Bio.Motif now has a chapter in the Tutorial. (At least) 13 people have contributed to this release, including 6 new people: Andrea Pierleoni (first contribution) Bart de Koning (first contribution) Bartek Wilczynski Bartosz Telenczuk (first contribution) Cymon Cox Eric Talevich Frank Kauff Michiel de Hoon Peter Cock Phillip Garland (first contribution) Siong Kong (first contribution) Tiago Antao Uri Laserson (first contribution) =================================================================== 31 August 2010: Biopython 1.55 released. See the notes below for the Biopython 1.55 beta release for changes since Biopython 1.54 was released. Since the beta release we have marked a few modules as obsolete or deprecated, and removed some deprecated code. There have also been a few bug fixes, extra unit tests, and documentation improvements. (At least) 12 people have contributed to this release, including 6 new people: Andres Colubri (first contribution) Carlos Rios Vera (first contribution) Claude Paroz (first contribution) Cymon Cox Eric Talevich Frank Kauff Joao Rodrigues (first contribution) Konstantin Okonechnikov (first contribution) Michiel de Hoon Nathan Edwards (first contribution) Peter Cock Tiago Antao =================================================================== 18 August 2010: Biopython 1.55 beta released. This is a beta release for testing purposes, both for new features added, and more importantly updates to avoid code deprecated in Python 2.7 or in Python 3. This is an important step towards Python 3 support. We are phasing out support for Python 2.4. We will continue to support it for at least one further release (Biopython 1.56). This could be delayed given feedback from our users (e.g. if this proves to be a problem in combination with other libraries or a popular Linux distribution). The SeqRecord object now has upper and lower methods (like the Seq object and Python strings), which return a new SeqRecord with the sequence in upper or lower case and a copy of all the annotation unchanged. Several small issues with Bio.PDB have been resolved, which includes better handling of model numbers, and files missing the element column. Feature location parsing for GenBank and EMBL files has been rewritten, making the parser much faster. Ace parsing by SeqIO now uses zero rather than None for the quality score of any gaps (insertions) in the contig sequence. The BioSQL classes DBServer and BioSeqDatabase now act more like Python dictionaries, making it easier to count, delete, iterate over, or check for membership of namespaces and records. The command line tool application wrapper classes are now executable, so you can use them to call the tool (using the subprocess module internally) and capture the output and any error messages as strings (stdout and stderr). This avoids having to worry about the details of how best to use subprocess. (At least) 10 people have contributed to this release, including 5 new people: Andres Colubri (first contribution) Carlos Rios Vera (first contribution) Claude Paroz (first contribution) Eric Talevich Frank Kauff Joao Rodrigues (first contribution) Konstantin Okonechnikov (first contribution) Michiel de Hoon Peter Cock Tiago Antao =================================================================== May 20, 2010: Biopython 1.54 released. See the notes below for the Biopython 1.54 beta release for changes since Biopython 1.53 was released. Since then there have been some changes to the new Bio.Phylo module, more documentation, and a number of smaller bug fixes. =================================================================== April 2, 2010: Biopython 1.54 beta released. We are phasing out support for Python 2.4. We will continue to support it for at least two further releases, and at least one year (whichever takes longer), before dropping support for Python 2.4. This could be delayed given feedback from our users (e.g. if this proves to be a problem in combination with other libraries or a popular Linux distribution). New module Bio.Phylo includes support for reading, writing and working with phylogenetic trees from Newick, Nexus and phyloXML files. This was work by Eric Talevich on a Google Summer of Code 2009 project, under The National Evolutionary Synthesis Center (NESCent), mentored by Brad Chapman and Christian Zmasek. Bio.Entrez includes some more DTD files, in particular eLink_090910.dtd, needed for our NCBI Entrez Utilities XML parser. The parse, read and write functions in Bio.SeqIO and Bio.AlignIO will now accept filenames as well as handles. This follows a general shift from other Python libraries, and does make usage a little simpler. Also the write functions will now accept a single SeqRecord or alignment. Bio.SeqIO now supports writing EMBL files (DNA and RNA sequences only). The dictionary-like objects from Bio.SeqIO.index() now support a get_raw method for most file formats, giving you the original unparsed data from the file as a string. This is useful for selecting a subset of records from a file where Bio.SeqIO.write() does not support the file format (e.g. the "swiss" format) or where you need to exactly preserve the original layout. Based on code from Jose Blanca (author of sff_extract), Bio.SeqIO now supports reading, indexing and writing Standard Flowgram Format (SFF) files which are used by 454 Life Sciences (Roche) sequencers. This means you can use SeqIO to convert from SFF to FASTQ, FASTA and QUAL (as trimmed or untrimmed reads). An improved multiple sequence alignment object has been introduced, and is used by Bio.AlignIO for input. This is a little stricter than the old class but should otherwise be backwards compatible. (At least) 11 people contributed to this release, including 5 new people: Anne Pajon (first contribution) Brad Chapman Christian Zmasek Diana Jaunzeikare (first contribution) Eric Talevich Jose Blanca (first contribution) Kevin Jacobs (first contribution) Leighton Pritchard Michiel de Hoon Peter Cock Thomas Holder (first contribution) =================================================================== December 15, 2009: Biopython 1.53 released. Biopython is now using git for source code control, currently on github. Our old CVS repository will remain on the OBF servers in the short/medium term as a backup, but will not be updated in future. The Bio.Blast.Applications wrappers now covers the new NCBI BLAST C++ tools (where blastall is replaced by blastp, blastn, etc, and the command line switches have all been renamed). These will be replacing the old wrappers in Bio.Blast.NCBIStandalone which are now obsolete, and will be deprecated in our next release. The plain text BLAST parser has been updated, and should cope with recent versions of NCBI BLAST, including the new C++ based version. Nevertheless, we (and the NCBI) still recommend using the XML output for parsing. The Seq (and related UnknownSeq) objects gained upper and lower methods, like the string methods of the same name but alphabet aware. The Seq object also gained a new ungap method for removing gap characters in an alphabet aware manner. The SeqFeature object now has an extract method, used with the parent sequence (as a string or Seq object) to get the region of that sequence described by the feature's location information (including the strand and any sub-features for a join). As an example, this is useful to get the nucleotide sequence for features in GenBank or EMBL files. SeqRecord objects now support addition, giving a new SeqRecord with the combined sequence, all the SeqFeatures, and any common annotation. Bio.Entrez includes the new (Jan 2010) DTD files from the NCBI for parsing MedLine/PubMed data. The NCBI codon tables have been updated from version 3.4 to 3.9, which adds a few extra start codons, and a few new tables (Tables 16, 21, 22 and 23). Note that Table 14 which used to be called "Flatworm Mitochondrial" is now called "Alternative Flatworm Mitochondrial", and "Flatworm Mitochondrial" is now an alias for Table 9 ("Echinoderm Mitochondrial"). The restriction enzyme list in Bio.Restriction has been updated to the Nov 2009 release of REBASE. The Bio.PDB parser and output code has been updated to understand the element column in ATOM and HETATM lines (based on patches contributed by Hongbo Zhu and Frederik Gwinner). Bio.PDB.PDBList has also been updated for recent changes to the PDB FTP site (Paul T. Bathen). SQLite support was added for BioSQL databases (Brad Chapman), allowing access to BioSQL through a lightweight embedded SQL engine. Python 2.5+ includes support for SQLite built in, but on Python 2.4 the optional sqlite3 library must be installed to use this. We currently use a draft BioSQL on SQLite schema, which will be merged with the main BioSQL release for use in other projects. Support for running Biopython under Jython (using the Java Virtual Machine) has been much improved thanks to input from Kyle Ellrott. Note that Jython does not support C code - this means NumPy isn't available, and nor are a selection of Biopython modules (including Bio.Cluster, Bio.PDB and BioSQL). Also, currently Jython does not parse DTD files, which means the XML parser in Bio.Entrez won't work. However, most of the Biopython modules seem fine from testing Jython 2.5.0 and 2.5.1. (At least) 12 people contributed to this release, including 3 first timers: Bartek Wilczynski Brad Chapman Chris Lasher Cymon Cox Frank Kauff Frederik Gwinner (first contribution) Hongbo Zhu (first contribution) Kyle Ellrott Leighton Pritchard Michiel de Hoon Paul Bathen (first contribution) Peter Cock =================================================================== September 22, 2009: Biopython 1.52 released. The Population Genetics module now allows the calculation of several tests, and statistical estimators via a wrapper to GenePop. Supported are tests for Hardy-Weinberg equilibrium, linkage disequilibrium and estimates for various F statistics (Cockerham and Wier Fst and Fis, Robertson and Hill Fis, etc), null allele frequencies and number of migrants among many others. Isolation By Distance (IBD) functionality is also supported. New helper functions Bio.SeqIO.convert() and Bio.AlignIO.convert() allow an easier way to use Biopython for simple file format conversions. Additionally, these new functions allow Biopython to offer important file format specific optimisations (e.g. FASTQ to FASTA, and interconverting FASTQ variants). New function Bio.SeqIO.index() allows indexing of most sequence file formats (but not alignment file formats), allowing dictionary like random access to all the entries in the file as SeqRecord objects, keyed on the record id. This is especially useful for very large sequencing files, where all the records cannot be held in memory at once. This supplements the more flexible but memory demanding Bio.SeqIO.to_dict() function. Bio.SeqIO can now write "phd" format files (used by PHRED, PHRAD and CONSED), allowing interconversion with FASTQ files, or FASTA+QUAL files. Bio.Emboss.Applications now includes wrappers for the "new" PHYLIP EMBASSY package (e.g. fneighbor) which replace the "old" PHYLIP EMBASSY package (e.g. eneighbor) whose Biopython wrappers are now obsolete. See also the DEPRECATED file, as several old deprecated modules have finally been removed (e.g. Bio.EUtils which had been replaced by Bio.Entrez). On a technical note, this will be the last release using CVS for source code control. Biopython is moving from CVS to git. =================================================================== August 17, 2009: Biopython 1.51 released. FASTQ support in Bio.SeqIO has been improved, extended and sped up since Biopython 1.50. Support for Illumina 1.3+ style FASTQ files was added in the 1.51 beta release. Furthermore, we now follow the interpretation agreed on the OBF mailing lists with EMBOSS, BioPerl, BioJava and BioRuby for inter- conversion and the valid score range for each FASTQ variant. This means Solexa FASTQ scores can be from -5 to 62 (format name "fastq-solexa" in Bio.SeqIO), Illumina 1.3+ FASTQ files have PHRED scores from 0 to 62 (format name "fastq-illumina"), and Sanger FASTQ files have PHRED scores from 0 to 93 (format name "fastq" or "fastq-sanger"). Bio.Sequencing.Phd has been updated, for example to cope with missing peak positions. The "phd" support in Bio.SeqIO has also been updated to record the PHRED qualities (and peak positions) in the SeqRecord's per-letter annotation. This allows conversion of PHD files into FASTQ or QUAL which may be useful for meta-assembly. See the notes below for the Biopython 1.50 beta release for changes since Biopython 1.49 was released. This includes dropping support for Python 2.3, removing our deprecated parsing infrastructure (Martel and Bio.Mindy), and hence removing any dependence on mxTextTools. Additionally, since the beta, a number of small bugs have been fixed, and there have been further additions to the test suite and documentation. =================================================================== June 23, 2009: Biopython 1.51 beta released. Biopython no longer supports Python 2.3. Currently we support Python 2.4, 2.5 and 2.6. Our deprecated parsing infrastructure (Martel and Bio.Mindy) has been removed. This means Biopython no longer has any dependence on mxTextTools. A few cosmetic issues in GenomeDiagram with arrow sigils and labels on circular diagrams have been fixed. Bio.SeqIO will now write GenBank files with the feature table (previously omitted), and a couple of obscure errors parsing ambiguous locations have been fixed. Bio.SeqIO can now read and write Illumina 1.3+ style FASTQ files (which use PHRED quality scores with an ASCII offset of 64) under the format name "fastq-illumina". Biopython 1.50 supported just "fastq" (the original Sanger style FASTQ files using PHRED scores with an ASCII offset of 33), and "fastq-solexa" (the original Solexa/Illumina FASTQ format variant holding Solexa scores with an ASCII offset of 64) . For parsing the "swiss" format, Bio.SeqIO now uses the new Bio.SwissProt parser, making it about twice as fast as in Biopython 1.50, where the older now deprecated Bio.SwissProt.SProt was used. There should be no functional differences as a result of this change. Our command line wrapper objects have been updated to support accessing parameters via python properties, and setting of parameters at initiation with keyword arguments. Additionally Cymon Cox has contributed several new multiple alignment wrappers under Bio.Align.Applications. A few more issues with Biopython's BioSQL support have been fixed (mostly by Cymon Cox). In particular, the default PostgreSQL schema includes some rules intended for BioPerl support only, which were causing problems in Biopython (see BioSQL bug 2839). There have also been additions to the tutorial, such as the new alignment wrappers, with a whole chapter for the SeqRecord object. We have also added to the unit test coverage. =================================================================== April 20, 2009: Biopython 1.50 released. See the notes below for the Biopython 1.50 beta release for more details, but the highlights are: * The SeqRecord supports slicing and per-letter-annotation * Bio.SeqIO can read and write FASTQ and QUAL files * Bio.Seq now has an UnknownSeq object * GenomeDiagram has been integrated into Biopython * New module Bio.Motif will later replace Bio.AlignAce and Bio.MEME * This will be the final release to support Python 2.3 * This will be the final release with Martel and Bio.Mindy Since the 1.50 beta release: * The NCBI's Entrez EFetch no longer supports rettype="genbank" and "gb" (or "gp") should be used instead. * Bio.SeqIO now supports "gb" as an alias for "genbank". * The Seq object now has string-like startswith and endswith methods * Bio.Blast.NCBIXML now has a read function for single record files * A few more unit tests were added * More documentation =================================================================== April 3, 2009: Biopython 1.50 beta released. The SeqRecord object has a new dictionary attribute, letter_annotations, which is for holding per-letter-annotation information like sequence quality scores or secondary structure predictions. As part of this work, the SeqRecord object can now be sliced to give a new SeqRecord covering just part of the sequence. This will slice the per-letter-annotation to match, and will also include any SeqFeature objects as appropriate. Bio.SeqIO can now read and write FASTQ and QUAL quality files using PHRED quality scores (Sanger style, also used for Roche 454 sequencing), and FASTQ files using Solexa/Illumina quality scores. The Bio.Seq module now has an UnknownSeq object, used for when we have a sequence of known length, but unknown content. This is used in parsing GenBank and EMBL files where the sequence may not be present (e.g. for a contig record) and when parsing QUAL files (which don't have the sequence) GenomeDiagram by Leighton Pritchard has been integrated into Biopython as the Bio.Graphics.GenomeDiagram module If you use this code, please cite the publication Pritchard et al. (2006), Bioinformatics 22 616-617. Note that like Bio.Graphics, this requires the ReportLab python library. A new module Bio.Motif has been added, which is intended to replace the existing Bio.AlignAce and Bio.MEME modules. The set of NCBI DTD files included with Bio.Entrez has been updated with the revised files the NCBI introduced on 1 Jan 2009. Minor fix to BioSQL for retrieving references and comments. Bio.SwissProt has a new faster parser which will be replacing the older slower code in Bio.SwissProt.SProt (which we expect to deprecate in the next release). We've also made some changes to our test framework, which is now given a whole chapter in the tutorial. This intended to help new developers or contributors wanting to improve our unit test coverage. =================================================================== November 21, 2008: Biopython 1.49 released. See the notes below for the Biopython 1.49 beta release for more details, but the highlights are: * Biopython has transitioned from Numeric to NumPy * Martel and Bio.Mindy are now deprecated Since the 1.49 beta release: * A couple of NumPy issues have been resolved * Further small improvements to BioSQL * Bio.PopGen.SimCoal should now work on Windows * A few more unit tests were added =================================================================== November 7, 2008: Biopython 1.49 beta released. Biopython has transitioned from Numeric to NumPy. Please move to NumPy. A number of small changes have been made to support Python 2.6 (mostly avoiding deprecated functionality), and further small changes have been made for better compatibility with Python 3 (this work is still ongoing). However, we intend to support Python 2.3 for only a couple more releases. As part of the Numeric to NumPy migration, Bio.KDTree has been rewritten in C instead of C++ which therefore simplifies building Biopython from source. Martel and Bio.Mindy are now considered to be deprecated, meaning mxTextTools is no longer required to use Biopython. See the DEPRECATED file for details of other deprecations. The Seq object now supports more string like methods (gaining find, rfind, split, rsplit, strip, lstrip and rstrip in addition to previously supported methods like count). Also, biological methods transcribe, back_transcribe and translate have been added, joining the pre-existing reverse_complement and complement methods. Together these changes allow a more object orientated programming style using the Seq object. The behaviour of the Bio.Seq module's translate function has changed so that ambiguous codons which could be a stop codon like "TAN" or "NNN" are now translated as "X" (consistent with EMBOSS and BioPerl - Biopython previously raised an exception), and a bug was fixed so that invalid codons (like "A-T") now raise an exception (previously these were translated as stop codons). BioSQL had a few bugs fixed, and can now optionally fetch the NCBI taxonomy on demand when loading sequences (via Bio.Entrez) allowing you to populate the taxon/taxon_name tables gradually. This has been tested in combination with the BioSQL load_ncbi_taxonomy.pl script used to populate or update the taxon/taxon_name tables. BioSQL should also now work with the psycopg2 driver for PostgreSQL as well as the older psycopg driver. The PDB and PopGen sections of the Tutorial have been promoted to full chapters, and a new chapter has been added on supervised learning methods like logistic regression. The "Cookbook" section now has a few graphical examples using Biopython to calculate sequence properties, and matplotlib (pylab) to plot them. The input functions in Bio.SeqIO and Bio.AlignIO now accept an optional argument to specify the expected sequence alphabet. The somewhat quirky unit test GUI has been removed, the unit tests are now run via the command line by default. =================================================================== September 8, 2008: Biopython 1.48 released. The SeqRecord and Alignment objects have a new method to format the object as a string in a requested file format (handled via Bio.SeqIO and Bio.AlignIO). Additional file formats supported in Bio.SeqIO and Bio.AlignIO: - reading and writing "tab" format (simple tab separated) - writing "nexus" files. - reading "pir" files (NBRF/PIR) - basic support for writing "genbank" files (GenBank plain text) Fixed some problems reading Clustal alignments (introduced in Biopython 1.46 when consolidating Bio.AlignIO and Bio.Clustalw). Updates to the Bio.Sequencing parsers. Bio.PubMed and the online code in Bio.GenBank are now considered obsolete, and we intend to deprecate them after the next release. For accessing PubMed and GenBank, please use Bio.Entrez instead. Bio.Fasta is now considered to be obsolete, please use Bio.SeqIO instead. We do intend to deprecate this module eventually, however, for several years this was the primary FASTA parsing module in Biopython and is likely to be in use in many existing scripts. Martel and Bio.Mindy are now considered to be obsolete, and are likely to be deprecated and removed in a future release. In addition a number of other modules have been deprecated, including: Bio.MetaTool, Bio.EUtils, Bio.Saf, Bio.NBRF, and Bio.IntelliGenetics See the DEPRECATED file for full details. =================================================================== July 5, 2008: Biopython 1.47 released. Improved handling of ambiguous nucleotides in Bio.Seq.Translate(). Better handling of stop codons in the alphabet from a translation. Fixed some codon tables (problem introduced in Biopython 1.46). Updated Nexus file handling. Fixed a bug in Bio.Cluster potentially causing segfaults in the single-linkage hierarchical clustering library. Added some DTDs to be able to parse EFetch results from the nucleotide database. Added IntelliGenetics/MASE parsing to Bio.SeqIO (as the "ig" format). =================================================================== June 29, 2008: Biopython 1.46 released. Bio.Entrez now has several Entrez format XML parsers, and a chapter in the tutorial. Addition of new Bio.AlignIO module for working with sequence alignments in the style introduced with Bio.SeqIO in recent releases, with a whole chapter in the tutorial. A problem parsing certain EMBL files was fixed. Several minor fixes were made to the NCBI BLAST XML parser, including support for the online version 2.2.18+ introduced in May 2008. The NCBIWWW.qblast() function now allows other programs (blastx, tblastn, tblastx) in addition to just blastn and blastp. Bio.EUtils has been updated to explicitly enforce the NCBI's rule of at most one query every 3 seconds, rather than assuming the user would obey this. Iterators in Bio.Medline, Bio.SCOP, Bio.Prosite, Bio.Prosite.Prodoc, Bio.SwissProt, and others to make them more generally usable. Phylip export added to Bio.Nexus. Improved handling of ambiguous nucleotides and stop codons in Bio.Seq.Translate (plus introduced a regression fixed in Biopython 1.47). =================================================================== March 22, 2008: Biopython 1.45 released. The Seq and MutableSeq objects act more like python strings, in particular str(object) now returns the full sequence as a plain string. The existing tostring() method is preserved for backwards compatibility. BioSQL has had some bugs fixed, and has an additional unit test which loads records into a database using Bio.SeqIO and then checks the records can be retrieved correctly. The DBSeq and DBSeqRecord classes now subclass the Seq and SeqRecord classes, which provides more functionality. The modules under Bio.WWW are being deprecated. Functionality in Bio.WWW.NCBI, Bio.WWW.SCOP, Bio.WWW.InterPro and Bio.WWW.ExPASy is now available from Bio.Entrez, Bio.SCOP, Bio.InterPro and Bio.ExPASy instead. Bio.Entrez was used to fix a nasty bug in Bio.GenBank. Tiago Antao has included more functionality in the Population Genetics module, Bio.PopGen. The Bio.Cluster module has been updated to be more consistent with other Biopython code. The tutorial has been updated, including devoting a whole chapter to Swiss-Prot, Prosite, Prodoc, and ExPASy. There is also a new chapter on Bio.Entrez. Bio.biblio was deprecated. =================================================================== October 28, 2007: Biopython 1.44 released. NOTE: This release includes some rather drastic code changes, which were necessary to get Biopython to work with the new release of mxTextTools. The (reverse)complement functions in Bio.Seq support ambiguous nucleotides. Bio.Kabat, which was previously deprecated, is now removed from Biopython. Bio.MarkupEditor was deprecated, as it does not appear to have any users. Bio.Blast.NCBI.qblast() updated with more URL options, thanks to a patch from Chang Soon Ong. Several fixes to the Blast parser. The deprecated Bio.Blast.NCBIWWW functions blast and blasturl were removed. The standalone Blast functions blastall, blastpgp now create XML output by default. Bio.SeqIO.FASTA and Bio.SeqIO.generic have been deprecated in favour of the new Bio.SeqIO module. Bio.FormatIO has been removed (a gradual deprecation was not possible). Please look at Bio.SeqIO for sequence input/output instead. Fix for a bug in Bio.Cluster, which caused kcluster() to hang on some platforms. Bio.expressions has been deprecated. Bio.SeqUtils.CheckSum created, including new methods from Sebastian Bassi, and functions crc32 and crc64 which were moved from Bio/crc.py. Bio.crc is now deprecated. Bio.lcc was updated and moved to Bio.SeqUtils.lcc. Bio.SwissProt parser updated to cope with recent file format updates. Bio.Fasta, Bio.KEGG and Bio.Geo updated to pure python parsers which don't rely on Martel. Numerous fixes in the Genbank parser. Several fixes in Bio.Nexus. Bio.MultiProc and Bio.Medline.NLMMedlineXML were deprecating, as they failed on some platforms, and seemed to have no users. Deprecated concurrent behavior in Bio.config.DBRegistry and timeouts in Bio.dbdefs.swissprot, which relies on Bio.MultiProc. Tiago Antao has started work on a Population Genetics module, Bio.PopGen Updates to the tutorial, including giving Bio.Seq and Bio.SeqIO a whole chapter each. =================================================================== March 17, 2007: Biopython 1.43 released. New Bio.SeqIO module for reading and writing biological sequence files in various formats, based on SeqRecord objects. This includes a new fasta parser which is much faster than Bio.Fasta, particularly for larger files. Easier to use, too. Various improvements in Bio.SeqRecord. Running Blast using Bio.Blast.NCBIStandalone now generates output in XML format by default. The new function Bio.Blast.NCBIXML.parse can parse multiple Blast records in XML format. Bio.Cluster no longer uses ranlib, but uses its own random number generator instead. Some modifications to make Bio.Cluster more compatible with the new NumPy (we're not quite there yet though). New Bio.UniGene parser. Numerous improvements in Bio.PDB. Bug fixes in Bio.SwissProt, BioSQL, Bio.Nexus, and other modules. Faster parsing of large GenBank files. New EMBL parser under Bio.GenBank and also integrated into (new) Bio.SeqIO Compilation of KDTree (C++ code) is optional (setup.py asks the user if it should be compiled). For the Windows installer, C++ code is now included. Nominating Bio.Kabat for removal. Believe it or not, even the documentation was updated. =================================================================== July 16, 2006: Biopython 1.42 released. Bio.GenBank: New parser by Peter, which doesn't rely on Martel. Numerous updates in Bio.Nexus and Bio.Geo. Bio.Cluster became (somewhat) object-oriented. Lots of bug fixes, and updates to the documentation. =================================================================== October 28, 2005: Biopython 1.41 released. Major changes: NEW: Bio.MEME -- thanks to Jason Hackney Added transcribe, translate, and reverse_complement functions to Bio.Seq that work both on Seq objects and plain strings. Major code optimization in cpairwise2module. CompareACE support added to AlignAce. Updates to Blast parsers in Bio.Blast, in particular use of the XML parser in NCBIXML contributed by Bertrand Frottier, and the BLAT parser by Yair Benita. Pairwise single-linkage hierarchical clustering in Bio.Cluster became much faster and memory-efficient, allowing clustering of large data sets. Bio.Emboss: Added command lines for einverted and palindrome. Bio.Nexus: Added support for StringIO objects. Numerous updates in Bio.PDB. Lots of fixes in the documentation. March 29, 2005: MEME parser added. Thanks to Jason Hackney =================================================================== Feb 18, 2005: Biopython 1.40 beta Major Changes since v1.30. For a full list of changes please see the CVS IMPORTANT: Biopython now works with Python version >= 2.3 NEW: Bio.Nexus -- thanks to Frank Kauff Bio.Nexus is a Nexus file parser. Nexus is a common format for phylogenetic trees. NEW: CAPS module -- Thanks to Jonathan Taylor NEW: Restriction enzyme package contributed by Frederic Sohm. This includes classes for manipulating enzymes, updating from Rebase, as well as documentation and Tests. CHANGED: Bio.PDB -- thanks to Thomas Hamelryck Added atom serial number. Epydoc style documentation. Added secondary structure support (through DSSP) Added Accessible Surface Area support (through DSSP) Added Residue Depth support (through MSMS) Added Half Sphere Exposure. Added Fragment classification of the protein backbone (see Kolodny et al., JMB, 2002) Corrected problem on Windows with PDBList (thanks to Matt Dimmic) Added StructureAlignment module to superimpose structures based on a FASTA sequence alignment. Various additions to Polypeptide. Various bug corrections in Vector. Lots of smaller bug corrections and additional features CHANGED: MutableSeq -- thanks to Michiel De Hoon Added the functions 'complement' and 'reverse_complement' to Bio.Seq's Seq and MutableSeq objects. Similar functions previous existed in various locations in BioPython: - forward_complement, reverse_complement in Bio.GFF.easy - complement, antiparallel in Bio.SeqUtils These functions have now been deprecated, and will generate a DeprecationWarning when used. The functions complement and reverse_complement, when applied to a Seq object, will return a new Seq object. The same function applied to a MutableSeq object will modify the MutableSeq object itself, and don't return anything. =================================================================== May 14, 2004: Biopython 1.30 Affy package added for dealing with Affymetrix cel files -- thanks to Harry Zuzan. Added code for parsing Blast XML output -- thanks to Bertrand Frottier. Added code for parsing Compass output -- thanks to James Casbon. New melting temperature calculation module -- thanks to Sebastian Bassi. Added lowess function for non-parameteric regression -- thanks to Michiel. Reduced protein alphabet supported added -- thanks to Iddo. Added documentation for Logistic Regression and Bio.PDB -- thanks to Michiel and Thomas. Documentation added for converting between file formats. Updates to install documentation for non-root users -- thanks to Jakob Fredslund. epydoc now used for automatic generation of documentation. Fasta parser updated to use Martel for parsing and indexing, allowing better speed and dealing with large data files. Updated to Registry code. Now 'from Bio import db' gives you a number of new retrieval options, including embl, fasta, genbak, interpro, prodoc and swissprot. GenBank parser uses new Martel format. GenBank retrieval now uses EUtils instead of the old non-working entrez scripts. GenBank indexing uses standard Mindy indexing. Fix for valueless qualifiers in feature keys -- thanks to Leighton Pritchard. Numerous updated to Bio.PDB modules -- thanks to Thomas. PDB can now parse headers -- thanks to Kristian Rother. Updates to the Ace parser -- thanks to Frank Kauff and Leighton Pritchard. Added pgdb (PyGreSQL) support to BioSQL -- thanks to Marc Colosimo. Fix problems with using py2exe and Biopython -- thanks to Michael Cariaso. PSIBlast parser fixes -- thanks to Jer-Yee John Chuang and James Casbon. Fix to NCBIWWW retrieval so that HTML results are returned correctly. Fix to Clustalw to handle question marks in title names -- thanks to Ashleigh Smythe. Fix to NBRF parsing to it accepts files produced by Clustalw -- thanks to Ashleigh Smythe. Fixes to the Enyzme module -- thanks to Marc Colosimo. Fix for bugs in SeqUtils -- thanks to Frank Kauff. Fix for optional hsps in ncbiblast Martel format -- thanks to Heiko. Fix to Fasta parsing to allow # comment lines -- thanks to Karl Diedrich. Updates to the C clustering library -- thanks to Michiel. Fixes for breakage in the SCOP module and addition of regression tests to framework -- thanks to Gavin. Various fixes to Bio.Wise -- thanks to Michael. Fix for bug in FastaReader -- thanks to Micheal. Fix EUtils bug where efetch would only return 500 sequences. Updates for Emboss commandlines, water and tranalign. Fixes to the FormatIO system of file conversion. C++ code (KDTree, Affy) now compiled by default on most platforms -- thanks to Michael for some nice distutils hacks and many people for testing. Deprecated Bio.sequtils -- use Bio.SeqUtils instead. Deprecated Bio.SVM -- use libsvm instead. Deprecated Bio.kMeans and Bio.xkMeans -- use Bio.cluster instead. Deprecated RecordFile -- doesn't appear to be finished code. Feb 16, 2004: Biopython 1.24 New parsers for Phred and Ace format files -- thanks to Frank Kauff New Code for dealing with NMR data -- thanks to Bob Bussell New SeqUtils modules for codon usage, isoelectric points and other protein properties -- thanks to Yair Benita New code for dealing with Wise contributed by Michael EZ-Retrieve sequence retrieval now supported thanks to Jeff Bio.Cluster updated along with documentation by Michiel BioSQL fixed so it now works with the current SQL schema -- thanks to Yves Bastide for patches Patches to Bio/__init__ to make it compatible with py2exe -- thanks to Leighton Pritchard Added __iter__ to all Biopython Iterators to make them Python 2.2 compatible Fixes to NCBIWWW for retrieving from NCBI -- thanks to Chris Wroe Retrieval of multiple alignment objects from BLAST records -- thanks to James Casbon Fixes to GenBank format for new tags by Peter Parsing fixes in clustalw parsed -- thanks to Greg Singer and Iddo Fasta Indexes can have a specified filename -- thanks to Chunlei Wu Fix to Prosite parser -- thanks to Mike Liang Fix in GenBank parsing -- mRNAs now get strand information Oct 18, 2003: Biopython 1.23 Fixed distribution of files in Bio/Cluster Now distributing Bio/KDTree/_KDTree.swig.C minor updates in installation code added mmCIF support for PDB files Oct 9, 2003: Biopython 1.22 Added Peter Slicker's patches for speeding up modules under Python 2.3 Fixed Martel installation. Does not install Bio.Cluster without Numeric. Distribute EUtils DTDs. Yves Bastide patched NCBIStandalone.Iterator to be Python 2.0 iterator Ashleigh's string coersion fixes in Clustalw. Yair Benita added precision to the protein molecular weights. Bartek updated AlignAce.Parser and added Motif.sim method bug fixes in Michiel De Hoon's clustering library Iddo's bug fixes to Bio.Enzyme and new RecordConsumer Guido Draheim added patches for fixing import path to xbb scripts regression tests updated to be Python 2.3 compatible GenBank.NCBIDictionary is smarter about guessing the format Jul 28, 2003: Biopython 1.21 Martel added back into the released package new AlignACE module by Bartek Wilczynski Andreas Kuntzagk fix for GenBank Iterator on empty files Jul 27, 2003: Biopython 1.20 added Andrew Dalke's EUtils library added Michiel de Hoon's gene expression analysis package updates to setup code, now smarter about dependencies updates to test suite, now smarter about code that is imported Michael Hoffman's fixes to DocSQL syntax fixes in triemodule.c to compile on SGI, Python 2.1 compatible updates in NCBIStandalone, short query error Sebastian Bassi submitted code to calculate LCC complexity Greg Kettler's NCBIStandalone fix for long query lengths slew of miscellaneous fixes from George Paci miscellaneous cleanups and updates from Andreas Kuntzagk Peter Bienstman's fixes to Genbank code -- now parses whole database Kayte Lindner's LocusLink package miscellaneous speedups and code cleanup in ParserSupport by Brad Chapman miscellaneous BLAST fixes and updates Iddo added new code to parse BLAST table output format Karl Diedrich's patch to read T_Coffee files Larry Heisler's fix for primer3 output Bio.Medline now uses proper iterator objects copen now handles SIGTERM correctly small bugfixes and updates in Thomas Hamelryck's PDB package bugfixes and updates to SeqIO.FASTA reader updates to Registry system, conforms to 2003 hackathon OBDA spec Yu Huang patch to support tblastn in wublast expression Dec 17, 2002: Biopython 1.10 Python requirement bumped up to 2.2 hierarchy reorg, many things moved upwards into Bio namespace pairwise2 replaces fastpairwise and pairwise removed deprecated Sequence.py package minor bug fix in File.SGMLStripper added Scripts/debug/debug_blast_parser.py to diagnose blast parsing errors IPI supported by SwissProt/SProt.py parser large speedup for kmeans new registry framework for generic access to databases and parsers small bug fix in stringfns.split scripts that access NCBI moved over to new EUtils system new crc module biblio.py supports the EBI Bibliographic database new CDD parser new Ndb parser new ECell parser new Geo parser access to GFF databases new KDTree data structure new LocusLink parser new MarkovModel algorithm new Saf parser miscellaneous sequence handling functions in sequtils new SVDSuperimpose algorithm Dec 18, 2001: Biopython1.00a4 minor bug fix in NCBIStandalone.blastall optimization in dynamic programming code new modules for logistic regression and maximum entropy minor bug fix in ParserSupport minor bug fixes in SCOP package minor updates in the kMeans cluster selection code minor bug fixes in SubsMat code support for XML-formatted MEDLINE files added MultiProc.run to simplify splitting code across processors listfns.items now supports lists with unhashable items new data type for pathways new support for intelligenetics format new support for metatool format new support for NBRF format new support for generalized launching of applications new support for genetic algorithms minor bug fixes in GenBank parsing new support for Primer in the Emboss package new support for chromosome graphics new support for HMMs new support for NeuralNetwork slew of Martel fixes (see Martel docs) Sept 3, 2001: Biopython1.00a3 added package to support KEGG added sequtils module for computations on sequences added pairwise sequence alignment algorithm major bug fixes in UndoHandle format updates in PubMed Tk interface to kMeans clustering July 5, 2001: Biopython1.00a2 deprecated old regression testing frameworks deprecated Sequence.py Swiss-Prot parser bug fixes GenBank parser bug fixes Can now output GenBank format can now download many sequences at a time from GenBank kMeans clustering algorithm Kabat format now supported FSSP format now supported more functionality for alignment code SubsMat bug fixes and updates fixed memory leak in listfns bug fixes Martel bundled and part of the install procedure Medline.Parser bug fixes PubMed.download_many handles broken IDs better Mar 3, 2001: Biopython 1.00a1 Refactoring of modules. X/X.py moved to X/__init__.py. Can search sequences for Prosite patterns at ExPASy Can do BLAST searches against stable URL at NCBI Prosite Pattern bug fixes GenBank parser Complete Seq and SeqFeatures framework distutils cleanup compile warning cleanups support for UniGene code for working with substitution matrices Tools.MultiProc package for rudimentary multiprocessing stuff Nov 10, 2000: Biopython 0.90d04 Added support for multiple alignments, ClustalW BLAST updates, bug fixes, and BlastErrorParser Fixes for PSI-BLAST in master-slave mode Minor update in stringfns, split separators can be negated Added download_many function to PubMed xbbtools updates Prodoc parser now accepts a copyright at the end of a record Swiss-Prot parser now handles taxonomy ID tag Sept 6, 2000: Biopython 0.90d03 Blast updates: - bug fixes in NCBIStandalone, NCBIWWW - some __str__ methods in Record.py implemented (incomplete) Tests - new BLAST regression tests - prosite tests fixed New parsers for Rebase, Gobase pure python implementation of C-based tools Thomas Sicheritz-Ponten's xbbtools can now generate documentation from docstrings using HappyDoc Aug17-18, 2000: Bioinformatics Open Source Conference 2000 We had a very good Birds-of-a-Feather meeting: http://www.biopython.org/pipermail/biopython/2000-August/000360.html Aug 2, 2000: Biopython 0.90d02 is released. Blast updates: - now works with v2.0.14 - HSP.identities and HSP.positives now tuples - HSP.gaps added SCOP updates: - Lin.Iterator now works with release 50 Starting a tutorial New regression tests for Prodoc July 6, 2000: Biopython 0.90d01 is released. February 8, 2000: Anonymous CVS made available. August 1999 Biopython project founded. Call for Participation sent out to relevant mailing lists, news groups. The Biopython Project (http://www.biopython.org/) is a new open collaborative effort to develop freely available Python libraries and applications that address the needs of current and future work in bioinformatics, including sequence analysis, structural biology, pathways, expression data, etc. When available, the source code will be released as open source (http://www.biopython.org/License.shtml) under terms similar to Python. This is a Call for Participation for interested people to join the project. We are hoping to attract people from a diverse set of backgrounds to help with code development, site maintenance, scientific discussion, etc. This project is open to everyone. If you're interested, please visit the web page, join the biopython mailing list, and let us know what you think! Jeffrey Chang Andrew Dalke biopython-1.63/setup.py0000644000175000017500000003031712250104543014566 0ustar tratra00000000000000"""Distutils based setup script for Biopython. This uses Distutils (http://python.org/sigs/distutils-sig/) the standard python mechanism for installing packages. For the easiest installation just type the command: python setup.py install For more in-depth instructions, see the installation section of the Biopython manual, linked to from: http://biopython.org/wiki/Documentation Or for more details about the options available from distutils, look at the 'Installing Python Modules' distutils documentation, available from: http://python.org/sigs/distutils-sig/doc/ Or, if all else fails, feel free to write to the sign up to the Biopython mailing list and ask for help. See: http://biopython.org/wiki/Mailing_lists """ from __future__ import print_function import sys import os import shutil from distutils.core import setup from distutils.core import Command from distutils.command.install import install from distutils.command.build_py import build_py from distutils.command.build_ext import build_ext from distutils.extension import Extension _CHECKED = None def is_pypy(): import platform try: if platform.python_implementation() == 'PyPy': return True except AttributeError: #New in Python 2.6, not in Jython yet either pass return False def is_ironpython(): return sys.platform == "cli" #TODO - Use platform as in Pypy test? def get_yes_or_no(question, default): if default: option_str = "(Y/n)" default_str = 'y' else: option_str = "(y/N)" default_str = 'n' while True: print("%s %s:" % (question, option_str)) if sys.version_info[0] == 3: response = input().lower() else: response = raw_input().lower() if not response: response = default_str if response[0] in ['y', 'n']: break print("Please answer y or n.") return response[0] == 'y' # Make sure we have the right Python version. if sys.version_info[:2] < (2, 6): print("Biopython requires Python 2.6 or 2.7 (or Python 3.3 or later). " "Python %d.%d detected" % sys.version_info[:2]) sys.exit(1) elif sys.version_info[0] == 3 and sys.version_info[:2] < (3, 3): print("Biopython requires Python 3.3 or later (or Python 2.6 or 2.7). " "Python %d.%d detected" % sys.version_info[:2]) sys.exit(1) def check_dependencies_once(): # Call check_dependencies, but cache the result for subsequent # calls. global _CHECKED if _CHECKED is None: _CHECKED = check_dependencies() return _CHECKED def is_automated(): """Check for installation with easy_install or pip. """ is_automated = False # easy_install: --dist-dir option passed try: dist_dir_i = sys.argv.index("--dist-dir") except ValueError: dist_dir_i = None if dist_dir_i is not None: dist_dir = sys.argv[dist_dir_i + 1] if "egg-dist-tmp" in dist_dir: is_automated = True # pip -- calls from python directly with "-c" if sys.argv in [["-c", "develop", "--no-deps"], ["--no-deps", "-c", "develop"], ["-c", "egg_info"]] \ or "pip-egg-info" in sys.argv \ or sys.argv[:3] == ["-c", "install", "--record"] \ or sys.argv[:4] == ['-c', 'install', '--single-version-externally-managed', '--record']: is_automated = True return is_automated def check_dependencies(): """Return whether the installation should continue.""" # There should be some way for the user to tell specify not to # check dependencies. For example, it probably should not if # the user specified "-q". However, I'm not sure where # distutils stores that information. Also, install has a # --force option that gets saved in self.user_options. It # means overwrite previous installations. If the user has # forced an installation, should we also ignore dependencies? # We only check for NumPy, as this is a compile time dependency if is_Numpy_installed(): return True if is_automated(): return True # For automated builds go ahead with installed packages if os.name == 'java': return True # NumPy is not avaliable for Jython (for now) if is_pypy(): return True # Full NumPy not available for PyPy (for now) if is_ironpython(): return True # We're ignoring NumPy under IronPython (for now) print(""" Numerical Python (NumPy) is not installed. This package is required for many Biopython features. Please install it before you install Biopython. You can install Biopython anyway, but anything dependent on NumPy will not work. If you do this, and later install NumPy, you should then re-install Biopython. You can find NumPy at http://www.numpy.org """) # exit automatically if running as part of some script # (e.g. PyPM, ActiveState's Python Package Manager) if not sys.stdout.isatty(): sys.exit(-1) # We can ask the user return get_yes_or_no("Do you want to continue this installation?", False) class install_biopython(install): """Override the standard install to check for dependencies. This will just run the normal install, and then print warning messages if packages are missing. """ # Adds support for the single-version-externally-managed flag # which is present in setuptools but not distutils. pip requires it. # In setuptools this forces installation the "old way" which we # only support here, so we just make it a no-op. user_options = install.user_options + [ ('single-version-externally-managed', None, "used by system package builders to create 'flat' eggs"), ] boolean_options = install.boolean_options + [ 'single-version-externally-managed', ] def initialize_options(self): install.initialize_options(self) self.single_version_externally_managed = None def run(self): if check_dependencies_once(): # Run the normal install. install.run(self) class build_py_biopython(build_py): def run(self): if not check_dependencies_once(): return # Add software that requires Numpy to be installed. if is_Numpy_installed(): self.packages.extend(NUMPY_PACKAGES) build_py.run(self) class build_ext_biopython(build_ext): def run(self): if not check_dependencies_once(): return build_ext.run(self) class test_biopython(Command): """Run all of the tests for the package. This is a automatic test run class to make distutils kind of act like perl. With this you can do: python setup.py build python setup.py install python setup.py test """ description = "Automatically run the test suite for Biopython." user_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): this_dir = os.getcwd() # change to the test dir and run the tests os.chdir("Tests") sys.path.insert(0, '') import run_tests run_tests.main([]) # change back to the current directory os.chdir(this_dir) def can_import(module_name): """can_import(module_name) -> module or None""" try: return __import__(module_name) except ImportError: return None def is_Numpy_installed(): if is_pypy(): return False return bool(can_import("numpy")) # --- set up the packages we are going to install # standard biopython packages PACKAGES = [ 'Bio', 'Bio.Align', 'Bio.Align.Applications', 'Bio.AlignIO', 'Bio.Alphabet', 'Bio.Application', 'Bio.Blast', 'Bio.CAPS', 'Bio.Compass', 'Bio.Crystal', 'Bio.Data', 'Bio.Emboss', 'Bio.Entrez', 'Bio.ExPASy', 'Bio.FSSP', 'Bio.GA', 'Bio.GA.Crossover', 'Bio.GA.Mutation', 'Bio.GA.Repair', 'Bio.GA.Selection', 'Bio.GenBank', 'Bio.Geo', 'Bio.Graphics', 'Bio.Graphics.GenomeDiagram', 'Bio.HMM', 'Bio.KEGG', 'Bio.KEGG.Compound', 'Bio.KEGG.Enzyme', 'Bio.KEGG.Map', 'Bio.Medline', 'Bio.Motif', 'Bio.Motif.Parsers', 'Bio.Motif.Applications', 'Bio.motifs', 'Bio.motifs.applications', 'Bio.motifs.jaspar', 'Bio.NeuralNetwork', 'Bio.NeuralNetwork.BackPropagation', 'Bio.NeuralNetwork.Gene', 'Bio.Nexus', 'Bio.NMR', 'Bio.Pathway', 'Bio.Pathway.Rep', 'Bio.PDB', 'Bio.PopGen', 'Bio.PopGen.Async', 'Bio.PopGen.FDist', 'Bio.PopGen.GenePop', 'Bio.PopGen.SimCoal', 'Bio.Restriction', 'Bio.SCOP', 'Bio.SearchIO', 'Bio.SearchIO._model', 'Bio.SearchIO.BlastIO', 'Bio.SearchIO.HmmerIO', 'Bio.SearchIO.ExonerateIO', 'Bio.SeqIO', 'Bio.SeqUtils', 'Bio.Sequencing', 'Bio.Sequencing.Applications', 'Bio.Statistics', 'Bio.SubsMat', 'Bio.SVDSuperimposer', 'Bio.SwissProt', 'Bio.TogoWS', 'Bio.Phylo', 'Bio.Phylo.Applications', 'Bio.Phylo.PAML', 'Bio.UniGene', 'Bio.UniProt', 'Bio.Wise', 'Bio._py3k', #Other top level packages, 'BioSQL', ] # packages that require Numeric Python NUMPY_PACKAGES = [ 'Bio.Affy', 'Bio.Cluster', 'Bio.KDTree', ] if os.name == 'java': # Jython doesn't support C extensions EXTENSIONS = [] elif is_pypy() or is_ironpython(): # Skip C extensions for now EXTENSIONS = [] else: EXTENSIONS = [ Extension('Bio.cpairwise2', ['Bio/cpairwise2module.c'], ), Extension('Bio.trie', ['Bio/triemodule.c', 'Bio/trie.c'], include_dirs=["Bio"] ), Extension('Bio.Nexus.cnexus', ['Bio/Nexus/cnexus.c'] ), ] #Add extensions that requires NumPy to build if is_Numpy_installed(): import numpy numpy_include_dir = numpy.get_include() EXTENSIONS.append( Extension('Bio.Cluster.cluster', ['Bio/Cluster/clustermodule.c', 'Bio/Cluster/cluster.c'], include_dirs=[numpy_include_dir], )) EXTENSIONS.append( Extension('Bio.KDTree._CKDTree', ["Bio/KDTree/KDTree.c", "Bio/KDTree/KDTreemodule.c"], include_dirs=[numpy_include_dir], )) EXTENSIONS.append( Extension('Bio.Motif._pwm', ["Bio/Motif/_pwm.c"], include_dirs=[numpy_include_dir], )) EXTENSIONS.append( Extension('Bio.motifs._pwm', ["Bio/motifs/_pwm.c"], include_dirs=[numpy_include_dir], )) #We now define the Biopython version number in Bio/__init__.py #Here we can't use "import Bio" then "Bio.__version__" as that would #tell us the version of Biopython already installed (if any). __version__ = "Undefined" for line in open('Bio/__init__.py'): if (line.startswith('__version__')): exec(line.strip()) #Simple trick to use the 2to3 converted source under Python 3, #change the current directory before/after running setup. #Note as a side effect there will be a build folder underneath #the python3_source folder. old_path = os.getcwd() try: src_path = python3_source except NameError: src_path = os.path.dirname(os.path.abspath(sys.argv[0])) os.chdir(src_path) sys.path.insert(0, src_path) setup_args = { "name": 'biopython', "version": __version__, "author": 'The Biopython Consortium', "author_email": 'biopython@biopython.org', "url": 'http://www.biopython.org/', "description": 'Freely available tools for computational molecular biology.', "download_url": 'http://biopython.org/DIST/', "cmdclass": { "install": install_biopython, "build_py": build_py_biopython, "build_ext": build_ext_biopython, "test": test_biopython, }, "packages": PACKAGES, "ext_modules": EXTENSIONS, "package_data": { 'Bio.Entrez': ['DTDs/*.dtd', 'DTDs/*.ent', 'DTDs/*.mod'], 'Bio.PopGen': ['SimCoal/data/*.par'], }, } try: setup(**setup_args) finally: del sys.path[0] os.chdir(old_path) biopython-1.63/BioSQL/0000755000175000017500000000000012250104773014146 5ustar tratra00000000000000biopython-1.63/BioSQL/Loader.py0000644000175000017500000013512012250104543015723 0ustar tratra00000000000000# Copyright 2002 by Andrew Dalke. All rights reserved. # Revisions 2007-2009 copyright by Peter Cock. All rights reserved. # Revisions 2008 copyright by Cymon J. Cox. All rights reserved. # This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. # # Note that BioSQL (including the database schema and scripts) is # available and licensed separately. Please consult www.biosql.org """Load biopython objects into a BioSQL database for persistent storage. This code makes it possible to store biopython objects in a relational database and then retrieve them back. You shouldn't use any of the classes in this module directly. Rather, call the load() method on a database object. """ # standard modules from __future__ import print_function from time import gmtime, strftime # biopython from Bio import Alphabet from Bio.SeqUtils.CheckSum import crc64 from Bio import Entrez from Bio.Seq import UnknownSeq from Bio._py3k import _is_int_or_long from Bio._py3k import range from Bio._py3k import basestring class DatabaseLoader: """Object used to load SeqRecord objects into a BioSQL database.""" def __init__(self, adaptor, dbid, fetch_NCBI_taxonomy=False): """Initialize with connection information for the database. Creating a DatabaseLoader object is normally handled via the BioSeqDatabase DBServer object, for example: from BioSQL import BioSeqDatabase server = BioSeqDatabase.open_database(driver="MySQLdb", user="gbrowse", passwd = "biosql", host = "localhost", db="test_biosql") try: db = server["test"] except KeyError: db = server.new_database("test", description="For testing GBrowse") """ self.adaptor = adaptor self.dbid = dbid self.fetch_NCBI_taxonomy = fetch_NCBI_taxonomy def load_seqrecord(self, record): """Load a Biopython SeqRecord into the database. """ bioentry_id = self._load_bioentry_table(record) self._load_bioentry_date(record, bioentry_id) self._load_biosequence(record, bioentry_id) self._load_comment(record, bioentry_id) self._load_dbxrefs(record, bioentry_id) references = record.annotations.get('references', ()) for reference, rank in zip(references, list(range(len(references)))): self._load_reference(reference, rank, bioentry_id) self._load_annotations(record, bioentry_id) for seq_feature_num in range(len(record.features)): seq_feature = record.features[seq_feature_num] self._load_seqfeature(seq_feature, seq_feature_num, bioentry_id) def _get_ontology_id(self, name, definition=None): """Returns the identifier for the named ontology (PRIVATE). This looks through the onotology table for a the given entry name. If it is not found, a row is added for this ontology (using the definition if supplied). In either case, the id corresponding to the provided name is returned, so that you can reference it in another table. """ oids = self.adaptor.execute_and_fetch_col0( "SELECT ontology_id FROM ontology WHERE name = %s", (name,)) if oids: return oids[0] self.adaptor.execute( "INSERT INTO ontology(name, definition) VALUES (%s, %s)", (name, definition)) return self.adaptor.last_id("ontology") def _get_term_id(self, name, ontology_id=None, definition=None, identifier=None): """Get the id that corresponds to a term (PRIVATE). This looks through the term table for a the given term. If it is not found, a new id corresponding to this term is created. In either case, the id corresponding to that term is returned, so that you can reference it in another table. The ontology_id should be used to disambiguate the term. """ # try to get the term id sql = r"SELECT term_id FROM term " \ r"WHERE name = %s" fields = [name] if ontology_id: sql += ' AND ontology_id = %s' fields.append(ontology_id) id_results = self.adaptor.execute_and_fetchall(sql, fields) # something is wrong if len(id_results) > 1: raise ValueError("Multiple term ids for %s: %r" % (name, id_results)) elif len(id_results) == 1: return id_results[0][0] else: sql = r"INSERT INTO term (name, definition," \ r" identifier, ontology_id)" \ r" VALUES (%s, %s, %s, %s)" self.adaptor.execute(sql, (name, definition, identifier, ontology_id)) return self.adaptor.last_id("term") def _add_dbxref(self, dbname, accession, version): """Insert a dbxref and return its id.""" self.adaptor.execute( "INSERT INTO dbxref(dbname, accession, version)" " VALUES (%s, %s, %s)", (dbname, accession, version)) return self.adaptor.last_id("dbxref") def _get_taxon_id(self, record): """Get the taxon id for this record (PRIVATE). record - a SeqRecord object This searches the taxon/taxon_name tables using the NCBI taxon ID, scientific name and common name to find the matching taxon table entry's id. If the species isn't in the taxon table, and we have at least the NCBI taxon ID, scientific name or common name, at least a minimal stub entry is created in the table. Returns the taxon id (database key for the taxon table, not an NCBI taxon ID), or None if the taxonomy information is missing. See also the BioSQL script load_ncbi_taxonomy.pl which will populate and update the taxon/taxon_name tables with the latest information from the NCBI. """ # To find the NCBI taxid, first check for a top level annotation ncbi_taxon_id = None if "ncbi_taxid" in record.annotations: #Could be a list of IDs. if isinstance(record.annotations["ncbi_taxid"], list): if len(record.annotations["ncbi_taxid"]) == 1: ncbi_taxon_id = record.annotations["ncbi_taxid"][0] else: ncbi_taxon_id = record.annotations["ncbi_taxid"] if not ncbi_taxon_id: # Secondly, look for a source feature for f in record.features: if f.type == 'source': quals = getattr(f, 'qualifiers', {}) if "db_xref" in quals: for db_xref in f.qualifiers["db_xref"]: if db_xref.startswith("taxon:"): ncbi_taxon_id = int(db_xref[6:]) break if ncbi_taxon_id: break try: scientific_name = record.annotations["organism"][:255] except KeyError: scientific_name = None try: common_name = record.annotations["source"][:255] except KeyError: common_name = None # Note: The maximum length for taxon names in the schema is 255. # Cropping it now should help in getting a match when searching, # and avoids an error if we try and add these to the database. if ncbi_taxon_id: #Good, we have the NCBI taxon to go on - this is unambiguous :) #Note that the scientific name and common name will only be #used if we have to record a stub entry. return self._get_taxon_id_from_ncbi_taxon_id(ncbi_taxon_id, scientific_name, common_name) if not common_name and not scientific_name: # Nothing to go on... and there is no point adding # a new entry to the database. We'll just leave this # sequence's taxon as a NULL in the database. return None # Next, we'll try to find a match based on the species name # (stored in GenBank files as the organism and/or the source). if scientific_name: taxa = self.adaptor.execute_and_fetch_col0( "SELECT taxon_id FROM taxon_name" " WHERE name_class = 'scientific name' AND name = %s", (scientific_name,)) if taxa: #Good, mapped the scientific name to a taxon table entry return taxa[0] # Last chance... if common_name: taxa = self.adaptor.execute_and_fetch_col0( "SELECT DISTINCT taxon_id FROM taxon_name" " WHERE name = %s", (common_name,)) #Its natural that several distinct taxa will have the same common #name - in which case we can't resolve the taxon uniquely. if len(taxa) > 1: raise ValueError("Taxa: %d species have name %r" % ( len(taxa), common_name)) if taxa: #Good, mapped the common name to a taxon table entry return taxa[0] # At this point, as far as we can tell, this species isn't # in the taxon table already. So we'll have to add it. # We don't have an NCBI taxonomy ID, so if we do record just # a stub entry, there is no simple way to fix this later. # # TODO - Should we try searching the NCBI taxonomy using the # species name? # # OK, let's try inserting the species. # Chances are we don't have enough information ... # Furthermore, it won't be in the hierarchy. lineage = [] for c in record.annotations.get("taxonomy", []): lineage.append([None, None, c]) if lineage: lineage[-1][1] = "genus" lineage.append([None, "species", record.annotations["organism"]]) # XXX do we have them? if "subspecies" in record.annotations: lineage.append([None, "subspecies", record.annotations["subspecies"]]) if "variant" in record.annotations: lineage.append([None, "varietas", record.annotations["variant"]]) lineage[-1][0] = ncbi_taxon_id left_value = self.adaptor.execute_one( "SELECT MAX(left_value) FROM taxon")[0] if not left_value: left_value = 0 left_value += 1 # XXX -- Brad: Fixing this for now in an ugly way because # I am getting overlaps for right_values. I need to dig into this # more to actually understand how it works. I'm not sure it is # actually working right anyhow. right_start_value = self.adaptor.execute_one( "SELECT MAX(right_value) FROM taxon")[0] if not right_start_value: right_start_value = 0 right_value = right_start_value + 2 * len(lineage) - 1 parent_taxon_id = None for taxon in lineage: self.adaptor.execute( "INSERT INTO taxon(parent_taxon_id, ncbi_taxon_id, node_rank," " left_value, right_value)" " VALUES (%s, %s, %s, %s, %s)", (parent_taxon_id, taxon[0], taxon[1], left_value, right_value)) taxon_id = self.adaptor.last_id("taxon") self.adaptor.execute( "INSERT INTO taxon_name(taxon_id, name, name_class)" "VALUES (%s, %s, 'scientific name')", (taxon_id, taxon[2][:255])) #Note the name field is limited to 255, some SwissProt files #have a multi-species name which can be longer. So truncate this. left_value += 1 right_value -= 1 parent_taxon_id = taxon_id if common_name: self.adaptor.execute( "INSERT INTO taxon_name(taxon_id, name, name_class)" "VALUES (%s, %s, 'common name')", ( taxon_id, common_name)) return taxon_id def _fix_name_class(self, entrez_name): """Map Entrez name terms to those used in taxdump (PRIVATE). We need to make this conversion to match the taxon_name.name_class values used by the BioSQL load_ncbi_taxonomy.pl script. e.g. "ScientificName" -> "scientific name", "EquivalentName" -> "equivalent name", "Synonym" -> "synonym", """ #Add any special cases here: # #known = {} #try: # return known[entrez_name] #except KeyError: # pass #Try automatically by adding spaces before each capital def add_space(letter): """Adds a space before a capital letter.""" if letter.isupper(): return " " + letter.lower() else: return letter answer = "".join(add_space(letter) for letter in entrez_name).strip() assert answer == answer.lower() return answer def _get_taxon_id_from_ncbi_taxon_id(self, ncbi_taxon_id, scientific_name=None, common_name=None): """Get the taxon id for this record from the NCBI taxon ID (PRIVATE). ncbi_taxon_id - string containing an NCBI taxon id scientific_name - string, used if a stub entry is recorded common_name - string, used if a stub entry is recorded This searches the taxon table using ONLY the NCBI taxon ID to find the matching taxon table entry's ID (database key). If the species isn't in the taxon table, and the fetch_NCBI_taxonomy flag is true, Biopython will attempt to go online using Bio.Entrez to fetch the official NCBI lineage, recursing up the tree until an existing entry is found in the database or the full lineage has been fetched. Otherwise the NCBI taxon ID, scientific name and common name are recorded as a minimal stub entry in the taxon and taxon_name tables. Any partial information about the lineage from the SeqRecord is NOT recorded. This should mean that (re)running the BioSQL script load_ncbi_taxonomy.pl can fill in the taxonomy lineage. Returns the taxon id (database key for the taxon table, not an NCBI taxon ID). """ assert ncbi_taxon_id taxon_id = self.adaptor.execute_and_fetch_col0( "SELECT taxon_id FROM taxon WHERE ncbi_taxon_id = %s", (int(ncbi_taxon_id),)) if taxon_id: #Good, we have mapped the NCBI taxid to a taxon table entry return taxon_id[0] # At this point, as far as we can tell, this species isn't # in the taxon table already. So we'll have to add it. parent_taxon_id = None rank = "species" genetic_code = None mito_genetic_code = None species_names = [] if scientific_name: species_names.append(("scientific name", scientific_name)) if common_name: species_names.append(("common name", common_name)) if self.fetch_NCBI_taxonomy: #Go online to get the parent taxon ID! handle = Entrez.efetch(db="taxonomy", id=ncbi_taxon_id, retmode="XML") taxonomic_record = Entrez.read(handle) if len(taxonomic_record) == 1: assert taxonomic_record[0]["TaxId"] == str(ncbi_taxon_id), \ "%s versus %s" % (taxonomic_record[0]["TaxId"], ncbi_taxon_id) parent_taxon_id = self._get_taxon_id_from_ncbi_lineage( taxonomic_record[0]["LineageEx"]) rank = taxonomic_record[0]["Rank"] genetic_code = taxonomic_record[0]["GeneticCode"]["GCId"] mito_genetic_code = taxonomic_record[0]["MitoGeneticCode"]["MGCId"] species_names = [("scientific name", taxonomic_record[0]["ScientificName"])] try: for name_class, names in taxonomic_record[0]["OtherNames"].items(): name_class = self._fix_name_class(name_class) if not isinstance(names, list): #The Entrez parser seems to return single entry #lists as just a string which is annoying. names = [names] for name in names: #Want to ignore complex things like ClassCDE entries if isinstance(name, basestring): species_names.append((name_class, name)) except KeyError: #OtherNames isn't always present, #e.g. NCBI taxon 41205, Bromheadia finlaysoniana pass else: pass # If we are not allowed to go online, we will record the bare minimum; # as long as the NCBI taxon id is present, then (re)running # load_ncbi_taxonomy.pl should fill in the taxonomomy lineage # (and update the species names). # # I am NOT going to try and record the lineage, even if it # is in the record annotation as a list of names, as we won't # know the NCBI taxon IDs for these parent nodes. self.adaptor.execute( "INSERT INTO taxon(parent_taxon_id, ncbi_taxon_id, node_rank," " genetic_code, mito_genetic_code, left_value, right_value)" " VALUES (%s, %s, %s, %s, %s, %s, %s)", (parent_taxon_id, ncbi_taxon_id, rank, genetic_code, mito_genetic_code, None, None)) taxon_id = self.adaptor.last_id("taxon") #Record the scientific name, common name, etc for name_class, name in species_names: self.adaptor.execute( "INSERT INTO taxon_name(taxon_id, name, name_class)" " VALUES (%s, %s, %s)", (taxon_id, name[:255], name_class)) return taxon_id def _get_taxon_id_from_ncbi_lineage(self, taxonomic_lineage): """This is recursive! (PRIVATE). taxonomic_lineage - list of taxonomy dictionaries from Bio.Entrez First dictionary in list is the taxonomy root, highest would be the species. Each dictionary includes: - TaxID (string, NCBI taxon id) - Rank (string, e.g. "species", "genus", ..., "phylum", ...) - ScientificName (string) (and that is all at the time of writing) This method will record all the lineage given, returning the taxon id (database key, not NCBI taxon id) of the final entry (the species). """ ncbi_taxon_id = taxonomic_lineage[-1]["TaxId"] #Is this in the database already? Check the taxon table... taxon_id = self.adaptor.execute_and_fetch_col0( "SELECT taxon_id FROM taxon" " WHERE ncbi_taxon_id=%s" % ncbi_taxon_id) if taxon_id: # we could verify that the Scientific Name etc in the database # is the same and update it or print a warning if not... if isinstance(taxon_id, list): assert len(taxon_id) == 1 return taxon_id[0] else: return taxon_id #We have to record this. if len(taxonomic_lineage) > 1: #Use recursion to find out the taxon id (database key) of the parent. parent_taxon_id = self._get_taxon_id_from_ncbi_lineage(taxonomic_lineage[:-1]) assert _is_int_or_long(parent_taxon_id), repr(parent_taxon_id) else: parent_taxon_id = None # INSERT new taxon rank = taxonomic_lineage[-1].get("Rank", None) self.adaptor.execute( "INSERT INTO taxon(ncbi_taxon_id, parent_taxon_id, node_rank)" " VALUES (%s, %s, %s)", (ncbi_taxon_id, parent_taxon_id, rank)) taxon_id = self.adaptor.last_id("taxon") assert isinstance(taxon_id, (int, long)), repr(taxon_id) # ... and its name in taxon_name scientific_name = taxonomic_lineage[-1].get("ScientificName", None) if scientific_name: self.adaptor.execute( "INSERT INTO taxon_name(taxon_id, name, name_class)" " VALUES (%s, %s, 'scientific name')", (taxon_id, scientific_name[:255])) return taxon_id def _load_bioentry_table(self, record): """Fill the bioentry table with sequence information (PRIVATE). record - SeqRecord object to add to the database. """ # get the pertinent info and insert it if record.id.count(".") == 1: # try to get a version from the id #This assumes the string is something like "XXXXXXXX.123" accession, version = record.id.split('.') try: version = int(version) except ValueError: accession = record.id version = 0 else: # otherwise just use a version of 0 accession = record.id version = 0 if "accessions" in record.annotations \ and isinstance(record.annotations["accessions"], list) \ and record.annotations["accessions"]: #Take the first accession (one if there is more than one) accession = record.annotations["accessions"][0] #Find the taxon id (this is not just the NCBI Taxon ID) #NOTE - If the species isn't defined in the taxon table, #a new minimal entry is created. taxon_id = self._get_taxon_id(record) if "gi" in record.annotations: identifier = record.annotations["gi"] else: identifier = record.id #Allow description and division to default to NULL as in BioPerl. description = getattr(record, 'description', None) division = record.annotations.get("data_file_division", None) sql = """ INSERT INTO bioentry ( biodatabase_id, taxon_id, name, accession, identifier, division, description, version) VALUES ( %s, %s, %s, %s, %s, %s, %s, %s)""" #print self.dbid, taxon_id, record.name, accession, identifier, \ # division, description, version self.adaptor.execute(sql, (self.dbid, taxon_id, record.name, accession, identifier, division, description, version)) # now retrieve the id for the bioentry bioentry_id = self.adaptor.last_id('bioentry') return bioentry_id def _load_bioentry_date(self, record, bioentry_id): """Add the effective date of the entry into the database. record - a SeqRecord object with an annotated date bioentry_id - corresponding database identifier """ # dates are GenBank style, like: # 14-SEP-2000 date = record.annotations.get("date", strftime("%d-%b-%Y", gmtime()).upper()) if isinstance(date, list): date = date[0] annotation_tags_id = self._get_ontology_id("Annotation Tags") date_id = self._get_term_id("date_changed", annotation_tags_id) sql = r"INSERT INTO bioentry_qualifier_value" \ r" (bioentry_id, term_id, value, rank)" \ r" VALUES (%s, %s, %s, 1)" self.adaptor.execute(sql, (bioentry_id, date_id, date)) def _load_biosequence(self, record, bioentry_id): """Record a SeqRecord's sequence and alphabet in the database (PRIVATE). record - a SeqRecord object with a seq property bioentry_id - corresponding database identifier """ if record.seq is None: #The biosequence table entry is optional, so if we haven't #got a sequence, we don't need to write to the table. return # determine the string representation of the alphabet if isinstance(record.seq.alphabet, Alphabet.DNAAlphabet): alphabet = "dna" elif isinstance(record.seq.alphabet, Alphabet.RNAAlphabet): alphabet = "rna" elif isinstance(record.seq.alphabet, Alphabet.ProteinAlphabet): alphabet = "protein" else: alphabet = "unknown" if isinstance(record.seq, UnknownSeq): seq_str = None else: seq_str = str(record.seq) sql = r"INSERT INTO biosequence (bioentry_id, version, " \ r"length, seq, alphabet) " \ r"VALUES (%s, 0, %s, %s, %s)" self.adaptor.execute(sql, (bioentry_id, len(record.seq), seq_str, alphabet)) def _load_comment(self, record, bioentry_id): """Record a SeqRecord's annotated comment in the database (PRIVATE). record - a SeqRecord object with an annotated comment bioentry_id - corresponding database identifier """ comments = record.annotations.get('comment') if not comments: return if not isinstance(comments, list): #It should be a string then... comments = [comments] for index, comment in enumerate(comments): comment = comment.replace('\n', ' ') #TODO - Store each line as a separate entry? This would preserve #the newlines, but we should check BioPerl etc to be consistent. sql = "INSERT INTO comment (bioentry_id, comment_text, rank)" \ " VALUES (%s, %s, %s)" self.adaptor.execute(sql, (bioentry_id, comment, index + 1)) def _load_annotations(self, record, bioentry_id): """Record a SeqRecord's misc annotations in the database (PRIVATE). The annotation strings are recorded in the bioentry_qualifier_value table, except for special cases like the reference, comment and taxonomy which are handled with their own tables. record - a SeqRecord object with an annotations dictionary bioentry_id - corresponding database identifier """ mono_sql = "INSERT INTO bioentry_qualifier_value" \ "(bioentry_id, term_id, value)" \ " VALUES (%s, %s, %s)" many_sql = "INSERT INTO bioentry_qualifier_value" \ "(bioentry_id, term_id, value, rank)" \ " VALUES (%s, %s, %s, %s)" tag_ontology_id = self._get_ontology_id('Annotation Tags') for key, value in record.annotations.items(): if key in ["references", "comment", "ncbi_taxid", "date"]: #Handled separately continue term_id = self._get_term_id(key, ontology_id=tag_ontology_id) if isinstance(value, list) or isinstance(value, tuple): rank = 0 for entry in value: if isinstance(entry, str) or isinstance(entry, int): #Easy case rank += 1 self.adaptor.execute(many_sql, (bioentry_id, term_id, str(entry), rank)) else: pass #print "Ignoring annotation '%s' sub-entry of type '%s'" \ # % (key, str(type(entry))) elif isinstance(value, str) or isinstance(value, int): #Have a simple single entry, leave rank as the DB default self.adaptor.execute(mono_sql, (bioentry_id, term_id, str(value))) else: pass #print "Ignoring annotation '%s' entry of type '%s'" \ # % (key, type(value)) def _load_reference(self, reference, rank, bioentry_id): """Record a SeqRecord's annotated references in the database (PRIVATE). record - a SeqRecord object with annotated references bioentry_id - corresponding database identifier """ refs = None if reference.medline_id: refs = self.adaptor.execute_and_fetch_col0( "SELECT reference_id" " FROM reference JOIN dbxref USING (dbxref_id)" " WHERE dbname = 'MEDLINE' AND accession = %s", (reference.medline_id,)) if not refs and reference.pubmed_id: refs = self.adaptor.execute_and_fetch_col0( "SELECT reference_id" " FROM reference JOIN dbxref USING (dbxref_id)" " WHERE dbname = 'PUBMED' AND accession = %s", (reference.pubmed_id,)) if not refs: s = [] for f in reference.authors, reference.title, reference.journal: s.append(f or "") crc = crc64("".join(s)) refs = self.adaptor.execute_and_fetch_col0( "SELECT reference_id FROM reference" r" WHERE crc = %s", (crc,)) if not refs: if reference.medline_id: dbxref_id = self._add_dbxref("MEDLINE", reference.medline_id, 0) elif reference.pubmed_id: dbxref_id = self._add_dbxref("PUBMED", reference.pubmed_id, 0) else: dbxref_id = None authors = reference.authors or None title = reference.title or None #The location/journal field cannot be Null, so default #to an empty string rather than None: journal = reference.journal or "" self.adaptor.execute( "INSERT INTO reference (dbxref_id, location," " title, authors, crc)" " VALUES (%s, %s, %s, %s, %s)", (dbxref_id, journal, title, authors, crc)) reference_id = self.adaptor.last_id("reference") else: reference_id = refs[0] if reference.location: start = 1 + int(str(reference.location[0].start)) end = int(str(reference.location[0].end)) else: start = None end = None sql = "INSERT INTO bioentry_reference (bioentry_id, reference_id," \ " start_pos, end_pos, rank)" \ " VALUES (%s, %s, %s, %s, %s)" self.adaptor.execute(sql, (bioentry_id, reference_id, start, end, rank + 1)) def _load_seqfeature(self, feature, feature_rank, bioentry_id): """Load a biopython SeqFeature into the database (PRIVATE). """ seqfeature_id = self._load_seqfeature_basic(feature.type, feature_rank, bioentry_id) self._load_seqfeature_locations(feature, seqfeature_id) self._load_seqfeature_qualifiers(feature.qualifiers, seqfeature_id) def _load_seqfeature_basic(self, feature_type, feature_rank, bioentry_id): """Load the first tables of a seqfeature and returns the id (PRIVATE). This loads the "key" of the seqfeature (ie. CDS, gene) and the basic seqfeature table itself. """ ontology_id = self._get_ontology_id('SeqFeature Keys') seqfeature_key_id = self._get_term_id(feature_type, ontology_id=ontology_id) # XXX source is always EMBL/GenBank/SwissProt here; it should depend on # the record (how?) source_cat_id = self._get_ontology_id('SeqFeature Sources') source_term_id = self._get_term_id('EMBL/GenBank/SwissProt', ontology_id=source_cat_id) sql = r"INSERT INTO seqfeature (bioentry_id, type_term_id, " \ r"source_term_id, rank) VALUES (%s, %s, %s, %s)" self.adaptor.execute(sql, (bioentry_id, seqfeature_key_id, source_term_id, feature_rank + 1)) seqfeature_id = self.adaptor.last_id('seqfeature') return seqfeature_id def _load_seqfeature_locations(self, feature, seqfeature_id): """Load all of the locations for a SeqFeature into tables (PRIVATE). This adds the locations related to the SeqFeature into the seqfeature_location table. Fuzzies are not handled right now. For a simple location, ie (1..2), we have a single table row with seq_start = 1, seq_end = 2, location_rank = 1. For split locations, ie (1..2, 3..4, 5..6) we would have three row tables with: start = 1, end = 2, rank = 1 start = 3, end = 4, rank = 2 start = 5, end = 6, rank = 3 """ # TODO - Record an ontology for the locations (using location.term_id) # which for now as in BioPerl we leave defaulting to NULL. if feature.location_operator and feature.location_operator != "join": # e.g. order locations... we don't record "order" so it # will become a "join" on reloading. What does BioPerl do? import warnings from Bio import BiopythonWarning warnings.warn("%s location operators are not fully supported" % feature.location_operator, BiopythonWarning) #This will be a list of length one for simple FeatureLocation: parts = feature.location.parts if parts and set(loc.strand for loc in parts)==set([-1]): #To mimic prior behaviour of Biopython+BioSQL, reverse order parts = parts[::-1] #TODO - Check what BioPerl does; see also BioSeq.py code for rank, loc in enumerate(parts): self._insert_location(loc, rank + 1, seqfeature_id) def _insert_location(self, location, rank, seqfeature_id): """Add a location of a SeqFeature to the seqfeature_location table (PRIVATE). TODO - Add location operator to location_qualifier_value? """ # convert biopython locations to the 1-based location system # used in bioSQL # XXX This could also handle fuzzies start = int(location.start) + 1 end = int(location.end) # Biopython uses None when we don't know strand information but # BioSQL requires something (non null) and sets this as zero # So we'll use the strand or 0 if Biopython spits out None strand = location.strand or 0 # TODO - Record an ontology term for the location (location.term_id) # which for now like BioPerl we'll leave as NULL. # This might allow us to record "between" positions properly, but I # doesn't really see how it could work for before/after fuzzy positions loc_term_id = None if location.ref: # sub_feature remote locations when they are in the same db as the current # record do not have a value for ref_db, which the SeqFeature object # stores as None. BioSQL schema requires a varchar and is not NULL dbxref_id = self._get_dbxref_id(location.ref_db or "", location.ref) else: dbxref_id = None sql = r"INSERT INTO location (seqfeature_id, dbxref_id, term_id," \ r"start_pos, end_pos, strand, rank) " \ r"VALUES (%s, %s, %s, %s, %s, %s, %s)" self.adaptor.execute(sql, (seqfeature_id, dbxref_id, loc_term_id, start, end, strand, rank)) """ # See Bug 2677 # TODO - Record the location_operator (e.g. "join" or "order") # using the location_qualifier_value table (which we and BioPerl # have historically left empty). # Note this will need an ontology term for the location qualifer # (location_qualifier_value.term_id) for which oddly the schema # does not allow NULL. if feature.location_operator: #e.g. "join" (common), #or "order" (see Tests/GenBank/protein_refseq2.gb) location_id = self.adaptor.last_id('location') loc_qual_term_id = None # Not allowed in BioSQL v1.0.1 sql = r"INSERT INTO location_qualifier_value" \ r"(location_id, term_id, value)" \ r"VALUES (%s, %s, %s)" self.adaptor.execute(sql, (location_id, loc_qual_term_id, feature.location_operator)) """ def _load_seqfeature_qualifiers(self, qualifiers, seqfeature_id): """Insert the (key, value) pair qualifiers relating to a feature (PRIVATE). Qualifiers should be a dictionary of the form: {key : [value1, value2]} """ tag_ontology_id = self._get_ontology_id('Annotation Tags') for qualifier_key in qualifiers: # Treat db_xref qualifiers differently to sequence annotation # qualifiers by populating the seqfeature_dbxref and dbxref # tables. Other qualifiers go into the seqfeature_qualifier_value # and (if new) term tables. if qualifier_key != 'db_xref': qualifier_key_id = self._get_term_id(qualifier_key, ontology_id=tag_ontology_id) # now add all of the values to their table entries = qualifiers[qualifier_key] if not isinstance(entries, list): # Could be a plain string, or an int or a float. # However, we exect a list of strings here. entries = [entries] for qual_value_rank in range(len(entries)): qualifier_value = entries[qual_value_rank] sql = r"INSERT INTO seqfeature_qualifier_value "\ r" (seqfeature_id, term_id, rank, value) VALUES"\ r" (%s, %s, %s, %s)" self.adaptor.execute(sql, (seqfeature_id, qualifier_key_id, qual_value_rank + 1, qualifier_value)) else: # The dbxref_id qualifier/value sets go into the dbxref table # as dbname, accession, version tuples, with dbxref.dbxref_id # being automatically assigned, and into the seqfeature_dbxref # table as seqfeature_id, dbxref_id, and rank tuples self._load_seqfeature_dbxref(qualifiers[qualifier_key], seqfeature_id) def _load_seqfeature_dbxref(self, dbxrefs, seqfeature_id): """Add database crossreferences of a SeqFeature to the database (PRIVATE). o dbxrefs List, dbxref data from the source file in the format : o seqfeature_id Int, the identifier for the seqfeature in the seqfeature table Insert dbxref qualifier data for a seqfeature into the seqfeature_dbxref and, if required, dbxref tables. The dbxref_id qualifier/value sets go into the dbxref table as dbname, accession, version tuples, with dbxref.dbxref_id being automatically assigned, and into the seqfeature_dbxref table as seqfeature_id, dbxref_id, and rank tuples """ # NOTE - In older versions of Biopython, we would map the GenBank # db_xref "name", for example "GI" to "GeneIndex", and give a warning # for any unknown terms. This was a long term maintainance problem, # and differed from BioPerl and BioJava's implementation. See bug 2405 for rank, value in enumerate(dbxrefs): # Split the DB:accession format string at colons. We have to # account for multiple-line and multiple-accession entries try: dbxref_data = value.replace(' ', '').replace('\n', '').split(':') db = dbxref_data[0] accessions = dbxref_data[1:] except: raise ValueError("Parsing of db_xref failed: '%s'" % value) # Loop over all the grabbed accessions, and attempt to fill the # table for accession in accessions: # Get the dbxref_id value for the dbxref data dbxref_id = self._get_dbxref_id(db, accession) # Insert the seqfeature_dbxref data self._get_seqfeature_dbxref(seqfeature_id, dbxref_id, rank + 1) def _get_dbxref_id(self, db, accession): """ _get_dbxref_id(self, db, accession) -> Int o db String, the name of the external database containing the accession number o accession String, the accession of the dbxref data Finds and returns the dbxref_id for the passed data. The method attempts to find an existing record first, and inserts the data if there is no record. """ # Check for an existing record sql = r'SELECT dbxref_id FROM dbxref WHERE dbname = %s ' \ r'AND accession = %s' dbxref_id = self.adaptor.execute_and_fetch_col0(sql, (db, accession)) # If there was a record, return the dbxref_id, else create the # record and return the created dbxref_id if dbxref_id: return dbxref_id[0] return self._add_dbxref(db, accession, 0) def _get_seqfeature_dbxref(self, seqfeature_id, dbxref_id, rank): """ Check for a pre-existing seqfeature_dbxref entry with the passed seqfeature_id and dbxref_id. If one does not exist, insert new data """ # Check for an existing record sql = r"SELECT seqfeature_id, dbxref_id FROM seqfeature_dbxref " \ r"WHERE seqfeature_id = %s AND dbxref_id = %s" result = self.adaptor.execute_and_fetch_col0(sql, (seqfeature_id, dbxref_id)) # If there was a record, return without executing anything, else create # the record and return if result: return result return self._add_seqfeature_dbxref(seqfeature_id, dbxref_id, rank) def _add_seqfeature_dbxref(self, seqfeature_id, dbxref_id, rank): """ Insert a seqfeature_dbxref row and return the seqfeature_id and dbxref_id """ sql = r'INSERT INTO seqfeature_dbxref ' \ '(seqfeature_id, dbxref_id, rank) VALUES' \ r'(%s, %s, %s)' self.adaptor.execute(sql, (seqfeature_id, dbxref_id, rank)) return (seqfeature_id, dbxref_id) def _load_dbxrefs(self, record, bioentry_id): """Load any sequence level cross references into the database (PRIVATE). See table bioentry_dbxref.""" for rank, value in enumerate(record.dbxrefs): # Split the DB:accession string at first colon. # We have to cope with things like: # "MGD:MGI:892" (db="MGD", accession="MGI:892") # "GO:GO:123" (db="GO", accession="GO:123") # # Annoyingly I have seen the NCBI use both the style # "GO:GO:123" and "GO:123" in different vintages. assert value.count("\n") == 0 try: db, accession = value.split(':', 1) db = db.strip() accession = accession.strip() except: raise ValueError("Parsing of dbxrefs list failed: '%s'" % value) # Get the dbxref_id value for the dbxref data dbxref_id = self._get_dbxref_id(db, accession) # Insert the bioentry_dbxref data self._get_bioentry_dbxref(bioentry_id, dbxref_id, rank + 1) def _get_bioentry_dbxref(self, bioentry_id, dbxref_id, rank): """ Check for a pre-existing bioentry_dbxref entry with the passed seqfeature_id and dbxref_id. If one does not exist, insert new data """ # Check for an existing record sql = r"SELECT bioentry_id, dbxref_id FROM bioentry_dbxref " \ r"WHERE bioentry_id = %s AND dbxref_id = %s" result = self.adaptor.execute_and_fetch_col0(sql, (bioentry_id, dbxref_id)) # If there was a record, return without executing anything, else create # the record and return if result: return result return self._add_bioentry_dbxref(bioentry_id, dbxref_id, rank) def _add_bioentry_dbxref(self, bioentry_id, dbxref_id, rank): """ Insert a bioentry_dbxref row and return the seqfeature_id and dbxref_id """ sql = r'INSERT INTO bioentry_dbxref ' \ '(bioentry_id,dbxref_id,rank) VALUES ' \ '(%s, %s, %s)' self.adaptor.execute(sql, (bioentry_id, dbxref_id, rank)) return (bioentry_id, dbxref_id) class DatabaseRemover: """Complement the Loader functionality by fully removing a database. This probably isn't really useful for normal purposes, since you can just do a: DROP DATABASE db_name and then recreate the database. But, it's really useful for testing purposes. YB: now use the cascaded deletions """ def __init__(self, adaptor, dbid): """Initialize with a database id and adaptor connection. """ self.adaptor = adaptor self.dbid = dbid def remove(self): """Remove everything related to the given database id. """ sql = r"DELETE FROM bioentry WHERE biodatabase_id = %s" self.adaptor.execute(sql, (self.dbid,)) sql = r"DELETE FROM biodatabase WHERE biodatabase_id = %s" self.adaptor.execute(sql, (self.dbid,)) biopython-1.63/BioSQL/__init__.py0000644000175000017500000000105312250104543016251 0ustar tratra00000000000000# Copyright 2002 by Andrew Dalke. All rights reserved. # Revisions 2007-2008 by Peter Cock. # This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. # # Note that BioSQL (including the database schema and scripts) is # available and licensed separately. Please consult www.biosql.org """Code for storing and retrieving biological sequences from a BioSQL relational database. See: http://biopython.org/wiki/BioSQL http://www.biosql.org/ """ biopython-1.63/BioSQL/BioSeq.py0000644000175000017500000005415412250104543015706 0ustar tratra00000000000000# Copyright 2002 by Andrew Dalke. All rights reserved. # Revisions 2007-2009 copyright by Peter Cock. All rights reserved. # Revisions 2008-2009 copyright by Cymon J. Cox. All rights reserved. # This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. # # Note that BioSQL (including the database schema and scripts) is # available and licensed separately. Please consult www.biosql.org """Implementations of Biopython-like Seq objects on top of BioSQL. This allows retrival of items stored in a BioSQL database using a biopython-like SeqRecord and Seq interface. Note: Currently we do not support recording per-letter-annotations (like quality scores) in BioSQL. """ from Bio._py3k import unicode from Bio import Alphabet from Bio.Seq import Seq, UnknownSeq from Bio.SeqRecord import SeqRecord, _RestrictedDict from Bio import SeqFeature class DBSeq(Seq): # This implements the biopython Seq interface def __init__(self, primary_id, adaptor, alphabet, start, length): """Create a new DBSeq object referring to a BioSQL entry. You wouldn't normally create a DBSeq object yourself, this is done for you when retreiving a DBSeqRecord object from the database. """ self.primary_id = primary_id self.adaptor = adaptor self.alphabet = alphabet self._length = length self.start = start def __len__(self): return self._length def __getitem__(self, index): # Seq API requirement #Note since Python 2.0, __getslice__ is deprecated #and __getitem__ is used instead. #See http://docs.python.org/ref/sequence-methods.html if isinstance(index, int): #Return a single letter as a string i = index if i < 0: if -i > self._length: raise IndexError(i) i = i + self._length elif i >= self._length: raise IndexError(i) return self.adaptor.get_subseq_as_string(self.primary_id, self.start + i, self.start + i + 1) if not isinstance(index, slice): raise ValueError("Unexpected index type") #Return the (sub)sequence as another DBSeq or Seq object #(see the Seq obect's __getitem__ method) if index.start is None: i = 0 else: i = index.start if i < 0: #Map to equavilent positive index if -i > self._length: raise IndexError(i) i = i + self._length elif i >= self._length: #Trivial case, should return empty string! i = self._length if index.stop is None: j = self._length else: j = index.stop if j < 0: #Map to equavilent positive index if -j > self._length: raise IndexError(j) j = j + self._length elif j >= self._length: j = self._length if i >= j: #Trivial case, empty string. return Seq("", self.alphabet) elif index.step is None or index.step == 1: #Easy case - can return a DBSeq with the start and end adjusted return self.__class__(self.primary_id, self.adaptor, self.alphabet, self.start + i, j - i) else: #Tricky. Will have to create a Seq object because of the stride full = self.adaptor.get_subseq_as_string(self.primary_id, self.start + i, self.start + j) return Seq(full[::index.step], self.alphabet) def tostring(self): """Returns the full sequence as a python string. Although not formally deprecated, you are now encouraged to use str(my_seq) instead of my_seq.tostring().""" return self.adaptor.get_subseq_as_string(self.primary_id, self.start, self.start + self._length) def __str__(self): """Returns the full sequence as a python string.""" return self.adaptor.get_subseq_as_string(self.primary_id, self.start, self.start + self._length) data = property(tostring, doc="Sequence as string (DEPRECATED)") def toseq(self): """Returns the full sequence as a Seq object.""" #Note - the method name copies that of the MutableSeq object return Seq(str(self), self.alphabet) def __add__(self, other): #Let the Seq object deal with the alphabet issues etc return self.toseq() + other def __radd__(self, other): #Let the Seq object deal with the alphabet issues etc return other + self.toseq() def _retrieve_seq(adaptor, primary_id): #The database schema ensures there will be only one matching #row in the table. #If an UnknownSeq was recorded, seq will be NULL, #but length will be populated. This means length(seq) #will return None. seqs = adaptor.execute_and_fetchall( "SELECT alphabet, length, length(seq) FROM biosequence" " WHERE bioentry_id = %s", (primary_id,)) if not seqs: return assert len(seqs) == 1 moltype, given_length, length = seqs[0] try: length = int(length) given_length = int(length) assert length == given_length have_seq = True except TypeError: assert length is None seqs = adaptor.execute_and_fetchall( "SELECT alphabet, length, seq FROM biosequence" " WHERE bioentry_id = %s", (primary_id,)) assert len(seqs) == 1 moltype, given_length, seq = seqs[0] assert seq is None or seq == "" length = int(given_length) have_seq = False del seq del given_length moltype = moltype.lower() # might be upper case in database #We have no way of knowing if these sequences will use IUPAC #alphabets, and we certainly can't assume they are unambiguous! if moltype == "dna": alphabet = Alphabet.generic_dna elif moltype == "rna": alphabet = Alphabet.generic_rna elif moltype == "protein": alphabet = Alphabet.generic_protein elif moltype == "unknown": #This is used in BioSQL/Loader.py and would happen #for any generic or nucleotide alphabets. alphabet = Alphabet.single_letter_alphabet else: raise AssertionError("Unknown moltype: %s" % moltype) if have_seq: return DBSeq(primary_id, adaptor, alphabet, 0, int(length)) else: return UnknownSeq(length, alphabet) def _retrieve_dbxrefs(adaptor, primary_id): """Retrieve the database cross references for the sequence.""" _dbxrefs = [] dbxrefs = adaptor.execute_and_fetchall( "SELECT dbname, accession, version" " FROM bioentry_dbxref join dbxref using (dbxref_id)" " WHERE bioentry_id = %s" " ORDER BY rank", (primary_id,)) for dbname, accession, version in dbxrefs: if version and version != "0": v = "%s.%s" % (accession, version) else: v = accession _dbxrefs.append("%s:%s" % (dbname, v)) return _dbxrefs def _retrieve_features(adaptor, primary_id): sql = "SELECT seqfeature_id, type.name, rank" \ " FROM seqfeature join term type on (type_term_id = type.term_id)" \ " WHERE bioentry_id = %s" \ " ORDER BY rank" results = adaptor.execute_and_fetchall(sql, (primary_id,)) seq_feature_list = [] for seqfeature_id, seqfeature_type, seqfeature_rank in results: # Get qualifiers [except for db_xref which is stored separately] qvs = adaptor.execute_and_fetchall( "SELECT name, value" " FROM seqfeature_qualifier_value join term using (term_id)" " WHERE seqfeature_id = %s" " ORDER BY rank", (seqfeature_id,)) qualifiers = {} for qv_name, qv_value in qvs: qualifiers.setdefault(qv_name, []).append(qv_value) # Get db_xrefs [special case of qualifiers] qvs = adaptor.execute_and_fetchall( "SELECT dbxref.dbname, dbxref.accession" " FROM dbxref join seqfeature_dbxref using (dbxref_id)" " WHERE seqfeature_dbxref.seqfeature_id = %s" " ORDER BY rank", (seqfeature_id,)) for qv_name, qv_value in qvs: value = "%s:%s" % (qv_name, qv_value) qualifiers.setdefault("db_xref", []).append(value) # Get locations results = adaptor.execute_and_fetchall( "SELECT location_id, start_pos, end_pos, strand" " FROM location" " WHERE seqfeature_id = %s" " ORDER BY rank", (seqfeature_id,)) locations = [] # convert to Python standard form # Convert strand = 0 to strand = None # re: comment in Loader.py: # Biopython uses None when we don't know strand information but # BioSQL requires something (non null) and sets this as zero # So we'll use the strand or 0 if Biopython spits out None for location_id, start, end, strand in results: if start: start -= 1 if strand == 0: strand = None if strand not in (+1, -1, None): raise ValueError("Invalid strand %s found in database for " "seqfeature_id %s" % (strand, seqfeature_id)) if end < start: import warnings from Bio import BiopythonWarning warnings.warn("Inverted location start/end (%i and %i) for " "seqfeature_id %s" % (start, end, seqfeature_id), BiopythonWarning) locations.append((location_id, start, end, strand)) # Get possible remote reference information remote_results = adaptor.execute_and_fetchall( "SELECT location_id, dbname, accession, version" " FROM location join dbxref using (dbxref_id)" " WHERE seqfeature_id = %s", (seqfeature_id,)) lookup = {} for location_id, dbname, accession, version in remote_results: if version and version != "0": v = "%s.%s" % (accession, version) else: v = accession # subfeature remote location db_ref are stored as a empty string when # not present if dbname == "": dbname = None lookup[location_id] = (dbname, v) feature = SeqFeature.SeqFeature(type=seqfeature_type) feature._seqfeature_id = seqfeature_id # Store the key as a private property feature.qualifiers = qualifiers if len(locations) == 0: pass elif len(locations) == 1: location_id, start, end, strand = locations[0] #See Bug 2677, we currently don't record the location_operator #For consistency with older versions Biopython, default to "". feature.location_operator = \ _retrieve_location_qualifier_value(adaptor, location_id) dbname, version = lookup.get(location_id, (None, None)) feature.location = SeqFeature.FeatureLocation(start, end) feature.strand = strand feature.ref_db = dbname feature.ref = version else: sub_features = feature.sub_features assert sub_features == [] for location in locations: location_id, start, end, strand = location dbname, version = lookup.get(location_id, (None, None)) subfeature = SeqFeature.SeqFeature() subfeature.type = seqfeature_type subfeature.location = SeqFeature.FeatureLocation(start, end) #subfeature.location_operator = \ # _retrieve_location_qualifier_value(adaptor, location_id) subfeature.strand = strand subfeature.ref_db = dbname subfeature.ref = version sub_features.append(subfeature) # Locations are in order, but because of remote locations for # sub-features they are not necessarily in numerical order: strands = set(sf.strand for sf in sub_features) if len(strands)==1 and -1 in strands: #Evil hack time for backwards compatibility #TODO - Check if BioPerl and (old) Biopython did the same, #we may have an existing incompatibility lurking here... locs = [f.location for f in sub_features[::-1]] else: #All forward, or mixed strands locs = [f.location for f in sub_features] feature.location = SeqFeature.CompoundLocation(locs, seqfeature_type) #TODO - See Bug 2677 - we don't yet record location_operator, #so for consistency with older versions of Biopython default #to assuming its a join. feature.location_operator = "join" seq_feature_list.append(feature) return seq_feature_list def _retrieve_location_qualifier_value(adaptor, location_id): value = adaptor.execute_and_fetch_col0( "SELECT value FROM location_qualifier_value" " WHERE location_id = %s", (location_id,)) try: return value[0] except IndexError: return "" def _retrieve_annotations(adaptor, primary_id, taxon_id): annotations = {} annotations.update(_retrieve_qualifier_value(adaptor, primary_id)) annotations.update(_retrieve_reference(adaptor, primary_id)) annotations.update(_retrieve_taxon(adaptor, primary_id, taxon_id)) annotations.update(_retrieve_comment(adaptor, primary_id)) # Convert values into strings in cases of unicode from the database. # BioSQL could eventually be expanded to be unicode aware. str_anns = {} for key, val in annotations.items(): if isinstance(val, list): val = [_make_unicode_into_string(x) for x in val] elif isinstance(val, unicode): val = str(val) str_anns[key] = val return str_anns def _make_unicode_into_string(text): if isinstance(text, unicode): return str(text) else: return text def _retrieve_qualifier_value(adaptor, primary_id): qvs = adaptor.execute_and_fetchall( "SELECT name, value" " FROM bioentry_qualifier_value JOIN term USING (term_id)" " WHERE bioentry_id = %s" " ORDER BY rank", (primary_id,)) qualifiers = {} for name, value in qvs: if name == "keyword": name = "keywords" #See handling of "date" in Loader.py elif name == "date_changed": name = "date" elif name == "secondary_accession": name = "accessions" qualifiers.setdefault(name, []).append(value) return qualifiers def _retrieve_reference(adaptor, primary_id): # XXX dbxref_qualifier_value refs = adaptor.execute_and_fetchall( "SELECT start_pos, end_pos, " " location, title, authors," " dbname, accession" " FROM bioentry_reference" " JOIN reference USING (reference_id)" " LEFT JOIN dbxref USING (dbxref_id)" " WHERE bioentry_id = %s" " ORDER BY rank", (primary_id,)) references = [] for start, end, location, title, authors, dbname, accession in refs: reference = SeqFeature.Reference() #If the start/end are missing, reference.location is an empty list if (start is not None) or (end is not None): if start is not None: start -= 1 # python counting reference.location = [SeqFeature.FeatureLocation(start, end)] #Don't replace the default "" with None. if authors: reference.authors = authors if title: reference.title = title reference.journal = location if dbname == 'PUBMED': reference.pubmed_id = accession elif dbname == 'MEDLINE': reference.medline_id = accession references.append(reference) if references: return {'references': references} else: return {} def _retrieve_taxon(adaptor, primary_id, taxon_id): a = {} common_names = adaptor.execute_and_fetch_col0( "SELECT name FROM taxon_name WHERE taxon_id = %s" " AND name_class = 'genbank common name'", (taxon_id,)) if common_names: a['source'] = common_names[0] scientific_names = adaptor.execute_and_fetch_col0( "SELECT name FROM taxon_name WHERE taxon_id = %s" " AND name_class = 'scientific name'", (taxon_id,)) if scientific_names: a['organism'] = scientific_names[0] ncbi_taxids = adaptor.execute_and_fetch_col0( "SELECT ncbi_taxon_id FROM taxon WHERE taxon_id = %s", (taxon_id,)) if ncbi_taxids and ncbi_taxids[0] and ncbi_taxids[0] != "0": a['ncbi_taxid'] = ncbi_taxids[0] #Old code used the left/right values in the taxon table to get the #taxonomy lineage in one SQL command. This was actually very slow, #and would fail if the (optional) left/right values were missing. # #The following code is based on a contribution from Eric Gibert, and #relies on the taxon table's parent_taxon_id field only (ignoring the #optional left/right values). This means that it has to make a #separate SQL query for each entry in the lineage, but it does still #appear to be *much* faster. See Bug 2494. taxonomy = [] while taxon_id: name, rank, parent_taxon_id = adaptor.execute_one( "SELECT taxon_name.name, taxon.node_rank, taxon.parent_taxon_id" " FROM taxon, taxon_name" " WHERE taxon.taxon_id=taxon_name.taxon_id" " AND taxon_name.name_class='scientific name'" " AND taxon.taxon_id = %s", (taxon_id,)) if taxon_id == parent_taxon_id: # If the taxon table has been populated by the BioSQL script # load_ncbi_taxonomy.pl this is how top parent nodes are stored. # Personally, I would have used a NULL parent_taxon_id here. break if rank != "no rank": #For consistency with older versions of Biopython, we are only #interested in taxonomy entries with a stated rank. #Add this to the start of the lineage list. taxonomy.insert(0, name) taxon_id = parent_taxon_id if taxonomy: a['taxonomy'] = taxonomy return a def _retrieve_comment(adaptor, primary_id): qvs = adaptor.execute_and_fetchall( "SELECT comment_text FROM comment" " WHERE bioentry_id=%s" " ORDER BY rank", (primary_id,)) comments = [comm[0] for comm in qvs] #Don't want to add an empty list... if comments: return {"comment": comments} else: return {} class DBSeqRecord(SeqRecord): """BioSQL equivalent of the biopython SeqRecord object. """ def __init__(self, adaptor, primary_id): self._adaptor = adaptor self._primary_id = primary_id (self._biodatabase_id, self._taxon_id, self.name, accession, version, self._identifier, self._division, self.description) = self._adaptor.execute_one( "SELECT biodatabase_id, taxon_id, name, accession, version," " identifier, division, description" " FROM bioentry" " WHERE bioentry_id = %s", (self._primary_id,)) if version and version != "0": self.id = "%s.%s" % (accession, version) else: self.id = accession #We don't yet record any per-letter-annotations in the #BioSQL database, but we should set this property up #for completeness (and the __str__ method). try: length = len(self.seq) except: #Could be no sequence in the database! length = 0 self._per_letter_annotations = _RestrictedDict(length=length) def __get_seq(self): if not hasattr(self, "_seq"): self._seq = _retrieve_seq(self._adaptor, self._primary_id) return self._seq def __set_seq(self, seq): self._seq = seq def __del_seq(self): del self._seq seq = property(__get_seq, __set_seq, __del_seq, "Seq object") def __get_dbxrefs(self): if not hasattr(self, "_dbxrefs"): self._dbxrefs = _retrieve_dbxrefs(self._adaptor, self._primary_id) return self._dbxrefs def __set_dbxrefs(self, dbxrefs): self._dbxrefs = dbxrefs def __del_dbxrefs(self): del self._dbxrefs dbxrefs = property(__get_dbxrefs, __set_dbxrefs, __del_dbxrefs, "Database cross references") def __get_features(self): if not hasattr(self, "_features"): self._features = _retrieve_features(self._adaptor, self._primary_id) return self._features def __set_features(self, features): self._features = features def __del_features(self): del self._features features = property(__get_features, __set_features, __del_features, "Features") def __get_annotations(self): if not hasattr(self, "_annotations"): self._annotations = _retrieve_annotations(self._adaptor, self._primary_id, self._taxon_id) if self._identifier: self._annotations["gi"] = self._identifier if self._division: self._annotations["data_file_division"] = self._division return self._annotations def __set_annotations(self, annotations): self._annotations = annotations def __del_annotations(self): del self._annotations annotations = property(__get_annotations, __set_annotations, __del_annotations, "Annotations") biopython-1.63/BioSQL/BioSeqDatabase.py0000644000175000017500000006603612250104543017335 0ustar tratra00000000000000# Copyright 2002 by Andrew Dalke. All rights reserved. # Revisions 2007-2009 copyright by Peter Cock. All rights reserved. # Revisions 2009 copyright by Cymon J. Cox. All rights reserved. # Revisions 2013 copyright by Tiago Antao. All rights reserved. # This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. # # Note that BioSQL (including the database schema and scripts) is # available and licensed separately. Please consult www.biosql.org """Connect with a BioSQL database and load Biopython like objects from it. This provides interfaces for loading biological objects from a relational database, and is compatible with the BioSQL standards. """ import os from Bio import BiopythonDeprecationWarning from . import BioSeq from . import Loader from . import DBUtils _POSTGRES_RULES_PRESENT = False # Hack for BioSQL Bug 2839 def open_database(driver="MySQLdb", **kwargs): """Main interface for loading a existing BioSQL-style database. This function is the easiest way to retrieve a connection to a database, doing something like: >>> from BioSeq import BioSeqDatabase >>> server = BioSeqDatabase.open_database(user="root", db="minidb") the various options are: driver -> The name of the database driver to use for connecting. The driver should implement the python DB API. By default, the MySQLdb driver is used. user -> the username to connect to the database with. password, passwd -> the password to connect with host -> the hostname of the database database or db -> the name of the database """ if driver == "psycopg": raise ValueError("Using BioSQL with psycopg (version one) is no " "longer supported. Use psycopg2 instead.") if os.name == "java": from com.ziclix.python.sql import zxJDBC module = zxJDBC if driver in ["MySQLdb"]: jdbc_driver = "com.mysql.jdbc.Driver" url_pref = "jdbc:mysql://" + kwargs["host"] + "/" elif driver in ["psycopg2"]: jdbc_driver = "org.postgresql.Driver" url_pref = "jdbc:postgresql://" + kwargs["host"] + "/" else: module = __import__(driver) connect = getattr(module, "connect") # Different drivers use different keywords... kw = kwargs.copy() if driver == "MySQLdb" and os.name != "java": if "database" in kw: kw["db"] = kw["database"] del kw["database"] if "password" in kw: kw["passwd"] = kw["password"] del kw["password"] else: # DB-API recommendations if "db" in kw: kw["database"] = kw["db"] del kw["db"] if "passwd" in kw: kw["password"] = kw["passwd"] del kw["passwd"] if driver in ["psycopg2", "pgdb"] and not kw.get("database"): kw["database"] = "template1" # SQLite connect takes the database name as input if os.name == "java": if driver in ["MySQLdb"]: conn = connect(url_pref + kw.get("database", "mysql"), kw["user"], kw["password"], jdbc_driver) elif driver in ["psycopg2"]: conn = connect(url_pref + kw.get("database", "postgresql") + "?stringtype=unspecified", kw["user"], kw["password"], jdbc_driver) elif driver in ["sqlite3"]: conn = connect(kw["database"]) else: try: conn = connect(**kw) except module.InterfaceError: # Ok, so let's try building a DSN # (older releases of psycopg need this) if "database" in kw: kw["dbname"] = kw["database"] del kw["database"] elif "db" in kw: kw["dbname"] = kw["db"] del kw["db"] dsn = ' '.join('='.join(i) for i in kw.items()) conn = connect(dsn) if os.name == "java": server = DBServer(conn, module, driver) else: server = DBServer(conn, module) # TODO - Remove the following once BioSQL Bug 2839 is fixed. # Test for RULES in PostgreSQL schema, see also Bug 2833. if driver in ["psycopg2", "pgdb"]: sql = "SELECT ev_class FROM pg_rewrite WHERE " + \ "rulename='rule_bioentry_i1' OR " + \ "rulename='rule_bioentry_i2';" if server.adaptor.execute_and_fetchall(sql): import warnings from Bio import BiopythonWarning warnings.warn("Your BioSQL PostgreSQL schema includes some " "rules currently required for bioperl-db but " "which may cause problems loading data using " "Biopython (see BioSQL Bug 2839). If you do not " "use BioPerl, please remove these rules. " "Biopython should cope with the rules present, " "but with a performance penalty when loading " "new records.", BiopythonWarning) global _POSTGRES_RULES_PRESENT _POSTGRES_RULES_PRESENT = True return server class DBServer: """Represents a BioSQL database continaing namespaces (sub-databases). This acts like a Python dictionary, giving access to each namespace (defined by a row in the biodatabase table) as a BioSeqDatabase object. """ def __init__(self, conn, module, module_name=None): self.module = module if module_name is None: module_name = module.__name__ self.adaptor = Adaptor(conn, DBUtils.get_dbutils(module_name)) self.module_name = module_name def __repr__(self): return self.__class__.__name__ + "(%r)" % self.adaptor.conn def __getitem__(self, name): return BioSeqDatabase(self.adaptor, name) def __len__(self): """Number of namespaces (sub-databases) in this database.""" sql = "SELECT COUNT(name) FROM biodatabase;" return int(self.adaptor.execute_and_fetch_col0(sql)[0]) def __contains__(self, value): """Check if a namespace (sub-database) in this database.""" sql = "SELECT COUNT(name) FROM biodatabase WHERE name=%s;" return bool(self.adaptor.execute_and_fetch_col0(sql, (value,))[0]) def __iter__(self): """Iterate over namespaces (sub-databases) in the database.""" #TODO - Iterate over the cursor, much more efficient return iter(self.adaptor.list_biodatabase_names()) if hasattr(dict, "iteritems"): #Python 2, use iteritems etc def keys(self): """List of namespaces (sub-databases) in the database.""" return self.adaptor.list_biodatabase_names() def values(self): """List of BioSeqDatabase objects in the database.""" return [self[key] for key in self] def items(self): """List of (namespace, BioSeqDatabase) for entries in the database.""" return [(key, self[key]) for key in self] def iterkeys(self): """Iterate over namespaces (sub-databases) in the database.""" return iter(self) def itervalues(self): """Iterate over BioSeqDatabase objects in the database.""" for key in self: yield self[key] def iteritems(self): """Iterate over (namespace, BioSeqDatabase) in the database.""" for key in self: yield key, self[key] else: #Python 3, items etc are all iterators def keys(self): """Iterate over namespaces (sub-databases) in the database.""" return iter(self) def values(self): """Iterate over BioSeqDatabase objects in the database.""" for key in self: yield self[key] def items(self): """Iterate over (namespace, BioSeqDatabase) in the database.""" for key in self: yield key, self[key] def __delitem__(self, name): """Remove a namespace and all its entries.""" if name not in self: raise KeyError(name) self.remove_database(name) def remove_database(self, db_name): """Remove a namespace and all its entries (OBSOLETE). Try to remove all references to items in a database. server.remove_database(name) In keeping with the dictionary interface, you can now do this: del server[name] """ import warnings warnings.warn("This method is deprecated. In keeping with the " "dictionary interface, you can now use 'del " "server[name]' instead", BiopythonDeprecationWarning) db_id = self.adaptor.fetch_dbid_by_dbname(db_name) remover = Loader.DatabaseRemover(self.adaptor, db_id) remover.remove() def new_database(self, db_name, authority=None, description=None): """Add a new database to the server and return it. """ # make the database sql = r"INSERT INTO biodatabase (name, authority, description)" \ r" VALUES (%s, %s, %s)" self.adaptor.execute(sql, (db_name, authority, description)) return BioSeqDatabase(self.adaptor, db_name) def load_database_sql(self, sql_file): """Load a database schema into the given database. This is used to create tables, etc when a database is first created. sql_file should specify the complete path to a file containing SQL entries for building the tables. """ # Not sophisticated enough for PG schema. Is it needed by MySQL? # Looks like we need this more complicated way for both. Leaving it # the default and removing the simple-minded approach. # read the file with all comment lines removed sql_handle = open(sql_file, "rU") sql = r"" for line in sql_handle: if line.startswith("--"): # don't include comment lines pass elif line.startswith("#"): # ditto for MySQL comments pass elif line.strip(): # only include non-blank lines sql += line.strip() sql += ' ' # two ways to load the SQL # 1. PostgreSQL can load it all at once and actually needs to # due to FUNCTION defines at the end of the SQL which mess up # the splitting by semicolons if self.module_name in ["psycopg2", "pgdb"]: self.adaptor.cursor.execute(sql) # 2. MySQL needs the database loading split up into single lines of # SQL executed one at a time elif self.module_name in ["MySQLdb", "sqlite3"]: sql_parts = sql.split(";") # one line per sql command for sql_line in sql_parts[:-1]: # don't use the last item, it's blank self.adaptor.cursor.execute(sql_line) else: raise ValueError("Module %s not supported by the loader." % (self.module_name)) def commit(self): """Commits the current transaction to the database.""" return self.adaptor.commit() def rollback(self): """Rolls backs the current transaction.""" return self.adaptor.rollback() def close(self): """Close the connection. No further activity possible.""" return self.adaptor.close() class Adaptor: def __init__(self, conn, dbutils): self.conn = conn self.cursor = conn.cursor() self.dbutils = dbutils def last_id(self, table): return self.dbutils.last_id(self.cursor, table) def autocommit(self, y=True): """Set the autocommit mode. True values enable; False value disable.""" return self.dbutils.autocommit(self.conn, y) def commit(self): """Commits the current transaction.""" return self.conn.commit() def rollback(self): """Rolls backs the current transaction.""" return self.conn.rollback() def close(self): """Close the connection. No further activity possible.""" return self.conn.close() def fetch_dbid_by_dbname(self, dbname): self.execute( r"select biodatabase_id from biodatabase where name = %s", (dbname,)) rv = self.cursor.fetchall() if not rv: raise KeyError("Cannot find biodatabase with name %r" % dbname) # Cannot happen (UK) ## assert len(rv) == 1, "More than one biodatabase with name %r" % dbname return rv[0][0] def fetch_seqid_by_display_id(self, dbid, name): sql = r"select bioentry_id from bioentry where name = %s" fields = [name] if dbid: sql += " and biodatabase_id = %s" fields.append(dbid) self.execute(sql, fields) rv = self.cursor.fetchall() if not rv: raise IndexError("Cannot find display id %r" % name) if len(rv) > 1: raise IndexError("More than one entry with display id %r" % name) return rv[0][0] def fetch_seqid_by_accession(self, dbid, name): sql = r"select bioentry_id from bioentry where accession = %s" fields = [name] if dbid: sql += " and biodatabase_id = %s" fields.append(dbid) self.execute(sql, fields) rv = self.cursor.fetchall() if not rv: raise IndexError("Cannot find accession %r" % name) if len(rv) > 1: raise IndexError("More than one entry with accession %r" % name) return rv[0][0] def fetch_seqids_by_accession(self, dbid, name): sql = r"select bioentry_id from bioentry where accession = %s" fields = [name] if dbid: sql += " and biodatabase_id = %s" fields.append(dbid) return self.execute_and_fetch_col0(sql, fields) def fetch_seqid_by_version(self, dbid, name): acc_version = name.split(".") if len(acc_version) > 2: raise IndexError("Bad version %r" % name) acc = acc_version[0] if len(acc_version) == 2: version = acc_version[1] else: version = "0" sql = r"SELECT bioentry_id FROM bioentry WHERE accession = %s" \ r" AND version = %s" fields = [acc, version] if dbid: sql += " and biodatabase_id = %s" fields.append(dbid) self.execute(sql, fields) rv = self.cursor.fetchall() if not rv: raise IndexError("Cannot find version %r" % name) if len(rv) > 1: raise IndexError("More than one entry with version %r" % name) return rv[0][0] def fetch_seqid_by_identifier(self, dbid, identifier): # YB: was fetch_seqid_by_seqid sql = "SELECT bioentry_id FROM bioentry WHERE identifier = %s" fields = [identifier] if dbid: sql += " and biodatabase_id = %s" fields.append(dbid) self.execute(sql, fields) rv = self.cursor.fetchall() if not rv: raise IndexError("Cannot find display id %r" % identifier) return rv[0][0] def list_biodatabase_names(self): return self.execute_and_fetch_col0( "SELECT name FROM biodatabase") def list_bioentry_ids(self, dbid): return self.execute_and_fetch_col0( "SELECT bioentry_id FROM bioentry WHERE biodatabase_id = %s", (dbid,)) def list_bioentry_display_ids(self, dbid): return self.execute_and_fetch_col0( "SELECT name FROM bioentry WHERE biodatabase_id = %s", (dbid,)) def list_any_ids(self, sql, args): """Return ids given a SQL statement to select for them. This assumes that the given SQL does a SELECT statement that returns a list of items. This parses them out of the 2D list they come as and just returns them in a list. """ return self.execute_and_fetch_col0(sql, args) def execute_one(self, sql, args=None): self.execute(sql, args or ()) rv = self.cursor.fetchall() assert len(rv) == 1, "Expected 1 response, got %d" % len(rv) return rv[0] def execute(self, sql, args=None): """Just execute an sql command. """ if os.name == "java": sql = sql.replace("%s", "?") self.dbutils.execute(self.cursor, sql, args) def get_subseq_as_string(self, seqid, start, end): length = end - start # XXX Check this on MySQL and PostgreSQL. substr should be general, # does it need dbutils? #return self.execute_one( # """select SUBSTRING(seq FROM %s FOR %s) # from biosequence where bioentry_id = %s""", # (start+1, length, seqid))[0] # # Convert to a string on returning for databases that give back # unicode. Shouldn't need unicode for sequences so this seems safe. return str(self.execute_one( """select SUBSTR(seq, %s, %s) from biosequence where bioentry_id = %s""", (start + 1, length, seqid))[0]) def execute_and_fetch_col0(self, sql, args=None): self.execute(sql, args or ()) return [field[0] for field in self.cursor.fetchall()] def execute_and_fetchall(self, sql, args=None): self.execute(sql, args or ()) return self.cursor.fetchall() _allowed_lookups = { # Lookup name / function name to get id, function to list all ids 'primary_id': "fetch_seqid_by_identifier", 'gi': "fetch_seqid_by_identifier", 'display_id': "fetch_seqid_by_display_id", 'name': "fetch_seqid_by_display_id", 'accession': "fetch_seqid_by_accession", 'version': "fetch_seqid_by_version", } class BioSeqDatabase: """Represents a namespace (sub-database) within the BioSQL database. i.e. One row in the biodatabase table, and all all rows in the bioentry table associated with it. """ def __init__(self, adaptor, name): self.adaptor = adaptor self.name = name self.dbid = self.adaptor.fetch_dbid_by_dbname(name) def __repr__(self): return "BioSeqDatabase(%r, %r)" % (self.adaptor, self.name) def get_Seq_by_id(self, name): """Gets a DBSeqRecord object by its name Example: seq_rec = db.get_Seq_by_id('ROA1_HUMAN') The name of this method is misleading since it returns a DBSeqRecord rather than a DBSeq ojbect, and presumably was to mirror BioPerl. """ seqid = self.adaptor.fetch_seqid_by_display_id(self.dbid, name) return BioSeq.DBSeqRecord(self.adaptor, seqid) def get_Seq_by_acc(self, name): """Gets a DBSeqRecord object by accession number Example: seq_rec = db.get_Seq_by_acc('X77802') The name of this method is misleading since it returns a DBSeqRecord rather than a DBSeq ojbect, and presumably was to mirror BioPerl. """ seqid = self.adaptor.fetch_seqid_by_accession(self.dbid, name) return BioSeq.DBSeqRecord(self.adaptor, seqid) def get_Seq_by_ver(self, name): """Gets a DBSeqRecord object by version number Example: seq_rec = db.get_Seq_by_ver('X77802.1') The name of this method is misleading since it returns a DBSeqRecord rather than a DBSeq ojbect, and presumably was to mirror BioPerl. """ seqid = self.adaptor.fetch_seqid_by_version(self.dbid, name) return BioSeq.DBSeqRecord(self.adaptor, seqid) def get_Seqs_by_acc(self, name): """Gets a list of DBSeqRecord objects by accession number Example: seq_recs = db.get_Seq_by_acc('X77802') The name of this method is misleading since it returns a list of DBSeqRecord objects rather than a list of DBSeq ojbects, and presumably was to mirror BioPerl. """ seqids = self.adaptor.fetch_seqids_by_accession(self.dbid, name) return [BioSeq.DBSeqRecord(self.adaptor, seqid) for seqid in seqids] def get_all_primary_ids(self): """All the primary_ids of the sequences in the database (OBSOLETE). These maybe ids (display style) or accession numbers or something else completely different - they *are not* meaningful outside of this database implementation. Please use .keys() instead of .get_all_primary_ids() """ import warnings warnings.warn("Use bio_seq_database.keys() instead of " "bio_seq_database.get_all_primary_ids()", BiopythonDeprecationWarning) return list(self.keys()) def __getitem__(self, key): return BioSeq.DBSeqRecord(self.adaptor, key) def __delitem__(self, key): """Remove an entry and all its annotation.""" if key not in self: raise KeyError(key) #Assuming this will automatically cascade to the other tables... sql = "DELETE FROM bioentry " + \ "WHERE biodatabase_id=%s AND bioentry_id=%s;" self.adaptor.execute(sql, (self.dbid, key)) def __len__(self): """Number of records in this namespace (sub database).""" sql = "SELECT COUNT(bioentry_id) FROM bioentry " + \ "WHERE biodatabase_id=%s;" return int(self.adaptor.execute_and_fetch_col0(sql, (self.dbid, ))[0]) def __contains__(self, value): """Check if a primary (internal) id is this namespace (sub database).""" sql = "SELECT COUNT(bioentry_id) FROM bioentry " + \ "WHERE biodatabase_id=%s AND bioentry_id=%s;" #The bioentry_id field is an integer in the schema. #PostgreSQL will throw an error if we use a non integer in the query. try: bioentry_id = int(value) except ValueError: return False return bool(self.adaptor.execute_and_fetch_col0(sql, (self.dbid, bioentry_id))[0]) def __iter__(self): """Iterate over ids (which may not be meaningful outside this database).""" #TODO - Iterate over the cursor, much more efficient return iter(self.adaptor.list_bioentry_ids(self.dbid)) if hasattr(dict, "iteritems"): #Python 2, use iteritems etc def keys(self): """List of ids which may not be meaningful outside this database.""" return self.adaptor.list_bioentry_ids(self.dbid) def values(self): """List of DBSeqRecord objects in the namespace (sub database).""" return [self[key] for key in self] def items(self): """List of (id, DBSeqRecord) for the namespace (sub database).""" return [(key, self[key]) for key in self] def iterkeys(self): """Iterate over ids (which may not be meaningful outside this database).""" return iter(self) def itervalues(self): """Iterate over DBSeqRecord objects in the namespace (sub database).""" for key in self: yield self[key] def iteritems(self): """Iterate over (id, DBSeqRecord) for the namespace (sub database).""" for key in self: yield key, self[key] else: #Python 3, items etc are all iterators def keys(self): """Iterate over ids (which may not be meaningful outside this database).""" return iter(self) def values(self): """Iterate over DBSeqRecord objects in the namespace (sub database).""" for key in self: yield self[key] def items(self): """Iterate over (id, DBSeqRecord) for the namespace (sub database).""" for key in self: yield key, self[key] def lookup(self, **kwargs): if len(kwargs) != 1: raise TypeError("single key/value parameter expected") k, v = list(kwargs.items())[0] if k not in _allowed_lookups: raise TypeError("lookup() expects one of %r, not %r" % (list(_allowed_lookups.keys()), k)) lookup_name = _allowed_lookups[k] lookup_func = getattr(self.adaptor, lookup_name) seqid = lookup_func(self.dbid, v) return BioSeq.DBSeqRecord(self.adaptor, seqid) def get_Seq_by_primary_id(self, seqid): """Get a DBSeqRecord by the primary (internal) id (OBSOLETE). Rather than db.get_Seq_by_primary_id(my_id) use db[my_id] The name of this method is misleading since it returns a DBSeqRecord rather than a DBSeq ojbect, and presumably was to mirror BioPerl. """ import warnings warnings.warn("Use bio_seq_database[my_id] instead of " "bio_seq_database.get_Seq_by_primary_id(my_id)", BiopythonDeprecationWarning) return self[seqid] def load(self, record_iterator, fetch_NCBI_taxonomy=False): """Load a set of SeqRecords into the BioSQL database. record_iterator is either a list of SeqRecord objects, or an Iterator object that returns SeqRecord objects (such as the output from the Bio.SeqIO.parse() function), which will be used to populate the database. fetch_NCBI_taxonomy is boolean flag allowing or preventing connection to the taxonomic database on the NCBI server (via Bio.Entrez) to fetch a detailed taxonomy for each SeqRecord. Example: from Bio import SeqIO count = db.load(SeqIO.parse(open(filename), format)) Returns the number of records loaded. """ db_loader = Loader.DatabaseLoader(self.adaptor, self.dbid, fetch_NCBI_taxonomy) num_records = 0 global _POSTGRES_RULES_PRESENT for cur_record in record_iterator: num_records += 1 #Hack to work arround BioSQL Bug 2839 - If using PostgreSQL and #the RULES are present check for a duplicate record before loading if _POSTGRES_RULES_PRESENT: #Recreate what the Loader's _load_bioentry_table will do: if cur_record.id.count(".") == 1: accession, version = cur_record.id.split('.') try: version = int(version) except ValueError: accession = cur_record.id version = 0 else: accession = cur_record.id version = 0 gi = cur_record.annotations.get("gi", None) sql = "SELECT bioentry_id FROM bioentry WHERE (identifier " + \ "= '%s' AND biodatabase_id = '%s') OR (accession = " + \ "'%s' AND version = '%s' AND biodatabase_id = '%s')" self.adaptor.execute(sql % (gi, self.dbid, accession, version, self.dbid)) if self.adaptor.cursor.fetchone(): raise self.adaptor.conn.IntegrityError("Duplicate record " "detected: record has not been inserted") #End of hack db_loader.load_seqrecord(cur_record) return num_records biopython-1.63/BioSQL/DBUtils.py0000644000175000017500000000701012250104543016017 0ustar tratra00000000000000# Copyright 2002 by Andrew Dalke. All rights reserved. # Revisions 2007-2010 copyright by Peter Cock. All rights reserved. # Revisions 2009 copyright by Brad Chapman. All rights reserved. # Revisions 2013 copyright by Tiago Antao. All rights reserved. # This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. # # Note that BioSQL (including the database schema and scripts) is # available and licensed separately. Please consult www.biosql.org import os _dbutils = {} class Generic_dbutils: """Default database utilities.""" def __init__(self): pass def tname(self, table): if table != 'biosequence': return table else: return 'bioentry' def last_id(self, cursor, table): # XXX: Unsafe without transactions isolation table = self.tname(table) sql = r"select max(%s_id) from %s" % (table, table) cursor.execute(sql) rv = cursor.fetchone() return rv[0] def execute(self, cursor, sql, args=None): """Just execute an sql command. """ cursor.execute(sql, args or ()) def autocommit(self, conn, y=1): # Let's hope it was not really needed pass class Sqlite_dbutils(Generic_dbutils): """Custom database utilities for SQLite.""" def execute(self, cursor, sql, args=None): """Execute SQL command, replacing %s with ? for variable substitution in sqlite3. """ cursor.execute(sql.replace("%s", "?"), args or ()) _dbutils["sqlite3"] = Sqlite_dbutils class Mysql_dbutils(Generic_dbutils): """Custom database utilities for MySQL.""" def last_id(self, cursor, table): if os.name == "java": return Generic_dbutils.last_id(self, cursor, table) try: #This worked on older versions of MySQL return cursor.insert_id() except AttributeError: #See bug 2390 #Google suggests this is the new way, #same fix also suggested by Eric Gibert: return cursor.lastrowid _dbutils["MySQLdb"] = Mysql_dbutils class _PostgreSQL_dbutils(Generic_dbutils): """Base class for any PostgreSQL adaptor.""" def next_id(self, cursor, table): table = self.tname(table) sql = r"select nextval('%s_pk_seq')" % table cursor.execute(sql) rv = cursor.fetchone() return rv[0] def last_id(self, cursor, table): table = self.tname(table) sql = r"select currval('%s_pk_seq')" % table cursor.execute(sql) rv = cursor.fetchone() return rv[0] class Psycopg2_dbutils(_PostgreSQL_dbutils): """Custom database utilities for Psycopg2 (PostgreSQL).""" def autocommit(self, conn, y=True): if y: if os.name == "java": conn.autocommit = 1 else: conn.set_isolation_level(0) else: if os.name == "java": conn.autocommit = 0 else: conn.set_isolation_level(1) _dbutils["psycopg2"] = Psycopg2_dbutils class Pgdb_dbutils(_PostgreSQL_dbutils): """Custom database utilities for Pgdb (aka PyGreSQL, for PostgreSQL).""" def autocommit(self, conn, y=True): raise NotImplementedError("pgdb does not support this!") _dbutils["pgdb"] = Pgdb_dbutils def get_dbutils(module_name): try: return _dbutils[module_name]() except KeyError: return Generic_dbutils() biopython-1.63/DEPRECATED0000644000175000017500000005371512250104543014406 0ustar tratra00000000000000This file provides documentation for modules in Biopython that have been moved or deprecated in favor of other modules. This provides some quick and easy to find documentation about how to update your code to work again. Python 2.3 ========== No longer supported as of Release 1.51, having triggered a warning with Release 1.50, with a mention in the release notes for Release 1.49. Python 2.4 ========== No longer supported as of Release 1.59, having triggered a warning since Release 1.55, with advance notice in the release notes for Release 1.54. Python 2.5 ========== No longer supported as of Release 1.63, having triggered a warning with Release 1.62, with advance notice in the release notes for Release 1.61. Python 3.0, 3.1, 3.2 ==================== Never officially supported, these trigger a warning in Release 1.62 recommending Python 3.3 or later if you wish to use Python 3. Iterator .next() methods ======================== The .next() method defined for any Biopython iterator is deprecated as of Biopython 1.63 under Python 2 (and not present on Python 3). Please replace my_iterator.next() with next(my_iterator) using the new built-in function next() instead. Bio.SVDSuperimposer =================== As of Release 1.63, the main class (confusingly also called) SVDSuperimposer is best imported as follows: >>> from Bio.SVDSuperimposer import SVDSuperimposer >>> super_imposer = SVDSuperimposer() This short form also works on older releases. The longer even more confusing historical alternatives dependent on the double module name no longer work, e.g. you can no longer do this: >>> from Bio.SVDSuperimposer.SVDSuperimposer import SVDSuperimposer >>> super_imposer = SVDSuperimposer() Bio.PDB.mmCIF ============= This was removed in Release 1.62, when MMCIF2Dict was updated to use shlex from the standard library instead. This had required manual intervention to include when installing Biopython from source due to a dependency on flex. Bio.SeqFeature ============== With the introduction of the CompoundLocation in Release 1.62, the SeqFeature attribute sub_features was deprecated. Bio.Motif ========= Declared obsolete with a PendingDeprecationWarning in Release 1.61, formally deprecated in Release 1.62. Please use the newer Bio.motifs module instead. AlignAceCommandline and CompareAceCommandline ============================================= Deprecated in release 1.62. An up to date version of the software cannot be obtained anymore (affects Bio.Motif and its replacement Bio.motifs). Bio.SeqIO.Interfaces ==================== Unused class InterlacedSequenceIterator was deprecated in Release 1.61. Bio.HotRand =========== Obsolete file Bio/HotRand.py was deprecated in Release 1.61, consider using an alternative RNG, or the Python module "randomdotorg". Bio.Search ========== Long obsolete file Bio/Search.py was deprecated in Release 1.61. Bio.Pathway.Rep.HashSet ======================= Deprecated in Release 1.59, removed in Release 1.62. Use Python's built in set object. Bio.SeqFeature.WithinPosition and OneOfPosition =============================================== The arguments to create these fuzzy positions changed in Release 1.59. Bio.Encodings ============= Explicitly declared obsolete in Release 1.55, deprecated in Release 1.56, and removed in Release 1.57. Bio.PropertyManager =================== Explicitly declared obsolete in Release 1.55, deprecated in Release 1.56, and removed in Release 1.57. Bio.InterPro ============ This module was a parser for the EBI InterPro webpages, but no longer worked with their current website. Deprecated in Release 1.55, and removed in Release 1.58. Bio.GenBank.LocationParser ========================== This module used to be used for parsing GenBank and EMBL feature locations. It has been replaced with faster code using regular expressions, and is no longer needed. Declared obsolete in Release 1.55, deprecated in Release 1.56, and removed in Release 1.59. Bio.Parsers and Bio.Parsers.spark ================================= This module was a copy of John Aycock's SPARK parser included with Biopython solely for use in Bio.GenBank.LocationParser. Declared obsolete in Release 1.55, deprecated in Release 1.56, and removed in Release 1.59. Bio.Restriction.DNAUtils and check_bases ======================================== This module (originally in C) offered complement and antiparallel functions (duplicating functionality in Bio.Seq) and a rather odd function called check_bases (also available as Bio.Restriction.Restriction.check_bases). Deprecated in Release 1.53, removed in Release 1.57. Bio.Blast.NCBIStandalone ======================== The three functions for calling the "legacy" NCBI BLAST command line tools blastall, blastpgp and rpsblast were declared obsolete in Biopython Release 1.53, and deprecated in Release 1.61. Please use the BLAST+ wrappers in Bio.Blast.Applications instead. The remainder of this module is a parser for the plain text BLAST output, which was declared obsolete in Release 1.54, and deprecated in Release 1.63. For some time now, both the NCBI and Biopython have encouraged people to parse the XML output instead, however Bio.SearchIO will initially attempt to support plain text BLAST output. Bio.Blast.Applications ====================== NCBI "legacy" BLAST tool wrappers FastacmdCommandline, BlastallCommandline, BlastpgpCommandline and RpsBlastCommandline were declared obsolete in Release 1.53 and deprecated in Release 1.61, having been replaced with wrappers for the new NCBI BLAST+ tools (e.g. NcbiblastpCommandline and NcbipsiblastCommandline). Bio.Clustalw ============ Declared obsolete in Release 1.52, deprecated in Release 1.55 final, and removed in Release 1.58. Replaced with Bio.AlignIO for parsing and writing clustal format alignments (since Release 1.46), and Bio.Align.Applications for calling the ClustalW command line tool (since Release 1.51). See the Tutorial for examples. BioSQL and psycopg ================== Support for psycopg (version one) in Biopython's BioSQL code was deprecated in Release 1.51, and removed in Release 1.55. Please use psycopg2 instead. Bio.Application.generic_run and ApplicationResult ================================================= Declared obsolete in Release 1.51, deprecated in Release 1.53, and removed in Release 1.57. Please use the Python subprocess module instead, or as of Release 1.55 the application wrappers can be used directly to execute the command. Bio.Entrez.efetch and rettype="genbank" ======================================= As of Easter 2009, the NCBI have stopped supporting the unofficial return type of "genbank" in EFetch. Instead we should be using "gb" (GenBank) or "gp" (GenPept). As of Biopython 1.50, Bio.Entrez.efetch will correct this automatically, but issues a deprecation warning. The code to check and correct for "genbank" was removed in Biopython 1.55 final. Bio.Entrez.query function ========================= Deprecated in Release 1.47, removed in Release 1.52. Bio.SwissProt.SProt =================== Declared obsolete in Release 1.50, deprecated in Release 1.51, and removed in Release 1.56. Most of the functionality in Bio.SwissProt.SProt is available from Bio.SwissProt. Bio.Prosite and Bio.Enzyme ========================== Declared obsolete in Release 1.50, deprecated in Release 1.53, and removed in Release 1.57. Most of the functionality has moved to Bio.ExPASy.Prosite and Bio.ExPASy.Enzyme, respectively. Bio.EZRetrieve, Bio.NetCatch, Bio.FilteredReader ================================================ Declared obsolete in Release 1.50, deprecated in Release 1.52, and removed in Release 1.56. Bio.File ======== Bio.File.SGMLHandle was declared obsolete in Release 1.50, deprecated in Release 1.52, and removed in Release 1.56. Bio.File.SGMLStripper was deprecated in Release 1.57, removed in Release 1.61. Bio.File.StringHandle was deprecated in Release 1.59, removed in Release 1.61. Bio.Graphics.GenomeDiagram and colour/color, centre/center ========================================================== GenomeDiagram originally used colour and centre (UK spelling of color and center) for argument names. As part of its integration into Biopython 1.50, this will support both colour and color, and both centre and center, to help people port existing scripts written for the standalone version of GenomeDiagram. However, these were deprecated in Release 1.55 final. Support for centre was removed in Release 1.62, and we intend to eventually remove support for colour in later releases of Biopython. Bio.AlignAce and Bio.MEME ========================= Declared obsolete in Release 1.50, deprecated in Release 1.52, and removed in Release 1.56. Please use Bio.Motif instead. Numeric support =============== Following the Release of 1.48, Numeric support in Biopython is discontinued. Please move to NumPy for Biopython 1.49 or later. Bio.Seq and the data property ============================= Direct use of the Seq object (and MutableSeq object) .data property is deprecated. As of Release 1.49, writing to the Seq object's .data property triggered a warning, and this property was made read only in Release 1.53. In Release 1.55 final, accessing the .data property gives a DeprecationWarning. The Seq object's .data property was removed in Release 1.61. Bio.Transcribe and Bio.Translate ================================ Declared obsolete in Release 1.49, deprecated in Release 1.51, and removed in Release 1.57. Please use the methods or functions in Bio.Seq instead. Bio.mathfns, Bio.stringfns and Bio.listfns (and their C code variants) ========================================== Declared obsolete in Release 1.49. Bio.mathfns and Bio.stringfns were deprecated in Release 1.50, Bio.listfns was deprecated in Release 1.53. The three C implementations were all removed in Release 1.53. Bio.mathfns and Bio.stringfns were removed in Release 1.55. Bio.listfns was removed in Release 1.57. Bio.distance (and Bio.cdistance) ================================ Bio.distance was deprecated in Release 1.49, at which point its C code implementation Bio.cdistance was removed (this was not intended as a public API). Removed in Release 1.53. Bio.Ndb ======= Deprecated in Release 1.49, as the website this parsed has been redesigned. Removed in Release 1.53. Martel ====== Declared obsolete in Release 1.48, deprecated in Release 1.49, and removed in Release 1.51. The source code for Martel is still in our repository if anyone wanted to develop this outside of Biopython. Bio.Mindy and associated modules. ================================= Declared obsolete in Release 1.48, deprecated in Release 1.49, removed in Release 1.51. This includes the Bio.Writer, Bio.writers, Bio.builders, Bio.Std, Bio.StdHandler, Bio.Decode and Bio.DBXRef modules Bio.Fasta index_file and Dictionary =================================== Deprecated in Release 1.44, removed in Biopython 1.46. For small to medium sized files, use Bio.SeqIO.to_dict() to make an in memory dictionary of SeqRecord objects. Biopython 1.52 onwards provides Bio.SeqIO.index() which is suitable even for very large files. Bio.Fasta (including Bio.Fasta.FastaAlign) ========================================== Declared obsolete in Release 1.48, deprecated in Release 1.51, and removed in Release 1.55 final. Please use the "fasta" support in Bio.SeqIO or Bio.AlignIO instead. Bio.Align.FormatConvert ====================== Declared obsolete in Release 1.48, deprecated in Release 1.51, and removed in Release 1.55 final. Please use Bio.AlignIO or the Alignment object's format method instead. Bio.Emboss.Primer ================= Deprecated in Release 1.48, and removed in Release 1.51, this parser was replaced by Bio.Emboss.Primer3 and Bio.Emboss.PrimerSearch instead. Bio.Emboss.Applications ======================= The wrappers for the "old" EMBOSS PHYLIP tools (e.g. eneighbor) were declared obsolete in Biopython 1.52, deprecated in Release 1.55 final, and removed in release 1.58. please use the wrappers for the "new" EMBOSS PHYLIP tools (e.g. fneighbor) instead. Specifically, EProtDistCommandline, ENeighborCommandline, EProtParsCommandline, EConsenseCommandline, and ESeqBootCommandline are replaced by FProtDistCommandline, FNeighborCommandline, FProtParsCommandline, FConsenseCommandline, and FSeqBootCommandline, respectively. Bio.MetaTool ============ Deprecated in Release 1.48, and removed in Release 1.51, this was a parser for the output of MetaTool 3.5 which is now obsolete. Bio.GenBank =========== The online functionality (search_for, download_many, and NCBIDictionary) was declared obsolete in Release 1.48, deprecated in Release 1.50, and removed in Release 1.54. Please use Bio.Entrez instead. Bio.PubMed ========== Declared obsolete in Release 1.48, deprecated in Release 1.49, and removed in Release 1.53. Please use Bio.Entrez instead. Bio.EUtils ========== Deprecated in favor of Bio.Entrez in Release 1.48, removed in Release 1.52. Bio.Sequencing & Bio.Medline ============================ A revised API was added and the old one deprecated in Release 1.48, and removed in Biopython 1.52: Bio.Sequencing.Ace.RecordParser --> Bio.Sequencing.Ace.read(handle) Bio.Sequencing.Ace.Iterator --> Bio.Sequencing.Ace.parse(handle) Bio.Sequencing.Phd.RecordParser --> Bio.Sequencing.Phd.read(handle) Bio.Sequencing.Phd.Iterator --> Bio.Sequencing.Phd.parse(handle) Bio.Medline.RecordParser --> Bio.Medline.read(handle) Bio.Medline.Iterator --> Bio.Medline.parse(handle) Bio.Blast.NCBIWWW ================= The HTML BLAST parser was deprecated in Release 1.48, and removed in 1.52. The deprecated functions blast and blasturl were removed in Release 1.44. Bio.Saf ======= Deprecated as of Release 1.48, removed in Release 1.51. If useful, a parser for this "simple alignment format" could be developed for Bio.AlignIO instead. Bio.NBRF ======== Deprecated as of Release 1.48 in favor of the "pir" format in Bio.SeqIO, removed in Release 1.51. Bio.IntelliGenetics =================== Deprecated as of Release 1.48 in favor of the "ig" format in Bio.SeqIO, removed in Release 1.51. Bio.SeqIO submodules PhylipIO, ClustalIO, NexusIO and StockholmIO ================================================================= You can still use the "phylip", "clustal", "nexus" and "stockholm" formats in Bio.SeqIO, however these are now supported via Bio.AlignIO, with the old code deprecated in Releases 1.46 or 1.47, and removed in Release 1.49. Bio.SeqIO.to_alignment() ======================== This function was made obsolete with the introduction of Bio.AlignIO, deprecated in Release 1.54, and removed in Release 1.58. Use either the Bio.AlignIO functions, or the Bio.Align.MultipleSeqAlignment class directly instead. Bio.ECell ========= Deprecated as of Release 1.47, as it appears to have no users, and the code does not seem relevant for ECell 3. Removed in Release 1.49. Bio.Ais ======= Deprecated as of Release 1.45, removed in Release 1.49. Bio.LocusLink ============= Deprecated as of Release 1.45, removed in Release 1.49. The NCBI's LocusLink was superseded by Entrez Gene. Bio.SGMLExtractor ================= Deprecated as of Release 1.46, removed in Release 1.49. Bio.Rebase ========== Deprecated as of Release 1.46, removed in Release 1.49. Bio.Gobase ========== Deprecated as of Release 1.46, removed in Release 1.49. Bio.CDD ======= Deprecated as of Release 1.46, removed in Release 1.49. Bio.biblio ========== Deprecated as of Release 1.45, removed in Release 1.48 Bio.WWW ======= The modules under Bio.WWW were deprecated in Release 1.45, and removed in Release 1.48. The remaining stub Bio.WWW was deprecated in Release 1.48, and removed in Release 1.53. The functionality in Bio.WWW.SCOP, Bio.WWW.InterPro, Bio.WWW.ExPASy and Bio.WWW.NCBI is now available from Bio.SCOP, Bio.InterPro, Bio.ExPASy and Bio.Entrez instead. Bio.SeqIO ========= The old Bio.SeqIO.FASTA and Bio.SeqIO.generic were deprecated in favour of the new Bio.SeqIO module as of Release 1.44, removed in Release 1.47. Bio.Medline.NLMMedlineXML ========================= Deprecated in Release 1.44, removed in 1.46. Bio.MultiProc ============= Deprecated in Release 1.44, removed in 1.46. Bio.MarkupEditor ================ Deprecated in Release 1.44, removed in 1.46. Bio.lcc ======= Deprecated in favor of Bio.SeqUtils.lcc in Release 1.44, removed in 1.46. Bio.crc ======= Deprecated in favor of Bio.SeqUtils.CheckSum in Release 1.44, removed in 1.46. Bio.FormatIO ============ This was removed in Release 1.44 (a deprecation was not possible). Bio.expressions, Bio.config, Bio.dbdefs, Bio.formatdefs and Bio.dbdefs ====================================================================== These were deprecated in Release 1.44, and removed in Release 1.49. Bio.Kabat ========= This was deprecated in Release 1.43 and removed in Release 1.44. Bio.SeqUtils ============ Functions 'complement' and 'antiparallel' in Bio.SeqUtils were deprecated in Release 1.31, and removed in Release 1.43. Function 'translate' was deprecated in Release 1.49, and removed in Release 1.53. Use the functions and methods in Bio.Seq instead. Function makeTableX and classes ProteinX and MissingTable were deprecated in Release 1.54, and removed in Release 1.58. These were remnants of the removed translate function, and no longer served any useful purpose. Function 'reverse' in Bio.SeqUtils was deprecated in Release 1.54, and removed in Release 1.58. Instead just use the string's slice method with a step of minus one. Functions GC_Frame, fasta_uniqids, apply_on_multi_fasta, and quicker_apply_on_multi_fasta were deprecated in Release 1.55, and removed in Release 1.58. Function quick_FASTA_reader was declared obsolete in Release 1.61. Use function list(SimpleFastaParser(handle)) in Bio.SeqIO.FastaIO instead (but ideally convert your code to using an iterator approach). Bio.GFF (for accessing a MySQL database created with BioPerl, etc) ======= The functions 'forward_complement' and 'antiparallel' in Bio.GFF.easy have been deprecated as of Release 1.31, and removed in Release 1.43. Use the functions 'complement' and 'reverse_complement' in Bio.Seq instead. The whole of the old Bio.GFF module was deprecated in Release 1.53, and removed in Release 1.57 (with the intention of reusing this name space for a GFF parser). Bio.sequtils ============ Deprecated as of Release 1.30, removed in Release 1.42. Use Bio.SeqUtils instead. Bio.SVM ======= Deprecated as of Release 1.30, removed in Release 1.42. The Support Vector Machine code in Biopython has been superseded by a more robust (and maintained) SVM library, which includes a python interface. We recommend using LIBSVM: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Bio.RecordFile ============== Deprecated as of Release 1.30, removed in Release 1.42. RecordFile wasn't completely implemented and duplicates the work of most standard parsers. Bio.kMeans and Bio.xkMeans ========================== Deprecated as of Release 1.30, removed in Release 1.42. Instead, please use the function kcluster in Bio.Cluster which performs k-means or k-medians clustering. Bio.InterPro ============ The function pairlist_to_dict was deprecated in Release 1.45, and removed in Release 1.53. Bio.SCOP ======== The module Bio.SCOP.FileIndex was deprecated in Release 1.46, and removed in Release 1.53. The class Parser in Bio.SCOP.Dom was removed in Release 1.55 final. The class Iterator in Bio.SCOP.Dom was removed in Release 1.56. Bio.utils ========= Functions 'translate', 'translate_to_stop', 'back_translate', 'transcribe', and 'back_transcribe' were deprecated in Release 1.49, and removed in Release 1.53. Function 'ungap' was deprecated in Release 1.53. Use Bio.Seq instead. The whole of Bio.utils was declared obsolete in Release 1.55, deprecated in Release 1.56, and removed in Release 1.57. Bio.Motif ========= CompareAceParser and CompareAceConsumer from Bio.Motif.Parsers.AlignAce were declared obsolete in Release 1.53, deprecated in Release 1.55 final, and removed in Release 1.57. AlignAceConsumer, AlignAceParser, and AlignAceScanner were declared obsolete in Release 1.53 and deprecated in Release 1.55 final; their functionality is now available through a read() function in Bio.Motif.Parsers.AlignAce. MEMEParser, _MEMEScanner, _MEMEConsumer, _MASTConsumer, MASTParser, _MASTScanner, and MASTRecord were declared obsolete in Release 1.54 and deprecated in Release 1.55 final; their functionality is now available through a read() function in Bio.Motif.Parsers.MEME and Bio.Motif.Parsers.MAST, respectively. Bio.Compass =========== The RecordParser and Iterator classes were declared obsolete in Release 1.54, deprecated in Release 1.55, removed in Release 1.59. Their functionality is now available through a read() and a parse() function, respectively. Bio.Affy.CelFile ================ The CelScanner, CelConsumer, CelRecord, and CelParser were declared obsolete in Release 1.54, deprecated in Release 1.55 and removed in Release 1.59. Their functionality is now available through a read() function. Bio.PopGen.FDist ================ The RecordParser, _Scanner, and _RecordConsumer classes were declared obsolete in Release 1.54, deprecated in Release 1.55, and removed in Release 1.58. Their functionality is now available through a read() function. Bio.UniGene =========== The classes UnigeneSequenceRecord, UnigeneProtsimRecord, UnigeneSTSRecord, UnigeneRecord, _RecordConsumer, _Scanner, RecordParser, and Iterator in Bio.UniGene were declared obsolete in Release 1.54, deprecated in Release 1.55, and removed in Release 1.59. Their functionality is now available through a read() and a parse() function in Bio.UniGene. The HTML parser in Bio.UniGene.UniGene was declared obsolete in Release 1.59, and deprecated in Release 1.61. Bio.SubsMat =========== The methods letter_sum and all_letters_sum were removed from the SeqMat class in Bio.SubsMat in Release 1.57. Bio.Align ========= The methods get_column and add_sequence of the MultipleSeqAlignment class were deprecated in Release 1.57. The Alignment class in Bio.Align.Generic and its method get_seq_by_num were deprecated in Release 1.57. Bio.ParserSupport ================= Bio.ParserSupport was declared obsolete in Release 1.59, and deprecated in Release 1.63. Bio.ParserSupport.SGMLStrippingConsumer was deprecated in Release 1.59, and removed in Release 1.61. biopython-1.63/CONTRIB0000644000175000017500000001064512250104543014101 0ustar tratra00000000000000CONTRIBUTORS ============ This is a list of people who have made contributions to Biopython. This is certainly not comprehensive, and if you've been overlooked (sorry!), please mention it on the development mailing list. People are listed alphabetically by surname. Cecilia Alsmark Tiago Antao Wibowo Arindrarto Sebastian Bassi Bill Barnard Yves Bastide Paul T. Bathen Yair Benita Peter Bienstman Jose Blanca Bob Bussell Diego Brouard Christian Brueffer David Cain James Casbon Hye-Shik Chang Jeffrey Chang Brad Chapman Peter Cock Marc Colosimo Andres Colubri Cymon J Cox Gavin E Crooks Andrew Dalke Wayne Decatur < https://github.com/fomightez > Michiel de Hoon Bart de Koning Sjoerd de Vries Nathan J. Edwards Kyle Ellrott Gokcen Eraslan < https://github.com/gokceneraslan > Jeffrey Finkelstein Konrad Förstner < https://github.com/konrad > Iddo Friedberg Bertrand Frottier Aaron Gallagher Phillip Garland Walter Gillett < https://github.com/wgillett > Frederik Gwinner Jason A. Hackney Thomas Hamelryck Michael Hoffman Thomas Holder Yu Huang Jeff Hussmann Kevin Jacobs Diana Jaunzeikare Joanna & Dominik Kasprzak Frank Kauff Siong Kong Andreas Kuntzagk Michal Kurowski Uri Laserson Chris Lasher Sergei Lebedev < https://github.com/superbobry > Gaetan Lehman Katharine Lindner Bryan Lunt < https://github.com/bryan-lunt > Erick Matsen Connor McCoy Tarjei Mikkelsen Chris Mitchell < https://github.com/chrismit > Ben Morris Konstantin Okonechnikov Cheng Soon Ong Anne Pajon Alessio Papini Claude Paroz Lenna Peterson Andrea Pierleoni Mike Poidinger Leighton Pritchard Andrea Rizzi < https://github.com/andrrizzi > Joao Rodrigues Thomas Rosleff Soerensen Thomas Schmitt Wolfgang Schueler Peter Slickers Kamil Slowikowski Thomas Sicheritz-Ponten Frederic Sohm Nate Sutton Eric Talevich Bartosz Telenczuk Bogdan T. (bogdan at pearlgen dot com) Carlos Rios Vera Johann Visagie Dan Vogel David Weisman Bartek Wilczynski David Winter Hongbo Zhu Christian Zmasek Harry Zuzan biopython-1.63/Doc/0000755000175000017500000000000012250104773013562 5ustar tratra00000000000000biopython-1.63/Doc/images/0000755000175000017500000000000012250104773015027 5ustar tratra00000000000000biopython-1.63/Doc/images/plasmid_circular.png0000644000175000017500000004203412250104543021050 0ustar tratra00000000000000PNG  IHDRݡ pHYs  tIME ]0ѫ IDATx}\?W&$ A4M7X`[zz C=%k˷G5=GۡkCOMYdv V(^($G*n+\Iуu}>7" BAt T1pe 'F+ /k?IHTJP`) 1ΆRCGEPZF+?r9]Q ,BXJH$\DD@ LEGF<_J}uXLN*kMH⣂@ G2LRj/'#,"\ PCP\B*X>b׏6`ƅi< [$T*lق ,"x0jZP ˇ#cé3Lf)`@`߶DRlFEtzƖBGi*l6@hr@ X- ,mmm)K |;VKpv{]]穟\.ϧ)/zj|Wj.8 *7XdL&^0V-//OHHPҌ)ڰXƗmG\ͧ`XSa^o2JN[hf f|@P TWC )N}n0TT__iX%P(1\ &4q^~TT>tM|llhFO5jb Ksj4)A4Jl݊bZOL$ @gJno>,2fqUV5999??oK |ܖZFۂpA@*RYYYt}[ju: T@+04E6s#}jXz}MM 0U渭PlT^G|dWʖ(Fbl]ZZޥ܋{, {>P`~lEe,L&۹sZ|$`2+**, 5Xr%KD)1YXDUSShZp8fMXx|F=߀ ;2n[Y]';7(Rb"D" MLժRlRגZFh4KeHrղtqpYXWVVVQQ>77. 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They provide methods and a data class for performing # some general analysis on NMR data taken directly from peaklist. # The code in this script will demonstrate how they are used. # ** Who wrote this code? ** # Robert Bussell, Jr. # rgb2003@med.cornell.edu # ** Running this script ** # # To run this script on a UNIX/Linux system, make it executable and # modify the first line of this script to point to python if necessary. # First try running the code with the peaklist that I provide to get # the feel of how things work, then you can use your own peaklist if you # modify the variables under the "INITS" code block to make it work # with your data. The modules xpktools and NOEtools can be called from # your own scripts when you have them in place on your computer. # NOTE: It is very important to have an intact peaklist. If you copy and # paste mine into a file be prepared to remove inappropriate line breaks. # ** Output of this script ** # # This script generates a human readable standard output version of the # NOE coordinates as well as an nmrview peaklist out_example.xpk. # *********************************************************************** # ***** LOAD MODULES ***** from __future__ import print_function import getopt import string import sys # -- don't need to modify sys.path with the *tools in Biopython # -- just need Biopython installed somewhere in the PYTHONPATH #sys.path=[sys.path,"./"] #sys.path=[sys.path,"/usr/people/robert/development/xpktools"] from Bio.NMR import xpktools # Contains data classes and functions for .xpk files from Bio.NMR import NOEtools # A module specific for generate NOE predictions # * * * * * * * * * * MAIN * * * * * * * * * * # ***** INITS ***** inc = 1 # The NOE increment (n where i->i+n and i->i-n are noes) infn = "./noed.xpk" # Input peaklist outfn = "./out_example.xpk" # Output peaklist detectatom = "H1" # Directly detected atom relayatom = "N15" # J-coupling from here to detected atom fromatom = "15N2" # The other labelled nucleus # *-*-* First the peaklist is read into a data class from xpktools # *-*-* that contains methods for easily extracting information from # *-*-* the peaklist file peaklist = xpktools.Peaklist(infn) # infn is the name of the xpk file # *-*-* The class attribute residue_dict allows the data lines # *-*-* to be separated from the header and returned here to # *-*-* the dictionary as a list indexed by the assignment # *-*-* of any of the nuclei in the file -- here, the detected atom # *-*-* is used dict = peaklist.residue_dict(detectatom) # *-*-* As well as the data, the dictionary contains two other entries, # *-*-* corresponding to the maximum and minimum residues indexed MAXRES = dict["maxres"] MINRES = dict["minres"] #****** CALCULATE AND WRITE CROSSPEAK PEAKLIST ***** # *-*-* The peaklist class has a method for writing out the header # *-*-* information in a format recognizable by nmrview peaklist.write_header(outfn) # Write the header to the output file # *-*-* Predict the i->i+inc and i->i-inc noe positions if possible # *-*-* Write each one to the output file as they are calculated count = 0 # A counter that number the output data lines in order res = MINRES # minimum residue number in the set outlist = [] # Holds the output data while (res <= MAXRES): # *-*-* Predicting the NOE positions based on peak assignment data # *-*-* is done by supplying the peaklist to and specifying the label # *-*-* of the origin and detected atom in the NOE transfer as well as # *-*-* the residues between which the NOE transfer takes places. noe1 = NOEtools.predictNOE(peaklist, "15N2", "H1", res, res + inc) noe2 = NOEtools.predictNOE(peaklist, "15N2", "H1", res, res - inc) # *-*-* The output of predictNOE is in the form of an xpk entry line # *-*-* suitable for printing to an output file # *-*-* Additionally, it is possible to extract information easily from # *-*-* these output lines by using the xpktools.XpkEntry class entry1 = xpktools.XpkEntry(noe1, peaklist.datalabels) if noe1 != "": # *-*-* Here I'm using the XpkEntry class to gain access to # *-*-* specific fields in the that make the information # *-*-* more readable and suitable for creating data tables # *-*-* This output will be printed to the screen. # *-*-* The data table contains the assignment, coordinates and # *-*-* intensity of the resonance. print(string.split(entry1.fields["15N2.L"], ".")[0], "-->", \ string.split(entry1.fields["N15.L"], ".")[0], "\t", \ entry1.fields["H1.P"], entry1.fields["N15.P"], \ entry1.fields["15N2.P"], entry1.fields["int"]) noe1 = noe1 + "\012" noe1 = xpktools.replace_entry(noe1, 1, count) outlist.append(noe1) count += 1 if noe2 != "": noe2 = noe2 + "\012" noe2 = xpktools.replace_entry(noe2, 1, count) outlist.append(noe2) count += 1 res += 1 # Open the output file and write the data outfile = open(outfn, 'a') outfile.writelines(outlist) # Write the output lines to the file outfile.close() biopython-1.63/Doc/examples/fasta_dictionary.py0000644000175000017500000000276712250104543021304 0ustar tratra00000000000000# In Memory # ========= # This next bit of code uses Bio.SeqIO.parse() to load a FASTA file, # and then turns it into an in-memory python dictionary. # This is *not* suitable for FASTA files with millions of entries. from __future__ import print_function from Bio.Alphabet import generic_dna from Bio import SeqIO def get_accession_num(seq_record): accession_atoms = seq_record.id.split('|') gb_name = accession_atoms[3] # strip the version info before returning return gb_name[:-2] rec_iterator = SeqIO.parse("ls_orchid.fasta", "fasta", generic_dna) orchid_dict = SeqIO.to_dict(rec_iterator, get_accession_num) for id_num in orchid_dict: print('id number: %s' % id_num) print('description: %s' % orchid_dict[id_num].description) print('sequence: %s' % orchid_dict[id_num].seq) # Indexed # ======= # This next version uses the Bio.SeqIO.index() function which will index # the FASTA file without loading all the records into memory at once. # This is suitable for FASTA files with millions of entries. from Bio.Alphabet import generic_dna from Bio import SeqIO def get_accession_num(record_id): accession_atoms = record_id.split('|') gb_name = accession_atoms[3] # strip the version info before returning return gb_name[:-2] orchid_dict = SeqIO.index("ls_orchid.fasta", "fasta", generic_dna) for id_num in orchid_dict: print('id number: %s' % id_num) print('description: %s' % orchid_dict[id_num].description) print('sequence: %s' % orchid_dict[id_num].seq) biopython-1.63/Doc/examples/swissprot.py0000644000175000017500000000137612250104543020031 0ustar tratra00000000000000# This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. # """Example of connecting with exPASy and parsing SwissProt records.""" # biopython from __future__ import print_function from Bio import ExPASy, SwissProt # 'O23729', 'O23730', 'O23731', Chalcone synthases from Orchid ids = ['O23729', 'O23730', 'O23731'] for id in ids: handle = ExPASy.get_sprot_raw(id) record = SwissProt.read(handle) print("description: %s" % record.description) for ref in record.references: print("authors: %s" % ref.authors) print("title: %s" % ref.title) print("classification: %s" % record.organism_classification) print("") biopython-1.63/Doc/examples/my_blat.psl0000644000175000017500000000402412250104543017542 0ustar tratra00000000000000psLayout version 4 DNA DNA match mis- rep. N's Q gap Q gap T gap T gap strand Q Q Q Q T T T T block blockSizes qStarts tStarts match match count bases count bases name size start end name size start end count --------------------------------------------------------------------------------------------------------------------------------------------------------------- 61 0 0 0 0 0 0 0 + mystery_seq 61 0 61 chr19 59128983 54204480 54204541 1 61, 0, 54204480, 59 2 0 0 0 0 1 31298 + mystery_seq 61 0 61 chr19 59128983 54233104 54264463 2 18,43, 0,18, 54233104,54264420, 59 2 0 0 0 0 1 5533 + mystery_seq 61 0 61 chr19 59128983 54254477 54260071 2 18,43, 0,18, 54254477,54260028, 58 2 0 0 0 0 0 0 + mystery_seq 61 1 61 chr19 59128983 54210720 54210780 1 60, 1, 54210720, 58 2 0 0 0 0 0 0 + mystery_seq 61 0 60 chr19 59128983 54198476 54198536 1 60, 0, 54198476, 58 3 0 0 0 0 0 0 + mystery_seq 61 0 61 chr19 59128983 54265610 54265671 1 61, 0, 54265610, 58 3 0 0 0 0 1 1971 + mystery_seq 61 0 61 chr19 59128983 54238143 54240175 2 34,27, 0,34, 54238143,54240148, 57 3 0 0 0 0 0 0 + mystery_seq 61 0 60 chr19 59128983 54189735 54189795 1 60, 0, 54189735, 57 4 0 0 0 0 0 0 + mystery_seq 61 0 61 chr19 59128983 54185425 54185486 1 61, 0, 54185425, 56 4 0 0 0 0 0 0 + mystery_seq 61 0 60 chr19 59128983 54197657 54197717 1 60, 0, 54197657, 56 5 0 0 0 0 0 0 + mystery_seq 61 0 61 chr19 59128983 54255662 54255723 1 61, 0, 54255662, 56 5 0 0 0 0 0 0 + mystery_seq 61 0 61 chr19 59128983 54201651 54201712 1 61, 0, 54201651, 48 4 0 0 0 0 0 0 + mystery_seq 61 8 60 chr19 59128983 54206009 54206061 1 52, 8, 54206009, 47 4 0 0 0 0 0 0 + mystery_seq 61 10 61 chr19 59128983 54178987 54179038 1 51, 10, 54178987, 48 4 0 0 1 1 1 1 + mystery_seq 61 8 61 chr19 59128983 54212018 54212071 3 42,4,6, 8,50,55, 54212018,54212061,54212065, 40 3 0 0 0 0 0 0 + mystery_seq 61 8 51 chr19 59128983 54234278 54234321 1 43, 8, 54234278, 42 11 0 0 0 0 0 0 - mystery_seq 61 8 61 chr19 59128983 54238143 54238196 1 53, 0, 54238143, biopython-1.63/Doc/examples/PF05371_seed.sth0000644000175000017500000000305012250104543020016 0ustar tratra00000000000000# STOCKHOLM 1.0 #=GS COATB_BPIKE/30-81 AC P03620.1 #=GS COATB_BPIKE/30-81 DR PDB; 1ifl ; 1-52; #=GS Q9T0Q8_BPIKE/1-52 AC Q9T0Q8.1 #=GS COATB_BPI22/32-83 AC P15416.1 #=GS COATB_BPM13/24-72 AC P69541.1 #=GS COATB_BPM13/24-72 DR PDB; 2cpb ; 1-49; #=GS COATB_BPM13/24-72 DR PDB; 2cps ; 1-49; #=GS COATB_BPZJ2/1-49 AC P03618.1 #=GS Q9T0Q9_BPFD/1-49 AC Q9T0Q9.1 #=GS Q9T0Q9_BPFD/1-49 DR PDB; 1nh4 A; 1-49; #=GS COATB_BPIF1/22-73 AC P03619.2 #=GS COATB_BPIF1/22-73 DR PDB; 1ifk ; 1-50; COATB_BPIKE/30-81 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA #=GR COATB_BPIKE/30-81 SS -HHHHHHHHHHHHHH--HHHHHHHH--HHHHHHHHHHHHHHHHHHHHH---- Q9T0Q8_BPIKE/1-52 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA COATB_BPI22/32-83 DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPM13/24-72 AEGDDP...AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA #=GR COATB_BPM13/24-72 SS ---S-T...CHCHHHHCCCCTCCCTTCHHHHHHHHHHHHHHHHHHHHCTT-- COATB_BPZJ2/1-49 AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA Q9T0Q9_BPFD/1-49 AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA #=GR Q9T0Q9_BPFD/1-49 SS ------...-HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH-- COATB_BPIF1/22-73 FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA #=GR COATB_BPIF1/22-73 SS XX-HHHH--HHHHHH--HHHHHHH--HHHHHHHHHHHHHHHHHHHHHHH--- #=GC SS_cons XHHHHHHHHHHHHHHHCHHHHHHHHCHHHHHHHHHHHHHHHHHHHHHHHC-- #=GC seq_cons AEssss...AptAhDSLpspAT-hIu.sWshVsslVsAsluIKLFKKFsSKA // biopython-1.63/Doc/examples/make_subsmat.py0000644000175000017500000000177212250104543020427 0ustar tratra00000000000000#!/usr/bin/env python """Example of generating a substitution matrix from an alignment. """ # standard library from __future__ import print_function import sys # Biopython from Bio import SubsMat from Bio import AlignIO from Bio.Alphabet import IUPAC, Gapped from Bio.Align import AlignInfo # get an alignment object from a Clustalw alignment output c_align = AlignIO.read('protein.aln', 'clustal', alphabet=Gapped(IUPAC.protein)) summary_align = AlignInfo.SummaryInfo(c_align) # get a replacement dictionary and accepted replacement matrix # exclude all amino acids that aren't charged polar replace_info = summary_align.replacement_dictionary(["G", "A", "V", "L", "I", "M", "P", "F", "W", "S", "T", "N", "Q", "Y", "C"]) my_arm = SubsMat.SeqMat(replace_info) print(replace_info) my_lom = SubsMat.make_log_odds_matrix(my_arm) print('log_odds_mat: %s' % my_lom) my_lom.print_mat() biopython-1.63/Doc/examples/ls_orchid.gbk.bgz0000644000175000017500000007207612250104543020623 0ustar tratra00000000000000BCy#]s7+P٤QDrq/~ {~Xyn~fӴːqsfգ.ۻo/=}<_goyX6/~~_ϒ>_Oi{/bv},?|uMxLj{y>[_|<=;Or&~Gџ̖?.7lg7x/?|?( ~߻~̓/ˇ'gt{XGrvz)wO2yX.<ϝKJo^pR6lܼn90[7W^g7vz|n݁s_=]Kg6i^~x~\NG)=ۇҔԽ8w_8_.}yxb1^؍o^<~Y== >}|XهEu7X"-U>jmN>~*/<&C7y??~gOF#7OjۙO3P_OR~NRu䆧ӡ?~e->f>go>wos2^Wx~:kgBLct)P,/cfH1D|;|7'.M국V/ߐ?mҖ//Rn7J rF;&ikt0r)<<6_n+ߗRo=r|#>Bж~;^TK[B[k<59Ѷ&<5fCY^љNW6_}<,?#OG#\@z2ydxgЕ-U/,H𘏲 m+=~oM/3geS6!Qs`҈ *s7s_By(W`偔x`nWm+|lČx{Dg eXh0T,؉^(t`Di `<dA2DB]My*kpXCgHwQZn)OЖНW^1НH0JY-Ԃ1b ];]Uv1b239w0pSvk)yN7҈x<@zJZX58LSCyd{疟; c=  숱Pv2,Fi @ߨh`;{!Oo}o1BMݵ[ }w&6z_Mwз77}4Z(ܥ"E^)@E@FBh{8;Ej{E\2+xFI(%jF Q }<)U!<Ce> xJ{?((D׃H] b B }Nt=ršW^Aerl EK@; /&h3,EV}.$_ɐ .DДsb"+d*i/vҞ?Pȅq}T쫠/&>G$vFc3  |y~gdDXXg:77Yk9|wu׎n'MM6"f_oSZ/|^tkM=:_2/HNКOђoY $:Sht LCU3y&>*a͍7r*䋴3 k1B_93Z[EK }Ό r 3&>X|j˅:G΃)&uZ<*q֘$>܂b5 ?/ E4 4H }Nm^*3@R{\[k.Y@eG0eqe.'^&phI$Bs?Ɩ-ixڛ,ckzwȤ;̤[oB6DEԃl _Q åoꕷ\JuEG aPG+ b^JNiRDCHc8ss5E:|b{ %]TNHNu Fvrы- zLkL*Q QA+C]?&ݫC]?U\mҽ:\m]:?u(W{WLQ۲}wpp4x1cQw7 wѮv`w+nt̶̶F&wX! .Mmy'ƺ=BC'p%΢L(m#4 ^K}:O<=AN< OLɋɋX2Ib͕*m'dJOQ u8%54]WSapu撇 pjeÞ_qCۓ:66Yr r[uCwVRю7 m~]R܎6n>vcf`MTh A}lɠy6.blG2 13d#Xp+f3dc(58g. ulp؊ ْ3d ΐ D6 DEhG 3dCp0-9C 4ЧȆD6ْ3dkM Ddc6o/Êl⇉j[8["nzg [Z=G[:~8ھ:]ir7 r2t'X*z|3w|n?!ͼɯ2 l v)`4*lTC<CPb܊C{uk` qUBaipEثc\jc!KMrwE.G`a/b+Vx&Z鱦Z%YwcM 8ƙԜ,ItĻ`T'(S7 ;М$ȗV5B؉-U*]}ZvsծĶƊ}Gnh AUGI" QTn!7G+o;]Ht0[N'=[g{{xd&5Mo8F8i-ݵ[ jDvѯK;dׁݐ >xS|iCB\ UN\xO\08r1Ht䣀s=oc$CW[N% {#}RN$u̦W&*8 rus0J3׸!ΰ I80ǞyYLC[+X3'9SHpHt#Bf5qK^N}Ԡ t(q sC Uʉ8OO MZ.QtwLV3*=<ägR{C$H'M gPHR];bvn_mSbq_~bK:kKl4yl4Uϴ{rf+8Pgu͙፼47$T_&xM?5"IɌboiU6&?^p OUF'Me.WM#L0ε0_5i] L8ifg'bz&Ws)vv+!@FfB<1چ6h'(JE9p`2H00>MV^N\ tKQBuVA u򠀧yk!g2zt+^-*=8餀+³*a'I~8g2 Lt]fE+\m5.:ps&Eg2uYB81jD::,yp&!X;g2v\JgoԗxK/|xΖ!R["h=;̿5{rȯ{FեPV؆9MX=xpE,j%$bC+]/= uq%f!1S>6-YO4@I?3<(Yb@V|nE-ûᮣr=.c{ 67xKw6ktozoz9o!hG~n$Šcō(჆[Z1"&=#5 P!Ab'(-&9$HZ(p!5q^LY CᲸؑPȎmԆb^q4`F\&g\̈+3# fgk1 4hF\͈뒎*Tbzi$0r0OBch*,xeOtPǝG;ex~Ac编pBQ< ݘy>5 iV1o@ИR#$)ybz+@Lulm^aԔ@|^{ݵc[z;7Aw_Lwim?TzDψzxv[CD ^F=V5WHB NL[2 "g!z)AU@XVz$%_s)mWkyF}zhR֋U'!)sHa6iZ%I?¯Dx^js$1_5dKeg8dO]3 pQ-N66Ppnc;A"P0]k ^Y Z_4Om~Ь!9#A' B ՎtkP0O3 RR"g,lѪh}ʂ[~5  n %+ƂLVCg,肱 X[,X`2?^h(,袱 ǕoX1* 2V߾Pdogf]cAǼ*4 h,袱SnQ?wO/ت6{/SpKUR(Uўm6 xvwKYoz^.}ockL?RK33 4"!!W!|Ra`IMF:<;ZwBLzk*aIBe'ks,(Ф&I[&4 OM4Z44̈I7IX.E&K9 |nEOኘ`݊. ֍Unyn08 5)twHҪ3WD{K<;:l!NJU՘N#7q;G #%rHyc*}%ݹtﳗl>M냽Gvwo mM%+(Ym2 @vwK1o<;7myӯK9m y+x{6.p܋{:ǽb^9 y hy(:a\\V8G^ꏾ 7Qr[BLQw5=r ysUdN=\4 /ѦiS7cC 0#JsH{3yFq!#yF(9 Jr/IdsF6CCPD-wLx (KYA$/N/U8l t'gq6z%*QY5 vpwKnrn)UoM;"'Bޅj$!"eZ{ _d~%mY~rko>ƕ*M^\>_c_*^sT0Ց"3zYdKKm-N[foaoʡ&#[kxvVu6knw_/035xES3o I{J#/ ϙH 8qsfOio<5z5Bi.򝿄;:mwt[y.Jy;;)zƌFw mBC[sǕ)N)i$.?(2ږ8j5&*$O613 0 c1?d^gn[5_?^zQjvu3f0<t0Mͮwyp}˗۹?~|OSG_}}2:pw]eq~?u7.ҕ,Nj\ٽO'Wpd3;ܿosG޽%= ӻ4 2_~խni7t1ݸYtbrwן򋗷S3 Udq]?0L*=~F:n&NIpq8*X˿^|33H|y/~>zK ,S">.kw?yߏ߿z%Oy^80owi0ßuy3? 7"o⴨p?߇Oӛ3-?7}=|_Oѝc= gύ-t1=|ORtiip98s>ԽJ/i{̦w90y1ҷS};]=O\,&/_Ço/ԡLNtO4jz|5_Գoh_v~niixi]_}o/~o)MŇń{:>n9lW|O_R8m'ͿBi$ԓԚ-W>JKe2'U||VNx_ѷ0yCt;}3? 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2Ztڭpd.![0AC#[5ضpZq푢[tЭөntU\W n=W߂k;`⚴ ɁXԒ G-WlvkMU/zvUos\eZ ]gP{!|Vˬ7HXЬG[ycVEy%7a]U[5~X6:z)YΓ]lx KbWh>XnJ)b{ŊJ>oz Ѐu|eB,x˕:܁X1։bB)\ Ɗɪ \ ƺ*w*d)"HxWzpq*G $L@p\51ڱmƌGTF5޴%sŖB]נwXS#g_~u5hR;{t O\[+][ZUMieNx Hb |AұILY;ZIgKVSjUgi/X݄ Px%zUخ6XmJ J fk xZ=U((p5]#+_kX[%X|ͺ\O0Zoz 5ъ6ⓊcipkkUN&RAܚW3桪a*jq΀|+enc'5JGh!#],ywBDsE@47:2A`bR%TV,-cJaK@u׏Pa9rt\*y+]+ҵrr6^+P]V Յ,r1 ՁD0I3[+oG7ϯ&MVq %'ǎ+=tͪYɾt;zJo#/ezr}9ItN#V1~ t\kIʅ*d+z[/)v te!. ʅ*d[m)uJ62;ٌ: ׉w 4sp^qHqf<sm}[R\sMӕa-3kҏԐ(9W4/`1=u "Նp^*. e.UksժY82X#Y8os]sVZҿu,%VJη<:pݢWD8JqtAl#gMhmyND[N+=NVsSɰkkEV2tattUt7Aj(]޷,DoVVvwǸrZo:Jcio ݾ*V-ѻν%8bX3 `L.|(Dݼ&(L[?{#FQ#VPbYپ #:;H+Gqsi;Sђ,~Ҝ!F%(+F=8/bsUq*: #HE%S,h_/z4pq1rCmA_(S z}sxT*2ORA:Kh^W!Ȝ ߡ DVօ;sG#HO¾Nv{5kw{AOxW#Z ,gOnd}D+XI" tW ۫N J} ^52sse$p986gKrC[r/ AIR+Qsk=ITꖷ$[C|kk.98vHH6kTZn]lΫzޝU;XKVZQv9V^Ic}n!jUD=[y- 郎!sѭ6.Q[ІdsĔ,QD0 q ~- B`|8doD~ZE HbRrxhtXl΀$Po7gi|6273291|gb|AF191665.1|AF191665 Opuntia marenae rpl16 gene; chloroplast gene for chloroplast product, partial intron sequence TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAATCTAAATGATA TAGGATTCCACTATGTAAGGTCTTTGAATCATATCATAAAAGACAATGTAATAAAGCATGAATACAGATT CACACATAATTATCTGATATGAATCTATTCATAGAAAAAAGAAAAAAGTAAGAGCCTCCGGCCAATAAAG ACTAAGAGGGTTGGCTCAAGAACAAAGTTCATTAAGAGCTCCATTGTAGAATTCAGACCTAATCATTAAT CAAGAAGCGATGGGAACGATGTAATCCATGAATACAGAAGATTCAATTGAAAAAGATCCTATGNTCATTG GAAGGATGGCGGAACGAACCAGAGACCAATTCATCTATTCTGAAAAGTGATAAACTAATCCTATAAAACT AAAATAGATATTGAAAGAGTAAATATTCGCCCGCGAAAATTCCTTTTTTATTAAATTGCTCATATTTTCT TTTAGCAATGCAATCTAATAAAATATATCTATACAAAAAAACATAGACAAACTATATATATATATATATA TAATATATTTCAAATTCCCTTATATATCCAAATATAAAAATATCTAATAAATTAGATGAATATCAAAGAA TCTATTGATTTAGTGTATTATTAAATGTATATATTAATTCAATATTATTATTCTATTCATTTTTATTCAT TTTCAAATTTATAATATATTAATCTATATATTAATTTAGAATTCTATTCTAATTCGAATTCAATTTTTAA ATATTCATATTCAATTAAAATTGAAATTTTTTCATTCGCGAGGAGCCGGATGAGAAGAAACTCTCATGTC CGGTTCTGTAGTAGAGATGGAATTAAGAAAAAACCATCAACTATAACCCCAAAAGAACCAGA >gi|6273290|gb|AF191664.1|AF191664 Opuntia clavata rpl16 gene; chloroplast gene for chloroplast product, partial intron sequence TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAATCTAAATGATA TAGGATTCCACTATGTAAGGTCTTTGAATCATATCATAAAAGACAATGTAATAAAGCATGAATACAGATT CACACATAATTATCTGATATGAATCTATTCATAGAAAAAAGAAAAAAGTAAGAGCCTCCGGCCAATAAAG ACTAAGAGGGTTGGCTCAAGAACAAAGTTCATTAAGAGCTCCATTGTAGAATTCAGACCTAATCATTAAT CAAGAAGCGATGGGAACGATGTAATCCATGAATACAGAAGATTCAATTGAAAAAGATCCTAATGNTNCAT TGGGAAGGATGGCGGAACGAACCAGAGACCAATTCATCTATTCTGAAAAGTGATAAACTAATCCTATAAA ACTAAAATAGATATTGAAAGAGTAAATATTCGCCCGCGAAAATTCCTTTTTTATTAAATTGCTCATATTT TCTTTTAGCAATGCAATCTAATAAAATATATCTATACAAAAAAACATAGACAAACTATATATATATATAA TATATTTCAAATTCCCTTATATATCCAAATATAAAAATATCTAATAAATTAGATGAATATCAAAGAATCT ATTGATTTAGTGTATTATTAAATGTATATATTAATTCAATATTATTATTCTATTCATTTTTATTCATTTT CAAATTTATAATATATTAATCTATATATTAATTTAGAATTCTATTCTAATTCGAATTCAATTTTTAAATA TTCATATTCAATTAAAATTGAAATTTTTTCATTCGCGAGGAGCCGGATGAGAAGAAACTCTCATGTCCGG TTCTGTAGTAGAGATGGAATTAAGAAAAAACCATCAACTATAACCCCAAAAGAACCAGA >gi|6273289|gb|AF191663.1|AF191663 Opuntia bradtiana rpl16 gene; chloroplast gene for chloroplast product, partial intron sequence TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAATCTAAATGATA TAGGATTCCACTATGTAAGGTCTTTGAATCATATCATAAAAGACAATGTAATAAAGCATGAATACAGATT CACACATAATTATCTGATATGAATCTATTCATAGAAAAAAGAAAAAAGTAAGAGCCTCCGGCCAATAAAG ACTAAGAGGGTTGGCTCAAGAACAAAGTTCATTAAGAGCTCCATTGTAGAATTCAGACCTAATCATTAAT CAAGAAGCGATGGGAACGATGTAATCCATGAATACAGAAGATTCAATTGAAAAAGATCCTAATGATTCAT TGGGAAGGATGGCGGAACGAACCAGAGACCAATTCATCTATTCTGAAAAGTGATAAACTAATCCTATAAA ACTAAAATAGATATTGAAAGAGTAAATATTCGCCCGCGAAAATTCCTTTTTTATTAAATTGCTCATATTT TCTTTTAGCAATGCAATCTAATAAAATATATCTATACAAAAAAACATAGACAAACTATATATATATATAA TATATTTCAAATTCCCTTATATATCCAAATATAAAAATATCTAATAAATTAGATGAATATCAAAGAATCT ATTGATTTAGTATATTATTAAATGTATATATTAATTCAATATTATTATTCTATTCATTTTTATTCATTTT CAAATTTATAATATATTAATCTATATATTAATTTAGAATTCTATTCTAATTCGAATTCAATTTTTAAATA TTCATATTCAATTAAAATTGAAATTTTTTCATTCGCGAGGAGCCGGATGAGAAGAAACTCTCATGTCCGG TTCTGTAGTAGAGATGGAATTAAGAAAAAACCATCAACTATAACCCCAAAAGAACCAGA >gi|6273287|gb|AF191661.1|AF191661 Opuntia kuehnrichiana rpl16 gene; chloroplast gene for chloroplast product, partial intron sequence TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAATCTAAATGATA TACGATTCCACTATGTAAGGTCTTTGAATCATATCATAAAAGACAATGTAATAAAGCATGAATACAGATT CACACATAATTATCTGATATGAATCTATTCATAGAAAAAAGAAAAAAGTAAGAGCCTCCGGCCAATAAAG ACTAAGAGGGTTGGCTCAAGAACAAAGTTCATTAAGAGCTCCATTGTAGAATTCAGACCTAATCATTAAT CAAGAAGCGATGGGAACGATGTAATCCATGAATACAGAAGATTCAATTGAAAAAGATCCTATGATCCATT GGGAAGGATGGCGGAACGAACCAGAGACCAATTCATCTATTCTGAAAAGTGATAAACTAATCCTATAAAA CTAAAATAGATATTGAAAGAGTAAATATTCGCCCGCGAAAATTCCTTTTTTTTTTAAATTGCTCATATTT TATTTTAGCAATGCAATCTAATAAAATATATCTATACAAAAAAATAAAGACAAACTATATATATAATATA TTTCAAATTTCCTTATATATCCAAATATAAAAATATCTAATAAATTAGATGAATATCAAAGAATCTATTG ATTTAGTGTATTATTAAATGTATATCTTAATTCAATATTATTATTCTATTCATTTTTATTCATTTTCAAT TTTATAATATATTAATCTATATATTAATTTATAATTCTATTCTAATTCGAATTCAATTTTTAAATATTCA TATTCAATTAAAATTGAAATTTTTTCATTCGCGAGGAGCCGGATGAGAAGAAACTCTCATGTCCGGTTCT GTAGTAGAGATGGAATTAAGAAAAAACCATCAACTATAACCCCAAGAGAACCAGA >gi|6273286|gb|AF191660.1|AF191660 Opuntia echinacea rpl16 gene; chloroplast gene for chloroplast product, partial intron sequence TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAATCTAAATGATA TACGATTCCACTATGTAAGGTCTTTGAATCATATCATAAAAGACAATGTAATAAAGCATGAATACAGATT CACACATAATTATCTGATATGAATCTATTCATAGAAAAAAGAAAAAAGTAAGAGCCTCCGGCCAATAAAG ACTAAGAGGGTTGGCTCAAGAACAAAGTTCATTAAGAGCTCCATTGTAGAATTCAGACCTAATCATTAAT CAAGAAGCGATGGGAACGATGTAATCCATGAATACAGAAGATTCAATTGAAAAAGATCCTAATGATCATT GGAAGGATGGCGGAACGAACCAGAGACCAATTCATCTATTCTGAAAAGTGATAAACTAATCCTATAAAAC TAAAATAGATATTGAAAGAGTAAATATTCGCCCGCGAAAATTCCTTTTTTATTAAATTGCTCATATTTTA TTTTAGCAATGCAATCTAATAAAATATATCTATACAAAAAAATATAGACAAACTATATATATAATATATT TATAATTTCCTTATATATCCAAATATAAAAATATCTAATAAATTAGATGAATATCAAAGAATCTATTGAT TTAGTGTATTATTAAATGTATATCTTAATTCAATATTATTATTCTATTCATTTTTATTCATTTTCAAATT TATAATATATTAATCTATATATTAATTTATAATTCTATTCTAATTCGAATTCAATTTTTAAATATTCATA TTCAATTAAAATTGAAATTTTTTCATTCGCGAGGAGCCGGATGAGAAGAAACTCTCATGTCCGGTTCTGT AGTAGAGATGGAATTAAGAAAAAACCATCAACTATAACCCCAAGAGAACCAGA >gi|6273285|gb|AF191659.1|AF191659 Opuntia pachypus rpl16 gene; chloroplast gene for chloroplast product, partial intron sequence TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAATCTAAATGATA TACGATTCCACTATGTAAGGTCTTTGAATCATATCATAAAAGACAATGTAATAAAGCATGAATACAGATT CACACATAATTATCTGATATGAATCTATTCATAGAAAAAAGAAAAAAGTAAGAGCCTCCGGCCAATAAAG ACTAAGAGGGTTGGCTCAAGAACAAAGTTCATTAAGAGCTCCATTGTAGAATTCAGACCTAATCATTAAT CAAGAAGCGATGGGAACGATGTAATCCATGAATACAGAAGATTCAATTGAAAAAGATCCTAATGATCATT GGGAAGGATGGCGGAACGAACCAGAGACCAATTCATCTATTCTGAAAAGTGATAAACTAATCCTATAAAA CTAAAATAGATATTGAAAGAGTAAATATTCGCCCGCGAAAATTCCTTTTTTATTAAATTGCTCATATTTT ATTTTAGCAATGCAATCTAATAAAATATATCTATACAAAAAAATATAGACAAACTATATATATAATATAT TTCAAATTTCCTTATATACCCAAATATAAAAATATCTAATAAATTAGATGAATATCAAAGAATCCATTGA TTTAGTGTATTATTAAATGTATATCTTAATTCAATATTATTATTCTATTCATTTTTATTCATTTTCAAAT TTATAATATATTAATCTATATATTAATTTATAATTCTATTCTAATTCGAATTCAATTTTTAAATATTCAT ATTCAATTAAAATTGAAATTTTTTCATTCGCGAGGAGCCGGATGAGAAGAAACTCTCATGTCCGGTTCTG TAGTAGAGATGGAATTAAGAAAAAACCATCAACTATAACCCCAAGAGAACCAGA >gi|6273284|gb|AF191658.1|AF191658 Opuntia subulata rpl16 gene; chloroplast gene for chloroplast product, partial intron sequence TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAATCTAAATGATA TACGATTCCACTATGTAAGGTCTTTGAATCATATCATAAAAGACAATGTAATAAAGCATGAATACAGATT CACACATAATTATCTGATATGAATCTATTCATAGAAAAAAGAAAAAAGTAAGAGCCTCCGGCCAATAAAG ACTAAGAGGGTTGGCTCAAGAACAAAGTTCATTAAGAGCTCCATTGTAGAATTCAGACCTAATCATTAAT CAAGAAGCGATGGGAACGATGTAATCCATGAATACAGAAGATTCAATTGAAAAAGATCCTAATGATCATT GGGAAGGATGGCGGAACGAACCAGAGACCAATTCATCTATTCTGAAAAGTGATAAACTAATCCTATAAAA CTAAAATAGATATTGAAAGAGTAAATATTCGCCCGCGAAAATTCCTTTTTTATTAAATTGCTCATATTTT ATTTTAGCAATGCAATCTAATAAAATATATCTATACAAAAAAATATAGACAAACTATATATATATAATAT ATTTCAAATTTCCTTATATACCCAAATATAAAAATATCTAATAAATTAGATGAATATCAAAGAATCTATT GATTTAGTGTATTATTAAATGTATATCTTAATTCAATATTATTATTCTATTCATTTTTATTCATTTTCAA ATTTATAATATATTAATCTATATATTAATTTATAATTCTATTCTAATTCGAATTCAATTTTTAAATATTC ATATTCAATTAAAATTGAAATTTTTTCATTCGCGAGGAGCCGGATGAGAAGAAACTCTCATGTCCGGTTC TGTAGTAGAGATGGAATTAAGAAAAAACCATCAACTATAACCCCAAGAGAACCAGA biopython-1.63/Doc/examples/ACT_example.py0000644000175000017500000001051712250104543020073 0ustar tratra00000000000000# This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. # from __future__ import print_function import sys import os import time from reportlab.lib import colors from reportlab.lib.units import cm from Bio.Blast.Applications import NcbitblastxCommandline from Bio.Graphics.GenomeDiagram import Diagram, CrossLink from Bio.SeqFeature import SeqFeature, FeatureLocation from Bio import SeqIO #Modify this line to point at the Artemis/ACT example data which is online at: # https://github.com/sanger-pathogens/Artemis/tree/master/etc input_folder = "/Applications/Artemis/Artemis.app/Contents/artemis/etc" name = "af063097_v_b132222" file_a = "af063097.embl" file_b = "b132222.embl" format_a = "embl" format_b = "embl" file_a_vs_b = "af063097_v_b132222.crunch" for f in [file_a, file_b, file_a_vs_b]: if not os.path.isfile(os.path.join(input_folder, f)): print("Missing input file %s.fna" % f) sys.exit(1) #Only doing a_vs_b here, could also have b_vs_c and c_vs_d etc genomes = [ (os.path.join(input_folder, file_a), format_a), (os.path.join(input_folder, file_b), format_b), ] comparisons = [os.path.join(input_folder, file_a_vs_b)] #Create diagram with tracks, each with a feature set assert len(genomes) >= 2 and len(genomes) == len(comparisons)+1 gd_diagram = Diagram(name, track_size=0.35, circular=False) tracks = dict() feature_sets = dict() records = dict() for f, format in genomes: records[f] = SeqIO.read(f, format) tracks[f] = gd_diagram.new_track(1, name=f, start=0, end=len(records[f]), scale_smalltick_interval=1000, scale_largetick_interval=10000, greytrack=True, greytrack_labels=0) feature_sets[f] = tracks[f].new_set() print("Drawing matches...") for i, crunch_file in enumerate(comparisons): q = genomes[i+1][0] # query file s = genomes[i][0] # subject file q_set = feature_sets[q] s_set = feature_sets[s] handle = open(crunch_file) for line in handle: if line[0]=="#": continue parts = line.rstrip("\n").split(None, 7) #0 = score #1 = id #2 = S1 #3 = E1 #4 = seq1 #5 = S2 #6 = E2 #7 = seq2 try: q_start, q_end = int(parts[2]), int(parts[3]) s_start, s_end = int(parts[5]), int(parts[6]) except IndexError: sys.stderr.write(repr(line) + "\n") sys.stderr.write(repr(parts) + "\n") raise flip = False if q_start > q_end: flip = not flip q_start, q_end = q_end, q_start if s_start > s_end: flip = not flip s_start, s_end = s_end, s_start if flip: c = colors.Color(0, 0, 1, alpha=0.25) b = False else: c = colors.Color(1, 0, 0, alpha=0.25) b = False q_feature = q_set.add_feature(SeqFeature(FeatureLocation(q_start-1, q_end)), color=c, border=b) s_feature = s_set.add_feature(SeqFeature(FeatureLocation(s_start-1, s_end)), color=c, border=b) gd_diagram.cross_track_links.append(CrossLink(q_feature, s_feature, c, b)) #NOTE: We are using the same colour for all the matches, #with transparency. This means overlayed matches will appear darker. #It also means the drawing order not very important. #Note ACT puts long hits at the back, and colours by hit score handle.close() print("Drawing CDS features...") for f, format in genomes: record = records[f] feature_set = feature_sets[f] #Mark the CDS features for cds in record.features: if cds.type != "CDS": continue feature_set.add_feature(cds, sigil="ARROW", color=colors.lightblue, border=colors.blue) gd_diagram.draw(format="linear", fragments=3, orientation="landscape", pagesize=(20*cm, 10*cm)) gd_diagram.write(name + ".pdf", "PDF") gd_diagram.draw(format="circular", orientation="landscape", pagesize=(20*cm, 20*cm)) gd_diagram.write(name + "_c.pdf", "PDF") biopython-1.63/Doc/examples/protein.aln0000644000175000017500000003647712250104543017570 0ustar tratra00000000000000CLUSTAL W (1.81) multiple sequence alignment gi|14150838|gb|AAK54648.1|AF37 -------------------------------------------------- gi|12644515|sp|Q9MBB1|CHSY_EQU MTVLEESADASSRRLAQRANGPATVLAIGTANPANVFEQSSYPDFYFDIT gi|13774971|gb|AAK39113.1|AF35 ------MVTVEEVRKAQRAEGPATILAIGTATPANCVDQSTYPDYYFRIT gi|13774967|gb|AAK39111.1|AF35 ------MVTVEEVRKAQRAEGPATILAIGTATPANCVDQSTYPDYYFRIT gi|13774975|gb|AAK39115.1|AF35 ------MVTVEEVRKAQRAEGPATILAIGTATPANCVDQSTYPDYYFRIT gi|13774965|gb|AAK39110.1|AF35 ------MVTVEEVRKAQRAEGPATILAIGTATPANCVDQSTYPDYYFRIT gi|13774973|gb|AAK39114.1|AF35 ------MVTVEEVRKAQRAEGPATILAIGTATPANCVDQSTYPDYYFRIT gi|13774969|gb|AAK39112.1|AF35 ------MVTVEEVRKAQRAEGPATILAIGTATPANCVDQSTYPDYYFRIT gi|13919613|gb|AAK33142.1| -----------------------------------------YPDYYFRIT gi|13919597|gb|AAK33134.1| -----------------------------------------YPDYYFRIT gi|13925890|gb|AAK49457.1| ------MVTVEEFRRAQCAEGPATVMAIGTATPSNCVDQSTYPDYYFRIT gi|12644514|sp|Q9MB41|CHS2_IPO ------MVTVEEVRKAQRAEGPATILAIGTVTPANCVNQSTYPDYYFRIT gi|12644512|sp|Q9MB39|CHS4_IPO ------MVTVEEVRKAQRAEGPATILAIGTVTPANCVNQSTYPDYYFRIT gi|12644509|sp|Q9MB36|CHS8_IPO ------MVTVEEVRKAQRAEGPATILAIGTVTPANCVNQSTYPDYYFRIT gi|12644511|sp|Q9MB38|CHS6_IPO ------MVTVEEVRKAQRAEGPATILAIGTATPANCVNQSTYPDYYFRIT gi|12644510|sp|Q9MB37|CHS7_IPO ------MVTVEEVRKAKRAEGPATILAIGTATPANCVNQSTYPDYYFRIT gi|12644513|sp|Q9MB40|CHS3_IPO ------MVTVGEVRKAQRAEGPATILAIGTATPANCVNQSTYPDYYFRIT gi|13936397|dbj|BAB47195.1| ----MTSMTVDQIRRPLRAEGLATILAIGTANPANYITQADYPDYYFRVT gi|13936395|dbj|BAB47194.1| ----MTSMTVDQIRRPLRAEGLATILAIGTANPANYITQADYPDYYFRVT gi|13936399|dbj|BAB47196.1| ---MSSSITVDQIRKAQRAEGPATILAIGTATPANFIIQADYPDYYFRVT gi|14150838|gb|AAK54648.1|AF37 -------------------------------------------------- gi|12644515|sp|Q9MBB1|CHSY_EQU NSQHMTELKLKFSRMCQKSGIKKRYMHLNSEILKANPSLCAYWEKSLDVR gi|13774971|gb|AAK39113.1|AF35 KSDHMTDLKQKFQRMCDKSMITKRYMHLTEEILKENPSFCEYMAPSLDAR gi|13774967|gb|AAK39111.1|AF35 KSDHMTDLKQKFQRMCDKSMITKRYMHLTEEILKENPSFCEYMAPSLDAR gi|13774975|gb|AAK39115.1|AF35 KSDHMTDLKQKFQRMCDKSMITKRYMHLTEEILKENPSFCEYMAPSLDAR gi|13774965|gb|AAK39110.1|AF35 KSDHMTDLKQKFQRMCDKSMITKRYMHLTEEILKENPSFCEYMAPSLDAR gi|13774973|gb|AAK39114.1|AF35 KSDHMTDLKQKFQRMCDKSMITKRYMHLTEEILKENPSFCEYMAPSLDAR gi|13774969|gb|AAK39112.1|AF35 NSDHMTDLKQKFQRMCDKSMITKRYMHLTEEILKENPSFCEYMAPSLDAR gi|13919613|gb|AAK33142.1| NREHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDAR gi|13919597|gb|AAK33134.1| NSEHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDAR gi|13925890|gb|AAK49457.1| NSEHKVELKEKFKRMCEKSMIKKRYMHLTEEILKENPNICAYMAPSLDAR gi|12644514|sp|Q9MB41|CHS2_IPO NSEHKTELKEKFQRMCDKSMITKRYMHLTEEILKENPSFCEYMAPSLDAR gi|12644512|sp|Q9MB39|CHS4_IPO NSEHKTELKERFQRMCDKSMITKRYMHLTEEILKENPSFCEYMAPSLDAR gi|12644509|sp|Q9MB36|CHS8_IPO NSEHKTELKEKFQRMCDKSMITKRYMHLTEEILKENPSFCEYMAPSLDAR gi|12644511|sp|Q9MB38|CHS6_IPO NSEHKTELKEKFQRMCDKSMITKRYMHLTEEILKENPSFCEYMAPSLDAR gi|12644510|sp|Q9MB37|CHS7_IPO NSEHKTELKEKFQRMCDKSMITKRYMHLTEEILKENPSFCEYMAPSLDAR gi|12644513|sp|Q9MB40|CHS3_IPO NSEHKTELKEKFQRMCDKSMITKRYMHLTEEILKENPSFCEYMAPSLDAR gi|13936397|dbj|BAB47195.1| KSEHMTDLKNKFQRMCDRSMIKKRHMYFTEEHLKQNPNMCDYTAPSLDAR gi|13936395|dbj|BAB47194.1| KSEHMTDLKNKFQRMCDRSMIKKRHMYFTEEHLKQNPNMCDYTAPSLDAR gi|13936399|dbj|BAB47196.1| KSEHMTNLKKRFQRICDRTMIKKRHLVLSEDHLKENPNMCEFMAPSLDVR gi|14150838|gb|AAK54648.1|AF37 -------------------------------------------------- gi|12644515|sp|Q9MBB1|CHSY_EQU QDIAVVEVPKLGKEASLKAIKEWGQPKSKITHLVFCTTSGVDMPGADWAL gi|13774971|gb|AAK39113.1|AF35 QDIVVVEVPKLGKEAAQSAIKEWGQPKSKITHVIFCTTSGVDMPGADYQL gi|13774967|gb|AAK39111.1|AF35 QDIVVVEVPKLGKEAAQSAIKEWGQPKSKITHVIFCTTSGVDMPGADYQL gi|13774975|gb|AAK39115.1|AF35 QDIVVVEVPKLGKEAAQSAIKEWGQPKSKITHVIFCTTSGVDMPGADYQL gi|13774965|gb|AAK39110.1|AF35 QDIVVVEVPKLGKEAAQSAIKEWGQPKSKITHVIFCTTSGVDMPGADYQL gi|13774973|gb|AAK39114.1|AF35 QDIVVVEVPKLGKEAAQSAIKEWGQPKSKITHVIFCTTSGVDMPGADYQL gi|13774969|gb|AAK39112.1|AF35 QDIVVVEVPKLGKEAAQSAIKEWGQPKSKITHVIFCTTSGVDMPGADYQL gi|13919613|gb|AAK33142.1| QDMVVVEIPKLGKEAAVKAIKEWGQ------------------------- gi|13919597|gb|AAK33134.1| QDMVVVEIPKLGKEAAVKAIKEWGQ------------------------- gi|13925890|gb|AAK49457.1| QDIVVVEVPKLGKEAAQKAIKEWGQPKSKITHLVFCTTSGVDMPGCDYQL gi|12644514|sp|Q9MB41|CHS2_IPO QDIAVVEVPKLGKEAAQSAIKEWGQPKSKITHVVFCTTSGVDMPGADYQL gi|12644512|sp|Q9MB39|CHS4_IPO QDIAVVEVPKLGKEAAQSAIKEWGQPKSKITHVVFCTTSGVDMPGADYQL gi|12644509|sp|Q9MB36|CHS8_IPO QDIAVVEVPKLGKEAAQSAIKEWGQPKSKITHVVFCTTSGIDMPGADYQL gi|12644511|sp|Q9MB38|CHS6_IPO QDIAVVEVPKLGKEAAQSAIKEWGQPKSKITHVVFCTTSGVDMPGADYQL gi|12644510|sp|Q9MB37|CHS7_IPO QDIAVVEVPKLGKEAAQSAIKEWGQPKSKITHVFFCTTSGVDMPGADYQL gi|12644513|sp|Q9MB40|CHS3_IPO QDIAVVEVPKLGKEAAQSAIKGWGQPKSKITHVVFCTTSGVDMPGADYQL gi|13936397|dbj|BAB47195.1| QSILVVEVPKLGKEACIKAIKEWNQPKSKITHFIFTTISGIDMPGADYQC gi|13936395|dbj|BAB47194.1| QSILVVEVPKLGKEACIKAIKEWNQPKSKITHFIFTTISGIDMPGADYQC gi|13936399|dbj|BAB47196.1| QDILVVEVPKLGKEACMKAIKEWDQPKSKITHFIFATTSGVDMPGADYQC gi|14150838|gb|AAK54648.1|AF37 -------------MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEIT gi|12644515|sp|Q9MBB1|CHSY_EQU TKLLGLRPSVKRLMMYQQGCFAGGTVLRVAKDVAENNKGARVLVVCSEIT gi|13774971|gb|AAK39113.1|AF35 TKLLGLRPSVKRLMMYQQGLFAGGTVLRVAKDLAENNKGARVLVVCSEIT gi|13774967|gb|AAK39111.1|AF35 TKLLGLRPSVKRLMMYQQGCFAGGTVLRVAKDLAENNKGARVLVVCSEIT gi|13774975|gb|AAK39115.1|AF35 TKLLGLRPSVKRLMMYQQGCFAGGTVLRVAKDLAENNKGARVLVVCSEIT gi|13774965|gb|AAK39110.1|AF35 TKLLGLRPSVKRLMMYQQGCFAGGTVLRVAKDLVENNKGARVLVVCSEIT gi|13774973|gb|AAK39114.1|AF35 TKLLGLRPSVKRLMMYQQGCFAGGTVLRVAKDLAENNKGARVLVVCSEIT gi|13774969|gb|AAK39112.1|AF35 TKLLGLRPSVKRLMMYQQGCFAGGTVLRVAKDLAENNKGARVLVVCSEIT gi|13919613|gb|AAK33142.1| -------------------------------------------------- gi|13919597|gb|AAK33134.1| -------------------------------------------------- gi|13925890|gb|AAK49457.1| TKLLGLRPSVKRFMMYQQGCFAGGTVLRMAKDLAENNKGARVLVVCSEIT gi|12644514|sp|Q9MB41|CHS2_IPO TKLLGLSPSVKRLMMYQQGCFAGGTVLRLAKDLAENNKGARVLIVCSEIT gi|12644512|sp|Q9MB39|CHS4_IPO TKLLGLRPSVKRLMMYQQGCFAGGTVLRLAKDLAENNKGARVLIVCSEIT gi|12644509|sp|Q9MB36|CHS8_IPO TKLLGLRPSVKRLMMYQQGCFAGGTVLRLAKDLAENNKGARVLIVCSEIT gi|12644511|sp|Q9MB38|CHS6_IPO TKLLGLRPSVKRLMMYQQGCFAGGTVLRLAKDLAENNKGARVLIVCSEIT gi|12644510|sp|Q9MB37|CHS7_IPO TKLLGLRPSVKRLMMYQQGCFAGGTVLRLAKDLAENNKGARVLIVCSEIT gi|12644513|sp|Q9MB40|CHS3_IPO TKLLGLRPSVKRLMMYQQGCFAGGTVLRLAKDLAENNKGARVLIVCSEIT gi|13936397|dbj|BAB47195.1| SKLLGLNPSVKRVMLYNLGCHASGTILRMAKDIAENNKGARVLAVCSDIM gi|13936395|dbj|BAB47194.1| SKLLGLNPSVKRVMLYNLGCHASGTILRMAKDIAENNKGARVLAVCSDIM gi|13936399|dbj|BAB47196.1| AKLLGLSSSVKRVMMYQQGCFAGGTVLRIAKDIAENNKGARVLALCSEIT gi|14150838|gb|AAK54648.1|AF37 AVTFRGPSETHLDSMVGQALFGDGAGAVIVGSDPDLS-VERPLYELVWTG gi|12644515|sp|Q9MBB1|CHSY_EQU CVTFRGPSETHLDSLVGQALFGDGAAAVILGSDP-LP-EENPCFELHWSG gi|13774971|gb|AAK39113.1|AF35 VVTFRGPNETHLDSLVGQALFGDGAAAIIVGSDPTP--VEKALFQLVSAA gi|13774967|gb|AAK39111.1|AF35 VVTFRGPNETHLDSLVGQALFGDGAAAIIVGSDPTP--VEKPLFQLVSAA gi|13774975|gb|AAK39115.1|AF35 VVTFRGPNETHLDSLVGQALFGDGAAAIIVGSDPTP--VEKPLFQLVSAA gi|13774965|gb|AAK39110.1|AF35 VVTFRGPNETHLDSLVGQALFGDGAAAIIVGSDPTP--VEEPLFQLVSAA gi|13774973|gb|AAK39114.1|AF35 VVTFRGPNETHLDSLVGQALFGDGAAAIIVGSDPTP--VEKPLFQLVSAA gi|13774969|gb|AAK39112.1|AF35 VVTFRGPNETHLDSLVGQALFGDGAAAIIVGSDPTP--VEKPLFQLVSAA gi|13919613|gb|AAK33142.1| -------------------------------------------------- gi|13919597|gb|AAK33134.1| -------------------------------------------------- gi|13925890|gb|AAK49457.1| AVTFRGPNDTHLDSLVGQALFGDGAAAVIIGSDPIPE-VERPLFELVSAA gi|12644514|sp|Q9MB41|CHS2_IPO VVTFRGPSETHLDSLVGQALFGDGAAAVIVGADPTP--AEKPLFQLVSAA gi|12644512|sp|Q9MB39|CHS4_IPO VVTFRGPSETHLDSLVGQALFGDGAAAVIVGADPTP--AEKPLFQLVSAA gi|12644509|sp|Q9MB36|CHS8_IPO VVTFRGPSETHLDSLVGQALFGDGAAAVIVGADPTP--AEKPLFQLVSAA gi|12644511|sp|Q9MB38|CHS6_IPO VVTFRGPSETHLDSLVGQALFGDGAAAVIVGADPTP--AEKPLFQLVSAA gi|12644510|sp|Q9MB37|CHS7_IPO VVTFRGPSETHLDSLVGQALFGDGAAAVIVGADPTP--AEKPLFQLVSAA gi|12644513|sp|Q9MB40|CHS3_IPO VVTFRGPSEAHLDSLVGQALFGDGAAAVIVGADPTP--AEKPLFQLVSAA gi|13936397|dbj|BAB47195.1| TGHFRGPAESHLDSMIGQALFGDGASAIIVGAEPDESAGEQPIFELVSTA gi|13936395|dbj|BAB47194.1| TGHFRGPAESHLDSMIGQALFGDGASAIIVGAEPDESAGEQPIFELVSTA gi|13936399|dbj|BAB47196.1| TCMFHGPTESHLDSMVGQALFGDGASAVIVGAEPDESAGERPIYELVSAA gi|14150838|gb|AAK54648.1|AF37 ATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLKEAFTPLG-- gi|12644515|sp|Q9MBB1|CHSY_EQU SNILPDSDGAIDGHLREVGLTFHLMKDVPGIISKNIGKVLNDAFRSAFDE gi|13774971|gb|AAK39113.1|AF35 QTLAPDSCGAIDGHLREVGLTFHLLKDVPSIVSNNIEKCLSEAFNPLG-- gi|13774967|gb|AAK39111.1|AF35 QTLAPDSCGAIDGHLREVGLTFHLLKDVPSIVSNNIEKCLSEAFNPLG-- gi|13774975|gb|AAK39115.1|AF35 QTLAPDSCGAIDGHLREVGLTFHLLKDVPSIVSNNIEKCLSEAFNPLG-- gi|13774965|gb|AAK39110.1|AF35 QTLAPDSCGAIDGHLREVGLTFHLLKDVPSIVSNNIEKCLSEAFNPLG-- gi|13774973|gb|AAK39114.1|AF35 QTLAPDSCGAIDGHLREVGLTFHLLKDVPSIVSNNIEKCLSEAFNPLG-- gi|13774969|gb|AAK39112.1|AF35 QTLAPDSCGAIDGHLREVGLTFHLLKDVPSIVSNNIEKCLSEAFNPLG-- gi|13919613|gb|AAK33142.1| -------------------------------------------------- gi|13919597|gb|AAK33134.1| -------------------------------------------------- gi|13925890|gb|AAK49457.1| QTLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLVEAFQPLG-- gi|12644514|sp|Q9MB41|CHS2_IPO QTLAPDSCGAIDGHLREVGLTFHLLKDVPSVVSNNIEKCLFEAFNPLG-- gi|12644512|sp|Q9MB39|CHS4_IPO QNLAPDSCGAIDGHLREVGLTFHLLKDVPSVVSNNIEKCLFEAFNPLG-- gi|12644509|sp|Q9MB36|CHS8_IPO QTLAPNSCGAIDGHLREVGLTFHLLKDVPSVVSNNIEKCLFEAFNPLG-- gi|12644511|sp|Q9MB38|CHS6_IPO QTLAPNSCGAIDGHLREVGLTFHLLKDVPSVVSNNIEKCLFEAFNPLG-- gi|12644510|sp|Q9MB37|CHS7_IPO QTLAPDSCGAIDGHLREVGLTFHLLKDVPSVVSNNIEKCLFEAFNPLG-- gi|12644513|sp|Q9MB40|CHS3_IPO QTLAPDSCGAIDGHLREVGLTFHLLKDVPSVVSNNIEKCLFEAFNPLG-- gi|13936397|dbj|BAB47195.1| QTTLPDSDGVARGHLKEAGVVIHLHQSLPGLISTNIEKSLTEAFAPIG-- gi|13936395|dbj|BAB47194.1| QTTLPDSDGVARGHLKEAGVVIHLHQSLPGLISTNIEKSLTEAFAPIG-- gi|13936399|dbj|BAB47196.1| QTILPNSEGAIDGHLMETRLTFHLLKDVPGLISNNIEKSLIEAFTPIG-- gi|14150838|gb|AAK54648.1|AF37 -------ISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEY gi|12644515|sp|Q9MBB1|CHSY_EQU SGNAEDRPASVNDIFWIAHPGGPAILDQVEEKMKLAPEKMRATRDVLSEY gi|13774971|gb|AAK39113.1|AF35 -------ISDWNSIFWIAHPGGPAILDQVEDKLGLKPEKLRATRHVLSEY gi|13774967|gb|AAK39111.1|AF35 -------ISDWNSIFWIAHPGGPAILDQVEDKLGLKPEKLRATRHVLSEY gi|13774975|gb|AAK39115.1|AF35 -------ISDWNSIFWIAHPGGPAILDQVEDKLGLKPEKLRATRHVLSEY gi|13774965|gb|AAK39110.1|AF35 -------ISDWNSIFWIAHPGGPAILDQVEDKLGLKPEKLRATRHVLSEY gi|13774973|gb|AAK39114.1|AF35 -------ISDWNSIFWIAHPGGPAILDQVEDKLGLKPEKLRATRHVLSEY gi|13774969|gb|AAK39112.1|AF35 -------ISDWNSIFWIAHPGGPAILDQVEDKLGLKPEKLRATRHVLSEY gi|13919613|gb|AAK33142.1| -------------------------------------------------- gi|13919597|gb|AAK33134.1| -------------------------------------------------- gi|13925890|gb|AAK49457.1| -------ISDWNSLFWIAHPGGPAILDQVELKLGLKQEKLKATRKVLSNY gi|12644514|sp|Q9MB41|CHS2_IPO -------ISDWNSVFWIAHPGGPAILDQVEDKLGLKPEKLRATRHVLSEY gi|12644512|sp|Q9MB39|CHS4_IPO -------ISDWNSVFWIAHPGGPAILDQVEDKLGLKPEKLRATRHVLSEY gi|12644509|sp|Q9MB36|CHS8_IPO -------ISDWNSVFWIAHPGGPAILDQVEDKLGLKPEKLRATRHVLSEY gi|12644511|sp|Q9MB38|CHS6_IPO -------ISDWNSVFWIAHPGGPAILDQVEDKLGLKPEKLRATRHVLSEY gi|12644510|sp|Q9MB37|CHS7_IPO -------ISDWNSVFWIAHPGGPAILDQVEDKLGLKPEKLRATRHVLSEY gi|12644513|sp|Q9MB40|CHS3_IPO -------ISDWNSVFWIAHPGGPAILDQVEDKLGLKPEKLRATRHVLSEY gi|13936397|dbj|BAB47195.1| -------ISDWNSIFWITHPAGRAVLEEIEAKLQLKNEKLADSRHVLSEY gi|13936395|dbj|BAB47194.1| -------ISDWNSIFWITHPAGRAVLEEIEAKLQLKNEKLADSRHVLSEY gi|13936399|dbj|BAB47196.1| -------INDWNSIFWVTHPGGPAILDEVEAKLELKKEKLAISRHVLSEY gi|14150838|gb|AAK54648.1|AF37 GNMSSAC------------------------------------------- gi|12644515|sp|Q9MBB1|CHSY_EQU GNMSSACVLFIMDHMRRMSAQNKLQTTGEGLDWGVLLGFGPGLTVETVLL gi|13774971|gb|AAK39113.1|AF35 GNMSSACVLFILDEMRKASSNDGLGTTGEGLEWGVLFGFGPGLTIETVVL gi|13774967|gb|AAK39111.1|AF35 GNMSSACVLFILDEMRKASSNDGLGTTGEGLEWGVLFGFGPGLTIETVVL gi|13774975|gb|AAK39115.1|AF35 GNMSSACVLFILDEMRKASSNDGLGTTGEGLEWGVLFGFGPGLTIETVVL gi|13774965|gb|AAK39110.1|AF35 GNMSSACVLFILDEMRKASSNDGLGTTGEGLEWGVLFGFGPGLTIETVVL gi|13774973|gb|AAK39114.1|AF35 GNMSSACVLFILDEMRKAASNDGLGTTGEGLEWGVLFGLGPGLTIETVAL gi|13774969|gb|AAK39112.1|AF35 GNMSSACVLFILDEMRKASSNDGLGTTGEGLEWGV--------------- gi|13919613|gb|AAK33142.1| -------------------------------------------------- gi|13919597|gb|AAK33134.1| -------------------------------------------------- gi|13925890|gb|AAK49457.1| GNMSSACVLFILDEMRKASAKEGLGTTGEGLEWGVLFGFGPGLTVETVVL gi|12644514|sp|Q9MB41|CHS2_IPO GNMSSACVLFILDEMRKASSNAGLGTTGEGLEWGVLFGFGPGLTIETVVL gi|12644512|sp|Q9MB39|CHS4_IPO GNMSSACVLFILDEMRKASSNAGLGTTGEGLEWGVLFGFGPGLTIETVVL gi|12644509|sp|Q9MB36|CHS8_IPO GNMSSACVLFILDEMRKASSNAGLGTTGEGLEWGVLFGFGPGLTIETVVL gi|12644511|sp|Q9MB38|CHS6_IPO GNMSSACVLFILDEMRKASSNAGLGTTGEGLEWGVLFGFGPGLTIETVVL gi|12644510|sp|Q9MB37|CHS7_IPO GNMSSACVLFILDEMRKASSNAGLGTTGEGLEWGVLFGFGPGLTIETVVL gi|12644513|sp|Q9MB40|CHS3_IPO GNMSSACVLFILDEMRKASSNDGLGTTGEGLEWGVLFGFGPGLTIETVVL gi|13936397|dbj|BAB47195.1| GNMSSACVFFIMDKLRKRSLEQRKSTTGDGLEWGVLFGFGPGLTVETVVL gi|13936395|dbj|BAB47194.1| GNMSSACVFFIMDKLRKRSLEQRKSTTGDGLEWGVLFGFGPGLTVETVVL gi|13936399|dbj|BAB47196.1| GNMSSASVFFVMDELRKRSLEEGKSTTGDGLDWGVLFGFGPGLTVEMVVL gi|14150838|gb|AAK54648.1|AF37 ----------- gi|12644515|sp|Q9MBB1|CHSY_EQU KSIRLAC---- gi|13774971|gb|AAK39113.1|AF35 HSVPA------ gi|13774967|gb|AAK39111.1|AF35 HSVPA------ gi|13774975|gb|AAK39115.1|AF35 HSVPA------ gi|13774965|gb|AAK39110.1|AF35 HSVPA------ gi|13774973|gb|AAK39114.1|AF35 HSVPA------ gi|13774969|gb|AAK39112.1|AF35 ----------- gi|13919613|gb|AAK33142.1| ----------- gi|13919597|gb|AAK33134.1| ----------- gi|13925890|gb|AAK49457.1| HSVAT------ gi|12644514|sp|Q9MB41|CHS2_IPO HSVPIKPGPH- gi|12644512|sp|Q9MB39|CHS4_IPO HSVPIKPGPH- gi|12644509|sp|Q9MB36|CHS8_IPO HSVPT------ gi|12644511|sp|Q9MB38|CHS6_IPO HSVLIKPGPH- gi|12644510|sp|Q9MB37|CHS7_IPO HSVPIKPGPH- gi|12644513|sp|Q9MB40|CHS3_IPO HSVPT------ gi|13936397|dbj|BAB47195.1| HSVANKF---- gi|13936395|dbj|BAB47194.1| HSVANKF---- gi|13936399|dbj|BAB47196.1| HSVENKVKSET biopython-1.63/Doc/examples/getgene.py0000755000175000017500000002126612250104543017375 0ustar tratra00000000000000#!/usr/bin/env python # Created: Sun Oct 15 16:16:20 2000 # Last changed: Time-stamp: <01/02/15 09:01:27 thomas> # thomas@cbs.dtu.dk, http://www.cbs.dtu.dk/thomas # File: getgene.py """ Example code to index a non-reduntant protein database of SwissProt + TrEMBL for fast lookup and retrieval. To build the database and index it: cd /opt/bio/data/ wget -N -nd -r -l1 -A'.dat.Z' ftp://expasy.cbr.nrc.ca/databases/sp_tr_nrdb/ zcat *.dat.Z > nr.dat ./getgene.py --index nr.dat setenv PYPHY '/opt/bio/data' To retrieve entries from the command line: ./getgene.py EFTU_ECOLI To use from a python script: from getgene import DB_Index db_index = DB_Index() # retrieve a complete entry: db_index.Get('EFTU_ECOLI') # get organism, lineage and gene db_index.Get_OS_OC_GN('EFTU_ECOLI') """ from __future__ import print_function import os import re import string import sys try: import gdbm # Python 2 except ImportError: from dbm import gnu as gdbm # Python 3 class DB_Index: def __init__(self, open=1): if open: self.Open() def Create(self, infile, outfile): db = gdbm.open(outfile, 'n') fid = open(infile) db['datafile'] = os.path.abspath(infile) while True: line = fid.readline() if not line or not len(line): break if line[:3] == 'ID ': id = string.split(line)[1] start = fid.tell() - len(line) elif line[:3] == 'AC ': acc = string.split(line)[1] if acc[-1] == ';': acc = acc[:-1] elif line[:2] == '//': stop = fid.tell() try: value = '%d %d' % (start, stop) db[id] = value db[acc] = value id, acc, start, stop = None, None, None, None except: print("AARRGGGG %d %d %s %s" % (start, stop, type(start), type(stop))) print("%s %s" % (id, acc)) db.close() fid.close() def Open(self, indexfile=None): if not indexfile: indexfile = os.path.join(os.environ['PYPHY'], 'nr.dat.indexed') self.db = gdbm.open(indexfile) self.datafile = self.db['datafile'] self.fid = open(self.datafile) def Close(self): self.db.close() def Get(self, id): try: values = self.db[id] except: return None start, stop = [int(x) for x in values.split()] self.fid.seek(start) txt = self.fid.read(stop - start) return txt def Get_Organism(self, id): entry = self.Get(id) if not entry: return None for line in string.split(entry, '\n'): if line[0:5] == 'OS ': OS = string.strip(line[5:]) if OS[-1] == ".": OS = OS[0:-1] return OS if line[0:2] == "//": break return OS def FixOS(self, os): os = string.split(os, ',')[0] os = string.split(os, '(')[0] return string.strip(os) def Get_Taxonomy(self, id): entry = self.Get(id) if not entry: return None OC = "" for line in string.split(entry, '\n'): if line[0:5] == 'OC ': OC = OC + string.strip(line[5:]) if OC[-1] == ".": OC = OC[0:-1] if line[0:2] == "//": break return OC def Get_Kingdom(self, id): res = self.Get_Taxonomy(id) #print("%s %s" % (id, res)) if not res: return "U" kd = string.strip(string.split(res, ";")[0]) if kd == "Eubacteria" or kd == "Prokaryota" or kd == "Bacteria": return "B" elif kd == "Eukaryota" or kd == "Eukaryotae": return "E" elif kd == "Archaebacteria" or kd == "Archaea": return "A" elif kd == "Viridae" or kd == "Viruses": return "V" else: print("%s UNKNOWN" % kd) return "U" def Get_Gene(self, id): entry = self.Get(id) if not entry: return None GN = '' for line in string.split(entry, '\n'): if line[0:5] == 'GN ': GN = string.strip(line[5:]) if GN[-1] == ".": GN = GN[0:-1] return GN if line[0:2] == "//": break return GN def Get_OS_OC_GN(self, id): entry = self.Get(id) if not entry: return None, None, None OS, OC, GN = "", "", "" for line in string.split(entry, '\n'): if line[0:5] == 'OS ': OS = string.strip(line[5:]) if OS[-1] == ".": OS = OS[0:-1] if line[0:5] == 'OC ': OC = OC + string.strip(line[5:]) if OC[-1] == ".": OC = OC[0:-1] if line[0:5] == 'GN ': GN = string.strip(line[5:]) if GN[-1] == ".": GN = GN[0:-1] if line[0:2] == "//": break return OS, OC, GN def Get_OS_OC_OG(self, id): entry = self.Get(id) if not entry: return None, None, None OS, OC, OG = "", "", "" for line in string.split(entry, '\n'): if line[0:5] == 'OS ': OS = string.strip(line[5:]) if OS[-1] == ".": OS = OS[0:-1] if line[0:5] == 'OC ': OC = OC + string.strip(line[5:]) if OC[-1] == ".": OC = OC[0:-1] if line[0:5] == 'OG ': OG = string.strip(line[5:]) if OG[-1] == ".": OG = OG[0:-1] if line[0:2] == "//": break return OS, OC, OG def Get_SQ(self, id, fasta=1): entry = self.Get(id) if not entry: return "" SQ = "" record = 0 for line in string.split(entry, '\n'): if record: SQ = SQ + string.strip(line[5:]) if line[0:5] == 'SQ ': record = 1 if line[0:2] == "//": break SQ = re.sub('[ \n]', '', SQ) if fasta: SQ = '>%s\n%s' % (id, re.sub('(.{60})', '\\1\n', SQ)) return SQ def Get_XX(self, id, xx): entry = self.Get(id) if not entry: return "" XX = "" for line in string.split(entry, '\n'): if line[0:5] == '%s ' % xx: XX = XX + string.strip(line[5:]) if XX[-1] == ".": XX = XX[0:-1] if line[0:2] == "//": break return XX def Get_Keywords(self, id): entry = self.Get(id) if not entry: return [] keywords = [] for line in string.split(entry, '\n'): if line[0:5] == 'KW ': for i in string.split(string.strip(line[5:]), ';'): kw = string.strip(i) if len(kw) < 2: continue if kw[-1] == '.': kw = kw[:-1] keywords.append(kw) if line[0:2] == "//": break return keywords def help(exit=0): name = os.path.basename(sys.argv[0]) print('Usage: %s ' % name) print(' or %s --index ' % name) if exit: sys.exit(0) if __name__ == '__main__': pyphy_home = os.environ.get('PYPHY', None) if len(sys.argv) == 1: help(exit=1) db_index = DB_Index(open=0) func = db_index.Get for arg in sys.argv[1:]: if arg == '--index': sys.argv.remove(arg) infile = sys.argv[1] outfile = os.path.basename(infile) + '.indexed' db_index.Create(infile, outfile) sys.exit(0) elif arg[:4] == '-Get': sys.argv.remove(arg) func = getattr(db_index, arg[1:]) elif arg == '-h' or arg == '--help': help(exit=1) db = 'nr.dat' if len(sys.argv) == 2: # shortcut, mostly we use nr.dat so dont bother to name it ids = sys.argv[1:] else: try: db = sys.argv[1] ids = sys.argv[2:] except: help(exit=1) dbfile = os.path.join(pyphy_home, db + '.indexed') db_index.Open(dbfile) for id in ids: #print(db_index.Get(id)) print(func(id)) biopython-1.63/Doc/examples/www_blast.py0000644000175000017500000000317012250104543017757 0ustar tratra00000000000000#!/usr/bin/env python """Example showing how to deal with internet BLAST from Biopython. This code is described in great detail in the BLAST section of the Biopython documentation. """ # standard library from __future__ import print_function try: from StringIO import StringIO # Python 2 except ImportError: from io import StringIO # Python 3 # biopython from Bio import SeqIO from Bio.Blast import NCBIWWW from Bio.Blast import NCBIXML # first get the sequence we want to parse from a FASTA file f_record = next(SeqIO.parse('m_cold.fasta', 'fasta')) print('Doing the BLAST and retrieving the results...') result_handle = NCBIWWW.qblast('blastn', 'nr', f_record.format('fasta')) # save the results for later, in case we want to look at it save_file = open('m_cold_blast.out', 'w') blast_results = result_handle.read() save_file.write(blast_results) save_file.close() print('Parsing the results and extracting info...') # option 1 -- open the saved file to parse it # option 2 -- create a handle from the string and parse it string_result_handle = StringIO(blast_results) b_record = NCBIXML.read(string_result_handle) # now get the alignment info for all e values greater than some threshold E_VALUE_THRESH = 0.1 for alignment in b_record.alignments: for hsp in alignment.hsps: if hsp.expect < E_VALUE_THRESH: print('****Alignment****') print('sequence: %s' % alignment.title) print('length: %i' % alignment.length) print('e value: %f' % hsp.expect) print(hsp.query[0:75] + '...') print(hsp.match[0:75] + '...') print(hsp.sbjct[0:75] + '...') biopython-1.63/Doc/examples/clustal_run.py0000644000175000017500000000400512250104543020277 0ustar tratra00000000000000#!/usr/bin/env python """clustal_run.py Example code to show how to create a clustalw command line, run clustalw and parse the results into an object that can be dealt with easily.""" # standard library from __future__ import print_function import os import sys import subprocess # biopython from Bio.Alphabet import Gapped, IUPAC from Bio.Align.Applications import ClustalwCommandline from Bio import AlignIO from Bio.Align import AlignInfo from Bio.SubsMat import FreqTable # create the command line to run clustalw # this assumes you've got clustalw somewhere on your path, otherwise # you need to pass the full path of the executable to this via cmd="..." cline = ClustalwCommandline(infile='opuntia.fasta', outfile='test.aln') # actually perform the alignment return_code = subprocess.call(str(cline), shell=(sys.platform != "win32")) assert return_code == 0, "Calling ClustalW failed" # Parse the output alignment = AlignIO.read("test.aln", "clustal", alphabet=Gapped(IUPAC.unambiguous_dna)) print(alignment) print('first description: %s' % alignment[0].description) print('first sequence: %s' % alignment[0].seq) # get the length of the alignment print('length %i' % alignment.get_alignment_length()) print(alignment) # print out interesting information about the alignment summary_align = AlignInfo.SummaryInfo(alignment) consensus = summary_align.dumb_consensus() print('consensus %s' % consensus) my_pssm = summary_align.pos_specific_score_matrix(consensus, chars_to_ignore=['N']) print(my_pssm) expect_freq = { 'A': .3, 'G': .2, 'T': .3, 'C': .2} freq_table_info = FreqTable.FreqTable(expect_freq, FreqTable.FREQ, IUPAC.unambiguous_dna) info_content = summary_align.information_content(5, 30, chars_to_ignore=['N'], e_freq_table=freq_table_info) print("relative info content: %f" % info_content) biopython-1.63/Doc/examples/my_blast.xml0000644000175000017500000041463212250104543017741 0ustar tratra00000000000000 blastn BLASTN 2.2.27+ Stephen F. Altschul, Thomas L. Madden, Alejandro A. Sch&auml;ffer, Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997), "Gapped BLAST and PSI-BLAST: a new generation of protein database search programs", Nucleic Acids Res. 25:3389-3402. refseq_rna 42291 mystery_seq 61 10 2 -3 5 2 L;m; 1 42291 mystery_seq 61 1 gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 520b (MIR520B), microRNA NR_030195 61 1 111.292 122 4.91307e-23 1 61 1 61 1 1 61 61 0 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 2 gi|301171311|ref|NR_035856.1| Pan troglodytes microRNA mir-520b (MIR520B), microRNA NR_035856 60 1 109.488 120 1.71483e-22 2 61 1 60 1 1 60 60 0 60 CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 3 gi|270133242|ref|NR_032573.1| Macaca mulatta microRNA mir-519a (MIR519A), microRNA NR_032573 85 1 102.275 112 2.54503e-20 1 61 13 73 1 1 59 59 0 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCTAGAGGGAAGCGCTTTCTGTGGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG ||||||| ||||||||||||||||| ||||||||||||||||||||||||||||||||||| 4 gi|301171322|ref|NR_035857.1| Pan troglodytes microRNA mir-520c (MIR520C), microRNA NR_035857 86 1 100.472 110 8.88303e-20 2 61 14 73 1 1 58 58 0 60 CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCTCTAGAGGGAAGCACTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG |||||| |||||||| |||||||||||||||||||||||||||||||||||||||||||| 2 55.3874 60 3.31332e-06 1 60 73 14 1 -1 48 48 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTAAAAGGAAGCACTTTCTTTTCTTTCAGACAACAGAAAGTGCTTCCCTCTAGAGG ||||||| | |||||| |||||| || | | | | || ||||||||||||| | |||||| 5 gi|301171267|ref|NR_035851.1| Pan troglodytes microRNA mir-519b (MIR519B), microRNA NR_035851 80 1 100.472 110 8.88303e-20 1 60 10 69 1 1 58 58 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTAGAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCATCCTTTTAGAGG ||||||| ||||||||||||||||||||||||||||||||||||||| |||||||||||| 6 gi|262205330|ref|NR_030198.1| Homo sapiens microRNA 520c (MIR520C), microRNA NR_030198 87 1 100.472 110 8.88303e-20 2 61 15 74 1 1 58 58 0 60 CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCTCTAGAGGGAAGCACTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG |||||| |||||||| |||||||||||||||||||||||||||||||||||||||||||| 2 55.3874 60 3.31332e-06 1 60 74 15 1 -1 48 48 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTAAAAGGAAGCACTTTCTTTTCTTTCAGACAACAGAAAGTGCTTCCCTCTAGAGG ||||||| | |||||| |||||| || | | | | || ||||||||||||| | |||||| 7 gi|262205302|ref|NR_030191.1| Homo sapiens microRNA 519b (MIR519B), microRNA NR_030191 81 1 100.472 110 8.88303e-20 1 60 11 70 1 1 58 58 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTAGAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCATCCTTTTAGAGG ||||||| ||||||||||||||||||||||||||||||||||||||| |||||||||||| 8 gi|301171259|ref|NR_035850.1| Pan troglodytes microRNA mir-519a (MIR519A), microRNA NR_035850 86 1 98.6682 108 3.10048e-19 1 59 13 71 1 1 57 57 0 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAGGAAAGTGCATCCTTTTAGAG |||||||||||||||||||||||||||||||||||||| |||||||| ||||||||||| 9 gi|262205451|ref|NR_030222.1| Homo sapiens microRNA 519a-2 (MIR519A2), microRNA NR_030222 87 1 98.6682 108 3.10048e-19 1 59 14 72 1 1 57 57 0 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAGGAAAGTGCATCCTTTTAGAG |||||||||||||||||||||||||||||||||||||| |||||||| ||||||||||| 10 gi|301171447|ref|NR_035871.1| Pan troglodytes microRNA mir-526b (MIR526B), microRNA NR_035871 82 1 95.0615 104 3.77716e-18 1 60 11 70 1 1 57 57 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTTGAGGGAAGCACTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG |||||| |||||||| ||||||||||||||||||||||||||||||||||||||||||| 2 49.9773 54 0.000140886 2 61 70 11 1 -1 47 47 0 60 CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCTCTAAAAGGAAGCACTTTCTTTTCTTTCAGACAACAGAAAGTGCTTCCCTCAAGAGGG |||||| | |||||| |||||| || | | | | || ||||||||||||| | |||||| 11 gi|301171276|ref|NR_035852.1| Pan troglodytes microRNA mir-519c (MIR519C), microRNA NR_035852 86 1 95.0615 104 3.77716e-18 1 60 13 72 1 1 57 57 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTAGAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCATCTTTTTAGAGG ||||||| ||||||||||||||||||||||||||||||||||||||| || ||||||||| 12 gi|262205290|ref|NR_030188.1| Homo sapiens microRNA 519c (MIR519C), microRNA NR_030188 87 1 95.0615 104 3.77716e-18 1 60 14 73 1 1 57 57 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTAGAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCATCTTTTTAGAGG ||||||| ||||||||||||||||||||||||||||||||||||||| || ||||||||| 13 gi|301171354|ref|NR_035860.1| Pan troglodytes microRNA mir-520f (MIR520F), microRNA NR_035860 86 1 93.2581 102 1.31836e-17 1 61 13 73 1 1 57 57 0 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCTAAAGGGAAGCGCTTTCTGTGGTCAGAAAGAAAAGCAAGTGCTTCCTTTTAGAGGG ||||||| ||||||||||||||||| ||| |||||||||| |||||||||||||||||||| 14 gi|262205281|ref|NR_030186.1| Homo sapiens microRNA 520f (MIR520F), microRNA NR_030186 87 1 93.2581 102 1.31836e-17 1 61 14 74 1 1 57 57 0 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCTAAAGGGAAGCGCTTTCTGTGGTCAGAAAGAAAAGCAAGTGCTTCCTTTTAGAGGG ||||||| ||||||||||||||||| ||| |||||||||| |||||||||||||||||||| 15 gi|262205298|ref|NR_030190.1| Homo sapiens microRNA 526b (MIR526B), microRNA NR_030190 83 1 91.4547 100 4.60152e-17 1 60 12 71 1 1 56 56 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTTGAGGGAAGCACTTTCTGTTGTCTGAAAGAAGAGAAAGTGCTTCCTTTTAGAGG |||||| |||||||| |||||||||||||||||||| |||||||||||||||||||||| 2 49.9773 54 0.000140886 2 61 71 12 1 -1 47 47 0 60 CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCTCTAAAAGGAAGCACTTTCTCTTCTTTCAGACAACAGAAAGTGCTTCCCTCAAGAGGG |||||| | |||||| |||||| || | | | | || ||||||||||||| | |||||| 16 gi|301171394|ref|NR_035865.1| Pan troglodytes microRNA mir-522 (MIR522), microRNA NR_035865 86 1 89.6514 98 1.60609e-16 1 59 13 71 1 1 55 55 0 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAATGGTTCCCTTTAGAG ||||||| ||||||||||||||||||||||||||||||||||| || |||| ||||||| 17 gi|262205429|ref|NR_030218.1| Homo sapiens microRNA 519a-1 (MIR519A1), microRNA NR_030218 85 1 89.6514 98 1.60609e-16 3 59 15 71 1 1 54 54 0 57 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CTCTAGAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAGGAAAGTGCATCCTTTTAGAG ||||| |||||||||||||||||||||||||||||| |||||||| ||||||||||| 18 gi|262205423|ref|NR_030217.1| Homo sapiens microRNA 522 (MIR522), microRNA NR_030217 87 1 89.6514 98 1.60609e-16 1 59 14 72 1 1 55 55 0 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAATGGTTCCCTTTAGAG ||||||| ||||||||||||||||||||||||||||||||||| || |||| ||||||| 19 gi|301171401|ref|NR_035866.1| Pan troglodytes microRNA mir-523 (MIR523), microRNA NR_035866 79 1 87.848 96 5.6058e-16 1 61 6 66 1 1 56 56 0 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCTAGAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAACGCGCTTCCCTATAGAGGG ||||||| |||||||||||||||||||||||||||||||||| | |||||| | ||||||| 20 gi|270133247|ref|NR_032574.1| Macaca mulatta microRNA mir-519b (MIR519B), microRNA NR_032574 81 1 87.848 96 5.6058e-16 1 61 11 71 1 1 56 56 0 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCTGGAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAACGTGCATCCCTTTAGAGGG |||||| |||||||||||||||||||||||||||||||||| |||| ||| ||||||||| 21 gi|262205309|ref|NR_030193.1| Homo sapiens microRNA 523 (MIR523), microRNA NR_030193 87 1 87.848 96 5.6058e-16 1 61 14 74 1 1 56 56 0 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCTAGAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAACGCGCTTCCCTATAGAGGG ||||||| |||||||||||||||||||||||||||||||||| | |||||| | ||||||| 22 gi|270132717|ref|NR_032716.1| Macaca mulatta microRNA mir-526b (MIR526B), microRNA NR_032716 83 1 86.0446 94 1.95662e-15 1 60 12 71 1 1 55 55 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTTGAGGGAAGCACTTTCTGTTGTCTGAATAAAAAGAAAGTGCTTCCTTTTAGAGG |||||| |||||||| |||||||||||||||| ||||||||||||||||||||||||| 2 46.3705 50 0.00171634 2 61 71 12 1 -1 46 46 0 60 CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCTCTAAAAGGAAGCACTTTCTTTTTATTCAGACAACAGAAAGTGCTTCCCTCAAGAGGG |||||| | |||||| |||||| || | | | || ||||||||||||| | |||||| 23 gi|301171437|ref|NR_035870.1| Pan troglodytes microRNA mir-526a-2 (MIR526A-2), microRNA NR_035870 68 1 84.2413 92 6.82927e-15 1 61 4 64 1 1 55 55 0 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCTAGAGGGAAGCACTTTCTGTTGTCTGAAAGAAAAGAACATGCATCCTTTCAGAGGG ||||||| |||||||| ||||||||||||||||||||||||| ||| |||||| |||||| 2 39.157 42 0.254728 1 61 64 4 1 -1 45 45 0 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCTGAAAGGATGCATGTTCTTTTCTTTCAGACAACAGAAAGTGCTTCCCTCTAGAGGG |||||| | ||| || |||| || | | | | || ||||||||||||| | ||||||| 24 gi|270133306|ref|NR_032587.1| Macaca mulatta microRNA mir-523a (MIR523A), microRNA NR_032587 87 1 84.2413 92 6.82927e-15 1 61 14 74 1 1 55 55 0 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCTAGAGGGAAGCACTTTCTGTTGTCTGGAAGAAAAGAATGCGCTTCCCTTTAGAGGG ||||||| |||||||| |||||||||||||| |||||||||| | |||||| ||||||||| 2 57.1907 62 9.49283e-07 1 61 74 14 1 -1 49 49 0 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCTAAAGGGAAGCGCATTCTTTTCTTCCAGACAACAGAAAGTGCTTCCCTCTAGAGGG ||||||| |||||||||| |||| || | | | || ||||||||||||| | ||||||| 25 gi|301171428|ref|NR_035869.1| Pan troglodytes microRNA mir-526a-1 (MIR526A-1), microRNA NR_035869 84 1 82.4379 90 2.38365e-14 1 60 12 71 1 1 54 54 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTAGAGGGAAGCACTTTCTGTTGCTTGAAAGAAGAGAAAGCGCTTCCTTTTAGAGG ||||||| |||||||| |||||||||| |||||||| |||||| ||||||||||||||| 2 58.9941 64 2.71974e-07 2 61 71 12 1 -1 49 49 0 60 CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCTCTAAAAGGAAGCGCTTTCTCTTCTTTCAAGCAACAGAAAGTGCTTCCCTCTAGAGGG |||||| | ||||||||||||| || | | || || ||||||||||||| | ||||||| 26 gi|301171211|ref|NR_035845.1| Pan troglodytes microRNA mir-518b (MIR518B), microRNA NR_035845 82 1 82.4379 90 2.38365e-14 1 60 11 70 1 1 54 54 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCCAGAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAACAAAGCGCTCCCCTTTAGAGG ||||| | ||||||||||||||||||||||||||||||| |||| ||| || |||||||| 27 gi|301171153|ref|NR_035838.1| Pan troglodytes microRNA mir-516a-2 (MIR516A-2), microRNA NR_035838 89 1 82.4379 90 2.38365e-14 12 61 27 76 1 1 48 48 0 50 GAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG GAAGCACTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTCAGAGGG ||||| ||||||||||||||||||||||||||||||||||||| |||||| 2 42.7638 46 0.0209094 1 53 76 24 1 -1 41 41 0 53 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTT CCCTCTGAAAGGAAGCACTTTCTTTTCTTTCAGACAACAGAAAGTGCTTCTTT |||||| | |||||| |||||| || | | | | || |||||||||||| || 28 gi|301171146|ref|NR_035837.1| Pan troglodytes microRNA mir-516a-1 (MIR516A-1), microRNA NR_035837 89 1 82.4379 90 2.38365e-14 12 61 27 76 1 1 48 48 0 50 GAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG GAAGCACTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTCAGAGGG ||||| ||||||||||||||||||||||||||||||||||||| |||||| 2 42.7638 46 0.0209094 1 53 76 24 1 -1 41 41 0 53 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTT CCCTCTGAAAGGAAGCACTTTCTTTTCTTTCAGACAACAGAAAGTGCTTCTTT |||||| | |||||| |||||| || | | | | || |||||||||||| || 29 gi|270133254|ref|NR_032575.1| Macaca mulatta microRNA mir-519c (MIR519C), microRNA NR_032575 87 1 82.4379 90 2.38365e-14 1 60 14 73 1 1 54 54 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTAGAAGGAAGCACTTTCTGTTGTTTGAAAGAAAAGAAAGTGCATCATTTTAGAGG ||||||| | |||||| ||||||||||| |||||||||||||||||| || ||||||||| 2 55.3874 60 3.31332e-06 2 61 73 14 1 -1 48 48 0 60 CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCTCTAAAATGATGCACTTTCTTTTCTTTCAAACAACAGAAAGTGCTTCCTTCTAGAGGG |||||| | || || |||||| || | | ||| || ||||||||||||||| ||||||| 30 gi|262205445|ref|NR_030221.1| Homo sapiens microRNA 516a-2 (MIR516A2), microRNA NR_030221 90 1 82.4379 90 2.38365e-14 12 61 28 77 1 1 48 48 0 50 GAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG GAAGCACTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTCAGAGGG ||||| ||||||||||||||||||||||||||||||||||||| |||||| 2 42.7638 46 0.0209094 1 53 77 25 1 -1 41 41 0 53 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTT CCCTCTGAAAGGAAGCACTTTCTTTTCTTTCAGACAACAGAAAGTGCTTCTTT |||||| | |||||| |||||| || | | | | || |||||||||||| || 31 gi|262205441|ref|NR_030220.1| Homo sapiens microRNA 516a-1 (MIR516A1), microRNA NR_030220 90 1 82.4379 90 2.38365e-14 12 61 28 77 1 1 48 48 0 50 GAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG GAAGCACTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTCAGAGGG ||||| ||||||||||||||||||||||||||||||||||||| |||||| 2 42.7638 46 0.0209094 1 53 77 25 1 -1 41 41 0 53 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTT CCCTCTGAAAGGAAGCACTTTCTTTTCTTTCAGACAACAGAAAGTGCTTCTTT |||||| | |||||| |||||| || | | | | || |||||||||||| || 32 gi|262205325|ref|NR_030197.1| Homo sapiens microRNA 526a-1 (MIR526A1), microRNA NR_030197 85 1 82.4379 90 2.38365e-14 1 60 13 72 1 1 54 54 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTAGAGGGAAGCACTTTCTGTTGCTTGAAAGAAGAGAAAGCGCTTCCTTTTAGAGG ||||||| |||||||| |||||||||| |||||||| |||||| ||||||||||||||| 2 58.9941 64 2.71974e-07 2 61 72 13 1 -1 49 49 0 60 CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCTCTAAAAGGAAGCGCTTTCTCTTCTTTCAAGCAACAGAAAGTGCTTCCCTCTAGAGGG |||||| | ||||||||||||| || | | || || ||||||||||||| | ||||||| 33 gi|262205321|ref|NR_030196.1| Homo sapiens microRNA 518b (MIR518B), microRNA NR_030196 83 1 82.4379 90 2.38365e-14 1 60 12 71 1 1 54 54 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCCAGAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAACAAAGCGCTCCCCTTTAGAGG ||||| | ||||||||||||||||||||||||||||||| |||| ||| || |||||||| 34 gi|301171236|ref|NR_035848.1| Pan troglodytes microRNA mir-518e (MIR518E), microRNA NR_035848 87 1 80.6345 88 8.31975e-14 1 59 13 71 1 1 53 53 0 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGAGGGAAGCGCTTTCTGTTGGCTAAAAGAAAAGAAAGCGCTTCCCTTCAGAG ||||||| ||||||||||||||||||| || ||||||||||||| |||||| || |||| 2 48.1739 52 0.000491741 3 61 71 13 1 -1 46 46 0 59 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CTCTGAAGGGAAGCGCTTTCTTTTCTTTTAGCCAACAGAAAGCGCTTCCCTCTAGAGGG |||| ||||||||||||||| || | | | || |||||| |||||| | ||||||| 35 gi|262205383|ref|NR_030209.1| Homo sapiens microRNA 518e (MIR518E), microRNA NR_030209 88 1 80.6345 88 8.31975e-14 1 59 14 72 1 1 53 53 0 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGAGGGAAGCGCTTTCTGTTGGCTAAAAGAAAAGAAAGCGCTTCCCTTCAGAG ||||||| ||||||||||||||||||| || ||||||||||||| |||||| || |||| 2 48.1739 52 0.000491741 3 61 72 14 1 -1 46 46 0 59 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CTCTGAAGGGAAGCGCTTTCTTTTCTTTTAGCCAACAGAAAGCGCTTCCCTCTAGAGGG |||| ||||||||||||||| || | | | || |||||| |||||| | ||||||| 36 gi|301171342|ref|NR_035859.1| Pan troglodytes microRNA mir-520e (MIR520E), microRNA NR_035859 86 1 78.8312 86 2.90388e-13 1 61 13 73 1 1 54 54 0 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCAAGATGGAAGCAGTTTCTGTTGTCTGAAAGGAAAGAAAGTGCTTCCTTTTTGAGGG ||||| | | |||||| ||||||||||||||||| ||||||||||||||||||| ||||| 37 gi|262205265|ref|NR_030183.1| Homo sapiens microRNA 520e (MIR520E), microRNA NR_030183 87 1 78.8312 86 2.90388e-13 1 61 14 74 1 1 54 54 0 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCAAGATGGAAGCAGTTTCTGTTGTCTGAAAGGAAAGAAAGTGCTTCCTTTTTGAGGG ||||| | | |||||| ||||||||||||||||| ||||||||||||||||||| ||||| 38 gi|301171247|ref|NR_035849.1| Pan troglodytes microRNA mir-518f (MIR518F), microRNA NR_035849 86 1 77.0278 84 1.01355e-12 1 60 13 72 1 1 53 53 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTAGAGGGAAGCACTTTCTCTTGTCTAAAAGAAAAGAAAGCGCTTCTCTTTAGAGG ||||||| |||||||| |||||| |||||| ||||||||||||| ||||| |||||||| 2 58.9941 64 2.71974e-07 2 61 72 13 1 -1 49 49 0 60 CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCTCTAAAGAGAAGCGCTTTCTTTTCTTTTAGACAAGAGAAAGTGCTTCCCTCTAGAGGG |||||| || |||||||||||| || | | | | || ||||||||||||| | ||||||| 39 gi|270133223|ref|NR_032569.1| Macaca mulatta microRNA mir-518c (MIR518C), microRNA NR_032569 101 1 77.0278 84 1.01355e-12 3 59 25 81 1 1 51 51 0 57 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CTCTGGAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAACAAAGCGCTTCTCTTTAGAG |||| ||||||||||||||||||||||||||||||| |||| ||||| ||||||| 40 gi|262205313|ref|NR_030194.1| Homo sapiens microRNA 518f (MIR518F), microRNA NR_030194 87 1 77.0278 84 1.01355e-12 1 60 14 73 1 1 53 53 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCTAGAGGGAAGCACTTTCTCTTGTCTAAAAGAAAAGAAAGCGCTTCTCTTTAGAGG ||||||| |||||||| |||||| |||||| ||||||||||||| ||||| |||||||| 2 58.9941 64 2.71974e-07 2 61 73 14 1 -1 49 49 0 60 CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCTCTAAAGAGAAGCGCTTTCTTTTCTTTTAGACAAGAGAAAGTGCTTCCCTCTAGAGGG |||||| || |||||||||||| || | | | | || ||||||||||||| | ||||||| 41 gi|301171225|ref|NR_035847.1| Pan troglodytes microRNA mir-518d (MIR518D), microRNA NR_035847 86 1 75.2244 82 3.53765e-12 1 59 13 71 1 1 52 52 0 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGAGGGAAGCACTTTCTGTTGTCTGAAAGAAACCAAAGCGCTTCCCTTTGGAG ||||||| |||||||| ||||||||||||||||||||| |||| |||||| ||| ||| 2 48.1739 52 0.000491741 3 61 71 13 1 -1 46 46 0 59 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CTCCAAAGGGAAGCGCTTTGGTTTCTTTCAGACAACAGAAAGTGCTTCCCTCTAGAGGG ||| | ||||||||||||| || | | | | || ||||||||||||| | ||||||| 42 gi|270133232|ref|NR_032571.1| Macaca mulatta microRNA mir-518e (MIR518E), microRNA NR_032571 87 1 75.2244 82 3.53765e-12 1 59 14 72 1 1 52 52 0 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGAGGGAAGCGATTTCTGTGATCTGAAAGAAAAGAAAATGGTTCCCTTTAGAG ||||||| ||||||||| ||||||| |||||||||||||||| || |||| ||||||| 43 gi|262205392|ref|NR_030211.1| Homo sapiens microRNA 518d (MIR518D), microRNA NR_030211 87 1 75.2244 82 3.53765e-12 1 59 14 72 1 1 52 52 0 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGAGGGAAGCACTTTCTGTTGTCTGAAAGAAACCAAAGCGCTTCCCTTTGGAG ||||||| |||||||| ||||||||||||||||||||| |||| |||||| ||| ||| 2 48.1739 52 0.000491741 3 61 72 14 1 -1 46 46 0 59 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CTCCAAAGGGAAGCGCTTTGGTTTCTTTCAGACAACAGAAAGTGCTTCCCTCTAGAGGG ||| | ||||||||||||| || | | | | || ||||||||||||| | ||||||| 44 gi|301171300|ref|NR_035855.1| Pan troglodytes microRNA mir-520a (MIR520A), microRNA NR_035855 84 1 73.4211 80 1.23476e-11 1 58 12 69 1 1 51 51 0 58 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGA CCCTCCAGAGGGAAGTACTTTCTGTTGTCTGAGAGAAAAGAAAGTGCTTCCCTTTGGA ||||| | ||||||| ||||||||||||||| |||||||||||||||||| ||| || 45 gi|301171161|ref|NR_035839.1| Pan troglodytes microRNA mir-516b-1 (MIR516B-1), microRNA NR_035839 89 1 73.4211 80 1.23476e-11 12 61 27 76 1 1 46 46 0 50 GAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG GAAGCACTTTCTGTTTTGTGAAAGAAAAGAAAGTGCTTCCTTTCAGAGGG ||||| ||||||||| | ||||||||||||||||||||||||| |||||| 2 40.9604 44 0.0729808 1 50 76 27 1 -1 39 39 0 50 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTC CCCTCTGAAAGGAAGCACTTTCTTTTCTTTCACAAAACAGAAAGTGCTTC |||||| | |||||| |||||| || | | | | || |||||||||||| 46 gi|270133259|ref|NR_032576.1| Macaca mulatta microRNA mir-519d (MIR519D), microRNA NR_032576 91 1 73.4211 80 1.23476e-11 1 59 14 76 1 1 54 54 4 63 CCCTCTACAGGGAAGCGCTTTCTGTT----GTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCCAAAGGGAAGCACTTTCTGTTTGTTGTCTGAGAGAAAACAAAGTGCTTCCTTTTAGAG ||||| | |||||||| ||||||||| |||||| |||||| ||||||||||||||||||| 2 46.3705 50 0.00171634 3 61 76 14 1 -1 49 49 4 63 CTCTACAGGGAAGCGCTTTCTGTTGTCTGA---AAGAAA-AGAAAGTGCTTCCTTTTAGAGGG CTCTAAAAGGAAGCACTTTGTTTTCTCTCAGACAACAAACAGAAAGTGCTTCCCTTTGGAGGG ||||| | |||||| |||| | || ||| | || ||| ||||||||||||| ||| ||||| 47 gi|270133218|ref|NR_032568.1| Macaca mulatta microRNA mir-518b (MIR518B), microRNA NR_032568 83 1 73.4211 80 1.23476e-11 1 60 12 71 1 1 52 52 0 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGG CCCTCCAGAGGGAAGCACTTTCTGTTGTCTGAAAGAAAGCAAAGCGCTCCCCTTTAGAGG ||||| | |||||||| ||||||||||||||||||||| |||| ||| || |||||||| 2 46.3705 50 0.00171634 2 61 71 12 1 -1 46 46 0 60 CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCTCTAAAGGGGAGCGCTTTGCTTTCTTTCAGACAACAGAAAGTGCTTCCCTCTGGAGGG |||||| |||| |||||||| || | | | | || ||||||||||||| | | ||||| 48 gi|270132725|ref|NR_032718.1| Macaca mulatta microRNA mir-516 (MIR516), microRNA NR_032718 90 1 73.4211 80 1.23476e-11 12 61 28 77 1 1 46 46 0 50 GAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG GAAGCACTTTCTGTTTTGTGAAAGAAAAGAAAGTGCTTCCTTTCAGAGGG ||||| ||||||||| | ||||||||||||||||||||||||| |||||| 2 40.9604 44 0.0729808 1 50 77 28 1 -1 39 39 0 50 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTC CCCTCTGAAAGGAAGCACTTTCTTTTCTTTCACAAAACAGAAAGTGCTTC |||||| | |||||| |||||| || | | | | || |||||||||||| 49 gi|262205396|ref|NR_030212.1| Homo sapiens microRNA 516b-1 (MIR516B1), microRNA NR_030212 90 1 73.4211 80 1.23476e-11 12 61 28 77 1 1 46 46 0 50 GAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG GAAGCACTTTCTGTTTTGTGAAAGAAAAGAAAGTGCTTCCTTTCAGAGGG ||||| ||||||||| | ||||||||||||||||||||||||| |||||| 2 40.9604 44 0.0729808 1 50 77 28 1 -1 39 39 0 50 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTC CCCTCTGAAAGGAAGCACTTTCTTTTCTTTCACAAAACAGAAAGTGCTTC |||||| | |||||| |||||| || | | | | || |||||||||||| 50 gi|262205294|ref|NR_030189.1| Homo sapiens microRNA 520a (MIR520A), microRNA NR_030189 85 1 73.4211 80 1.23476e-11 1 58 13 70 1 1 51 51 0 58 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGA CCCTCCAGAGGGAAGTACTTTCTGTTGTCTGAGAGAAAAGAAAGTGCTTCCCTTTGGA ||||| | ||||||| ||||||||||||||| |||||||||||||||||| ||| || 51 gi|301171602|ref|NR_035634.1| Pan troglodytes microRNA mir-1283 (MIR1283), microRNA NR_035634 86 1 71.6177 78 4.30974e-11 3 61 12 72 1 1 53 53 2 61 CTCTACA--GGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CTCTACAAAGGAAAGCGCTTTCTGTTGTCAGAAAGAAGAGAAAGCGCTTCCCTTTTGAGGG ||||||| || ||||||||||||||||| ||||||| |||||| |||||| ||| ||||| 2 44.5672 48 0.00599063 1 59 72 12 1 -1 48 48 2 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGC-TTCCTTT-TAGAG CCCTCAAAAGGGAAGCGCTTTCTCTTCTTTCTGACAACAGAAAGCGCTTTCCTTTGTAGAG ||||| | ||||||||||||||| || | | | || |||||| || ||||||| ||||| 52 gi|301171454|ref|NR_035872.1| Pan troglodytes microRNA mir-527 (MIR527), microRNA NR_035872 86 1 71.6177 78 4.30974e-11 9 55 21 67 1 1 44 44 0 47 AGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTT AGGGAAGCCCTTTCTGTTGTCTAAAAGAAAAGAAAGTGCTTCCCTTT |||||||| ||||||||||||| |||||||||||||||||||| ||| 53 gi|301171333|ref|NR_035858.1| Pan troglodytes microRNA mir-520d (MIR520D), microRNA NR_035858 86 1 71.6177 78 4.30974e-11 3 61 13 73 1 1 53 53 2 61 CTCTACA--GGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CTCTACAAAGGGAAGCCCTTTCTGTTGTCTAAAAGAAAAGAAAGTGCTTCTCTTTGGTGGG ||||||| ||||||| ||||||||||||| ||||||||||||||||||| ||| | ||| 54 gi|301171291|ref|NR_035854.1| Pan troglodytes microRNA mir-519e (MIR519E), microRNA NR_035854 86 1 71.6177 78 4.30974e-11 3 59 15 71 1 1 50 50 0 57 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CTCCAAAAGGGAGCACTTTCTGTTGTCTGAAAGAAAACAAAGTGCCTCCTTTTAGAG ||| | | || ||| |||||||||||||||||||||| ||||||| ||||||||||| 55 gi|301171219|ref|NR_035846.1| Pan troglodytes microRNA mir-518c (MIR518C), microRNA NR_035846 100 1 71.6177 78 4.30974e-11 3 59 24 80 1 1 50 50 0 57 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CTCTGGAGGGAAGCACTTTCTGTTGTCTGAAAGAAAACAAAGCGCTTCTCTTTAGAG |||| |||||||| |||||||||||||||||||||| |||| ||||| ||||||| 2 44.5672 48 0.00599063 3 59 80 24 1 -1 44 44 0 57 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CTCTAAAGAGAAGCGCTTTGTTTTCTTTCAGACAACAGAAAGTGCTTCCCTCCAGAG ||||| || |||||||||| | || | | | | || ||||||||||||| | |||| 56 gi|269847477|ref|NR_031696.1| Homo sapiens microRNA 1283-2 (MIR1283-2), microRNA NR_031696 87 1 71.6177 78 4.30974e-11 4 59 14 71 1 1 51 51 2 58 TCTACA--GGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG TCTACAAAGGAAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAATCGCTTCCCTTTGGAG |||||| || ||||||||||||||||||||||||||||||| |||||| ||| ||| 57 gi|262205435|ref|NR_030219.1| Homo sapiens microRNA 527 (MIR527), microRNA NR_030219 85 1 71.6177 78 4.30974e-11 9 55 20 66 1 1 44 44 0 47 AGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTT AGGGAAGCCCTTTCTGTTGTCTAAAAGAAAAGAAAGTGCTTCCCTTT |||||||| ||||||||||||| |||||||||||||||||||| ||| 58 gi|262205336|ref|NR_030199.1| Homo sapiens microRNA 518c (MIR518C), microRNA NR_030199 101 1 71.6177 78 4.30974e-11 3 59 25 81 1 1 50 50 0 57 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CTCTGGAGGGAAGCACTTTCTGTTGTCTGAAAGAAAACAAAGCGCTTCTCTTTAGAG |||| |||||||| |||||||||||||||||||||| |||| ||||| ||||||| 2 44.5672 48 0.00599063 3 59 81 25 1 -1 44 44 0 57 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CTCTAAAGAGAAGCGCTTTGTTTTCTTTCAGACAACAGAAAGTGCTTCCCTCCAGAG ||||| || |||||||||| | || | | | | || ||||||||||||| | |||| 59 gi|270133264|ref|NR_032577.1| Macaca mulatta microRNA mir-520a (MIR520A), microRNA NR_032577 85 1 69.8143 76 1.50425e-10 1 58 13 70 1 1 50 50 0 58 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGA CCCTCCAGAGGGAAGTATTTTCTGTTGTCTGAAGGAAAAGAAAGTGCTTCCCTTTGGA ||||| | ||||||| ||||||||||||||| ||||||||||||||||| ||| || 60 gi|269846946|ref|NR_031573.1| Homo sapiens microRNA 1283-1 (MIR1283-1), microRNA NR_031573 87 1 69.8143 76 1.50425e-10 4 61 14 73 1 1 52 52 2 60 TCTACA--GGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG TCTACAAAGGAAAGCGCTTTCTGTTGTCAGAAAGAAGAGAAAGCGCTTCCCTTTTGAGGG |||||| || ||||||||||||||||| ||||||| |||||| |||||| ||| ||||| 2 42.7638 46 0.0209094 1 58 73 14 1 -1 47 47 2 60 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGC-TTCCTTT-TAGA CCCTCAAAAGGGAAGCGCTTTCTCTTCTTTCTGACAACAGAAAGCGCTTTCCTTTGTAGA ||||| | ||||||||||||||| || | | | || |||||| || ||||||| |||| 61 gi|262205358|ref|NR_030204.1| Homo sapiens microRNA 520d (MIR520D), microRNA NR_030204 87 1 69.8143 76 1.50425e-10 4 61 15 74 1 1 52 52 2 60 TCTACA--GGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG TCTACAAAGGGAAGCCCTTTCTGTTGTCTAAAAGAAAAGAAAGTGCTTCTCTTTGGTGGG |||||| ||||||| ||||||||||||| ||||||||||||||||||| ||| | ||| 62 gi|301171206|ref|NR_035844.1| Pan troglodytes microRNA mir-518a (MIR518A), microRNA NR_035844 86 1 68.0109 74 5.25034e-10 9 55 21 67 1 1 43 43 0 47 AGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTT AGGGAAGCCCTTTCTGTTGTCTAAAAGAAAAGAAAGCGCTTCCCTTT |||||||| ||||||||||||| ||||||||||||| |||||| ||| 2 44.5672 48 0.00599063 9 55 65 19 1 -1 38 38 0 47 AGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTT AGGGAAGCGCTTTCTTTTCTTTTAGACAACAGAAAGGGCTTCCCTTT ||||||||||||||| || | | | | || |||||| |||||| ||| 63 gi|301171169|ref|NR_035840.1| Pan troglodytes microRNA mir-516b-2 (MIR516B-2), microRNA NR_035840 89 1 68.0109 74 5.25034e-10 12 61 27 76 1 1 45 45 0 50 GAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG GAAGCACTTTGTGTTTTGTGAAAGAAAAGAAAGTGCTTCCTTTCAGAGGG ||||| |||| |||| | ||||||||||||||||||||||||| |||||| 64 gi|262205387|ref|NR_030210.1| Homo sapiens microRNA 518a-1 (MIR518A1), microRNA NR_030210 85 1 68.0109 74 5.25034e-10 9 55 20 66 1 1 43 43 0 47 AGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTT AGGGAAGCCCTTTCTGTTGTCTGAAAGAAGAGAAAGCGCTTCCCTTT |||||||| |||||||||||||||||||| |||||| |||||| ||| 2 44.5672 48 0.00599063 9 55 64 18 1 -1 38 38 0 47 AGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTT AGGGAAGCGCTTTCTCTTCTTTCAGACAACAGAAAGGGCTTCCCTTT ||||||||||||||| || | | | | || |||||| |||||| ||| 65 gi|301171381|ref|NR_035863.1| Pan troglodytes microRNA mir-521-1 (MIR521-1), microRNA NR_035863 86 1 66.2076 72 1.83255e-09 1 59 13 71 1 1 50 50 0 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCCAAAGGGAAGAACTTTCTGTTGTCTAAAAGAAAAGAACGCACTTCCCTTTAGAG ||||| | ||||||| ||||||||||||| ||||||||||| | ||||| ||||||| 66 gi|270133201|ref|NR_032565.1| Macaca mulatta microRNA mir-517 (MIR517), microRNA NR_032565 86 1 66.2076 72 1.83255e-09 1 59 13 71 1 1 50 50 0 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGATGGAAGCACTGTCTGTGGTCTAAAAGAAAAGATCGTGCATCCTTTTAGAG ||||||| | |||||| || ||||| |||| |||||||||| |||| ||||||||||| 67 gi|262205417|ref|NR_030216.1| Homo sapiens microRNA 521-1 (MIR521-1), microRNA NR_030216 87 1 66.2076 72 1.83255e-09 1 59 14 72 1 1 50 50 0 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCCAAAGGGAAGAACTTTCTGTTGTCTAAAAGAAAAGAACGCACTTCCCTTTAGAG ||||| | ||||||| ||||||||||||| ||||||||||| | ||||| ||||||| 68 gi|270133214|ref|NR_032567.1| Macaca mulatta microRNA mir-518a (MIR518A), microRNA NR_032567 87 1 64.4042 70 6.39622e-09 1 54 14 68 1 1 48 48 1 55 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTC-CTTT CCCTACAAAGGGAAGCCCTTTCTGTTGTCTAAACGAAAAGAAAGTGCTTCTCTTT |||| | |||||||| ||||||||||||| || |||||||||||||||| |||| 69 gi|262205407|ref|NR_030214.1| Homo sapiens microRNA 517c (MIR517C), microRNA NR_030214 95 1 64.4042 70 6.39622e-09 1 59 19 75 1 1 50 50 2 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGATGGAAGCACTGTCTGTTGTCT--AAGAAAAGATCGTGCATCCTTTTAGAG ||||||| | |||||| || |||||||||| ||||||||| |||| ||||||||||| 70 gi|262205377|ref|NR_030208.1| Homo sapiens microRNA 526a-2 (MIR526A2), microRNA NR_030208 65 1 64.4042 70 6.39622e-09 1 61 5 61 1 1 51 51 4 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCCTCTAGAGGGAAGCACTTTCTGTTG----AAAGAAAAGAACATGCATCCTTTCAGAGGG ||||||| |||||||| |||||||||| ||||||||||| ||| |||||| |||||| 71 gi|270133311|ref|NR_032588.1| Macaca mulatta microRNA mir-523b (MIR523B), microRNA NR_032588 89 1 62.6008 68 2.2325e-08 1 59 14 74 1 1 51 51 2 61 CCCTCTACAGGGAAGCGCTTTCTGTTGTCT--GAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGAGCGAAGCGCTTTCTGTTGGCTAGAAAAGAATAGGAAGCGCTTCCCTTTAGAG ||||||| || |||||||||||||||| || |||||| || ||| |||||| ||||||| 2 40.9604 44 0.0729808 3 61 74 14 1 -1 46 46 2 61 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAG--AAAAGAAAGTGCTTCCTTTTAGAGGG CTCTAAAGGGAAGCGCTTCCTATTCTTTTCTAGCCAACAGAAAGCGCTTCGCTCTAGAGGG ||||| |||||||||||| || || | | || || |||||| ||||| | ||||||| 72 gi|270133297|ref|NR_032585.1| Macaca mulatta microRNA mir-521 (MIR521), microRNA NR_032585 87 1 62.6008 68 2.2325e-08 1 59 14 72 1 1 49 49 0 59 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCCAAAGGGAAGTACTTTCTGTTGTCTAAAAGAAAAGAACGCACTTCCCTTTGGAG ||||| | ||||||| ||||||||||||| ||||||||||| | ||||| ||| ||| 73 gi|262205401|ref|NR_030213.1| Homo sapiens microRNA 518a-2 (MIR518A2), microRNA NR_030213 87 1 62.6008 68 2.2325e-08 9 55 22 68 1 1 42 42 0 47 AGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTT AGGGAAGCCCTTTCTGTTGTCTAAAAGAAGAGAAAGCGCTTCCCTTT |||||||| ||||||||||||| |||||| |||||| |||||| ||| 2 44.5672 48 0.00599063 9 55 66 20 1 -1 38 38 0 47 AGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTT AGGGAAGCGCTTTCTCTTCTTTTAGACAACAGAAAGGGCTTCCCTTT ||||||||||||||| || | | | | || |||||| |||||| ||| 74 gi|301171372|ref|NR_035862.1| Pan troglodytes microRNA mir-520h (MIR520H), microRNA NR_035862 89 1 60.7975 66 7.79219e-08 1 59 13 74 1 1 52 52 5 63 CCCTCTACAGGGAAGCGCTTTCTGTT----GTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGAGG-AAGCACTTTCTGTTTGTTGTCTGAGAAAAAACAAAGTGCTTCCCTTTAGAG ||||||| ||| |||| ||||||||| |||||| | |||| ||||||||||| ||||||| 2 46.3705 50 0.00171634 3 61 74 13 1 -1 50 50 5 63 CTCTACAGGGAAGCGCTTTCTGTTGTCTGA---AAGAAA-AGAAAGTGCTTCCTTTTAGAGGG CTCTAAAGGGAAGCACTTTGTTTTTTCTCAGACAACAAACAGAAAGTGCTTCC-TCTAGAGGG ||||| |||||||| |||| | || ||| | || ||| ||||||||||||| | ||||||| 75 gi|262205412|ref|NR_030215.1| Homo sapiens microRNA 520h (MIR520H), microRNA NR_030215 88 1 60.7975 66 7.79219e-08 1 59 14 75 1 1 52 52 5 63 CCCTCTACAGGGAAGCGCTTTCTGTT----GTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGAGG-AAGCACTTTCTGTTTGTTGTCTGAGAAAAAACAAAGTGCTTCCCTTTAGAG ||||||| ||| |||| ||||||||| |||||| | |||| ||||||||||| ||||||| 2 46.3705 50 0.00171634 3 61 75 14 1 -1 50 50 5 63 CTCTACAGGGAAGCGCTTTCTGTTGTCTGA---AAGAAA-AGAAAGTGCTTCCTTTTAGAGGG CTCTAAAGGGAAGCACTTTGTTTTTTCTCAGACAACAAACAGAAAGTGCTTCC-TCTAGAGGG ||||| |||||||| |||| | || ||| | || ||| ||||||||||||| | ||||||| 76 gi|262205366|ref|NR_030206.1| Homo sapiens microRNA 520g (MIR520G), microRNA NR_030206 90 1 60.7975 66 7.79219e-08 1 59 14 75 1 1 52 52 5 63 CCCTCTACAGGGAAGCGCTTTCTGTT----GTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGAGG-AAGCACTTTCTGTTTGTTGTCTGAGAAAAAACAAAGTGCTTCCCTTTAGAG ||||||| ||| |||| ||||||||| |||||| | |||| ||||||||||| ||||||| 2 46.3705 50 0.00171634 3 61 75 14 1 -1 50 50 5 63 CTCTACAGGGAAGCGCTTTCTGTTGTCTGA---AAGAAA-AGAAAGTGCTTCCTTTTAGAGGG CTCTAAAGGGAAGCACTTTGTTTTTTCTCAGACAACAAACAGAAAGTGCTTCC-TCTAGAGGG ||||| |||||||| |||| | || ||| | || ||| ||||||||||||| | ||||||| 77 gi|270132721|ref|NR_032717.1| Macaca mulatta microRNA mir-524 (MIR524), microRNA NR_032717 85 1 58.9941 64 2.71974e-07 1 55 12 66 1 1 46 46 0 55 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTT CCCTACAAAGGCAAGCACTTTCTCTTGTCTAAAAGAAAAGAAGGTGCTTCCCTTT |||| | ||| |||| |||||| |||||| ||||||||||| |||||||| ||| 2 37.3537 40 0.889088 9 61 64 12 1 -1 40 40 0 53 AGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG AGGGAAGCACCTTCTTTTCTTTTAGACAAGAGAAAGTGCTTGCCTTTGTAGGG |||||||| | |||| || | | | | || ||||||||||| | ||| |||| 78 gi|262205276|ref|NR_030185.1| Homo sapiens microRNA 519e (MIR519E), microRNA NR_030185 84 1 57.1907 62 9.49283e-07 3 59 16 69 1 1 47 47 3 57 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CTCCAAAAGGGAGCACTTTCTGTT---TGAAAGAAAACAAAGTGCCTCCTTTTAGAG ||| | | || ||| ||||||||| |||||||||| ||||||| ||||||||||| 79 gi|301171363|ref|NR_035861.1| Pan troglodytes microRNA mir-520g (MIR520G), microRNA NR_035861 89 1 55.3874 60 3.31332e-06 1 59 13 74 1 1 51 51 5 63 CCCTCTACAGGGAAGCGCTTTCTGTT----GTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGAGG-ACGCACTTTCTGTTTGTTGTCTGAGAAAAAACAAAGTGCTTCCCTTTAGAG ||||||| ||| | || ||||||||| |||||| | |||| ||||||||||| ||||||| 80 gi|270133196|ref|NR_032564.1| Macaca mulatta microRNA mir-516a-2 (MIR516A-2), microRNA NR_032564 89 1 55.3874 60 3.31332e-06 12 61 28 76 1 1 43 43 1 50 GAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG GAAGCACTTTCTGTTGTCT-AAAGAAAAGGAAGTGTTTCCTTCCCGAGGG ||||| ||||||||||||| ||||||||| ||||| |||||| ||||| 81 gi|270133191|ref|NR_032563.1| Macaca mulatta microRNA mir-516a-1 (MIR516A-1), microRNA NR_032563 89 1 55.3874 60 3.31332e-06 12 61 28 76 1 1 43 43 1 50 GAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG GAAGCACTTTCTGTTGTCT-AAAGAAAAGGAAGTGTTTCCTTCCCGAGGG ||||| ||||||||||||| ||||||||| ||||| |||||| ||||| 82 gi|301171387|ref|NR_035864.1| Pan troglodytes microRNA mir-521-2 (MIR521-2), microRNA NR_035864 86 1 53.584 58 1.15646e-05 3 59 15 71 1 1 46 46 0 57 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CTCCAAAGGGAAGAATTTTCTCTTGTCTAAAAGAAAAGAACGCACTTCCCTTTAGAG ||| | ||||||| ||||| |||||| ||||||||||| | ||||| ||||||| 83 gi|262205353|ref|NR_030203.1| Homo sapiens microRNA 521-2 (MIR521-2), microRNA NR_030203 87 1 53.584 58 1.15646e-05 3 59 16 72 1 1 46 46 0 57 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CTCCAAAGGGAAGAATTTTCTCTTGTCTAAAAGAAAAGAACGCACTTCCCTTTAGAG ||| | ||||||| ||||| |||||| ||||||||||| | ||||| ||||||| 84 gi|270133237|ref|NR_032572.1| Macaca mulatta microRNA mir-518f (MIR518F), microRNA NR_032572 87 1 49.9773 54 0.000140886 2 61 73 14 1 -1 47 47 0 60 CCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CCTCTGAAGGGAAGCGCTTTCTTTCCTTTCACACAAGATAAAGTGCTTCCCTCTAGAGGG ||||| ||||||||||||||| | | | | | || | ||||||||||| | ||||||| 85 gi|301171283|ref|NR_035853.1| Pan troglodytes microRNA mir-519d (MIR519D), microRNA NR_035853 87 1 46.3705 50 0.00171634 1 59 13 72 1 1 47 47 1 60 CCCTCTACAGGGAAGCGCTTTCTG-TTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCCAAAGGGAAGCGCTTTCTGTTTGTTTTCTCTCAAACAAAGTGCCTCCCTTTAGAG ||||| | |||||||||||||||| |||| | ||| ||||||| ||| ||||||| 86 gi|270133288|ref|NR_032583.1| Macaca mulatta microRNA mir-520g (MIR520G), microRNA NR_032583 90 1 46.3705 50 0.00171634 1 59 14 75 1 1 49 49 5 63 CCCTCTACAGGGAAGCGCTTTCTGTT----GTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCTAGAGA-AAGCACTTTCTGTTTGTTGTCTGAGGAAAAACAAAGTGCTTCCCTTCAGAG ||||||| || |||| ||||||||| |||||| |||| ||||||||||| || |||| 87 gi|262205371|ref|NR_030207.1| Homo sapiens microRNA 516b-2 (MIR516B2), microRNA NR_030207 85 1 46.3705 50 0.00171634 12 61 28 72 1 1 40 40 5 50 GAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG GAAGCACTTTGTGTTTTGTGAAAG-----AAAGTGCTTCCTTTCAGAGGG ||||| |||| |||| | |||||| |||||||||||||| |||||| 88 gi|262205349|ref|NR_030202.1| Homo sapiens microRNA 519d (MIR519D), microRNA NR_030202 88 1 46.3705 50 0.00171634 1 59 14 73 1 1 47 47 1 60 CCCTCTACAGGGAAGCGCTTTCTG-TTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG CCCTCCAAAGGGAAGCGCTTTCTGTTTGTTTTCTCTTAAACAAAGTGCCTCCCTTTAGAG ||||| | |||||||||||||||| |||| | ||| ||||||| ||| ||||||| 89 gi|301171138|ref|NR_035836.1| Pan troglodytes microRNA mir-515-2 (MIR515-2), microRNA NR_035836 82 1 39.157 42 0.254728 13 59 25 67 1 1 37 37 4 47 AAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG AAGCACTTTCTGTTGTCTGAAAG----CAGAGTGCCTTCTTTTGGAG |||| |||||||||||||||||| | ||||| | ||||| ||| 90 gi|301171409|ref|NR_035867.1| Pan troglodytes microRNA mir-524 (MIR524), microRNA NR_035867 86 1 39.157 42 0.254728 3 61 71 13 1 -1 44 44 0 59 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CTCCAAAGGGAAGCGCCTTCTTTTCCTTTGGACAAGAGAAAGTGCTTCCCTTTGTAGGG ||| | |||||||||| |||| || | | || ||||||||||||| ||| |||| 91 gi|301173375|ref|NR_035835.1| Pan troglodytes microRNA mir-515-1 (MIR515-1), microRNA NR_035835 82 1 39.157 42 0.254728 13 59 25 67 1 1 37 37 4 47 AAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG AAGCACTTTCTGTTGTCTGAAAG----CAGAGTGCCTTCTTTTGGAG |||| |||||||||||||||||| | ||||| | ||||| ||| 92 gi|262205340|ref|NR_030200.1| Homo sapiens microRNA 524 (MIR524), microRNA NR_030200 87 1 39.157 42 0.254728 3 61 72 14 1 -1 44 44 0 59 CTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG CTCCAAAGGGAAGCGCCTTCTTTTCCTTTGGACAAGAGAAAGTGCTTCCCTTTGTAGGG ||| | |||||||||| |||| || | | || ||||||||||||| ||| |||| 93 gi|262205285|ref|NR_030187.1| Homo sapiens microRNA 515-2 (MIR515-2), microRNA NR_030187 83 1 39.157 42 0.254728 13 59 26 68 1 1 37 37 4 47 AAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG AAGCACTTTCTGTTGTCTGAAAG----CAGAGTGCCTTCTTTTGGAG |||| |||||||||||||||||| | ||||| | ||||| ||| 94 gi|262205270|ref|NR_030184.1| Homo sapiens microRNA 515-1 (MIR515-1), microRNA NR_030184 83 1 39.157 42 0.254728 13 59 26 68 1 1 37 37 4 47 AAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAG AAGCACTTTCTGTTGTCTGAAAG----CAGAGTGCCTTCTTTTGGAG |||| |||||||||||||||||| | ||||| | ||||| ||| 95 gi|390332045|ref|XM_776818.2| PREDICTED: Strongylocentrotus purpuratus TM2 domain-containing protein almondex-like, transcript variant 2 (LOC576518), mRNA XM_776818 4082 1 37.3537 40 0.889088 32 56 2287 2263 1 -1 23 23 0 25 AAAGAAAAGAAAGTGCTTCCTTTTA AATGAAAAGAAAGTGCTTCATTTTA || |||||||||||||||| ||||| 96 gi|390332043|ref|XM_003723358.1| PREDICTED: Strongylocentrotus purpuratus TM2 domain-containing protein almondex-like, transcript variant 1 (LOC576518), mRNA XM_003723358 4079 1 37.3537 40 0.889088 32 56 2284 2260 1 -1 23 23 0 25 AAAGAAAAGAAAGTGCTTCCTTTTA AATGAAAAGAAAGTGCTTCATTTTA || |||||||||||||||| ||||| 97 gi|356543101|ref|XM_003539954.1| PREDICTED: Glycine max probable alpha,alpha-trehalose-phosphate synthase [UDP-forming] 7-like (LOC100811463), mRNA XM_003539954 2936 1 37.3537 40 0.889088 30 49 2157 2176 1 1 20 20 0 20 TGAAAGAAAAGAAAGTGCTT TGAAAGAAAAGAAAGTGCTT |||||||||||||||||||| 98 gi|356517317|ref|XM_003527287.1| PREDICTED: Glycine max probable alpha,alpha-trehalose-phosphate synthase [UDP-forming] 7-like (LOC100800847), mRNA XM_003527287 3251 1 37.3537 40 0.889088 30 49 2494 2513 1 1 20 20 0 20 TGAAAGAAAAGAAAGTGCTT TGAAAGAAAAGAAAGTGCTT |||||||||||||||||||| 99 gi|297814701|ref|XM_002875188.1| Arabidopsis lyrata subsp. lyrata hypothetical protein, mRNA XM_002875188 593 1 37.3537 40 0.889088 19 43 119 143 1 1 23 23 0 25 TTTCTGTTGTCTGAAAGAAAAGAAA TTTCTGATGTCTGGAAGAAAAGAAA |||||| |||||| ||||||||||| 100 gi|397513516|ref|XM_003827011.1| PREDICTED: Pan paniscus pumilio homolog 2 (Drosophila), transcript variant 2 (PUM2), mRNA XM_003827011 6002 1 35.5503 38 3.10322 20 49 4867 4896 1 1 28 28 2 31 TTCTGTTGTCTGAAAGA-AAAGAAAGTGCTT TTCAGTT-TCTGAAAGATAAAGAAAGTGCTT ||| ||| ||||||||| ||||||||||||| 3056429 673143725 0 0 0.41 0.625 0.78 biopython-1.63/Doc/examples/m_cold.fasta0000644000175000017500000000235712250104543017657 0ustar tratra00000000000000>gi|8332116|gb|BE037100.1|BE037100 MP14H09 MP Mesembryanthemum crystallinum cDNA 5' similar to cold acclimation protein, mRNA sequence CACTAGTACTCGAGCGTNCTGCACCAATTCGGCACGAGCAAGTGACTACGTTNTGTGAACAGAAAATGGG GAGAGAAATGAAGTACTTGGCCATGAAAACTGATCAATTGGCCGTGGCTAATATGATCGATTCCGATATC AATGAGCTTAAAATGGCAACAATGAGGCTCATCAATGATGCTAGTATGCTCGGTCATTACGGGTTTGGCA CTCATTTCCTCAAATGGCTCGCCTGCCTTGCGGCTATTTACTTGTTGATATTGGATCGAACAAACTGGAG AACCAACATGCTCACGTCACTTTTAGTCCCTTACATATTCCTCAGTCTTCCATCCGGGCCATTTCATCTG TTCAGAGGCGAGGTCGGGAAATGGATTGCCATCATTGCAGTCGTGTTAAGGCTGTTCTTCAACCGGCATT TCCCAGTTTGGCTGGAAATGCCTGGATCGTTGATACTCCTCCTGGTGGTGGCACCAGACTTCTTTACACA CAAAGTGAAGGAGAGCTGGATCGGAATTGCAATTATGATAGCGATAGGGTGTCACCTGATGCAAGAACAT ATCAGAGCCACTGGTGGCTTTTGGAATTCCTTCACACAGAGCCACGGAACTTTTAACACAATTGGGCTTA TCCTTCTACTGGCTTACCCTGTCTGTTTATGGTCATCTTCATGATGTAGTAGCTTAGTCTTGATCCTAAT CCTCAAATNTACTTTTCCAGCTCTTTCGACGCTCTTGCTAAAGCCCATTCAATTCGCCCCATATTTCGCA CACATTCATTTCACCACCCAATACGTGCTCTCCTTCTCCCTCTCTCCCTCTCCTCCCTCTTTTCTTCCTC TCACTTCTCTTCTCTTCTCTTCTTCAATACTCCCCTGGAGCGCCCTCTTCACCTCCCTACTCTCTACTCC TCTCTCTCACTCTCTCTTCCTCTCTTATCTCTCTCCTCCTCTCCTTCTCATCCCTCCTCCTTCTCTTCCT TTTCTTCTTTCTATCCACGCGCCATCCTCCCTCTTCCCTCTTCCCTTCTCTCTCCTCTCTTTCTCTCTCC TCTCTTCCTCATCTCACCACCTCCTCCTCTCTTTCTTCCGTCCTCCTTCCCTTCCTTCTTC biopython-1.63/Doc/examples/Proux_et_al_2002_Figure_6.py0000644000175000017500000001513712250104543022426 0ustar tratra00000000000000# This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. # """GenomeDiagram script to mimic Proux et al 2002 Figure 6 You can use the Entrez module to download the 3 required GenBank files This is an extended version of the example in the Biopython Tutorial which produces a GenomeDiagram figure close to Proux et al 2002 Figure 6. See http://dx.doi.org/10.1128/JB.184.21.6026-6036.2002 """ import os from reportlab.lib import colors from reportlab.lib.colors import red, grey, orange, green, brown, blue, lightblue, purple from reportlab.lib.units import cm from Bio.Graphics import GenomeDiagram from Bio.Graphics.GenomeDiagram import CrossLink from Bio import SeqIO from Bio.SeqFeature import SeqFeature, FeatureLocation name = "Proux Fig 6" #As explained in the Biopython Tutorial, these are three phage genomes. The first #two are self-containged GenBank files, but the third phage is integrated into a #bacterial genome, thus we slice the full record and also take the reverse #complement to match the strand orientation of the other two phage: A_rec = SeqIO.read("NC_002703.gbk", "gb") B_rec = SeqIO.read("AF323668.gbk", "gb") C_rec = SeqIO.read("NC_003212.gbk", "gb")[2587879:2625807].reverse_complement(name=True) records = dict((rec.name, rec) for rec in [A_rec, B_rec, C_rec]) #Here we hard code the gene colors for simiplicity and to match the target image. #In practice you might have an automatic mapping based on the gene annotation #or some other classification: A_colors = [red]*5 + [grey]*7 + [orange]*2 + [grey]*2 + [orange] + [grey]*11 + [green]*4 \ + [grey] + [green]*2 + [grey, green] + [brown]*5 + [blue]*4 + [lightblue]*5 \ + [grey, lightblue] + [purple]*2 + [grey] B_colors = [red]*6 + [grey]*8 + [orange]*2 + [grey] + [orange] + [grey]*21 + [green]*5 \ + [grey] + [brown]*4 + [blue]*3 + [lightblue]*3 + [grey]*5 + [purple]*2 C_colors = [grey]*30 + [green]*5 + [brown]*4 + [blue]*2 + [grey, blue] + [lightblue]*2 \ + [grey]*5 #Here we hard code a list of cross-links with percentage identity scores, based #on a manual inspection of the target image (there could be mistakes here). #In practice you might generate the list of cross-mappings from a BLAST report #or similar computational analysis: #Tuc2009 (NC_002703) vs bIL285 (AF323668) A_vs_B = [ (99, "Tuc2009_01", "int"), (33, "Tuc2009_03", "orf4"), (94, "Tuc2009_05", "orf6"), (100, "Tuc2009_06", "orf7"), (97, "Tuc2009_07", "orf8"), (98, "Tuc2009_08", "orf9"), (98, "Tuc2009_09", "orf10"), (100, "Tuc2009_10", "orf12"), (100, "Tuc2009_11", "orf13"), (94, "Tuc2009_12", "orf14"), (87, "Tuc2009_13", "orf15"), (94, "Tuc2009_14", "orf16"), (94, "Tuc2009_15", "orf17"), (88, "Tuc2009_17", "rusA"), (91, "Tuc2009_18", "orf20"), (93, "Tuc2009_19", "orf22"), (71, "Tuc2009_20", "orf23"), (51, "Tuc2009_22", "orf27"), (97, "Tuc2009_23", "orf28"), (88, "Tuc2009_24", "orf29"), (26, "Tuc2009_26", "orf38"), (19, "Tuc2009_46", "orf52"), (77, "Tuc2009_48", "orf54"), (91, "Tuc2009_49", "orf55"), (95, "Tuc2009_52", "orf60"), ] #bIL285 (AF323668) vs Listeria innocua prophage 5 (in NC_003212) B_vs_C = [ (42, "orf39", "lin2581"), (31, "orf40", "lin2580"), (49, "orf41", "lin2579"), # terL (54, "orf42", "lin2578"), # portal (55, "orf43", "lin2577"), # protease (33, "orf44", "lin2576"), # mhp (51, "orf46", "lin2575"), (33, "orf47", "lin2574"), (40, "orf48", "lin2573"), (25, "orf49", "lin2572"), (50, "orf50", "lin2571"), (48, "orf51", "lin2570"), (24, "orf52", "lin2568"), (30, "orf53", "lin2567"), (28, "orf54", "lin2566"), ] def get_feature(features, id, tags=["locus_tag", "gene"]): """Search list of SeqFeature objects for an identifier under the given tags.""" for f in features: for key in tags: #tag may not be present in this feature for x in f.qualifiers.get(key, []): if x == id: return f raise KeyError(id) gd_diagram = GenomeDiagram.Diagram(name) feature_sets = {} max_len = 0 for i, record in enumerate([A_rec, B_rec, C_rec]): max_len = max(max_len, len(record)) #Allocate tracks 5 (top), 3, 1 (bottom) for A, B, C #(empty tracks 2 and 4 add useful white space to emphasise the cross links #and also serve to make the tracks vertically more compressed) gd_track_for_features = gd_diagram.new_track(5-2*i, name=record.name, greytrack=True, height=0.5, start=0, end=len(record)) assert record.name not in feature_sets feature_sets[record.name] = gd_track_for_features.new_set() #We add dummy features to the tracks for each cross-link BEFORE we add the #arrow features for the genes. This ensures the genes appear on top: for X, Y, X_vs_Y in [("NC_002703", "AF323668", A_vs_B), ("AF323668", "NC_003212", B_vs_C)]: features_X = records[X].features features_Y = records[Y].features set_X = feature_sets[X] set_Y = feature_sets[Y] for score, x, y in X_vs_Y: color = colors.linearlyInterpolatedColor(colors.white, colors.firebrick, 0, 100, score) border = colors.lightgrey f_x = get_feature(features_X, x) F_x = set_X.add_feature(SeqFeature(FeatureLocation(f_x.location.start, f_x.location.end, strand=0)), color=color, border=border) f_y = get_feature(features_Y, y) F_y = set_Y.add_feature(SeqFeature(FeatureLocation(f_y.location.start, f_y.location.end, strand=0)), color=color, border=border) gd_diagram.cross_track_links.append(CrossLink(F_x, F_y, color, border)) for record, gene_colors in zip([A_rec, B_rec, C_rec], [A_colors, B_colors, C_colors]): gd_feature_set = feature_sets[record.name] i = 0 for feature in record.features: if feature.type != "gene": #Exclude this feature continue gd_feature_set.add_feature(feature, sigil="BIGARROW", color=gene_colors[i], label=True, name=str(i+1), label_position="start", label_size=6, label_angle=0) i += 1 gd_diagram.draw(format="linear", pagesize='A4', fragments=1, start=0, end=max_len) gd_diagram.write(name + ".pdf", "PDF") gd_diagram.write(name + ".eps", "EPS") gd_diagram.write(name + ".svg", "SVG") biopython-1.63/Doc/examples/fasta_iterator.py0000644000175000017500000000164612250104543020763 0ustar tratra00000000000000# This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. # """Example using Bio.SeqIO to parse a FASTA file.""" from __future__ import print_function from Bio import SeqIO def extract_organisms(file_to_parse, format): all_species = [] for cur_record in SeqIO.parse(open(file_to_parse), format): # extract the info from the description new_species = cur_record.description.split()[1] # append the new species to the list if it isn't there if new_species not in all_species: all_species.append(new_species) return all_species if __name__ == "__main__": print("Using Bio.SeqIO on a FASTA file") all_species = extract_organisms("ls_orchid.fasta", "fasta") print("number of species: %i" % len(all_species)) print("species names: %s" % all_species) biopython-1.63/Doc/examples/ls_orchid.fasta0000644000175000017500000022530012250104543020363 0ustar tratra00000000000000>gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG CCGCCTCGGGAGCGTCCATGGCGGGTTTGAACCTCTAGCCCGGCGCAGTTTGGGCGCCAAGCCATATGAA AGCATCACCGGCGAATGGCATTGTCTTCCCCAAAACCCGGAGCGGCGGCGTGCTGTCGCGTGCCCAATGA ATTTTGATGACTCTCGCAAACGGGAATCTTGGCTCTTTGCATCGGATGGAAGGACGCAGCGAAATGCGAT AAGTGGTGTGAATTGCAAGATCCCGTGAACCATCGAGTCTTTTGAACGCAAGTTGCGCCCGAGGCCATCA GGCTAAGGGCACGCCTGCTTGGGCGTCGCGCTTCGTCTCTCTCCTGCCAATGCTTGCCCGGCATACAGCC AGGCCGGCGTGGTGCGGATGTGAAAGATTGGCCCCTTGTGCCTAGGTGCGGCGGGTCCAAGAGCTGGTGT TTTGATGGCCCGGAACCCGGCAAGAGGTGGACGGATGCTGGCAGCAGCTGCCGTGCGAATCCCCCATGTT GTCGTGCTTGTCGGACAGGCAGGAGAACCCTTCCGAACCCCAATGGAGGGCGGTTGACCGCCATTCGGAT GTGACCCCAGGTCAGGCGGGGGCACCCGCTGAGTTTACGC >gi|2765657|emb|Z78532.1|CCZ78532 C.californicum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAACAGAATATATGATCGAGTG AATCTGGAGGACCTGTGGTAACTCAGCTCGTCGTGGCACTGCTTTTGTCGTGACCCTGCTTTGTTGTTGG GCCTCCTCAAGAGCTTTCATGGCAGGTTTGAACTTTAGTACGGTGCAGTTTGCGCCAAGTCATATAAAGC ATCACTGATGAATGACATTATTGTCAGAAAAAATCAGAGGGGCAGTATGCTACTGAGCATGCCAGTGAAT TTTTATGACTCTCGCAACGGATATCTTGGCTCTAACATCGATGAAGAACGCAGCTAAATGCGATAAGTGG TGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCTCGAGGCCATCAGGCTAAG GGCACGCCTGCCTGGGCGTCGTGTGTTGCGTCTCTCCTACCAATGCTTGCTTGGCATATCGCTAAGCTGG CATTATACGGATGTGAATGATTGGCCCCTTGTGCCTAGGTGCGGTGGGTCTAAGGATTGTTGCTTTGATG GGTAGGAATGTGGCACGAGGTGGAGAATGCTAACAGTCATAAGGCTGCTATTTGAATCCCCCATGTTGTT GTATTTTTTCGAACCTACACAAGAACCTAATTGAACCCCAATGGAGCTAAAATAACCATTGGGCAGTTGA TTTCCATTCAGATGCGACCCCAGGTCAGGCGGGGCCACCCGCTGAGTTGAGGC >gi|2765656|emb|Z78531.1|CFZ78531 C.fasciculatum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGCAGAACATACGATCGAGTG AATCCGGAGGACCCGTGGTTACACGGCTCACCGTGGCTTTGCTCTCGTGGTGAACCCGGTTTGCGACCGG GCCGCCTCGGGAACTTTCATGGCGGGTTTGAACGTCTAGCGCGGCGCAGTTTGCGCCAAGTCATATGGAG CGTCACCGATGGATGGCATTTTTGTCAAGAAAAACTCGGAGGGGCGGCGTCTGTTGCGCGTGCCAATGAA TTTATGACGACTCTCGGCAACGGGATATCTGGCTCTTGCATCGATGAAGAACGCAGCGAAATGCGATAAG TGGTGTGAATTGCAGAATCCCGCGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCT AAGGGCACGCCTGCCTGGGCGTCGTGTGCTGCGTCTCTCCTGATAATGCTTGATTGGCATGCGGCTAGTC TGTCATTGTGAGGACGTGAAAGATTGGCCCCTTGCGCCTAGGTGCGGCGGGTCTAAGCATCGGTGTTCTG ATGGCCCGGAACTTGGCAGTAGGTGGAGGATGCTGGCAGCCGCAAGGCTGCCGTTCGAATCCCCCGTGTT GTCGTACTCGTCAGGCCTACAGAAGAACCTGTTTGAACCCCCAGTGGACGCAAAACCGCCCTCGGGCGGT GATTTCCATTCAGATGCGACCCCAGTCAGGCGGGCCACCCGTGAGTAA >gi|2765655|emb|Z78530.1|CMZ78530 C.margaritaceum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAAACAACATAATAAACGATTGAGTG AATCTGGAGGACTTGTGGTAATTTGGCTCGCTAGGGATATCCTTTTGTGGTGACCATGATTTGTCATTGG GCCTCATTGAGAGCTTTCATGGCGGGTTTGAACCTCTAGCACGGTCCAGTTTGCACCAAGGTATATAAAG AATCACCGATGAATGACATTATTGCCCCACACAACGTCGGAGGTGTGGTGTGTTAATGTTCATTCCAATG AATTTTGATGACTCTCGGCAGACGGATATCTTGACTCTTGCATCGATGAAGAACGCACCGAAATGTGATA AGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGG CTAAGGGCACGCCTGCCTGGGCGTCGTATGTTTTATCTCTCCTTCCAATGCTTGTCCAGCATATAGCTAG GCCATCATTGTGTGGATGTGAAAGATTGGCCCCTTGTGCTTAGGTGCGGTGGGTCTAAGGATATGTGTTT TGATGGTCTGAAACTTGGCAAGAGGTGGAGGATGCTGGCAGCCGCAAGGCTATTGTTTGAATCCCCCATG TTGTCATGTTTGTTGGGCCTATAGAACAACTTGTTTGGACCCTAATTAAGGCAAAACAATCCTTGGGTGG TTGATTTCCAATCAGATGCGACCCCAGTCAGGGGGCCACCCCAT >gi|2765654|emb|Z78529.1|CLZ78529 C.lichiangense 5.8S rRNA gene and ITS1 and ITS2 DNA ACGGCGAGCTGCCGAAGGACATTGTTGAGACAGCAGAATATACGATTGAGTGAATCTGGAGGACTTGTGG TTATTTGGCTCGCTAGGGATTTCCTTTTGTGGTGACCATGATTTGTCATTGGGCCTCATTGAGAGCTTTC ATGGCGGGTTTGAACCTCTAGCACGGTGCAGTTTGCACCAAGGTATATAAAGAATCACCGATGAATGACA TTATTGTCAAAAAAGTCGGAGGTGTGGTGTGTTATTGGTCATGCCAATGAATTGTTGATGACTCTCGCCG AGGGATATCTTGGCTCTTGCATCGATGAAGAATCCCACCGAAATGTGATAAGTGGTGTGAATTGCAGAAT CCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCTAAGGGCACGCCTGCCTGG GCGTCGTATGTTTTATCTCTCCTTCCAATGCTTGTCCAGCATATAGCTAGGCCATCATTGTGTGGATGTG AAAGATTGGCCCCTTGTGCTTAGGTGCGGTGGGTCTAAGGATATGTGTTTTGATGGTCTGAAACTTGGCA AGAGGTGGAGGATGCTGGCAGCCGCAAGGCTATTGTTTGAATCCCCCATGTTGTCATATTTGTTGGGCCT ATAGAACAACTTGTTTGGACCCTAATTAAGGCAAAACAATCCTTGGGTGGTTGATTTCCAATCAGATGCG ACCCCAGTCAGCGGGCCACCAGCTGAGCTAAAA >gi|2765652|emb|Z78527.1|CYZ78527 C.yatabeanum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAGAATATATGATCGAGTG AATCTGGATGACCTGTGGTTACTCGGCTCGCCTGATTTGTTGTTGGGCTTCCTTAGGAGCTTACATGGCG GGTTTGAACCTCTACCACGGTGCAGTTTGCGCCAAGTCATATGAAGCATAACTGCAAATGGCACTACTGT CACCAAAAGTTGGAGTGGCAGTGTGTTATTGCATGCGCTAATGGATTTTTGATGACTCTCGGCCAACGGA TATCTGGCTCTTTGCATCGATGAAGGAACGCAGCGAAATGCGATAAGTGGTGTGAATTGCAGAATCCCGC GAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCTAAGGGCACGCCTGCCTGGGCGTC GTGTATCGCATCTCTCTTGCCAATGCTTACCCGGCATACAACTAGGCTGGCATTGGGTGGATGTGAAAGA TTGGCCCCTTGTGCCTAGGTGCGGTGGGTCTAAGGATTGATGTTTTGATGGATCGAAACCTGGCAGGAGG TGGAGGATGCTGGAAGCCGCAAGGCTGTCGTTCGAATCCCCCATGTTGTCATATTTGTCGAACCTATAAA AGAACATGCTTGAACCCCAATGGATTGTAAAATGACCCTTGGCGGTTGATTTCCATTTAGATGCGACCCC AGGTCAGGCGGGCCACCC >gi|2765651|emb|Z78526.1|CGZ78526 C.guttatum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAGAATATATGATCGAGTG AATCTGGAGGACCTGTGGTTACTCGGCTCCCCTGATTTGTTGTTGGGCTTCCTTAGGAGCTTACATGCCG GGTTTGAACCTCTACCACGGTGCAGTTTGCGCCAAGTCATATGAAGCATAACTGAACAATGGCATTATTG TCACCAAAAGTTGGAGTGGCAGTGTGCTATTGCATGCGCTAATGGATTTTTGATGACTCTCGCCAACGGA TATCTTGGCTCTTGCATCGATGAAGAACCCAGCGAAATGCGATAAGTGGTGTGAATTGCAGAATCCCGCG AACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCTAAGGGCACGCCTGCCTGGGCGTCG TGTATCGCATCTCTCTTGCCAATGCTTACCCGGCATACAACTAGGCTGGCATTGTGCGGATGTGAAAGAT TGGCCCCTTGTGCCTAGGTGCGGTGGGTCTAAGGATTGATGTTTTGATGGATCGAAACCTGGCAGGAGGT GGAGGATGCTGGAAGCCGCAAGGCTGTCGTTCGAATCCCCCATGTTGTCATATTTGTCGAACCTATAAAA GAACATGCTTGAACCCCAATGGATGTAAAATGACCCTTGGCGGTTGATTTCCATTTAGATGCGACCCCAG GTCAGGCGGGGCCACCCGCTGAGTTTATGT >gi|2765650|emb|Z78525.1|CAZ78525 C.acaule 5.8S rRNA gene and ITS1 and ITS2 DNA TGTTGAGATAGCAGAATATACATCGAGTGAATCCGGAGGACCTGTGGTTATTCGGCTTGCCGAGGGCTTT GCTTTTGTGGTGACCCAAATTTGTCGTTGGGCCTCCTCGGGAGCTTTCATGGCAGGTTTCAAACTCTAGC ACGGCACAGTTTGTGCCAAGTCATATGAAGCATCACCGACAAATGACATTATTGTCAAAAAAGTTGGAGG GGCGGTGTCCTCCTGTGCATGCCGAATATAATTTTGATGACTCTCGACAAGCGGATATCTTGGCTCTTGC ATCGGATGAAGAACCCAGCGAAATGCGATAAGTGGTGTGAATTGCAAGATCCCGTGAACCATCGAGTCTT TTGAACGCAAGTTGCGCCCGAGGCCATCAGGCTAAGGGCACGCCTGCCTGGGCGTCGTGTGTTGTGTCTC TCTTGCCAATGCCTGTCCATATATCTAGGCTTGCATTGTGCGGATGCGAAAGATTGGCCCCTTGTGCCTT GGCGCGTTGGGTCTAAGGAATGTTTTGATGGCCCGAAACTTGGCAGGAGGTGGAGGACATTGGCTGCCAC AAGGTTGTCATTTAAATCCCCCATGTTATCGTATTTATCGGACCTATATAATAACTTGTTTGAACCCCAG TGGAGGCAAAATGACCCTTGGATGGTTGACTGCCATTCATATGCGACCCCAGGTCAGGCGGGCCACCCGC TGCA >gi|2765649|emb|Z78524.1|CFZ78524 C.formosanum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATAGTAGAATATATGATTGAGTG AATATGGAGGACATGTGGTTACTTGGCTCGTCAGTGCTTTGCTTTTGTGGTGACCTTAATTGGGCCTCCT TAGGAGCTTTCATGGCGGGTTCAAACCTTTAGCACGGCGCAGTTTGTGCCAAGTCATATAAGCATCCCTG ATGAATGGCATTGTTGTTAAAAAAGTCGGAGGGGCGGCATGCTGTTGTGCATGCTAATGAATTTTTTGAT GACTCTCGCAACGGAACTTGGCTCTTTACATCCGATGAAGAACGCAGCGAAATGCGATAAGTGGTGTGAA TTGCAGAATCCCGTGAACCATCGAGTATTTGAACGCAAGTTGCGCCCAAGGCCATCAGGCTAAGGGCACG CCTGCCTGGGCGTCGTGTGCTGCATCTCTCCTNCCAATGCTTGCCCAGCATATAGCTAGGTTAGCATTGT GCGGATGAAATATTGGCCCCTTGTGCTTAGGTGCGGTGGGTCTAAGGATTAGTATTTTGATAGCTCGGAA CTCGGCAGGAGGTGGAGAATGTTGGTAGCTGCAAGGCTGCCATTTGAATTCCCCATGTTGTCGTATTTGT TGAACCTATAAAAGAACTTGTTTGAACCCCAATGAAGGAAAAATGACCCTTGGGCGGTTGATTTTCATTC AGATGCAACCCCAGGTCAGGTGGCCACCCTGAGATTAAGC >gi|2765648|emb|Z78523.1|CHZ78523 C.himalaicum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACCAGGTTTCCGTAGGTGAACCTGCGGCAGGATCATTGTTGAGACAGCAGAATATATGATCGAGTG AATCCGGTGGACTTGTGGTTACTCAGCTCGACATAGGCTTTGCTTTTGCGGTGACCCTAACTTGTCATTG GGCCTCCTCCCAAGCTTTCCTTGTGGGTGTGAACCTCTAGCACGGTGCAGTATGCGCCAAGTCATATGAA GCATCACTGAGGAATGACATTATTGTCCCAAAAGCTAGAGTGGAAGCGTGCTCTTGCATGCATGCATAAT GAATTTTTTATTGACTCTCGACATATGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAAC GCAAGTTGCGCCCGATGCCATCAGGCTAAGGGCACGCCTGCCTGGGCGTCGTGTGCTGCGTCTCTCCTAT CAATGCTTTCCTATCATATAGATTGGTTTGCATTGCGTGGATGCGAAAGATTGGCCCCTTGTGCCTAGGT GCGGTGGGTCTAAGGACTAGTGTTTTGATGGTTCGAAACCTGGCAGGAGGTGGAGGATGTTGGCAGCTAT AAGGCTATCGTTTGAACCCCCCATATTGTCGTGTTTGTCGGACCTAGAGAAGAACCTGTTTGAATCCCAA TGGAGGCAAACAACCCTCGGGCGGTTGATTGCCATTCATATTGCGACCCAGGTCAGGCGGGGCCACCGCT GAGTTTAAG >gi|2765647|emb|Z78522.1|CMZ78522 C.macranthum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGCAGAATATATGATCGAGTG AATCCGGTGGACTTGTGGTTACTCAGCTCGACATAGGCTTTGCTTTTGCGGTGACCCTAATTTGTCATTG GGCCTCCCCCCAAGCTTTCCTTGTGGGTTTGAACCTCTAGCACGGTGCAGTATGCGCCAAGTCATATGAA GCATCACTGATGAATGACATTATTGTCCAAAAAGTTGGAGTGGAAGCGTGCTATTGCATACATGCAAATG AATTTTTTATGACTCTCGACATATCGTGGTGTGATCGCAGAATCCCGTGAACCATCGAGTCTTTGAACGC AAGTTGCGCCCGATGCCATCAGGCTAAGGGAACGCCTGCCTGGGCGTCGTGTGCTGCGTCTCTCCTGTCA ATGGTTATACGTCATATAGACAGGTTTGCATTGCGTGGATGTGAAAGATTGGCCCCTTGTGCCTAGGTGC GGTGGGTCTAAGGACTAGTGTTTTGATGGTTCGAAACCTGGCAGGAGGTGGAGGATGTTGGCAGCTATAA GGCTATCATTTGAATCCCCCATATTGTCGTGTTTGTCGGACCTAGAGAAGAACATGTTTGAATCCCAATG GGGGCAAACAACCCTCGGGCGGTTGATTGCCATTCATATGCGACCCCAGGTCAGGCGGCCACCCGCTGAG >gi|2765646|emb|Z78521.1|CCZ78521 C.calceolus 5.8S rRNA gene and ITS1 and ITS2 DNA GTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAGAATATATGATCGAGTGAATCCGGTGGACTTG TGGTTACTCAGCTCGACATAGGCTTGGCTTTGGCGGTGACCCTAATTTGCCATTGGGCCTCCTCCAAAGC TTTCCTTGTGGGTTTGAACCTCTAGCACGGTGCAGTATGCGCCAAGTCATATGAAGCATCACCGATGAAT GACATTATTGTCAAAAAGTTGGAGTGGAAGCGTGCTATTGCATGCATGCAAATGAATTTTTTATGACTCT CGACAACGGATATCTTGGCTCTTGCATCGATGAAGAACGCAGCAAAATGCGATAAGTGGTGTGAATTGCA GAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGATGCCATCAGGCTAAGGGCACGCCTGC CTGGGCGTCGTGTGCTGCGTCTCTCCTGTCAATGCTTTCATGTCATATAGATAGGTTTGCATTGCGTGGA TGTGAAAGAATGGCCCCTTGTGCCTAGGTACGGTGGGTCTAAGGACTAGTGTTTTGATGGTTCGAAACCT GGCAGGAGGTGGAGGATGTTGGCAGCTATAAGGCTATCATTTGAATCCCCCATATTGTCGTGTTTGTCGG ACCTAGAGAAAAACATGTTTGAATCCCAATGGAGGCAAACAACTCTCGGGCGGTTGATTGCCATTCATAT GCGACCCCAGGTCAGGCGGGGCCACC >gi|2765645|emb|Z78520.1|CSZ78520 C.segawai 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGCAGAATATATGATCGAGTG AATCCGGTGGACTTGTGGTTACTCAGCTCGCCATAGGCTTTGCTTTTGCGGTGACCCTAATTTGTCATTG GGCCTCCTCCAAAGCTTTCCTTGTGGGTTTGAACCTCTAGCACGGTGCAGTATGCGCCAAGTCATATGAA TCATCACCGATGAATGACATTATTGTCCAAAAAGTTGGAGTAGAAGCGTGCTATTGCATGCATGCAATAG AATATTTTATGACTCTCGACAAGGGATATCTTGGCTCTTGCATCGGATGAAGAACGCAGCGAAATTGTGA TAAGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTATGTGAACGCAAGTTGCGCCCGATGCCATCA GGCTAAGGGCACGCCTGCCTGGGCGTCGTGTGCTGCGTCTCTCCTGTCAATGCTTTCCTGTCATATCGAT AGGTTTGCATTGTGTGGATGTGAAAGATTGGCCCCTTGTGCCTAGGTGCGGTGGGTCTAAGGACTAGTGT TTTGATGGTTCGAAACCTGGCAGGAGGTGGAGGATGTTGGCAGCTATAAGGTTGTCATGTGAATCCCCCA TATTGTCGTGTTTGTCGGACCTGGAGAAGAACCTGTTTGAATCCCAATGGAGGCAAACAACCCTCGGGCG GTTGATTGCCATTCATATGCGACCCCAGGTCAGGCGGGCCACCCGCTGAGTTT >gi|2765644|emb|Z78519.1|CPZ78519 C.pubescens 5.8S rRNA gene and ITS1 and ITS2 DNA ATATGATCGAGTGAATCTGGTGGACTTGTGGTTACTCAGCTCGCCATAGGCTTTGCTTTTGCGGTGACCC TAATTTGTCATTGGGCCTCCTCCCAAGCTTTCCTTGTGGGTTTGAACCTCTAGCACGGTGCAGTATGCAC CAAGTCATATGAAGCATTGCCGATGAATGACATTATTGTCCAAAAGTTGGAGTGGAAGCGTGCTACTGCA TGCATGCAAATGAATTTTTTTATGACTCTCGACAACGGATATCTTGGCTCTTGCATCGATGAAGAACGCA GCGAAATGCGATAAGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCC CGATGCCATCAGGCTAAGGGCACGCCTGCCTGGGCGTCGTGTGCTGCGTCTCTCCTGTCAATGCTTTCCC ATCATATAGATAGGTTTGCATTGTGTGGATGTGAAAGATTGGCCCCTTGTGCCTAGGTGCGGTGGGTCTA AGAACTTAATGTTTTGATGGTTCGAAACCTGGCAGGAGGTGGAGGATGTTGGCAGCTATATAAGGCTATC ATTTGAATCCCCCATATTGTCGCGTTTGTTGGACCTAGAGAAGAACATGTTTGAATCCCAATGGAGGCAA ACAACCCTCGGGCGGTTGATTGCCATTCATATGCGACCCCAGGTCAGGCGGGGCCACCCGCTGAGTTTA >gi|2765643|emb|Z78518.1|CRZ78518 C.reginae 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGGAGGATCATTGTTGAGATAGTAGAATATTCGATCGAGTG AATCCGGAGGACTTGTGGTTACTCGGCTCGTCGAAGGCTTAACTTTTGTGGTGACCCTGATTTGTAGTTG GGCATCCTCGAGAGCTTTTATGGCGGGTTTGAGCCTCTATCACGGCGCAGTTTGCGCCAAGTCATATGAA GCATCGCCGACGAATTACATTATTGTCCCCCAAACTCGGATGGACGGTGTGTGTTACGCATACCCGTGAA CCATCGAGTCTTTGAACGCAAGTTGCGCCCAAGGCCATCAGGCTAAGGGCACGCCTGCCTGGGCGTCGTG TGCTGCATCTCTTTTGCCAATGCTTGCTCGACATACAATTAGGCCAGTATTGCATAGATATGAAAGATTG ACCCCTTGTGCTTAGGTGCGGTGGGTCTAAGGATTGGTGTTTTGATGGCCCAAACCCCGGCAGGAGGTGG AGCATGTTGATAGTCGCAAGGCTGTCGTTCAAATCCCACATGTTGTCGTATTTGCCGGACCCACAGAAGA ACCTATTTGAACCCCTATGGAGGCAAAACAACCCTTGAGCGATTGATTGCCATTCAGATACGACCCCAGG TCAGGCGGGGCCACCCGCTGAGTTTTCC >gi|2765642|emb|Z78517.1|CFZ78517 C.flavum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAGAATATACGATCGAGTG AATCCAGAGGACTTGTGGTTACTCGGCTCGTCCAAGTCTTAGCTTTTGTGGCCCTGATTTGTCGTTGGGC ATCCTCAGGAGATTTTATGGCGGGTTTGAACCTCTATCACGGTGCAGTTTGCGCCAAGTCATATGAAGCA TCACCGATGAATGACATTATTGTCACAAAAGTCGGATGGGCGGTGTGCTGTTGTGCATGCCAATAAATTT TTGATGACTCTCGGCCAACGGATATCTTGCTCTTCCATCGATGAAGAACGCAGCGAAATGCGATAAGTGG TGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCAAGGCCATCAGGCTAAG GGCACGCCTGCCTGGGCGTCGTGTGCTGCATCTCTTTTGCCAATGCTTGCCCGACATACAATTAGGACAG TATTGCATAGATATGAAAGATTGACCCCTTGTGCTTAGGTGCGGTGGGTCTAAGGATTGGTGTTTTGGTG GCCCGAACCCCGGCAGGAGGTGGATCATGTTGATAGTCGCAAGACTGTCATTCAAATCCCACATGTTGTC GTATTTGTCGGACCCACAGAAGAACCTATTTGAACCCCAATGGAGGCAAAACAACCCTCGAGCGATTGAT TGCCATTCAGATACGACCCCAGGTCAGGCGGGGCCACCCGCTGAGTTTAAGC >gi|2765641|emb|Z78516.1|CPZ78516 C.passerinum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTATAATATATGATCGAGTG AATCCGGAGGACTTGTGGTTACTCGGCTCGTCCAAGTCTTAGCTTTTGTGGCCCTGATTTGTCGTTGGGC ATCCTCAGGAGCTTTTATGGCGGGTTTGAACCTCTATCACGGCGCAGTTTGCGCCAAGTCATATGAAGCA TCACCGACGAATGACATTATTGTCACCAAAGTCGGATGGGCGGTGTGCTATTGTGCATACCAATAATTTT TTGATGACTCTCGTCAACGGATATCTTAGCGAAATGCGATAAGTGGTGTGAATTGCAGAATCCCGTGAAC CATCGAGTCTTTGAACGCAAGTTGCGCCCAAGGCCATCAGGCTAAGGGCACGCCTGCCTGGGCGTCGTGT GCTGCATCTCTTTTGCCAATGCTTGCCCGACATACAATTAGGCCAGTATTGCATAGATATGAAAGATTGA CCCCTTGTGCTTAGGTGCGGTGGGTCTAAGGATTGGTGTTTTGATGTCCCGAACCCCGGCAGGAGGTGGA GCATGTTGATAGTCGCAAGACTATCGTTCAAATCCCACATGTTGTCGTATTTGTCGGACCCACAGAAGAA CCTATTTGAACCCCAATGGAGGCAAAACAACCCTCGAGTGATTGATTGCCATTCAGATATGACCCCAGGT CAGGCGGGGCCACCCGCTGAGTTTAA >gi|2765640|emb|Z78515.1|MXZ78515 M.xerophyticum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGCTGAGACCGTAGAATAAACGATCGAGTT AATCTGGAGGACCGGTGTACTTCGATCTCCCAGGGGCTTTCGCTACTGTGGTGGCCTTTGATCTGCCATC GGGCCTCCTTGGGAGTGTTCTTGAAGGCGATCAAACTTCTAGCCCGGCGCAGCTTCGCGCCAAGTCTTTT GAGACAAGAATCGGCATAGGGGTGTTCCCTTGTAGCCGGTGGGGGTGGCACTTGATTCGTGCCCCCCAAG TTACATTTTTACAACTCTCGGCAACGGATATCTCGGCTCTTGCATCGATGGAGGACGCAGCGAAATGCGA TAAGTGGTGTGAATTGCAGAATCCCGTGAACATCGAGTCTTTGAACGCAAGTTGCGCTCGAGGCCGTCAG GCTAAGGGCACGCCTGCCTGGGCGTTGTGTACTGTGTCTCTCCCACCAATGGTTGTCCAAGAATACCGTT CGGTCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTTCGGTTCAATGGGTCTAAGACCTCGGGCTTTTGAT GGTCCAAAATTCGACAAGAGGTGGATGGGCAATGGCTGAGCCAAAGCTGTTGTGCGAATGCCGTATGTTG TCCGATTTGATGGGCCATCATAATTCGTAGGACCCTTCGGAACCCCATTGGTGCCACATCAACCCAACCC ATGGGCGGTTGATTGCCATTTGGAAGCAACCCCAGGTCAGGTGAGGCCACCCGCTGAGTTTAAGC >gi|2765639|emb|Z78514.1|PSZ78514 P.schlimii 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGGACCTTCGGGAGGATCATTTTTGAAGCCCCCAAATATACGATCGAGAT AATCCGGAGGACCGGTGTACTTTGGTCTCCCAGGGGCTTCCGCCGCTGTGGTGACCATGATTTCCCATCG AACCTCCTTGGGAGAGTTCTCTATGGCAATTGAACCCTTGGCCCGGCGCAGTTTCGCTCCAAGTCAAATG AGATAGAACCGACAAGGGTCTTCGTCCTCCATGGATGGACCGGGGAGGGCCGGCGTGCTGCTGTGCCCCC TTAAGATTCGTCTGACAACTCTCGTCAACGGATATCTCGGCTCTTGCATCGATGAAGAACCCAGCGAAAT GTGATAAGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCC ATCAGGCTAAGGGCACGCCTGCCTGGGCGTTGCGTGCTGCATCTCTCCCAATGCTAAGGTTGAACAGCCA TACTGTTCGGCCGGCGAGGATGAGAGTTTGGCCCCTTGTTCTTCGGTGCGATGGGTCCAAGACTTGGGCT TGTGACGGTCCACAAATCCGGCAAGAGGTGGACGGACGGTGGCTGCGACAAAGCTGTTGTGCGAATGCCC TATGTTGTCGTATTTGATGGGCCGGAATGATCCCTTTCGAACCCCGTCGGAGGCACGTCGACCCAACCCA TGGGCGGTTGACGGCCATTAGGATGCAACCCCAGGTCAGGTGAGACTACCCGCTA >gi|2765638|emb|Z78513.1|PBZ78513 P.besseae 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCCAAATACACGATCGAGTT AATCCGGAGGACCGGTGTACTTTGGTCTCCCAGGGGCTTCCGCCGCTGTGGTGACCATGATTTGCCATCG AGCCTCATTGGGAGATTTCTCCATGGCAATTGAACCCTTGGCCCGGCGCAGTTTCGCGCCAAGTCAAATG AGATAGAACCGGCAAGGGCATCGTCCTCCATGGAGCCGGGGAGGCCGGCATGCTGCTGTGCCCGAAAGAA TTTTCGACAACTCTCGGCAACGGATATCTCGGCTCTTGCATCGATGAAGAACGCAGCGAAATGTGATAAG TGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCT AAGGGCACGCCTGCCTGGGCGTTGCGTGCTGCATCTCTCCCAATGCTAAGGTTGAACAGCCATACTGTTC GGCCGGCGAGGATGAGAGTTTGGCCCCTTGTTCTTCTGTGCGATGGGTCCAAGACTTGGGCTTGTGACGG TCCACAAATCCGGCAAGAGGTGGACGGACGGTGGCTGCGACGAAGCTTTTGTGCGAATGCCCTATGTTGT CGTATTTGATGTGCCAGAACGATCCCTTTCGAACCCCTTCGGAGGCACGTCGACCCAACCCATGGGCGGT GACGCCATTAGGATGCAACCCAGTCAGTGAGCTACCGCTGAG >gi|2765637|emb|Z78512.1|PWZ78512 P.wallisii 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGGACCTTCGGGAGGATCATTTTTGAAGCCCCCAAAAAAACGATCGAATT AATCCGGAGGGCCGGTGTAGTTTGGTCTCCCAGGGGCCTTGGCTACTGTGGTGACCGTGAATTTCCGTCG AACCCTCTGGGAGGAATTCTTGATGGCAAATGAAACCCTTGGCCCGGCGCAGTTTCGCCCCAAATCAAAT GAGATGGAACCGGCGGAGGGCATCGTCCCCCATGGAACCGGGGAGGGCCGGCGTTCTTCCGTTCCCCCAA TGAAATTATTTTTTGACAACTCTCGCAACGGATATCTCGGCTCTTGCATCGATGAAGAACGCAGCGAAAT GTGATAAGCTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGC CATCAGGCTAAGGGCACGCCTGCCTGGGCGTTGCGTGCTGCATCTCTCTCCCATTGCTAAGGCTGAACAG GCATACTGTTCGGCCGGCGCGGATGAGTGTTTGGCCCCTTGTTCTTCGGTGCGATGGGTCCAAGACCTGG GCTTTTGACGGCCGGAAATCCGGCAAGAGGTGGACGGACGGTGGCTGCGACGAAGCTGTCGTGCGAATGC CCTGCGTTGTCGTATTTGATGGGCCGGAATAAATCCCTTTTGAACCCCATTGGAGGCACGTCAACCCGTG GGCGGTCGACGGTCATTTGGATCCAACCCCAGGTCAGGTGAGGCCACCCGCTGAGTTTAAGC >gi|2765636|emb|Z78511.1|PEZ78511 P.exstaminodium 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTTCGGAAGGATCATTGTTGAGACCCCCAAAAAAACGATCGAGTT AATCCGGAGGACCGGTGTAGTTTGGTCTCCCAGGGGCTTTGGCTACTGTGGTGGCCGTGAATTTCCGTCG AACCTCCTTGGGAGAATTCTTGATGGCAATTGAACCCTTGGCCCGGCGCAGTTTCGCCCCAAGTCAAATG AGATGGAACCGGCGGAGGGCATCGTCCTCCATGGAACCGGGGAGGGCCGGCGTTCTTCCGTTCCCCCCAT GAATTTTTTTTTGACAACTCTCGGCAACGGATATCTCGGCTCTTTGCATCCGATGAAAGAACCCAGCGAA ATGTGATAAGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGG CCATCAGGCTAAGGGCACGCCTGCCTGGGCGTTGCGTGCTGCATCTCTCTCCCATTGCTAAGGCTGAACA GGCATACTGTTCGGCCGGCGCGGATGAGTGTTTGGCCCCTTGTTCTTCGGTGCGATGGGTCCAAGACCTG GGCTTTTGACGGCCGGAAATCCGGCAAGAGGTGGACGGACGGTGGCTGCGACGAAGCTGTCGTGCGAATG CCCTACGCTGTCGTATTTGATGGGCCGGAATAAATCCCTTTTGAGCCCCATTGGAGGCACGTCAACCCGT GGGCGGTCGACGGCCATTTGGATGCAACCCCAGGTCAGGTGAGGA >gi|2765635|emb|Z78510.1|PCZ78510 P.caricinum 5.8S rRNA gene and ITS1 and ITS2 DNA CTAACCAGGGTTCCGAGGTGACCTTCGGGAGGATTCCTTTTTAAGCCCCCGAAAAAACGATCGAATTAAA CCGGAGGACCGGTTTAATTTGGTCTCCCCAGGGGCTTTCCCCCCTTGGTGGCCGTGAATTTCCATCGAAC CCCCCTGGGAGAATTCTTGGTGGCCAATGGACCCTTGGCCCGGCGCAATTTCCCCCCCAATCAAATGAGA TAGGACCGGCAGGGGGCGTCCCCCCCCATGGAACCGGGGAGGGCCGGCATTCTTCCGTTCCCCCCTCGGA TTTTTTGACAACTCTCGCAACGGATATCTCGCCTCTTTGCATCGGATGGAAGAACGCAGCGAAATGTGAT AAGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAG GCTAAGGGCACGCCTGCCTGGGCGTTGCGTGCTGCATCTCTCCCATTGCTAAGGTTGAACGGGCATACTG TTCGGCCGGCGCGGATGAGAGATTGGCCCCTTGTTCTTCGGTGCGATGGGTCCAAGACCTGGGCTTTTGA CGGTCCAAAATCCGGCAAGAGGTGGACGGACGGTGGCTGCGACAAAGCTGTCGTGCGAATGCCCTGCGTT GTCGTTTTTGATGGGCCGGAATAAATCCCTTTTGAACCCCATTGGAGGCACGTCAACCCATGGGCGGTTG ACGGCCATTTGGATGCAACCCCAGGTCAGGTGAGCCACCCGCTGAGTTTA >gi|2765634|emb|Z78509.1|PPZ78509 P.pearcei 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCCAAATATACGATCGAGTT AATCCGGAGGACCGGTGTAGTTTGGTCTCCCAGGGGCTTTCGCCGCTGTGGTGACCGTGATTTGCCATCG AGCCTCCTTGGGAGATTTCTTGATGGCAATTGAACCCTTGGCCCGGCGCAGTTTCGCGCCAAGTCATATG AGATAGAACCGGCGGAGGGCGTCGTCCTCCATGGAGCGGGGAGGGCCGGCATGCTCCGTGCCCCCCCATG AATTTTTCTGACAACTCTCGGCAACGGACGTAACAAGGTTTAAATGTGATAAGCAGGTGTGAATTGCAGA ATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGTTAAGGGCACGCCTGCCT GGGCGTTGCGTGCTGCATCTCTCCCATTGCTAAGGTTGAACGGGCATACTGTTCGGCCGGCGCGGATGAG AGTTTGGCCCCTTGTTCTTCGGTGCGATGGGTCCAAGACCTGGGCTTTTGACGGTCCAAAATCCGGCAAG AGGTGGACGGACGGTGGCTGCGACAGAGCTGTCGTGCGAATGCCCTACGTTGTCGTTTTTGATGGGCCAG AATAAATCCCTTTTGAACCCCATTGGAGGCACGTCAACCCAATGGGGGGTGACGGGCATTTGGTTAACCC CGGCAAGTTAAGGCACCCGTTAATTTTAGGA >gi|2765633|emb|Z78508.1|PLZ78508 P.longifolium 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCCAAATATACGATCGAGTT AATCCGGAGGACCGGTGTAGTTTGGTCTCCCAGGGGCTTTCGCCGCTGTGGTGACCGTGATTTGCCATCG AGCCTCCTTGGGAGATTTCTTGATGGCAATTGAACCCTTGGCCCGGCGCAGTTTCGCGCCAAGTCATATG AGATAGAACCGGCAGGGGGCGTCGTCCTCCATGGAGCCGGGGAGGGCCGGCATGCTGCCGTGCCCCCAAT GAATTTTTTGACAACTCTCGGCAACGGATATCTCGGCTCTTGCATCGATGAAGAACGCAGCGAAATGTGA TAAGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCA GGCTAAGGGCACGCCTGCCTGGGCGTTGCGTGCTGCATCTCTCCCATTGCTAAGGTTGAACGGGCATACT GTTCGGCCGGCGCGGATGAGAGTTTGGCCCCTTGTTCTTCGGTGCGATGGGTCCAAGACCTGGGCTTTTG ACGGTCCAAAATCCGGCAAGAGGTGGACGGACGGTGGCTGCGACAGAGCTGTCGTGCGAATGCCCTACGT TGTCGTATTTGATGGGCCGGAATAAATCCCTTTGAACCCCATTGGAGGCACGTCAACCCCATGGGCGGTG ACGGCCACTTGGATGCAACCCAGTCAGTGAGCCACCGCTGA >gi|2765632|emb|Z78507.1|PLZ78507 P.lindenii 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCCCCAAAAAAACGATCGAGTT AATCCGGAGGACCGGTGTAGTTTGGTCTCCCAGGGGCTTTGGCTACTGTGGTGACCGTGAATTTCCGTCG AGCCTCCTTGGGAGATTTCTTGATGGCAATTGAACCCTTGGCCCGGCGCAGTTTCGCCCCAAGTCAAATG AGATGGAACCGGCGGAGGGCATCGTCCTCCATGGAACCGGGGAGGGCCGGCGTGCTTCCGTCCCCCAATG AATTTTTTTGACAACTCTCGCAACGGATATCTCGCTCTTGCATCGATGAAGAACACAGCGAAATGTGATA AGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGG CTAAGGGCACGCCTGCCTGGGCGTTGCGTGCTGCATCTCTCTCCCATTGCTAAGGCTGAACAGGCATACT GTTCGGCCGGCGCGGATGAGTGTTTGGCCCCTTGTTCTTCGGTGCGATGGGTCCAAGACCTGGGCTTTTG ACGGCCGGAAATCCGGCAAGAGGTGGACGGACGGTGGCTGCGACGAAGCTGTCGTGCGAATGCCCTGCGT TGTCGTATTTGATGGGCCGGAATAAATCCCTTTGAACCCCATTGGAGGCACGTCAACCCGTGGCGGTCGA CGGCCATTTGGATGCAACCCAGTCAGTGAGCCACCGCTGA >gi|2765631|emb|Z78506.1|PLZ78506 P.lindleyanum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCAAAATATACGATCGAGTT AATCCGGAGGACTGGTGTAGTTTGGTCTCCCAGGGGCGTTGGCTACTGCGGTGACCATGATTTTCCATCG AGCCTCCTCGGGAGATTTCTTGATGGCAATTGAACCCTTGGCCCGGCGCAGTTTCGCGCCAAGTCGTATG AGATAGAACCGGCAGAGGGCTTCGTCCTCCATGGAACCGGGGAGGACCGGCATGCCGCCGTACCCCCTTT GAATCGGGCTGACAACTCTCGTTCAAGGAGCCCTCAGCTCATAATGTGATAAGTGGTGTGAATTGCAGAA TCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCTAAGGGCACGCCTGCCTG GGCGTTGCGTGCTGCATCTCTCCCGTTGCTAAGGTCGAACAGGCATATACTGTTCGGCCGGCGCGGATGA GAGTTTGGCCCCTTGTTCTTCGGTGCGATGGGTCCAAGACCTGGGCTTTTGACGGTAAAAAATCCGGCAA GAGGTGGACGGACGGTGGCTGCGACAAAGCTGTCGTGCGAATGCCCTACGTTGTCGTATTTGATGGGCCA GAATAAATCCCTTTTGAACCCCATTGGAGGCACGTCAACCCATGGGCGGTTTGACGGCCATTGGATGCCA ACCCAGTCAGGTGAGGCCACCCGCTGA >gi|2765630|emb|Z78505.1|PSZ78505 P.sargentianum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCCAAATATACGATCGAGTT AATCCGGAGGACTGGTGTAGTTTGGTCTCCCAGGGGCGGTGGCTACTGCGGTGACCATGATATTCCATCG AGCCTCCTCGGGAGATTTCTTGATGGCAATTGAACCCGTGGCCCGGCGCAGTTTCGCGCCAAGTCGTATG AGATAGAACCGGCAGAGGTCTTCGTCCTCCATGGAACCGGGGAGGCCCGGCATACCACCATACCCCCAAT GAATCTTTGTGACAACTCTCGTCAATGTGATAAGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTC TTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCTAAGGGCACGCCTGCCTGGGCGTTGCGTGCTGCATCT CTCCCGTTGCTAAGGTCGAACAGGCATACTGTTCGGTCGGCGCGGATGAGAGTTTGGCCCCTTGTTCTTC GGTGCGATGGGTCCAAGACCTGGGCTTTTGACGGTAAAAAATCCGGCAAGAGGTGGACGGACGGTGGCTG CGACAAAGCTGTCGTGCGAATGCCCTGCGTTGTCGTATTTGATGGGCCGGAATAAATCCCTTTTGAACCC CATTGGAGGCACGTCAACCCATGGGCGGTTGACGGCCATTTGGATGCAACCCCAGGTCAGGTGAGCCACC CGCTGAGTTTT >gi|2765629|emb|Z78504.1|PKZ78504 P.kaiteurum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTTCGGAAGGATCATTGTTGAGACCGCAAAATATACGATCGAGTT AATCCGGAGGACTGGTGTAGTTTGTCTCCCAGGGGCGTTGGCTACTGCGGTGACCATGATTTTCCATCGA GCCTCCTCGGGAGATTTCTTGATGGCAATTGAACCCTTGGCCCGGCGCAGTTTCGCGCCAAGTCGTATGA GATAGAACCGTCAGAGGGCATCGTCCCTCCATGGAGCCGGGGAGGGCCGGCATGCCCGCCGTGCCCCCAA TGAATCTTTTTGACAACTCTCGCAACGGATATCTCGCTCTTCATCGATGAAGAACGCAGCGAAATGTGAT AAGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGCCATCAGG CTAAGGGCACGCCTGCCTGGGCGTTGCGTGCTGCATCTCTCCCGTTGCTAAGGTCGAACAGACATACTGT TCGGCCGGCGCGGATGAGAGTTTGGCCCCTTGTTCTTCGGTGCGATGGGTCCAAGACCTGGGCTTTTGAC GGTACCAAATCCGACAAGAGGTGGACGGACGGTGGCTGCGACAAAACTGTCGTGCGAATGCCCTACGTTG TCGTATTTGATGGGCCAGAATAAATCCCTTTTGAACCCCATTGGAGGCACGTCAACCCATGGGCGGTGAC GGCCATTGGATGCAACCCCAGTCAGTGAGCCACCGCTGAGTAA >gi|2765628|emb|Z78503.1|PCZ78503 P.czerwiakowianum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACCAGGTTTCCGTAGGTGAACCTCCGGAAGGATCCTTGTTGAGACCGCCAAATATACGATCGAGTT AATCCGGAGGACCGGTGTAGTTTGGTCTCCCAGGGGCTTTCGCCGCTGTGGTGTCCGTGATTTACCATCG AGCCTCCTTGGGAGATTTCTTGATGGCAATTGAACCCATGGCCCGGCGCAGTTTCGCGCCAAGTCATATG AGATAGAACCGGCAGGGGGCGTCGTCCTCCATGGAACCGGGGAGGGCCGGCATGCTACCGTACCCCCAAT GAATTATTTTGACAACTCTCGGCAACGGAGTCTCAGCTCTTTGTGATAAGTGGTGTGAATTGCAGAATCC CGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCTAAGGGCACGCCTGCCTGGGC GTTGCGTGCTGCATCTCTCCCATTGCTAAGGTTGAACGGGCATACTGTTCGGCCGGCGCGGACGAGAGTT TGGCCCCTTGTTCTTCGGTGCGATGGGTCCAAGACCTGGGCTTTTGACGGTCCAAAATCCGGCAAGAGGT GGACGGACGGTGGCTGCGACAAAGCTGTCGTGCGAATGCCCTACGTTGTCGTATTTGATGGGCCAGAATA AATCCCTTTTGAACCCCATTGGAGGCACGTCAACCCATGGGCGGTTGACGGCCATTTGGATGCAACCCCA GGTCAGGTGAGCCACCCGCTGAGATAA >gi|2765627|emb|Z78502.1|PBZ78502 P.boissierianum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACCAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGACCGCCAAATATACGATCGAGTT AATCCGGAGGACCGGTGTAGTTTGGTCTCCCAGGGGCTTTCGCCCCTGTGGTGACCGTGATTTGCCATCG AGCCTCCTTGGGAGATTTCTTGATGGCAATTGAACCCATGACCCGGCGCAGTTTCGCGCCAAGTCATATG AGATAGAACCGGCAGGGGTCGTCGTCCTCCATGGAGCCGGGGAGGGCCGGCATACTACCGTCCCCCCAAT GAATTTTATATTGACAACTCTCGGCCGGAATCTCGGCTCTTGCATCGATGGAAGGACGCAGCGAAAATGT GATAAGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCAT CAGGCTAAGGGCACGCCTGCCTGGGCGTTGCGTGCTGCATCTCTCCCATTGCTAAGGTTGAACGGGCGTA CTGTTCGGCCGGCGCGGACGAGAGTTTGGCCCCTTGTTCTTCGGTGCGATGGGTCCAAGACCTGGGCTTT TGACGGTCCAAAATCCGGCAAGAGGTGGACGGACGGTGGCTGCGACAAAGCTGTCGTGCGAATGCCCTAC GTTGTCGTATTTGATGGGCCAGAATAAATCCCTTTTGAACCCCATTGGAGGCACGTCAACCCATGGGCGG TTGACGGCCATTTGGATGCAACCCCAGGTCAGGTGAGGCCACCCCGTGAGTTTACGC >gi|2765626|emb|Z78501.1|PCZ78501 P.caudatum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCAAAATATACGATCGAGTT AATCCGGAGGACCGGTGTAGTTTGGTCTCCCAGGGGCTTTGGTTACTGTGGTGACCGTGACTTTGCCGTC GAGCCTCCTTGGGAGATTTCTTGATGGAAATTGAACCCTTGGCCCGGCGCAGTTTCGCGCCAAGTCATAT GAGATGGAACCGGCGGAGGGAATCGTCCTCCATGGAGCCGGGGAGGGCCGGCGTGTTGCCGTGCCCCCAA TGAATTTTTTTTTGGGCAACTCTCGGCCAAACGGGAAATCTCGGCTCCTTGCATCGGATGGAAGGACGCA GCGAAATTGTGATAAGTGTTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTTGAACGCAAGTTGCG CCCGAGGCCATCAGGCTAAGGGCACGCCTGCCTGGGCGTTGCGTGCTGCATCTCTCTCCCATTGCTAAGG CTGAACAGGCATACTGTTCGGCCGGCGCGGATGAGTGTTTGGCCCCTTGTTCTTCGGTGCGATGGGTCCA AGACCTGGGCTTTTGACGGCCGGAAATCCGGCAAGAGGTGGACGGACGGTGGCTGCGACGAAGCTGTCGA GCGAATGCCCTACGCTGTCGTATTTGATGGGCCGGAATAAATCCCTTTTGAACCCCATTGGAGGCACGTC AACCCGTGGGCGGTCGACGGCCATTTGGATGCAACCCCAGGTCAGGTGAGGCCACCCGCTGAGTTTAAGA >gi|2765625|emb|Z78500.1|PWZ78500 P.warszewiczianum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGCTCATTGTTGAGACCGCAAAATAATTGATCGAGTT AATCCGGAGGACCGGTGTAGTTTGGTCTCCCAGGGGCTTTGGTTACTGTGGTGACCGTGACTTTGCCGTC GAGCCTCCTTGGGAGATTTCTTGATGGCAATTGAACCCTTGGCCCGGCGCAGTTTCGCGCCAAGTCATAT GAGATGGAACCGGCGGAGGGCATCGCCCTCCATGGAGCCGGGGAGGGCCGGCGTCCTGCCGTGCCCCCAA TGAATTTTTCTTTGGGAAAACTTCTCGGCCAACGGGAATCTCGGCTCTTTGCATCGGATGGAAGGACGCA GCGAATTGTGATAAGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCC CGAGGCCATCAGGCTAAGGGCACGCCTGCCTGGGCGTTGCGTGCTGCATCTCTCTCCCATTGCTAAGGCT GAACAGGCATACTGTTCGGCCGGCGCGGATGAGTGTTTGGCCCCTTGTTCTTCGGTGCGATGGGTCCAAG ACCTGGGCTTTTGACGGCCGGAAATCCGGCAAGAGGTGGACGGACGGTGGCTGCGACGAAGCTGTCGTGC GAATGCCCTACGCTGTCGTATTTGATGGGCCGGAATAAATCCCTTTTGAACCCCATTGGAGGCACGTCAA CCCGTGGGCGGTCGACGGCCATTTGGATGCAACCCCAGGTCAGGTGAGGCCACCCGCTGAGTTTAAG >gi|2765624|emb|Z78499.1|PMZ78499 P.micranthum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAGGGATCATTGTTGAGATCGCATAATAATTGATCGAGAT AATCCAGAGGATCGGTTTACTTTGGTCACCCTTGGGCGCCTGCTATTGCGGTGACCTAGATTTTCCATGG AGGGAGCCTCCTTGGGAGCTTTCTTGCCGGCGATCTAACCCTTGCCCGTCGCAGTTTTGCACCAAGTCAT ATGACACATAAATGGTGAAGGACATACCCCCTTCGTTGAATTCGAGGAGGGGACGGCATGTGGCCTTGAC CTCTCCCAATTATTTTTTTGACAACTCTCAGCCAACGGATATCTCGGCTCTTTGCATCGGATGGAGGACG CAGCGAAATGCGATAAGTGTGTGAATTGCAAGATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGC CCAAGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTC CATGCATACTGTTCAGCCGGTGCGGATGTGAGTTTGCCCCCTTGTTCTTTGGTGCTGGGGGTCTAAGAGC TGCAGGGGCTTTTGATGGTCCTAAATTCGGCAAGAGGTGGACGAATCATGCTACAACAAAACTGTTGGGC GAATGCTCCAGGTTGTCGTATTAGATGGGCCATCATAATCTAGAGACCCTTGTGAACCCCATTGGAGGCC CATCAACCCATGATCAGTTGATGGCCTTTTGGATGCGACCCCAGGTCAGGTGAGCAACC >gi|2765623|emb|Z78498.1|PMZ78498 P.malipoense 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAAGGTCATTGTTGAGATCACATAATAATTGATCGAGTT ACTCCAGAGGATCAGTTTACTCCGGTCACCCATGGGTGCTTGCTATTGCGGTGACCTAGATTTGCCATGG AGCCTCCTTGGGAGTTTTCTTGCCGGCGATCTTACCCTTGCCCGGGGCAGTTTTGCCCCAAGTCATATGA CACAATAATGGCGAAGGAGGGGGCGGCATTTTGCCCTTGACCCTACAATCACTCCCCCTCTCCAAATTAT TTTTGGACAACTCTCAGCAACGGATATCTCGGCTCTTGCATCGATGAAGAACCCAGCGAAATGCGATAAG TGGTGTGAATTGCAGGATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCAAGGCCATCAGGCC AAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTACATGCATACTGTTCA GCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTTCGGTGCTGGGGGTCTAAGAGCTGCAGGGGCTTTTGA TGGTCCTAAATTCGGCAAGAGGTGGACGAATTACAACAAAACTGTTGTGCGAATGCTCCAGGTTGTCGTA TTAGATGGGCCAGCATAATCTAAAGACCCTTGTGAACCCCACTGGAGGCACATCAACCCGTGATCAGTTG ATGGCCATTTGTTGCGACCCCAGTCAGTGAGCAACCCGCTGAGTTTAAGC >gi|2765622|emb|Z78497.1|PDZ78497 P.delenatii 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTG AATCCAGAGGATCAGTTTACTTTGGTCGCCCATGGGCGCTTGCTATTGCGGTGACCTAGATTTGCCATGG AGCCTCCTTGGGAGCTTTCTTGCCGGCGATCTAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCACATGA CACATAAATGGTGAAGGGCACGGCCCTTTGTGAATTCAAGGAGGTGAAGGGCATGTGGCCTTGAGCCTAC ACTCCCTCCCCCTCTCCAAATTATTTTTTGTGAAACAACTCTCAGCAACGGAGATCTCAGCTCTTGCATC GATGAAGGAACGCAGCGAAATGCGATAAGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGA ACGCAAGTTGCGCCCAAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTCTCCC TTAACGAGGCTGTCCAGGCATACTGTTCAGCCGGTGCGGGTGTGAGTTTGGCCCCTTGTTCTTTGGTGCT GGGGGTCTAAGAGCTGCAGGGGCTTTTGATGGTCCTAAATTCGGCAAGAGGTGGACGCAACGCGCTACAA CAAAACTGTTGTGCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAGCATAATCTAAACACCCTTGTGAA CCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTTGGTTGCGACCCCAGGTCAGGTGAGGC AACCCGCTGAGTTTAAGC >gi|2765621|emb|Z78496.1|PAZ78496 P.armeniacum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCATAATAATTGATCGAGTT AATCCAGAGGATCGGTTTACTTTGGTCACCCTTGGGCGCCTGCTATTGCGGTGACCTAGATTTGCCATGG AGGGAGCCTCCTTGGGAGCTTTCTTGCCGGCGATCTAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCAT ATGACACATAATTGGTGAAGGGCATATCCCTTCGTGAATTCGAGGAGGGGCCGGCATGTGGCCTTGACCT CTCCAATTAATTTTTTGAACAACTCTCAGCAACGGATATCTCGGCTCTTGCATCGATGGAGGGACGCACC GAAATGCGATAAGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTTGAACGCAAGTTGCGCCC AAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCC ATGCATACTGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTTTGGTGCTGGGGGTCTAAGAGCT GCAGGGGCTTTTGATGGTCCTAAATTCGGCAAGAGGTGGACGAATCATGCTACAACAAAACTGTTGGGCG AATGCTCCAGGTTGTCGTATTAGATGGGCCATCATAATCTAGAGACCCTTGTGAACCCCATTGGAGGCCC ATCAACCCATGATCAGTTGATGGCCTTTTGGGTTGCGACCCCAGGTCAGGTGAGGCAACCAGCTGAGTTT AAGC >gi|2765620|emb|Z78495.1|PEZ78495 P.emersonii 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGAT AATCCAGAGGATCGGTTTACTTTGGTCACCCATGGGCGCTTGCTATTGCGGTGACCTAGAGTTGCCATGG AGAGCCTCCTTGGGAGCTTTCTTGCCGGCGATCTAACCCTTGTCCGGCGCGGTTTTGCGCCAAGTCATAT GACACATAATTGGTGAAGGGCATAGCCCTTCGTGTATTCAAGGAGGGGGGGCGGCATGTGGCCTTGACAC TGCACTCGCTCTCCCCCTCTCCAAAGTATTTTTCTGAACAACTCTCAGCAACGGATATCTCAGCTCTTGC ATCGGATGGAGGAACGCAGCGAAATCCGATAAGTGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCT TTTGAACGCAAGTTGCGCCCGAGGCCATGAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATCTCT CTCCCTCAATGAGGCTGTCCATGCATACTGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTTTG GTGCTGGGGGTCTAAGAGCAGCAGGGGCTTTTGATGGTCCTAAATTCGGCAAGAGGTGGACGAATCAGGC TACAACAACACTGTTGTTGTGCGAATGCTCCAGGTTGTCGTATTAGATGGGCCGGCATAATCCAGAGACC CTTGTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTCGGTTGCGACCCCAGGTCA GTGAGCAACCCGCTGAGTG >gi|2765619|emb|Z78494.1|PNZ78494 P.niveum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGGTCGCATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCATGGACATTTACTGTTGCAGTGACCTAGATTTACCATCG GGCCTCCTTGGGAACTTTCCTGCTGGCGATCTATACCCTTGCCCGGCGCAGTAATGCGCCAAATCAAATG ACCCATAATTAATGAAGGGGGACGGCATACTGCCTTGACCAACTCCCCATTATTGAGGTAACACTCTCAA CTTCGGATATCTCAGGTGTGAATTGCAGAATCCCGTTAACATCGAGTCTTTGAACGCAAGTTGGGCCCGA GGCCAACAGGCCAAGGGAACGCCTGTCTGGGCATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCAT ACGTACTGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTTCGGTACGGGGGGTCTAAGAGCTGC ATGGGCTTTTGATGGTCCTAAATACGGCAAGAGGTGGACGAACTATGCTACAACAAAACTGTTGTGCGAA TGCCCCGGGTTGTTGTGTTTAATCAGAAGACCCTTTTGAACCCCATTGGAGGCCCATCGACCCATGATCA GTTGAATGGCCATTTGGTTGCGATCCCAGGTCAGGTGAGGCAACCCGCTGAGTTTAAG >gi|2765618|emb|Z78493.1|PGZ78493 P.godefroyae 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGATTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCATAATAATTGATCGAGTT AATCTGGAGGATCGGTTTACTTTGGTCACCCATGGGCATTTTCTGTTGCAGTGACCTAGAATTTCCATCG GGCCTCCCTGGGAGCTTTCCTGCTGGCGATCTATACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCAAATT TTACATAAGTGGTGAAGGGGGGCGGCATTCTTCCTTGACCAGCCCCCAAGTATTTAGGTAACAACTCTCA GCAACGGATATCTCGGCTCATGCATCGATGAAGGAACGCAGCGAGATTCGATAAATGGTGTGCCTTGCAG AATCCCGTGAACCATCGAATCTTTGAACGCAAGTTGCGCGCGAGGCCATCAGCCCAAGGGGACGCCTGCC TGGACATTGCGACTCATATCTCTCCCTTAATGAGGCTGTCCAAACGTACTGGTCATCCGGTGTGGATGTG AGTTTGGCCCCTTGTTCTTCGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGG CAAGAGGTGGACGAACTATGCTACAACAAAACTGTTGTGCGAATGCCCCGGGTTGTTGTGTTTAATCAGA AGACCCTTTTGAACCCCATTGGAGGCCCATCGACCCATGATCAGTTGAATGGCCATTTGGTTGCGATCCC AGGTCAGGTGAGGCAACCC >gi|2765617|emb|Z78492.1|PBZ78492 P.bellatulum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCACGGGCATTTGCTGTTGCAGTGACCTAGAATTTGCCATC GAGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTATACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATAT GTCACATAATTGGTGAAGGGGGGGAGCGTACTGCCTTGACCCACTCCAAATTATTTTTTTAACAACTCTC AGCAACGGATATCTCGGCTCTTGCATCGATGTAGAACCCAGCGAAATGCGATACTGGTGTGATTTGCAGA ATCCCGTGGACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCT GGGCATTGCGAGTCAAATCTCTCCCTTAATGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTGA GTTTGGCCCCTTGTTCTTTGGGACGGGGGGTCTAAGAGCTGCACGGGCTTTTGATGGTCCTAAATACGGC AAGAGGTGGACGAATTATGCTACAACAAAACTGTTGTGCGAATGCCCCGGGTTGTTGTGTTACATGGGCC AGCTTAATCAGAAGACCCTTTTGGACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATTGGCCATTT GGTTGCGATCCCAGTCAGGTGAGGCACACCGCTGAGTTTAATA >gi|2765616|emb|Z78491.1|PCZ78491 P.concolor 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCACGGGCATTTGCTGTTGCAGTGACCTAGATTTGCCATCG GGCCTCCTTAGGAGCTTTCTTGCTGGCGATCTATACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATATG ACACATAATTGGTGTTGGGGGGAGGCGTGCTGCCTTGACCCGCTCCAAATTATATTTTTAACAACTCTCA GCAACGGATATCTCGGCTCTTGCATCGATGAAGAACCAGCGAAATGCGATAAATGGTGTGAATTGCAGAA TCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTG GGCATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTGAG TTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGGCA AGAGGTGGACGAACTATGCTACAACAAAATGTTGTGCGAATGCCCCGGGTTGTTGTGTTACATGGACCAG CATAATCAGAAGACCCTTTGGACCCCATTAGAGGCCCATCAACCCATGATCAGTTGAATGGCCATTTGTT GCGATCCCAGTCAGGTGAGCAACCCGCTGAGATAAGC >gi|2765615|emb|Z78490.1|PFZ78490 P.fairrieanum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCATGGGCATTTGCTGTTGCAGTGACCGAGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTGAACCCTAGTCCGGCGCAGTTTTGCGCCAAGTCATATG GCACATAATTGGTGAAGGGGGCGGCATGCTGCCTCGACCCTCCCCAAATTATTTTTTAACAACTCTCAGC AACGGATATCTCGGCTCTTGCATCGATGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAAT CCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGG GCATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCAAACATACTGTTCAGCCGGTGCGGATGTGAGT TTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAACGCTGCATGGGCTTTTGATGGTCCTAAATACGGCAA GAGGTGGACGACTGTTCTGCAACAAGTGTTGTGCGAATGCCCCGCGTTGTCGTATTAGATGGGCCAGCAT AATTTGAAGACCCTTTTGAACCCCATTGGAGGCCCGTCAACCCATGATCAGTTGACGGCCATTTGGTTGC GACCCCAGTCAGGTGAGCAACCAGCTGA >gi|2765614|emb|Z78489.1|PDZ78489 P.druryi 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCGAGGGCATTTGCTATTGCAGTGACCGAGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAACCCGAGCCCGGCGCAGTTTTGCGCCAAGTCATATG ACACATAATTGGTGAAGGGGGCGGCATGCTGCCTTGACCCTCCCCAGATTATTTTTTAACAACTCTCAGC AACGGATATCTCGGCTCTTGCATCGATGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAAT CCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGG GCATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATACATACTGTTCAGCCGGTGGGGATGTGGGT TTGGCCCCTTGTTCATTGGTACGGGGGGTCTAAGAGCTGCATGGGATTTTGATGGTCCTAAATACGGCAA GAGGTGGACGAACTATGCTACAACAAAACTGTTGTGCGAATGCCCCGAGTTGTCGTATTAGATGGGCCCA GCATATCTGAAGACCCTTTTGAACCCCATGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTTGGTT GCGACCCCAGGTCAGGTGAGGCAACCCGCTGAGTTTAGGC >gi|2765613|emb|Z78488.1|PTZ78488 P.tigrinum 5.8S rRNA gene and ITS1 and ITS2 DNA CTGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACGCAATAATTGATCGAGTTAATCTGGAGGATC AGTTTACTTTGGTCACCCATGGGCATTTGTTATTGCAGTGACCGAGATTTGCCATCGAGCCTCCTTGGGA GCTTTCTTGCTGGCGATCTTAACCCTAGCCCGGCGCAGTTTTGCGCCAAGTCATACGACACATAATTGGT GAAGGGGGCGGCATGGTGCCTTGACCCTCCCCTAATTATTTTCTAACAACTCTCATCAACGGGATGGAGA ACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACCCAAGTT GCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCAAATCTCTCCCTTAATGAGG CTGTCCATACATACTGTTCAACCGGTGCGGATGTGAGTTTGCCCCCTTGTTCATTGGTACGGGGGGTCTA AGAGCTGCATGGGCTTTTGATGGTCCTAAAAACGGCAAGAGGTGGACGAACAACGCCACAACCAAACTGT TGTGCGGATCCCCCGGGTTGTCGTATTAGATGGGCCATCATAATCTGAAGACCCTTTTGAACCCCATCGG AGGCCCATCAACCCATGATCAGTTGATGGACATTTGTTGCGACCCCAGTCAGGTGAGCAACCGGCT >gi|2765612|emb|Z78487.1|PHZ78487 P.hirsutissimum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT ACTCTGGAGGATCAGTTTACTTTGGTCACCCATGGGCATTTGCTATTGCAGTGACCGGGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAACCCTAGCCCGGCGCAGTTTTGCGCCAAGTCATATG ACACATAATTGGTGAAGGGGGCGGCATGCTGCCTTTACCCTCCCCAAATTATTTTTTAACAACTCTCAGC AACGGATATCTCGGCTCTTGCATCGATGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAAT CCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGG GCATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATACATATTGTTCAGCCGGTGCGGATGTGAGT TCGGCCCCTTGTTCTTCGGTACGGGGGGTCTAAGAGCAGCATGGGCTATCGATGGTCCTAAATGCGGCAA GAGGTGGACGAACTATGCTACAACAAAACTGTTGTGCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAG CGTAATCTAAAGACCCTTTTGAACCCCATTAGAGGCCCATCAACCCATGATCAGTTGGGGCCACTTGTTG CGACCCCAGTCAGGTGAGCAACCGCTGAGTAA >gi|2765611|emb|Z78486.1|PBZ78486 P.barbigerum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTCACGAGGTTTCCGTAGGTGAATCTGCGGGAGGATCATTGTTGAGATCACATAATAATTGATCGAGAT TATCTGGAGGATCAGTTTACTTTGGTCACCCATGGACATTTGCTATTGCAGTGCCCGAGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAACCCTAGCCCGGCGCAGTTTTGCGCCAAGTCATATG ACACATAATTGGTGAAGGGTGCGGCATGGTGCCTGACCCTCCCCAAATTATTTCTTAACAACTCTCAGCA ACGGATATCTCGCTCTTGCATCGATGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAATCC CGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGC ATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTGAGTTT GGCCCCTTGTTCATTGGTACGGGGGGTCTAAGAGCTGCGTGGGCTTTTGATGGTCCTAAATACGGCAAGA GGTGGACGAACTATGCTACAACAAAACTGTTGTGCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAGCA TAATCTAAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTTGGTAG CGACCCCAGGTCAGGTGAGGCAACCCGCTGA >gi|2765610|emb|Z78485.1|PHZ78485 P.henryanum 5.8S rRNA gene and ITS1 and ITS2 DNA CTGAACCTGGTGTCCGAAGGTGAATCTGCGGATGGATCATTGTTGAGATATCATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCATGGGCATTTGCTATTGCAGTGACCGAGATTTGCCATCGA GCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAACCCTAGCCCGGGGCAGTTTTGCGCCAAGTCATATGA CACATAATTGGTGAAGGGTGCGGCATGGTGCCTTGACCCTCCCCAAATTATTTCTTAACAACTCTCAGCA ACGGATATCTCGGCTCTTGCATCGATGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAATC CCCGTGAACATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGG CATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATACATACTGTGCAGCCGGTGCGGATGTGAGTT TGGGCCCTTGTTCATTGGTACGGGGGGTCTAAGAGCTGCGTGGGCTTTTGATGGTCCTAAATACGCCAAG AGGTGGACGAACTATGCTACAACAAAACTGTTGTGCGAATGCCCCGGGTTGTCGTATTAGACGGGCCAGC ACAATCTAAAGACCCCTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATGTGTTG CGACCCCAGTCAGCTGAGCAACCCCTGATTTAGTA >gi|2765609|emb|Z78484.1|PCZ78484 P.charlesworthii 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGGGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCATGGGCATTTGGCTATTGCAGTGACCGAGAGTTTACCAT CGAGCCTCCATGGGAGCTTTCTTGCTGGCGATCTAAACCCTAGCCCGGCGCAGTTTTGGCCCAAGTCATA TGACACATAATTGTGAAGGGGCGGCATGTTTCCTACCCTCCCAACTCTCAGCAACGGATATCTCGCTCTT GCATCGATGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCT TGGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTC TCCCTTAATGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCATTGG TACGGGGGGTCTAAGAGCTGCGTGGGCTTTTGATGGTCCTAAATACGGCAAGAGGTGGACGAACTATGCT ACAACAAAACTGTTGTGCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAGCATAATCTAAAGACCCTTT GACACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGTCCATTGGGTTGCGACCCCAAGTCAGGT GAAGCAACTCCCTGAGTTTT >gi|2765608|emb|Z78483.1|PVZ78483 P.villosum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCATGGGCATTTGCTATTGCAGTGACCGAGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAACCCTAGCCCGGCGCAGTTTTGCGCCAAGTCATATG ACACATAATTGGTGAAGGGGGCGGCATGCTGCCTTGACCCTCCCCCAAATTATTTTTTAACAACTCTCAG CAACGGAAATCTGGCCTCTTGCATGGATGAAGACCAAACCGAAATCATAAATGGTGTGAATTGCAGAATC CCGTGAACAATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGG CATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCACACATATCTGTTCAGCCGGTGCGGATGTGAGT TTGGCCCCTTGTTCATTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGTTGGTCCTAAATACGGCAA GAGGTGGACGAACTATGCTACAACAAAACTGTTGTGCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAG CATAATCTAAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTTGGT TGCGACCCCAGGTCAAGTGAAGCAACCCCTGAGTTTAGCA >gi|2765607|emb|Z78482.1|PEZ78482 P.exul 5.8S rRNA gene and ITS1 and ITS2 DNA TCTACTGCAGTGACCGAGATTTGCCATCGAGCCTCCTGGGAGCTTTCTTGCTGGCGATCTAAACCGTAGC CCGGCGCAGTTTTGCGCCAAGTCATATGACACATAATTGGTGAAGGGGGCGGCATGCTGCCTTGACCCTC CCCAAATTATTTTTTAACAACTCTCNGCAACGGATATCTCGCCTCTTCATCGATGAAGAACGCAGCGAAA TGCGATAAATGGTGTGAATTGCAGAATCCCGTGAANCCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGG CCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATAC ATACTGTTCAGCCGGTGCGGATGTGCGTTTGGCCCCTTGTTCATTGGTACGGGGGGTCTAAGAGCTGCGT GGGCTTTTGATGGTCCTAAATACGGCAAGAGGTGGACGAACTATGCTACAACAAAACTGTTGTGCGAATG CCCCGGGTTGTCGTATTAGATGGGCCAGCATAATCTAAAGACCCTTTTGAACCCCATTGGAGGCCCATCA ACCCATGATCAGTTGATGGCCATTTGGTTGCGACCCCAGGTCAGGTGAGGCAACCCGCTGAGTTTAGCA >gi|2765606|emb|Z78481.1|PIZ78481 P.insigne 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCATGGGCATTTGCTATTGCAGTGACCGAGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAACCCTAGCCCGGCGCAGTTTTGCGCCAAGTCATATG ACACATAATTGGCGAAGGGGGCGGCATGGTGCCTTGACCCTCCCCAAATCATTTTTTTAACAACTCTCAG CAACGGAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCACACATAC TGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCATTGGTACGGGGGGTCTAAGAGCTGCGTGGGC TTTTGTTGGTCCTAAATACGGCAAGAGGTGGACGAACTATGCTACAACAAAACTGTTGTGCGAATGCCCC GGGTTGTCGTATTAGATGGGCCAGCATAATCTAAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCC ATGATCAGTTGA >gi|2765605|emb|Z78480.1|PGZ78480 P.gratrixianum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCATGGGCATTTGCTATTGCAGTGACCGAGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAACCCTAGCCCGGCGCAGTTTTGCGCCAAGTCATATG ACACATAATTGGCGAAGGGGGCGGCATGCTGCCTTGACCCTCCCCAAATTATTTTTTAACAACTCTCAGC AACGGATAGGCCATCAGGCTAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTCTCCCTTCAATGAG GCTGTCCACACATACTGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCATTGGTACGGGGGGTCT AAGAGCTGCGTGGGCTTTTGTTGGTACCTAAATACGGCAAGAGGTGGACGAACTATGCTACAACAAAAAT GTTGTGCGAATGCCCCGGGTTGTCGTATAGATGGGCCAGCATAATCTAAAGACCCTTTTGAACCCCATTG GAGGCCCATCAACCCATGATCAGTTGA >gi|2765604|emb|Z78479.1|PPZ78479 P.primulinum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCAGGAGGATCAGGTTACTTTGGTCACCCATGGGCATTTGCTGTTTCAGTGACCTAGATTTGCCATCG AGCCGAACTAAACCCTTGCCCGGCGCAGATTTGCGCCAATTTATATGAGACATTACTGGAAGGGGAGGGA TGCTGCCTTGCCTTGCCCCCCAAATTATTTTTTAACAACTCTCAGCAACGGATATCTCGCCTCTTGCATC GATGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATACAGAGATCCCGTGAACCATCGAGTCTTGAAC GCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGACATATCTCTCCCTT AATGAGGCTGTCGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGA GCTGCATGGGCTTTTGATGGTCCTAAATACGGCAAGAGGTGGACGAACTATGCGACAACAGAACTGTTGT GCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAGCATAATCTAAAGACCCTTTTGAACCCCATTGGAGG CCCATCAACCCAAGATCAGTTGACGGCCATTTGGATGCGACCCCAGGTCAGGTGAGGCAACCCTCTGAGT >gi|2765603|emb|Z78478.1|PVZ78478 P.victoria 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCAGTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGGGGATCAGTTTACTTTGGTCACCCATGGGCATTTGCTGTTGCAGTGGCCTAGAGTTTGCCATC GAGCCGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATATGACACATAACTGGAAGGGGCGGC ATGCTGCCATGGCTCCCCCAAATATTGATAATGGCGTGATAGCAGATCCCTGAACATCGAGTTTTGAACG CATGTTGCGTCCGAGGCCATCAGGTCAAGGGGACGCCTGCCTGGAAATTGTGAGACATATCTCTCCCTTA ATGAGGCTGCCATACATACTGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTCCTTTGGTACGGGGG TCTAAGAGCTGTATGGGTTGGATGGTCCTAAATACGGCAAGAGGTGGACGAGCTATGTGACAACAGAACT GTTGTGCGAATGCCCCGGTTGTCGTTTAGATGGGTCAGCATAATCTAAAGACCCTTTTGACCCCCATTGG AGGCCCATCAACCCATTATCAGTTGAGGACATTTGGTTGCGACCCAGGTCAGATGAGGCACCCGCTGAGT TTAGGC >gi|2765602|emb|Z78477.1|PVZ78477 P.victoria 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATGATAATTGATCGAGTT AATCTGGAGGATCAGTTTGCTTTGGTCACCCATGGGCATTTGCTGTTGCAGTGACCTAGATTTGCCATCG AGCCGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATATGACACATAATTGGAAGGGGCGGCA TGCTGCCTTGCCCTCCCCAAATATTTGTTAACCAACTCTCAGCAACGAGTATCTCGGCTCTTGCATCGGA TGGAAGGACGCAGCGAAATGCGATAAATGGTGTGAATTGCAAGAATCCCTGAACCATCGAGTCTTTGGAA CGCAAGTTGCGGCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGACATATCTCTCCCT TAATGAGGCTGTCCACACATACTGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTTTGGTACGG GGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGGCAAGAGGTGGACGAACTATGCGACAAC AGAACTGTTGTGCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAGCATAATCTAAAGACCCTTTTGAAC CCCATTGGAGGCCCATCAACCCATGATCAGTTGACGGCCATTTGGTTGCGACCCCAGGTCAGGTGAGGCA ACCGCTGAGTTTATGC >gi|2765601|emb|Z78476.1|PGZ78476 P.glaucophyllum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATGATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCATGGGCATTTGCTGTTGTAGTGACCTAGATTTGCCATCG AGCCGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATATGACACATAATTGGAAGGGCGGCAT GCTGCCTTGGCCCTCCCCAAATTATTTTTTTAACAACTCTCAGCAACGGATATCTCGGCTCTTAGGCCAT CCGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGACATATCTCTCCCTTAATGAGGCTGTCCATACATAC AGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGC TTTTGATCCACCTAAATACGGCAAGAGGTGGACGAACTATGCGACAACAGAACTGTTGCGCGAATGCCCC GGGTTGTCGTATTAGATGGGCCAGCATAATCTAAAGACCCTTTTGAATCCCATTGGAGGCCCATCAACCC ATGATCAGTTGACGGCCATTTGGTTGCGACCC >gi|2765600|emb|Z78475.1|PSZ78475 P.supardii 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCATGGGCATCTGCTCTTGCAGTGACCTGGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAACCCGTCCCGGCGCAGTTTTGCGCCAAGTCATATGA CACATAATTGGAAGGGGGTGGCATGCTGCCTTGACCCTCCCCAAATTATTTTTTTGACAACTCTCAGCAA CGGATATCTCGGCTCTTNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNATCAGGCCAAGGGCACGCCTGCCTGGG CATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATACATACTGTTCAGCCAATGCGGATGTGAGTT TGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCAAAATACGGCAAG AGGTGGACGAACTATGCTACAACAAAATTGTTGTGCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAGC ATAATCTAAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGACGGCCATTTGGTT GCGACCCAGGTCAGGT >gi|2765599|emb|Z78474.1|PKZ78474 P.kolopakingii 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACGTAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCATGGGCATCTGCTCTTTCAGTGACCTGGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCCAATCATATG ACACATAATTGGAAGGGCGGCATCCTGCCTTGACCCTCCCCAAATTATATTTTTGACAACTCTCAGCAAC GGATATCTCGCCTCTTGCATCGATGAAGACACAGCGAAATGCGATAAATGGTGTGAATTGCAGAATCCCG TGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCAT TGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTGAGTTTGG CCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCAAAATACGGCAAGAGG TGGACGAACTATGCTACAACAAAATTGTTGTGCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAGCATA ATCTACAGACCCTTCTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGACGGCCATTTGGTTGCG ACCCCAGGTCAGGTGAAGCAACCCGTTGAGCTT >gi|2765598|emb|Z78473.1|PSZ78473 P.sanderianum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGGGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCATGGGCATCTGCTCTTGTAGTGACCTGGATTTGCCATCG AGCCTCCATGGGAGCTTTCTTGGTGGCGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATATG ACACATAATTGGAAGGGGGCGGCATGCTGCCTTGACCCTCCCCAAATTATTTTGTTAACAACTCTCAGCA ACGGATATCTCGGCTCTTCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATC TCTCCCTTCAATGAGGCTGTCCATACATACTGTTCAGTCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTT GCCTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTACCTAAATACGGCAAGAGGTGGACGAACTA TGCTACAACAAAATTGTTGTGCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAGCATAATCTAAAGACC CTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGACGGCCATTTGGTTGCGACCCAGG >gi|2765597|emb|Z78472.1|PLZ78472 P.lowii 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTAGTCACCCATGGGCATCTGCTCTTGCAGTGACCTGGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAATCGTTGCCCGGCGCAGTCTTGCGCCAAGTCATTTC ACACATAATTGGAAGGGGGCGGCATGCAGTCTAAGACCCTCCCCAAATTATTTTTTGATAACTCTCAGCA ACGGAATCTCGGCTCTTGCATCGATGGAGGACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAATCC CGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGC ATTGCGAGTCATATCTCTCCTTTAATGAGGCTGTCTATACATACTGTTCAGCCACAGCGGATGTGAGTTT GGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCTTAAATACGGCAAGA GGTGGACGAAGTATGCTACAACAAAATTGTAGTTCGAATGCCCAGGGTTGTCGTATTAGATGGGCCAGCA TAATCTAAAGACCCTTTGAACCCCTTTAGAGGCCAATCAACCCATGATCAGTTGATGGCCATTTGGTTGC GACCCCAAGTCAGGTGAGGCAACCGCTGAGTTTAAGC >gi|2765596|emb|Z78471.1|PDZ78471 P.dianthum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTAGAGGATCGGTTTACTTTGGTCACCCATGGGCATCTGCTCTTACAGTGACCTGGATTTGCCATCG AGCCTCCTTGGGAGCTGTCTTGCTGGCGATCTAAATCGTTGCCCGACGCAGCCTTGCGTCAAGTCACCCC GACACATAATGGAAGGGGGCGGCATGCTGCCTTGACCCTTCCCCAAATTAATTTTTTGACAACTCTCAAC ATCGGATATCTCGGCTCTTGCATCGATGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAAT CCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCTTGG GCATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATACATACTGTTCAGCCAGTGCGGATGTGAGT TTGGCCCCTTGTTCTTTAGTACGGGGGGTCTAAAAGCTGCATGGGCTTTTGCTGGTCCTAAATACGGCAA GAGGTGGACAAAGTATGCTACAACAAAATTGTAGTGCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAG CATAATTTAAAGACCCTTTTGAACCCCATTAGAGGCCCATCAACCCTGATCAGTTGATGGCCATTTGGTT GCGACCCCAAGTCAGGTGAGGCAACCCGCTGAGTTTAAGC >gi|2765595|emb|Z78470.1|PPZ78470 P.parishii 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTAGAGGATCAGTTTACTTTGGTCACCCATGGGCATCTGCTCTTACAGTGACCTGGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAATCGTTGCCCGACGCAGCCTTGCGTCAAGTCATATG ACACATAATTGGAAGGGGGCGGCATGCTGCCTTGACCCTTCCCAAATTATTTTTTTAAGTTGCGCCCGAG GCCATCAGGCCAAGGGCACGCCTGCTTGGGCATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATA CATACTGTTCAGCCAGTGCGGATGTGAGTTTGGCCCCTTGTTCTTTACTACGGGGGGTCTAAAAGCTGCA TGGGCTTTTGCTGGTCCTAAATACGGCAAGAGGTGGACAAAGTATGCTACAACAAAATTGTAGTGCGAAT GCCCCGGGTTGTCGTATTAGATGGGCCAGCATAATTTAAAGACCCTTTTGAACCCCATTAGAGGCCCATC AACCCTGATCAGTT >gi|2765594|emb|Z78469.1|PHZ78469 P.haynaldianum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTAGTCACCCATGGGCATCTGCTCTTGCAGTGACCTGGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAATTCGTTGCCCGGCGCAGTCTTGCGCCAAGTCATAT CACACATAATTGGAAGGGGGCGGCATGCGGTCTAGACCCTCCCCAAATTATTTTTTTGACAACTCTCAGC AACGGATATCTCGGCTCTTCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTCTCCC TTAATGAGGCTGTCCATACATACTGTTCAGCCACAGCGGATGTGAGTTTGGCCCCTTGTTCTTTGGTACG GGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTACCTAAATACGGCAAGAGGTGGACGAAGTATGCTACA ACAAAATGATAGTGCGAATGCCCAGGGTTGTCGTATTAGATGGGCCAGCATAATCTAAAGACCCTTTTGA ACCCCATTAGAGGCCCATCAACCCATGATCAGTT >gi|2765593|emb|Z78468.1|PAZ78468 P.adductum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCATAATAATTGATTGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACTCATGGGAATCTGCTCTTGTAGTGACCTGGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGATGGCGATCTGAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATATG ACACATAATTGGGAGGGGCAGCATGCTGCCTTGACCCTCCCCATATCATTTTTTTAACGACTCTCAGCTC CGGAACTCAAGTTGCGCCCGAGGCCATCAGGCTAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTC TCCCTTAATGAGGCTGTCCATACATACTGTTCAGCCAGTGCGGATGTGAGTTTGGCCCCTTGTTCTTCGG TACGGGGGGTCTAAGAGCAGCTGGGCTTTTGATGGACCTAAATACGGCAAGAGGTGGACGAAGTATGCTA CAACAAAATTATTGTGTGAATGCCCCGGGTTGTCGTATAGATGGGCCAGCATAATCTAAAGACCCTTTTG AACCCCATTGGAGGCCCATCAACCCATGATCAGTTGACGGCCATTTGGTT >gi|2765592|emb|Z78467.1|PSZ78467 P.stonei 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCATGGGCATCTGCTCTTGCAGTGACCTGGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATATG ACACATAATTGGAAGGGGGTGGCATGCTGCCTTGACCCTCCCCAAATTATTTTTTGACAACTCTCAGCAA CGGATATCTCGGCTCTTGCATCGATGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAATCC CGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGTACGCCTGCCTGGGC ATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTGAGTTT GGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGTGCCTTTGGTGGTCCAAAATACGGCAAGA GGTGGACGAACTATGCTACAACAAAATTGTTGTGCGAATGACCCGGGTTGTCGTATTAGCTGGGCCAGCA TAATCTAAAGACCCTCTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGACGGCCATATGTTTG CGACCCAGGTCAGGTGAGGCAACCCGCTGA >gi|2765591|emb|Z78466.1|PPZ78466 P.philippinense 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCATGGGCATCTGCTCTTGTAGTGACCTGGATTTGCCATCG AGCCTTCATGGGAGCTTTCTTGCTGGCGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATATG ACACATAATTGGAAGGGGGCGGCATGCTGCCTTGACCCTCCCCAAATTATTTTTGTAACAACTCTCAGCA ACGGATATCTCGGCTCTTTGCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATAT CTCTCCCTTAATGAGGCTGTCCATACATACAGTTCAGCCTGTGCGGATGTGAGTTTGGCCCCTTGTTCTT GCGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGGCAAGAGGTGGACGAACTAT GCTACAACAAAATTGTTGTGCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAGCATAATCTAAAGACCC TTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGACGGCCATTTGGTTGCGACCCAGGTCAGG TGAGGCAACCC >gi|2765590|emb|Z78465.1|PRZ78465 P.rothschildianum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCAGTTTACTTTGGTCACCCATGGGCATCTGCTCTTGCAGTGACCTGGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGCTGGCGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATATG ACACATAATTGGAAGGGGGCGGCATGCTGCCTTGACCCTCCCCAAATTATTTTTTTGACAACTCTCAGCA ACGGATTTCTCGGTTCTTGAATCGATGAGGAACGCAGCGAAATGCGATAATTGGTGTGAATTGAAGAATC CCGTAAACCATCGAGTCTTTGAACGAAAGTTGCGCCCGAGGCCATCAGGCCAAGGGAACGCCTGCCTGGG CATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATACATACTGTTCAGCGAGTGCGGATGTGAGTT TGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGGCAAG AGGTGGACGAACTATGCTACAACAAAATTGTTGTGCGAATGCCCCGGGTTGTCGTATTAGATGGGCCAGC ATAATCTAAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGACGGCCATTTGGTT GC >gi|2765589|emb|Z78464.1|PGZ78464 P.glanduliferum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAGCGGAAGGGTCATTGTTGAGATCACATAATAATTGATCGGTTGAGTC TGGAGGATCAGTTTACTTTGGTCACCCATGGGCATCTGCTCTTGTAGTGACCTGGATTGCCATCGAGCCT CCTTGGGAGCTTTCTTGCTGGAGATCTAAACCCTTGCCCGGAGCAGTTTTGCGCCAAGTCATATGACACA TAATTGGAAGGGGGCGGCATGCTGCCTTGACCCTCCCCAAATTATTTTCTTAACAACTCTCAGCAACGGA TATCTCGGCTCTTGCATCGATGAAGAACGCAGCGAAATCCGATAAATGGTGTGAATTTCAGAATCCCGTG AACCATCGAGTCTTTGAACGCCAAGTTCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTT CGAATCAAATCTCTCCCTTAATGAGGCTGTCCAAACATACTGTTCAACCGGTTCGGATGTGAGTTTGGCC CCTTTTTCTTTGGGGCGGGGGGTCTAAGAACTTCATGGGCTTTTGGTGGTCCTAAAAACGGCAAGAGGTG GGCGGACTATTCTTCAACCAAATTTCTGTTCGGATTCCCCGGGCTTTCGTAATAGATGGGCCAGCATAAT CTAAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGACGGCCATTTGGTGCGACC CCAGGTCAGGTGAGCAACCCGCTGAGTTTAAGC >gi|2765588|emb|Z78463.1|PGZ78463 P.glanduliferum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGTTCATTGTTGAGATCACATAATCATTGTTCGGTTG AGTCTGGAGGATCAGTTTACTTTGGTCACCATGGGCATCTGCTCTTGTAGTGACCTGGATTGCCATCGAG CCTCCTTGGGAGCTTTCTTGCTGGGGATCTAAACCCTCGCCCGGGGGAGTTTTGCGCCAAGTCATATGAC ACATAATTGGGAGGGGGCGGCATGTTGCCTTGACCCTCCCCAAATTATTTTCTTAACAACTCTCAGCAAC GGATATCTCGGCTCTTGCATCGATGGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAATCC CGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGC ATTGCGAGTCATATCTCTCCCTTAATGAGGCTGTCCATACATACTGTTCAACCGGTGCGGATGTGAGTTT GGCCCCTTTTTCTTTGGGGCGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGGCAAGA GGTGGACGAACTATGCTGCAACAAAATTGCTGTGCGAATACCCCGGGTTGTCGTATTAGATGGGCCAGCA TAATCTAAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGACGGCCATTTGGTTG CGACCCCAGATCAGGTGAGCAACCCGCTGAGTTTAAGC >gi|2765587|emb|Z78462.1|PSZ78462 P.sukhakulii 5.8S rRNA gene and ITS1 and ITS2 DNA CGTCACGAGGTCTCCGGATGTGACCCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGCAGGATCTGTTTACTTTGGTCACCCATGGGCATTTGCTGTTGAAGTGACCTAGATTTGCCATCG AGCCTCCTTGGGAGATTTCTTGTTGGCGAGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATA NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNCGCCTGCCTGGGCAT TGCGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTGAGTTTGG CCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGGCAAGAGG TGGACGAACTATGCTACAACAAAATTGTTGTGCAAAGGCCCCGGGTTGTCGTATTAGATGGGCCACCGTA ATCTGAAGACCCTTTTGAACCCCATTGGAGGCCCAT >gi|2765586|emb|Z78461.1|PWZ78461 P.wardii 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGGT AATCTGGAGGATCTGTTTACTTTGGTCACCCATGGGCAGTTGCTGCTGGAGTGACCTAGAATTTCCATCG AGCCTCCTTGGGAGCTTTCTTGTTGGCGAGATCTAAACCCTTACCCGGCGCAATTTTTCCCCCAGTCAAA TGACACATAATTGGTGGAGGGGGTGGCATTCTTCCCTGACCCTCCCCCAAATAATTTTTTCACAACTCTC AGCAACGGATATCTCGGCTCTTGCATCGATGAAGAACGCACCGAAATCCGATAAATGGTGTGAATTCCAG AATCCCGTGAACCATCGAGTCTTTGAACCCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGAACGCCTGCC TGGGCATTGCGAGTCATATCTCTCCCTTAACGAGCCTGTCCATACATCCTGTTCAGCCGGTGCGGATGTG AGTTTGCCCCCTTGTTCTTTGGTACGGGGGGTCTAAGACCTGCATGGGCTTTTGATGGTCCTAAATACGG CAAGAGGTGGACGAACTATGCTACACCAAAATTGTTGTCCAAAGCCCCCGGGTTGTCGTATAAGATGGGC CACCGTAATCTGAAGACCCTTTGAACCCCATTGGAGCCCATCAACCCATGATCAGTTGATGCCATTGTTG CGACCCAGTCAGTGAGCAAGCCGCTGAGTTAA >gi|2765585|emb|Z78460.1|PCZ78460 P.ciliolare 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCTGTTTACTTTGGTCACCCATGAGCATTTGCTGTTGAAGTGACCTAGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGTTGGCGAGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATA TGACACATAATTGGTGAAGGGGGTGGCATCCTGCCCTGACCCTCCCCAAATTATTTTTTTAACAACTCTC AGCAACGGGATATCTCGGCTCTTGCATCGGATGGAGGAACGCAGCGAAATGCGATAAATGGTGTGAATTG CAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCT GCCTGGGCATTGCGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGAT GTGAGTTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCCAAATA CGGCAAGAGGTGGACGAACTATGCTACAACAAAATTGTTGTGCAAAGGCCCCGGGTTGTCGTATTAGATG GGCCACCGTAATCTGAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCA TTTGGGTTGCGACCCCAGGTCAGGTGAGGCAACCCGCTGAGTTTA >gi|2765584|emb|Z78459.1|PDZ78459 P.dayanum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCTGTTTACTTTGGTCACCCATGAGCATTTGCTGTTGAAGTGACCTAGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGTTGGCGAGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATA TGACACATAATTGGTGAAGGGGGTGGCATCCTGCCCTGACCCTCCCCAAATTATTTTTTTAACAACTCTC AGCAACGGATATCTCGGCTCTTGCATCGGATGGAGGAACGCAGCGAAATTGCGATAAATTGGTGTGAATT GCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCC TGCCTGGGCATTGCGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGA TGTGAGTTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAAT ACGGCAAGAGGTGGACGAACTATGCTACAACAAAATTGTTGTGCAAAGGCCCCGGGTTGTCGTATTAGAT GGGCCACCGTAATCTGAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCC ATTTGGTTGCGACCCCAGGTCAGGTGAGGCAACCCGCTGAGTTT >gi|2765583|emb|Z78458.1|PHZ78458 P.hennisianum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCTGTTTACTTTGGTCACCCATGAGCATTTGCTGTTGAAGTGACCTAGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGTTGGCGAGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCGTA CGACACATAATTGGTGAAGGGGGTGGCATCCTTCCCTGACCCTCCCCAAATTATTTTTTAACAACTCTCA GCAACGGATATCTCGGCTCTTGCATCGATGGAGGACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGA TCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTG GGCATTGCGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTGAG TTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGGCA AGAGGTGGACGAACTATGCTACAACAAAATTGTTGTGCAGAGGCCCCGGGTTGTCGTATTAGATGGGCCA CCGTAATCTGAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTTGG TTGCGACCCCAGGTCAGGTGAGGCAACCCGCTGAGTTG >gi|2765582|emb|Z78457.1|PCZ78457 P.callosum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCTGTTTACTTTGGTCACCCATGGGCATTTTCTGTTGTAGTGACCTAGAATTTCCATCG AGCCTCCTTGGGAGCTTTCTTGTTGGCGAGATCTAAACCCTTGCCCGGCGCAGTTTTTCCCCCAGTCAAA TGGCACATAATTGGTGGAGGGGGTGGCATTCTTCCCTGACCCTCCCCCAAATATTTTTTTAACAACTCTC AACAACGGATATCTCGGCTCTTTCATCGGATGAAGGACCGCAGCGAAATGCGATAAATGGTGTGAATTGC AGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTG CCTGGGCATTGCGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATG TGAGTTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATAC GGCAAGAGGTGGACGAACTATGCTACAACAAAATCGTTGTGCAAAGGCCCCGGGTTGTCGTATTAGATGG GCCACCGTAATCTAAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCAT TTGGTTGCGACCCCAGGTCAGGTGAGGCAACCCGCTGAG >gi|2765581|emb|Z78456.1|PTZ78456 P.tonsum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCTGTTTACTTTGGTCACCCATGAGCATTTGCTGTTGAAGTGACCTAGAATTTGCCATC GAGCCTCCTTGGGAGCTTTCTTGTTGGCGAGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCAT ATGACACATAATTGGTGGAGGGGGTGGCATCCTGCCCTGACCCTCCCCAAATTATTTTTTTAACAACTCT CAGCAACGGATATCTCCGGTCTGGCATCGATGAAGAACGCAGCGAAATGCGATAAATGGTGTGATTGCAG AATCCCGTGAACATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCT GGGCATTGCGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTGA GTTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGGC AAGAGGTGGACGAACTATGCTACAACAAAATTGTTGTGCAAAGGCCCCGGGTTGTCGTATTAGATGGGCC ACCGTAATCTGAAGACCCTTTTGGACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTTG GGTTGCGACCCAGGTCAGGTGAGGCAACCCTGAGTTTAGC >gi|2765580|emb|Z78455.1|PJZ78455 P.javanicum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACCAGGTTTCCGTAGGTGGACCTTCGGGAGGATCATTTTTGAGATCACATAAAAATTGATCGAGTT AATCTGGAGGAACTTTTTACTTTGGTCACCCATGAACAATTTCTTTTGGAGTGACCTAGAATTTCCATCG AACCCCCTTGGGAACTTTCTTTTTGGCGAGAACTAAACCCTTTCCCGGCGCAGTTTTTCCCCCAATCAAA TTACACAAAAATGGTGGAGGGGGTGGCATCCTTCCCTTACCCTCCCCCAAATAATTTTTTAACAACTCTC AGCAACGGATATCTCGGCTCTTGCATCGGTTGAAGAACCAGCGAAATGCGATAAATGGTGTGAATTGCAG AATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCC TGGGCATTGCGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTG AGTTTGGCCCCTTGTTCTTTGGCACGGGGGGTCTAAGAGCTTCATGGGCTTTTGATGGTCCTAAATACGG CAAGAGGTGGACGAACTATGCTACAACAAAATTGTTGTGCAAAGGCCCCGGGTTGTCGTATTAGATGGGC CACCGTAATCTGAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTT GGGTTGCGACCCCAGGTCAGGTGAGGCAACCCGCTGAGTTTAGCA >gi|2765579|emb|Z78454.1|PFZ78454 P.fowliei 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGAT AATCTGGAGGATCTGTTTACTTTGGTCACCCATGAGCATTTGCTGTTGAAGTGACCTAGATTTACCATCG AGCCTCCTTGGGAGCTTTCTTGTTGGCGAGATCTAAACCCTTGCCCGGCGCAGTTTTTCTCCAAGTCATT TGACACATAATCGGTGAAGGGGGTGGCATCCTGCCCTGACCCTCCCCCCACTATTGTTAACAACTCTCAT CAACGGAACTCATGCGATAAATGGTGTGATTTGCAGAATCCCGTGAACCATCGAGTCTGTGAACGCAAGT TGCGGCCGAGGCCATCAGACCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTCTCCCTTAACGAG GCTGTCCATACATACTGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTCTGGTACGGGGGGTCT AAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGGCAAGAGGTGGACGAACTATGCTACAACAAAATTG TTGTGCAAAGGCCCCGGGTTGTCGTATTAGATGGGCCACCGTAATCTGAAGCCCCTTTTGAACCCATTGG AGGCCCATCAACCNCATGATCAGTTGATGGCCATTTGGATGCGACCCCAGGTCAGGTGAGGCAAC >gi|2765578|emb|Z78453.1|PSZ78453 P.schoseri 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCTGTTTACTTTGGTCACCCATGAGCATTTGCTGTTGAAGTGACCTAGAGTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGTTGGCGAGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAACTCATT TGACACATAACTGGTGAAGGGGGTGGCATCCTTCCCTGACCCTCCCCCAAATTATTTTTTAACAACTCTC AGCAACCGGATATCTCAGCTCTTGCATCGATGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCA GAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGC CTGGGCATTGCGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGT GAGTTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACG GCAAGAGGTGGACGAACTATGCTACAACAAAATTGTTGTGCAAAGGCCCCGGGTTGTCGTATTAGATGGG CCACCGTAATCTGAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATT TGGTTGCGACCCCAGGTCAGGTGAGGCAACCCGCTGAGTTTAGCA >gi|2765577|emb|Z78452.1|PBZ78452 P.bougainvilleanum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCTATTTACTTTGGTCACCCATGAGCATTTGCTGTTGAAGTGACCTAGATTTGCCATCA AGCCTCCTTGGGAGCTTTCTTTTTGGCGAGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATA TGACACATAATTGGTGAAGGGGGTGGCATCCTGCCCTGACCCTCCCCAAATTATTTTTTTAACAACTCTC AGCAACGGATATCTCGGCTCTTGAATCGATGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAG AATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCC TGGGCATTGCGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTG AGTTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGG CAAGAGGTGGATGAACTATGCTACAACAAAATTGTTGTGCAAAGGCCCCGGGTTGTCGTATTAGATGGGC CACTGTAATCTGAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTT GGATGCGACCCCAGGTCAGGTGAGGCAACCGCTGAGTTTAGCA >gi|2765576|emb|Z78451.1|PHZ78451 P.hookerae 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGTACCTCCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGAT AATCTGGAGGATCTGTTTACTTTGGTCACCCATGGGCATTTGCTGTTGTAGTGACCTAGATTTCCATGGA GCCCCCCTGGGAGCTTTCTTGCTGGCGAGATCTAAACCCGTGCCCGGCGCAGTTTTGCGCCAAGTCATAT GACACATAATTGGTGTAGGGGGCGGGCATCCTGCCCTGACCCTCCCCAAAGTATTTTTTGCAACTCTCAA CAACGGAAATCTCGCTCTTTCATCGATGAAGAACCAGCGAAATCGATAAATGGTGTGAATTGCAGAATCC CGTGAACCATCGAGTCTTTGAACCCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACCCCTCCCTGGGC ATCGCGAGTCAAATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCACCCGGTGCGGATGTGAGTTT GCCCCCTTGTTCTTTGGTACGGGGGGTCTAAGACCTGCATGGGATTTTGATGGTCCTAAAAACGGCAAGA GGTGGACGAACTATCCCTACAATAAAATTGTTGTGCGAAGCCCCCGGGTTGTCGTATTAGATGGGCCACC GTAATCTGAAGACCCTATAGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTTGATG CGACACCAGTCAGTGAGCAACCAGCTGAGC >gi|2765575|emb|Z78450.1|PPZ78450 P.papuanum 5.8S rRNA gene and ITS1 and ITS2 DNA GGAAGGATCATTGCTGATATCACATAATAATTGATCGAGTTAAGCTGGAGGATCTGTTTACTTTGGTCAT CCATGAGCATTTACTGTTGAAGTGACCTAGATTTGCCATCGAGCCTCCTTGGGAGCTTTCTTGTTGCCGA GATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATATGACACATAATTGGTGAAGGGGGTGGCAT CCTGCCCTGACCCTCCCCAAATTATTTTTTTAACAACTCTCAGCAACGGATATCTCGGCTCTTGCATCGA TGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACG CAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTCCGAGTCATATCTCTCCCCTA ACGAGGCTGTCCATACATACTGTTCATCCGGTGCGGATGTGAGTGTGGCCCCTTGTTCTTTGGTCCGGGG GGTCTAAGAACTGCATGGGCATATGATGGTCCTCAAAACTGGCAAGAGGTGGACGAACTATGCTACAACC AAATTGTTGTGCCAAGGCCCCGGGTTGTCGTATTAGATGGGCCACCGTAACCTGTAGACCCTGTTGAACC CCATTGGAGGACCATCAACCCATGATCAGTTGATGACCATGTGTTGCGACCCCAGATCAGCTGAGCAACC CCTGAG >gi|2765574|emb|Z78449.1|PMZ78449 P.mastersianum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCTGTTTACTTTGGTCACCCATGAGCATTTGCTGTTGAAGTGACCTAGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGTTGGCGAGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAACTCATT TGACACATAATTGGTGAAGGGTGGCATCCTGCCCTGACCCTCCCCCAAATTATTTTTTTAACAACTCTCA GCAACGGATATCTCAGCTCTTGCATCGATGAAGAACGCAGCGAAAATGCGATAAATGGTGTGAATTGCAG AATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCC TGGGCATTGCGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTG AGTTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGG CAAGAGGTGGACGAACTATGCTACAACTAAATTGTTGTGCAAAGGCCCCGGGTTGTCGTATTAGATGGGC CACCGTAATCTGAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTT GGTTGCGACCCCAGGTCAGGTGAGGCAACCCGCTGAGTTTATGC >gi|2765573|emb|Z78448.1|PAZ78448 P.argus 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCTGTTTACTTTGGTCACCCATGAGCATTTGCTGTTGAAGTGACCTAGAATTGCCATCG AGCCTCCTTGGGAGCTTTCTTGTTGGCGAGATCTAAACCCTTTCCCGGCGCAGTTTTGCGCCAAGTCGTA TGACACATAATTGGTGAAGGGGGGTGGCATCCTTCCCTTGACCCTCCCCAAATTATTTTTTTAACAACTC TCAGCAACGGATATCTCAGCTCTTGCATCGGATGGAAGAACGCACCGAAATGCGATAAATGGTGTGAATT GCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCC TGCCTGGGCATTGCGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGA TGTGAGTTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAAT ACGGCAAGAGGTGGACGAACTATGCTACAANAAAATTGTTGTGCAAAGGCCCCGGGTTGTCGTATTAGAT GGGCCACCGTAATCTGAAGACCCTTTTGAACCCCATTGGAGGCCCATCAGCCCATGATCAGTTGATGGCC ATTTGGNTGCGACCCCAGGTCAGGTGAGGCAACCCGCTGAGG >gi|2765572|emb|Z78447.1|PVZ78447 P.venustum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGATTCCGTAGGTGAACCTGCGGGAGGATCATTGTTGAGATCACATAATAATTGATCGAGTA ATCTGGAGGATCTGTTTACTTTGGTCACCCATGGGCATTTCCTGTTGAAGTGACCTAGAGTTGCCATCGA GCCTCCCTGGGAGCTTTCTTGTTGGCGAGAACTAAACCCTTACCCGGCGCAATTTTGCCCCCAGTCAAAT GACACATAAGTGGTGAAGGGGGTGGCATCCTATCTCGGCTCTTGCATCGATGAAGAACCCAGCGAAATGC GATAAATGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCAT CAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATAC TGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGC TTTTGATGGTCCTAAATACGGCAAGAGGTGGACGAACTATGCTACACCAAAATTGTTGTGCAAAGGCCCC GGGTTGTCGTATTAGATGGGCCACCGTAATCTGAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCC ATGATCAGTTGATGGCCATTTGGTTGCGACCCCAGTCAGGTGAGGCAACCCGCTGAGTTTAAGC >gi|2765571|emb|Z78446.1|PAZ78446 P.acmodontum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCTGTTTACTTTGGTCACCCATGAGCATTTCCTGTTGAAGTGACCTAGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGTTGGCGAGATCTAAACCCTTGCCCGGCGCAGTTTTGCGCCAAGTCATA TGACACATAATTGGTGAAGGGGGTGGCATCCTCCCCAAATAATTTTTTAACAACTCTCAGCAACGGATAC TCGGCTCTTCATCGATGGAGGACCAGCGAAATTGCGATAATGGTGTGATTGCAGAATCCCGTGAACATCG AGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGATTGCGAGTCATA TCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCAGATGTGAGTTTGGCCCCTTGTTCT TCGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGCCCTAAATACGGCAAGAGGTGGACGAACTA TGCTACAACAAAATTGTTGTGCAAAGGCTCCGGGTTGTCGTATTAGATGGGCCACCGTAATCTGAAGACC CTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTTGGTTGCGACCCAGGTCAG GTGAGGCAACCC >gi|2765570|emb|Z78445.1|PUZ78445 P.urbanianum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGGGTT AATCTGGAGGATCTGTTTACTTTGGTCACCCATGAGCATTTGCTGTTGAAGTGACCTAGAATTGCCATCG AGCCTCCTTGGGAGCTTTCTTGTTGGCGAGATCTAAACCCTTGCCCGGCGCAGTTTTGCTCCAAGTCGTT TGACACATAATTGGTGAAGGGGGTGGCATCCTTCCCTGACCCTCCCCCAACTATTTTTTTAACAACTCTC AGCAACGGAGACTCAGTCTTCGGCAAATGCGATAAATGGTGTGAATTGCAGAATCCCGTGCACCATCGAG TCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATAT CTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTT TGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGGCAAGAGGTGGACGAACTAT GCTACAACAAAATTGTTGTGCAGAGGCCCCGGGTTGTCGTATTAGATGGGCCACCGTAATCTGAAGACCC TTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTTGGTTGCGACCCCAGGTCAG GTGAGCAACAGCTGT >gi|2765569|emb|Z78444.1|PAZ78444 P.appletonianum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGGTGAACTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCTGTTTACTTGGGTCACCCATGGGCATTTGCTGTTGAAGTGACCTAGATTTGCCATCG AGCCTCCTTGGGAGCATCCTTGTTGGCGATATCTAAACCCTCAATTTTTCCCCCAATCAAATTACACAAA ATTGGTGGAGGGGGTGGCATTCTTCCCTTACCCTCCCCCAAATATTTTTTTAACAACTCTCAGCAACGGA TATCTCAGCTCTTGCATCGATGAAGAACCCACCGAAATGCGATAAATGGTGTGAATTGCAGAATCCCGTG AACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTG CGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGTGAGTTTGGCC CCTTGTTCTTTGGTACGGGGGGTCTAAGAGATGCATGGGCTTTTGATGGTCCTAAATACGGCAAGAGGTG GACGAACTATGCTACAACAAAATTGTTGTGCAAAGGCCCCGGGTTGTCGTATAAGATGGGCCACCGATAT CTGAAGACCCTTTTGGACCCCATTGGAGCCCATCAACCCATGTCAGTTGATGGCCATT >gi|2765568|emb|Z78443.1|PLZ78443 P.lawrenceanum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTT AATCTGGAGGATCTGTTTACTTGGGTCACCCATGGGCATTTGCTGTTGAAGTGACCTAGATTTGCCATCG AGCCTCCTTGGGAGCTTTCTTGTTGGCGATATCTAAACCCTTGCCCGGCAGAGTTTTGGGCCACANNNNN NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNNNNNNNNNNNNNNCAGCGAAATGCGATAATGGTGTGAATTGCAGAATCCCGTGA ACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGC GAGTCATATCTCTCCCTTAACGAGGCTGTCCATACACACCTGTTCAGCCGGTGCGGATGTGAGTTTGGCC CCTTGTTCTTTGGTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACGGCAAGAGGTG GACGAACTATGCTACAACAAAATTGTTGTGCAAAGGCCCCGGGTTGTCGTATTAGATGGGCCACCATAAT CTGAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATTTGGTTGCGAC CCAGTCAGGTGAGG >gi|2765567|emb|Z78442.1|PBZ78442 P.bullenianum 5.8S rRNA gene and ITS1 and ITS2 DNA GTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTG TTTACTTTGGTCACCCATGGGCATTTGCTGTTGAAGTGACCTAGATTTGCCATCGAGCCTCCTTGGGAGC TTTCTTGTTGGCGAGATCTAAACCCTTGCCCGGCGGAGTTTGGCGCCAAGTCATATGACACATAATTGGT GAAGGGGGTGGCATCCTGCCCTGACCCTCCCCAAATTATTTTTTTAACAACTCTCAGCAACGGATATCTC GGCTCTTGCATCGATGAAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAATCCCGTGAACCAT CGAGTCTTTGGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGGGAAT CATATCTCTCCCCTAACGAGGCTATCCAAACATACTGTTCATCCGGTGCGGATGTGAGTTTGGCCCCTTG TTCTTTGGTACCGGGGGTCTAAGAGCTGCATGGGCATTTGATGGTCCTCAAAACGGCAAGAGGTGGACGA ACTATGCCACAACAAAATTGTTGTCCCAAGGCCCCGGGTTGTCGTATTAGATGGGCCACCGTAACCTGAA GACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGACCATTTGTTGCGACCCCAGT CAGCTGAGCAACCCGCTGAGT >gi|2765566|emb|Z78441.1|PSZ78441 P.superbiens 5.8S rRNA gene and ITS1 and ITS2 DNA GGAAGGTCATTGCCGATATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACTTGGTCACCC ATGAGCATTTGCTGTTGAAGTGACCTAGATTTGCCATCGAGCCTCCTTGGGAGTTTTCTTGTTGGCGAGA TCTAAACCCTTGCCCGGCGGAGTTGTGCGCCAAGTCATATGACACATAATTGGTGAAGGGGGTGGCATCC TGCCCTGACCCTCCCCAAATTATTTTTTTAACAACTCTCAGCAACGGATATCTCGGCTCTTGCATCGATG AAGAACGCAGCGAAATGCGATAAATGGTGTGAATTGCAGAATCCCGTGAACCATCGAGTCTTTGAACGCA AGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTCTCCCTTAAC GAGGCTGTCCATACATACTGTTCATCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTTTGGTACGGGGGG TCTAAGAGCTGCATGGGCATTTGATGGTCCTCAAAACGGCAAGAGGTGGACGAACTATGCTACAACCAAA TTGTTGTCCCAAGGCCCCGGGTTGTCGTATTAGATGGGCCACCGTAACCTGAAGACCCTTTTGAACCCCA TTGGAGGCCCATCAACCCATGATCAGTTGATGACCATGTGTTGCGACCCCAGTCAGCTGAGCAACGCGCT GAG >gi|2765565|emb|Z78440.1|PPZ78440 P.purpuratum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGGACCTCCGGGAGGATCATTGTTGAGATCACATAATAATTGATCGAGGT AATCTGGAGGATCTGCATATTTTGGTCACCCATGGGCAATTTCTGTTGTAGTGACCTAGAATTTCCATCG ACCCTCCTTGGGAGCATTCTTGTTGGCGAGAACTAAACCCTTACCCGGCGCAGTTTTTCCCCCAATCAAA TTACACAAAAATGGTGGAGGGGGTGGCATATCTTCCCTTACCCTCCCCCAAATAATTTTTTAACAACTCT CAGCAACGGATATCTCAGCTCTTGCATCGTTGAAGAACCCACCGAAATGCGATAAATGGTGTGAATTGCA GAATCCCGTGAACCATCGAGTCTTTGAACGCAAGTTGCGCCCGAGGCCATCAGGCCAAGGGCACGCCTGC CTGGGCATTGCGAGTCATATCTCTCCCTTAACGAGGCTGTCCATACATACTGTTCAGCCGGTGCGGATGT GAGTTTGGCCCCTTGTTCTTTGCTACGGGGGGTCTAAGAGCTGCATGGGCTTTTGATGGTCCTAAATACG GCAAGAGGTGGACGAACTATGCTACAGCAAAATTGTTGTGCAAAGGCCCCGGGTTGTCGTATTAGATGGG CCACCGTAATCTGAAGACCCTTTTGAACCCCATTGGAGGCCCATCAACCCATGATCAGTTGATGGCCATT TGGTTGCGACCCCAGGTCAGGTGAGGCAACCCCTGAGTTTAGCA >gi|2765564|emb|Z78439.1|PBZ78439 P.barbatum 5.8S rRNA gene and ITS1 and ITS2 DNA CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACTTTGGTCACCCATGGGC ATTTGCTGTTGAAGTGACCTAGATTTGCCATCGAGCCTCCTTGGGAGCTTTCTTGTTGGCGAGATCTAAA CCCCTGCCCGGCGGAGTTGGGCGCCAAGTCATATGACACATAATTGGTGAAGGGGGTGGTAATCCTGCCC TGACCCTCCCCAAATTATTTTTTTAACAACTCTCAGCAACGGATATCTCGGCTCTTGCATCGATGAAGAA CGCAGCGAAATGCGATAATGGTGTGAATTGCAGAATCCCGTGAACATCGAGTCTTTGAACGCAAGTTGCG CCCGAGGCCATCAGGCCAAGGGCACGCCTGCCTGGGCATTGCGAGTCATATCTCTCCCTTAATGAGGCTG TCCATACATACTGTTCAGCCGGTGCGGATGTGAGTTTGGCCCCTTGTTCTTTGGTACGGGGGGTCTAAGA GCTGCATGGGCTTTGGATGGTCCTAAATACGGAAAGAGGTGGACGAACTATGCTACAACAAAATTGTTGT GCAAATGCCCCGGTTGGCCGTTTAGTTGGGCC biopython-1.63/Doc/biopdb_faq.tex0000644000175000017500000012130612250104543016370 0ustar tratra00000000000000%% LyX 1.3 created this file. For more info, see http://www.lyx.org/. %% Do not edit unless you really know what you are doing. \documentclass[english]{article} \usepackage{bookman} \usepackage[T1]{fontenc} \usepackage[latin1]{inputenc} \usepackage{geometry} \geometry{verbose,a4paper,tmargin=20mm,bmargin=20mm} \usepackage{graphicx} \IfFileExists{url.sty}{\usepackage{url}} {\newcommand{\url}{\texttt}} \makeatletter %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% LyX specific LaTeX commands. %% Bold symbol macro for standard LaTeX users \newcommand{\boldsymbol}[1]{\mbox{\boldmath $#1$}} %% Because html converters don't know tabularnewline \providecommand{\tabularnewline}{\\} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Textclass specific LaTeX commands. \newenvironment{lyxcode} {\begin{list}{}{ \setlength{\rightmargin}{\leftmargin} \setlength{\listparindent}{0pt}% needed for AMS classes \raggedright \setlength{\itemsep}{0pt} \setlength{\parsep}{0pt} \normalfont\ttfamily}% \item[]} {\end{list}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% User specified LaTeX commands. % header \usepackage{fancyhdr} \pagestyle{fancy} \lhead{Structural Biopython FAQ} \rhead{} % remove date \date{} % make everything have section numbers % Make links between references \usepackage{hyperref} \newif\ifpdf \ifx\pdfoutput\undefined \pdffalse \else \pdfoutput=1 \pdftrue \fi \ifpdf \hypersetup{colorlinks=true, hyperindex=true, citecolor=red, urlcolor=blue} \fi \usepackage{babel} \makeatother \begin{document} \title{{\huge The Biopython}\\ {\huge Structural Bioinformatics FAQ}} \author{Thomas Hamelryck} \author{{\normalsize Bioinformatics center}\\ {\normalsize Institute of Molecular Biology}\\ {\normalsize University of Copenhagen }\\ {\normalsize Universitetsparken 15, Bygning 10}\\ {\normalsize DK-2100 Kbenhavn }\\ {\normalsize Denmark }\\ {\normalsize thamelry@binf.ku.dk}\\ \url{http://www.binf.ku.dk/users/thamelry/}} \maketitle \section{Introduction} The Biopython Project is an international association of developers of freely available Python (\url{http://www.python.org}) tools for computational molecular biology. Python is an object oriented, interpreted, flexible language that is becoming increasingly popular for scientific computing. Python is easy to learn, has a very clear syntax and can easily be extended with modules written in C, C++ or FORTRAN. The Biopython web site (\url{http://www.biopython.org}) provides an online resource for modules, scripts, and web links for developers of Python-based software for bioinformatics use and research. Basically, the goal of biopython is to make it as easy as possible to use python for bioinformatics by creating high-quality, reusable modules and classes. Biopython features include parsers for various Bioinformatics file formats (BLAST, Clustalw, FASTA, Genbank,...), access to online services (NCBI, Expasy,...), interfaces to common and not-so-common programs (Clustalw, DSSP, MSMS...), a standard sequence class, various clustering modules, a KD tree data structure etc. and even documentation. Bio.PDB is a biopython module that focuses on working with crystal structures of biological macromolecules. This document gives a fairly complete overview of Bio.PDB. \section{Bio.PDB's installation} Bio.PDB is automatically installed as part of Biopython. Biopython can be obtained from \url{http://www.biopython.org}. It runs on many platforms (Linux/Unix, windows, Mac,...). However, the \verb|Bio.PDB.mmCIF.MMCIFlex| module (used internally by \verb|Bio.PDB.MMCIFParser| to parse mmCIF files) is \emph{not} currently installed by default. This module relies on a third party tool called flex (fast lexical analyzer generator). At the time of writing, in order to parse mmCIF files you'll have to install flex, then tweak your \verb|setup.py| file and (re)install Biopython from source. \section{Who's using Bio.PDB?} Bio.PDB was used in the construction of DISEMBL, a web server that predicts disordered regions in proteins (\url{http://dis.embl.de/}), and COLUMBA, a website that provides annotated protein structures (\url{http://www.columba-db.de/}). Bio.PDB has also been used to perform a large scale search for active sites similarities between protein structures in the PDB (see \textit{Proteins Struct. Func. Gen.}, \textbf{2003}, 51, 96-108), and to develop a new algorithm that identifies linear secondary structure elements (\emph{BMC Bioinformatics}, \textbf{2005}, 6, 202, \url{http://www.biomedcentral.com/1471-2105/6/202}). Judging from requests for features and information, Bio.PDB is also used by several LPCs (Large Pharmaceutical Companies :-). \section{Is there a Bio.PDB reference?} Yes, and I'd appreciate it if you would refer to Bio.PDB in publications if you make use of it. The reference is: \begin{quote} Hamelryck, T., Manderick, B. (2003) PDB parser and structure class implemented in Python. \textit{Bioinformatics}, \textbf{19}, 2308-2310. \end{quote} The article can be freely downloaded via the Bioinformatics journal website (\url{http://www.binf.ku.dk/users/thamelry/references.html}). I welcome e-mails telling me what you are using Bio.PDB for. Feature requests are welcome too. \section{How well tested is Bio.PDB?} Pretty well, actually. Bio.PDB has been extensively tested on nearly 5500 structures from the PDB - all structures seemed to be parsed correctly. More details can be found in the Bio.PDB Bioinformatics article. Bio.PDB has been used/is being used in many research projects as a reliable tool. In fact, I'm using Bio.PDB almost daily for research purposes and continue working on improving it and adding new features. \section{How fast is it?} The \texttt{PDBParser} performance was tested on about 800 structures (each belonging to a unique SCOP superfamily). This takes about 20 minutes, or on average 1.5 seconds per structure. Parsing the structure of the large ribosomal subunit (1FKK), which contains about 64000 atoms, takes 10 seconds on a 1000 MHz PC. In short: it's more than fast enough for many applications. \section{Why should I use Bio.PDB?} Bio.PDB might be exactly what you want, and then again it might not. If you are interested in data mining the PDB header, you might want to look elsewhere because there is only limited support for this. If you look for a powerful, complete data structure to access the atomic data Bio.PDB is probably for you. \section{Usage} \subsection{General questions} \subsubsection*{Importing Bio.PDB} That's simple: \begin{lyxcode} from~Bio.PDB~import~{*} \end{lyxcode} \subsubsection*{Is there support for molecular graphics?} Not directly, mostly since there are quite a few Python based/Python aware solutions already, that can potentially be used with Bio.PDB. My choice is Pymol, BTW (I've used this successfully with Bio.PDB, and there will probably be specific PyMol modules in Bio.PDB soon/some day). Python based/aware molecular graphics solutions include: \begin{itemize} \item PyMol: \url{http://pymol.sourceforge.net/} \item Chimera: \url{http://www.cgl.ucsf.edu/chimera/} \item PMV: \url{http://www.scripps.edu/~sanner/python/} \item Coot: \url{http://www.ysbl.york.ac.uk/~emsley/coot/} \item CCP4mg: \url{http://www.ysbl.york.ac.uk/~lizp/molgraphics.html} \item mmLib: \url{http://pymmlib.sourceforge.net/} \item VMD: \url{http://www.ks.uiuc.edu/Research/vmd/} \item MMTK: \url{http://starship.python.net/crew/hinsen/MMTK/} \end{itemize} I'd be crazy to write another molecular graphics application (been there - done that, actually :-). \subsection{Input/output} \subsubsection*{How do I create a structure object from a PDB file?} First, create a \texttt{PDBParser} object: \begin{lyxcode} parser=PDBParser() \end{lyxcode} Then, create a structure object from a PDB file in the following way (the PDB file in this case is called '1FAT.pdb', 'PHA-L' is a user defined name for the structure): \begin{lyxcode} structure=parser.get\_structure('PHA-L',~'1FAT.pdb') \end{lyxcode} \subsubsection*{How do I create a structure object from an mmCIF file?} Similarly to the case the case of PDB files, first create an \texttt{MMCIFParser} object: \begin{lyxcode} parser=MMCIFParser() \end{lyxcode} Then use this parser to create a structure object from the mmCIF file: \begin{lyxcode} structure=parser.get\_structure('PHA-L',~'1FAT.cif') \end{lyxcode} \subsubsection*{...and what about the new PDB XML format?} That's not yet supported, but I'm definitely planning to support that in the future (it's not a lot of work). Contact me if you need this, it might encourage me :-). \subsubsection*{I'd like to have some more low level access to an mmCIF file...} You got it. You can create a python dictionary that maps all mmCIF tags in an mmCIF file to their values. If there are multiple values (like in the case of tag \texttt{\_atom\_site.Cartn\_y}, which holds the y coordinates of all atoms), the tag is mapped to a list of values. The dictionary is created from the mmCIF file as follows: \begin{lyxcode} mmcif\_dict=MMCIF2Dict('1FAT.cif') \end{lyxcode} Example: get the solvent content from an mmCIF file: \begin{lyxcode} sc=mmcif\_dict{[}'\_exptl\_crystal.density\_percent\_sol'{]} \end{lyxcode} Example: get the list of the y coordinates of all atoms \begin{lyxcode} y\_list=mmcif\_dict{[}'\_atom\_site.Cartn\_y'{]} \end{lyxcode} \subsubsection*{Can I access the header information?} Thanks to Christian Rother you can access some information from the PDB header. Note however that many PDB files contain headers with incomplete or erroneous information. Many of the errors have been fixed in the equivalent mmCIF files. \emph{Hence, if you are interested in the header information, it is a good idea to extract information from mmCIF files using the} \texttt{\emph{MMCIF2Dict}} \emph{tool described above, instead of parsing the PDB header. } Now that is clarified, let's return to parsing the PDB header. The structure object has an attribute called \texttt{header} which is a python dictionary that maps header records to their values. Example: \begin{lyxcode} resolution=structure.header{[}'resolution'{]} keywords=structure.header{[}'keywords'{]} \end{lyxcode} The available keys are \texttt{name, head, deposition\_\-date, release\_\-date, structure\_\-method, resolution, structure\_\-reference} (maps to a list of references), \texttt{journal\_\-reference, author} and \texttt{compound} (maps to a dictionary with various information about the crystallized compound). The dictionary can also be created without creating a \texttt{Structure} object, ie. directly from the PDB file: \begin{lyxcode} file=open(filename,'r') header\_dict=parse\_pdb\_header(file) file.close() \end{lyxcode} \subsubsection*{Can I use Bio.PDB with NMR structures (ie. with more than one model)?} Sure. Many PDB parsers assume that there is only one model, making them all but useless for NMR structures. The design of the \texttt{Structure} object makes it easy to handle PDB files with more than one model (see section \ref{sub:The-Structure-object}). \subsubsection*{How do I download structures from the PDB?} This can be done using the \texttt{PDBList} object, using the \texttt{retrieve\_pdb\_file} method. The argument for this method is the PDB identifier of the structure. \begin{lyxcode} pdbl=PDBList() pdbl.retrieve\_pdb\_file('1FAT') \end{lyxcode} The \texttt{PDBList} class can also be used as a command-line tool: \begin{lyxcode} python~PDBList.py~1fat \end{lyxcode} The downloaded file will be called \texttt{pdb1fat.ent} and stored in the current working directory. Note that the \texttt{retrieve\_pdb\_file} method also has an optional argument \texttt{pdir} that specifies a specific directory in which to store the downloaded PDB files. The \texttt{retrieve\_pdb\_file} method also has some options to specify the compression format used for the download, and the program used for local decompression (default \texttt{.Z} format and \texttt{gunzip}). In addition, the PDB ftp site can be specified upon creation of the \texttt{PDBList} object. By default, the RCSB PDB server (\url{ftp://ftp.rcsb.org/pub/pdb/data/structures/divided/pdb/}) is used. See the API documentation for more details. Thanks again to Kristian Rother for donating this module. \subsubsection*{How do I download the entire PDB?} The following commands will store all PDB files in the \texttt{/data/pdb} directory: \begin{lyxcode} python~PDBList.py~all~/data/pdb~ python~PDBList.py~all~/data/pdb~-d~ \end{lyxcode} \noindent The API method for this is called \texttt{download\_entire\_pdb}. Adding the \texttt{-d} option will store all files in the same directory. Otherwise, they are sorted into PDB-style subdirectories according to their PDB ID's. Depending on the traffic, a complete download will take 2-4 days. \subsubsection*{How do I keep a local copy of the PDB up-to-date?} This can also be done using the \texttt{PDBList} object. One simply creates a \texttt{PDBList} object (specifying the directory where the local copy of the PDB is present) and calls the \texttt{update\_pdb} method: \begin{lyxcode} pl=PDBList(pdb='/data/pdb') pl.update\_pdb() \end{lyxcode} One can of course make a weekly \texttt{cronjob} out of this to keep the local copy automatically up-to-date. The PDB ftp site can also be specified (see API documentation). \texttt{PDBList} has some additional methods that can be of use. The \texttt{get\_all\_obsolete} method can be used to get a list of all obsolete PDB entries. The \texttt{changed\_this\_week} method can be used to obtain the entries that were added, modified or obsoleted during the current week. For more info on the possibilities of \texttt{PDBList}, see the API documentation. \subsubsection*{What about all those buggy PDB files?} It is well known that many PDB files contain semantic errors (I'm not talking about the structures themselves know, but their representation in PDB files). Bio.PDB tries to handle this in two ways. The PDBParser object can behave in two ways: a restrictive way and a permissive way (THIS IS NOW THE DEFAULT). The restrictive way used to be the default, but people seemed to think that Bio.PDB 'crashed' due to a bug (hah!), so I changed it. If you ever encounter a real bug, please tell me immediately! Example: \begin{lyxcode} \#~Permissive~parser parser=PDBParser(PERMISSIVE=1) parser=PDBParser()~\#~The~same~(default) \#~Strict~parser strict\_parser=PDBParser(PERMISSIVE=0) \end{lyxcode} In the permissive state (DEFAULT), PDB files that obviously contain errors are 'corrected' (ie. some residues or atoms are left out). These errors include: \begin{itemize} \item Multiple residues with the same identifier \item Multiple atoms with the same identifier (taking into account the altloc identifier) \end{itemize} These errors indicate real problems in the PDB file (for details see the Bioinformatics article). In the restrictive state, PDB files with errors cause an exception to occur. This is useful to find errors in PDB files. Some errors however are automatically corrected. Normally each disordered atom should have a non-blanc altloc identifier. However, there are many structures that do not follow this convention, and have a blank and a non-blank identifier for two disordered positions of the same atom. This is automatically interpreted in the right way. Sometimes a structure contains a list of residues belonging to chain A, followed by residues belonging to chain B, and again followed by residues belonging to chain A, i.e. the chains are 'broken'. This is also correctly interpreted. \subsubsection*{Can I write PDB files?} Use the PDBIO class for this. It's easy to write out specific parts of a structure too, of course. Example: saving a structure \begin{lyxcode} io=PDBIO() io.set\_structure(s) io.save('out.pdb') \end{lyxcode} If you want to write out a part of the structure, make use of the \texttt{Select} class (also in \texttt{PDBIO}). Select has four methods: \begin{lyxcode} accept\_model(model) accept\_chain(chain) accept\_residue(residue) accept\_atom(atom) \end{lyxcode} By default, every method returns 1 (which means the model/\-chain/\-residue/\-atom is included in the output). By subclassing \texttt{Select} and returning 0 when appropriate you can exclude models, chains, etc. from the output. Cumbersome maybe, but very powerful. The following code only writes out glycine residues: \begin{lyxcode} class~GlySelect(Select): ~~~~def~accept\_residue(self,~residue): ~~~~~~~~if~residue.get\_name()=='GLY': ~~~~~~~~~~~~return~1 ~~~~~~~~else: ~~~~~~~~~~~~return~0 io=PDBIO() io.set\_structure(s) io.save('gly\_only.pdb',~GlySelect()) \end{lyxcode} If this is all too complicated for you, the \texttt{Dice} module contains a handy \texttt{extract} function that writes out all residues in a chain between a start and end residue. \subsubsection*{Can I write mmCIF files?} No, and I also don't have plans to add that functionality soon (or ever - I don't need it at all, and it's a lot of work, plus no-one has ever asked for it). People who want to add this can contact me. \subsection{The Structure object\label{sub:The-Structure-object}} \subsubsection*{What's the overall layout of a Structure object?} The \texttt{Structure} object follows the so-called \texttt{SMCRA} (Structure/\-Model/\-Chain/\-Residue/\-Atom) architecture : \begin{itemize} \item A structure consists of models \item A model consists of chains \item A chain consists of residues \item A residue consists of atoms \end{itemize} This is the way many structural biologists/bioinformaticians think about structure, and provides a simple but efficient way to deal with structure. Additional stuff is essentially added when needed. A UML diagram of the \texttt{Structure} object (forget about the \texttt{Disordered} classes for now) is shown in Fig. \ref{cap:SMCRA}. % \begin{figure}[tbh] \begin{center}\includegraphics[% width=100mm, keepaspectratio]{images/smcra.png}\end{center} \caption{\label{cap:SMCRA}UML diagram of SMCRA architecture of the \texttt{Structure} object. Full lines with diamonds denote aggregation, full lines with arrows denote referencing, full lines with triangles denote inheritance and dashed lines with triangles denote interface realization. } \end{figure} \subsubsection*{How do I navigate through a Structure object?} The following code iterates through all atoms of a structure: \begin{lyxcode} p=PDBParser() structure=p.get\_structure('X',~'pdb1fat.ent') for~model~in~structure: ~~for~chain~in~model:~ ~~~~for~residue~in~chain: ~~~~~~for~atom~in~residue: ~~~~~~~~print~atom \end{lyxcode} There are also some shortcuts: \begin{lyxcode} \#~Iterate~over~all~atoms~in~a~structure for~atom~in~structure.get\_atoms(): ~~~~print~atom \#~Iterate~over~all~residues~in~a~model for~residue~in~model.get\_residues(): ~~~~print~residue \end{lyxcode} Structures, models, chains, residues and atoms are called \texttt{Entities} in Biopython. You can always get a parent \texttt{Entity} from a child \texttt{Entity}, eg.: \begin{lyxcode} residue=atom.get\_parent() chain=residue.get\_parent() \end{lyxcode} You can also test wether an \texttt{Entity} has a certain child using the \texttt{has\_id} method. \subsubsection*{Can I do that a bit more conveniently?} You can do things like: \begin{lyxcode} atoms=structure.get\_atoms() residue=structure.get\_residues() atoms=chain.get\_atoms() \end{lyxcode} You can also use the \texttt{Selection.unfold\_entities} function: \begin{lyxcode} \#~Get~all~residues~from~a~structure res\_list=Selection.unfold\_entities(structure,~'R') \#~Get~all~atoms~from~a~chain atom\_list=Selection.unfold\_entities(chain,~'A') \end{lyxcode} Obviously, \texttt{A=atom, R=residue, C=chain, M=model, S=structure}. You can use this to go up in the hierarchy, eg.\ to get a list of (unique) \texttt{Residue} or \texttt{Chain} parents from a list of \texttt{Atoms}: \begin{lyxcode} residue\_list=Selection.unfold\_entities(atom\_list,~'R') chain\_list=Selection.unfold\_entities(atom\_list,~'C') \end{lyxcode} For more info, see the API documentation. \subsubsection*{How do I extract a specific \texttt{Atom/\-Residue/\-Chain/\-Model} from a Structure?} Easy. Here are some examples: \begin{lyxcode} model=structure{[}0{]} chain=model{[}'A'{]} residue=chain{[}100{]} atom=residue{[}'CA'{]} \end{lyxcode} Note that you can use a shortcut: \begin{lyxcode} atom=structure{[}0{]}{[}'A'{]}{[}100{]}{[}'CA'{]} \end{lyxcode} \subsubsection*{What is a model id?} The model id is an integer which denotes the rank of the model in the PDB/mmCIF file. The model is starts at 0. Crystal structures generally have only one model (with id 0), while NMR files usually have several models. \subsubsection*{What is a chain id?} The chain id is specified in the PDB/mmCIF file, and is a single character (typically a letter). \subsubsection*{What is a residue id?} This is a bit more complicated, due to the clumsy PDB format. A residue id is a tuple with three elements: \begin{itemize} \item The \textbf{hetero-flag}: this is \texttt{'H\_'} plus the name of the hetero-residue (eg. \texttt{'H\_GLC'} in the case of a glucose molecule), or \texttt{'W'} in the case of a water molecule. \item The \textbf{sequence identifier} in the chain, eg. 100 \item The \textbf{insertion code}, eg. 'A'. The insertion code is sometimes used to preserve a certain desirable residue numbering scheme. A Ser 80 insertion mutant (inserted e.g. between a Thr 80 and an Asn 81 residue) could e.g. have sequence identifiers and insertion codes as follows: Thr 80 A, Ser 80 B, Asn 81. In this way the residue numbering scheme stays in tune with that of the wild type structure. \end{itemize} The id of the above glucose residue would thus be \texttt{('H\_GLC', 100, 'A')}. If the hetero-flag and insertion code are blanc, the sequence identifier alone can be used: \begin{lyxcode} \#~Full~id residue=chain{[}('~',~100,~'~'){]} \#~Shortcut~id residue=chain{[}100{]} \end{lyxcode} The reason for the hetero-flag is that many, many PDB files use the same sequence identifier for an amino acid and a hetero-residue or a water, which would create obvious problems if the hetero-flag was not used. \subsubsection*{What is an atom id?} The atom id is simply the atom name (eg. \texttt{'CA'}). In practice, the atom name is created by stripping all spaces from the atom name in the PDB file. However, in PDB files, a space can be part of an atom name. Often, calcium atoms are called \texttt{'CA..'} in order to distinguish them from C$\alpha$ atoms (which are called \texttt{'.CA.'}). In cases were stripping the spaces would create problems (ie. two atoms called \texttt{'CA'} in the same residue) the spaces are kept. \subsubsection*{How is disorder handled?} This is one of the strong points of Bio.PDB. It can handle both disordered atoms and point mutations (ie. a Gly and an Ala residue in the same position). Disorder should be dealt with from two points of view: the atom and the residue points of view. In general, I have tried to encapsulate all the complexity that arises from disorder. If you just want to loop over all C$\alpha$ atoms, you do not care that some residues have a disordered side chain. On the other hand it should also be possible to represent disorder completely in the data structure. Therefore, disordered atoms or residues are stored in special objects that behave as if there is no disorder. This is done by only representing a subset of the disordered atoms or residues. Which subset is picked (e.g. which of the two disordered OG side chain atom positions of a Ser residue is used) can be specified by the user. \textbf{Disordered atom positions} are represented by ordinary \texttt{Atom} objects, but all \texttt{Atom} objects that represent the same physical atom are stored in a \texttt{Disordered\-Atom} object (see Fig. \ref{cap:SMCRA}). Each \texttt{Atom} object in a \texttt{Disordered\-Atom} object can be uniquely indexed using its altloc specifier. The \texttt{Disordered\-Atom} object forwards all uncaught method calls to the selected Atom object, by default the one that represents the atom with the highest occupancy. The user can of course change the selected \texttt{Atom} object, making use of its altloc specifier. In this way atom disorder is represented correctly without much additional complexity. In other words, if you are not interested in atom disorder, you will not be bothered by it. Each disordered atom has a characteristic altloc identifier. You can specify that a \texttt{Disordered\-Atom} object should behave like the \texttt{Atom} object associated with a specific altloc identifier: \begin{lyxcode} atom.disordered\_select('A')~\#~select~altloc~A~atom atom.disordered\_select('B')~\#~select~altloc~B~atom~ \end{lyxcode} A special case arises when disorder is due to \textbf{point mutations}, i.e. when two or more point mutants of a polypeptide are present in the crystal. An example of this can be found in PDB structure 1EN2. Since these residues belong to a different residue type (e.g. let's say Ser 60 and Cys 60) they should not be stored in a single \texttt{Residue} object as in the common case. In this case, each residue is represented by one \texttt{Residue} object, and both \texttt{Residue} objects are stored in a single \texttt{Disordered\-Residue} object (see Fig. \ref{cap:SMCRA}). The \texttt{Dis\-ordered\-Residue} object forwards all un\-caught methods to the selected \texttt{Residue} object (by default the last \texttt{Residue} object added), and thus behaves like an ordinary residue. Each \texttt{Residue} object in a \texttt{Disordered\-Residue} object can be uniquely identified by its residue name. In the above example, residue Ser 60 would have id 'SER' in the \texttt{Disordered\-Residue} object, while residue Cys 60 would have id 'CYS'. The user can select the active \texttt{Residue} object in a \texttt{Disordered\-Residue} object via this id. Example: suppose that a chain has a point mutation at position 10, consisting of a Ser and a Cys residue. Make sure that residue 10 of this chain behaves as the Cys residue. \begin{lyxcode} residue=chain{[}10{]} residue.disordered\_select('CYS') \end{lyxcode} In addition, you can get a list of all \texttt{Atom} objects (ie. all \texttt{DisorderedAtom} objects are 'unpacked' to their individual \texttt{Atom} objects) using the \texttt{get\_unpacked\_list} method of a \texttt{(Disordered)\-Residue} object. \subsubsection*{Can I sort residues in a chain somehow?} Yes, kinda, but I'm waiting for a request for this feature to finish it :-). \subsubsection*{How are ligands and solvent handled?} See 'What is a residue id?'. \subsubsection*{What about B factors?} Well, yes! Bio.PDB supports isotropic and anisotropic B factors, and also deals with standard deviations of anisotropic B factor if present (see \ref{sub:Analysis}). \subsubsection*{What about standard deviation of atomic positions?} Yup, supported. See section \ref{sub:Analysis}. \subsubsection*{I think the SMCRA data structure is not flexible/\-sexy/\-whatever enough...} Sure, sure. Everybody is always coming up with (mostly vaporware or partly implemented) data structures that handle all possible situations and are extensible in all thinkable (and unthinkable) ways. The prosaic truth however is that 99.9\% of people using (and I mean really using!) crystal structures think in terms of models, chains, residues and atoms. The philosophy of Bio.PDB is to provide a reasonably fast, clean, simple, but complete data structure to access structure data. The proof of the pudding is in the eating. Moreover, it is quite easy to build more specialised data structures on top of the \texttt{Structure} class (eg. there's a \texttt{Polypeptide} class). On the other hand, the \texttt{Structure} object is built using a Parser/\-Consumer approach (called \texttt{PDBParser/\-MMCIFParser} and \texttt{Structure\-Builder}, respectively). One can easily re-use the PDB/mmCIF parsers by implementing a specialised \texttt{Structure\-Builder} class. It is of course also trivial to add support for new file formats by writing new parsers. \subsection{\label{sub:Analysis}Analysis} \subsubsection*{How do I extract information from an \texttt{Atom} object?} Using the following methods: \begin{lyxcode} a.get\_name()~\#~atom~name~(spaces~stripped,~e.g.~'CA') a.get\_id()~\#~id~(equals~atom~name) a.get\_coord()~\#~atomic~coordinates a.get\_vector()~\#~atomic~coordinates~as~Vector~object a.get\_bfactor()~\#~isotropic~B~factor a.get\_occupancy()~\#~occupancy a.get\_altloc()~\#~alternative~location~specifier a.get\_sigatm()~\#~std.~dev.~of~atomic~parameters a.get\_siguij()~\#~std.~dev.~of~anisotropic~B~factor a.get\_anisou()~\#~anisotropic~B~factor a.get\_fullname()~\#~atom~name~(with~spaces,~e.g.~'.CA.') \end{lyxcode} \subsubsection*{How do I extract information from a \texttt{Residue} object?} Using the following methods: \begin{lyxcode} r.get\_resname()~\#~return~the~residue~name~(eg.~'GLY') r.is\_disordered()~\#~1~if~the~residue~has~disordered~atoms r.get\_segid()~\#~return~the~SEGID r.has\_id(name)~\#~test~if~a~residue~has~a~certain~atom \end{lyxcode} \subsubsection*{How do I measure distances?} That's simple: the minus operator for atoms has been overloaded to return the distance between two atoms. Example: \begin{lyxcode} \#~Get~some~atoms ca1=residue1{[}'CA'{]} ca2=residue2{[}'CA'{]} \#~Simply~subtract~the~atoms~to~get~their~distance distance=ca1-ca2 \end{lyxcode} \subsubsection*{How do I measure angles?} This can easily be done via the vector representation of the atomic coordinates, and the \texttt{calc\_angle} function from the \texttt{Vector} module: \begin{lyxcode} vector1=atom1.get\_vector() vector2=atom2.get\_vector() vector3=atom3.get\_vector() angle=calc\_angle(vector1,~vector2,~vector3) \end{lyxcode} \subsubsection*{How do I measure torsion angles?} Again, this can easily be done via the vector representation of the atomic coordinates, this time using the \texttt{calc\_dihedral} function from the \texttt{Vector} module: \begin{lyxcode} vector1=atom1.get\_vector() vector2=atom2.get\_vector() vector3=atom3.get\_vector() vector4=atom4.get\_vector() angle=calc\_dihedral(vector1,~vector2,~vector3,~vector4) \end{lyxcode} \subsubsection*{How do I determine atom-atom contacts?} Use \texttt{NeighborSearch}. This uses a KD tree data structure coded in C++ behind the screens, so it's pretty darn fast (see \texttt{Bio.KDTree}). \subsubsection*{How do I extract polypeptides from a \texttt{Structure} object?} Use \texttt{PolypeptideBuilder}. You can use the resulting \texttt{Polypeptide} object to get the sequence as a \texttt{Seq} object or to get a list of C$\alpha$ atoms as well. Polypeptides can be built using a C-N or a C$\alpha$-C$\alpha$ distance criterion. Example: \begin{lyxcode} \#~Using~C-N~ ppb=PPBuilder() for~pp~in~ppb.build\_peptides(structure):~ ~~~~print~pp.get\_sequence() \#~Using~CA-CA ppb=CaPPBuilder() for~pp~in~ppb.build\_peptides(structure):~ ~~~~print~pp.get\_sequence() \end{lyxcode} Note that in the above case only model 0 of the structure is considered by \texttt{PolypeptideBuilder}. However, it is possible to use \texttt{PolypeptideBuilder} to build \texttt{Polypeptide} objects from \texttt{Model} and \texttt{Chain} objects as well. \subsubsection*{How do I get the sequence of a structure?} The first thing to do is to extract all polypeptides from the structure (see previous entry). The sequence of each polypeptide can then easily be obtained from the \texttt{Polypeptide} objects. The sequence is represented as a Biopython \texttt{Seq} object, and its alphabet is defined by a \texttt{ProteinAlphabet} object. Example: \begin{lyxcode} >\,{}>\,{}>~seq=polypeptide.get\_sequence() >\,{}>\,{}>~print~seq Seq('SNVVE...',~) \end{lyxcode} \subsubsection*{How do I determine secondary structure?} For this functionality, you need to install DSSP (and obtain a license for it - free for academic use, see \url{http://www.cmbi.kun.nl/gv/dssp/}). Then use the \texttt{DSSP} class, which maps \texttt{Residue} objects to their secondary structure (and accessible surface area). The DSSP codes are listed in Table \ref{cap:DSSP-codes}. Note that DSSP (the program, and thus by consequence the class) cannot handle multiple models! % \begin{table} \subsubsection*{\begin{tabular}{|c|c|} \hline Code& Secondary structure\tabularnewline \hline \hline H& $\alpha$-helix\tabularnewline \hline B& Isolated $\beta$-bridge residue\tabularnewline \hline E& Strand \tabularnewline \hline G& 3-10 helix \tabularnewline \hline I& $\Pi$-helix \tabularnewline \hline T& Turn\tabularnewline \hline S& Bend \tabularnewline \hline -& Other\tabularnewline \hline \end{tabular}} \caption{\label{cap:DSSP-codes}DSSP codes in Bio.PDB.} \end{table} \subsubsection*{How do I calculate the accessible surface area of a residue?} Use the \texttt{DSSP} class (see also previous entry). But see also next entry. \subsubsection*{How do I calculate residue depth?} Residue depth is the average distance of a residue's atoms from the solvent accessible surface. It's a fairly new and very powerful parameterization of solvent accessibility. For this functionality, you need to install Michel Sanner's MSMS program (\url{http://www.scripps.edu/pub/olson-web/people/sanner/html/msms_home.html}). Then use the \texttt{ResidueDepth} class. This class behaves as a dictionary which maps \texttt{Residue} objects to corresponding (residue depth, C$\alpha$ depth) tuples. The C$\alpha$ depth is the distance of a residue's C$\alpha$ atom to the solvent accessible surface. Example: \begin{lyxcode} model=structure{[}0{]} rd=ResidueDepth(model,~pdb\_file) residue\_depth,~ca\_depth=rd{[}some\_residue{]} \end{lyxcode} You can also get access to the molecular surface itself (via the \texttt{get\_surface} function), in the form of a Numeric python array with the surface points. \subsubsection*{How do I calculate Half Sphere Exposure?} Half Sphere Exposure (HSE) is a new, 2D measure of solvent exposure. Basically, it counts the number of C$\alpha$ atoms around a residue in the direction of its side chain, and in the opposite direction (within a radius of 13 ). Despite its simplicity, it outperforms many other measures of solvent exposure. An article describing this novel 2D measure has been submitted. HSE comes in two flavors: HSE$\alpha$ and HSE$\beta$. The former only uses the C$\alpha$ atom positions, while the latter uses the C$\alpha$ and C$\beta$ atom positions. The HSE measure is calculated by the \texttt{HSExposure} class, which can also calculate the contact number. The latter class has methods which return dictionaries that map a \texttt{Residue} object to its corresponding HSE$\alpha$, HSE$\beta$ and contact number values. Example: \begin{lyxcode} model=structure{[}0{]} hse=HSExposure() \#~Calculate~HSEalpha exp\_ca=hse.calc\_hs\_exposure(model,~option='CA3') \#~Calculate~HSEbeta exp\_cb=hse.calc\_hs\_exposure(model,~option='CB') \#~Calculate~classical~coordination~number~exp\_fs=hse.calc\_fs\_exposure(model) \#~Print~HSEalpha~for~a~residue print~exp\_ca{[}some\_residue{]} \end{lyxcode} \subsubsection*{How do I map the residues of two related structures onto each other?} First, create an alignment file in FASTA format, then use the \texttt{StructureAlignment} class. This class can also be used for alignments with more than two structures. \subsubsection*{How do I test if a Residue object is an amino acid?} Use \texttt{is\_aa(residue)}. \subsubsection*{Can I do vector operations on atomic coordinates?} \texttt{Atom} objects return a \texttt{Vector} object representation of the coordinates with the \texttt{get\_vector} method. \texttt{Vector} implements the full set of 3D vector operations, matrix multiplication (left and right) and some advanced rotation-related operations as well. See also next question. \subsubsection*{How do I put a virtual C$\beta$ on a Gly residue?} OK, I admit, this example is only present to show off the possibilities of Bio.PDB's \texttt{Vector} module (though this code is actually used in the \texttt{HSExposure} module, which contains a novel way to parametrize residue exposure - publication underway). Suppose that you would like to find the position of a Gly residue's C$\beta$ atom, if it had one. How would you do that? Well, rotating the N atom of the Gly residue along the C$\alpha$-C bond over -120 degrees roughly puts it in the position of a virtual C$\beta$ atom. Here's how to do it, making use of the \texttt{rotaxis} method (which can be used to construct a rotation around a certain axis) of the \texttt{Vector} module: \begin{lyxcode} \#~get~atom~coordinates~as~vectors n=residue{[}'N'{]}.get\_vector()~ c=residue{[}'C'{]}.get\_vector()~ ca=residue{[}'CA'{]}.get\_vector() \#~center~at~origin n=n-ca~ c=c-ca~ \#~find~rotation~matrix~that~rotates~n~ \#~-120~degrees~along~the~ca-c~vector rot=rotaxis(-pi{*}120.0/180.0,~c) \#~apply~rotation~to~ca-n~vector cb\_at\_origin=n.left\_multiply(rot) \#~put~on~top~of~ca~atom cb=cb\_at\_origin+ca \end{lyxcode} This example shows that it's possible to do some quite nontrivial vector operations on atomic data, which can be quite useful. In addition to all the usual vector operations (cross (use \texttt{{*}{*}}), and dot (use \texttt{{*}}) product, angle, norm, etc.) and the above mentioned \texttt{rotaxis} function, the \texttt{Vector} module also has methods to rotate (\texttt{rotmat}) or reflect (\texttt{refmat}) one vector on top of another. \subsection{Manipulating the structure} \subsubsection*{How do I superimpose two structures?} Surprisingly, this is done using the \texttt{Superimposer} object. This object calculates the rotation and translation matrix that rotates two lists of atoms on top of each other in such a way that their RMSD is minimized. Of course, the two lists need to contain the same amount of atoms. The \texttt{Superimposer} object can also apply the rotation/translation to a list of atoms. The rotation and translation are stored as a tuple in the \texttt{rotran} attribute of the \texttt{Superimposer} object (note that the rotation is right multiplying!). The RMSD is stored in the \texttt{rmsd} attribute. The algorithm used by \texttt{Superimposer} comes from \textit{Matrix computations, 2nd ed. Golub, G. \& Van Loan (1989)} and makes use of singular value decomposition (this is implemented in the general \texttt{Bio.\-SVDSuperimposer} module). Example: \begin{lyxcode} sup=Superimposer() \#~Specify~the~atom~lists \#~'fixed'~and~'moving'~are~lists~of~Atom~objects \#~The~moving~atoms~will~be~put~on~the~fixed~atoms sup.set\_atoms(fixed,~moving) \#~Print~rotation/translation/rmsd print~sup.rotran print~sup.rms~ \#~Apply~rotation/translation~to~the~moving~atoms sup.apply(moving) \end{lyxcode} \subsubsection*{How do I superimpose two structures based on their active sites?} Pretty easily. Use the active site atoms to calculate the rotation/translation matrices (see above), and apply these to the whole molecule. \subsubsection*{Can I manipulate the atomic coordinates?} Yes, using the \texttt{transform} method of the \texttt{Atom} object, or directly using the \texttt{set\_coord} method. \section{Other Structural Bioinformatics modules} \subsubsection*{Bio.SCOP} Info coming soon. \subsubsection*{Bio.FSSP} Info coming soon. \section{You haven't answered my question yet!} Woah! It's late and I'm tired, and a glass of excellent \textit{Pedro Ximenez} sherry is waiting for me. Just drop me a mail, and I'll answer you in the morning (with a bit of luck...). \section{Contributors} The main author/maintainer of Bio.PDB is yours truly. Kristian Rother donated code to interact with the PDB database, and to parse the PDB header. Indraneel Majumdar sent in some bug reports and assisted in coding the \texttt{Polypeptide} module. Many thanks to Brad Chapman, Jeffrey Chang, Andrew Dalke and Iddo Friedberg for suggestions, comments, help and/or biting criticism :-). \section{Can I contribute?} Yes, yes, yes! Just send me an e-mail (thamelry@binf.ku.dk) if you have something useful to contribute! Eternal fame awaits! \section{Biopython License Agreement} Permission to use, copy, modify, and distribute this software and its documentation with or without modifications and for any purpose and without fee is hereby granted, provided that any copyright notices appear in all copies and that both those copyright notices and this permission notice appear in supporting documentation, and that the names of the contributors or copyright holders not be used in advertising or publicity pertaining to distribution of the software without specific prior permission. THE CONTRIBUTORS AND COPYRIGHT HOLDERS OF THIS SOFTWARE DISCLAIM ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. \end{document} biopython-1.63/Doc/install/0000755000175000017500000000000012250104773015230 5ustar tratra00000000000000biopython-1.63/Doc/install/Installation.pdf0000644000175000017500000044556112250104653020400 0ustar tratra00000000000000%PDF-1.5 % 171 0 obj << /Length 1336 /Filter /FlateDecode >> stream xZs8~_#9\gz}F}WH86`ǦԱbaV~+lclƇ {C{5/ȳ0r1a`,JBy&vE>INؼܟN<Ak dqRNS3-GZN0$]OYǗCOg&Ds'O_YhB] N՞"QZSgek!K/{߅#(}Efg;KۨbD׽, ^`(5o=~@ q/728bR;kcu3zL5Zs6; n3x/6CNҜw3 K2 {jjІP QQVvљmf+ A*Rj @DāG 9 ==V .<i8JCvlx LEveyBbD\kP> stream xڵY]oF}篸 HRl n-䁖D $~ϥFE CgΜ9Ν;"I$OSIIJϒ"ã_ Hk]hMH{tDȠ5!PX5l]$'#YSXM.#/Qx֐M7PXtEH(X@2p&`XiHԢFI 0)I`XJD14nʏD}#4<06A,g$ `(f /,͏C$jW)B*AZ!$O ZAS*Zi0pa hmj+H:XW`0(K؄ na8k 2ޞXYa Ѡ |` U YY~jI8C/tuč`! a~p4Tܮ{ l(uD$ܱ=_U$&F?;Z^~PP觟ίmWT6+zכ-?=OWf[Wwoꛉ\菉fuj:7{C^g:Axs'ؖ]~{Pmǁڣ=?n/UGkA{L=} s2} ˺VնjVU4^߮7UW/u6}p]|^RW-͆WM o ?ڋOX1NO,OUT{-3ַeom o+HҎ\#Aeгɐq~.Dz\V}1n_$fJ;ěfh55-oXMU=j= 6j(tG2} u1nfgru5.Td!x/8fQ>>GA',pP?iGber\g#Zv/ T7Ҹ8soOm_w"4mc-Ut;IoizϟmEsضXjjmӇZ.# I,ѡ/rEӴI]svTԹ4tx6ٌg3x.㹌2x.㹌2x.3x>3x~NQbj`mQRqեoˁ. g]IhdEGt8/ qk:$#^c8:mEqYHl|&s^2)LS_G-'NN*iI/ pYkz_"h#>1szQDxH"S;$N/b x%|tG?Ы#vGB I !'>( h'>);?V}G'Db5_J鼙vP Q8v{jTK*wUɍ477 l~qFr#ĝ w @ aROS9\V,QG#V;MO&!*$RVqP`+k@DmVHw$-I^jڗy 1蟎`2S#6!?G3$6B!]Orb%,žV endstream endobj 186 0 obj << /Length 2453 /Filter /FlateDecode >> stream xڍ˒۸m9U#ߏI8l\)o -ߧ_H&Fh[Ew"Vv޽zmR.b]ɪ0JCT7:Ƀ_͠:`k,@nhN;^8a+M{нvmސG%6&V_qeGUiV$WaV|chj_AӷH\aƞ-pRF " 8P ~/9!]R^p0rn4{9"{ . 4H{jp3 _HT E5pp5)~dp-Ĕl3asR+[\zPGN|t2n$%?C'Csxq(Y~ݡpx7{d0p+YLJN@ވ&< ox*ёUFm۰VۣsNkCd)+BZ.qg0lE H[k@6 yD0 VQ˜]†׌Ŏb BJz,K2mYn +i0OF薬 6=hyeQJ:rb)_!RB͒B17Jc }I߯P\i-^y2Gxc]ߋ.)`E5o}BэCN-acE\dwC27ANVZǭi^I>!agBsI&iSDJ{~ó'6>*ާR E]"e_[oc8".W2)_Lt*NyOM&aܔ,iVrkFqо3+m%f]mo>va=U- SfiRJ$E `P=jHst:w>k tfkQp.f !LR\+aJ[~> stream xڭYY~LMR"6"1Y$A6Gj 4k}j5Y#E*Z}"ﻻ>a}2Q?_-Jכtqk/ܵEި@$OdzkYu3qHk&9JG6CYBLa&fd[Z-gj{@tɱsw0p3OCבVZxH 5`оQIrZ#!BS,mֱh~3\/ڽcNq>wvvۡVSVA:7pώ3Y!1(oG1%vy4/?;stISl0Q[/x +tpW8;fR*= ] .?ps.†$<$~ !@##oB.[1X}p=WP.oMB~5q3vf;ےqt/ @6`Gi<"dpnT-` Mxw: .؄/m(u{* $x59r뀓tD@S6݄XY)Ͼ1:Y۠|u~@ET2I R)yR[ڞۜ>E&TZH-׋IQ5)F2 u Kҹg%*pC|m3 r0J9_^wb.fPb\ĸEIv":l#fݳџpa871 Ðr.! 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One popular one is the RPM (RedHat Package Manager) system. Each of these package managing systems has its own quirks and tricks and I certainly can't pretend to understand them all so I won't try to describe them all here. While these package repositories may include Biopython all ready to install, you will typically want to install Biopython from source to get the very latest version. However, there is one general point which it is important to remember when installing from any of these systems: you need to download and install the development packages for python. A number of distributions contain a "basic" python which contains libraries and enough stuff to run simple python programs. However, they do not contain the python libraries necessary to build third-party python applications (like Biopython and it's dependencies). You'll need to install these libraries and header files, which are often found in a separate package called \verb|python-devel| or something similar. \subsection{Python installation on Windows} Installation on Windows is most easily done using handy windows installers. As described above in the UNIX section, you should go to the webpage for the current stable version of Python to download this installer. At the current time, you'd go to \ahrefurl{\url{http://www.python.org/download/releases/2.7.5/}} and download \verb|Python-2.7.5.msi|. The installer is an executable program, so you only need to double click it to run it. Then just follow the friendly instructions. On all newer Windows machines you'll need to have Administrator privileges to do this installation. \subsection{Python installation on Mac OS X} Apple includes python on Mac OS X, and while you can use this many people have preferred to install the latest version of python as well in parallel. We refer you to the \ahrefurl{\url{http://www.python.org}} for more details, although otherwise the UNIX instructions apply. (See note above about installing XCode to get the compiler tools.) \section{Installing Biopython dependencies} Once python is installed, the next step is getting the dependencies for Biopython installed. Since not all functionality is included in the main python installation, Biopython needs some support libraries to save us a lot of work re-writing code that already exists. We try to keep as few dependencies as possible to make installation as easy as possible. \subsection{Numerical Python (NumPy) (strongly recommended)} The Numerical Python distribution is a fast implementation of arrays and associated array functionality. This is important for a number of Biopython modules that deal with number processing (e.g. Bio.Cluster and Bio.PDB). As of release 1.49, Biopython supports the standard NumPy distribution. Previous releases instead used the older Numeric module (which is no longer being maintained). The main web site for NumPy is: \ahrefurl{\url{http://numpy.scipy.org/}}. \subsubsection{UNIX and Mac OS X systems} You should download the \verb|tar.gz| file, and follow the standard python build process. Note you will need a C compiler installed (see above): \begin{verbatim} $ tar -zxvf numpy-1.7.1.tar.gz $ cd numpy-1.7.1/ $ python setup.py build \end{verbatim} Once it is built, you should become root, and then install it: \begin{verbatim} $ python setup.py install \end{verbatim} One important note if you use an package system and not installing NumPy from source: you may also need to install the header files which are not included with some packages. As with the main python distribution, this means you'll need to look for something like \verb|python-numpy-devel| and make sure to install this as well as the basic package. \subsubsection{Windows systems} We recommend using the NumPy provided windows installers for your installed version of python. For Python 2.7, at the current time this would be \verb|numpy-1.7.1-win32-superpack-python2.7.exe|. You should follow the now-standard procedure of downloading the installer, double clicking it and then following the installation instructions. As before, you will need to have administrator permissions to do this. \subsubsection{Making sure it installed correctly} To make sure everything went okay during the install, fire up the python interpreter and ensure you can import NumPy without any errors: \begin{verbatim} $ python2.7 Python 2.7.4 (default, Apr 8 2013, 15:01:09) [GCC 4.4.6 20120305 (Red Hat 4.4.6-4)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import numpy >>> \end{verbatim} Note that for the import statement, NumPy should be in lower case! \subsection{ReportLab (optional)} The ReportLab package is a library for generating PDF documents. It is used in the Biopython Graphics modules, which contains basic functionality for drawing biological objects like chromosomes. If you are not planning on using this, installing ReportLab is not necessary. ReportLab in itself is very useful for a number of tasks besides just Biopython, so you may want to check out \ahrefurl{\url{http://www.reportlab.org}} before making your decision. The main download page for ReportLab is \ahrefurl{\url{http://www.reportlab.org/downloads.html}}. The ReportLab company has some commercial products as well, but just scroll down their page to the Open Source software section for the base ReportLab downloads. If you want to generate bitmap images, you will also need the ReportLab module renderPM. This in turn requires the \href{http://www.pythonware.com/products/pil/}{Python Imaging Library (PIL)}. \subsubsection{UNIX and Mac OS X systems} For UNIX installs, you should download the tarred and gzipped version of the ReportLab distribution. At the time of this writing, this is called \verb|ReportLab_2_3.tar.gz|. First, unpack the distribution and change into the created directory: \begin{verbatim} $ gunzip ReportLab_2_3.tar.gz $ tar -xvpf ReportLab_2_3.tar $ cd reportlab_2_3/ \end{verbatim} Once again, ReportLab uses the standard python installation system which you are probably feeling really comfortable with by now. So, first build the package: \begin{verbatim} $ python setup.py build \end{verbatim} Now become root, and install it: \begin{verbatim} $ python setup.py install \end{verbatim} \subsubsection{Windows systems} ReportLab now has graphical windows installers. Nice and easy. \subsubsection{Making sure it installed correctly} If reportlab is installed correctly, you should be able to do the following: \begin{verbatim} $ python2.7 Python 2.7.4 (default, Apr 8 2013, 15:01:09) [GCC 4.4.6 20120305 (Red Hat 4.4.6-4)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> from reportlab.graphics import renderPDF >>> \end{verbatim} Depending on your version of python and what you have installed, you may get the following warning message: \verb|Warn: Python Imaging Library not available|. This isn't anything to worry about unless you want to produce bitmap images, since the Biopython parts that use ReportLab will work just fine without it. \subsection{Database Access (MySQLdb, ...) (optional)} The MySQLdb package is a library for accessing MySQL databases. Biopython includes an accessory module, DocSQL, which provides a convenient interface to MySQLdb. If you are not planning on using Bio.DocSQL, installing MySQLdb is not necessary. Additionally, both MySQLdb and psycopg (a PostgreSQL database adaptor) can be used for accessing BioSQL databases through Biopython (see \ahrefurl{\url{http://biopython.org/wiki/BioSQL}}). Again if you are not going to use BioSQL, there shouldn't be any need to install these modules. \subsection{mxTextTools (no longer needed)} Historically this was an important Biopython dependency, used extensively in a number of parsers. However, we have gradually phased out its use, and as of Biopython 1.50, mxTextTools is no longer used at all. mxTextTools is available along with the entire mx-base system (which contains a number of other useful utilities as well) and the latest version is available for download at: \ahrefurl{\url{http://www.egenix.com/products/python/mxBase/mxTextTools/}}. \section{Installing Biopython} \subsection{Obtaining Biopython} Biopython's internet home is at, naturally enough, \ahrefurl{\url{http://www.biopython.org}}. This is the home of all things Biopython, so it is the best place to start looking around. You have two choices for obtaining Biopython: \begin{enumerate} \item Release code -- We made available releases on the download page (\ahrefurl{\url{http://biopython.org/wiki/Download}}). The releases are also available both as source and as installers (windows installers right now), so you have some choices to pick from on releases if you prefer not to deal with source code directly. \item git -- The current working copy of the Biopython sources is available via git hosted on github -- \ahrefurl{\url{http://github.com/biopython/biopython}}). Concise instructions for accessing this copy are available at \ahrefurl{\url{http://biopython.org/wiki/SourceCode}}. Our code in git is normally quite stable but there is always the caveat that the code there is under development. \end{enumerate} Based on which way you choose, you'll need to follow one of the following installation options. Read on for the platform you are working on. \subsection{Installing on UNIX and Mac OS X} \label{sec:unix_install} \subsubsection{Installation from source on UNIX and Mac OS X} Biopython uses Distutils, the standard python installation package, for its installation. If you read the install instructions above you are already quite familiar with its workings. Distutils comes standard with Python 1.6 and beyond. Now that we've got what we need, let's get into the installation: \begin{enumerate} \item First you need to unpack the distribution. If you got the git version, you are all set to go and can skip on ahead. Otherwise, you'll need to unpack it. On UN*X machines, a tar.gz package is provided, which you can unpack with \verb|tar -xzvpf biopython-X.X.tar.gz|. A zip file is also provided for other platforms. \item Now that everything is unpacked, move into the \verb| biopython*| directory (this will just be \verb|biopython| for git users, and will be \verb|biopython-X.X| for those using a packaged download). \item Now you are ready for your one step install -- \verb|python setup.py install|. This performs the default install, and will put Biopython into the \verb|site-packages| directory of your python library tree (on my machine this is \verb|/usr/local/lib/python2.4/site-packages|). You will have to have permissions to write to this directory, so you'll need to have root access on the machine. \begin{enumerate} \item This install requires that you have the python source available. You can check this by looking for \verb|Python.h| and \verb|config.h| in some place like \verb|/usr/local/include/python2.5|. If you installed python with RPMs or some other packaging system, this means you'll also have to install the header files. This requires installing the python development libraries as well (normally called something like \verb|python-devel-2.5.rpm|). \item The distutils setup process allows you to do some customization of your install so you don't have to stick everything in the default location (in case you don't have write permissions there, or just want to test Biopython out). You have quite a few choices, which are covered in detail in the distutils installation manual (\ahrefurl{\url{http://www.python.org/sigs/distutils-sig/doc/inst/inst.html}}), specifically in the Alternative installation section. For example, to install Biopython into your home directory, you need to type \verb|python setup.py install --home=$HOME|. This will install the package into someplace like \verb|$HOME/lib/python2.5/site-packages|. You'll need to subsequently modify the \verb|PYTHONPATH| environmental variable to include this directory so python will be able to find the installation. \end{enumerate} \item That's it! Biopython is installed. Wasn't that easy? Now let's check and make sure it worked properly. Skip on ahead to section~\ref{sec:is_working}. \end{enumerate} \subsubsection{Using the Python package index} Another simple option is to use the Python package index (\ahrefurl{\url{http://pypi.python.org/pypi}}) with the \verb|easy_install| command: \begin{verbatim} $ easy_install -f http://biopython.org/DIST/ biopython \end{verbatim} If Python is installed in the standard location, you will need administrator privileges to do this; the \verb|sudo| command works well: \begin{verbatim} $ sudo easy_install -f http://biopython.org/DIST/ biopython \end{verbatim} % FreeBSD port not up to date % \subsubsection{Installation on FreeBSD} % % Johann Visagie has been kind enough to create (and keep updated) a FreeBSD port of Biopython. Thanks to the wonders of the ports system, this means that all you need to do to install Biopython on FreeBSD is do the following as root: % % \begin{verbatim} % cd /usr/ports/biology/py-biopython % make install % \end{verbatim} % % And voila! It's installed. % % If you want more information on FreeBSD and things, Johann has written a nice primer for his FreeBSD EMBOSS port. This has lots of generally useful information, such as how to keep your ports tree up to date. If you are new to FreeBSD, you should definitely check it out at \ahrefurl{\url{ftp://ftp.no.embnet.org/pub/EMBOSS-extras/EMBOSS-FreeBSD-HOWTO.txt}}. \subsubsection{Installation on Mac OS X using the fink package manager} Instead of installing from source, on Mac OS X you can also use the fink package manager, see \ahrefurl{\url{http://fink.sf.net}}. Fink should take care of downloading the source code and installing all needed packages for Biopython, including Python itself. Once you have installed fink, you can install biopython using: \begin{verbatim} $ fink install biopython-pyXX \end{verbatim} \noindent where XX is the python version you would like to use. Currently, python 2.4, 2.5, and 2.6 are available through fink on Mac OS X 10.4, so you would have to replace XX with 24, 25, or 26, respectively. Most likely, you will have to enable the unstable tree of fink in order to install the most recent versions of the package, see also this item in the Fink FAQ: \url{http://fink.sourceforge.net/faq/usage-fink.php#unstable}. Note that 'unstable' doesn't mean that a package won't work, but only that there has not been feedback to the fink team from users. \subsubsection{Installation on UNIX systems using RPMs} Warning. Right now we're not making RPMs for biopython (because I stopped using an RPM system, basically). If anyone wants to pick this up, or feels especially strongly that they'd like RPMs, please let us know. To simplify things for people running RPM-based systems, biopython can also be installed via the RPM system. Additionally, this saves the necessity of having a C compiler to install biopython. Installing Biopython from a RPM package should be much the same process as used for other RPMs. If you need general information about how RPMs work, the best place to go is \ahrefurl{\url{http://www.rpm.org}}. To install it, you should just need to do: \begin{verbatim} $ rpm -i your_biopython.rpm \end{verbatim} To see what you installed try doing \verb|rpm -qpl your_biopython.rpm| which will list all of the installed files. RPMs do not install the documentation, tests, or example code, so you might want to also grab a source distribution, so you can use these resources (and also look at the source code if you want to). \subsection{Installing with a Windows Installer} Installing things on Windows with the installer should be really easy (hey, that's why they've got graphical installers, right?). You should just need to download the \verb|Biopython-version.exe| installer from biopython web site. Then you just need to double click and voila, a nice little installer will come up and you can stick the libraries where you need to. No need for a C compiler or anything fancy. You will need to have Administrator privileges on the machine to do the installation. This does not install the documentation, tests, example code or source code, so it is probably also a good idea to download the zip file containing this so you can test your installation and learn how to use it. \subsection{Installing from source on Windows} \label{sec:windows_install} This section deals with installing the source (i.~e.~from git or from a source zip file) on a Windows machine. Much of the information from the UNIX install applies here, so it would be good to read section~\ref{sec:unix_install} before starting. You will need a suitable C compiler. What you choose may depend on your version of Python. For Python 2.6 we currently use Microsoft's free VC++ 2008 Express Edition from \ahrefurl{\url{http://www.microsoft.com/express/download/}}, installation of this is pretty simple. Then go to the Biopython source directory and run: \begin{verbatim} c:\python26\python setup.py build c:\python26\python setup.py test c:\python26\python setup.py install \end{verbatim} For older versions of Python, we use mingw32 installed from cygwin (\ahrefurl{\url{http://www.cygwin.com}}). Once everything is setup (which is a bit complicated), you would again get the source, and from that directory run: \begin{verbatim} c:\python25\python setup.py build --compiler=mingw32 c:\python25\python setup.py test c:\python25\python setup.py install \end{verbatim} Previously (back on Python 2.0), Brad has also managed to use Borland's free C++ compiler (available from \ahrefurl{\url{http://www.inprise.com/bcppbuilder/freecompiler/}}), but this required extra work. Now that you've got everything installed, carry on ahead to section~\ref{sec:is_working} to make sure everything worked. \section{Making sure everything worked} \label{sec:is_working} First, we'll just do a quick test to make sure Biopython is installed correctly. The most important thing is that python can find the biopython installation. Biopython installs into top level \verb|Bio| and \verb|BioSQL| directories, so you'll want to make sure these directories are located in a directory specified in your\verb| $PYTHONPATH| environmental variable. If you used the default install, this shouldn't be a problem, but if not, you'll need to set the \verb|PYTHONPATH| with something like \verb|export PYTHONPATH = $PYTHONPATH':/directory/where/you/put/Biopython'| (on UNIX). Now that we think we are ready, fire up your python interpreter and follow along with the following code: \begin{verbatim} $ python Python 2.5 (r25:51908, Nov 23 2006, 18:40:28) [GCC 4.1.1 20061011 (Red Hat 4.1.1-30)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> from Bio.Seq import Seq >>> from Bio.Alphabet.IUPAC import unambiguous_dna >>> new_seq = Seq('GATCAGAAG', unambiguous_dna) >>> new_seq[0:2] Seq('GA', IUPACUnambiguousDNA()) >>> new_seq.translate() Seq('DQK', HasStopCodon(IUPACProtein(), '*')) >>> \end{verbatim} If this worked properly, then it looks like Biopython is in a happy place where python can find it, so now you might want to do some more rigorous tests. The \verb|Tests| directory inside the distribution contains a number of tests you can run to make sure all of the different parts of biopython are working. These should all work just by running \verb|python test_WhateverTheTestIs.py|. If you didn't do this earlier, you should also all of our tests. To do this, you just need to be in the source code installation directory and type: \begin{verbatim} $ python setup.py test \end{verbatim} You can also run them by typing \verb|python run_tests.py| in the Tests sub directory. See the main Tutorial for further details (there is a whole chapter on the test framework). If you've made it this far, you've gotten Biopython installed and running. Congratulations! \section{Third Party Tools} Note that Biopython includes support for interfacing with or parsing the output from a number of third party command line tools. These are not required to install Biopython, but may be of interest. This includes: \begin{itemize} \item NCBI Standalone BLAST, which can used with the \verb|Bio.Blast| module and parsed with the \verb|Bio.SearchIO| module. \item EMBOSS tools, which can be invoked using the \verb|Bio.Emboss| module. The \verb|Bio.AlignIO| module can also parse some alignment formats output by the EMBOSS suite. \item ClustalW, which can be parsed using \verb|Bio.AlignIO| and invoked using the \verb|Bio.Align.Applications| module. \item SIMCOAL2 and FDist tools for population genetics can be used via the \verb|Bio.PopGen| module. \item Bill Pearson's FASTA tools output can be parsed using the \verb|Bio.AlignIO| and \verb|Bio.SearchIO| module. \item Wise2 includes the useful tool dnal. \end{itemize} \noindent See also the listing on \ahrefurl{\url{http://biopython.org/wiki/Download}} which should include URLs for these tools, and may also be more up to date. \section{Notes for installing with non-administrator permissions} Although I mentioned above that I wouldn't go much into installing in non-root directories, if you are stuck installing Biopython and it's dependencies into your home directory here are a few notes and tricks to keep you going: \begin{itemize} \item Building some C modules, such as \verb|Bio.Cluster| require that the NumPy include files (normally installed in \verb|your_dir/include/python/Numeric|) be available. If the compiler can't find these directories you'll normally get an error like: \begin{verbatim} Bio/Cluster/clustermodule.c:2: NumpPy/arrayobject.h: No such file or directory \end{verbatim} Followed by a long messy list of syntax errors. To fix this, you'll have to edit the \verb|setup.py| file to let it know where the include directories are located. Look for the line in \verb|setup.py| that looks like: \begin{verbatim} include_dirs=["Bio/Cluster"] \end{verbatim} and adjust it so that it includes the include directory where the NumPy libraries were installed: \begin{verbatim} include_dirs=["Bio/Cluster", "your_dir/include/python"] \end{verbatim} Then you should be able to install everything happily. \end{itemize} Yes, it's a bit of a mess installing lots of packages in non-standard locations. The best solution is to talk with your friendly system administrator and get them to assist with the installation of at least the required packages (they are generally quite useful for any python install) before going ahead with Biopython installation. \end{document} biopython-1.63/Doc/install/Installation.html0000644000175000017500000011651712250104654020570 0ustar tratra00000000000000 Biopython Installation

Biopython Installation

Brad Chapman, with other contributors

Contents

1  Purpose and Assumptions

For those of you familiar with installing python packages and who don’t care for following details instructions can try going to http://biopython.org/wiki/Download, installing the relevant prerequisites, and Biopython.

This document describes installing Biopython on your computer. To make things as simple as possible, it basically assumes you have nothing related to Python or Biopython on your computer and want to end up with a working installation of Biopython when you are finished following through this documentation.

Biopython should work on just any operating system where Python works, so these instructions contain directions for installation on UNIX/Linux, Windows and Macintosh machines. The directions assume that you have permission to install programs on the machine (root access on UNIX and Administrator privileges on Windows or Mac machines). While it is certainly possible to install things without these privileges, this is a serious pain and all the tedious workarounds aren’t something that I’ll go into very much in this documentation.

With all this said, hopefully these directions will make it straightforward to get Biopython installed on your machine so you can begin using it as quick as possible.

2  C Compiler

Although mostly written in Python, Biopython (and some of its dependencies) include C code, which must be compiled to run. If you are going to be installing from source you will therefore need a C compiler. However, in many cases this can be avoided by using pre-compiled packages (which is what we recommend on Windows).

2.1  Unix

We recommend GCC as the C compiler, this is usually available as part of the standard set of packages on any Unix or Linux system.

2.2  Mac OS X

Please install Apple’s XCode suite from the App Store, and then from the Xcode options also install the optional command line utilities.

2.3  Windows

We recommend you install Biopython and its dependencies using the provided pre-compiled Windows Installers. i.e. You don’t need a C compiler. See Section 5.4 for more details.

3  Installing Python

Python is a interpreting, interactive object-oriented programming language and the home for all things python is http://www.python.org. Presumedly you have some idea of python and what it can do if you are interested in Biopython, but if not the python website contains tons of documentation and reasons to learn to program in python.

Biopython is designed to work with Python 2.5 to 2.7 inclusive. Python 2.7 is the final 2.x series release, and this would be our recommended version (assuming all other Python libraries you plan to use support it).

Upgrading bug-fix releases (for example. 2.6.1 to 2.6.2) is incredibly easy and won’t require any re-installation of libraries.

Upgrading between versions (e.g. 2.6 to 2.7) is more time consuming since you need to re-install all libraries you have added to python.

As of Biopython 1.62 we officially support Python 3, specifically Python 3.3. Python 3.0, 3.1 and 3.2 will not be supported.

Let’s get started with installation on various platforms.

3.1  Python installation on UNIX systems

First, you should go the main python web site and head over to the information page for the latest python release. At the time of this writing the latest stable Python 2 release is 2.7.5, which is available from http://www.python.org/download/releases/2.7.5/. This page contains links to all released files for the given release. For UNIX, we’ll want to use the tarred and gzipped file, which is called Python-2.7.5.tgz at the time of this writing.

Download this file and then unpack it with the following command:

$ tar -zxvf Python-2.7.5.tar.gz

Then enter into the created directory:

$ cd Python-2.7

Now, start the build process by configuring everything to your system:

$ ./configure

Build all of the files with:

$ make

Finally, you’ll need to have root permissions on the system and then install everything:

$ make install

If there were no errors and everything worked correctly, you should now be able to type python at a command prompt and enter into the python interpreter:

$ python
Python 2.7.5 (...)
...
Type "help", "copyright", "credits" or "license" for more information.
>>>

(The precise version text and details will depend on the version you installed and your operating system.)

3.1.1  RPM and other Package Manager Installation

There are a multitude of package manager systems out there for which python is available. One popular one is the RPM (RedHat Package Manager) system. Each of these package managing systems has its own quirks and tricks and I certainly can’t pretend to understand them all so I won’t try to describe them all here.

While these package repositories may include Biopython all ready to install, you will typically want to install Biopython from source to get the very latest version.

However, there is one general point which it is important to remember when installing from any of these systems: you need to download and install the development packages for python. A number of distributions contain a "basic" python which contains libraries and enough stuff to run simple python programs. However, they do not contain the python libraries necessary to build third-party python applications (like Biopython and it’s dependencies). You’ll need to install these libraries and header files, which are often found in a separate package called python-devel or something similar.

3.2  Python installation on Windows

Installation on Windows is most easily done using handy windows installers. As described above in the UNIX section, you should go to the webpage for the current stable version of Python to download this installer. At the current time, you’d go to http://www.python.org/download/releases/2.7.5/ and download Python-2.7.5.msi.

The installer is an executable program, so you only need to double click it to run it. Then just follow the friendly instructions. On all newer Windows machines you’ll need to have Administrator privileges to do this installation.

3.3  Python installation on Mac OS X

Apple includes python on Mac OS X, and while you can use this many people have preferred to install the latest version of python as well in parallel. We refer you to the http://www.python.org for more details, although otherwise the UNIX instructions apply.

(See note above about installing XCode to get the compiler tools.)

4  Installing Biopython dependencies

Once python is installed, the next step is getting the dependencies for Biopython installed. Since not all functionality is included in the main python installation, Biopython needs some support libraries to save us a lot of work re-writing code that already exists. We try to keep as few dependencies as possible to make installation as easy as possible.

4.1  Numerical Python (NumPy) (strongly recommended)

The Numerical Python distribution is a fast implementation of arrays and associated array functionality. This is important for a number of Biopython modules that deal with number processing (e.g. Bio.Cluster and Bio.PDB).

As of release 1.49, Biopython supports the standard NumPy distribution. Previous releases instead used the older Numeric module (which is no longer being maintained).

The main web site for NumPy is: http://numpy.scipy.org/.

4.1.1  UNIX and Mac OS X systems

You should download the tar.gz file, and follow the standard python build process. Note you will need a C compiler installed (see above):

$ tar -zxvf numpy-1.7.1.tar.gz
$ cd numpy-1.7.1/
$ python setup.py build

Once it is built, you should become root, and then install it:

$ python setup.py install

One important note if you use an package system and not installing NumPy from source: you may also need to install the header files which are not included with some packages. As with the main python distribution, this means you’ll need to look for something like python-numpy-devel and make sure to install this as well as the basic package.

4.1.2  Windows systems

We recommend using the NumPy provided windows installers for your installed version of python. For Python 2.7, at the current time this would be numpy-1.7.1-win32-superpack-python2.7.exe. You should follow the now-standard procedure of downloading the installer, double clicking it and then following the installation instructions. As before, you will need to have administrator permissions to do this.

4.1.3  Making sure it installed correctly

To make sure everything went okay during the install, fire up the python interpreter and ensure you can import NumPy without any errors:

$ python2.7
Python 2.7.4 (default, Apr  8 2013, 15:01:09) 
[GCC 4.4.6 20120305 (Red Hat 4.4.6-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
>>>

Note that for the import statement, NumPy should be in lower case!

4.2  ReportLab (optional)

The ReportLab package is a library for generating PDF documents. It is used in the Biopython Graphics modules, which contains basic functionality for drawing biological objects like chromosomes. If you are not planning on using this, installing ReportLab is not necessary. ReportLab in itself is very useful for a number of tasks besides just Biopython, so you may want to check out http://www.reportlab.org before making your decision.

The main download page for ReportLab is http://www.reportlab.org/downloads.html. The ReportLab company has some commercial products as well, but just scroll down their page to the Open Source software section for the base ReportLab downloads.

If you want to generate bitmap images, you will also need the ReportLab module renderPM. This in turn requires the Python Imaging Library (PIL).

4.2.1  UNIX and Mac OS X systems

For UNIX installs, you should download the tarred and gzipped version of the ReportLab distribution. At the time of this writing, this is called ReportLab_2_3.tar.gz. First, unpack the distribution and change into the created directory:

$ gunzip ReportLab_2_3.tar.gz
$ tar -xvpf ReportLab_2_3.tar
$ cd reportlab_2_3/

Once again, ReportLab uses the standard python installation system which you are probably feeling really comfortable with by now. So, first build the package:

$ python setup.py build

Now become root, and install it:

$ python setup.py install

4.2.2  Windows systems

ReportLab now has graphical windows installers. Nice and easy.

4.2.3  Making sure it installed correctly

If reportlab is installed correctly, you should be able to do the following:

$ python2.7
Python 2.7.4 (default, Apr  8 2013, 15:01:09) 
[GCC 4.4.6 20120305 (Red Hat 4.4.6-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from reportlab.graphics import renderPDF
>>>

Depending on your version of python and what you have installed, you may get the following warning message: Warn: Python Imaging Library not available. This isn’t anything to worry about unless you want to produce bitmap images, since the Biopython parts that use ReportLab will work just fine without it.

4.3  Database Access (MySQLdb, ...) (optional)

The MySQLdb package is a library for accessing MySQL databases. Biopython includes an accessory module, DocSQL, which provides a convenient interface to MySQLdb. If you are not planning on using Bio.DocSQL, installing MySQLdb is not necessary.

Additionally, both MySQLdb and psycopg (a PostgreSQL database adaptor) can be used for accessing BioSQL databases through Biopython (see http://biopython.org/wiki/BioSQL). Again if you are not going to use BioSQL, there shouldn’t be any need to install these modules.

4.4  mxTextTools (no longer needed)

Historically this was an important Biopython dependency, used extensively in a number of parsers. However, we have gradually phased out its use, and as of Biopython 1.50, mxTextTools is no longer used at all.

mxTextTools is available along with the entire mx-base system (which contains a number of other useful utilities as well) and the latest version is available for download at: http://www.egenix.com/products/python/mxBase/mxTextTools/.

5  Installing Biopython

5.1  Obtaining Biopython

Biopython’s internet home is at, naturally enough, http://www.biopython.org. This is the home of all things Biopython, so it is the best place to start looking around. You have two choices for obtaining Biopython:

  1. Release code – We made available releases on the download page (http://biopython.org/wiki/Download). The releases are also available both as source and as installers (windows installers right now), so you have some choices to pick from on releases if you prefer not to deal with source code directly.
  2. git – The current working copy of the Biopython sources is available via git hosted on github – http://github.com/biopython/biopython). Concise instructions for accessing this copy are available at http://biopython.org/wiki/SourceCode. Our code in git is normally quite stable but there is always the caveat that the code there is under development.

Based on which way you choose, you’ll need to follow one of the following installation options. Read on for the platform you are working on.

5.2  Installing on UNIX and Mac OS X

5.2.1  Installation from source on UNIX and Mac OS X

Biopython uses Distutils, the standard python installation package, for its installation. If you read the install instructions above you are already quite familiar with its workings. Distutils comes standard with Python 1.6 and beyond.

Now that we’ve got what we need, let’s get into the installation:

  1. First you need to unpack the distribution. If you got the git version, you are all set to go and can skip on ahead. Otherwise, you’ll need to unpack it. On UN*X machines, a tar.gz package is provided, which you can unpack with tar -xzvpf biopython-X.X.tar.gz. A zip file is also provided for other platforms.
  2. Now that everything is unpacked, move into the biopython* directory (this will just be biopython for git users, and will be biopython-X.X for those using a packaged download).
  3. Now you are ready for your one step install – python setup.py install. This performs the default install, and will put Biopython into the site-packages directory of your python library tree (on my machine this is /usr/local/lib/python2.4/site-packages). You will have to have permissions to write to this directory, so you’ll need to have root access on the machine.
    1. This install requires that you have the python source available. You can check this by looking for Python.h and config.h in some place like /usr/local/include/python2.5. If you installed python with RPMs or some other packaging system, this means you’ll also have to install the header files. This requires installing the python development libraries as well (normally called something like python-devel-2.5.rpm).
    2. The distutils setup process allows you to do some customization of your install so you don’t have to stick everything in the default location (in case you don’t have write permissions there, or just want to test Biopython out). You have quite a few choices, which are covered in detail in the distutils installation manual (http://www.python.org/sigs/distutils-sig/doc/inst/inst.html), specifically in the Alternative installation section. For example, to install Biopython into your home directory, you need to type python setup.py install --home=$HOME. This will install the package into someplace like $HOME/lib/python2.5/site-packages. You’ll need to subsequently modify the PYTHONPATH environmental variable to include this directory so python will be able to find the installation.
  4. That’s it! Biopython is installed. Wasn’t that easy? Now let’s check and make sure it worked properly. Skip on ahead to section 6.

5.2.2  Using the Python package index

Another simple option is to use the Python package index (http://pypi.python.org/pypi) with the easy_install command:

$ easy_install -f http://biopython.org/DIST/ biopython

If Python is installed in the standard location, you will need administrator privileges to do this; the sudo command works well:

$ sudo easy_install -f http://biopython.org/DIST/ biopython

5.2.3  Installation on Mac OS X using the fink package manager

Instead of installing from source, on Mac OS X you can also use the fink package manager, see http://fink.sf.net. Fink should take care of downloading the source code and installing all needed packages for Biopython, including Python itself. Once you have installed fink, you can install biopython using:

$ fink install biopython-pyXX

where XX is the python version you would like to use. Currently, python 2.4, 2.5, and 2.6 are available through fink on Mac OS X 10.4, so you would have to replace XX with 24, 25, or 26, respectively. Most likely, you will have to enable the unstable tree of fink in order to install the most recent versions of the package, see also this item in the Fink FAQ: http://fink.sourceforge.net/faq/usage-fink.php#unstable. Note that ’unstable’ doesn’t mean that a package won’t work, but only that there has not been feedback to the fink team from users.

5.2.4  Installation on UNIX systems using RPMs

Warning. Right now we’re not making RPMs for biopython (because I stopped using an RPM system, basically). If anyone wants to pick this up, or feels especially strongly that they’d like RPMs, please let us know.

To simplify things for people running RPM-based systems, biopython can also be installed via the RPM system. Additionally, this saves the necessity of having a C compiler to install biopython.

Installing Biopython from a RPM package should be much the same process as used for other RPMs. If you need general information about how RPMs work, the best place to go is http://www.rpm.org.

To install it, you should just need to do:

$ rpm -i your_biopython.rpm

To see what you installed try doing rpm -qpl your_biopython.rpm which will list all of the installed files.

RPMs do not install the documentation, tests, or example code, so you might want to also grab a source distribution, so you can use these resources (and also look at the source code if you want to).

5.3  Installing with a Windows Installer

Installing things on Windows with the installer should be really easy (hey, that’s why they’ve got graphical installers, right?). You should just need to download the Biopython-version.exe installer from biopython web site. Then you just need to double click and voila, a nice little installer will come up and you can stick the libraries where you need to. No need for a C compiler or anything fancy. You will need to have Administrator privileges on the machine to do the installation.

This does not install the documentation, tests, example code or source code, so it is probably also a good idea to download the zip file containing this so you can test your installation and learn how to use it.

5.4  Installing from source on Windows

This section deals with installing the source (i. e. from git or from a source zip file) on a Windows machine. Much of the information from the UNIX install applies here, so it would be good to read section 5.2 before starting. You will need a suitable C compiler. What you choose may depend on your version of Python.

For Python 2.6 we currently use Microsoft’s free VC++ 2008 Express Edition from http://www.microsoft.com/express/download/, installation of this is pretty simple. Then go to the Biopython source directory and run:

c:\python26\python setup.py build
c:\python26\python setup.py test
c:\python26\python setup.py install

For older versions of Python, we use mingw32 installed from cygwin (http://www.cygwin.com). Once everything is setup (which is a bit complicated), you would again get the source, and from that directory run:

c:\python25\python setup.py build --compiler=mingw32
c:\python25\python setup.py test
c:\python25\python setup.py install

Previously (back on Python 2.0), Brad has also managed to use Borland’s free C++ compiler (available from http://www.inprise.com/bcppbuilder/freecompiler/), but this required extra work.

Now that you’ve got everything installed, carry on ahead to section 6 to make sure everything worked.

6  Making sure everything worked

First, we’ll just do a quick test to make sure Biopython is installed correctly. The most important thing is that python can find the biopython installation. Biopython installs into top level Bio and BioSQL directories, so you’ll want to make sure these directories are located in a directory specified in your $PYTHONPATH environmental variable. If you used the default install, this shouldn’t be a problem, but if not, you’ll need to set the PYTHONPATH with something like export PYTHONPATH = $PYTHONPATH':/directory/where/you/put/Biopython' (on UNIX). Now that we think we are ready, fire up your python interpreter and follow along with the following code:

$ python
Python 2.5 (r25:51908, Nov 23 2006, 18:40:28) 
[GCC 4.1.1 20061011 (Red Hat 4.1.1-30)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from Bio.Seq import Seq
>>> from Bio.Alphabet.IUPAC import unambiguous_dna
>>> new_seq = Seq('GATCAGAAG', unambiguous_dna)
>>> new_seq[0:2]
Seq('GA', IUPACUnambiguousDNA())
>>> new_seq.translate()
Seq('DQK', HasStopCodon(IUPACProtein(), '*'))
>>>

If this worked properly, then it looks like Biopython is in a happy place where python can find it, so now you might want to do some more rigorous tests. The Tests directory inside the distribution contains a number of tests you can run to make sure all of the different parts of biopython are working. These should all work just by running python test_WhateverTheTestIs.py.

If you didn’t do this earlier, you should also all of our tests. To do this, you just need to be in the source code installation directory and type:

$ python setup.py test

You can also run them by typing python run_tests.py in the Tests sub directory. See the main Tutorial for further details (there is a whole chapter on the test framework).

If you’ve made it this far, you’ve gotten Biopython installed and running. Congratulations!

7  Third Party Tools

Note that Biopython includes support for interfacing with or parsing the output from a number of third party command line tools. These are not required to install Biopython, but may be of interest. This includes:

  • NCBI Standalone BLAST, which can used with the Bio.Blast module and parsed with the Bio.SearchIO module.
  • EMBOSS tools, which can be invoked using the Bio.Emboss module. The Bio.AlignIO module can also parse some alignment formats output by the EMBOSS suite.
  • ClustalW, which can be parsed using Bio.AlignIO and invoked using the Bio.Align.Applications module.
  • SIMCOAL2 and FDist tools for population genetics can be used via the Bio.PopGen module.
  • Bill Pearson’s FASTA tools output can be parsed using the Bio.AlignIO and Bio.SearchIO module.
  • Wise2 includes the useful tool dnal.

See also the listing on http://biopython.org/wiki/Download which should include URLs for these tools, and may also be more up to date.

8  Notes for installing with non-administrator permissions

Although I mentioned above that I wouldn’t go much into installing in non-root directories, if you are stuck installing Biopython and it’s dependencies into your home directory here are a few notes and tricks to keep you going:

  • Building some C modules, such as Bio.Cluster require that the NumPy include files (normally installed in your_dir/include/python/Numeric) be available. If the compiler can’t find these directories you’ll normally get an error like:
    Bio/Cluster/clustermodule.c:2: NumpPy/arrayobject.h: No such file or directory

    Followed by a long messy list of syntax errors. To fix this, you’ll have to edit the setup.py file to let it know where the include directories are located. Look for the line in setup.py that looks like:

        include_dirs=["Bio/Cluster"]

    and adjust it so that it includes the include directory where the NumPy libraries were installed:

        include_dirs=["Bio/Cluster", "your_dir/include/python"]

    Then you should be able to install everything happily.

Yes, it’s a bit of a mess installing lots of packages in non-standard locations. The best solution is to talk with your friendly system administrator and get them to assist with the installation of at least the required packages (they are generally quite useful for any python install) before going ahead with Biopython installation.

This document was translated from LATEX by HEVEA.
biopython-1.63/Doc/install/Makefile0000644000175000017500000000113012250104543016656 0ustar tratra00000000000000# create pdf, txt and html documentation FILENAME=Installation all: ${FILENAME}.pdf ${FILENAME}.html ${FILENAME}.txt clean: rm -f ${FILENAME}.aux rm -f ${FILENAME}.log rm -f ${FILENAME}.out rm -f ${FILENAME}.toc rm -f ${FILENAME}.haux rm -f ${FILENAME}.htoc distclean: clean rm -f ${FILENAME}.pdf rm -f ${FILENAME}.txt rm -f ${FILENAME}.html ${FILENAME}.pdf: ${FILENAME}.tex pdflatex ${FILENAME}.tex pdflatex ${FILENAME}.tex pdflatex ${FILENAME}.tex ${FILENAME}.html: ${FILENAME}.tex hevea -fix ${FILENAME}.tex ${FILENAME}.txt: ${FILENAME}.tex hevea -fix -text ${FILENAME}.tex biopython-1.63/Doc/Tutorial.html0000644000175000017500000374636012250104613016267 0ustar tratra00000000000000 Biopython Tutorial and Cookbook

[Biopython Logo]

Biopython Tutorial and Cookbook

Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck,
Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek Wilczyński

Last Update – 1 December 2013 (Biopython 1.63)

Contents

Chapter 1  Introduction

1.1  What is Biopython?

The Biopython Project is an international association of developers of freely available Python (http://www.python.org) tools for computational molecular biology. Python is an object oriented, interpreted, flexible language that is becoming increasingly popular for scientific computing. Python is easy to learn, has a very clear syntax and can easily be extended with modules written in C, C++ or FORTRAN.

The Biopython web site (http://www.biopython.org) provides an online resource for modules, scripts, and web links for developers of Python-based software for bioinformatics use and research. Basically, the goal of Biopython is to make it as easy as possible to use Python for bioinformatics by creating high-quality, reusable modules and classes. Biopython features include parsers for various Bioinformatics file formats (BLAST, Clustalw, FASTA, Genbank,...), access to online services (NCBI, Expasy,...), interfaces to common and not-so-common programs (Clustalw, DSSP, MSMS...), a standard sequence class, various clustering modules, a KD tree data structure etc. and even documentation.

Basically, we just like to program in Python and want to make it as easy as possible to use Python for bioinformatics by creating high-quality, reusable modules and scripts.

1.2  What can I find in the Biopython package

The main Biopython releases have lots of functionality, including:

  • The ability to parse bioinformatics files into Python utilizable data structures, including support for the following formats:
    • Blast output – both from standalone and WWW Blast
    • Clustalw
    • FASTA
    • GenBank
    • PubMed and Medline
    • ExPASy files, like Enzyme and Prosite
    • SCOP, including ‘dom’ and ‘lin’ files
    • UniGene
    • SwissProt
  • Files in the supported formats can be iterated over record by record or indexed and accessed via a Dictionary interface.
  • Code to deal with popular on-line bioinformatics destinations such as:
    • NCBI – Blast, Entrez and PubMed services
    • ExPASy – Swiss-Prot and Prosite entries, as well as Prosite searches
  • Interfaces to common bioinformatics programs such as:
    • Standalone Blast from NCBI
    • Clustalw alignment program
    • EMBOSS command line tools
  • A standard sequence class that deals with sequences, ids on sequences, and sequence features.
  • Tools for performing common operations on sequences, such as translation, transcription and weight calculations.
  • Code to perform classification of data using k Nearest Neighbors, Naive Bayes or Support Vector Machines.
  • Code for dealing with alignments, including a standard way to create and deal with substitution matrices.
  • Code making it easy to split up parallelizable tasks into separate processes.
  • GUI-based programs to do basic sequence manipulations, translations, BLASTing, etc.
  • Extensive documentation and help with using the modules, including this file, on-line wiki documentation, the web site, and the mailing list.
  • Integration with BioSQL, a sequence database schema also supported by the BioPerl and BioJava projects.

We hope this gives you plenty of reasons to download and start using Biopython!

1.3  Installing Biopython

All of the installation information for Biopython was separated from this document to make it easier to keep updated.

The short version is go to our downloads page (http://biopython.org/wiki/Download), download and install the listed dependencies, then download and install Biopython. Biopython runs on many platforms (Windows, Mac, and on the various flavors of Linux and Unix). For Windows we provide pre-compiled click-and-run installers, while for Unix and other operating systems you must install from source as described in the included README file. This is usually as simple as the standard commands:

python setup.py build
python setup.py test
sudo python setup.py install

(You can in fact skip the build and test, and go straight to the install – but its better to make sure everything seems to be working.)

The longer version of our installation instructions covers installation of Python, Biopython dependencies and Biopython itself. It is available in PDF (http://biopython.org/DIST/docs/install/Installation.pdf) and HTML formats (http://biopython.org/DIST/docs/install/Installation.html).

1.4  Frequently Asked Questions (FAQ)

  1. How do I cite Biopython in a scientific publication?
    Please cite our application note [1, Cock et al., 2009] as the main Biopython reference. In addition, please cite any publications from the following list if appropriate, in particular as a reference for specific modules within Biopython (more information can be found on our website):
    • For the official project announcement: [13, Chapman and Chang, 2000];
    • For Bio.PDB: [18, Hamelryck and Manderick, 2003];
    • For Bio.Cluster: [14, De Hoon et al., 2004];
    • For Bio.Graphics.GenomeDiagram: [2, Pritchard et al., 2006];
    • For Bio.Phylo and Bio.Phylo.PAML: [9, Talevich et al., 2012];
    • For the FASTQ file format as supported in Biopython, BioPerl, BioRuby, BioJava, and EMBOSS: [7, Cock et al., 2010].
  2. How should I capitalize “Biopython”? Is “BioPython” OK?
    The correct capitalization is “Biopython”, not “BioPython” (even though that would have matched BioPerl, BioJava and BioRuby).
  3. What is going wrong with my print commands?
    This tutorial now uses the Python 3 style print function. As of Biopython 1.62, we support both Python 2 and Python 3. The most obvious language difference is the print statement in Python 2 became a print function in Python 3.

    For example, this will only work under Python 2:

    >>> print "Hello World!"
Hello World!

    If you try that on Python 3 you’ll get a SyntaxError. Under Python 3 you must write:

    >>> print("Hello World!")
Hello World!

    Surprisingly that will also work on Python 2 – but only for simple examples printing one thing. In general you need to add this magic line to the start of your Python scripts to use the print function under Python 2.6 and 2.7:

    from __future__ import print_function

    If you forget to add this magic import, under Python 2 you’ll see extra brackets produced by trying to use the print function when Python 2 is interpretting it as a print statement and a tuple.

  4. How do I find out what version of Biopython I have installed?
    Use this: 
      >>> import Bio
  >>> print(Bio.__version__)
  ...
    If the “import Bio” line fails, Biopython is not installed. If the second line fails, your version is very out of date. If the version string ends with a plus, you don’t have an official release, but a snapshot of the in development code.
  5. Where is the latest version of this document?
    If you download a Biopython source code archive, it will include the relevant version in both HTML and PDF formats. The latest published version of this document (updated at each release) is online: If you are using the very latest unreleased code from our repository you can find copies of the in-progress tutorial here:
  6. Why is the Seq object missing the upper & lower methods described in this Tutorial?
    You need Biopython 1.53 or later. Alternatively, use str(my_seq).upper() to get an upper case string. If you need a Seq object, try Seq(str(my_seq).upper()) but be careful about blindly re-using the same alphabet.
  7. Why doesn’t the Seq object translation method support the cds option described in this Tutorial?
    You need Biopython 1.51 or later.
  8. What file formats do Bio.SeqIO and Bio.AlignIO read and write?
    Check the built in docstrings (from Bio import SeqIO, then help(SeqIO)), or see http://biopython.org/wiki/SeqIO and http://biopython.org/wiki/AlignIO on the wiki for the latest listing.
  9. Why won’t the Bio.SeqIO and Bio.AlignIO functions parse, read and write take filenames? They insist on handles!
    You need Biopython 1.54 or later, or just use handles explicitly (see Section 22.1). It is especially important to remember to close output handles explicitly after writing your data.
  10. Why won’t the Bio.SeqIO.write() and Bio.AlignIO.write() functions accept a single record or alignment? They insist on a list or iterator!
    You need Biopython 1.54 or later, or just wrap the item with [...] to create a list of one element.
  11. Why doesn’t str(...) give me the full sequence of a Seq object?
    You need Biopython 1.45 or later. Alternatively, rather than str(my_seq), use my_seq.tostring() (which will also work on recent versions of Biopython).
  12. Why doesn’t Bio.Blast work with the latest plain text NCBI blast output?
    The NCBI keep tweaking the plain text output from the BLAST tools, and keeping our parser up to date is/was an ongoing struggle. If you aren’t using the latest version of Biopython, you could try upgrading. However, we (and the NCBI) recommend you use the XML output instead, which is designed to be read by a computer program.
  13. Why doesn’t Bio.Entrez.parse() work? The module imports fine but there is no parse function!
    You need Biopython 1.52 or later.
  14. Why has my script using Bio.Entrez.efetch() stopped working?
    This could be due to NCBI changes in February 2012 introducing EFetch 2.0. First, they changed the default return modes - you probably want to add retmode="text" to your call. Second, they are now stricter about how to provide a list of IDs – Biopython 1.59 onwards turns a list into a comma separated string automatically.
  15. Why doesn’t Bio.Blast.NCBIWWW.qblast() give the same results as the NCBI BLAST website?
    You need to specify the same options – the NCBI often adjust the default settings on the website, and they do not match the QBLAST defaults anymore. Check things like the gap penalties and expectation threshold.
  16. Why doesn’t Bio.Blast.NCBIXML.read() work? The module imports but there is no read function!
    You need Biopython 1.50 or later. Or, use next(Bio.Blast.NCBIXML.parse(...)) instead.
  17. Why doesn’t my SeqRecord object have a letter_annotations attribute?
    Per-letter-annotation support was added in Biopython 1.50.
  18. Why can’t I slice my SeqRecord to get a sub-record?
    You need Biopython 1.50 or later.
  19. Why can’t I add SeqRecord objects together?
    You need Biopython 1.53 or later.
  20. Why doesn’t Bio.SeqIO.convert() or Bio.AlignIO.convert() work? The modules import fine but there is no convert function!
    You need Biopython 1.52 or later. Alternatively, combine the parse and write functions as described in this tutorial (see Sections 5.5.2 and 6.2.1).
  21. Why doesn’t Bio.SeqIO.index() work? The module imports fine but there is no index function!
    You need Biopython 1.52 or later.
  22. Why doesn’t Bio.SeqIO.index_db() work? The module imports fine but there is no index_db function!
    You need Biopython 1.57 or later (and a Python with SQLite3 support).
  23. Where is the MultipleSeqAlignment object? The Bio.Align module imports fine but this class isn’t there!
    You need Biopython 1.54 or later. Alternatively, the older Bio.Align.Generic.Alignment class supports some of its functionality, but using this is now discouraged.
  24. Why can’t I run command line tools directly from the application wrappers?
    You need Biopython 1.55 or later. Alternatively, use the Python subprocess module directly.
  25. I looked in a directory for code, but I couldn’t find the code that does something. Where’s it hidden?
    One thing to know is that we put code in __init__.py files. If you are not used to looking for code in this file this can be confusing. The reason we do this is to make the imports easier for users. For instance, instead of having to do a “repetitive” import like from Bio.GenBank import GenBank, you can just use from Bio import GenBank.
  26. Why does the code from CVS seem out of date?
    In late September 2009, just after the release of Biopython 1.52, we switched from using CVS to git, a distributed version control system. The old CVS server will remain available as a static and read only backup, but if you want to grab the latest code, you’ll need to use git instead. See our website for more details.

For more general questions, the Python FAQ pages http://www.python.org/doc/faq/ may be useful.

Chapter 2  Quick Start – What can you do with Biopython?

This section is designed to get you started quickly with Biopython, and to give a general overview of what is available and how to use it. All of the examples in this section assume that you have some general working knowledge of Python, and that you have successfully installed Biopython on your system. If you think you need to brush up on your Python, the main Python web site provides quite a bit of free documentation to get started with (http://www.python.org/doc/).

Since much biological work on the computer involves connecting with databases on the internet, some of the examples will also require a working internet connection in order to run.

Now that that is all out of the way, let’s get into what we can do with Biopython.

2.1  General overview of what Biopython provides

As mentioned in the introduction, Biopython is a set of libraries to provide the ability to deal with “things” of interest to biologists working on the computer. In general this means that you will need to have at least some programming experience (in Python, of course!) or at least an interest in learning to program. Biopython’s job is to make your job easier as a programmer by supplying reusable libraries so that you can focus on answering your specific question of interest, instead of focusing on the internals of parsing a particular file format (of course, if you want to help by writing a parser that doesn’t exist and contributing it to Biopython, please go ahead!). So Biopython’s job is to make you happy!

One thing to note about Biopython is that it often provides multiple ways of “doing the same thing.” Things have improved in recent releases, but this can still be frustrating as in Python there should ideally be one right way to do something. However, this can also be a real benefit because it gives you lots of flexibility and control over the libraries. The tutorial helps to show you the common or easy ways to do things so that you can just make things work. To learn more about the alternative possibilities, look in the Cookbook (Chapter 18, this has some cools tricks and tips), the Advanced section (Chapter 20), the built in “docstrings” (via the Python help command, or the API documentation) or ultimately the code itself.

2.2  Working with sequences

Disputably (of course!), the central object in bioinformatics is the sequence. Thus, we’ll start with a quick introduction to the Biopython mechanisms for dealing with sequences, the Seq object, which we’ll discuss in more detail in Chapter 3.

Most of the time when we think about sequences we have in my mind a string of letters like ‘AGTACACTGGT’. You can create such Seq object with this sequence as follows - the “>>>” represents the Python prompt followed by what you would type in:

>>> from Bio.Seq import Seq
>>> my_seq = Seq("AGTACACTGGT")
>>> my_seq
Seq('AGTACACTGGT', Alphabet())
>>> print(my_seq)
AGTACACTGGT
>>> my_seq.alphabet
Alphabet()

What we have here is a sequence object with a generic alphabet - reflecting the fact we have not specified if this is a DNA or protein sequence (okay, a protein with a lot of Alanines, Glycines, Cysteines and Threonines!). We’ll talk more about alphabets in Chapter 3.

In addition to having an alphabet, the Seq object differs from the Python string in the methods it supports. You can’t do this with a plain string:

>>> my_seq
Seq('AGTACACTGGT', Alphabet())
>>> my_seq.complement()
Seq('TCATGTGACCA', Alphabet())
>>> my_seq.reverse_complement()
Seq('ACCAGTGTACT', Alphabet())

The next most important class is the SeqRecord or Sequence Record. This holds a sequence (as a Seq object) with additional annotation including an identifier, name and description. The Bio.SeqIO module for reading and writing sequence file formats works with SeqRecord objects, which will be introduced below and covered in more detail by Chapter 5.

This covers the basic features and uses of the Biopython sequence class. Now that you’ve got some idea of what it is like to interact with the Biopython libraries, it’s time to delve into the fun, fun world of dealing with biological file formats!

2.3  A usage example

Before we jump right into parsers and everything else to do with Biopython, let’s set up an example to motivate everything we do and make life more interesting. After all, if there wasn’t any biology in this tutorial, why would you want you read it?

Since I love plants, I think we’re just going to have to have a plant based example (sorry to all the fans of other organisms out there!). Having just completed a recent trip to our local greenhouse, we’ve suddenly developed an incredible obsession with Lady Slipper Orchids (if you wonder why, have a look at some Lady Slipper Orchids photos on Flickr, or try a Google Image Search).

Of course, orchids are not only beautiful to look at, they are also extremely interesting for people studying evolution and systematics. So let’s suppose we’re thinking about writing a funding proposal to do a molecular study of Lady Slipper evolution, and would like to see what kind of research has already been done and how we can add to that.

After a little bit of reading up we discover that the Lady Slipper Orchids are in the Orchidaceae family and the Cypripedioideae sub-family and are made up of 5 genera: Cypripedium, Paphiopedilum, Phragmipedium, Selenipedium and Mexipedium.

That gives us enough to get started delving for more information. So, let’s look at how the Biopython tools can help us. We’ll start with sequence parsing in Section 2.4, but the orchids will be back later on as well - for example we’ll search PubMed for papers about orchids and extract sequence data from GenBank in Chapter 9, extract data from Swiss-Prot from certain orchid proteins in Chapter 10, and work with ClustalW multiple sequence alignments of orchid proteins in Section 6.4.1.

2.4  Parsing sequence file formats

A large part of much bioinformatics work involves dealing with the many types of file formats designed to hold biological data. These files are loaded with interesting biological data, and a special challenge is parsing these files into a format so that you can manipulate them with some kind of programming language. However the task of parsing these files can be frustrated by the fact that the formats can change quite regularly, and that formats may contain small subtleties which can break even the most well designed parsers.

We are now going to briefly introduce the Bio.SeqIO module – you can find out more in Chapter 5. We’ll start with an online search for our friends, the lady slipper orchids. To keep this introduction simple, we’re just using the NCBI website by hand. Let’s just take a look through the nucleotide databases at NCBI, using an Entrez online search (http://www.ncbi.nlm.nih.gov:80/entrez/query.fcgi?db=Nucleotide) for everything mentioning the text Cypripedioideae (this is the subfamily of lady slipper orchids).

When this tutorial was originally written, this search gave us only 94 hits, which we saved as a FASTA formatted text file and as a GenBank formatted text file (files ls_orchid.fasta and ls_orchid.gbk, also included with the Biopython source code under docs/tutorial/examples/).

If you run the search today, you’ll get hundreds of results! When following the tutorial, if you want to see the same list of genes, just download the two files above or copy them from docs/examples/ in the Biopython source code. In Section 2.5 we will look at how to do a search like this from within Python.

2.4.1  Simple FASTA parsing example

If you open the lady slipper orchids FASTA file ls_orchid.fasta in your favourite text editor, you’ll see that the file starts like this:

>gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA
CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG
AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG
...

It contains 94 records, each has a line starting with “>” (greater-than symbol) followed by the sequence on one or more lines. Now try this in Python:

from Bio import SeqIO
for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
    print(seq_record.id)
    print(repr(seq_record.seq))
    print(len(seq_record))

You should get something like this on your screen:

gi|2765658|emb|Z78533.1|CIZ78533
Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', SingleLetterAlphabet())
740
...
gi|2765564|emb|Z78439.1|PBZ78439
Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', SingleLetterAlphabet())
592

Notice that the FASTA format does not specify the alphabet, so Bio.SeqIO has defaulted to the rather generic SingleLetterAlphabet() rather than something DNA specific.

2.4.2  Simple GenBank parsing example

Now let’s load the GenBank file ls_orchid.gbk instead - notice that the code to do this is almost identical to the snippet used above for the FASTA file - the only difference is we change the filename and the format string:

from Bio import SeqIO
for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"):
    print(seq_record.id)
    print(repr(seq_record.seq))
    print(len(seq_record))

This should give:

Z78533.1
Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', IUPACAmbiguousDNA())
740
...
Z78439.1
Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', IUPACAmbiguousDNA())
592

This time Bio.SeqIO has been able to choose a sensible alphabet, IUPAC Ambiguous DNA. You’ll also notice that a shorter string has been used as the seq_record.id in this case.

2.4.3  I love parsing – please don’t stop talking about it!

Biopython has a lot of parsers, and each has its own little special niches based on the sequence format it is parsing and all of that. Chapter 5 covers Bio.SeqIO in more detail, while Chapter 6 introduces Bio.AlignIO for sequence alignments.

While the most popular file formats have parsers integrated into Bio.SeqIO and/or Bio.AlignIO, for some of the rarer and unloved file formats there is either no parser at all, or an old parser which has not been linked in yet. Please also check the wiki pages http://biopython.org/wiki/SeqIO and http://biopython.org/wiki/AlignIO for the latest information, or ask on the mailing list. The wiki pages should include an up to date list of supported file types, and some additional examples.

The next place to look for information about specific parsers and how to do cool things with them is in the Cookbook (Chapter 18 of this Tutorial). If you don’t find the information you are looking for, please consider helping out your poor overworked documentors and submitting a cookbook entry about it! (once you figure out how to do it, that is!)

2.5  Connecting with biological databases

One of the very common things that you need to do in bioinformatics is extract information from biological databases. It can be quite tedious to access these databases manually, especially if you have a lot of repetitive work to do. Biopython attempts to save you time and energy by making some on-line databases available from Python scripts. Currently, Biopython has code to extract information from the following databases:

  • Entrez (and PubMed) from the NCBI – See Chapter 9.
  • ExPASy – See Chapter 10.
  • SCOP – See the Bio.SCOP.search() function.

The code in these modules basically makes it easy to write Python code that interact with the CGI scripts on these pages, so that you can get results in an easy to deal with format. In some cases, the results can be tightly integrated with the Biopython parsers to make it even easier to extract information.

2.6  What to do next

Now that you’ve made it this far, you hopefully have a good understanding of the basics of Biopython and are ready to start using it for doing useful work. The best thing to do now is finish reading this tutorial, and then if you want start snooping around in the source code, and looking at the automatically generated documentation.

Once you get a picture of what you want to do, and what libraries in Biopython will do it, you should take a peak at the Cookbook (Chapter 18), which may have example code to do something similar to what you want to do.

If you know what you want to do, but can’t figure out how to do it, please feel free to post questions to the main Biopython list (see http://biopython.org/wiki/Mailing_lists). This will not only help us answer your question, it will also allow us to improve the documentation so it can help the next person do what you want to do.

Enjoy the code!

Chapter 3  Sequence objects

Biological sequences are arguably the central object in Bioinformatics, and in this chapter we’ll introduce the Biopython mechanism for dealing with sequences, the Seq object. Chapter 4 will introduce the related SeqRecord object, which combines the sequence information with any annotation, used again in Chapter 5 for Sequence Input/Output.

Sequences are essentially strings of letters like AGTACACTGGT, which seems very natural since this is the most common way that sequences are seen in biological file formats.

There are two important differences between Seq objects and standard Python strings. First of all, they have different methods. Although the Seq object supports many of the same methods as a plain string, its translate() method differs by doing biological translation, and there are also additional biologically relevant methods like reverse_complement(). Secondly, the Seq object has an important attribute, alphabet, which is an object describing what the individual characters making up the sequence string “mean”, and how they should be interpreted. For example, is AGTACACTGGT a DNA sequence, or just a protein sequence that happens to be rich in Alanines, Glycines, Cysteines and Threonines?

3.1  Sequences and Alphabets

The alphabet object is perhaps the important thing that makes the Seq object more than just a string. The currently available alphabets for Biopython are defined in the Bio.Alphabet module. We’ll use the IUPAC alphabets (http://www.chem.qmw.ac.uk/iupac/) here to deal with some of our favorite objects: DNA, RNA and Proteins.

Bio.Alphabet.IUPAC provides basic definitions for proteins, DNA and RNA, but additionally provides the ability to extend and customize the basic definitions. For instance, for proteins, there is a basic IUPACProtein class, but there is an additional ExtendedIUPACProtein class providing for the additional elements “U” (or “Sec” for selenocysteine) and “O” (or “Pyl” for pyrrolysine), plus the ambiguous symbols “B” (or “Asx” for asparagine or aspartic acid), “Z” (or “Glx” for glutamine or glutamic acid), “J” (or “Xle” for leucine isoleucine) and “X” (or “Xxx” for an unknown amino acid). For DNA you’ve got choices of IUPACUnambiguousDNA, which provides for just the basic letters, IUPACAmbiguousDNA (which provides for ambiguity letters for every possible situation) and ExtendedIUPACDNA, which allows letters for modified bases. Similarly, RNA can be represented by IUPACAmbiguousRNA or IUPACUnambiguousRNA.

The advantages of having an alphabet class are two fold. First, this gives an idea of the type of information the Seq object contains. Secondly, this provides a means of constraining the information, as a means of type checking.

Now that we know what we are dealing with, let’s look at how to utilize this class to do interesting work. You can create an ambiguous sequence with the default generic alphabet like this:

>>> from Bio.Seq import Seq
>>> my_seq = Seq("AGTACACTGGT")
>>> my_seq
Seq('AGTACACTGGT', Alphabet())
>>> my_seq.alphabet
Alphabet()

However, where possible you should specify the alphabet explicitly when creating your sequence objects - in this case an unambiguous DNA alphabet object:

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> my_seq = Seq("AGTACACTGGT", IUPAC.unambiguous_dna)
>>> my_seq
Seq('AGTACACTGGT', IUPACUnambiguousDNA())
>>> my_seq.alphabet
IUPACUnambiguousDNA()

Unless of course, this really is an amino acid sequence:

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> my_prot = Seq("AGTACACTGGT", IUPAC.protein)
>>> my_prot
Seq('AGTACACTGGT', IUPACProtein())
>>> my_prot.alphabet
IUPACProtein()

3.2  Sequences act like strings

In many ways, we can deal with Seq objects as if they were normal Python strings, for example getting the length, or iterating over the elements:

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> my_seq = Seq("GATCG", IUPAC.unambiguous_dna)
>>> for index, letter in enumerate(my_seq):
...     print("%i %s" % (index, letter))
0 G
1 A
2 T
3 C
4 G
>>> print(len(my_seq))
5

You can access elements of the sequence in the same way as for strings (but remember, Python counts from zero!):

>>> print(my_seq[0]) #first letter
G
>>> print(my_seq[2]) #third letter
T
>>> print(my_seq[-1]) #last letter
G

The Seq object has a .count() method, just like a string. Note that this means that like a Python string, this gives a non-overlapping count:

>>> from Bio.Seq import Seq
>>> "AAAA".count("AA")
2
>>> Seq("AAAA").count("AA")
2

For some biological uses, you may actually want an overlapping count (i.e. 3 in this trivial example). When searching for single letters, this makes no difference:

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> my_seq = Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPAC.unambiguous_dna)
>>> len(my_seq)
32
>>> my_seq.count("G")
9
>>> 100 * float(my_seq.count("G") + my_seq.count("C")) / len(my_seq)
46.875

While you could use the above snippet of code to calculate a GC%, note that the Bio.SeqUtils module has several GC functions already built. For example:

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> from Bio.SeqUtils import GC
>>> my_seq = Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPAC.unambiguous_dna)
>>> GC(my_seq)
46.875

Note that using the Bio.SeqUtils.GC() function should automatically cope with mixed case sequences and the ambiguous nucleotide S which means G or C.

Also note that just like a normal Python string, the Seq object is in some ways “read-only”. If you need to edit your sequence, for example simulating a point mutation, look at the Section 3.12 below which talks about the MutableSeq object.

3.3  Slicing a sequence

A more complicated example, let’s get a slice of the sequence:

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC", IUPAC.unambiguous_dna)
>>> my_seq[4:12]
Seq('GATGGGCC', IUPACUnambiguousDNA())

Two things are interesting to note. First, this follows the normal conventions for Python strings. So the first element of the sequence is 0 (which is normal for computer science, but not so normal for biology). When you do a slice the first item is included (i.e. 4 in this case) and the last is excluded (12 in this case), which is the way things work in Python, but of course not necessarily the way everyone in the world would expect. The main goal is to stay consistent with what Python does.

The second thing to notice is that the slice is performed on the sequence data string, but the new object produced is another Seq object which retains the alphabet information from the original Seq object.

Also like a Python string, you can do slices with a start, stop and stride (the step size, which defaults to one). For example, we can get the first, second and third codon positions of this DNA sequence:

>>> my_seq[0::3]
Seq('GCTGTAGTAAG', IUPACUnambiguousDNA())
>>> my_seq[1::3]
Seq('AGGCATGCATC', IUPACUnambiguousDNA())
>>> my_seq[2::3]
Seq('TAGCTAAGAC', IUPACUnambiguousDNA())

Another stride trick you might have seen with a Python string is the use of a -1 stride to reverse the string. You can do this with a Seq object too:

>>> my_seq[::-1]
Seq('CGCTAAAAGCTAGGATATATCCGGGTAGCTAG', IUPACUnambiguousDNA())

3.4  Turning Seq objects into strings

If you really do just need a plain string, for example to write to a file, or insert into a database, then this is very easy to get: 

>>> str(my_seq)
'GATCGATGGGCCTATATAGGATCGAAAATCGC'

Since calling str() on a Seq object returns the full sequence as a string, you often don’t actually have to do this conversion explicitly. Python does this automatically in the print function (and the print statement under Python 2): 

>>> print(my_seq)
GATCGATGGGCCTATATAGGATCGAAAATCGC

You can also use the Seq object directly with a %s placeholder when using the Python string formatting or interpolation operator (%): 

>>> fasta_format_string = ">Name\n%s\n" % my_seq
>>> print(fasta_format_string)
>Name
GATCGATGGGCCTATATAGGATCGAAAATCGC
<BLANKLINE>

This line of code constructs a simple FASTA format record (without worrying about line wrapping). Section 4.5 describes a neat way to get a FASTA formatted string from a SeqRecord object, while the more general topic of reading and writing FASTA format sequence files is covered in Chapter 5.

NOTE: If you are using Biopython 1.44 or older, using str(my_seq) will give just a truncated representation. Instead use my_seq.tostring() (which is still available in the current Biopython releases for backwards compatibility):

>>> my_seq.tostring()
'GATCGATGGGCCTATATAGGATCGAAAATCGC'

3.5  Concatenating or adding sequences

Naturally, you can in principle add any two Seq objects together - just like you can with Python strings to concatenate them. However, you can’t add sequences with incompatible alphabets, such as a protein sequence and a DNA sequence:

>>> from Bio.Alphabet import IUPAC
>>> from Bio.Seq import Seq
>>> protein_seq = Seq("EVRNAK", IUPAC.protein)
>>> dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
>>> protein_seq + dna_seq
Traceback (most recent call last):
...
TypeError: Incompatible alphabets IUPACProtein() and IUPACUnambiguousDNA()

If you really wanted to do this, you’d have to first give both sequences generic alphabets:

>>> from Bio.Alphabet import generic_alphabet
>>> protein_seq.alphabet = generic_alphabet
>>> dna_seq.alphabet = generic_alphabet
>>> protein_seq + dna_seq
Seq('EVRNAKACGT', Alphabet())

Here is an example of adding a generic nucleotide sequence to an unambiguous IUPAC DNA sequence, resulting in an ambiguous nucleotide sequence:

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import generic_nucleotide
>>> from Bio.Alphabet import IUPAC
>>> nuc_seq = Seq("GATCGATGC", generic_nucleotide)
>>> dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
>>> nuc_seq
Seq('GATCGATGC', NucleotideAlphabet())
>>> dna_seq
Seq('ACGT', IUPACUnambiguousDNA())
>>> nuc_seq + dna_seq
Seq('GATCGATGCACGT', NucleotideAlphabet())

3.6  Changing case

Python strings have very useful upper and lower methods for changing the case. As of Biopython 1.53, the Seq object gained similar methods which are alphabet aware. For example,

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import generic_dna
>>> dna_seq = Seq("acgtACGT", generic_dna)
>>> dna_seq
Seq('acgtACGT', DNAAlphabet())
>>> dna_seq.upper()
Seq('ACGTACGT', DNAAlphabet())
>>> dna_seq.lower()
Seq('acgtacgt', DNAAlphabet())

These are useful for doing case insensitive matching:

>>> "GTAC" in dna_seq
False
>>> "GTAC" in dna_seq.upper()
True

Note that strictly speaking the IUPAC alphabets are for upper case sequences only, thus:

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
>>> dna_seq
Seq('ACGT', IUPACUnambiguousDNA())
>>> dna_seq.lower()
Seq('acgt', DNAAlphabet())

3.7  Nucleotide sequences and (reverse) complements

For nucleotide sequences, you can easily obtain the complement or reverse complement of a Seq object using its built-in methods:

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC", IUPAC.unambiguous_dna)
>>> my_seq
Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPACUnambiguousDNA())
>>> my_seq.complement()
Seq('CTAGCTACCCGGATATATCCTAGCTTTTAGCG', IUPACUnambiguousDNA())
>>> my_seq.reverse_complement()
Seq('GCGATTTTCGATCCTATATAGGCCCATCGATC', IUPACUnambiguousDNA())

As mentioned earlier, an easy way to just reverse a Seq object (or a Python string) is slice it with -1 step:

>>> my_seq[::-1]
Seq('CGCTAAAAGCTAGGATATATCCGGGTAGCTAG', IUPACUnambiguousDNA())

In all of these operations, the alphabet property is maintained. This is very useful in case you accidentally end up trying to do something weird like take the (reverse)complement of a protein sequence:

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> protein_seq = Seq("EVRNAK", IUPAC.protein)
>>> protein_seq.complement()
Traceback (most recent call last):
...
ValueError: Proteins do not have complements!

The example in Section 5.5.3 combines the Seq object’s reverse complement method with Bio.SeqIO for sequence input/output.

3.8  Transcription

Before talking about transcription, I want to try and clarify the strand issue. Consider the following (made up) stretch of double stranded DNA which encodes a short peptide:

 
 DNA coding strand (aka Crick strand, strand +1) 
5’ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG3’
 ||||||||||||||||||||||||||||||||||||||| 
3’TACCGGTAACATTACCCGGCGACTTTCCCACGGGCTATC5’
 DNA template strand (aka Watson strand, strand −1) 
 
 | 
 Transcription 
 ↓ 
 
5’AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG3’
 Single stranded messenger RNA 
 

The actual biological transcription process works from the template strand, doing a reverse complement (TCAG → CUGA) to give the mRNA. However, in Biopython and bioinformatics in general, we typically work directly with the coding strand because this means we can get the mRNA sequence just by switching T → U.

Now let’s actually get down to doing a transcription in Biopython. First, let’s create Seq objects for the coding and template DNA strands: 

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG", IUPAC.unambiguous_dna)
>>> coding_dna
Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())
>>> template_dna = coding_dna.reverse_complement()
>>> template_dna
Seq('CTATCGGGCACCCTTTCAGCGGCCCATTACAATGGCCAT', IUPACUnambiguousDNA())

These should match the figure above - remember by convention nucleotide sequences are normally read from the 5’ to 3’ direction, while in the figure the template strand is shown reversed.

Now let’s transcribe the coding strand into the corresponding mRNA, using the Seq object’s built in transcribe method: 

>>> coding_dna
Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())
>>> messenger_rna = coding_dna.transcribe()
>>> messenger_rna
Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA())

As you can see, all this does is switch T → U, and adjust the alphabet.

If you do want to do a true biological transcription starting with the template strand, then this becomes a two-step process: 

>>> template_dna.reverse_complement().transcribe()
Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA())

The Seq object also includes a back-transcription method for going from the mRNA to the coding strand of the DNA. Again, this is a simple U → T substitution and associated change of alphabet: 

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> messenger_rna = Seq("AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG", IUPAC.unambiguous_rna)
>>> messenger_rna
Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA())
>>> messenger_rna.back_transcribe()
Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())

Note: The Seq object’s transcribe and back_transcribe methods were added in Biopython 1.49. For older releases you would have to use the Bio.Seq module’s functions instead, see Section 3.14.

3.9  Translation

Sticking with the same example discussed in the transcription section above, now let’s translate this mRNA into the corresponding protein sequence - again taking advantage of one of the Seq object’s biological methods:

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> messenger_rna = Seq("AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG", IUPAC.unambiguous_rna)
>>> messenger_rna
Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA())
>>> messenger_rna.translate()
Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*'))

You can also translate directly from the coding strand DNA sequence: 

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG", IUPAC.unambiguous_dna)
>>> coding_dna
Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())
>>> coding_dna.translate()
Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*'))

You should notice in the above protein sequences that in addition to the end stop character, there is an internal stop as well. This was a deliberate choice of example, as it gives an excuse to talk about some optional arguments, including different translation tables (Genetic Codes).

The translation tables available in Biopython are based on those from the NCBI (see the next section of this tutorial). By default, translation will use the standard genetic code (NCBI table id 1). Suppose we are dealing with a mitochondrial sequence. We need to tell the translation function to use the relevant genetic code instead: 

>>> coding_dna.translate(table="Vertebrate Mitochondrial")
Seq('MAIVMGRWKGAR*', HasStopCodon(IUPACProtein(), '*'))

You can also specify the table using the NCBI table number which is shorter, and often included in the feature annotation of GenBank files: 

>>> coding_dna.translate(table=2)
Seq('MAIVMGRWKGAR*', HasStopCodon(IUPACProtein(), '*'))

Now, you may want to translate the nucleotides up to the first in frame stop codon, and then stop (as happens in nature): 

>>> coding_dna.translate()
Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*'))
>>> coding_dna.translate(to_stop=True)
Seq('MAIVMGR', IUPACProtein())
>>> coding_dna.translate(table=2)
Seq('MAIVMGRWKGAR*', HasStopCodon(IUPACProtein(), '*'))
>>> coding_dna.translate(table=2, to_stop=True)
Seq('MAIVMGRWKGAR', IUPACProtein())

Notice that when you use the to_stop argument, the stop codon itself is not translated - and the stop symbol is not included at the end of your protein sequence.

You can even specify the stop symbol if you don’t like the default asterisk: 

>>> coding_dna.translate(table=2, stop_symbol="@")
Seq('MAIVMGRWKGAR@', HasStopCodon(IUPACProtein(), '@'))

Now, suppose you have a complete coding sequence CDS, which is to say a nucleotide sequence (e.g. mRNA – after any splicing) which is a whole number of codons (i.e. the length is a multiple of three), commences with a start codon, ends with a stop codon, and has no internal in-frame stop codons. In general, given a complete CDS, the default translate method will do what you want (perhaps with the to_stop option). However, what if your sequence uses a non-standard start codon? This happens a lot in bacteria – for example the gene yaaX in E. coli K12:

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import generic_dna
>>> gene = Seq("GTGAAAAAGATGCAATCTATCGTACTCGCACTTTCCCTGGTTCTGGTCGCTCCCATGGCA" + \
...            "GCACAGGCTGCGGAAATTACGTTAGTCCCGTCAGTAAAATTACAGATAGGCGATCGTGAT" + \
...            "AATCGTGGCTATTACTGGGATGGAGGTCACTGGCGCGACCACGGCTGGTGGAAACAACAT" + \
...            "TATGAATGGCGAGGCAATCGCTGGCACCTACACGGACCGCCGCCACCGCCGCGCCACCAT" + \
...            "AAGAAAGCTCCTCATGATCATCACGGCGGTCATGGTCCAGGCAAACATCACCGCTAA",
...            generic_dna)
>>> gene.translate(table="Bacterial")
Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HR*',
HasStopCodon(ExtendedIUPACProtein(), '*')
>>> gene.translate(table="Bacterial", to_stop=True)
Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HHR',
ExtendedIUPACProtein())

In the bacterial genetic code GTG is a valid start codon, and while it does normally encode Valine, if used as a start codon it should be translated as methionine. This happens if you tell Biopython your sequence is a complete CDS:

>>> gene.translate(table="Bacterial", cds=True)
Seq('MKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HHR',
ExtendedIUPACProtein())

In addition to telling Biopython to translate an alternative start codon as methionine, using this option also makes sure your sequence really is a valid CDS (you’ll get an exception if not).

The example in Section 18.1.3 combines the Seq object’s translate method with Bio.SeqIO for sequence input/output.

3.10  Translation Tables

In the previous sections we talked about the Seq object translation method (and mentioned the equivalent function in the Bio.Seq module – see Section 3.14). Internally these use codon table objects derived from the NCBI information at ftp://ftp.ncbi.nlm.nih.gov/entrez/misc/data/gc.prt, also shown on http://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi in a much more readable layout.

As before, let’s just focus on two choices: the Standard translation table, and the translation table for Vertebrate Mitochondrial DNA. 

>>> from Bio.Data import CodonTable
>>> standard_table = CodonTable.unambiguous_dna_by_name["Standard"]
>>> mito_table = CodonTable.unambiguous_dna_by_name["Vertebrate Mitochondrial"]

Alternatively, these tables are labeled with ID numbers 1 and 2, respectively: 

>>> from Bio.Data import CodonTable
>>> standard_table = CodonTable.unambiguous_dna_by_id[1]
>>> mito_table = CodonTable.unambiguous_dna_by_id[2]

You can compare the actual tables visually by printing them: 

>>> print(standard_table)
Table 1 Standard, SGC0

  |  T      |  C      |  A      |  G      |
--+---------+---------+---------+---------+--
T | TTT F   | TCT S   | TAT Y   | TGT C   | T
T | TTC F   | TCC S   | TAC Y   | TGC C   | C
T | TTA L   | TCA S   | TAA Stop| TGA Stop| A
T | TTG L(s)| TCG S   | TAG Stop| TGG W   | G
--+---------+---------+---------+---------+--
C | CTT L   | CCT P   | CAT H   | CGT R   | T
C | CTC L   | CCC P   | CAC H   | CGC R   | C
C | CTA L   | CCA P   | CAA Q   | CGA R   | A
C | CTG L(s)| CCG P   | CAG Q   | CGG R   | G
--+---------+---------+---------+---------+--
A | ATT I   | ACT T   | AAT N   | AGT S   | T
A | ATC I   | ACC T   | AAC N   | AGC S   | C
A | ATA I   | ACA T   | AAA K   | AGA R   | A
A | ATG M(s)| ACG T   | AAG K   | AGG R   | G
--+---------+---------+---------+---------+--
G | GTT V   | GCT A   | GAT D   | GGT G   | T
G | GTC V   | GCC A   | GAC D   | GGC G   | C
G | GTA V   | GCA A   | GAA E   | GGA G   | A
G | GTG V   | GCG A   | GAG E   | GGG G   | G
--+---------+---------+---------+---------+--

and: 

>>> print(mito_table)
Table 2 Vertebrate Mitochondrial, SGC1

  |  T      |  C      |  A      |  G      |
--+---------+---------+---------+---------+--
T | TTT F   | TCT S   | TAT Y   | TGT C   | T
T | TTC F   | TCC S   | TAC Y   | TGC C   | C
T | TTA L   | TCA S   | TAA Stop| TGA W   | A
T | TTG L   | TCG S   | TAG Stop| TGG W   | G
--+---------+---------+---------+---------+--
C | CTT L   | CCT P   | CAT H   | CGT R   | T
C | CTC L   | CCC P   | CAC H   | CGC R   | C
C | CTA L   | CCA P   | CAA Q   | CGA R   | A
C | CTG L   | CCG P   | CAG Q   | CGG R   | G
--+---------+---------+---------+---------+--
A | ATT I(s)| ACT T   | AAT N   | AGT S   | T
A | ATC I(s)| ACC T   | AAC N   | AGC S   | C
A | ATA M(s)| ACA T   | AAA K   | AGA Stop| A
A | ATG M(s)| ACG T   | AAG K   | AGG Stop| G
--+---------+---------+---------+---------+--
G | GTT V   | GCT A   | GAT D   | GGT G   | T
G | GTC V   | GCC A   | GAC D   | GGC G   | C
G | GTA V   | GCA A   | GAA E   | GGA G   | A
G | GTG V(s)| GCG A   | GAG E   | GGG G   | G
--+---------+---------+---------+---------+--

You may find these following properties useful – for example if you are trying to do your own gene finding: 

>>> mito_table.stop_codons
['TAA', 'TAG', 'AGA', 'AGG']
>>> mito_table.start_codons
['ATT', 'ATC', 'ATA', 'ATG', 'GTG']
>>> mito_table.forward_table["ACG"]
'T'

3.11  Comparing Seq objects

Sequence comparison is actually a very complicated topic, and there is no easy way to decide if two sequences are equal. The basic problem is the meaning of the letters in a sequence are context dependent - the letter “A” could be part of a DNA, RNA or protein sequence. Biopython uses alphabet objects as part of each Seq object to try and capture this information - so comparing two Seq objects means considering both the sequence strings and the alphabets.

For example, you might argue that the two DNA Seq objects Seq("ACGT", IUPAC.unambiguous_dna) and Seq("ACGT", IUPAC.ambiguous_dna) should be equal, even though they do have different alphabets. Depending on the context this could be important.

This gets worse – suppose you think Seq("ACGT", IUPAC.unambiguous_dna) and Seq("ACGT") (i.e. the default generic alphabet) should be equal. Then, logically, Seq("ACGT", IUPAC.protein) and Seq("ACGT") should also be equal. Now, in logic if A=B and B=C, by transitivity we expect A=C. So for logical consistency we’d require Seq("ACGT", IUPAC.unambiguous_dna) and Seq("ACGT", IUPAC.protein) to be equal – which most people would agree is just not right. This transitivity problem would also have implications for using Seq objects as Python dictionary keys.

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> seq1 = Seq("ACGT", IUPAC.unambiguous_dna)
>>> seq2 = Seq("ACGT", IUPAC.unambiguous_dna)

So, what does Biopython do? Well, the equality test is the default for Python objects – it tests to see if they are the same object in memory. This is a very strict test: 

>>> seq1 == seq2
False
>>> seq1 == seq1
True

If you actually want to do this, you can be more explicit by using the Python id function, 

>>> id(seq1) == id(seq2)
False
>>> id(seq1) == id(seq1)
True

Now, in every day use, your sequences will probably all have the same alphabet, or at least all be the same type of sequence (all DNA, all RNA, or all protein). What you probably want is to just compare the sequences as strings – so do this explicitly: 

>>> str(seq1) == str(seq2)
True
>>> str(seq1) == str(seq1)
True

As an extension to this, while you can use a Python dictionary with Seq objects as keys, it is generally more useful to use the sequence a string for the key. See also Section 3.4.

3.12  MutableSeq objects

Just like the normal Python string, the Seq object is “read only”, or in Python terminology, immutable. Apart from wanting the Seq object to act like a string, this is also a useful default since in many biological applications you want to ensure you are not changing your sequence data:

>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> my_seq = Seq("GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA", IUPAC.unambiguous_dna)

Observe what happens if you try to edit the sequence: 

>>> my_seq[5] = "G"
Traceback (most recent call last):
...
TypeError: 'Seq' object does not support item assignment

However, you can convert it into a mutable sequence (a MutableSeq object) and do pretty much anything you want with it:

>>> mutable_seq = my_seq.tomutable()
>>> mutable_seq
MutableSeq('GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA())

Alternatively, you can create a MutableSeq object directly from a string: 

>>> from Bio.Seq import MutableSeq
>>> from Bio.Alphabet import IUPAC
>>> mutable_seq = MutableSeq("GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA", IUPAC.unambiguous_dna)

Either way will give you a sequence object which can be changed: 

>>> mutable_seq
MutableSeq('GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA())
>>> mutable_seq[5] = "C"
>>> mutable_seq
MutableSeq('GCCATCGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA())
>>> mutable_seq.remove("T")
>>> mutable_seq
MutableSeq('GCCACGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA())
>>> mutable_seq.reverse()
>>> mutable_seq
MutableSeq('AGCCCGTGGGAAAGTCGCCGGGTAATGCACCG', IUPACUnambiguousDNA())

Do note that unlike the Seq object, the MutableSeq object’s methods like reverse_complement() and reverse() act in-situ!

An important technical difference between mutable and immutable objects in Python means that you can’t use a MutableSeq object as a dictionary key, but you can use a Python string or a Seq object in this way.

Once you have finished editing your a MutableSeq object, it’s easy to get back to a read-only Seq object should you need to:

>>> new_seq = mutable_seq.toseq()
>>> new_seq
Seq('AGCCCGTGGGAAAGTCGCCGGGTAATGCACCG', IUPACUnambiguousDNA())

You can also get a string from a MutableSeq object just like from a Seq object (Section 3.4).

3.13  UnknownSeq objects

The UnknownSeq object is a subclass of the basic Seq object and its purpose is to represent a sequence where we know the length, but not the actual letters making it up. You could of course use a normal Seq object in this situation, but it wastes rather a lot of memory to hold a string of a million “N” characters when you could just store a single letter “N” and the desired length as an integer.

>>> from Bio.Seq import UnknownSeq
>>> unk = UnknownSeq(20)
>>> unk
UnknownSeq(20, alphabet = Alphabet(), character = '?')
>>> print(unk)
????????????????????
>>> len(unk)
20

You can of course specify an alphabet, meaning for nucleotide sequences the letter defaults to “N” and for proteins “X”, rather than just “?”.

>>> from Bio.Seq import UnknownSeq
>>> from Bio.Alphabet import IUPAC
>>> unk_dna = UnknownSeq(20, alphabet=IUPAC.ambiguous_dna)
>>> unk_dna
UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N')
>>> print(unk_dna)
NNNNNNNNNNNNNNNNNNNN

You can use all the usual Seq object methods too, note these give back memory saving UnknownSeq objects where appropriate as you might expect:

>>> unk_dna
UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N')
>>> unk_dna.complement()
UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N')
>>> unk_dna.reverse_complement()
UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N')
>>> unk_dna.transcribe()
UnknownSeq(20, alphabet = IUPACAmbiguousRNA(), character = 'N')
>>> unk_protein = unk_dna.translate()
>>> unk_protein
UnknownSeq(6, alphabet = ProteinAlphabet(), character = 'X')
>>> print(unk_protein)
XXXXXX
>>> len(unk_protein)
6

You may be able to find a use for the UnknownSeq object in your own code, but it is more likely that you will first come across them in a SeqRecord object created by Bio.SeqIO (see Chapter 5). Some sequence file formats don’t always include the actual sequence, for example GenBank and EMBL files may include a list of features but for the sequence just present the contig information. Alternatively, the QUAL files used in sequencing work hold quality scores but they never contain a sequence – instead there is a partner FASTA file which does have the sequence.

3.14  Working with strings directly

To close this chapter, for those you who really don’t want to use the sequence objects (or who prefer a functional programming style to an object orientated one), there are module level functions in Bio.Seq will accept plain Python strings, Seq objects (including UnknownSeq objects) or MutableSeq objects:

>>> from Bio.Seq import reverse_complement, transcribe, back_transcribe, translate
>>> my_string = "GCTGTTATGGGTCGTTGGAAGGGTGGTCGTGCTGCTGGTTAG"
>>> reverse_complement(my_string)
'CTAACCAGCAGCACGACCACCCTTCCAACGACCCATAACAGC'
>>> transcribe(my_string)
'GCUGUUAUGGGUCGUUGGAAGGGUGGUCGUGCUGCUGGUUAG'
>>> back_transcribe(my_string)
'GCTGTTATGGGTCGTTGGAAGGGTGGTCGTGCTGCTGGTTAG'
>>> translate(my_string)
'AVMGRWKGGRAAG*'

You are, however, encouraged to work with Seq objects by default.

Chapter 4  Sequence annotation objects

Chapter 3 introduced the sequence classes. Immediately “above” the Seq class is the Sequence Record or SeqRecord class, defined in the Bio.SeqRecord module. This class allows higher level features such as identifiers and features (as SeqFeature objects) to be associated with the sequence, and is used throughout the sequence input/output interface Bio.SeqIO described fully in Chapter 5.

If you are only going to be working with simple data like FASTA files, you can probably skip this chapter for now. If on the other hand you are going to be using richly annotated sequence data, say from GenBank or EMBL files, this information is quite important.

While this chapter should cover most things to do with the SeqRecord and SeqFeature objects in this chapter, you may also want to read the SeqRecord wiki page (http://biopython.org/wiki/SeqRecord), and the built in documentation (also online – SeqRecord and SeqFeature):

>>> from Bio.SeqRecord import SeqRecord
>>> help(SeqRecord)
...

4.1  The SeqRecord object

The SeqRecord (Sequence Record) class is defined in the Bio.SeqRecord module. This class allows higher level features such as identifiers and features to be associated with a sequence (see Chapter 3), and is the basic data type for the Bio.SeqIO sequence input/output interface (see Chapter 5).

The SeqRecord class itself is quite simple, and offers the following information as attributes:

.seq
– The sequence itself, typically a Seq object.
.id
– The primary ID used to identify the sequence – a string. In most cases this is something like an accession number.
.name
– A “common” name/id for the sequence – a string. In some cases this will be the same as the accession number, but it could also be a clone name. I think of this as being analogous to the LOCUS id in a GenBank record.
.description
– A human readable description or expressive name for the sequence – a string.
.letter_annotations
– Holds per-letter-annotations using a (restricted) dictionary of additional information about the letters in the sequence. The keys are the name of the information, and the information is contained in the value as a Python sequence (i.e. a list, tuple or string) with the same length as the sequence itself. This is often used for quality scores (e.g. Section 18.1.6) or secondary structure information (e.g. from Stockholm/PFAM alignment files).
.annotations
– A dictionary of additional information about the sequence. The keys are the name of the information, and the information is contained in the value. This allows the addition of more “unstructured” information to the sequence.
.features
– A list of SeqFeature objects with more structured information about the features on a sequence (e.g. position of genes on a genome, or domains on a protein sequence). The structure of sequence features is described below in Section 4.3.
.dbxrefs
- A list of database cross-references as strings.

4.2  Creating a SeqRecord

Using a SeqRecord object is not very complicated, since all of the information is presented as attributes of the class. Usually you won’t create a SeqRecord “by hand”, but instead use Bio.SeqIO to read in a sequence file for you (see Chapter 5 and the examples below). However, creating SeqRecord can be quite simple.

4.2.1  SeqRecord objects from scratch

To create a SeqRecord at a minimum you just need a Seq object:

>>> from Bio.Seq import Seq
>>> simple_seq = Seq("GATC")
>>> from Bio.SeqRecord import SeqRecord
>>> simple_seq_r = SeqRecord(simple_seq)

Additionally, you can also pass the id, name and description to the initialization function, but if not they will be set as strings indicating they are unknown, and can be modified subsequently:

>>> simple_seq_r.id
'<unknown id>'
>>> simple_seq_r.id = "AC12345"
>>> simple_seq_r.description = "Made up sequence I wish I could write a paper about"
>>> print(simple_seq_r.description)
Made up sequence I wish I could write a paper about
>>> simple_seq_r.seq
Seq('GATC', Alphabet())

Including an identifier is very important if you want to output your SeqRecord to a file. You would normally include this when creating the object:

>>> from Bio.Seq import Seq
>>> simple_seq = Seq("GATC")
>>> from Bio.SeqRecord import SeqRecord
>>> simple_seq_r = SeqRecord(simple_seq, id="AC12345")

As mentioned above, the SeqRecord has an dictionary attribute annotations. This is used for any miscellaneous annotations that doesn’t fit under one of the other more specific attributes. Adding annotations is easy, and just involves dealing directly with the annotation dictionary:

>>> simple_seq_r.annotations["evidence"] = "None. I just made it up."
>>> print(simple_seq_r.annotations)
{'evidence': 'None. I just made it up.'}
>>> print(simple_seq_r.annotations["evidence"])
None. I just made it up.

Working with per-letter-annotations is similar, letter_annotations is a dictionary like attribute which will let you assign any Python sequence (i.e. a string, list or tuple) which has the same length as the sequence:

>>> simple_seq_r.letter_annotations["phred_quality"] = [40, 40, 38, 30]
>>> print(simple_seq_r.letter_annotations)
{'phred_quality': [40, 40, 38, 30]}
>>> print(simple_seq_r.letter_annotations["phred_quality"])
[40, 40, 38, 30]

The dbxrefs and features attributes are just Python lists, and should be used to store strings and SeqFeature objects (discussed later in this chapter) respectively.

4.2.2  SeqRecord objects from FASTA files

This example uses a fairly large FASTA file containing the whole sequence for Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, originally downloaded from the NCBI. This file is included with the Biopython unit tests under the GenBank folder, or online NC_005816.fna from our website.

The file starts like this - and you can check there is only one record present (i.e. only one line starting with a greater than symbol):

>gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus ... pPCP1, complete sequence
TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGGGGGTAATCTGCTCTCC
...

Back in Chapter 2 you will have seen the function Bio.SeqIO.parse(...) used to loop over all the records in a file as SeqRecord objects. The Bio.SeqIO module has a sister function for use on files which contain just one record which we’ll use here (see Chapter 5 for details):

>>> from Bio import SeqIO
>>> record = SeqIO.read("NC_005816.fna", "fasta")
>>> record
SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG',
SingleLetterAlphabet()), id='gi|45478711|ref|NC_005816.1|', name='gi|45478711|ref|NC_005816.1|',
description='gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus ... sequence',
dbxrefs=[])

Now, let’s have a look at the key attributes of this SeqRecord individually – starting with the seq attribute which gives you a Seq object:

>>> record.seq
Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', SingleLetterAlphabet())

Here Bio.SeqIO has defaulted to a generic alphabet, rather than guessing that this is DNA. If you know in advance what kind of sequence your FASTA file contains, you can tell Bio.SeqIO which alphabet to use (see Chapter 5).

Next, the identifiers and description:

>>> record.id
'gi|45478711|ref|NC_005816.1|'
>>> record.name
'gi|45478711|ref|NC_005816.1|'
>>> record.description
'gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus ... pPCP1, complete sequence'

As you can see above, the first word of the FASTA record’s title line (after removing the greater than symbol) is used for both the id and name attributes. The whole title line (after removing the greater than symbol) is used for the record description. This is deliberate, partly for backwards compatibility reasons, but it also makes sense if you have a FASTA file like this:

>Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1
TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGGGGGTAATCTGCTCTCC
...

Note that none of the other annotation attributes get populated when reading a FASTA file:

>>> record.dbxrefs
[]
>>> record.annotations
{}
>>> record.letter_annotations
{}
>>> record.features
[]

In this case our example FASTA file was from the NCBI, and they have a fairly well defined set of conventions for formatting their FASTA lines. This means it would be possible to parse this information and extract the GI number and accession for example. However, FASTA files from other sources vary, so this isn’t possible in general.

4.2.3  SeqRecord objects from GenBank files

As in the previous example, we’re going to look at the whole sequence for Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, originally downloaded from the NCBI, but this time as a GenBank file. Again, this file is included with the Biopython unit tests under the GenBank folder, or online NC_005816.gb from our website.

This file contains a single record (i.e. only one LOCUS line) and starts: 

LOCUS       NC_005816               9609 bp    DNA     circular BCT 21-JUL-2008
DEFINITION  Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete
            sequence.
ACCESSION   NC_005816
VERSION     NC_005816.1  GI:45478711
PROJECT     GenomeProject:10638
...

Again, we’ll use Bio.SeqIO to read this file in, and the code is almost identical to that for used above for the FASTA file (see Chapter 5 for details):

>>> from Bio import SeqIO
>>> record = SeqIO.read("NC_005816.gb", "genbank")
>>> record
SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG',
IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
dbxrefs=['Project:10638'])

You should be able to spot some differences already! But taking the attributes individually, the sequence string is the same as before, but this time Bio.SeqIO has been able to automatically assign a more specific alphabet (see Chapter 5 for details):

>>> record.seq
Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', IUPACAmbiguousDNA())

The name comes from the LOCUS line, while the id includes the version suffix. The description comes from the DEFINITION line:

>>> record.id
'NC_005816.1'
>>> record.name
'NC_005816'
>>> record.description
'Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.'

GenBank files don’t have any per-letter annotations:

>>> record.letter_annotations
{}

Most of the annotations information gets recorded in the annotations dictionary, for example:

>>> len(record.annotations)
11
>>> record.annotations["source"]
'Yersinia pestis biovar Microtus str. 91001'

The dbxrefs list gets populated from any PROJECT or DBLINK lines:

>>> record.dbxrefs
['Project:10638']

Finally, and perhaps most interestingly, all the entries in the features table (e.g. the genes or CDS features) get recorded as SeqFeature objects in the features list.

>>> len(record.features)
29

We’ll talk about SeqFeature objects next, in Section 4.3.

4.3  Feature, location and position objects

4.3.1  SeqFeature objects

Sequence features are an essential part of describing a sequence. Once you get beyond the sequence itself, you need some way to organize and easily get at the more “abstract” information that is known about the sequence. While it is probably impossible to develop a general sequence feature class that will cover everything, the Biopython SeqFeature class attempts to encapsulate as much of the information about the sequence as possible. The design is heavily based on the GenBank/EMBL feature tables, so if you understand how they look, you’ll probably have an easier time grasping the structure of the Biopython classes.

The key idea about each SeqFeature object is to describe a region on a parent sequence, typically a SeqRecord object. That region is described with a location object, typically a range between two positions (see Section 4.3.2 below).

The SeqFeature class has a number of attributes, so first we’ll list them and their general features, and then later in the chapter work through examples to show how this applies to a real life example. The attributes of a SeqFeature are:

.type
– This is a textual description of the type of feature (for instance, this will be something like ‘CDS’ or ‘gene’).
.location
– The location of the SeqFeature on the sequence that you are dealing with, see Section 4.3.2 below. The SeqFeature delegates much of its functionality to the location object, and includes a number of shortcut attributes for properties of the location:
.ref
– shorthand for .location.ref – any (different) reference sequence the location is referring to. Usually just None.
.ref_db
– shorthand for .location.ref_db – specifies the database any identifier in .ref refers to. Usually just None.
.strand
– shorthand for .location.strand – the strand on the sequence that the feature is located on. For double stranded nucleotide sequence this may either be 1 for the top strand, −1 for the bottom strand, 0 if the strand is important but is unknown, or None if it doesn’t matter. This is None for proteins, or single stranded sequences.
.qualifiers
– This is a Python dictionary of additional information about the feature. The key is some kind of terse one-word description of what the information contained in the value is about, and the value is the actual information. For example, a common key for a qualifier might be “evidence” and the value might be “computational (non-experimental).” This is just a way to let the person who is looking at the feature know that it has not be experimentally (i. e. in a wet lab) confirmed. Note that other the value will be a list of strings (even when there is only one string). This is a reflection of the feature tables in GenBank/EMBL files.
.sub_features
– This used to be used to represent features with complicated locations like ‘joins’ in GenBank/EMBL files. This has been deprecated with the introduction of the CompoundLocation object, and should now be ignored.

4.3.2  Positions and locations

The key idea about each SeqFeature object is to describe a region on a parent sequence, for which we use a location object, typically describing a range between two positions. Two try to clarify the terminology we’re using:

position
– This refers to a single position on a sequence, which may be fuzzy or not. For instance, 5, 20, <100 and >200 are all positions.
location
– A location is region of sequence bounded by some positions. For instance 5..20 (i. e. 5 to 20) is a location.

I just mention this because sometimes I get confused between the two.

4.3.2.1  FeatureLocation object

Unless you work with eukaryotic genes, most SeqFeature locations are extremely simple - you just need start and end coordinates and a strand. That’s essentially all the basic FeatureLocation object does.

In practise of course, things can be more complicated. First of all we have to handle compound locations made up of several regions. Secondly, the positions themselves may be fuzzy (inexact).

4.3.2.2  CompoundLocation object

Biopython 1.62 introduced the CompoundLocation as part of a restructuring of how complex locations made up of multiple regions are represented. The main usage is for handling ‘join’ locations in EMBL/GenBank files.

4.3.2.3  Fuzzy Positions

So far we’ve only used simple positions. One complication in dealing with feature locations comes in the positions themselves. In biology many times things aren’t entirely certain (as much as us wet lab biologists try to make them certain!). For instance, you might do a dinucleotide priming experiment and discover that the start of mRNA transcript starts at one of two sites. This is very useful information, but the complication comes in how to represent this as a position. To help us deal with this, we have the concept of fuzzy positions. Basically there are several types of fuzzy positions, so we have five classes do deal with them:

ExactPosition
– As its name suggests, this class represents a position which is specified as exact along the sequence. This is represented as just a number, and you can get the position by looking at the position attribute of the object.
BeforePosition
– This class represents a fuzzy position that occurs prior to some specified site. In GenBank/EMBL notation, this is represented as something like `<13', signifying that the real position is located somewhere less than 13. To get the specified upper boundary, look at the position attribute of the object.
AfterPosition
– Contrary to BeforePosition, this class represents a position that occurs after some specified site. This is represented in GenBank as `>13', and like BeforePosition, you get the boundary number by looking at the position attribute of the object.
WithinPosition
– Occasionally used for GenBank/EMBL locations, this class models a position which occurs somewhere between two specified nucleotides. In GenBank/EMBL notation, this would be represented as ‘(1.5)’, to represent that the position is somewhere within the range 1 to 5. To get the information in this class you have to look at two attributes. The position attribute specifies the lower boundary of the range we are looking at, so in our example case this would be one. The extension attribute specifies the range to the higher boundary, so in this case it would be 4. So object.position is the lower boundary and object.position + object.extension is the upper boundary.
OneOfPosition
– Occasionally used for GenBank/EMBL locations, this class deals with a position where several possible values exist, for instance you could use this if the start codon was unclear and there where two candidates for the start of the gene. Alternatively, that might be handled explicitly as two related gene features.
UnknownPosition
– This class deals with a position of unknown location. This is not used in GenBank/EMBL, but corresponds to the ‘?’ feature coordinate used in UniProt.

Here’s an example where we create a location with fuzzy end points:

>>> from Bio import SeqFeature
>>> start_pos = SeqFeature.AfterPosition(5)
>>> end_pos = SeqFeature.BetweenPosition(9, left=8, right=9)
>>> my_location = SeqFeature.FeatureLocation(start_pos, end_pos)

Note that the details of some of the fuzzy-locations changed in Biopython 1.59, in particular for BetweenPosition and WithinPosition you must now make it explicit which integer position should be used for slicing etc. For a start position this is generally the lower (left) value, while for an end position this would generally be the higher (right) value.

If you print out a FeatureLocation object, you can get a nice representation of the information:

>>> print(my_location)
[>5:(8^9)]

We can access the fuzzy start and end positions using the start and end attributes of the location:

>>> my_location.start
AfterPosition(5)
>>> print(my_location.start)
>5
>>> my_location.end
BetweenPosition(9, left=8, right=9)
>>> print(my_location.end)
(8^9)

If you don’t want to deal with fuzzy positions and just want numbers, they are actually subclasses of integers so should work like integers:

>>> int(my_location.start)
5
>>> int(my_location.end)
9

For compatibility with older versions of Biopython you can ask for the nofuzzy_start and nofuzzy_end attributes of the location which are plain integers:

>>> my_location.nofuzzy_start
5
>>> my_location.nofuzzy_end
9

Notice that this just gives you back the position attributes of the fuzzy locations.

Similarly, to make it easy to create a position without worrying about fuzzy positions, you can just pass in numbers to the FeaturePosition constructors, and you’ll get back out ExactPosition objects:

>>> exact_location = SeqFeature.FeatureLocation(5, 9)
>>> print(exact_location)
[5:9]
>>> exact_location.start
ExactPosition(5)
>>> int(exact_location.start)
5
>>> exact_location.nofuzzy_start
5

That is most of the nitty gritty about dealing with fuzzy positions in Biopython. It has been designed so that dealing with fuzziness is not that much more complicated than dealing with exact positions, and hopefully you find that true!

4.3.2.4  Location testing

You can use the Python keyword in with a SeqFeature or location object to see if the base/residue for a parent coordinate is within the feature/location or not.

For example, suppose you have a SNP of interest and you want to know which features this SNP is within, and lets suppose this SNP is at index 4350 (Python counting!). Here is a simple brute force solution where we just check all the features one by one in a loop:

>>> from Bio import SeqIO
>>> my_snp = 4350
>>> record = SeqIO.read("NC_005816.gb", "genbank")
>>> for feature in record.features:
...     if my_snp in feature:
...         print("%s %s" % (feature.type, feature.qualifiers.get('db_xref')))
... 
source ['taxon:229193']
gene ['GeneID:2767712']
CDS ['GI:45478716', 'GeneID:2767712']

Note that gene and CDS features from GenBank or EMBL files defined with joins are the union of the exons – they do not cover any introns.

4.3.3  Sequence described by a feature or location

A SeqFeature or location object doesn’t directly contain a sequence, instead the location (see Section 4.3.2) describes how to get this from the parent sequence. For example consider a (short) gene sequence with location 5:18 on the reverse strand, which in GenBank/EMBL notation using 1-based counting would be complement(6..18), like this:

>>> from Bio.Seq import Seq
>>> from Bio.SeqFeature import SeqFeature, FeatureLocation
>>> example_parent = Seq("ACCGAGACGGCAAAGGCTAGCATAGGTATGAGACTTCCTTCCTGCCAGTGCTGAGGAACTGGGAGCCTAC")
>>> example_feature = SeqFeature(FeatureLocation(5, 18), type="gene", strand=-1)

You could take the parent sequence, slice it to extract 5:18, and then take the reverse complement. If you are using Biopython 1.59 or later, the feature location’s start and end are integer like so this works:

>>> feature_seq = example_parent[example_feature.location.start:example_feature.location.end].reverse_complement()
>>> print(feature_seq)
AGCCTTTGCCGTC

This is a simple example so this isn’t too bad – however once you have to deal with compound features (joins) this is rather messy. Instead, the SeqFeature object has an extract method to take care of all this:

>>> feature_seq = example_feature.extract(example_parent)
>>> print(feature_seq)
AGCCTTTGCCGTC

The length of a SeqFeature or location matches that of the region of sequence it describes.

>>> print(example_feature.extract(example_parent))
AGCCTTTGCCGTC
>>> print(len(example_feature.extract(example_parent)))
13
>>> print(len(example_feature))
13
>>> print(len(example_feature.location))
13

For simple FeatureLocation objects the length is just the difference between the start and end positions. However, for a CompoundLocation the length is the sum of the constituent regions.

4.4  References

Another common annotation related to a sequence is a reference to a journal or other published work dealing with the sequence. We have a fairly simple way of representing a Reference in Biopython – we have a Bio.SeqFeature.Reference class that stores the relevant information about a reference as attributes of an object.

The attributes include things that you would expect to see in a reference like journal, title and authors. Additionally, it also can hold the medline_id and pubmed_id and a comment about the reference. These are all accessed simply as attributes of the object.

A reference also has a location object so that it can specify a particular location on the sequence that the reference refers to. For instance, you might have a journal that is dealing with a particular gene located on a BAC, and want to specify that it only refers to this position exactly. The location is a potentially fuzzy location, as described in section 4.3.2.

Any reference objects are stored as a list in the SeqRecord object’s annotations dictionary under the key “references”. That’s all there is too it. References are meant to be easy to deal with, and hopefully general enough to cover lots of usage cases.

4.5  The format method

The format() method of the SeqRecord class gives a string containing your record formatted using one of the output file formats supported by Bio.SeqIO, such as FASTA:

from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Alphabet import generic_protein

record = SeqRecord(Seq("MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD" \
                      +"GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK" \
                      +"NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM" \
                      +"SSAC", generic_protein),
                   id="gi|14150838|gb|AAK54648.1|AF376133_1",
                   description="chalcone synthase [Cucumis sativus]")
                   
print(record.format("fasta"))

which should give: 

>gi|14150838|gb|AAK54648.1|AF376133_1 chalcone synthase [Cucumis sativus]
MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD
GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK
NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM
SSAC

This format method takes a single mandatory argument, a lower case string which is supported by Bio.SeqIO as an output format (see Chapter 5). However, some of the file formats Bio.SeqIO can write to require more than one record (typically the case for multiple sequence alignment formats), and thus won’t work via this format() method. See also Section 5.5.4.

4.6  Slicing a SeqRecord

You can slice a SeqRecord, to give you a new SeqRecord covering just part of the sequence. What is important here is that any per-letter annotations are also sliced, and any features which fall completely within the new sequence are preserved (with their locations adjusted).

For example, taking the same GenBank file used earlier:

>>> from Bio import SeqIO
>>> record = SeqIO.read("NC_005816.gb", "genbank")

>>> record
SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG',
IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
dbxrefs=['Project:10638'])

>>> len(record)
9609
>>> len(record.features)
41

For this example we’re going to focus in on the pim gene, YP_pPCP05. If you have a look at the GenBank file directly you’ll find this gene/CDS has location string 4343..4780, or in Python counting 4342:4780. From looking at the file you can work out that these are the twelfth and thirteenth entries in the file, so in Python zero-based counting they are entries 11 and 12 in the features list:

>>> print(record.features[20])
type: gene
location: [4342:4780](+)
qualifiers: 
    Key: db_xref, Value: ['GeneID:2767712']
    Key: gene, Value: ['pim']
    Key: locus_tag, Value: ['YP_pPCP05']
<BLANKLINE>

>>> print(record.features[21])
type: CDS
location: [4342:4780](+)
qualifiers: 
    Key: codon_start, Value: ['1']
    Key: db_xref, Value: ['GI:45478716', 'GeneID:2767712']
    Key: gene, Value: ['pim']
    Key: locus_tag, Value: ['YP_pPCP05']
    Key: note, Value: ['similar to many previously sequenced pesticin immunity ...']
    Key: product, Value: ['pesticin immunity protein']
    Key: protein_id, Value: ['NP_995571.1']
    Key: transl_table, Value: ['11']
    Key: translation, Value: ['MGGGMISKLFCLALIFLSSSGLAEKNTYTAKDILQNLELNTFGNSLSH...']

Let’s slice this parent record from 4300 to 4800 (enough to include the pim gene/CDS), and see how many features we get:

>>> sub_record = record[4300:4800]

>>> sub_record
SeqRecord(seq=Seq('ATAAATAGATTATTCCAAATAATTTATTTATGTAAGAACAGGATGGGAGGGGGA...TTA',
IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
dbxrefs=[])

>>> len(sub_record)
500
>>> len(sub_record.features)
2

Our sub-record just has two features, the gene and CDS entries for YP_pPCP05:

>>> print(sub_record.features[0])
type: gene
location: [42:480](+)
qualifiers: 
    Key: db_xref, Value: ['GeneID:2767712']
    Key: gene, Value: ['pim']
    Key: locus_tag, Value: ['YP_pPCP05']
<BLANKLINE>

>>> print(sub_record.features[20])
type: CDS
location: [42:480](+)
qualifiers: 
    Key: codon_start, Value: ['1']
    Key: db_xref, Value: ['GI:45478716', 'GeneID:2767712']
    Key: gene, Value: ['pim']
    Key: locus_tag, Value: ['YP_pPCP05']
    Key: note, Value: ['similar to many previously sequenced pesticin immunity ...']
    Key: product, Value: ['pesticin immunity protein']
    Key: protein_id, Value: ['NP_995571.1']
    Key: transl_table, Value: ['11']
    Key: translation, Value: ['MGGGMISKLFCLALIFLSSSGLAEKNTYTAKDILQNLELNTFGNSLSH...']

Notice that their locations have been adjusted to reflect the new parent sequence!

While Biopython has done something sensible and hopefully intuitive with the features (and any per-letter annotation), for the other annotation it is impossible to know if this still applies to the sub-sequence or not. To avoid guessing, the annotations and dbxrefs are omitted from the sub-record, and it is up to you to transfer any relevant information as appropriate.

>>> sub_record.annotations
{}
>>> sub_record.dbxrefs
[]

The same point could be made about the record id, name and description, but for practicality these are preserved:

>>> sub_record.id
'NC_005816.1'
>>> sub_record.name
'NC_005816'
>>> sub_record.description
'Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.'

This illustrates the problem nicely though, our new sub-record is not the complete sequence of the plasmid, so the description is wrong! Let’s fix this and then view the sub-record as a reduced GenBank file using the format method described above in Section 4.5:

>>> sub_record.description = "Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, partial."
>>> print(sub_record.format("genbank"))
...

See Sections 18.1.7 and 18.1.8 for some FASTQ examples where the per-letter annotations (the read quality scores) are also sliced.

4.7  Adding SeqRecord objects

You can add SeqRecord objects together, giving a new SeqRecord. What is important here is that any common per-letter annotations are also added, all the features are preserved (with their locations adjusted), and any other common annotation is also kept (like the id, name and description).

For an example with per-letter annotation, we’ll use the first record in a FASTQ file. Chapter 5 will explain the SeqIO functions:

>>> from Bio import SeqIO
>>> record = next(SeqIO.parse("example.fastq", "fastq"))
>>> len(record)
25
>>> print(record.seq)
CCCTTCTTGTCTTCAGCGTTTCTCC

>>> print(record.letter_annotations["phred_quality"])
[26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26, 26, 26, 26,
26, 26, 26, 23, 23]

Let’s suppose this was Roche 454 data, and that from other information you think the TTT should be only TT. We can make a new edited record by first slicing the SeqRecord before and after the “extra” third T:

>>> left = record[:20]
>>> print(left.seq)
CCCTTCTTGTCTTCAGCGTT
>>> print(left.letter_annotations["phred_quality"])
[26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26, 26, 26, 26]
>>> right = record[21:]
>>> print(right.seq)
CTCC
>>> print(right.letter_annotations["phred_quality"])
[26, 26, 23, 23]

Now add the two parts together:

>>> edited = left + right
>>> len(edited)
24
>>> print(edited.seq)
CCCTTCTTGTCTTCAGCGTTCTCC

>>> print(edited.letter_annotations["phred_quality"])
[26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26, 26, 26, 26,
26, 26, 23, 23]

Easy and intuitive? We hope so! You can make this shorter with just:

>>> edited = record[:20] + record[21:]

Now, for an example with features, we’ll use a GenBank file. Suppose you have a circular genome:

>>> from Bio import SeqIO
>>> record = SeqIO.read("NC_005816.gb", "genbank")

>>> record
SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG',
IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
dbxrefs=['Project:10638'])

>>> len(record)
9609
>>> len(record.features)
41
>>> record.dbxrefs
['Project:58037']

>>> record.annotations.keys()
['comment', 'sequence_version', 'source', 'taxonomy', 'keywords', 'references',
'accessions', 'data_file_division', 'date', 'organism', 'gi']

You can shift the origin like this:

>>> shifted = record[2000:] + record[:2000]

>>> shifted
SeqRecord(seq=Seq('GATACGCAGTCATATTTTTTACACAATTCTCTAATCCCGACAAGGTCGTAGGTC...GGA',
IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
dbxrefs=[])

>>> len(shifted)
9609

Note that this isn’t perfect in that some annotation like the database cross references and one of the features (the source feature) have been lost:

>>> len(shifted.features)
40
>>> shifted.dbxrefs
[]
>>> shifted.annotations.keys()
[]

This is because the SeqRecord slicing step is cautious in what annotation it preserves (erroneously propagating annotation can cause major problems). If you want to keep the database cross references or the annotations dictionary, this must be done explicitly:

>>> shifted.dbxrefs = record.dbxrefs[:]
>>> shifted.annotations = record.annotations.copy()
>>> shifted.dbxrefs
['Project:10638']
>>> shifted.annotations.keys()
['comment', 'sequence_version', 'source', 'taxonomy', 'keywords', 'references',
'accessions', 'data_file_division', 'date', 'organism', 'gi']

Also note that in an example like this, you should probably change the record identifiers since the NCBI references refer to the original unmodified sequence.

4.8  Reverse-complementing SeqRecord objects

One of the new features in Biopython 1.57 was the SeqRecord object’s reverse_complement method. This tries to balance easy of use with worries about what to do with the annotation in the reverse complemented record.

For the sequence, this uses the Seq object’s reverse complement method. Any features are transferred with the location and strand recalculated. Likewise any per-letter-annotation is also copied but reversed (which makes sense for typical examples like quality scores). However, transfer of most annotation is problematical.

For instance, if the record ID was an accession, that accession should not really apply to the reverse complemented sequence, and transferring the identifier by default could easily cause subtle data corruption in downstream analysis. Therefore by default, the SeqRecord’s id, name, description, annotations and database cross references are all not transferred by default.

The SeqRecord object’s reverse_complement method takes a number of optional arguments corresponding to properties of the record. Setting these arguments to True means copy the old values, while False means drop the old values and use the default value. You can alternatively provide the new desired value instead.

Consider this example record:

>>> from Bio import SeqIO
>>> record = SeqIO.read("NC_005816.gb", "genbank")
>>> print("%s %i %i %i %i" % (record.id, len(record), len(record.features), len(record.dbxrefs), len(record.annotations)))
NC_005816.1 9609 41 1 11

Here we take the reverse complement and specify a new identifier – but notice how most of the annotation is dropped (but not the features):

>>> rc = record.reverse_complement(id="TESTING")
>>> print("%s %i %i %i %i" % (rc.id, len(rc), len(rc.features), len(rc.dbxrefs), len(rc.annotations)))
TESTING 9609 41 0 0

Chapter 5  Sequence Input/Output

In this chapter we’ll discuss in more detail the Bio.SeqIO module, which was briefly introduced in Chapter 2 and also used in Chapter 4. This aims to provide a simple interface for working with assorted sequence file formats in a uniform way. See also the Bio.SeqIO wiki page (http://biopython.org/wiki/SeqIO), and the built in documentation (also online):

>>> from Bio import SeqIO
>>> help(SeqIO)
...

The “catch” is that you have to work with SeqRecord objects (see Chapter 4), which contain a Seq object (see Chapter 3) plus annotation like an identifier and description.

5.1  Parsing or Reading Sequences

The workhorse function Bio.SeqIO.parse() is used to read in sequence data as SeqRecord objects. This function expects two arguments:

  1. The first argument is a handle to read the data from, or a filename. A handle is typically a file opened for reading, but could be the output from a command line program, or data downloaded from the internet (see Section 5.3). See Section 22.1 for more about handles.
  2. The second argument is a lower case string specifying sequence format – we don’t try and guess the file format for you! See http://biopython.org/wiki/SeqIO for a full listing of supported formats.

There is an optional argument alphabet to specify the alphabet to be used. This is useful for file formats like FASTA where otherwise Bio.SeqIO will default to a generic alphabet.

The Bio.SeqIO.parse() function returns an iterator which gives SeqRecord objects. Iterators are typically used in a for loop as shown below.

Sometimes you’ll find yourself dealing with files which contain only a single record. For this situation use the function Bio.SeqIO.read() which takes the same arguments. Provided there is one and only one record in the file, this is returned as a SeqRecord object. Otherwise an exception is raised.

5.1.1  Reading Sequence Files

In general Bio.SeqIO.parse() is used to read in sequence files as SeqRecord objects, and is typically used with a for loop like this:

from Bio import SeqIO
for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
    print(seq_record.id)
    print(repr(seq_record.seq))
    print(len(seq_record))

The above example is repeated from the introduction in Section 2.4, and will load the orchid DNA sequences in the FASTA format file ls_orchid.fasta. If instead you wanted to load a GenBank format file like ls_orchid.gbk then all you need to do is change the filename and the format string:

from Bio import SeqIO
for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"):
    print(seq_record.id)
    print(seq_record.seq)
    print(len(seq_record))

Similarly, if you wanted to read in a file in another file format, then assuming Bio.SeqIO.parse() supports it you would just need to change the format string as appropriate, for example “swiss” for SwissProt files or “embl” for EMBL text files. There is a full listing on the wiki page (http://biopython.org/wiki/SeqIO) and in the built in documentation (also online).

Another very common way to use a Python iterator is within a list comprehension (or a generator expression). For example, if all you wanted to extract from the file was a list of the record identifiers we can easily do this with the following list comprehension:

>>> from Bio import SeqIO
>>> identifiers = [seq_record.id for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank")]
>>> identifiers
['Z78533.1', 'Z78532.1', 'Z78531.1', 'Z78530.1', 'Z78529.1', 'Z78527.1', ..., 'Z78439.1']

There are more examples using SeqIO.parse() in a list comprehension like this in Section 18.2 (e.g. for plotting sequence lengths or GC%).

5.1.2  Iterating over the records in a sequence file

In the above examples, we have usually used a for loop to iterate over all the records one by one. You can use the for loop with all sorts of Python objects (including lists, tuples and strings) which support the iteration interface.

The object returned by Bio.SeqIO is actually an iterator which returns SeqRecord objects. You get to see each record in turn, but once and only once. The plus point is that an iterator can save you memory when dealing with large files.

Instead of using a for loop, can also use the next() function on an iterator to step through the entries, like this:

from Bio import SeqIO
record_iterator = SeqIO.parse("ls_orchid.fasta", "fasta")

first_record = next(record_iterator)
print(first_record.id)
print(first_record.description)

second_record = next(record_iterator)
print(second_record.id)
print(second_record.description)

Note that if you try to use next() and there are no more results, you’ll get the special StopIteration exception.

One special case to consider is when your sequence files have multiple records, but you only want the first one. In this situation the following code is very concise:

from Bio import SeqIO
first_record = next(SeqIO.parse("ls_orchid.gbk", "genbank"))

A word of warning here – using the next() function like this will silently ignore any additional records in the file. If your files have one and only one record, like some of the online examples later in this chapter, or a GenBank file for a single chromosome, then use the new Bio.SeqIO.read() function instead. This will check there are no extra unexpected records present.

5.1.3  Getting a list of the records in a sequence file

In the previous section we talked about the fact that Bio.SeqIO.parse() gives you a SeqRecord iterator, and that you get the records one by one. Very often you need to be able to access the records in any order. The Python list data type is perfect for this, and we can turn the record iterator into a list of SeqRecord objects using the built-in Python function list() like so:

from Bio import SeqIO
records = list(SeqIO.parse("ls_orchid.gbk", "genbank"))

print("Found %i records" % len(records))

print("The last record")
last_record = records[-1] #using Python's list tricks
print(last_record.id)
print(repr(last_record.seq))
print(len(last_record))

print("The first record")
first_record = records[0] #remember, Python counts from zero
print(first_record.id)
print(repr(first_record.seq))
print(len(first_record))

Giving:

Found 94 records
The last record
Z78439.1
Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', IUPACAmbiguousDNA())
592
The first record
Z78533.1
Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', IUPACAmbiguousDNA())
740

You can of course still use a for loop with a list of SeqRecord objects. Using a list is much more flexible than an iterator (for example, you can determine the number of records from the length of the list), but does need more memory because it will hold all the records in memory at once.

5.1.4  Extracting data

The SeqRecord object and its annotation structures are described more fully in Chapter 4. As an example of how annotations are stored, we’ll look at the output from parsing the first record in the GenBank file ls_orchid.gbk.

from Bio import SeqIO
record_iterator = SeqIO.parse("ls_orchid.gbk", "genbank")
first_record = next(record_iterator)
print(first_record)

That should give something like this:

ID: Z78533.1
Name: Z78533
Description: C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA.
Number of features: 5
/sequence_version=1
/source=Cypripedium irapeanum
/taxonomy=['Eukaryota', 'Viridiplantae', 'Streptophyta', ..., 'Cypripedium']
/keywords=['5.8S ribosomal RNA', '5.8S rRNA gene', ..., 'ITS1', 'ITS2']
/references=[...]
/accessions=['Z78533']
/data_file_division=PLN
/date=30-NOV-2006
/organism=Cypripedium irapeanum
/gi=2765658
Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', IUPACAmbiguousDNA())

This gives a human readable summary of most of the annotation data for the SeqRecord. For this example we’re going to use the .annotations attribute which is just a Python dictionary. The contents of this annotations dictionary were shown when we printed the record above. You can also print them out directly: 

print(first_record.annotations)

Like any Python dictionary, you can easily get a list of the keys: 

print(first_record.annotations.keys())

or values: 

print(first_record.annotations.values())

In general, the annotation values are strings, or lists of strings. One special case is any references in the file get stored as reference objects.

Suppose you wanted to extract a list of the species from the ls_orchid.gbk GenBank file. The information we want, Cypripedium irapeanum, is held in the annotations dictionary under ‘source’ and ‘organism’, which we can access like this:

>>> print(first_record.annotations["source"])
Cypripedium irapeanum

or:

>>> print(first_record.annotations["organism"])
Cypripedium irapeanum

In general, ‘organism’ is used for the scientific name (in Latin, e.g. Arabidopsis thaliana), while ‘source’ will often be the common name (e.g. thale cress). In this example, as is often the case, the two fields are identical.

Now let’s go through all the records, building up a list of the species each orchid sequence is from:

from Bio import SeqIO
all_species = []
for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"):
    all_species.append(seq_record.annotations["organism"])
print(all_species)

Another way of writing this code is to use a list comprehension:

from Bio import SeqIO
all_species = [seq_record.annotations["organism"] for seq_record in \
               SeqIO.parse("ls_orchid.gbk", "genbank")]
print(all_species)

In either case, the result is:

['Cypripedium irapeanum', 'Cypripedium californicum', ..., 'Paphiopedilum barbatum']

Great. That was pretty easy because GenBank files are annotated in a standardised way.

Now, let’s suppose you wanted to extract a list of the species from a FASTA file, rather than the GenBank file. The bad news is you will have to write some code to extract the data you want from the record’s description line - if the information is in the file in the first place! Our example FASTA format file ls_orchid.fasta starts like this:

>gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA
CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG
AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG
...

You can check by hand, but for every record the species name is in the description line as the second word. This means if we break up each record’s .description at the spaces, then the species is there as field number one (field zero is the record identifier). That means we can do this:

from Bio import SeqIO
all_species = []
for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
    all_species.append(seq_record.description.split()[1])
print(all_species)

This gives:

['C.irapeanum', 'C.californicum', 'C.fasciculatum', 'C.margaritaceum', ..., 'P.barbatum']

The concise alternative using list comprehensions would be:

from Bio import SeqIO
all_species == [seq_record.description.split()[1] for seq_record in \
                SeqIO.parse("ls_orchid.fasta", "fasta")]
print(all_species)

In general, extracting information from the FASTA description line is not very nice. If you can get your sequences in a well annotated file format like GenBank or EMBL, then this sort of annotation information is much easier to deal with.

5.2  Parsing sequences from compressed files

In the previous section, we looked at parsing sequence data from a file. Instead of using a filename, you can give Bio.SeqIO a handle (see Section 22.1), and in this section we’ll use handles to parse sequence from compressed files.

As you’ll have seen above, we can use Bio.SeqIO.read() or Bio.SeqIO.parse() with a filename - for instance this quick example calculates the total length of the sequences in a multiple record GenBank file using a generator expression:

>>> from Bio import SeqIO
>>> print(sum(len(r) for r in SeqIO.parse("ls_orchid.gbk", "gb")))
67518

Here we use a file handle instead, using the with statement to close the handle automatically:

>>> from Bio import SeqIO
>>> with open("ls_orchid.gbk") as handle:
...     print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
67518

Or, the old fashioned way where you manually close the handle:

>>> from Bio import SeqIO
>>> handle = open("ls_orchid.gbk")
>>> print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
67518
>>> handle.close()

Now, suppose we have a gzip compressed file instead? These are very commonly used on Linux. We can use Python’s gzip module to open the compressed file for reading - which gives us a handle object:

>>> import gzip
>>> from Bio import SeqIO
>>> handle = gzip.open("ls_orchid.gbk.gz", "r")
>>> print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
67518
>>> handle.close()

Similarly if we had a bzip2 compressed file (sadly the function name isn’t quite as consistent):

>>> import bz2
>>> from Bio import SeqIO
>>> handle = bz2.BZ2File("ls_orchid.gbk.bz2", "r")
>>> print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
67518
>>> handle.close()

If you are using Python 2.7 or later, the with-version works for gzip and bz2 as well. Unfortunately this is broken on older versions of Python (Issue 3860) and you’d get an AttributeError about __exit__ being missing.

There is a gzip (GNU Zip) variant called BGZF (Blocked GNU Zip Format), which can be treated like an ordinary gzip file for reading, but has advantages for random access later which we’ll talk about later in Section 5.4.4.

5.3  Parsing sequences from the net

In the previous sections, we looked at parsing sequence data from a file (using a filename or handle), and from compressed files (using a handle). Here we’ll use Bio.SeqIO with another type of handle, a network connection, to download and parse sequences from the internet.

Note that just because you can download sequence data and parse it into a SeqRecord object in one go doesn’t mean this is a good idea. In general, you should probably download sequences once and save them to a file for reuse.

5.3.1  Parsing GenBank records from the net

Section 9.6 talks about the Entrez EFetch interface in more detail, but for now let’s just connect to the NCBI and get a few Opuntia (prickly-pear) sequences from GenBank using their GI numbers.

First of all, let’s fetch just one record. If you don’t care about the annotations and features downloading a FASTA file is a good choice as these are compact. Now remember, when you expect the handle to contain one and only one record, use the Bio.SeqIO.read() function:

from Bio import Entrez
from Bio import SeqIO
Entrez.email = "A.N.Other@example.com"
handle = Entrez.efetch(db="nucleotide", rettype="fasta", retmode="text", id="6273291")
seq_record = SeqIO.read(handle, "fasta")
handle.close()
print("%s with %i features" % (seq_record.id, len(seq_record.features)))

Expected output:

gi|6273291|gb|AF191665.1|AF191665 with 0 features

The NCBI will also let you ask for the file in other formats, in particular as a GenBank file. Until Easter 2009, the Entrez EFetch API let you use “genbank” as the return type, however the NCBI now insist on using the official return types of “gb” (or “gp” for proteins) as described on EFetch for Sequence and other Molecular Biology Databases. As a result, in Biopython 1.50 onwards, we support “gb” as an alias for “genbank” in Bio.SeqIO.

from Bio import Entrez
from Bio import SeqIO
Entrez.email = "A.N.Other@example.com"
handle = Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", id="6273291")
seq_record = SeqIO.read(handle, "gb") #using "gb" as an alias for "genbank"
handle.close()
print("%s with %i features" % (seq_record.id, len(seq_record.features)))

The expected output of this example is:

AF191665.1 with 3 features

Notice this time we have three features.

Now let’s fetch several records. This time the handle contains multiple records, so we must use the Bio.SeqIO.parse() function:

from Bio import Entrez
from Bio import SeqIO
Entrez.email = "A.N.Other@example.com"
handle = Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", \
                       id="6273291,6273290,6273289")
for seq_record in SeqIO.parse(handle, "gb"):
    print seq_record.id, seq_record.description[:50] + "..."
    print "Sequence length %i," % len(seq_record),
    print "%i features," % len(seq_record.features),
    print "from: %s" % seq_record.annotations["source"]
handle.close()

That should give the following output:

AF191665.1 Opuntia marenae rpl16 gene; chloroplast gene for c...
Sequence length 902, 3 features, from: chloroplast Opuntia marenae
AF191664.1 Opuntia clavata rpl16 gene; chloroplast gene for c...
Sequence length 899, 3 features, from: chloroplast Grusonia clavata
AF191663.1 Opuntia bradtiana rpl16 gene; chloroplast gene for...
Sequence length 899, 3 features, from: chloroplast Opuntia bradtianaa

See Chapter 9 for more about the Bio.Entrez module, and make sure to read about the NCBI guidelines for using Entrez (Section 9.1).

5.3.2  Parsing SwissProt sequences from the net

Now let’s use a handle to download a SwissProt file from ExPASy, something covered in more depth in Chapter 10. As mentioned above, when you expect the handle to contain one and only one record, use the Bio.SeqIO.read() function:

from Bio import ExPASy
from Bio import SeqIO
handle = ExPASy.get_sprot_raw("O23729")
seq_record = SeqIO.read(handle, "swiss")
handle.close()
print(seq_record.id)
print(seq_record.name)
print(seq_record.description)
print(repr(seq_record.seq))
print("Length %i" % len(seq_record))
print(seq_record.annotations["keywords"])

Assuming your network connection is OK, you should get back:

O23729
CHS3_BROFI
RecName: Full=Chalcone synthase 3; EC=2.3.1.74; AltName: Full=Naringenin-chalcone synthase 3;
Seq('MAPAMEEIRQAQRAEGPAAVLAIGTSTPPNALYQADYPDYYFRITKSEHLTELK...GAE', ProteinAlphabet())
Length 394
['Acyltransferase', 'Flavonoid biosynthesis', 'Transferase']

5.4  Sequence files as Dictionaries

We’re now going to introduce three related functions in the Bio.SeqIO module which allow dictionary like random access to a multi-sequence file. There is a trade off here between flexibility and memory usage. In summary:

  • Bio.SeqIO.to_dict() is the most flexible but also the most memory demanding option (see Section 5.4.1). This is basically a helper function to build a normal Python dictionary with each entry held as a SeqRecord object in memory, allowing you to modify the records.
  • Bio.SeqIO.index() is a useful middle ground, acting like a read only dictionary and parsing sequences into SeqRecord objects on demand (see Section 5.4.2).
  • Bio.SeqIO.index_db() also acts like a read only dictionary but stores the identifiers and file offsets in a file on disk (as an SQLite3 database), meaning it has very low memory requirements (see Section 5.4.3), but will be a little bit slower.

See the discussion for an broad overview (Section 5.4.5).

5.4.1  Sequence files as Dictionaries – In memory

The next thing that we’ll do with our ubiquitous orchid files is to show how to index them and access them like a database using the Python dictionary data type (like a hash in Perl). This is very useful for moderately large files where you only need to access certain elements of the file, and makes for a nice quick ’n dirty database. For dealing with larger files where memory becomes a problem, see Section 5.4.2 below.

You can use the function Bio.SeqIO.to_dict() to make a SeqRecord dictionary (in memory). By default this will use each record’s identifier (i.e. the .id attribute) as the key. Let’s try this using our GenBank file:

>>> from Bio import SeqIO
>>> orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.gbk", "genbank"))

There is just one required argument for Bio.SeqIO.to_dict(), a list or generator giving SeqRecord objects. Here we have just used the output from the SeqIO.parse function. As the name suggests, this returns a Python dictionary.

Since this variable orchid_dict is an ordinary Python dictionary, we can look at all of the keys we have available:

>>> len(orchid_dict)
94

>>> orchid_dict.keys()
['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']

If you really want to, you can even look at all the records at once: 

>>> orchid_dict.values() #lots of output!
...

We can access a single SeqRecord object via the keys and manipulate the object as normal:

>>> seq_record = orchid_dict["Z78475.1"]
>>> print(seq_record.description)
P.supardii 5.8S rRNA gene and ITS1 and ITS2 DNA.
>>> print(repr(seq_record.seq))
Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGT', IUPACAmbiguousDNA())

So, it is very easy to create an in memory “database” of our GenBank records. Next we’ll try this for the FASTA file instead.

Note that those of you with prior Python experience should all be able to construct a dictionary like this “by hand”. However, typical dictionary construction methods will not deal with the case of repeated keys very nicely. Using the Bio.SeqIO.to_dict() will explicitly check for duplicate keys, and raise an exception if any are found.

5.4.1.1  Specifying the dictionary keys

Using the same code as above, but for the FASTA file instead:

from Bio import SeqIO
orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.fasta", "fasta"))
print(orchid_dict.keys())

This time the keys are:

['gi|2765596|emb|Z78471.1|PDZ78471', 'gi|2765646|emb|Z78521.1|CCZ78521', ...
 ..., 'gi|2765613|emb|Z78488.1|PTZ78488', 'gi|2765583|emb|Z78458.1|PHZ78458']

You should recognise these strings from when we parsed the FASTA file earlier in Section 2.4.1. Suppose you would rather have something else as the keys - like the accession numbers. This brings us nicely to SeqIO.to_dict()’s optional argument key_function, which lets you define what to use as the dictionary key for your records.

First you must write your own function to return the key you want (as a string) when given a SeqRecord object. In general, the details of function will depend on the sort of input records you are dealing with. But for our orchids, we can just split up the record’s identifier using the “pipe” character (the vertical line) and return the fourth entry (field three):

def get_accession(record):
    """"Given a SeqRecord, return the accession number as a string.
  
    e.g. "gi|2765613|emb|Z78488.1|PTZ78488" -> "Z78488.1"
    """
    parts = record.id.split("|")
    assert len(parts) == 5 and parts[0] == "gi" and parts[2] == "emb"
    return parts[3]

Then we can give this function to the SeqIO.to_dict() function to use in building the dictionary:

from Bio import SeqIO
orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.fasta", "fasta"), key_function=get_accession)
print(orchid_dict.keys())

Finally, as desired, the new dictionary keys:

>>> print(orchid_dict.keys())
['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']

Not too complicated, I hope!

5.4.1.2  Indexing a dictionary using the SEGUID checksum

To give another example of working with dictionaries of SeqRecord objects, we’ll use the SEGUID checksum function. This is a relatively recent checksum, and collisions should be very rare (i.e. two different sequences with the same checksum), an improvement on the CRC64 checksum.

Once again, working with the orchids GenBank file:

from Bio import SeqIO
from Bio.SeqUtils.CheckSum import seguid
for record in SeqIO.parse("ls_orchid.gbk", "genbank"):
    print(record.id, seguid(record.seq))

This should give:

Z78533.1 JUEoWn6DPhgZ9nAyowsgtoD9TTo
Z78532.1 MN/s0q9zDoCVEEc+k/IFwCNF2pY
...
Z78439.1 H+JfaShya/4yyAj7IbMqgNkxdxQ

Now, recall the Bio.SeqIO.to_dict() function’s key_function argument expects a function which turns a SeqRecord into a string. We can’t use the seguid() function directly because it expects to be given a Seq object (or a string). However, we can use Python’s lambda feature to create a “one off” function to give to Bio.SeqIO.to_dict() instead:

>>> from Bio import SeqIO
>>> from Bio.SeqUtils.CheckSum import seguid
>>> seguid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.gbk", "genbank"),
...                             lambda rec : seguid(rec.seq))
>>> record = seguid_dict["MN/s0q9zDoCVEEc+k/IFwCNF2pY"]
>>> print(record.id)
Z78532.1
>>> print(record.description)
C.californicum 5.8S rRNA gene and ITS1 and ITS2 DNA.

That should have retrieved the record Z78532.1, the second entry in the file.

5.4.2  Sequence files as Dictionaries – Indexed files

As the previous couple of examples tried to illustrate, using Bio.SeqIO.to_dict() is very flexible. However, because it holds everything in memory, the size of file you can work with is limited by your computer’s RAM. In general, this will only work on small to medium files.

For larger files you should consider Bio.SeqIO.index(), which works a little differently. Although it still returns a dictionary like object, this does not keep everything in memory. Instead, it just records where each record is within the file – when you ask for a particular record, it then parses it on demand.

As an example, let’s use the same GenBank file as before:

>>> from Bio import SeqIO
>>> orchid_dict = SeqIO.index("ls_orchid.gbk", "genbank")
>>> len(orchid_dict)
94

>>> orchid_dict.keys()
['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']

>>> seq_record = orchid_dict["Z78475.1"]
>>> print(seq_record.description)
P.supardii 5.8S rRNA gene and ITS1 and ITS2 DNA.
>>> seq_record.seq
Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGT', IUPACAmbiguousDNA())

Note that Bio.SeqIO.index() won’t take a handle, but only a filename. There are good reasons for this, but it is a little technical. The second argument is the file format (a lower case string as used in the other Bio.SeqIO functions). You can use many other simple file formats, including FASTA and FASTQ files (see the example in Section 18.1.11). However, alignment formats like PHYLIP or Clustal are not supported. Finally as an optional argument you can supply an alphabet, or a key function.

Here is the same example using the FASTA file - all we change is the filename and the format name:

>>> from Bio import SeqIO
>>> orchid_dict = SeqIO.index("ls_orchid.fasta", "fasta")
>>> len(orchid_dict)
94
>>> orchid_dict.keys()
['gi|2765596|emb|Z78471.1|PDZ78471', 'gi|2765646|emb|Z78521.1|CCZ78521', ...
 ..., 'gi|2765613|emb|Z78488.1|PTZ78488', 'gi|2765583|emb|Z78458.1|PHZ78458']

5.4.2.1  Specifying the dictionary keys

Suppose you want to use the same keys as before? Much like with the Bio.SeqIO.to_dict() example in Section 5.4.1.1, you’ll need to write a tiny function to map from the FASTA identifier (as a string) to the key you want:

def get_acc(identifier):
    """"Given a SeqRecord identifier string, return the accession number as a string.
  
    e.g. "gi|2765613|emb|Z78488.1|PTZ78488" -> "Z78488.1"
    """
    parts = identifier.split("|")
    assert len(parts) == 5 and parts[0] == "gi" and parts[2] == "emb"
    return parts[3]

Then we can give this function to the Bio.SeqIO.index() function to use in building the dictionary:

>>> from Bio import SeqIO
>>> orchid_dict = SeqIO.index("ls_orchid.fasta", "fasta", key_function=get_acc)
>>> print(orchid_dict.keys())
['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']

Easy when you know how?

5.4.2.2  Getting the raw data for a record

The dictionary-like object from Bio.SeqIO.index() gives you each entry as a SeqRecord object. However, it is sometimes useful to be able to get the original raw data straight from the file. For this use the get_raw() method which takes a single argument (the record identifier) and returns a string (extracted from the file without modification).

A motivating example is extracting a subset of a records from a large file where either Bio.SeqIO.write() does not (yet) support the output file format (e.g. the plain text SwissProt file format) or where you need to preserve the text exactly (e.g. GenBank or EMBL output from Biopython does not yet preserve every last bit of annotation).

Let’s suppose you have download the whole of UniProt in the plain text SwissPort file format from their FTP site (ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.dat.gz) and uncompressed it as the file uniprot_sprot.dat, and you want to extract just a few records from it:

>>> from Bio import SeqIO
>>> uniprot = SeqIO.index("uniprot_sprot.dat", "swiss")
>>> handle = open("selected.dat", "w")
>>> for acc in ["P33487", "P19801", "P13689", "Q8JZQ5", "Q9TRC7"]:
...     handle.write(uniprot.get_raw(acc))
>>> handle.close()

There is a longer example in Section 18.1.5 using the SeqIO.index() function to sort a large sequence file (without loading everything into memory at once).

5.4.3  Sequence files as Dictionaries – Database indexed files

Biopython 1.57 introduced an alternative, Bio.SeqIO.index_db(), which can work on even extremely large files since it stores the record information as a file on disk (using an SQLite3 database) rather than in memory. Also, you can index multiple files together (providing all the record identifiers are unique).

The Bio.SeqIO.index() function takes three required arguments:

  • Index filename, we suggest using something ending .idx. This index file is actually an SQLite3 database.
  • List of sequence filenames to index (or a single filename)
  • File format (lower case string as used in the rest of the SeqIO module).

As an example, consider the GenBank flat file releases from the NCBI FTP site, ftp://ftp.ncbi.nih.gov/genbank/, which are gzip compressed GenBank files. As of GenBank release 182, there are 16 files making up the viral sequences, gbvrl1.seq, …, gbvrl16.seq, containing in total almost one million records. You can index them like this:

>>> from Bio import SeqIO
>>> files = ["gbvrl%i.seq" % (i+1) for i in range(16)]
>>> gb_vrl = SeqIO.index_db("gbvrl.idx", files, "genbank")
>>> print("%i sequences indexed" % len(gb_vrl))
958086 sequences indexed

That takes about two minutes to run on my machine. If you rerun it then the index file (here gbvrl.idx) is reloaded in under a second. You can use the index as a read only Python dictionary - without having to worry about which file the sequence comes from, e.g.

>>> print(gb_vrl["GQ333173.1"].description)
HIV-1 isolate F12279A1 from Uganda gag protein (gag) gene, partial cds.

5.4.3.1  Getting the raw data for a record

Just as with the Bio.SeqIO.index() function discussed above in Section 5.4.2.2, the dictionary like object also lets you get at the raw text of each record:

>>> print(gb_vrl.get_raw("GQ333173.1"))
LOCUS       GQ333173                 459 bp    DNA     linear   VRL 21-OCT-2009
DEFINITION  HIV-1 isolate F12279A1 from Uganda gag protein (gag) gene, partial
            cds.
ACCESSION   GQ333173
...
//

5.4.4  Indexing compressed files

Very often when you are indexing a sequence file it can be quite large – so you may want to compress it on disk. Unfortunately efficient random access is difficult with the more common file formats like gzip and bzip2. In this setting, BGZF (Blocked GNU Zip Format) can be very helpful. This is a variant of gzip (and can be decompressed using standard gzip tools) popularised by the BAM file format, samtools, and tabix.

To create a BGZF compressed file you can use the command line tool bgzip which comes with samtools. In our examples we use a filename extension *.bgz, so they can be distinguished from normal gzipped files (named *.gz). You can also use the Bio.bgzf module to read and write BGZF files from within Python.

The Bio.SeqIO.index() and Bio.SeqIO.index_db() can both be used with BGZF compressed files. For example, if you started with an uncompressed GenBank file:

>>> from Bio import SeqIO
>>> orchid_dict = SeqIO.index("ls_orchid.gbk", "genbank")
>>> len(orchid_dict)
94

You could compress this (while keeping the original file) at the command line using the following command – but don’t worry, the compressed file is already included with the other example files:

$ bgzip -c ls_orchid.gbk > ls_orchid.gbk.bgz

You can use the compressed file in exactly the same way:

>>> from Bio import SeqIO
>>> orchid_dict = SeqIO.index("ls_orchid.gbk.bgz", "genbank")
>>> len(orchid_dict)
94

or:

>>> from Bio import SeqIO
>>> orchid_dict = SeqIO.index_db("ls_orchid.gbk.bgz.idx", "ls_orchid.gbk.bgz", "genbank")
>>> len(orchid_dict)
94

The SeqIO indexing automatically detects the BGZF compression. Note that you can’t use the same index file for the uncompressed and compressed files.

5.4.5  Discussion

So, which of these methods should you use and why? It depends on what you are trying to do (and how much data you are dealing with). However, in general picking Bio.SeqIO.index() is a good starting point. If you are dealing with millions of records, multiple files, or repeated analyses, then look at Bio.SeqIO.index_db().

Reasons to choose Bio.SeqIO.to_dict() over either Bio.SeqIO.index() or Bio.SeqIO.index_db() boil down to a need for flexibility despite its high memory needs. The advantage of storing the SeqRecord objects in memory is they can be changed, added to, or removed at will. In addition to the downside of high memory consumption, indexing can also take longer because all the records must be fully parsed.

Both Bio.SeqIO.index() and Bio.SeqIO.index_db() only parse records on demand. When indexing, they scan the file once looking for the start of each record and do as little work as possible to extract the identifier.

Reasons to choose Bio.SeqIO.index() over Bio.SeqIO.index_db() include:

  • Faster to build the index (more noticeable in simple file formats)
  • Slightly faster access as SeqRecord objects (but the difference is only really noticeable for simple to parse file formats).
  • Can use any immutable Python object as the dictionary keys (e.g. a tuple of strings, or a frozen set) not just strings.
  • Don’t need to worry about the index database being out of date if the sequence file being indexed has changed.

Reasons to choose Bio.SeqIO.index_db() over Bio.SeqIO.index() include:

  • Not memory limited – this is already important with files from second generation sequencing where 10s of millions of sequences are common, and using Bio.SeqIO.index() can require more than 4GB of RAM and therefore a 64bit version of Python.
  • Because the index is kept on disk, it can be reused. Although building the index database file takes longer, if you have a script which will be rerun on the same datafiles in future, this could save time in the long run.
  • Indexing multiple files together
  • The get_raw() method can be much faster, since for most file formats the length of each record is stored as well as its offset.

5.5  Writing Sequence Files

We’ve talked about using Bio.SeqIO.parse() for sequence input (reading files), and now we’ll look at Bio.SeqIO.write() which is for sequence output (writing files). This is a function taking three arguments: some SeqRecord objects, a handle or filename to write to, and a sequence format.

Here is an example, where we start by creating a few SeqRecord objects the hard way (by hand, rather than by loading them from a file):

from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Alphabet import generic_protein

rec1 = SeqRecord(Seq("MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD" \
                    +"GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK" \
                    +"NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM" \
                    +"SSAC", generic_protein),
                 id="gi|14150838|gb|AAK54648.1|AF376133_1",
                 description="chalcone synthase [Cucumis sativus]")

rec2 = SeqRecord(Seq("YPDYYFRITNREHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDARQ" \
                    +"DMVVVEIPKLGKEAAVKAIKEWGQ", generic_protein),
                 id="gi|13919613|gb|AAK33142.1|",
                 description="chalcone synthase [Fragaria vesca subsp. bracteata]")

rec3 = SeqRecord(Seq("MVTVEEFRRAQCAEGPATVMAIGTATPSNCVDQSTYPDYYFRITNSEHKVELKEKFKRMC" \
                    +"EKSMIKKRYMHLTEEILKENPNICAYMAPSLDARQDIVVVEVPKLGKEAAQKAIKEWGQP" \
                    +"KSKITHLVFCTTSGVDMPGCDYQLTKLLGLRPSVKRFMMYQQGCFAGGTVLRMAKDLAEN" \
                    +"NKGARVLVVCSEITAVTFRGPNDTHLDSLVGQALFGDGAAAVIIGSDPIPEVERPLFELV" \
                    +"SAAQTLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLVEAFQPLGISDWNSLFW" \
                    +"IAHPGGPAILDQVELKLGLKQEKLKATRKVLSNYGNMSSACVLFILDEMRKASAKEGLGT" \
                    +"TGEGLEWGVLFGFGPGLTVETVVLHSVAT", generic_protein),
                 id="gi|13925890|gb|AAK49457.1|",
                 description="chalcone synthase [Nicotiana tabacum]")
               
my_records = [rec1, rec2, rec3]

Now we have a list of SeqRecord objects, we’ll write them to a FASTA format file:

from Bio import SeqIO
SeqIO.write(my_records, "my_example.faa", "fasta")

And if you open this file in your favourite text editor it should look like this:

>gi|14150838|gb|AAK54648.1|AF376133_1 chalcone synthase [Cucumis sativus]
MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD
GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK
NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM
SSAC
>gi|13919613|gb|AAK33142.1| chalcone synthase [Fragaria vesca subsp. bracteata]
YPDYYFRITNREHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDARQ
DMVVVEIPKLGKEAAVKAIKEWGQ
>gi|13925890|gb|AAK49457.1| chalcone synthase [Nicotiana tabacum]
MVTVEEFRRAQCAEGPATVMAIGTATPSNCVDQSTYPDYYFRITNSEHKVELKEKFKRMC
EKSMIKKRYMHLTEEILKENPNICAYMAPSLDARQDIVVVEVPKLGKEAAQKAIKEWGQP
KSKITHLVFCTTSGVDMPGCDYQLTKLLGLRPSVKRFMMYQQGCFAGGTVLRMAKDLAEN
NKGARVLVVCSEITAVTFRGPNDTHLDSLVGQALFGDGAAAVIIGSDPIPEVERPLFELV
SAAQTLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLVEAFQPLGISDWNSLFW
IAHPGGPAILDQVELKLGLKQEKLKATRKVLSNYGNMSSACVLFILDEMRKASAKEGLGT
TGEGLEWGVLFGFGPGLTVETVVLHSVAT

Suppose you wanted to know how many records the Bio.SeqIO.write() function wrote to the handle? If your records were in a list you could just use len(my_records), however you can’t do that when your records come from a generator/iterator. The Bio.SeqIO.write() function returns the number of SeqRecord objects written to the file.

Note - If you tell the Bio.SeqIO.write() function to write to a file that already exists, the old file will be overwritten without any warning.

5.5.1  Round trips

Some people like their parsers to be “round-tripable”, meaning if you read in a file and write it back out again it is unchanged. This requires that the parser must extract enough information to reproduce the original file exactly. Bio.SeqIO does not aim to do this.

As a trivial example, any line wrapping of the sequence data in FASTA files is allowed. An identical SeqRecord would be given from parsing the following two examples which differ only in their line breaks:

>YAL068C-7235.2170 Putative promoter sequence
TACGAGAATAATTTCTCATCATCCAGCTTTAACACAAAATTCGCACAGTTTTCGTTAAGA
GAACTTAACATTTTCTTATGACGTAAATGAAGTTTATATATAAATTTCCTTTTTATTGGA

>YAL068C-7235.2170 Putative promoter sequence
TACGAGAATAATTTCTCATCATCCAGCTTTAACACAAAATTCGCA
CAGTTTTCGTTAAGAGAACTTAACATTTTCTTATGACGTAAATGA
AGTTTATATATAAATTTCCTTTTTATTGGA

To make a round-tripable FASTA parser you would need to keep track of where the sequence line breaks occurred, and this extra information is usually pointless. Instead Biopython uses a default line wrapping of 60 characters on output. The same problem with white space applies in many other file formats too. Another issue in some cases is that Biopython does not (yet) preserve every last bit of annotation (e.g. GenBank and EMBL).

Occasionally preserving the original layout (with any quirks it may have) is important. See Section 5.4.2.2 about the get_raw() method of the Bio.SeqIO.index() dictionary-like object for one potential solution.

5.5.2  Converting between sequence file formats

In previous example we used a list of SeqRecord objects as input to the Bio.SeqIO.write() function, but it will also accept a SeqRecord iterator like we get from Bio.SeqIO.parse() – this lets us do file conversion by combining these two functions.

For this example we’ll read in the GenBank format file ls_orchid.gbk and write it out in FASTA format:

from Bio import SeqIO
records = SeqIO.parse("ls_orchid.gbk", "genbank")
count = SeqIO.write(records, "my_example.fasta", "fasta")
print("Converted %i records" % count)

Still, that is a little bit complicated. So, because file conversion is such a common task, there is a helper function letting you replace that with just:

from Bio import SeqIO
count = SeqIO.convert("ls_orchid.gbk", "genbank", "my_example.fasta", "fasta")
print("Converted %i records" % count)

The Bio.SeqIO.convert() function will take handles or filenames. Watch out though – if the output file already exists, it will overwrite it! To find out more, see the built in help:

>>> from Bio import SeqIO
>>> help(SeqIO.convert)
...

In principle, just by changing the filenames and the format names, this code could be used to convert between any file formats available in Biopython. However, writing some formats requires information (e.g. quality scores) which other files formats don’t contain. For example, while you can turn a FASTQ file into a FASTA file, you can’t do the reverse. See also Sections 18.1.9 and 18.1.10 in the cookbook chapter which looks at inter-converting between different FASTQ formats.

Finally, as an added incentive for using the Bio.SeqIO.convert() function (on top of the fact your code will be shorter), doing it this way may also be faster! The reason for this is the convert function can take advantage of several file format specific optimisations and tricks.

5.5.3  Converting a file of sequences to their reverse complements

Suppose you had a file of nucleotide sequences, and you wanted to turn it into a file containing their reverse complement sequences. This time a little bit of work is required to transform the SeqRecord objects we get from our input file into something suitable for saving to our output file.

To start with, we’ll use Bio.SeqIO.parse() to load some nucleotide sequences from a file, then print out their reverse complements using the Seq object’s built in .reverse_complement() method (see Section 3.7):

>>> from Bio import SeqIO
>>> for record in SeqIO.parse("ls_orchid.gbk", "genbank"):
...     print(record.id)
...     print(record.seq.reverse_complement())

Now, if we want to save these reverse complements to a file, we’ll need to make SeqRecord objects. We can use the SeqRecord object’s built in .reverse_complement() method (see Section 4.8) but we must decide how to name our new records.

This is an excellent place to demonstrate the power of list comprehensions which make a list in memory: 

>>> from Bio import SeqIO
>>> records = [rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \
...            for rec in SeqIO.parse("ls_orchid.fasta", "fasta")]
>>> len(records)
94

Now list comprehensions have a nice trick up their sleeves, you can add a conditional statement:

>>> records = [rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \
...            for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if len(rec)<700]
>>> len(records)
18

That would create an in memory list of reverse complement records where the sequence length was under 700 base pairs. However, we can do exactly the same with a generator expression - but with the advantage that this does not create a list of all the records in memory at once:

>>> records = (rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \
...           for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if len(rec)<700)

As a complete example:

>>> from Bio import SeqIO
>>> records = (rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \
...            for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if len(rec)<700)
>>> SeqIO.write(records, "rev_comp.fasta", "fasta")
18

There is a related example in Section 18.1.3, translating each record in a FASTA file from nucleotides to amino acids.

5.5.4  Getting your SeqRecord objects as formatted strings

Suppose that you don’t really want to write your records to a file or handle – instead you want a string containing the records in a particular file format. The Bio.SeqIO interface is based on handles, but Python has a useful built in module which provides a string based handle.

For an example of how you might use this, let’s load in a bunch of SeqRecord objects from our orchids GenBank file, and create a string containing the records in FASTA format:

from Bio import SeqIO
from StringIO import StringIO
records = SeqIO.parse("ls_orchid.gbk", "genbank")
out_handle = StringIO()
SeqIO.write(records, out_handle, "fasta")
fasta_data = out_handle.getvalue()
print(fasta_data)

This isn’t entirely straightforward the first time you see it! On the bright side, for the special case where you would like a string containing a single record in a particular file format, use the the SeqRecord class’ format() method (see Section 4.5).

Note that although we don’t encourage it, you can use the format() method to write to a file, for example something like this: 

from Bio import SeqIO
out_handle = open("ls_orchid_long.tab", "w")
for record in SeqIO.parse("ls_orchid.gbk", "genbank"):
    if len(record) > 100:
        out_handle.write(record.format("tab"))
out_handle.close()

While this style of code will work for a simple sequential file format like FASTA or the simple tab separated format used here, it will not work for more complex or interlaced file formats. This is why we still recommend using Bio.SeqIO.write(), as in the following example: 

from Bio import SeqIO
records = (rec for rec in SeqIO.parse("ls_orchid.gbk", "genbank") if len(rec) > 100)
SeqIO.write(records, "ls_orchid.tab", "tab")

Making a single call to SeqIO.write(...) is also much quicker than multiple calls to the SeqRecord.format(...) method.

Chapter 6  Multiple Sequence Alignment objects

This chapter is about Multiple Sequence Alignments, by which we mean a collection of multiple sequences which have been aligned together – usually with the insertion of gap characters, and addition of leading or trailing gaps – such that all the sequence strings are the same length. Such an alignment can be regarded as a matrix of letters, where each row is held as a SeqRecord object internally.

We will introduce the MultipleSeqAlignment object which holds this kind of data, and the Bio.AlignIO module for reading and writing them as various file formats (following the design of the Bio.SeqIO module from the previous chapter). Note that both Bio.SeqIO and Bio.AlignIO can read and write sequence alignment files. The appropriate choice will depend largely on what you want to do with the data.

The final part of this chapter is about our command line wrappers for common multiple sequence alignment tools like ClustalW and MUSCLE.

6.1  Parsing or Reading Sequence Alignments

We have two functions for reading in sequence alignments, Bio.AlignIO.read() and Bio.AlignIO.parse() which following the convention introduced in Bio.SeqIO are for files containing one or multiple alignments respectively.

Using Bio.AlignIO.parse() will return an iterator which gives MultipleSeqAlignment objects. Iterators are typically used in a for loop. Examples of situations where you will have multiple different alignments include resampled alignments from the PHYLIP tool seqboot, or multiple pairwise alignments from the EMBOSS tools water or needle, or Bill Pearson’s FASTA tools.

However, in many situations you will be dealing with files which contain only a single alignment. In this case, you should use the Bio.AlignIO.read() function which returns a single MultipleSeqAlignment object.

Both functions expect two mandatory arguments:

  1. The first argument is a handle to read the data from, typically an open file (see Section 22.1), or a filename.
  2. The second argument is a lower case string specifying the alignment format. As in Bio.SeqIO we don’t try and guess the file format for you! See http://biopython.org/wiki/AlignIO for a full listing of supported formats.

There is also an optional seq_count argument which is discussed in Section 6.1.3 below for dealing with ambiguous file formats which may contain more than one alignment.

A further optional alphabet argument allowing you to specify the expected alphabet. This can be useful as many alignment file formats do not explicitly label the sequences as RNA, DNA or protein – which means Bio.AlignIO will default to using a generic alphabet.

6.1.1  Single Alignments

As an example, consider the following annotation rich protein alignment in the PFAM or Stockholm file format:

# STOCKHOLM 1.0
#=GS COATB_BPIKE/30-81  AC P03620.1
#=GS COATB_BPIKE/30-81  DR PDB; 1ifl ; 1-52;
#=GS Q9T0Q8_BPIKE/1-52  AC Q9T0Q8.1
#=GS COATB_BPI22/32-83  AC P15416.1
#=GS COATB_BPM13/24-72  AC P69541.1
#=GS COATB_BPM13/24-72  DR PDB; 2cpb ; 1-49;
#=GS COATB_BPM13/24-72  DR PDB; 2cps ; 1-49;
#=GS COATB_BPZJ2/1-49   AC P03618.1
#=GS Q9T0Q9_BPFD/1-49   AC Q9T0Q9.1
#=GS Q9T0Q9_BPFD/1-49   DR PDB; 1nh4 A; 1-49;
#=GS COATB_BPIF1/22-73  AC P03619.2
#=GS COATB_BPIF1/22-73  DR PDB; 1ifk ; 1-50;
COATB_BPIKE/30-81             AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA
#=GR COATB_BPIKE/30-81  SS    -HHHHHHHHHHHHHH--HHHHHHHH--HHHHHHHHHHHHHHHHHHHHH----
Q9T0Q8_BPIKE/1-52             AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA
COATB_BPI22/32-83             DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA
COATB_BPM13/24-72             AEGDDP...AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
#=GR COATB_BPM13/24-72  SS    ---S-T...CHCHHHHCCCCTCCCTTCHHHHHHHHHHHHHHHHHHHHCTT--
COATB_BPZJ2/1-49              AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA
Q9T0Q9_BPFD/1-49              AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
#=GR Q9T0Q9_BPFD/1-49   SS    ------...-HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH--
COATB_BPIF1/22-73             FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA
#=GR COATB_BPIF1/22-73  SS    XX-HHHH--HHHHHH--HHHHHHH--HHHHHHHHHHHHHHHHHHHHHHH---
#=GC SS_cons                  XHHHHHHHHHHHHHHHCHHHHHHHHCHHHHHHHHHHHHHHHHHHHHHHHC--
#=GC seq_cons                 AEssss...AptAhDSLpspAT-hIu.sWshVsslVsAsluIKLFKKFsSKA
//

This is the seed alignment for the Phage_Coat_Gp8 (PF05371) PFAM entry, downloaded from a now out of date release of PFAM from http://pfam.sanger.ac.uk/. We can load this file as follows (assuming it has been saved to disk as “PF05371_seed.sth” in the current working directory):

>>> from Bio import AlignIO
>>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")

This code will print out a summary of the alignment:

>>> print(alignment)
SingleLetterAlphabet() alignment with 7 rows and 52 columns
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRL...SKA COATB_BPIKE/30-81
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKL...SRA Q9T0Q8_BPIKE/1-52
DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRL...SKA COATB_BPI22/32-83
AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49
FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73

You’ll notice in the above output the sequences have been truncated. We could instead write our own code to format this as we please by iterating over the rows as SeqRecord objects:

>>> from Bio import AlignIO
>>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
>>> print("Alignment length %i" % alignment.get_alignment_length())
Alignment length 52
>>> for record in alignment:
...     print("%s - %s" % (record.seq, record.id))
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA - COATB_BPIKE/30-81
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA - Q9T0Q8_BPIKE/1-52
DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA - COATB_BPI22/32-83
AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - COATB_BPM13/24-72
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA - COATB_BPZJ2/1-49
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - Q9T0Q9_BPFD/1-49
FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA - COATB_BPIF1/22-73

You could also use the alignment object’s format method to show it in a particular file format – see Section 6.2.2 for details.

Did you notice in the raw file above that several of the sequences include database cross-references to the PDB and the associated known secondary structure? Try this:

>>> for record in alignment:
...     if record.dbxrefs:
...         print("%s %s" % (record.id, record.dbxrefs))
COATB_BPIKE/30-81 ['PDB; 1ifl ; 1-52;']
COATB_BPM13/24-72 ['PDB; 2cpb ; 1-49;', 'PDB; 2cps ; 1-49;']
Q9T0Q9_BPFD/1-49 ['PDB; 1nh4 A; 1-49;']
COATB_BPIF1/22-73 ['PDB; 1ifk ; 1-50;']

To have a look at all the sequence annotation, try this:

>>> for record in alignment:
...     print(record)

Sanger provide a nice web interface at http://pfam.sanger.ac.uk/family?acc=PF05371 which will actually let you download this alignment in several other formats. This is what the file looks like in the FASTA file format:

>COATB_BPIKE/30-81
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA
>Q9T0Q8_BPIKE/1-52
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA
>COATB_BPI22/32-83
DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA
>COATB_BPM13/24-72
AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
>COATB_BPZJ2/1-49
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA
>Q9T0Q9_BPFD/1-49
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
>COATB_BPIF1/22-73
FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA

Note the website should have an option about showing gaps as periods (dots) or dashes, we’ve shown dashes above. Assuming you download and save this as file “PF05371_seed.faa” then you can load it with almost exactly the same code:

from Bio import AlignIO
alignment = AlignIO.read("PF05371_seed.faa", "fasta")
print(alignment)

All that has changed in this code is the filename and the format string. You’ll get the same output as before, the sequences and record identifiers are the same. However, as you should expect, if you check each SeqRecord there is no annotation nor database cross-references because these are not included in the FASTA file format.

Note that rather than using the Sanger website, you could have used Bio.AlignIO to convert the original Stockholm format file into a FASTA file yourself (see below).

With any supported file format, you can load an alignment in exactly the same way just by changing the format string. For example, use “phylip” for PHYLIP files, “nexus” for NEXUS files or “emboss” for the alignments output by the EMBOSS tools. There is a full listing on the wiki page (http://biopython.org/wiki/AlignIO) and in the built in documentation (also online):

>>> from Bio import AlignIO
>>> help(AlignIO)
...

6.1.2  Multiple Alignments

The previous section focused on reading files containing a single alignment. In general however, files can contain more than one alignment, and to read these files we must use the Bio.AlignIO.parse() function.

Suppose you have a small alignment in PHYLIP format:

    5    6
Alpha     AACAAC
Beta      AACCCC
Gamma     ACCAAC
Delta     CCACCA
Epsilon   CCAAAC

If you wanted to bootstrap a phylogenetic tree using the PHYLIP tools, one of the steps would be to create a set of many resampled alignments using the tool bootseq. This would give output something like this, which has been abbreviated for conciseness:

    5     6
Alpha     AAACCA
Beta      AAACCC
Gamma     ACCCCA
Delta     CCCAAC
Epsilon   CCCAAA
    5     6
Alpha     AAACAA
Beta      AAACCC
Gamma     ACCCAA
Delta     CCCACC
Epsilon   CCCAAA
    5     6
Alpha     AAAAAC
Beta      AAACCC
Gamma     AACAAC
Delta     CCCCCA
Epsilon   CCCAAC
...
    5     6
Alpha     AAAACC
Beta      ACCCCC
Gamma     AAAACC
Delta     CCCCAA
Epsilon   CAAACC

If you wanted to read this in using Bio.AlignIO you could use:

from Bio import AlignIO
alignments = AlignIO.parse("resampled.phy", "phylip")
for alignment in alignments:
    print(alignment)
    print("")

This would give the following output, again abbreviated for display:

SingleLetterAlphabet() alignment with 5 rows and 6 columns
AAACCA Alpha
AAACCC Beta
ACCCCA Gamma
CCCAAC Delta
CCCAAA Epsilon

SingleLetterAlphabet() alignment with 5 rows and 6 columns
AAACAA Alpha
AAACCC Beta
ACCCAA Gamma
CCCACC Delta
CCCAAA Epsilon

SingleLetterAlphabet() alignment with 5 rows and 6 columns
AAAAAC Alpha
AAACCC Beta
AACAAC Gamma
CCCCCA Delta
CCCAAC Epsilon

...

SingleLetterAlphabet() alignment with 5 rows and 6 columns
AAAACC Alpha
ACCCCC Beta
AAAACC Gamma
CCCCAA Delta
CAAACC Epsilon

As with the function Bio.SeqIO.parse(), using Bio.AlignIO.parse() returns an iterator. If you want to keep all the alignments in memory at once, which will allow you to access them in any order, then turn the iterator into a list:

from Bio import AlignIO
alignments = list(AlignIO.parse("resampled.phy", "phylip"))
last_align = alignments[-1]
first_align = alignments[0]

6.1.3  Ambiguous Alignments

Many alignment file formats can explicitly store more than one alignment, and the division between each alignment is clear. However, when a general sequence file format has been used there is no such block structure. The most common such situation is when alignments have been saved in the FASTA file format. For example consider the following:

>Alpha
ACTACGACTAGCTCAG--G
>Beta
ACTACCGCTAGCTCAGAAG
>Gamma
ACTACGGCTAGCACAGAAG
>Alpha
ACTACGACTAGCTCAGG--
>Beta
ACTACCGCTAGCTCAGAAG
>Gamma
ACTACGGCTAGCACAGAAG

This could be a single alignment containing six sequences (with repeated identifiers). Or, judging from the identifiers, this is probably two different alignments each with three sequences, which happen to all have the same length.

What about this next example?

>Alpha
ACTACGACTAGCTCAG--G
>Beta
ACTACCGCTAGCTCAGAAG
>Alpha
ACTACGACTAGCTCAGG--
>Gamma
ACTACGGCTAGCACAGAAG
>Alpha
ACTACGACTAGCTCAGG--
>Delta
ACTACGGCTAGCACAGAAG

Again, this could be a single alignment with six sequences. However this time based on the identifiers we might guess this is three pairwise alignments which by chance have all got the same lengths.

This final example is similar:

>Alpha
ACTACGACTAGCTCAG--G
>XXX
ACTACCGCTAGCTCAGAAG
>Alpha
ACTACGACTAGCTCAGG
>YYY
ACTACGGCAAGCACAGG
>Alpha
--ACTACGAC--TAGCTCAGG
>ZZZ
GGACTACGACAATAGCTCAGG

In this third example, because of the differing lengths, this cannot be treated as a single alignment containing all six records. However, it could be three pairwise alignments.

Clearly trying to store more than one alignment in a FASTA file is not ideal. However, if you are forced to deal with these as input files Bio.AlignIO can cope with the most common situation where all the alignments have the same number of records. One example of this is a collection of pairwise alignments, which can be produced by the EMBOSS tools needle and water – although in this situation, Bio.AlignIO should be able to understand their native output using “emboss” as the format string.

To interpret these FASTA examples as several separate alignments, we can use Bio.AlignIO.parse() with the optional seq_count argument which specifies how many sequences are expected in each alignment (in these examples, 3, 2 and 2 respectively). For example, using the third example as the input data:

for alignment in AlignIO.parse(handle, "fasta", seq_count=2):
    print("Alignment length %i" % alignment.get_alignment_length())
    for record in alignment:
        print("%s - %s" % (record.seq, record.id))
    print("")

giving:

Alignment length 19
ACTACGACTAGCTCAG--G - Alpha
ACTACCGCTAGCTCAGAAG - XXX

Alignment length 17
ACTACGACTAGCTCAGG - Alpha
ACTACGGCAAGCACAGG - YYY

Alignment length 21
--ACTACGAC--TAGCTCAGG - Alpha
GGACTACGACAATAGCTCAGG - ZZZ

Using Bio.AlignIO.read() or Bio.AlignIO.parse() without the seq_count argument would give a single alignment containing all six records for the first two examples. For the third example, an exception would be raised because the lengths differ preventing them being turned into a single alignment.

If the file format itself has a block structure allowing Bio.AlignIO to determine the number of sequences in each alignment directly, then the seq_count argument is not needed. If it is supplied, and doesn’t agree with the file contents, an error is raised.

Note that this optional seq_count argument assumes each alignment in the file has the same number of sequences. Hypothetically you may come across stranger situations, for example a FASTA file containing several alignments each with a different number of sequences – although I would love to hear of a real world example of this. Assuming you cannot get the data in a nicer file format, there is no straight forward way to deal with this using Bio.AlignIO. In this case, you could consider reading in the sequences themselves using Bio.SeqIO and batching them together to create the alignments as appropriate.

6.2  Writing Alignments

We’ve talked about using Bio.AlignIO.read() and Bio.AlignIO.parse() for alignment input (reading files), and now we’ll look at Bio.AlignIO.write() which is for alignment output (writing files). This is a function taking three arguments: some MultipleSeqAlignment objects (or for backwards compatibility the obsolete Alignment objects), a handle or filename to write to, and a sequence format.

Here is an example, where we start by creating a few MultipleSeqAlignment objects the hard way (by hand, rather than by loading them from a file). Note we create some SeqRecord objects to construct the alignment from.

from Bio.Alphabet import generic_dna
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Align import MultipleSeqAlignment

align1 = MultipleSeqAlignment([
             SeqRecord(Seq("ACTGCTAGCTAG", generic_dna), id="Alpha"),
             SeqRecord(Seq("ACT-CTAGCTAG", generic_dna), id="Beta"),
             SeqRecord(Seq("ACTGCTAGDTAG", generic_dna), id="Gamma"),
         ])

align2 = MultipleSeqAlignment([
             SeqRecord(Seq("GTCAGC-AG", generic_dna), id="Delta"),
             SeqRecord(Seq("GACAGCTAG", generic_dna), id="Epsilon"),
             SeqRecord(Seq("GTCAGCTAG", generic_dna), id="Zeta"),
         ])

align3 = MultipleSeqAlignment([
             SeqRecord(Seq("ACTAGTACAGCTG", generic_dna), id="Eta"),
             SeqRecord(Seq("ACTAGTACAGCT-", generic_dna), id="Theta"),
             SeqRecord(Seq("-CTACTACAGGTG", generic_dna), id="Iota"),
         ])

my_alignments = [align1, align2, align3]

Now we have a list of Alignment objects, we’ll write them to a PHYLIP format file:

from Bio import AlignIO
AlignIO.write(my_alignments, "my_example.phy", "phylip")

And if you open this file in your favourite text editor it should look like this:

 3 12
Alpha      ACTGCTAGCT AG
Beta       ACT-CTAGCT AG
Gamma      ACTGCTAGDT AG
 3 9
Delta      GTCAGC-AG
Epislon    GACAGCTAG
Zeta       GTCAGCTAG
 3 13
Eta        ACTAGTACAG CTG
Theta      ACTAGTACAG CT-
Iota       -CTACTACAG GTG

Its more common to want to load an existing alignment, and save that, perhaps after some simple manipulation like removing certain rows or columns.

Suppose you wanted to know how many alignments the Bio.AlignIO.write() function wrote to the handle? If your alignments were in a list like the example above, you could just use len(my_alignments), however you can’t do that when your records come from a generator/iterator. Therefore the Bio.AlignIO.write() function returns the number of alignments written to the file.

Note - If you tell the Bio.AlignIO.write() function to write to a file that already exists, the old file will be overwritten without any warning.

6.2.1  Converting between sequence alignment file formats

Converting between sequence alignment file formats with Bio.AlignIO works in the same way as converting between sequence file formats with Bio.SeqIO (Section 5.5.2). We load generally the alignment(s) using Bio.AlignIO.parse() and then save them using the Bio.AlignIO.write() – or just use the Bio.AlignIO.convert() helper function.

For this example, we’ll load the PFAM/Stockholm format file used earlier and save it as a Clustal W format file:

from Bio import AlignIO
count = AlignIO.convert("PF05371_seed.sth", "stockholm", "PF05371_seed.aln", "clustal")
print("Converted %i alignments" % count)

Or, using Bio.AlignIO.parse() and Bio.AlignIO.write():

from Bio import AlignIO
alignments = AlignIO.parse("PF05371_seed.sth", "stockholm")
count = AlignIO.write(alignments, "PF05371_seed.aln", "clustal")
print("Converted %i alignments" % count)

The Bio.AlignIO.write() function expects to be given multiple alignment objects. In the example above we gave it the alignment iterator returned by Bio.AlignIO.parse().

In this case, we know there is only one alignment in the file so we could have used Bio.AlignIO.read() instead, but notice we have to pass this alignment to Bio.AlignIO.write() as a single element list:

from Bio import AlignIO
alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
AlignIO.write([alignment], "PF05371_seed.aln", "clustal")

Either way, you should end up with the same new Clustal W format file “PF05371_seed.aln” with the following content:

CLUSTAL X (1.81) multiple sequence alignment


COATB_BPIKE/30-81                   AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSS
Q9T0Q8_BPIKE/1-52                   AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVS
COATB_BPI22/32-83                   DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSS
COATB_BPM13/24-72                   AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTS
COATB_BPZJ2/1-49                    AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFAS
Q9T0Q9_BPFD/1-49                    AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTS
COATB_BPIF1/22-73                   FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVS

COATB_BPIKE/30-81                   KA
Q9T0Q8_BPIKE/1-52                   RA
COATB_BPI22/32-83                   KA
COATB_BPM13/24-72                   KA
COATB_BPZJ2/1-49                    KA
Q9T0Q9_BPFD/1-49                    KA
COATB_BPIF1/22-73                   RA

Alternatively, you could make a PHYLIP format file which we’ll name “PF05371_seed.phy”:

from Bio import AlignIO
AlignIO.convert("PF05371_seed.sth", "stockholm", "PF05371_seed.phy", "phylip")

This time the output looks like this:

 7 52
COATB_BPIK AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS
Q9T0Q8_BPI AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS
COATB_BPI2 DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS
COATB_BPM1 AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
COATB_BPZJ AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS
Q9T0Q9_BPF AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
COATB_BPIF FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS

           KA
           RA
           KA
           KA
           KA
           KA
           RA

One of the big handicaps of the PHYLIP alignment file format is that the sequence identifiers are strictly truncated at ten characters. In this example, as you can see the resulting names are still unique - but they are not very readable. In this particular case, there is no clear way to compress the identifiers, but for the sake of argument you may want to assign your own names or numbering system. This following bit of code manipulates the record identifiers before saving the output:

from Bio import AlignIO
alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
name_mapping = {}
for i, record in enumerate(alignment):
    name_mapping[i] = record.id
    record.id = "seq%i" % i
print(name_mapping)

AlignIO.write([alignment], "PF05371_seed.phy", "phylip")

This code used a Python dictionary to record a simple mapping from the new sequence system to the original identifier: 

{0: 'COATB_BPIKE/30-81', 1: 'Q9T0Q8_BPIKE/1-52', 2: 'COATB_BPI22/32-83', ...}

Here is the new PHYLIP format output: 

 7 52
seq0       AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS
seq1       AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS
seq2       DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS
seq3       AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
seq4       AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS
seq5       AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
seq6       FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS

           KA
           RA
           KA
           KA
           KA
           KA
           RA

In general, because of the identifier limitation, working with PHYLIP file formats shouldn’t be your first choice. Using the PFAM/Stockholm format on the other hand allows you to record a lot of additional annotation too.

6.2.2  Getting your alignment objects as formatted strings

The Bio.AlignIO interface is based on handles, which means if you want to get your alignment(s) into a string in a particular file format you need to do a little bit more work (see below). However, you will probably prefer to take advantage of the alignment object’s format() method. This takes a single mandatory argument, a lower case string which is supported by Bio.AlignIO as an output format. For example:

from Bio import AlignIO
alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
print(alignment.format("clustal"))

As described in Section 4.5), the SeqRecord object has a similar method using output formats supported by Bio.SeqIO.

Internally the format() method is using the StringIO string based handle and calling Bio.AlignIO.write(). You can do this in your own code if for example you are using an older version of Biopython:

from Bio import AlignIO
from StringIO import StringIO

alignments = AlignIO.parse("PF05371_seed.sth", "stockholm")

out_handle = StringIO()
AlignIO.write(alignments, out_handle, "clustal")
clustal_data = out_handle.getvalue()

print(clustal_data)

6.3  Manipulating Alignments

Now that we’ve covered loading and saving alignments, we’ll look at what else you can do with them.

6.3.1  Slicing alignments

First of all, in some senses the alignment objects act like a Python list of SeqRecord objects (the rows). With this model in mind hopefully the actions of len() (the number of rows) and iteration (each row as a SeqRecord) make sense:

>>> from Bio import AlignIO
>>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
>>> print("Number of rows: %i" % len(alignment))
Number of rows: 7
>>> for record in alignment:
...     print("%s - %s" % (record.seq, record.id))
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA - COATB_BPIKE/30-81
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA - Q9T0Q8_BPIKE/1-52
DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA - COATB_BPI22/32-83
AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - COATB_BPM13/24-72
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA - COATB_BPZJ2/1-49
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - Q9T0Q9_BPFD/1-49
FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA - COATB_BPIF1/22-73

You can also use the list-like append and extend methods to add more rows to the alignment (as SeqRecord objects). Keeping the list metaphor in mind, simple slicing of the alignment should also make sense - it selects some of the rows giving back another alignment object:

>>> print(alignment)
SingleLetterAlphabet() alignment with 7 rows and 52 columns
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRL...SKA COATB_BPIKE/30-81
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKL...SRA Q9T0Q8_BPIKE/1-52
DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRL...SKA COATB_BPI22/32-83
AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49
FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73
>>> print(alignment[3:7])
SingleLetterAlphabet() alignment with 4 rows and 52 columns
AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49
FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73

What if you wanted to select by column? Those of you who have used the NumPy matrix or array objects won’t be surprised at this - you use a double index.

>>> print(alignment[2, 6])
T

Using two integer indices pulls out a single letter, short hand for this:

>>> print(alignment[2].seq[6])
T

You can pull out a single column as a string like this:

>>> print(alignment[:, 6])
TTT---T

You can also select a range of columns. For example, to pick out those same three rows we extracted earlier, but take just their first six columns:

>>> print(alignment[3:6, :6])
SingleLetterAlphabet() alignment with 3 rows and 6 columns
AEGDDP COATB_BPM13/24-72
AEGDDP COATB_BPZJ2/1-49
AEGDDP Q9T0Q9_BPFD/1-49

Leaving the first index as : means take all the rows:

>>> print(alignment[:, :6])
SingleLetterAlphabet() alignment with 7 rows and 6 columns
AEPNAA COATB_BPIKE/30-81
AEPNAA Q9T0Q8_BPIKE/1-52
DGTSTA COATB_BPI22/32-83
AEGDDP COATB_BPM13/24-72
AEGDDP COATB_BPZJ2/1-49
AEGDDP Q9T0Q9_BPFD/1-49
FAADDA COATB_BPIF1/22-73

This brings us to a neat way to remove a section. Notice columns 7, 8 and 9 which are gaps in three of the seven sequences:

>>> print(alignment[:, 6:9])
SingleLetterAlphabet() alignment with 7 rows and 3 columns
TNY COATB_BPIKE/30-81
TNY Q9T0Q8_BPIKE/1-52
TSY COATB_BPI22/32-83
--- COATB_BPM13/24-72
--- COATB_BPZJ2/1-49
--- Q9T0Q9_BPFD/1-49
TSQ COATB_BPIF1/22-73

Again, you can slice to get everything after the ninth column:

>>> print(alignment[:, 9:])
SingleLetterAlphabet() alignment with 7 rows and 43 columns
ATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81
ATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52
ATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83
AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72
AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49
AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49
AKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73

Now, the interesting thing is that addition of alignment objects works by column. This lets you do this as a way to remove a block of columns:

>>> edited = alignment[:, :6] + alignment[:, 9:]
>>> print(edited)
SingleLetterAlphabet() alignment with 7 rows and 49 columns
AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81
AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52
DGTSTAATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83
AEGDDPAKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72
AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49
AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49
FAADDAAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73

Another common use of alignment addition would be to combine alignments for several different genes into a meta-alignment. Watch out though - the identifiers need to match up (see Section 4.7 for how adding SeqRecord objects works). You may find it helpful to first sort the alignment rows alphabetically by id:

>>> edited.sort()
>>> print(edited)
SingleLetterAlphabet() alignment with 7 rows and 49 columns
DGTSTAATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83
FAADDAAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73
AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81
AEGDDPAKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72
AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49
AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52
AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49

Note that you can only add two alignments together if they have the same number of rows.

6.3.2  Alignments as arrays

Depending on what you are doing, it can be more useful to turn the alignment object into an array of letters – and you can do this with NumPy:

>>> import numpy as np
>>> from Bio import AlignIO
>>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
>>> align_array = np.array([list(rec) for rec in alignment], np.character)
>>> align_array.shape
(7, 52)

If you will be working heavily with the columns, you can tell NumPy to store the array by column (as in Fortran) rather then its default of by row (as in C):

>>> align_array = np.array([list(rec) for rec in alignment], np.character, order="F")

Note that this leaves the original Biopython alignment object and the NumPy array in memory as separate objects - editing one will not update the other!

6.4  Alignment Tools

There are lots of algorithms out there for aligning sequences, both pairwise alignments and multiple sequence alignments. These calculations are relatively slow, and you generally wouldn’t want to write such an algorithm in Python. Instead, you can use Biopython to invoke a command line tool on your behalf. Normally you would:

  1. Prepare an input file of your unaligned sequences, typically this will be a FASTA file which you might create using Bio.SeqIO (see Chapter 5).
  2. Call the command line tool to process this input file, typically via one of Biopython’s command line wrappers (which we’ll discuss here).
  3. Read the output from the tool, i.e. your aligned sequences, typically using Bio.AlignIO (see earlier in this chapter).

All the command line wrappers we’re going to talk about in this chapter follow the same style. You create a command line object specifying the options (e.g. the input filename and the output filename), then invoke this command line via a Python operating system call (e.g. using the subprocess module).

Most of these wrappers are defined in the Bio.Align.Applications module:

>>> import Bio.Align.Applications
>>> dir(Bio.Align.Applications)
...
['ClustalwCommandline', 'DialignCommandline', 'MafftCommandline', 'MuscleCommandline',
'PrankCommandline', 'ProbconsCommandline', 'TCoffeeCommandline' ...]

(Ignore the entries starting with an underscore – these have special meaning in Python.) The module Bio.Emboss.Applications has wrappers for some of the EMBOSS suite, including needle and water, which are described below in Section 6.4.5, and wrappers for the EMBOSS packaged versions of the PHYLIP tools (which EMBOSS refer to as one of their EMBASSY packages - third party tools with an EMBOSS style interface). We won’t explore all these alignment tools here in the section, just a sample, but the same principles apply.

6.4.1  ClustalW

ClustalW is a popular command line tool for multiple sequence alignment (there is also a graphical interface called ClustalX). Biopython’s Bio.Align.Applications module has a wrapper for this alignment tool (and several others).

Before trying to use ClustalW from within Python, you should first try running the ClustalW tool yourself by hand at the command line, to familiarise yourself the other options. You’ll find the Biopython wrapper is very faithful to the actual command line API:

>>> from Bio.Align.Applications import ClustalwCommandline
>>> help(ClustalwCommandline)
...

For the most basic usage, all you need is to have a FASTA input file, such as opuntia.fasta (available online or in the Doc/examples subdirectory of the Biopython source code). This is a small FASTA file containing seven prickly-pear DNA sequences (from the cactus family Opuntia).

By default ClustalW will generate an alignment and guide tree file with names based on the input FASTA file, in this case opuntia.aln and opuntia.dnd, but you can override this or make it explicit:

>>> from Bio.Align.Applications import ClustalwCommandline
>>> cline = ClustalwCommandline("clustalw2", infile="opuntia.fasta")
>>> print(cline)
clustalw2 -infile=opuntia.fasta

Notice here we have given the executable name as clustalw2, indicating we have version two installed, which has a different filename to version one (clustalw, the default). Fortunately both versions support the same set of arguments at the command line (and indeed, should be functionally identical).

You may find that even though you have ClustalW installed, the above command doesn’t work – you may get a message about “command not found” (especially on Windows). This indicated that the ClustalW executable is not on your PATH (an environment variable, a list of directories to be searched). You can either update your PATH setting to include the location of your copy of ClustalW tools (how you do this will depend on your OS), or simply type in the full path of the tool. For example:

>>> import os
>>> from Bio.Align.Applications import ClustalwCommandline
>>> clustalw_exe = r"C:\Program Files\new clustal\clustalw2.exe"
>>> clustalw_cline = ClustalwCommandline(clustalw_exe, infile="opuntia.fasta")

>>> assert os.path.isfile(clustalw_exe), "Clustal W executable missing"
>>> stdout, stderr = clustalw_cline()

Remember, in Python strings \n and \t are by default interpreted as a new line and a tab – which is why we’re put a letter “r” at the start for a raw string that isn’t translated in this way. This is generally good practice when specifying a Windows style file name.

Internally this uses the subprocess module which is now the recommended way to run another program in Python. This replaces older options like the os.system() and the os.popen* functions.

Now, at this point it helps to know about how command line tools “work”. When you run a tool at the command line, it will often print text output directly to screen. This text can be captured or redirected, via two “pipes”, called standard output (the normal results) and standard error (for error messages and debug messages). There is also standard input, which is any text fed into the tool. These names get shortened to stdin, stdout and stderr. When the tool finishes, it has a return code (an integer), which by convention is zero for success.

When you run the command line tool like this via the Biopython wrapper, it will wait for it to finish, and check the return code. If this is non zero (indicating an error), an exception is raised. The wrapper then returns two strings, stdout and stderr.

In the case of ClustalW, when run at the command line all the important output is written directly to the output files. Everything normally printed to screen while you wait (via stdout or stderr) is boring and can be ignored (assuming it worked).

What we care about are the two output files, the alignment and the guide tree. We didn’t tell ClustalW what filenames to use, but it defaults to picking names based on the input file. In this case the output should be in the file opuntia.aln. You should be able to work out how to read in the alignment using Bio.AlignIO by now:

>>> from Bio import AlignIO
>>> align = AlignIO.read("opuntia.aln", "clustal")
>>> print(align)
SingleLetterAlphabet() alignment with 7 rows and 906 columns
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191
TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191

In case you are interested (and this is an aside from the main thrust of this chapter), the opuntia.dnd file ClustalW creates is just a standard Newick tree file, and Bio.Phylo can parse these:

>>> from Bio import Phylo
>>> tree = Phylo.read("opuntia.dnd", "newick")
>>> Phylo.draw_ascii(tree)
                             _______________ gi|6273291|gb|AF191665.1|AF191665
  __________________________|
 |                          |   ______ gi|6273290|gb|AF191664.1|AF191664
 |                          |__|
 |                             |_____ gi|6273289|gb|AF191663.1|AF191663
 |
_|_________________ gi|6273287|gb|AF191661.1|AF191661
 |
 |__________ gi|6273286|gb|AF191660.1|AF191660
 |
 |    __ gi|6273285|gb|AF191659.1|AF191659
 |___|
     | gi|6273284|gb|AF191658.1|AF191658

Chapter 13 covers Biopython’s support for phylogenetic trees in more depth.

6.4.2  MUSCLE

MUSCLE is a more recent multiple sequence alignment tool than ClustalW, and Biopython also has a wrapper for it under the Bio.Align.Applications module. As before, we recommend you try using MUSCLE from the command line before trying it from within Python, as the Biopython wrapper is very faithful to the actual command line API:

>>> from Bio.Align.Applications import MuscleCommandline
>>> help(MuscleCommandline)
...

For the most basic usage, all you need is to have a FASTA input file, such as opuntia.fasta (available online or in the Doc/examples subdirectory of the Biopython source code). You can then tell MUSCLE to read in this FASTA file, and write the alignment to an output file:

>>> from Bio.Align.Applications import MuscleCommandline
>>> cline = MuscleCommandline(input="opuntia.fasta", out="opuntia.txt")
>>> print(cline)
muscle -in opuntia.fasta -out opuntia.txt

Note that MUSCLE uses “-in” and “-out” but in Biopython we have to use “input” and “out” as the keyword arguments or property names. This is because “in” is a reserved word in Python.

By default MUSCLE will output the alignment as a FASTA file (using gapped sequences). The Bio.AlignIO module should be able to read this alignment using format="fasta". You can also ask for ClustalW-like output:

>>> from Bio.Align.Applications import MuscleCommandline
>>> cline = MuscleCommandline(input="opuntia.fasta", out="opuntia.aln", clw=True)
>>> print(cline)
muscle -in opuntia.fasta -out opuntia.aln -clw

Or, strict ClustalW output where the original ClustalW header line is used for maximum compatibility:

>>> from Bio.Align.Applications import MuscleCommandline
>>> cline = MuscleCommandline(input="opuntia.fasta", out="opuntia.aln", clwstrict=True)
>>> print(cline)
muscle -in opuntia.fasta -out opuntia.aln -clwstrict

The Bio.AlignIO module should be able to read these alignments using format="clustal".

MUSCLE can also output in GCG MSF format (using the msf argument), but Biopython can’t currently parse that, or using HTML which would give a human readable web page (not suitable for parsing).

You can also set the other optional parameters, for example the maximum number of iterations. See the built in help for details.

You would then run MUSCLE command line string as described above for ClustalW, and parse the output using Bio.AlignIO to get an alignment object.

6.4.3  MUSCLE using stdout

Using a MUSCLE command line as in the examples above will write the alignment to a file. This means there will be no important information written to the standard out (stdout) or standard error (stderr) handles. However, by default MUSCLE will write the alignment to standard output (stdout). We can take advantage of this to avoid having a temporary output file! For example:

>>> from Bio.Align.Applications import MuscleCommandline
>>> muscle_cline = MuscleCommandline(input="opuntia.fasta")
>>> print(muscle_cline)
muscle -in opuntia.fasta

If we run this via the wrapper, we get back the output as a string. In order to parse this we can use StringIO to turn it into a handle. Remember that MUSCLE defaults to using FASTA as the output format:

>>> from Bio.Align.Applications import MuscleCommandline
>>> muscle_cline = MuscleCommandline(input="opuntia.fasta")
>>> stdout, stderr = muscle_cline()
>>> from StringIO import StringIO
>>> from Bio import AlignIO
>>> align = AlignIO.read(StringIO(stdout), "fasta")
>>> print(align)
SingleLetterAlphabet() alignment with 7 rows and 906 columns
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191663
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191665
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191664
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191661
TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191660
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191659
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191658

The above approach is fairly simple, but if you are dealing with very large output text the fact that all of stdout and stderr is loaded into memory as a string can be a potential drawback. Using the subprocess module we can work directly with handles instead:

>>> import subprocess
>>> from Bio.Align.Applications import MuscleCommandline
>>> muscle_cline = MuscleCommandline(input="opuntia.fasta")
>>> child = subprocess.Popen(str(muscle_cline),
...                          stdout=subprocess.PIPE,
...                          stderr=subprocess.PIPE,
...                          shell=(sys.platform!="win32"))
>>> from Bio import AlignIO
>>> align = AlignIO.read(child.stdout, "fasta")
>>> print(align)
SingleLetterAlphabet() alignment with 7 rows and 906 columns
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191663
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191665
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191664
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191661
TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191660
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191659
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191658

6.4.4  MUSCLE using stdin and stdout

We don’t actually need to have our FASTA input sequences prepared in a file, because by default MUSCLE will read in the input sequence from standard input! Note this is a bit more advanced and fiddly, so don’t bother with this technique unless you need to.

First, we’ll need some unaligned sequences in memory as SeqRecord objects. For this demonstration I’m going to use a filtered version of the original FASTA file (using a generator expression), taking just six of the seven sequences:

>>> from Bio import SeqIO
>>> records = (r for r in SeqIO.parse("opuntia.fasta", "fasta") if len(r) < 900)

Then we create the MUSCLE command line, leaving the input and output to their defaults (stdin and stdout). I’m also going to ask for strict ClustalW format as for the output.

>>> from Bio.Align.Applications import MuscleCommandline
>>> muscle_cline = MuscleCommandline(clwstrict=True)
>>> print(muscle_cline)
muscle -clwstrict

Now for the fiddly bits using the subprocess module, stdin and stdout:

>>> import subprocess
>>> import sys
>>> child = subprocess.Popen(str(cline),
...                          stdin=subprocess.PIPE,
...                          stdout=subprocess.PIPE,
...                          stderr=subprocess.PIPE,
...                          shell=(sys.platform!="win32"))

That should start MUSCLE, but it will be sitting waiting for its FASTA input sequences, which we must supply via its stdin handle:

>>> SeqIO.write(records, child.stdin, "fasta")
6
>>> child.stdin.close()

After writing the six sequences to the handle, MUSCLE will still be waiting to see if that is all the FASTA sequences or not – so we must signal that this is all the input data by closing the handle. At that point MUSCLE should start to run, and we can ask for the output:

>>> from Bio import AlignIO
>>> align = AlignIO.read(child.stdout, "clustal")
>>> print(align)
SingleLetterAlphabet() alignment with 6 rows and 900 columns
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF19166
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF19166
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF19166
TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF19166
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF19165
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF19165

Wow! There we are with a new alignment of just the six records, without having created a temporary FASTA input file, or a temporary alignment output file. However, a word of caution: Dealing with errors with this style of calling external programs is much more complicated. It also becomes far harder to diagnose problems, because you can’t try running MUSCLE manually outside of Biopython (because you don’t have the input file to supply). There can also be subtle cross platform issues (e.g. Windows versus Linux), and how you run your script can have an impact (e.g. at the command line, from IDLE or an IDE, or as a GUI script). These are all generic Python issues though, and not specific to Biopython.

If you find working directly with subprocess like this scary, there is an alternative. If you execute the tool with muscle_cline() you can supply any standard input as a big string, muscle_cline(stdin=...). So, provided your data isn’t very big, you can prepare the FASTA input in memory as a string using StringIO (see Section 22.1):

>>> from Bio import SeqIO
>>> records = (r for r in SeqIO.parse("opuntia.fasta", "fasta") if len(r) < 900)
>>> from StringIO import StringIO
>>> handle = StringIO()
>>> SeqIO.write(records, handle, "fasta")
6
>>> data = handle.getvalue()

You can then run the tool and parse the alignment as follows:

>>> stdout, stderr = muscle_cline(stdin=data)
>>> from Bio import AlignIO
>>> align = AlignIO.read(StringIO(stdout), "clustal")
>>> print(align)
SingleLetterAlphabet() alignment with 6 rows and 900 columns
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF19166
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF19166
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF19166
TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF19166
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF19165
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF19165

You might find this easier, but it does require more memory (RAM) for the strings used for the input FASTA and output Clustal formatted data.

6.4.5  EMBOSS needle and water

The EMBOSS suite includes the water and needle tools for Smith-Waterman algorithm local alignment, and Needleman-Wunsch global alignment. The tools share the same style interface, so switching between the two is trivial – we’ll just use needle here.

Suppose you want to do a global pairwise alignment between two sequences, prepared in FASTA format as follows:

>HBA_HUMAN
MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHG
KKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTP
AVHASLDKFLASVSTVLTSKYR

in a file alpha.fasta, and secondly in a file beta.fasta:

>HBB_HUMAN
MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPK
VKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFG
KEFTPPVQAAYQKVVAGVANALAHKYH

Let’s start by creating a complete needle command line object in one go:

>>> from Bio.Emboss.Applications import NeedleCommandline
>>> needle_cline = NeedleCommandline(asequence="alpha.faa", bsequence="beta.faa",
...                                  gapopen=10, gapextend=0.5, outfile="needle.txt")
>>> print(needle_cline)
needle -outfile=needle.txt -asequence=alpha.faa -bsequence=beta.faa -gapopen=10 -gapextend=0.5

Why not try running this by hand at the command prompt? You should see it does a pairwise comparison and records the output in the file needle.txt (in the default EMBOSS alignment file format).

Even if you have EMBOSS installed, running this command may not work – you might get a message about “command not found” (especially on Windows). This probably means that the EMBOSS tools are not on your PATH environment variable. You can either update your PATH setting, or simply tell Biopython the full path to the tool, for example:

>>> from Bio.Emboss.Applications import NeedleCommandline
>>> needle_cline = NeedleCommandline(r"C:\EMBOSS\needle.exe",
...                                  asequence="alpha.faa", bsequence="beta.faa",
...                                  gapopen=10, gapextend=0.5, outfile="needle.txt")

Remember in Python that for a default string \n or \t means a new line or a tab – which is why we’re put a letter “r” at the start for a raw string.

At this point it might help to try running the EMBOSS tools yourself by hand at the command line, to familiarise yourself the other options and compare them to the Biopython help text:

>>> from Bio.Emboss.Applications import NeedleCommandline
>>> help(NeedleCommandline)
...

Note that you can also specify (or change or look at) the settings like this:

>>> from Bio.Emboss.Applications import NeedleCommandline
>>> needle_cline = NeedleCommandline()
>>> needle_cline.asequence="alpha.faa"
>>> needle_cline.bsequence="beta.faa"
>>> needle_cline.gapopen=10
>>> needle_cline.gapextend=0.5
>>> needle_cline.outfile="needle.txt"
>>> print(needle_cline)
needle -outfile=needle.txt -asequence=alpha.faa -bsequence=beta.faa -gapopen=10 -gapextend=0.5
>>> print(needle_cline.outfile)
needle.txt

Next we want to use Python to run this command for us. As explained above, for full control, we recommend you use the built in Python subprocess module, but for simple usage the wrapper object usually suffices:

>>> stdout, stderr = needle_cline()
>>> print(stdout + stderr)
Needleman-Wunsch global alignment of two sequences

Next we can load the output file with Bio.AlignIO as discussed earlier in this chapter, as the emboss format:

>>> from Bio import AlignIO
>>> align = AlignIO.read("needle.txt", "emboss")
>>> print(align)
SingleLetterAlphabet() alignment with 2 rows and 149 columns
MV-LSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTY...KYR HBA_HUMAN
MVHLTPEEKSAVTALWGKV--NVDEVGGEALGRLLVVYPWTQRF...KYH HBB_HUMAN

In this example, we told EMBOSS to write the output to a file, but you can tell it to write the output to stdout instead (useful if you don’t want a temporary output file to get rid of – use stdout=True rather than the outfile argument), and also to read one of the one of the inputs from stdin (e.g. asequence="stdin", much like in the MUSCLE example in the section above).

This has only scratched the surface of what you can do with needle and water. One useful trick is that the second file can contain multiple sequences (say five), and then EMBOSS will do five pairwise alignments.

Note - Biopython includes its own pairwise alignment code in the Bio.pairwise2 module (written in C for speed, but with a pure Python fallback available too). This doesn’t work with alignment objects, so we have not covered it within this chapter. See the module’s docstring (built in help) for details.

Chapter 7  BLAST

Hey, everybody loves BLAST right? I mean, geez, how can get it get any easier to do comparisons between one of your sequences and every other sequence in the known world? But, of course, this section isn’t about how cool BLAST is, since we already know that. It is about the problem with BLAST – it can be really difficult to deal with the volume of data generated by large runs, and to automate BLAST runs in general.

Fortunately, the Biopython folks know this only too well, so they’ve developed lots of tools for dealing with BLAST and making things much easier. This section details how to use these tools and do useful things with them.

Dealing with BLAST can be split up into two steps, both of which can be done from within Biopython. Firstly, running BLAST for your query sequence(s), and getting some output. Secondly, parsing the BLAST output in Python for further analysis.

Your first introduction to running BLAST was probably via the NCBI web-service. In fact, there are lots of ways you can run BLAST, which can be categorised several ways. The most important distinction is running BLAST locally (on your own machine), and running BLAST remotely (on another machine, typically the NCBI servers). We’re going to start this chapter by invoking the NCBI online BLAST service from within a Python script.

NOTE: The following Chapter 8 describes Bio.SearchIO, an experimental module in Biopython. We intend this to ultimately replace the older Bio.Blast module, as it provides a more general framework handling other related sequence searching tools as well. However, until that is declared stable, for production code please continue to use the Bio.Blast module for dealing with NCBI BLAST.

7.1  Running BLAST over the Internet

We use the function qblast() in the Bio.Blast.NCBIWWW module call the online version of BLAST. This has three non-optional arguments:

  • The first argument is the blast program to use for the search, as a lower case string. The options and descriptions of the programs are available at http://www.ncbi.nlm.nih.gov/BLAST/blast_program.shtml. Currently qblast only works with blastn, blastp, blastx, tblast and tblastx.
  • The second argument specifies the databases to search against. Again, the options for this are available on the NCBI web pages at http://www.ncbi.nlm.nih.gov/BLAST/blast_databases.shtml.
  • The third argument is a string containing your query sequence. This can either be the sequence itself, the sequence in fasta format, or an identifier like a GI number.

The qblast function also take a number of other option arguments which are basically analogous to the different parameters you can set on the BLAST web page. We’ll just highlight a few of them here:

  • The qblast function can return the BLAST results in various formats, which you can choose with the optional format_type keyword: "HTML", "Text", "ASN.1", or "XML". The default is "XML", as that is the format expected by the parser, described in section 7.3 below.
  • The argument expect sets the expectation or e-value threshold.

For more about the optional BLAST arguments, we refer you to the NCBI’s own documentation, or that built into Biopython:

>>> from Bio.Blast import NCBIWWW
>>> help(NCBIWWW.qblast)
...

Note that the default settings on the NCBI BLAST website are not quite the same as the defaults on QBLAST. If you get different results, you’ll need to check the parameters (e.g. the expectation value threshold and the gap values).

For example, if you have a nucleotide sequence you want to search against the nucleotide database (nt) using BLASTN, and you know the GI number of your query sequence, you can use:

>>> from Bio.Blast import NCBIWWW
>>> result_handle = NCBIWWW.qblast("blastn", "nt", "8332116")

Alternatively, if we have our query sequence already in a FASTA formatted file, we just need to open the file and read in this record as a string, and use that as the query argument:

>>> from Bio.Blast import NCBIWWW
>>> fasta_string = open("m_cold.fasta").read()
>>> result_handle = NCBIWWW.qblast("blastn", "nt", fasta_string)

We could also have read in the FASTA file as a SeqRecord and then supplied just the sequence itself:

>>> from Bio.Blast import NCBIWWW
>>> from Bio import SeqIO
>>> record = SeqIO.read("m_cold.fasta", format="fasta")
>>> result_handle = NCBIWWW.qblast("blastn", "nt", record.seq)

Supplying just the sequence means that BLAST will assign an identifier for your sequence automatically. You might prefer to use the SeqRecord object’s format method to make a fasta string (which will include the existing identifier):

>>> from Bio.Blast import NCBIWWW
>>> from Bio import SeqIO
>>> record = SeqIO.read("m_cold.fasta", format="fasta")
>>> result_handle = NCBIWWW.qblast("blastn", "nt", record.format("fasta"))

This approach makes more sense if you have your sequence(s) in a non-FASTA file format which you can extract using Bio.SeqIO (see Chapter 5).

Whatever arguments you give the qblast() function, you should get back your results in a handle object (by default in XML format). The next step would be to parse the XML output into Python objects representing the search results (Section 7.3), but you might want to save a local copy of the output file first. I find this especially useful when debugging my code that extracts info from the BLAST results (because re-running the online search is slow and wastes the NCBI computer time).

We need to be a bit careful since we can use result_handle.read() to read the BLAST output only once – calling result_handle.read() again returns an empty string.

>>> save_file = open("my_blast.xml", "w")
>>> save_file.write(result_handle.read())
>>> save_file.close()
>>> result_handle.close()

After doing this, the results are in the file my_blast.xml and the original handle has had all its data extracted (so we closed it). However, the parse function of the BLAST parser (described in 7.3) takes a file-handle-like object, so we can just open the saved file for input:

>>> result_handle = open("my_blast.xml")

Now that we’ve got the BLAST results back into a handle again, we are ready to do something with them, so this leads us right into the parsing section (see Section 7.3 below). You may want to jump ahead to that now ….

7.2  Running BLAST locally

7.2.1  Introduction

Running BLAST locally (as opposed to over the internet, see Section 7.1) has at least major two advantages:

  • Local BLAST may be faster than BLAST over the internet;
  • Local BLAST allows you to make your own database to search for sequences against.

Dealing with proprietary or unpublished sequence data can be another reason to run BLAST locally. You may not be allowed to redistribute the sequences, so submitting them to the NCBI as a BLAST query would not be an option.

Unfortunately, there are some major drawbacks too – installing all the bits and getting it setup right takes some effort:

  • Local BLAST requires command line tools to be installed.
  • Local BLAST requires (large) BLAST databases to be setup (and potentially kept up to date).

To further confuse matters there are several different BLAST packages available, and there are also other tools which can produce imitation BLAST output files, such as BLAT.

7.2.2  Standalone NCBI BLAST+

The “new” NCBI BLAST+ suite was released in 2009. This replaces the old NCBI “legacy” BLAST package (see below).

This section will show briefly how to use these tools from within Python. If you have already read or tried the alignment tool examples in Section 6.4 this should all seem quite straightforward. First, we construct a command line string (as you would type in at the command line prompt if running standalone BLAST by hand). Then we can execute this command from within Python.

For example, taking a FASTA file of gene nucleotide sequences, you might want to run a BLASTX (translation) search against the non-redundant (NR) protein database. Assuming you (or your systems administrator) has downloaded and installed the NR database, you might run:

blastx -query opuntia.fasta -db nr -out opuntia.xml -evalue 0.001 -outfmt 5

This should run BLASTX against the NR database, using an expectation cut-off value of 0.001 and produce XML output to the specified file (which we can then parse). On my computer this takes about six minutes - a good reason to save the output to a file so you and repeat any analysis as needed.

From within Biopython we can use the NCBI BLASTX wrapper from the Bio.Blast.Applications module to build the command line string, and run it:

>>> from Bio.Blast.Applications import NcbiblastxCommandline
>>> help(NcbiblastxCommandline)
...
>>> blastx_cline = NcbiblastxCommandline(query="opuntia.fasta", db="nr", evalue=0.001,
...                                      outfmt=5, out="opuntia.xml")
>>> blastx_cline
NcbiblastxCommandline(cmd='blastx', out='opuntia.xml', outfmt=5, query='opuntia.fasta',
db='nr', evalue=0.001)
>>> print(blastx_cline)
blastx -out opuntia.xml -outfmt 5 -query opuntia.fasta -db nr -evalue 0.001
>>> stdout, stderr = blastx_cline()

In this example there shouldn’t be any output from BLASTX to the terminal, so stdout and stderr should be empty. You may want to check the output file opuntia.xml has been created.

As you may recall from earlier examples in the tutorial, the opuntia.fasta contains seven sequences, so the BLAST XML output should contain multiple results. Therefore use Bio.Blast.NCBIXML.parse() to parse it as described below in Section 7.3.

7.2.3  Other versions of BLAST

NCBI BLAST+ (written in C++) was first released in 2009 as a replacement for the original NCBI “legacy” BLAST (written in C) which is no longer being updated. There were a lot of changes – the old version had a single core command line tool blastall which covered multiple different BLAST search types (which are now separate commands in BLAST+), and all the command line options were renamed. Biopython’s wrappers for the NCBI “legacy” BLAST tools have been deprecated and will be removed in a future release. To try to avoid confusion, we do not cover calling these old tools from Biopython in this tutorial.

You may also come across Washington University BLAST (WU-BLAST), and its successor, Advanced Biocomputing BLAST (AB-BLAST, released in 2009, not free/open source). These packages include the command line tools wu-blastall and ab-blastall, which mimicked blastall from the NCBI “legacy” BLAST stuie. Biopython does not currently provide wrappers for calling these tools, but should be able to parse any NCBI compatible output from them.

7.3  Parsing BLAST output

As mentioned above, BLAST can generate output in various formats, such as XML, HTML, and plain text. Originally, Biopython had parsers for BLAST plain text and HTML output, as these were the only output formats offered at the time. Unfortunately, the BLAST output in these formats kept changing, each time breaking the Biopython parsers. Our HTML BLAST parser has been removed, but the plain text BLAST parser is still available (see Section 7.5). Use it at your own risk, it may or may not work, depending on which BLAST version you’re using.

As keeping up with changes in BLAST became a hopeless endeavor, especially with users running different BLAST versions, we now recommend to parse the output in XML format, which can be generated by recent versions of BLAST. Not only is the XML output more stable than the plain text and HTML output, it is also much easier to parse automatically, making Biopython a whole lot more stable.

You can get BLAST output in XML format in various ways. For the parser, it doesn’t matter how the output was generated, as long as it is in the XML format.

  • You can use Biopython to run BLAST over the internet, as described in section 7.1.
  • You can use Biopython to run BLAST locally, as described in section 7.2.
  • You can do the BLAST search yourself on the NCBI site through your web browser, and then save the results. You need to choose XML as the format in which to receive the results, and save the final BLAST page you get (you know, the one with all of the interesting results!) to a file.
  • You can also run BLAST locally without using Biopython, and save the output in a file. Again, you need to choose XML as the format in which to receive the results.

The important point is that you do not have to use Biopython scripts to fetch the data in order to be able to parse it. Doing things in one of these ways, you then need to get a handle to the results. In Python, a handle is just a nice general way of describing input to any info source so that the info can be retrieved using read() and readline() functions (see Section sec:appendix-handles).

If you followed the code above for interacting with BLAST through a script, then you already have result_handle, the handle to the BLAST results. For example, using a GI number to do an online search:

>>> from Bio.Blast import NCBIWWW
>>> result_handle = NCBIWWW.qblast("blastn", "nt", "8332116")

If instead you ran BLAST some other way, and have the BLAST output (in XML format) in the file my_blast.xml, all you need to do is to open the file for reading:

>>> result_handle = open("my_blast.xml")

Now that we’ve got a handle, we are ready to parse the output. The code to parse it is really quite small. If you expect a single BLAST result (i.e. you used a single query):

>>> from Bio.Blast import NCBIXML
>>> blast_record = NCBIXML.read(result_handle)

or, if you have lots of results (i.e. multiple query sequences):

>>> from Bio.Blast import NCBIXML
>>> blast_records = NCBIXML.parse(result_handle)

Just like Bio.SeqIO and Bio.AlignIO (see Chapters 5 and 6), we have a pair of input functions, read and parse, where read is for when you have exactly one object, and parse is an iterator for when you can have lots of objects – but instead of getting SeqRecord or MultipleSeqAlignment objects, we get BLAST record objects.

To be able to handle the situation where the BLAST file may be huge, containing thousands of results, NCBIXML.parse() returns an iterator. In plain English, an iterator allows you to step through the BLAST output, retrieving BLAST records one by one for each BLAST search result:

>>> from Bio.Blast import NCBIXML
>>> blast_records = NCBIXML.parse(result_handle)
>>> blast_record = next(blast_records)
# ... do something with blast_record
>>> blast_record = next(blast_records)
# ... do something with blast_record
>>> blast_record = next(blast_records)
# ... do something with blast_record
>>> blast_record = next(blast_records)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration
# No further records

Or, you can use a for-loop: 

>>> for blast_record in blast_records:
...     # Do something with blast_record

Note though that you can step through the BLAST records only once. Usually, from each BLAST record you would save the information that you are interested in. If you want to save all returned BLAST records, you can convert the iterator into a list: 

>>> blast_records = list(blast_records)

Now you can access each BLAST record in the list with an index as usual. If your BLAST file is huge though, you may run into memory problems trying to save them all in a list.

Usually, you’ll be running one BLAST search at a time. Then, all you need to do is to pick up the first (and only) BLAST record in blast_records: 

>>> from Bio.Blast import NCBIXML
>>> blast_records = NCBIXML.parse(result_handle)
>>> blast_record = next(blast_records)

or more elegantly: 

>>> from Bio.Blast import NCBIXML
>>> blast_record = NCBIXML.read(result_handle)

I guess by now you’re wondering what is in a BLAST record.

7.4  The BLAST record class

A BLAST Record contains everything you might ever want to extract from the BLAST output. Right now we’ll just show an example of how to get some info out of the BLAST report, but if you want something in particular that is not described here, look at the info on the record class in detail, and take a gander into the code or automatically generated documentation – the docstrings have lots of good info about what is stored in each piece of information.

To continue with our example, let’s just print out some summary info about all hits in our blast report greater than a particular threshold. The following code does this:

>>> E_VALUE_THRESH = 0.04

>>> for alignment in blast_record.alignments:
...     for hsp in alignment.hsps:
...         if hsp.expect < E_VALUE_THRESH:
...             print('****Alignment****')
...             print('sequence:', alignment.title)
...             print('length:', alignment.length)
...             print('e value:', hsp.expect)
...             print(hsp.query[0:75] + '...')
...             print(hsp.match[0:75] + '...')
...             print(hsp.sbjct[0:75] + '...')

This will print out summary reports like the following:

****Alignment****
sequence: >gb|AF283004.1|AF283004 Arabidopsis thaliana cold acclimation protein WCOR413-like protein
alpha form mRNA, complete cds
length: 783
e value: 0.034
tacttgttgatattggatcgaacaaactggagaaccaacatgctcacgtcacttttagtcccttacatattcctc...
||||||||| | ||||||||||| || ||||  || || |||||||| |||||| |  | |||||||| ||| ||...
tacttgttggtgttggatcgaaccaattggaagacgaatatgctcacatcacttctcattccttacatcttcttc...

Basically, you can do anything you want to with the info in the BLAST report once you have parsed it. This will, of course, depend on what you want to use it for, but hopefully this helps you get started on doing what you need to do!

An important consideration for extracting information from a BLAST report is the type of objects that the information is stored in. In Biopython, the parsers return Record objects, either Blast or PSIBlast depending on what you are parsing. These objects are defined in Bio.Blast.Record and are quite complete.

Here are my attempts at UML class diagrams for the Blast and PSIBlast record classes. If you are good at UML and see mistakes/improvements that can be made, please let me know. The Blast class diagram is shown in Figure 7.4.

The PSIBlast record object is similar, but has support for the rounds that are used in the iteration steps of PSIBlast. The class diagram for PSIBlast is shown in Figure 7.4.

7.5  Deprecated BLAST parsers

Older versions of Biopython had parsers for BLAST output in plain text or HTML format. Over the years, we discovered that it is very hard to maintain these parsers in working order. Basically, any small change to the BLAST output in newly released BLAST versions tends to cause the plain text and HTML parsers to break. We therefore recommend parsing BLAST output in XML format, as described in section 7.3.

Depending on which BLAST versions or programs you’re using, our plain text BLAST parser may or may not work. Use it at your own risk!

7.5.1  Parsing plain-text BLAST output

The plain text BLAST parser is located in Bio.Blast.NCBIStandalone.

As with the XML parser, we need to have a handle object that we can pass to the parser. The handle must implement the readline() method and do this properly. The common ways to get such a handle are to either use the provided blastall or blastpgp functions to run the local blast, or to run a local blast via the command line, and then do something like the following:

>>> result_handle = open("my_file_of_blast_output.txt")

Well, now that we’ve got a handle (which we’ll call result_handle), we are ready to parse it. This can be done with the following code:

>>> from Bio.Blast import NCBIStandalone
>>> blast_parser = NCBIStandalone.BlastParser()
>>> blast_record = blast_parser.parse(result_handle)

This will parse the BLAST report into a Blast Record class (either a Blast or a PSIBlast record, depending on what you are parsing) so that you can extract the information from it. In our case, let’s just use print out a quick summary of all of the alignments greater than some threshold value.

>>> E_VALUE_THRESH = 0.04
>>> for alignment in blast_record.alignments:
...     for hsp in alignment.hsps:
...         if hsp.expect < E_VALUE_THRESH:
...             print('****Alignment****')
...             print('sequence:', alignment.title)
...             print('length:', alignment.length)
...             print('e value:', hsp.expect)
...             print(hsp.query[0:75] + '...')
...             print(hsp.match[0:75] + '...')
...             print(hsp.sbjct[0:75] + '...')

If you also read the section 7.3 on parsing BLAST XML output, you’ll notice that the above code is identical to what is found in that section. Once you parse something into a record class you can deal with it independent of the format of the original BLAST info you were parsing. Pretty snazzy!

Sure, parsing one record is great, but I’ve got a BLAST file with tons of records – how can I parse them all? Well, fear not, the answer lies in the very next section.

7.5.2  Parsing a plain-text BLAST file full of BLAST runs

We can do this using the blast iterator. To set up an iterator, we first set up a parser, to parse our blast reports in Blast Record objects:

>>> from Bio.Blast import NCBIStandalone
>>> blast_parser = NCBIStandalone.BlastParser()

Then we will assume we have a handle to a bunch of blast records, which we’ll call result_handle. Getting a handle is described in full detail above in the blast parsing sections.

Now that we’ve got a parser and a handle, we are ready to set up the iterator with the following command:

>>> blast_iterator = NCBIStandalone.Iterator(result_handle, blast_parser)

The second option, the parser, is optional. If we don’t supply a parser, then the iterator will just return the raw BLAST reports one at a time.

Now that we’ve got an iterator, we start retrieving blast records (generated by our parser) using next():

>>> blast_record = next(blast_iterator)

Each call to next will return a new record that we can deal with. Now we can iterate through this records and generate our old favorite, a nice little blast report:

>>> for blast_record in blast_iterator:
...     E_VALUE_THRESH = 0.04
...     for alignment in blast_record.alignments:
...         for hsp in alignment.hsps:
...             if hsp.expect < E_VALUE_THRESH:
...                 print('****Alignment****')
...                 print('sequence:', alignment.title)
...                 print('length:', alignment.length)
...                 print('e value:', hsp.expect)
...                 if len(hsp.query) > 75:
...                     dots = '...'
...                 else:
...                     dots = ''
...                 print(hsp.query[0:75] + dots)
...                 print(hsp.match[0:75] + dots)
...                 print(hsp.sbjct[0:75] + dots)

The iterator allows you to deal with huge blast records without any memory problems, since things are read in one at a time. I have parsed tremendously huge files without any problems using this.

7.5.3  Finding a bad record somewhere in a huge plain-text BLAST file

One really ugly problem that happens to me is that I’ll be parsing a huge blast file for a while, and the parser will bomb out with a ValueError. This is a serious problem, since you can’t tell if the ValueError is due to a parser problem, or a problem with the BLAST. To make it even worse, you have no idea where the parse failed, so you can’t just ignore the error, since this could be ignoring an important data point.

We used to have to make a little script to get around this problem, but the Bio.Blast module now includes a BlastErrorParser which really helps make this easier. The BlastErrorParser works very similar to the regular BlastParser, but it adds an extra layer of work by catching ValueErrors that are generated by the parser, and attempting to diagnose the errors.

Let’s take a look at using this parser – first we define the file we are going to parse and the file to write the problem reports to:

>>> import os
>>> blast_file = os.path.join(os.getcwd(), "blast_out", "big_blast.out")
>>> error_file = os.path.join(os.getcwd(), "blast_out", "big_blast.problems")

Now we want to get a BlastErrorParser:

>>> from Bio.Blast import NCBIStandalone
>>> error_handle = open(error_file, "w")
>>> blast_error_parser = NCBIStandalone.BlastErrorParser(error_handle)

Notice that the parser take an optional argument of a handle. If a handle is passed, then the parser will write any blast records which generate a ValueError to this handle. Otherwise, these records will not be recorded.

Now we can use the BlastErrorParser just like a regular blast parser. Specifically, we might want to make an iterator that goes through our blast records one at a time and parses them with the error parser:

>>> result_handle = open(blast_file)
>>> iterator = NCBIStandalone.Iterator(result_handle, blast_error_parser)

We can read these records one a time, but now we can catch and deal with errors that are due to problems with Blast (and not with the parser itself):

>>> try:
...     next_record = next(iterator)
... except NCBIStandalone.LowQualityBlastError as info:
...     print("LowQualityBlastError detected in id %s" % info[1])

The next() functionality is normally called indirectly via a for-loop. Right now the BlastErrorParser can generate the following errors:

  • ValueError – This is the same error generated by the regular BlastParser, and is due to the parser not being able to parse a specific file. This is normally either due to a bug in the parser, or some kind of discrepancy between the version of BLAST you are using and the versions the parser is able to handle.
  • LowQualityBlastError – When BLASTing a sequence that is of really bad quality (for example, a short sequence that is basically a stretch of one nucleotide), it seems that Blast ends up masking out the entire sequence and ending up with nothing to parse. In this case it will produce a truncated report that causes the parser to generate a ValueError. LowQualityBlastError is reported in these cases. This error returns an info item with the following information:
    • item[0] – The error message
    • item[1] – The id of the input record that caused the error. This is really useful if you want to record all of the records that are causing problems.

As mentioned, with each error generated, the BlastErrorParser will write the offending record to the specified error_handle. You can then go ahead and look and these and deal with them as you see fit. Either you will be able to debug the parser with a single blast report, or will find out problems in your blast runs. Either way, it will definitely be a useful experience!

Hopefully the BlastErrorParser will make it much easier to debug and deal with large Blast files.

7.6  Dealing with PSI-BLAST

You can run the standalone version of PSI-BLAST (the legacy NCBI command line tool blastpgp, or its replacement psiblast) using the wrappers in Bio.Blast.Applications module.

At the time of writing, the NCBI do not appear to support tools running a PSI-BLAST search via the internet.

Note that the Bio.Blast.NCBIXML parser can read the XML output from current versions of PSI-BLAST, but information like which sequences in each iteration is new or reused isn’t present in the XML file. If you care about this information you may have more joy with the plain text output and the PSIBlastParser in Bio.Blast.NCBIStandalone.

7.7  Dealing with RPS-BLAST

You can run the standalone version of RPS-BLAST (either the legacy NCBI command line tool rpsblast, or its replacement with the same name) using the wrappers in Bio.Blast.Applications module.

At the time of writing, the NCBI do not appear to support tools running an RPS-BLAST search via the internet.

You can use the Bio.Blast.NCBIXML parser to read the XML output from current versions of RPS-BLAST.

Chapter 8  BLAST and other sequence search tools (experimental code)

WARNING: This chapter of the Tutorial describes an experimental module in Biopython. It is being included in Biopython and documented here in the tutorial in a pre-final state to allow a period of feedback and refinement before we declare it stable. Until then the details will probably change, and any scripts using the current Bio.SearchIO would need to be updated. Please keep this in mind! For stable code working with NCBI BLAST, please continue to use Bio.Blast described in the preceding Chapter 7.

Biological sequence identification is an integral part of bioinformatics. Several tools are available for this, each with their own algorithms and approaches, such as BLAST (arguably the most popular), FASTA, HMMER, and many more. In general, these tools usually use your sequence to search a database of potential matches. With the growing number of known sequences (hence the growing number of potential matches), interpreting the results becomes increasingly hard as there could be hundreds or even thousands of potential matches. Naturally, manual interpretation of these searches’ results is out of the question. Moreover, you often need to work with several sequence search tools, each with its own statistics, conventions, and output format. Imagine how daunting it would be when you need to work with multiple sequences using multiple search tools.

We know this too well ourselves, which is why we created the Bio.SearchIO submodule in Biopython. Bio.SearchIO allows you to extract information from your search results in a convenient way, while also dealing with the different standards and conventions used by different search tools. The name SearchIO is a homage to BioPerl’s module of the same name.

In this chapter, we’ll go through the main features of Bio.SearchIO to show what it can do for you. We’ll use two popular search tools along the way: BLAST and BLAT. They are used merely for illustrative purposes, and you should be able to adapt the workflow to any other search tools supported by Bio.SearchIO in a breeze. You’re very welcome to follow along with the search output files we’ll be using. The BLAST output file can be downloaded here, and the BLAT output file here. Both output files were generated using this sequence:

>mystery_seq
CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG

The BLAST result is an XML file generated using blastn against the NCBI refseq_rna database. For BLAT, the sequence database was the February 2009 hg19 human genome draft and the output format is PSL.

We’ll start from an introduction to the Bio.SearchIO object model. The model is the representation of your search results, thus it is core to Bio.SearchIO itself. After that, we’ll check out the main functions in Bio.SearchIO that you may often use.

Now that we’re all set, let’s go to the first step: introducing the core object model.

8.1  The SearchIO object model

Despite the wildly differing output styles among many sequence search tools, it turns out that their underlying concept is similar:

  • The output file may contain results from one or more search queries.
  • In each search query, you will see one or more hits from the given search database.
  • In each database hit, you will see one or more regions containing the actual sequence alignment between your query sequence and the database sequence.
  • Some programs like BLAT or Exonerate may further split these regions into several alignment fragments (or blocks in BLAT and possibly exons in exonerate). This is not something you always see, as programs like BLAST and HMMER do not do this.

Realizing this generality, we decided use it as base for creating the Bio.SearchIO object model. The object model consists of a nested hierarchy of Python objects, each one representing one concept outlined above. These objects are:

  • QueryResult, to represent a single search query.
  • Hit, to represent a single database hit. Hit objects are contained within QueryResult and in each QueryResult there is zero or more Hit objects.
  • HSP (short for high-scoring pair), to represent region(s) of significant alignments between query and hit sequences. HSP objects are contained within Hit objects and each Hit has one or more HSP objects.
  • HSPFragment, to represent a single contiguous alignment between query and hit sequences. HSPFragment objects are contained within HSP objects. Most sequence search tools like BLAST and HMMER unify HSP and HSPFragment objects as each HSP will only have a single HSPFragment. However there are tools like BLAT and Exonerate that produce HSP containing multiple HSPFragment. Don’t worry if this seems a tad confusing now, we’ll elaborate more on these two objects later on.

These four objects are the ones you will interact with when you use Bio.SearchIO. They are created using one of the main Bio.SearchIO methods: read, parse, index, or index_db. The details of these methods are provided in later sections. For this section, we’ll only be using read and parse. These functions behave similarly to their Bio.SeqIO and Bio.AlignIO counterparts:

  • read is used for search output files with a single query and returns a QueryResult object
  • parse is used for search output files with multiple queries and returns a generator that yields QueryResult objects

With that settled, let’s start probing each Bio.SearchIO object, beginning with QueryResult.

8.1.1  QueryResult

The QueryResult object represents a single search query and contains zero or more Hit objects. Let’s see what it looks like using the BLAST file we have:

>>> from Bio import SearchIO
>>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
>>> print(blast_qresult)
Program: blastn (2.2.27+)
  Query: 42291 (61)
         mystery_seq
 Target: refseq_rna
   Hits: ----  -----  ----------------------------------------------------------
            #  # HSP  ID + description                                          
         ----  -----  ----------------------------------------------------------
            0      1  gi|262205317|ref|NR_030195.1|  Homo sapiens microRNA 52...
            1      1  gi|301171311|ref|NR_035856.1|  Pan troglodytes microRNA...
            2      1  gi|270133242|ref|NR_032573.1|  Macaca mulatta microRNA ...
            3      2  gi|301171322|ref|NR_035857.1|  Pan troglodytes microRNA...
            4      1  gi|301171267|ref|NR_035851.1|  Pan troglodytes microRNA...
            5      2  gi|262205330|ref|NR_030198.1|  Homo sapiens microRNA 52...
            6      1  gi|262205302|ref|NR_030191.1|  Homo sapiens microRNA 51...
            7      1  gi|301171259|ref|NR_035850.1|  Pan troglodytes microRNA...
            8      1  gi|262205451|ref|NR_030222.1|  Homo sapiens microRNA 51...
            9      2  gi|301171447|ref|NR_035871.1|  Pan troglodytes microRNA...
           10      1  gi|301171276|ref|NR_035852.1|  Pan troglodytes microRNA...
           11      1  gi|262205290|ref|NR_030188.1|  Homo sapiens microRNA 51...
           12      1  gi|301171354|ref|NR_035860.1|  Pan troglodytes microRNA...
           13      1  gi|262205281|ref|NR_030186.1|  Homo sapiens microRNA 52...
           14      2  gi|262205298|ref|NR_030190.1|  Homo sapiens microRNA 52...
           15      1  gi|301171394|ref|NR_035865.1|  Pan troglodytes microRNA...
           16      1  gi|262205429|ref|NR_030218.1|  Homo sapiens microRNA 51...
           17      1  gi|262205423|ref|NR_030217.1|  Homo sapiens microRNA 52...
           18      1  gi|301171401|ref|NR_035866.1|  Pan troglodytes microRNA...
           19      1  gi|270133247|ref|NR_032574.1|  Macaca mulatta microRNA ...
           20      1  gi|262205309|ref|NR_030193.1|  Homo sapiens microRNA 52...
           21      2  gi|270132717|ref|NR_032716.1|  Macaca mulatta microRNA ...
           22      2  gi|301171437|ref|NR_035870.1|  Pan troglodytes microRNA...
           23      2  gi|270133306|ref|NR_032587.1|  Macaca mulatta microRNA ...
           24      2  gi|301171428|ref|NR_035869.1|  Pan troglodytes microRNA...
           25      1  gi|301171211|ref|NR_035845.1|  Pan troglodytes microRNA...
           26      2  gi|301171153|ref|NR_035838.1|  Pan troglodytes microRNA...
           27      2  gi|301171146|ref|NR_035837.1|  Pan troglodytes microRNA...
           28      2  gi|270133254|ref|NR_032575.1|  Macaca mulatta microRNA ...
           29      2  gi|262205445|ref|NR_030221.1|  Homo sapiens microRNA 51...
           ~~~
           97      1  gi|356517317|ref|XM_003527287.1|  PREDICTED: Glycine ma...
           98      1  gi|297814701|ref|XM_002875188.1|  Arabidopsis lyrata su...
           99      1  gi|397513516|ref|XM_003827011.1|  PREDICTED: Pan panisc...

We’ve just begun to scratch the surface of the object model, but you can see that there’s already some useful information. By invoking print on the QueryResult object, you can see:

  • The program name and version (blastn version 2.2.27+)
  • The query ID, description, and its sequence length (ID is 42291, description is ‘mystery_seq’, and it is 61 nucleotides long)
  • The target database to search against (refseq_rna)
  • A quick overview of the resulting hits. For our query sequence, there are 100 potential hits (numbered 0–99 in the table). For each hit, we can also see how many HSPs it contains, its ID, and a snippet of its description. Notice here that Bio.SearchIO truncates the hit table overview, by showing only hits numbered 0–29, and then 97–99.

Now let’s check our BLAT results using the same procedure as above:

>>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl')
>>> print(blat_qresult)
Program: blat (<unknown version>)
  Query: mystery_seq (61)
         <unknown description>
 Target: <unknown target>
   Hits: ----  -----  ----------------------------------------------------------
            #  # HSP  ID + description                                          
         ----  -----  ----------------------------------------------------------
            0     17  chr19  <unknown description>

You’ll immediately notice that there are some differences. Some of these are caused by the way PSL format stores its details, as you’ll see. The rest are caused by the genuine program and target database differences between our BLAST and BLAT searches:

  • The program name and version. Bio.SearchIO knows that the program is BLAT, but in the output file there is no information regarding the program version so it defaults to ‘<unknown version>’.
  • The query ID, description, and its sequence length. Notice here that these details are slightly different from the ones we saw in BLAST. The ID is ‘mystery_seq’ instead of 42991, there is no known description, but the query length is still 61. This is actually a difference introduced by the file formats themselves. BLAST sometimes creates its own query IDs and uses your original ID as the sequence description.
  • The target database is not known, as it is not stated in the BLAT output file.
  • And finally, the list of hits we have is completely different. Here, we see that our query sequence only hits the ‘chr19’ database entry, but in it we see 17 HSP regions. This should not be surprising however, given that we are using a different program, each with its own target database.

All the details you saw when invoking the print method can be accessed individually using Python’s object attribute access notation (a.k.a. the dot notation). There are also other format-specific attributes that you can access using the same method.

>>> print("%s %s" % (blast_qresult.program, blast_qresult.version))
blastn 2.2.27+
>>> print("%s %s" % (blat_qresult.program, blat_qresult.version))
blat <unknown version>
>>> blast_qresult.param_evalue_threshold    # blast-xml specific
10.0

For a complete list of accessible attributes, you can check each format-specific documentation. Here are the ones for BLAST and for BLAT.

Having looked at using print on QueryResult objects, let’s drill down deeper. What exactly is a QueryResult? In terms of Python objects, QueryResult is a hybrid between a list and a dictionary. In other words, it is a container object with all the convenient features of lists and dictionaries.

Like Python lists and dictionaries, QueryResult objects are iterable. Each iteration returns a Hit object:

>>> for hit in blast_qresult:
...     hit
Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps)
Hit(id='gi|301171311|ref|NR_035856.1|', query_id='42291', 1 hsps)
Hit(id='gi|270133242|ref|NR_032573.1|', query_id='42291', 1 hsps)
Hit(id='gi|301171322|ref|NR_035857.1|', query_id='42291', 2 hsps)
Hit(id='gi|301171267|ref|NR_035851.1|', query_id='42291', 1 hsps)
...

To check how many items (hits) a QueryResult has, you can simply invoke Python’s len method:

>>> len(blast_qresult)
100
>>> len(blat_qresult)
1

Like Python lists, you can retrieve items (hits) from a QueryResult using the slice notation:

>>> blast_qresult[0]        # retrieves the top hit
Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps)
>>> blast_qresult[-1]       # retrieves the last hit
Hit(id='gi|397513516|ref|XM_003827011.1|', query_id='42291', 1 hsps)

To retrieve multiple hits, you can slice QueryResult objects using the slice notation as well. In this case, the slice will return a new QueryResult object containing only the sliced hits:

>>> blast_slice = blast_qresult[:3]     # slices the first three hits
>>> print(blast_slice)
Program: blastn (2.2.27+)
  Query: 42291 (61)
         mystery_seq
 Target: refseq_rna
   Hits: ----  -----  ----------------------------------------------------------
            #  # HSP  ID + description                                          
         ----  -----  ----------------------------------------------------------
            0      1  gi|262205317|ref|NR_030195.1|  Homo sapiens microRNA 52...
            1      1  gi|301171311|ref|NR_035856.1|  Pan troglodytes microRNA...
            2      1  gi|270133242|ref|NR_032573.1|  Macaca mulatta microRNA ...

Like Python dictionaries, you can also retrieve hits using the hit’s ID. This is particularly useful if you know a given hit ID exists within a search query results:

>>> blast_qresult['gi|262205317|ref|NR_030195.1|']
Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps)

You can also get a full list of Hit objects using hits and a full list of Hit IDs using hit_keys:

>>> blast_qresult.hits
[...]       # list of all hits
>>> blast_qresult.hit_keys
[...]       # list of all hit IDs

What if you just want to check whether a particular hit is present in the query results? You can do a simple Python membership test using the in keyword:

>>> 'gi|262205317|ref|NR_030195.1|' in blast_qresult
True
>>> 'gi|262205317|ref|NR_030194.1|' in blast_qresult
False

Sometimes, knowing whether a hit is present is not enough; you also want to know the rank of the hit. Here, the index method comes to the rescue:

>>> blast_qresult.index('gi|301171437|ref|NR_035870.1|')
22

Remember that we’re using Python’s indexing style here, which is zero-based. This means our hit above is ranked at no. 23, not 22.

Also, note that the hit rank you see here is based on the native hit ordering present in the original search output file. Different search tools may order these hits based on different criteria.

If the native hit ordering doesn’t suit your taste, you can use the sort method of the QueryResult object. It is very similar to Python’s list.sort method, with the addition of an option to create a new sorted QueryResult object or not.

Here is an example of using QueryResult.sort to sort the hits based on each hit’s full sequence length. For this particular sort, we’ll set the in_place flag to False so that sorting will return a new QueryResult object and leave our initial object unsorted. We’ll also set the reverse flag to True so that we sort in descending order.

>>> for hit in blast_qresult[:5]:   # id and sequence length of the first five hits
...     print("%s %i" % (hit.id, hit.seq_len))
... 
gi|262205317|ref|NR_030195.1| 61
gi|301171311|ref|NR_035856.1| 60
gi|270133242|ref|NR_032573.1| 85
gi|301171322|ref|NR_035857.1| 86
gi|301171267|ref|NR_035851.1| 80

>>> sort_key = lambda hit: hit.seq_len
>>> sorted_qresult = blast_qresult.sort(key=sort_key, reverse=True, in_place=False)
>>> for hit in sorted_qresult[:5]:
...     print("%s %i" % (hit.id, hit.seq_len))
... 
gi|397513516|ref|XM_003827011.1| 6002
gi|390332045|ref|XM_776818.2| 4082
gi|390332043|ref|XM_003723358.1| 4079
gi|356517317|ref|XM_003527287.1| 3251
gi|356543101|ref|XM_003539954.1| 2936

The advantage of having the in_place flag here is that we’re preserving the native ordering, so we may use it again later. You should note that this is not the default behavior of QueryResult.sort, however, which is why we needed to set the in_place flag to True explicitly.

At this point, you’ve known enough about QueryResult objects to make it work for you. But before we go on to the next object in the Bio.SearchIO model, let’s take a look at two more sets of methods that could make it even easier to work with QueryResult objects: the filter and map methods.

If you’re familiar with Python’s list comprehensions, generator expressions or the built in filter and map functions, you’ll know how useful they are for working with list-like objects (if you’re not, check them out!). You can use these built in methods to manipulate QueryResult objects, but you’ll end up with regular Python lists and lose the ability to do more interesting manipulations.

That’s why, QueryResult objects provide its own flavor of filter and map methods. Analogous to filter, there are hit_filter and hsp_filter methods. As their name implies, these methods filter its QueryResult object either on its Hit objects or HSP objects. Similarly, analogous to map, QueryResult objects also provide the hit_map and hsp_map methods. These methods apply a given function to all hits or HSPs in a QueryResult object, respectively.

Let’s see these methods in action, beginning with hit_filter. This method accepts a callback function that checks whether a given Hit object passes the condition you set or not. In other words, the function must accept as its argument a single Hit object and returns True or False.

Here is an example of using hit_filter to filter out Hit objects that only have one HSP:

>>> filter_func = lambda hit: len(hit.hsps) > 1     # the callback function
>>> len(blast_qresult)      # no. of hits before filtering
100
>>> filtered_qresult = blast_qresult.hit_filter(filter_func)
>>> len(filtered_qresult)   # no. of hits after filtering
37
>>> for hit in filtered_qresult[:5]:    # quick check for the hit lengths
...     print("%s %i" % (hit.id, len(hit.hsps)))
gi|301171322|ref|NR_035857.1| 2
gi|262205330|ref|NR_030198.1| 2
gi|301171447|ref|NR_035871.1| 2
gi|262205298|ref|NR_030190.1| 2
gi|270132717|ref|NR_032716.1| 2

hsp_filter works the same as hit_filter, only instead of looking at the Hit objects, it performs filtering on the HSP objects in each hits.

As for the map methods, they too accept a callback function as their arguments. However, instead of returning True or False, the callback function must return the modified Hit or HSP object (depending on whether you’re using hit_map or hsp_map).

Let’s see an example where we’re using hit_map to rename the hit IDs:

>>> def map_func(hit):
...     hit.id = hit.id.split('|')[3]   # renames 'gi|301171322|ref|NR_035857.1|' to 'NR_035857.1'
...     return hit
...
>>> mapped_qresult = blast_qresult.hit_map(map_func)
>>> for hit in mapped_qresult[:5]:
...     print(hit.id)
NR_030195.1
NR_035856.1
NR_032573.1
NR_035857.1
NR_035851.1

Again, hsp_map works the same as hit_map, but on HSP objects instead of Hit objects.

8.1.2  Hit

Hit objects represent all query results from a single database entry. They are the second-level container in the Bio.SearchIO object hierarchy. You’ve seen that they are contained by QueryResult objects, but they themselves contain HSP objects.

Let’s see what they look like, beginning with our BLAST search:

>>> from Bio import SearchIO
>>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
>>> blast_hit = blast_qresult[3]    # fourth hit from the query result
>>> print(blast_hit)
Query: 42291
       mystery_seq
  Hit: gi|301171322|ref|NR_035857.1| (86)
       Pan troglodytes microRNA mir-520c (MIR520C), microRNA
 HSPs: ----  --------  ---------  ------  ---------------  ---------------------
          #   E-value  Bit score    Span      Query range              Hit range
       ----  --------  ---------  ------  ---------------  ---------------------
          0   8.9e-20     100.47      60           [1:61]                [13:73]
          1   3.3e-06      55.39      60           [0:60]                [13:73]

You see that we’ve got the essentials covered here:

  • The query ID and description is present. A hit is always tied to a query, so we want to keep track of the originating query as well. These values can be accessed from a hit using the query_id and query_description attributes.
  • We also have the unique hit ID, description, and full sequence lengths. They can be accessed using id, description, and seq_len, respectively.
  • Finally, there’s a table containing quick information about the HSPs this hit contains. In each row, we’ve got the important HSP details listed: the HSP index, its e-value, its bit score, its span (the alignment length including gaps), its query coordinates, and its hit coordinates.

Now let’s contrast this with the BLAT search. Remember that in the BLAT search we had one hit with 17 HSPs.

>>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl')
>>> blat_hit = blat_qresult[0]      # the only hit
>>> print(blat_hit)
Query: mystery_seq
       <unknown description>
  Hit: chr19 (59128983)
       <unknown description>
 HSPs: ----  --------  ---------  ------  ---------------  ---------------------
          #   E-value  Bit score    Span      Query range              Hit range
       ----  --------  ---------  ------  ---------------  ---------------------
          0         ?          ?       ?           [0:61]    [54204480:54204541]
          1         ?          ?       ?           [0:61]    [54233104:54264463]
          2         ?          ?       ?           [0:61]    [54254477:54260071]
          3         ?          ?       ?           [1:61]    [54210720:54210780]
          4         ?          ?       ?           [0:60]    [54198476:54198536]
          5         ?          ?       ?           [0:61]    [54265610:54265671]
          6         ?          ?       ?           [0:61]    [54238143:54240175]
          7         ?          ?       ?           [0:60]    [54189735:54189795]
          8         ?          ?       ?           [0:61]    [54185425:54185486]
          9         ?          ?       ?           [0:60]    [54197657:54197717]
         10         ?          ?       ?           [0:61]    [54255662:54255723]
         11         ?          ?       ?           [0:61]    [54201651:54201712]
         12         ?          ?       ?           [8:60]    [54206009:54206061]
         13         ?          ?       ?          [10:61]    [54178987:54179038]
         14         ?          ?       ?           [8:61]    [54212018:54212071]
         15         ?          ?       ?           [8:51]    [54234278:54234321]
         16         ?          ?       ?           [8:61]    [54238143:54238196]

Here, we’ve got a similar level of detail as with the BLAST hit we saw earlier. There are some differences worth explaining, though:

  • The e-value and bit score column values. As BLAT HSPs do not have e-values and bit scores, the display defaults to ‘?’.
  • What about the span column? The span values is meant to display the complete alignment length, which consists of all residues and any gaps that may be present. The PSL format do not have this information readily available and Bio.SearchIO does not attempt to try guess what it is, so we get a ‘?’ similar to the e-value and bit score columns.

In terms of Python objects, Hit behaves almost the same as Python lists, but contain HSP objects exclusively. If you’re familiar with lists, you should encounter no difficulties working with the Hit object.

Just like Python lists, Hit objects are iterable, and each iteration returns one HSP object it contains:

>>> for hsp in blast_hit:
...     hsp
HSP(hit_id='gi|301171322|ref|NR_035857.1|', query_id='42291', 1 fragments)
HSP(hit_id='gi|301171322|ref|NR_035857.1|', query_id='42291', 1 fragments)

You can invoke len on a Hit to see how many HSP objects it has:

>>> len(blast_hit)
2
>>> len(blat_hit)
17

You can use the slice notation on Hit objects, whether to retrieve single HSP or multiple HSP objects. Like QueryResult, if you slice for multiple HSP, a new Hit object will be returned containing only the sliced HSP objects:

>>> blat_hit[0]                 # retrieve single items
HSP(hit_id='chr19', query_id='mystery_seq', 1 fragments)
>>> sliced_hit = blat_hit[4:9]  # retrieve multiple items
>>> len(sliced_hit)
5
>>> print(sliced_hit)
Query: mystery_seq
       <unknown description>
  Hit: chr19 (59128983)
       <unknown description>
 HSPs: ----  --------  ---------  ------  ---------------  ---------------------
          #   E-value  Bit score    Span      Query range              Hit range
       ----  --------  ---------  ------  ---------------  ---------------------
          0         ?          ?       ?           [0:60]    [54198476:54198536]
          1         ?          ?       ?           [0:61]    [54265610:54265671]
          2         ?          ?       ?           [0:61]    [54238143:54240175]
          3         ?          ?       ?           [0:60]    [54189735:54189795]
          4         ?          ?       ?           [0:61]    [54185425:54185486]

You can also sort the HSP inside a Hit, using the exact same arguments like the sort method you saw in the QueryResult object.

Finally, there are also the filter and map methods you can use on Hit objects. Unlike in the QueryResult object, Hit objects only have one variant of filter (Hit.filter) and one variant of map (Hit.map). Both of Hit.filter and Hit.map work on the HSP objects a Hit has.

8.1.3  HSP

HSP (high-scoring pair) represents region(s) in the hit sequence that contains significant alignment(s) to the query sequence. It contains the actual match between your query sequence and a database entry. As this match is determined by the sequence search tool’s algorithms, the HSP object contains the bulk of the statistics computed by the search tool. This also makes the distinction between HSP objects from different search tools more apparent compared to the differences you’ve seen in QueryResult or Hit objects.

Let’s see some examples from our BLAST and BLAT searches. We’ll look at the BLAST HSP first:

>>> from Bio import SearchIO
>>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
>>> blast_hsp = blast_qresult[0][0]    # first hit, first hsp
>>> print(blast_hsp)
      Query: 42291 mystery_seq
        Hit: gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 520b (MIR520...
Query range: [0:61] (1)
  Hit range: [0:61] (1)
Quick stats: evalue 4.9e-23; bitscore 111.29
  Fragments: 1 (61 columns)
     Query - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
             |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
       Hit - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG

Just like QueryResult and Hit, invoking print on an HSP shows its general details:

  • There are the query and hit IDs and descriptions. We need these to identify our HSP.
  • We’ve also got the matching range of the query and hit sequences. The slice notation we’re using here is an indication that the range is displayed using Python’s indexing style (zero-based, half open). The number inside the parenthesis denotes the strand. In this case, both sequences have the plus strand.
  • Some quick statistics are available: the e-value and bitscore.
  • There is information about the HSP fragments. Ignore this for now; it will be explained later on.
  • And finally, we have the query and hit sequence alignment itself.

These details can be accessed on their own using the dot notation, just like in QueryResult and Hit:

>>> blast_hsp.query_range
(0, 61)

>>> blast_hsp.evalue
4.91307e-23

They’re not the only attributes available, though. HSP objects come with a default set of properties that makes it easy to probe their various details. Here are some examples:

>>> blast_hsp.hit_start         # start coordinate of the hit sequence
0
>>> blast_hsp.query_span        # how many residues in the query sequence
61
>>> blast_hsp.aln_span          # how long the alignment is
61

Check out the HSP documentation for a full list of these predefined properties.

Furthermore, each sequence search tool usually computes its own statistics / details for its HSP objects. For example, an XML BLAST search also outputs the number of gaps and identical residues. These attributes can be accessed like so:

>>> blast_hsp.gap_num       # number of gaps
0
>>> blast_hsp.ident_num     # number of identical residues
61

These details are format-specific; they may not be present in other formats. To see which details are available for a given sequence search tool, you should check the format’s documentation in Bio.SearchIO. Alternatively, you may also use .__dict__.keys() for a quick list of what’s available:

>>> blast_hsp.__dict__.keys()
['bitscore', 'evalue', 'ident_num', 'gap_num', 'bitscore_raw', 'pos_num', '_items']

Finally, you may have noticed that the query and hit attributes of our HSP are not just regular strings:

>>> blast_hsp.query
SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='42291', name='aligned query sequence', description='mystery_seq', dbxrefs=[])
>>> blast_hsp.hit
SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='gi|262205317|ref|NR_030195.1|', name='aligned hit sequence', description='Homo sapiens microRNA 520b (MIR520B), microRNA', dbxrefs=[])

They are SeqRecord objects you saw earlier in Section 4! This means that you can do all sorts of interesting things you can do with SeqRecord objects on HSP.query and/or HSP.hit.

It should not surprise you now that the HSP object has an alignment property which is a MultipleSeqAlignment object:

>>> print(blast_hsp.aln)
DNAAlphabet() alignment with 2 rows and 61 columns
CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAG...GGG 42291
CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAG...GGG gi|262205317|ref|NR_030195.1|

Having probed the BLAST HSP, let’s now take a look at HSPs from our BLAT results for a different kind of HSP. As usual, we’ll begin by invoking print on it:

>>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl')
>>> blat_hsp = blat_qresult[0][0]       # first hit, first hsp
>>> print(blat_hsp)
      Query: mystery_seq <unknown description>
        Hit: chr19 <unknown description>
Query range: [0:61] (1)
  Hit range: [54204480:54204541] (1)
Quick stats: evalue ?; bitscore ?
  Fragments: 1 (? columns)

Some of the outputs you may have already guessed. We have the query and hit IDs and descriptions and the sequence coordinates. Values for evalue and bitscore is ‘?’ as BLAT HSPs do not have these attributes. But The biggest difference here is that you don’t see any sequence alignments displayed. If you look closer, PSL formats themselves do not have any hit or query sequences, so Bio.SearchIO won’t create any sequence or alignment objects. What happens if you try to access HSP.query, HSP.hit, or HSP.aln? You’ll get the default values for these attributes, which is None:

>>> blat_hsp.hit is None
True
>>> blat_hsp.query is None
True
>>> blat_hsp.aln is None
True

This does not affect other attributes, though. For example, you can still access the length of the query or hit alignment. Despite not displaying any attributes, the PSL format still have this information so Bio.SearchIO can extract them:

>>> blat_hsp.query_span     # length of query match
61
>>> blat_hsp.hit_span       # length of hit match
61

Other format-specific attributes are still present as well:

>>> blat_hsp.score          # PSL score
61
>>> blat_hsp.mismatch_num   # the mismatch column
0

So far so good? Things get more interesting when you look at another ‘variant’ of HSP present in our BLAT results. You might recall that in BLAT searches, sometimes we get our results separated into ‘blocks’. These blocks are essentially alignment fragments that may have some intervening sequence between them.

Let’s take a look at a BLAT HSP that contains multiple blocks to see how Bio.SearchIO deals with this:

>>> blat_hsp2 = blat_qresult[0][1]      # first hit, second hsp
>>> print(blat_hsp2)
      Query: mystery_seq <unknown description>
        Hit: chr19 <unknown description>
Query range: [0:61] (1)
  Hit range: [54233104:54264463] (1)
Quick stats: evalue ?; bitscore ?
  Fragments: ---  --------------  ----------------------  ----------------------
               #            Span             Query range               Hit range
             ---  --------------  ----------------------  ----------------------
               0               ?                  [0:18]     [54233104:54233122]
               1               ?                 [18:61]     [54264420:54264463]

What’s happening here? We still some essential details covered: the IDs and descriptions, the coordinates, and the quick statistics are similar to what you’ve seen before. But the fragments detail is all different. Instead of showing ‘Fragments: 1’, we now have a table with two data rows.

This is how Bio.SearchIO deals with HSPs having multiple fragments. As mentioned before, an HSP alignment may be separated by intervening sequences into fragments. The intervening sequences are not part of the query-hit match, so they should not be considered part of query nor hit sequence. However, they do affect how we deal with sequence coordinates, so we can’t ignore them.

Take a look at the hit coordinate of the HSP above. In the Hit range: field, we see that the coordinate is [54233104:54264463]. But looking at the table rows, we see that not the entire region spanned by this coordinate matches our query. Specifically, the intervening region spans from 54233122 to 54264420.

Why then, is the query coordinates seem to be contiguous, you ask? This is perfectly fine. In this case it means that the query match is contiguous (no intervening regions), while the hit match is not.

All these attributes are accessible from the HSP directly, by the way:

>>> blat_hsp2.hit_range         # hit start and end coordinates of the entire HSP
(54233104, 54264463)
>>> blat_hsp2.hit_range_all     # hit start and end coordinates of each fragment
[(54233104, 54233122), (54264420, 54264463)]
>>> blat_hsp2.hit_span          # hit span of the entire HSP
31359
>>> blat_hsp2.hit_span_all      # hit span of each fragment
[18, 43]
>>> blat_hsp2.hit_inter_ranges  # start and end coordinates of intervening regions in the hit sequence
[(54233122, 54264420)]
>>> blat_hsp2.hit_inter_spans   # span of intervening regions in the hit sequence
[31298]

Most of these attributes are not readily available from the PSL file we have, but Bio.SearchIO calculates them for you on the fly when you parse the PSL file. All it needs are the start and end coordinates of each fragment.

What about the query, hit, and aln attributes? If the HSP has multiple fragments, you won’t be able to use these attributes as they only fetch single SeqRecord or MultipleSeqAlignment objects. However, you can use their *_all counterparts: query_all, hit_all, and aln_all. These properties will return a list containing SeqRecord or MultipleSeqAlignment objects from each of the HSP fragment. There are other attributes that behave similarly, i.e. they only work for HSPs with one fragment. Check out the HSP documentation for a full list.

Finally, to check whether you have multiple fragments or not, you can use the is_fragmented property like so:

>>> blat_hsp2.is_fragmented     # BLAT HSP with 2 fragments
True
>>> blat_hsp.is_fragmented      # BLAT HSP from earlier, with one fragment
False

Before we move on, you should also know that we can use the slice notation on HSP objects, just like QueryResult or Hit objects. When you use this notation, you’ll get an HSPFragment object in return, the last component of the object model.

8.1.4  HSPFragment

HSPFragment represents a single, contiguous match between the query and hit sequences. You could consider it the core of the object model and search result, since it is the presence of these fragments that determine whether your search have results or not.

In most cases, you don’t have to deal with HSPFragment objects directly since not that many sequence search tools fragment their HSPs. When you do have to deal with them, what you should remember is that HSPFragment objects were written with to be as compact as possible. In most cases, they only contain attributes directly related to sequences: strands, reading frames, alphabets, coordinates, the sequences themselves, and their IDs and descriptions.

These attributes are readily shown when you invoke print on an HSPFragment. Here’s an example, taken from our BLAST search:

>>> from Bio import SearchIO
>>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
>>> blast_frag = blast_qresult[0][0][0]    # first hit, first hsp, first fragment
>>> print(blast_frag)
      Query: 42291 mystery_seq
        Hit: gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 520b (MIR520...
Query range: [0:61] (1)
  Hit range: [0:61] (1)
  Fragments: 1 (61 columns)
     Query - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
             |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
       Hit - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG

At this level, the BLAT fragment looks quite similar to the BLAST fragment, save for the query and hit sequences which are not present:

>>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl')
>>> blat_frag = blat_qresult[0][0][0]    # first hit, first hsp, first fragment
>>> print(blat_frag)
      Query: mystery_seq <unknown description>
        Hit: chr19 <unknown description>
Query range: [0:61] (1)
  Hit range: [54204480:54204541] (1)
  Fragments: 1 (? columns)

In all cases, these attributes are accessible using our favorite dot notation. Some examples:

>>> blast_frag.query_start      # query start coordinate
0
>>> blast_frag.hit_strand       # hit sequence strand
1
>>> blast_frag.hit              # hit sequence, as a SeqRecord object
SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='gi|262205317|ref|NR_030195.1|', name='aligned hit sequence', description='Homo sapiens microRNA 520b (MIR520B), microRNA', dbxrefs=[])

8.2  A note about standards and conventions

Before we move on to the main functions, there is something you ought to know about the standards Bio.SearchIO uses. If you’ve worked with multiple sequence search tools, you might have had to deal with the many different ways each program deals with things like sequence coordinates. It might not have been a pleasant experience as these search tools usually have their own standards. For example, one tools might use one-based coordinates, while the other uses zero-based coordinates. Or, one program might reverse the start and end coordinates if the strand is minus, while others don’t. In short, these often creates unnecessary mess must be dealt with.

We realize this problem ourselves and we intend to address it in Bio.SearchIO. After all, one of the goals of Bio.SearchIO is to create a common, easy to use interface to deal with various search output files. This means creating standards that extend beyond the object model you just saw.

Now, you might complain, "Not another standard!". Well, eventually we have to choose one convention or the other, so this is necessary. Plus, we’re not creating something entirely new here; just adopting a standard we think is best for a Python programmer (it is Biopython, after all).

There are three implicit standards that you can expect when working with Bio.SearchIO:

  • The first one pertains to sequence coordinates. In Bio.SearchIO, all sequence coordinates follows Python’s coordinate style: zero-based and half open. For example, if in a BLAST XML output file the start and end coordinates of an HSP are 10 and 28, they would become 9 and 28 in Bio.SearchIO. The start coordinate becomes 9 because Python indices start from zero, while the end coordinate remains 28 as Python slices omit the last item in an interval.
  • The second is on sequence coordinate orders. In Bio.SearchIO, start coordinates are always less than or equal to end coordinates. This isn’t always the case with all sequence search tools, as some of them have larger start coordinates when the sequence strand is minus.
  • The last one is on strand and reading frame values. For strands, there are only four valid choices: 1 (plus strand), -1 (minus strand), 0 (protein sequences), and None (no strand). For reading frames, the valid choices are integers from -3 to 3 and None.

Note that these standards only exist in Bio.SearchIO objects. If you write Bio.SearchIO objects into an output format, Bio.SearchIO will use the format’s standard for the output. It does not force its standard over to your output file.

8.3  Reading search output files

There are two functions you can use for reading search output files into Bio.SearchIO objects: read and parse. They’re essentially similar to read and parse functions in other submodules like Bio.SeqIO or Bio.AlignIO. In both cases, you need to supply the search output file name and the file format name, both as Python strings. You can check the documentation for a list of format names Bio.SearchIO recognizes.

Bio.SearchIO.read is used for reading search output files with only one query and returns a QueryResult object. You’ve seen read used in our previous examples. What you haven’t seen is that read may also accept additional keyword arguments, depending on the file format.

Here are some examples. In the first one, we use read just like previously to read a BLAST tabular output file. In the second one, we use a keyword argument to modify so it parses the BLAST tabular variant with comments in it:

>>> from Bio import SearchIO
>>> qresult = SearchIO.read('tab_2226_tblastn_003.txt', 'blast-tab')
>>> qresult
QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
>>> qresult2 = SearchIO.read('tab_2226_tblastn_007.txt', 'blast-tab', comments=True)
>>> qresult2
QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)

These keyword arguments differs among file formats. Check the format documentation to see if it has keyword arguments that modifies its parser’s behavior.

As for the Bio.SearchIO.parse, it is used for reading search output files with any number of queries. The function returns a generator object that yields a QueryResult object in each iteration. Like Bio.SearchIO.read, it also accepts format-specific keyword arguments:

>>> from Bio import SearchIO
>>> qresults = SearchIO.parse('tab_2226_tblastn_001.txt', 'blast-tab')
>>> for qresult in qresults:
...     print(qresult.id)
gi|16080617|ref|NP_391444.1|
gi|11464971:4-101
>>> qresults2 = SearchIO.parse('tab_2226_tblastn_005.txt', 'blast-tab', comments=True)
>>> for qresult in qresults2:
...     print(qresult.id)
random_s00
gi|16080617|ref|NP_391444.1|
gi|11464971:4-101

8.4  Dealing with large search output files with indexing

Sometimes, you’re handed a search output file containing hundreds or thousands of queries that you need to parse. You can of course use Bio.SearchIO.parse for this file, but that would be grossly inefficient if you need to access only a few of the queries. This is because parse will parse all queries it sees before it fetches your query of interest.

In this case, the ideal choice would be to index the file using Bio.SearchIO.index or Bio.SearchIO.index_db. If the names sound familiar, it’s because you’ve seen them before in Section 5.4.2. These functions also behave similarly to their Bio.SeqIO counterparts, with the addition of format-specific keyword arguments.

Here are some examples. You can use index with just the filename and format name:

>>> from Bio import SearchIO
>>> idx = SearchIO.index('tab_2226_tblastn_001.txt', 'blast-tab')
>>> sorted(idx.keys())
['gi|11464971:4-101', 'gi|16080617|ref|NP_391444.1|']
>>> idx['gi|16080617|ref|NP_391444.1|']
QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)

Or also with the format-specific keyword argument:

>>> idx = SearchIO.index('tab_2226_tblastn_005.txt', 'blast-tab', comments=True)
>>> sorted(idx.keys())
['gi|11464971:4-101', 'gi|16080617|ref|NP_391444.1|', 'random_s00']
>>> idx['gi|16080617|ref|NP_391444.1|']
QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)

Or with the key_function argument, as in Bio.SeqIO:

>>> key_function = lambda id: id.upper()    # capitalizes the keys
>>> idx = SearchIO.index('tab_2226_tblastn_001.txt', 'blast-tab', key_function=key_function)
>>> sorted(idx.keys())
['GI|11464971:4-101', 'GI|16080617|REF|NP_391444.1|']
>>> idx['GI|16080617|REF|NP_391444.1|']
QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)

Bio.SearchIO.index_db works like as index, only it writes the query offsets into an SQLite database file.

8.5  Writing and converting search output files

It is occasionally useful to be able to manipulate search results from an output file and write it again to a new file. Bio.SearchIO provides a write function that lets you do exactly this. It takes as its arguments an iterable returning QueryResult objects, the output filename to write to, the format name to write to, and optionally some format-specific keyword arguments. It returns a four-item tuple, which denotes the number or QueryResult, Hit, HSP, and HSPFragment objects that were written.

>>> from Bio import SearchIO
>>> qresults = SearchIO.parse('mirna.xml', 'blast-xml')     # read XML file
>>> SearchIO.write(qresults, 'results.tab', 'blast-tab')    # write to tabular file
(3, 239, 277, 277)

You should note different file formats require different attributes of the QueryResult, Hit, HSP and HSPFragment objects. If these attributes are not present, writing won’t work. In other words, you can’t always write to the output format that you want. For example, if you read a BLAST XML file, you wouldn’t be able to write the results to a PSL file as PSL files require attributes not calculated by BLAST (e.g. the number of repeat matches). You can always set these attributes manually, if you really want to write to PSL, though.

Like read, parse, index, and index_db, write also accepts format-specific keyword arguments. Check out the documentation for a complete list of formats Bio.SearchIO can write to and their arguments.

Finally, Bio.SearchIO also provides a convert function, which is simply a shortcut for Bio.SearchIO.parse and Bio.SearchIO.write. Using the convert function, our example above would be:

>>> from Bio import SearchIO
>>> SearchIO.convert('mirna.xml', 'blast-xml', 'results.tab', 'blast-tab')
(3, 239, 277, 277)

As convert uses write, it is only limited to format conversions that have all the required attributes. Here, the BLAST XML file provides all the default values a BLAST tabular file requires, so it works just fine. However, other format conversions are less likely to work since you need to manually assign the required attributes first.

Chapter 9  Accessing NCBI’s Entrez databases

Entrez (http://www.ncbi.nlm.nih.gov/Entrez) is a data retrieval system that provides users access to NCBI’s databases such as PubMed, GenBank, GEO, and many others. You can access Entrez from a web browser to manually enter queries, or you can use Biopython’s Bio.Entrez module for programmatic access to Entrez. The latter allows you for example to search PubMed or download GenBank records from within a Python script.

The Bio.Entrez module makes use of the Entrez Programming Utilities (also known as EUtils), consisting of eight tools that are described in detail on NCBI’s page at http://www.ncbi.nlm.nih.gov/entrez/utils/. Each of these tools corresponds to one Python function in the Bio.Entrez module, as described in the sections below. This module makes sure that the correct URL is used for the queries, and that not more than one request is made every three seconds, as required by NCBI.

The output returned by the Entrez Programming Utilities is typically in XML format. To parse such output, you have several options:

  1. Use Bio.Entrez’s parser to parse the XML output into a Python object;
  2. Use the DOM (Document Object Model) parser in Python’s standard library;
  3. Use the SAX (Simple API for XML) parser in Python’s standard library;
  4. Read the XML output as raw text, and parse it by string searching and manipulation.

For the DOM and SAX parsers, see the Python documentation. The parser in Bio.Entrez is discussed below.

NCBI uses DTD (Document Type Definition) files to describe the structure of the information contained in XML files. Most of the DTD files used by NCBI are included in the Biopython distribution. The Bio.Entrez parser makes use of the DTD files when parsing an XML file returned by NCBI Entrez.

Occasionally, you may find that the DTD file associated with a specific XML file is missing in the Biopython distribution. In particular, this may happen when NCBI updates its DTD files. If this happens, Entrez.read will show a warning message with the name and URL of the missing DTD file. The parser will proceed to access the missing DTD file through the internet, allowing the parsing of the XML file to continue. However, the parser is much faster if the DTD file is available locally. For this purpose, please download the DTD file from the URL in the warning message and place it in the directory ...site-packages/Bio/Entrez/DTDs, containing the other DTD files. If you don’t have write access to this directory, you can also place the DTD file in ~/.biopython/Bio/Entrez/DTDs, where ~ represents your home directory. Since this directory is read before the directory ...site-packages/Bio/Entrez/DTDs, you can also put newer versions of DTD files there if the ones in ...site-packages/Bio/Entrez/DTDs become outdated. Alternatively, if you installed Biopython from source, you can add the DTD file to the source code’s Bio/Entrez/DTDs directory, and reinstall Biopython. This will install the new DTD file in the correct location together with the other DTD files.

The Entrez Programming Utilities can also generate output in other formats, such as the Fasta or GenBank file formats for sequence databases, or the MedLine format for the literature database, discussed in Section 9.12.

9.1  Entrez Guidelines

Before using Biopython to access the NCBI’s online resources (via Bio.Entrez or some of the other modules), please read the NCBI’s Entrez User Requirements. If the NCBI finds you are abusing their systems, they can and will ban your access!

To paraphrase:

  • For any series of more than 100 requests, do this at weekends or outside USA peak times. This is up to you to obey.
  • Use the http://eutils.ncbi.nlm.nih.gov address, not the standard NCBI Web address. Biopython uses this web address.
  • Make no more than three requests every seconds (relaxed from at most one request every three seconds in early 2009). This is automatically enforced by Biopython.
  • Use the optional email parameter so the NCBI can contact you if there is a problem. You can either explicitly set this as a parameter with each call to Entrez (e.g. include email="A.N.Other@example.com" in the argument list), or you can set a global email address: 
    >>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"
    Bio.Entrez will then use this email address with each call to Entrez. The example.com address is a reserved domain name specifically for documentation (RFC 2606). Please DO NOT use a random email – it’s better not to give an email at all. The email parameter will be mandatory from June 1, 2010. In case of excessive usage, NCBI will attempt to contact a user at the e-mail address provided prior to blocking access to the E-utilities.
  • If you are using Biopython within some larger software suite, use the tool parameter to specify this. You can either explicitly set the tool name as a parameter with each call to Entrez (e.g. include tool="MyLocalScript" in the argument list), or you can set a global tool name: 
    >>> from Bio import Entrez
>>> Entrez.tool = "MyLocalScript"
    The tool parameter will default to Biopython.
  • For large queries, the NCBI also recommend using their session history feature (the WebEnv session cookie string, see Section 9.15). This is only slightly more complicated.

In conclusion, be sensible with your usage levels. If you plan to download lots of data, consider other options. For example, if you want easy access to all the human genes, consider fetching each chromosome by FTP as a GenBank file, and importing these into your own BioSQL database (see Section 18.5).

9.2  EInfo: Obtaining information about the Entrez databases

EInfo provides field index term counts, last update, and available links for each of NCBI’s databases. In addition, you can use EInfo to obtain a list of all database names accessible through the Entrez utilities: 

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
>>> handle = Entrez.einfo()
>>> result = handle.read()

The variable result now contains a list of databases in XML format: 

>>> print(result)
<?xml version="1.0"?>
<!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May 2002//EN"
 "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
<eInfoResult>
<DbList>
        <DbName>pubmed</DbName>
        <DbName>protein</DbName>
        <DbName>nucleotide</DbName>
        <DbName>nuccore</DbName>
        <DbName>nucgss</DbName>
        <DbName>nucest</DbName>
        <DbName>structure</DbName>
        <DbName>genome</DbName>
        <DbName>books</DbName>
        <DbName>cancerchromosomes</DbName>
        <DbName>cdd</DbName>
        <DbName>gap</DbName>
        <DbName>domains</DbName>
        <DbName>gene</DbName>
        <DbName>genomeprj</DbName>
        <DbName>gensat</DbName>
        <DbName>geo</DbName>
        <DbName>gds</DbName>
        <DbName>homologene</DbName>
        <DbName>journals</DbName>
        <DbName>mesh</DbName>
        <DbName>ncbisearch</DbName>
        <DbName>nlmcatalog</DbName>
        <DbName>omia</DbName>
        <DbName>omim</DbName>
        <DbName>pmc</DbName>
        <DbName>popset</DbName>
        <DbName>probe</DbName>
        <DbName>proteinclusters</DbName>
        <DbName>pcassay</DbName>
        <DbName>pccompound</DbName>
        <DbName>pcsubstance</DbName>
        <DbName>snp</DbName>
        <DbName>taxonomy</DbName>
        <DbName>toolkit</DbName>
        <DbName>unigene</DbName>
        <DbName>unists</DbName>
</DbList>
</eInfoResult>

Since this is a fairly simple XML file, we could extract the information it contains simply by string searching. Using Bio.Entrez’s parser instead, we can directly parse this XML file into a Python object: 

>>> from Bio import Entrez
>>> handle = Entrez.einfo()
>>> record = Entrez.read(handle)

Now record is a dictionary with exactly one key: 

>>> record.keys()
[u'DbList']

The values stored in this key is the list of database names shown in the XML above: 

>>> record["DbList"]
['pubmed', 'protein', 'nucleotide', 'nuccore', 'nucgss', 'nucest',
 'structure', 'genome', 'books', 'cancerchromosomes', 'cdd', 'gap',
 'domains', 'gene', 'genomeprj', 'gensat', 'geo', 'gds', 'homologene',
 'journals', 'mesh', 'ncbisearch', 'nlmcatalog', 'omia', 'omim', 'pmc',
 'popset', 'probe', 'proteinclusters', 'pcassay', 'pccompound',
 'pcsubstance', 'snp', 'taxonomy', 'toolkit', 'unigene', 'unists']

For each of these databases, we can use EInfo again to obtain more information: 

>>> handle = Entrez.einfo(db="pubmed")
>>> record = Entrez.read(handle)
>>> record["DbInfo"]["Description"]
'PubMed bibliographic record'
>>> record["DbInfo"]["Count"]
'17989604'
>>> record["DbInfo"]["LastUpdate"]
'2008/05/24 06:45'

Try record["DbInfo"].keys() for other information stored in this record. One of the most useful is a list of possible search fields for use with ESearch:

>>> for field in record["DbInfo"]["FieldList"]:
...     print("%(Name)s, %(FullName)s, %(Description)s" % field)
ALL, All Fields, All terms from all searchable fields
UID, UID, Unique number assigned to publication
FILT, Filter, Limits the records
TITL, Title, Words in title of publication
WORD, Text Word, Free text associated with publication
MESH, MeSH Terms, Medical Subject Headings assigned to publication
MAJR, MeSH Major Topic, MeSH terms of major importance to publication
AUTH, Author, Author(s) of publication
JOUR, Journal, Journal abbreviation of publication
AFFL, Affiliation, Author's institutional affiliation and address
...

That’s a long list, but indirectly this tells you that for the PubMed database, you can do things like Jones[AUTH] to search the author field, or Sanger[AFFL] to restrict to authors at the Sanger Centre. This can be very handy - especially if you are not so familiar with a particular database.

9.3  ESearch: Searching the Entrez databases

To search any of these databases, we use Bio.Entrez.esearch(). For example, let’s search in PubMed for publications related to Biopython: 

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
>>> handle = Entrez.esearch(db="pubmed", term="biopython")
>>> record = Entrez.read(handle)
>>> record["IdList"]
['19304878', '18606172', '16403221', '16377612', '14871861', '14630660', '12230038']

In this output, you see seven PubMed IDs (including 19304878 which is the PMID for the Biopython application note), which can be retrieved by EFetch (see section 9.6).

You can also use ESearch to search GenBank. Here we’ll do a quick search for the matK gene in Cypripedioideae orchids (see Section 9.2 about EInfo for one way to find out which fields you can search in each Entrez database):

>>> handle = Entrez.esearch(db="nucleotide", term="Cypripedioideae[Orgn] AND matK[Gene]")
>>> record = Entrez.read(handle)
>>> record["Count"]
'25'
>>> record["IdList"]
['126789333', '37222967', '37222966', '37222965', ..., '61585492']

Each of the IDs (126789333, 37222967, 37222966, …) is a GenBank identifier. See section 9.6 for information on how to actually download these GenBank records.

Note that instead of a species name like Cypripedioideae[Orgn], you can restrict the search using an NCBI taxon identifier, here this would be txid158330[Orgn]. This isn’t currently documented on the ESearch help page - the NCBI explained this in reply to an email query. You can often deduce the search term formatting by playing with the Entrez web interface. For example, including complete[prop] in a genome search restricts to just completed genomes.

As a final example, let’s get a list of computational journal titles: 

>>> handle = Entrez.esearch(db="journals", term="computational")
>>> record = Entrez.read(handle)
>>> record["Count"]
'16'
>>> record["IdList"]
['30367', '33843', '33823', '32989', '33190', '33009', '31986',
 '34502', '8799', '22857', '32675', '20258', '33859', '32534',
 '32357', '32249']

Again, we could use EFetch to obtain more information for each of these journal IDs.

ESearch has many useful options — see the ESearch help page for more information.

9.4  EPost: Uploading a list of identifiers

EPost uploads a list of UIs for use in subsequent search strategies; see the EPost help page for more information. It is available from Biopython through the Bio.Entrez.epost() function.

To give an example of when this is useful, suppose you have a long list of IDs you want to download using EFetch (maybe sequences, maybe citations – anything). When you make a request with EFetch your list of IDs, the database etc, are all turned into a long URL sent to the server. If your list of IDs is long, this URL gets long, and long URLs can break (e.g. some proxies don’t cope well).

Instead, you can break this up into two steps, first uploading the list of IDs using EPost (this uses an “HTML post” internally, rather than an “HTML get”, getting round the long URL problem). With the history support, you can then refer to this long list of IDs, and download the associated data with EFetch.

Let’s look at a simple example to see how EPost works – uploading some PubMed identifiers: 

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
>>> id_list = ["19304878", "18606172", "16403221", "16377612", "14871861", "14630660"]
>>> print(Entrez.epost("pubmed", id=",".join(id_list)).read())
<?xml version="1.0"?>
<!DOCTYPE ePostResult PUBLIC "-//NLM//DTD ePostResult, 11 May 2002//EN"
 "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/ePost_020511.dtd">
<ePostResult>
 <QueryKey>1</QueryKey>
 <WebEnv>NCID_01_206841095_130.14.22.101_9001_1242061629</WebEnv>
</ePostResult>

The returned XML includes two important strings, QueryKey and WebEnv which together define your history session. You would extract these values for use with another Entrez call such as EFetch: 

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
>>> id_list = ["19304878", "18606172", "16403221", "16377612", "14871861", "14630660"]
>>> search_results = Entrez.read(Entrez.epost("pubmed", id=",".join(id_list)))
>>> webenv = search_results["WebEnv"]
>>> query_key = search_results["QueryKey"]

Section 9.15 shows how to use the history feature.

9.5  ESummary: Retrieving summaries from primary IDs

ESummary retrieves document summaries from a list of primary IDs (see the ESummary help page for more information). In Biopython, ESummary is available as Bio.Entrez.esummary(). Using the search result above, we can for example find out more about the journal with ID 30367: 

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
>>> handle = Entrez.esummary(db="journals", id="30367")
>>> record = Entrez.read(handle)
>>> record[0]["Id"]
'30367'
>>> record[0]["Title"]
'Computational biology and chemistry'
>>> record[0]["Publisher"]
'Pergamon,'

9.6  EFetch: Downloading full records from Entrez

EFetch is what you use when you want to retrieve a full record from Entrez. This covers several possible databases, as described on the main EFetch Help page.

For most of their databases, the NCBI support several different file formats. Requesting a specific file format from Entrez using Bio.Entrez.efetch() requires specifying the rettype and/or retmode optional arguments. The different combinations are described for each database type on the pages linked to on NCBI efetch webpage (e.g. literature, sequences and taxonomy).

One common usage is downloading sequences in the FASTA or GenBank/GenPept plain text formats (which can then be parsed with Bio.SeqIO, see Sections 5.3.1 and 9.6). From the Cypripedioideae example above, we can download GenBank record 186972394 using Bio.Entrez.efetch:

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
>>> handle = Entrez.efetch(db="nucleotide", id="186972394", rettype="gb", retmode="text")
>>> print(handle.read())
LOCUS       EU490707                1302 bp    DNA     linear   PLN 05-MAY-2008
DEFINITION  Selenipedium aequinoctiale maturase K (matK) gene, partial cds;
            chloroplast.
ACCESSION   EU490707
VERSION     EU490707.1  GI:186972394
KEYWORDS    .
SOURCE      chloroplast Selenipedium aequinoctiale
  ORGANISM  Selenipedium aequinoctiale
            Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
            Spermatophyta; Magnoliophyta; Liliopsida; Asparagales; Orchidaceae;
            Cypripedioideae; Selenipedium.
REFERENCE   1  (bases 1 to 1302)
  AUTHORS   Neubig,K.M., Whitten,W.M., Carlsward,B.S., Blanco,M.A.,
            Endara,C.L., Williams,N.H. and Moore,M.J.
  TITLE     Phylogenetic utility of ycf1 in orchids
  JOURNAL   Unpublished
REFERENCE   2  (bases 1 to 1302)
  AUTHORS   Neubig,K.M., Whitten,W.M., Carlsward,B.S., Blanco,M.A.,
            Endara,C.L., Williams,N.H. and Moore,M.J.
  TITLE     Direct Submission
  JOURNAL   Submitted (14-FEB-2008) Department of Botany, University of
            Florida, 220 Bartram Hall, Gainesville, FL 32611-8526, USA
FEATURES             Location/Qualifiers
     source          1..1302
                     /organism="Selenipedium aequinoctiale"
                     /organelle="plastid:chloroplast"
                     /mol_type="genomic DNA"
                     /specimen_voucher="FLAS:Blanco 2475"
                     /db_xref="taxon:256374"
     gene            <1..>1302
                     /gene="matK"
     CDS             <1..>1302
                     /gene="matK"
                     /codon_start=1
                     /transl_table=11
                     /product="maturase K"
                     /protein_id="ACC99456.1"
                     /db_xref="GI:186972395"
                     /translation="IFYEPVEIFGYDNKSSLVLVKRLITRMYQQNFLISSVNDSNQKG
                     FWGHKHFFSSHFSSQMVSEGFGVILEIPFSSQLVSSLEEKKIPKYQNLRSIHSIFPFL
                     EDKFLHLNYVSDLLIPHPIHLEILVQILQCRIKDVPSLHLLRLLFHEYHNLNSLITSK
                     KFIYAFSKRKKRFLWLLYNSYVYECEYLFQFLRKQSSYLRSTSSGVFLERTHLYVKIE
                     HLLVVCCNSFQRILCFLKDPFMHYVRYQGKAILASKGTLILMKKWKFHLVNFWQSYFH
                     FWSQPYRIHIKQLSNYSFSFLGYFSSVLENHLVVRNQMLENSFIINLLTKKFDTIAPV
                     ISLIGSLSKAQFCTVLGHPISKPIWTDFSDSDILDRFCRICRNLCRYHSGSSKKQVLY
                     RIKYILRLSCARTLARKHKSTVRTFMRRLGSGLLEEFFMEEE"
ORIGIN      
        1 attttttacg aacctgtgga aatttttggt tatgacaata aatctagttt agtacttgtg
       61 aaacgtttaa ttactcgaat gtatcaacag aattttttga tttcttcggt taatgattct
      121 aaccaaaaag gattttgggg gcacaagcat tttttttctt ctcatttttc ttctcaaatg
      181 gtatcagaag gttttggagt cattctggaa attccattct cgtcgcaatt agtatcttct
      241 cttgaagaaa aaaaaatacc aaaatatcag aatttacgat ctattcattc aatatttccc
      301 tttttagaag acaaattttt acatttgaat tatgtgtcag atctactaat accccatccc
      361 atccatctgg aaatcttggt tcaaatcctt caatgccgga tcaaggatgt tccttctttg
      421 catttattgc gattgctttt ccacgaatat cataatttga atagtctcat tacttcaaag
      481 aaattcattt acgccttttc aaaaagaaag aaaagattcc tttggttact atataattct
      541 tatgtatatg aatgcgaata tctattccag tttcttcgta aacagtcttc ttatttacga
      601 tcaacatctt ctggagtctt tcttgagcga acacatttat atgtaaaaat agaacatctt
      661 ctagtagtgt gttgtaattc ttttcagagg atcctatgct ttctcaagga tcctttcatg
      721 cattatgttc gatatcaagg aaaagcaatt ctggcttcaa agggaactct tattctgatg
      781 aagaaatgga aatttcatct tgtgaatttt tggcaatctt attttcactt ttggtctcaa
      841 ccgtatagga ttcatataaa gcaattatcc aactattcct tctcttttct ggggtatttt
      901 tcaagtgtac tagaaaatca tttggtagta agaaatcaaa tgctagagaa ttcatttata
      961 ataaatcttc tgactaagaa attcgatacc atagccccag ttatttctct tattggatca
     1021 ttgtcgaaag ctcaattttg tactgtattg ggtcatccta ttagtaaacc gatctggacc
     1081 gatttctcgg attctgatat tcttgatcga ttttgccgga tatgtagaaa tctttgtcgt
     1141 tatcacagcg gatcctcaaa aaaacaggtt ttgtatcgta taaaatatat acttcgactt
     1201 tcgtgtgcta gaactttggc acggaaacat aaaagtacag tacgcacttt tatgcgaaga
     1261 ttaggttcgg gattattaga agaattcttt atggaagaag aa
//

The arguments rettype="gb" and retmode="text" let us download this record in the GenBank format.

Note that until Easter 2009, the Entrez EFetch API let you use “genbank” as the return type, however the NCBI now insist on using the official return types of “gb” or “gbwithparts” (or “gp” for proteins) as described on online. Also not that until Feb 2012, the Entrez EFetch API would default to returning plain text files, but now defaults to XML.

Alternatively, you could for example use rettype="fasta" to get the Fasta-format; see the EFetch Sequences Help page for other options. Remember – the available formats depend on which database you are downloading from - see the main EFetch Help page.

If you fetch the record in one of the formats accepted by Bio.SeqIO (see Chapter 5), you could directly parse it into a SeqRecord:

>>> from Bio import Entrez, SeqIO
>>> handle = Entrez.efetch(db="nucleotide", id="186972394", rettype="gb", retmode="text")
>>> record = SeqIO.read(handle, "genbank")
>>> handle.close()
>>> print(record)
ID: EU490707.1
Name: EU490707
Description: Selenipedium aequinoctiale maturase K (matK) gene, partial cds; chloroplast.
Number of features: 3
...
Seq('ATTTTTTACGAACCTGTGGAAATTTTTGGTTATGACAATAAATCTAGTTTAGTA...GAA', IUPACAmbiguousDNA())

Note that a more typical use would be to save the sequence data to a local file, and then parse it with Bio.SeqIO. This can save you having to re-download the same file repeatedly while working on your script, and places less load on the NCBI’s servers. For example:

import os
from Bio import SeqIO
from Bio import Entrez
Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
filename = "gi_186972394.gbk"
if not os.path.isfile(filename):
    # Downloading...
    net_handle = Entrez.efetch(db="nucleotide",id="186972394",rettype="gb", retmode="text")
    out_handle = open(filename, "w")
    out_handle.write(net_handle.read())
    out_handle.close()
    net_handle.close()
    print("Saved")

print("Parsing...")
record = SeqIO.read(filename, "genbank")
print(record)

To get the output in XML format, which you can parse using the Bio.Entrez.read() function, use retmode="xml":

>>> from Bio import Entrez
>>> handle = Entrez.efetch(db="nucleotide", id="186972394", retmode="xml")
>>> record = Entrez.read(handle)
>>> handle.close()
>>> record[0]["GBSeq_definition"] 
'Selenipedium aequinoctiale maturase K (matK) gene, partial cds; chloroplast'
>>> record[0]["GBSeq_source"] 
'chloroplast Selenipedium aequinoctiale'

So, that dealt with sequences. For examples of parsing file formats specific to the other databases (e.g. the MEDLINE format used in PubMed), see Section 9.12.

If you want to perform a search with Bio.Entrez.esearch(), and then download the records with Bio.Entrez.efetch(), you should use the WebEnv history feature – see Section 9.15.

9.7  ELink: Searching for related items in NCBI Entrez

ELink, available from Biopython as Bio.Entrez.elink(), can be used to find related items in the NCBI Entrez databases. For example, you can us this to find nucleotide entries for an entry in the gene database, and other cool stuff.

Let’s use ELink to find articles related to the Biopython application note published in Bioinformatics in 2009. The PubMed ID of this article is 19304878:

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"
>>> pmid = "19304878"
>>> record = Entrez.read(Entrez.elink(dbfrom="pubmed", id=pmid))

The record variable consists of a Python list, one for each database in which we searched. Since we specified only one PubMed ID to search for, record contains only one item. This item is a dictionary containing information about our search term, as well as all the related items that were found:

>>> record[0]["DbFrom"]
'pubmed'
>>> record[0]["IdList"]
['19304878']

The "LinkSetDb" key contains the search results, stored as a list consisting of one item for each target database. In our search results, we only find hits in the PubMed database (although sub-divided into categories):

>>> len(record[0]["LinkSetDb"])
5
>>> for linksetdb in record[0]["LinkSetDb"]:
...     print(linksetdb["DbTo"], linksetdb["LinkName"], len(linksetdb["Link"]))
... 
pubmed pubmed_pubmed 110
pubmed pubmed_pubmed_combined 6
pubmed pubmed_pubmed_five 6
pubmed pubmed_pubmed_reviews 5
pubmed pubmed_pubmed_reviews_five 5

The actual search results are stored as under the "Link" key. In total, 110 items were found under standard search. Let’s now at the first search result: 

>>> record[0]["LinkSetDb"][0]["Link"][0]
{u'Id': '19304878'}

This is the article we searched for, which doesn’t help us much, so let’s look at the second search result:

>>> record[0]["LinkSetDb"][0]["Link"][1]
{u'Id': '14630660'}

This paper, with PubMed ID 14630660, is about the Biopython PDB parser.

We can use a loop to print out all PubMed IDs: 

>>> for link in record[0]["LinkSetDb"][0]["Link"]:
...     print(link["Id"])
19304878
14630660
18689808
17121776
16377612
12368254
......

Now that was nice, but personally I am often more interested to find out if a paper has been cited. Well, ELink can do that too – at least for journals in Pubmed Central (see Section 9.15.3).

For help on ELink, see the ELink help page. There is an entire sub-page just for the link names, describing how different databases can be cross referenced.

9.8  EGQuery: Global Query - counts for search terms

EGQuery provides counts for a search term in each of the Entrez databases (i.e. a global query). This is particularly useful to find out how many items your search terms would find in each database without actually performing lots of separate searches with ESearch (see the example in 9.14.2 below).

In this example, we use Bio.Entrez.egquery() to obtain the counts for “Biopython”:

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
>>> handle = Entrez.egquery(term="biopython")
>>> record = Entrez.read(handle)
>>> for row in record["eGQueryResult"]:
...     print(row["DbName"], row["Count"])
... 
pubmed 6
pmc 62
journals 0
...

See the EGQuery help page for more information.

9.9  ESpell: Obtaining spelling suggestions

ESpell retrieves spelling suggestions. In this example, we use Bio.Entrez.espell() to obtain the correct spelling of Biopython:

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
>>> handle = Entrez.espell(term="biopythooon")
>>> record = Entrez.read(handle)
>>> record["Query"]
'biopythooon'
>>> record["CorrectedQuery"]
'biopython'

See the ESpell help page for more information. The main use of this is for GUI tools to provide automatic suggestions for search terms.

9.10  Parsing huge Entrez XML files

The Entrez.read function reads the entire XML file returned by Entrez into a single Python object, which is kept in memory. To parse Entrez XML files too large to fit in memory, you can use the function Entrez.parse. This is a generator function that reads records in the XML file one by one. This function is only useful if the XML file reflects a Python list object (in other words, if Entrez.read on a computer with infinite memory resources would return a Python list).

For example, you can download the entire Entrez Gene database for a given organism as a file from NCBI’s ftp site. These files can be very large. As an example, on September 4, 2009, the file Homo_sapiens.ags.gz, containing the Entrez Gene database for human, had a size of 116576 kB. This file, which is in the ASN format, can be converted into an XML file using NCBI’s gene2xml program (see NCBI’s ftp site for more information):

gene2xml -b T -i Homo_sapiens.ags -o Homo_sapiens.xml

The resulting XML file has a size of 6.1 GB. Attempting Entrez.read on this file will result in a MemoryError on many computers.

The XML file Homo_sapiens.xml consists of a list of Entrez gene records, each corresponding to one Entrez gene in human. Entrez.parse retrieves these gene records one by one. You can then print out or store the relevant information in each record by iterating over the records. For example, this script iterates over the Entrez gene records and prints out the gene numbers and names for all current genes:

>>> from Bio import Entrez
>>> handle = open("Homo_sapiens.xml")
>>> records = Entrez.parse(handle)

>>> for record in records:
...     status = record['Entrezgene_track-info']['Gene-track']['Gene-track_status']
...     if status.attributes['value']=='discontinued':
...         continue
...     geneid = record['Entrezgene_track-info']['Gene-track']['Gene-track_geneid']
...     genename = record['Entrezgene_gene']['Gene-ref']['Gene-ref_locus']
...     print(geneid, genename)

This will print: 

1 A1BG
2 A2M
3 A2MP
8 AA
9 NAT1
10 NAT2
11 AACP
12 SERPINA3
13 AADAC
14 AAMP
15 AANAT
16 AARS
17 AAVS1
...

9.11  Handling errors

Three things can go wrong when parsing an XML file:

  • The file may not be an XML file to begin with;
  • The file may end prematurely or otherwise be corrupted;
  • The file may be correct XML, but contain items that are not represented in the associated DTD.

The first case occurs if, for example, you try to parse a Fasta file as if it were an XML file: 

>>> from Bio import Entrez
>>> handle = open("NC_005816.fna") # a Fasta file
>>> record = Entrez.read(handle)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/__init__.py", line 257, in read
    record = handler.read(handle)
  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 164, in read
    raise NotXMLError(e)
Bio.Entrez.Parser.NotXMLError: Failed to parse the XML data (syntax error: line 1, column 0). Please make sure that the input data are in XML format.

Here, the parser didn’t find the <?xml ... tag with which an XML file is supposed to start, and therefore decides (correctly) that the file is not an XML file.

When your file is in the XML format but is corrupted (for example, by ending prematurely), the parser will raise a CorruptedXMLError. Here is an example of an XML file that ends prematurely: 

<?xml version="1.0"?>
<!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May 2002//EN" "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
<eInfoResult>
<DbList>
        <DbName>pubmed</DbName>
        <DbName>protein</DbName>
        <DbName>nucleotide</DbName>
        <DbName>nuccore</DbName>
        <DbName>nucgss</DbName>
        <DbName>nucest</DbName>
        <DbName>structure</DbName>
        <DbName>genome</DbName>
        <DbName>books</DbName>
        <DbName>cancerchromosomes</DbName>
        <DbName>cdd</DbName>

which will generate the following traceback: 

>>> Entrez.read(handle)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/__init__.py", line 257, in read
    record = handler.read(handle)
  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 160, in read
    raise CorruptedXMLError(e)
Bio.Entrez.Parser.CorruptedXMLError: Failed to parse the XML data (no element found: line 16, column 0). Please make sure that the input data are not corrupted.

>>>

Note that the error message tells you at what point in the XML file the error was detected.

The third type of error occurs if the XML file contains tags that do not have a description in the corresponding DTD file. This is an example of such an XML file:

<?xml version="1.0"?>
<!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May 2002//EN" "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
<eInfoResult>
        <DbInfo>
        <DbName>pubmed</DbName>
        <MenuName>PubMed</MenuName>
        <Description>PubMed bibliographic record</Description>
        <Count>20161961</Count>
        <LastUpdate>2010/09/10 04:52</LastUpdate>
        <FieldList>
                <Field>
...
                </Field>
        </FieldList>
        <DocsumList>
                <Docsum>
                        <DsName>PubDate</DsName>
                        <DsType>4</DsType>
                        <DsTypeName>string</DsTypeName>
                </Docsum>
                <Docsum>
                        <DsName>EPubDate</DsName>
...
        </DbInfo>
</eInfoResult>

In this file, for some reason the tag <DocsumList> (and several others) are not listed in the DTD file eInfo_020511.dtd, which is specified on the second line as the DTD for this XML file. By default, the parser will stop and raise a ValidationError if it cannot find some tag in the DTD:

>>> from Bio import Entrez
>>> handle = open("einfo3.xml")
>>> record = Entrez.read(handle)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/__init__.py", line 257, in read
    record = handler.read(handle)
  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 154, in read
    self.parser.ParseFile(handle)
  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 246, in startElementHandler
    raise ValidationError(name)
Bio.Entrez.Parser.ValidationError: Failed to find tag 'DocsumList' in the DTD. To skip all tags that are not represented in the DTD, please call Bio.Entrez.read or Bio.Entrez.parse with validate=False.

Optionally, you can instruct the parser to skip such tags instead of raising a ValidationError. This is done by calling Entrez.read or Entrez.parse with the argument validate equal to False: 

>>> from Bio import Entrez
>>> handle = open("einfo3.xml")
>>> record = Entrez.read(handle, validate=False)
>>>

Of course, the information contained in the XML tags that are not in the DTD are not present in the record returned by Entrez.read.

9.12  Specialized parsers

The Bio.Entrez.read() function can parse most (if not all) XML output returned by Entrez. Entrez typically allows you to retrieve records in other formats, which may have some advantages compared to the XML format in terms of readability (or download size).

To request a specific file format from Entrez using Bio.Entrez.efetch() requires specifying the rettype and/or retmode optional arguments. The different combinations are described for each database type on the NCBI efetch webpage.

One obvious case is you may prefer to download sequences in the FASTA or GenBank/GenPept plain text formats (which can then be parsed with Bio.SeqIO, see Sections 5.3.1 and 9.6). For the literature databases, Biopython contains a parser for the MEDLINE format used in PubMed.

9.12.1  Parsing Medline records

You can find the Medline parser in Bio.Medline. Suppose we want to parse the file pubmed_result1.txt, containing one Medline record. You can find this file in Biopython’s Tests\Medline directory. The file looks like this:

PMID- 12230038
OWN - NLM
STAT- MEDLINE
DA  - 20020916
DCOM- 20030606
LR  - 20041117
PUBM- Print
IS  - 1467-5463 (Print)
VI  - 3
IP  - 3
DP  - 2002 Sep
TI  - The Bio* toolkits--a brief overview.
PG  - 296-302
AB  - Bioinformatics research is often difficult to do with commercial software. The
      Open Source BioPerl, BioPython and Biojava projects provide toolkits with
...

We first open the file and then parse it: 

>>> from Bio import Medline
>>> input = open("pubmed_result1.txt")
>>> record = Medline.read(input)

The record now contains the Medline record as a Python dictionary: 

>>> record["PMID"]
'12230038'

>>> record["AB"]
'Bioinformatics research is often difficult to do with commercial software.
The Open Source BioPerl, BioPython and Biojava projects provide toolkits with
multiple functionality that make it easier to create customised pipelines or
analysis. This review briefly compares the quirks of the underlying languages
and the functionality, documentation, utility and relative advantages of the
Bio counterparts, particularly from the point of view of the beginning
biologist programmer.'

The key names used in a Medline record can be rather obscure; use 

>>> help(record)

for a brief summary.

To parse a file containing multiple Medline records, you can use the parse function instead: 

>>> from Bio import Medline
>>> input = open("pubmed_result2.txt")
>>> records = Medline.parse(input)
>>> for record in records:
...     print(record["TI"])
A high level interface to SCOP and ASTRAL implemented in python.
GenomeDiagram: a python package for the visualization of large-scale genomic data.
Open source clustering software.
PDB file parser and structure class implemented in Python.

Instead of parsing Medline records stored in files, you can also parse Medline records downloaded by Bio.Entrez.efetch. For example, let’s look at all Medline records in PubMed related to Biopython: 

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
>>> handle = Entrez.esearch(db="pubmed", term="biopython")
>>> record = Entrez.read(handle)
>>> record["IdList"]
['19304878', '18606172', '16403221', '16377612', '14871861', '14630660', '12230038']

We now use Bio.Entrez.efetch to download these Medline records: 

>>> idlist = record["IdList"]
>>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline", retmode="text")

Here, we specify rettype="medline", retmode="text" to obtain the Medline records in plain-text Medline format. Now we use Bio.Medline to parse these records: 

>>> from Bio import Medline
>>> records = Medline.parse(handle)
>>> for record in records:
...     print(record["AU"])
['Cock PJ', 'Antao T', 'Chang JT', 'Chapman BA', 'Cox CJ', 'Dalke A', ..., 'de Hoon MJ']
['Munteanu CR', 'Gonzalez-Diaz H', 'Magalhaes AL']
['Casbon JA', 'Crooks GE', 'Saqi MA']
['Pritchard L', 'White JA', 'Birch PR', 'Toth IK']
['de Hoon MJ', 'Imoto S', 'Nolan J', 'Miyano S']
['Hamelryck T', 'Manderick B']
['Mangalam H']

For comparison, here we show an example using the XML format: 

>>> idlist = record["IdList"]
>>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline", retmode="xml")
>>> records = Entrez.read(handle)
>>> for record in records:
...     print(record["MedlineCitation"]["Article"]["ArticleTitle"])
Biopython: freely available Python tools for computational molecular biology and
 bioinformatics.
Enzymes/non-enzymes classification model complexity based on composition, sequence,
 3D and topological indices.
A high level interface to SCOP and ASTRAL implemented in python.
GenomeDiagram: a python package for the visualization of large-scale genomic data.
Open source clustering software.
PDB file parser and structure class implemented in Python.
The Bio* toolkits--a brief overview.

Note that in both of these examples, for simplicity we have naively combined ESearch and EFetch. In this situation, the NCBI would expect you to use their history feature, as illustrated in Section 9.15.

9.12.2  Parsing GEO records

GEO (Gene Expression Omnibus) is a data repository of high-throughput gene expression and hybridization array data. The Bio.Geo module can be used to parse GEO-formatted data.

The following code fragment shows how to parse the example GEO file GSE16.txt into a record and print the record:

>>> from Bio import Geo
>>> handle = open("GSE16.txt")
>>> records = Geo.parse(handle)
>>> for record in records:
...     print(record)

You can search the “gds” database (GEO datasets) with ESearch:

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are
>>> handle = Entrez.esearch(db="gds", term="GSE16")
>>> record = Entrez.read(handle)
>>> record["Count"]
2
>>> record["IdList"]
['200000016', '100000028']

From the Entrez website, UID “200000016” is GDS16 while the other hit “100000028” is for the associated platform, GPL28. Unfortunately, at the time of writing the NCBI don’t seem to support downloading GEO files using Entrez (not as XML, nor in the Simple Omnibus Format in Text (SOFT) format).

However, it is actually pretty straight forward to download the GEO files by FTP from ftp://ftp.ncbi.nih.gov/pub/geo/ instead. In this case you might want ftp://ftp.ncbi.nih.gov/pub/geo/DATA/SOFT/by_series/GSE16/GSE16_family.soft.gz (a compressed file, see the Python module gzip).

9.12.3  Parsing UniGene records

UniGene is an NCBI database of the transcriptome, with each UniGene record showing the set of transcripts that are associated with a particular gene in a specific organism. A typical UniGene record looks like this:

ID          Hs.2
TITLE       N-acetyltransferase 2 (arylamine N-acetyltransferase)
GENE        NAT2
CYTOBAND    8p22
GENE_ID     10
LOCUSLINK   10
HOMOL       YES
EXPRESS      bone| connective tissue| intestine| liver| liver tumor| normal| soft tissue/muscle tissue tumor| adult
RESTR_EXPR   adult
CHROMOSOME  8
STS         ACC=PMC310725P3 UNISTS=272646
STS         ACC=WIAF-2120 UNISTS=44576
STS         ACC=G59899 UNISTS=137181
...
STS         ACC=GDB:187676 UNISTS=155563
PROTSIM     ORG=10090; PROTGI=6754794; PROTID=NP_035004.1; PCT=76.55; ALN=288
PROTSIM     ORG=9796; PROTGI=149742490; PROTID=XP_001487907.1; PCT=79.66; ALN=288
PROTSIM     ORG=9986; PROTGI=126722851; PROTID=NP_001075655.1; PCT=76.90; ALN=288
...
PROTSIM     ORG=9598; PROTGI=114619004; PROTID=XP_519631.2; PCT=98.28; ALN=288

SCOUNT      38
SEQUENCE    ACC=BC067218.1; NID=g45501306; PID=g45501307; SEQTYPE=mRNA
SEQUENCE    ACC=NM_000015.2; NID=g116295259; PID=g116295260; SEQTYPE=mRNA
SEQUENCE    ACC=D90042.1; NID=g219415; PID=g219416; SEQTYPE=mRNA
SEQUENCE    ACC=D90040.1; NID=g219411; PID=g219412; SEQTYPE=mRNA
SEQUENCE    ACC=BC015878.1; NID=g16198419; PID=g16198420; SEQTYPE=mRNA
SEQUENCE    ACC=CR407631.1; NID=g47115198; PID=g47115199; SEQTYPE=mRNA
SEQUENCE    ACC=BG569293.1; NID=g13576946; CLONE=IMAGE:4722596; END=5'; LID=6989; SEQTYPE=EST; TRACE=44157214
...
SEQUENCE    ACC=AU099534.1; NID=g13550663; CLONE=HSI08034; END=5'; LID=8800; SEQTYPE=EST
//

This particular record shows the set of transcripts (shown in the SEQUENCE lines) that originate from the human gene NAT2, encoding en N-acetyltransferase. The PROTSIM lines show proteins with significant similarity to NAT2, whereas the STS lines show the corresponding sequence-tagged sites in the genome.

To parse UniGene files, use the Bio.UniGene module: 

>>> from Bio import UniGene
>>> input = open("myunigenefile.data")
>>> record = UniGene.read(input)

The record returned by UniGene.read is a Python object with attributes corresponding to the fields in the UniGene record. For example, 

>>> record.ID
"Hs.2"
>>> record.title
"N-acetyltransferase 2 (arylamine N-acetyltransferase)"

The EXPRESS and RESTR_EXPR lines are stored as Python lists of strings: 

['bone', 'connective tissue', 'intestine', 'liver', 'liver tumor', 'normal', 'soft tissue/muscle tissue tumor', 'adult']

Specialized objects are returned for the STS, PROTSIM, and SEQUENCE lines, storing the keys shown in each line as attributes: 

>>> record.sts[0].acc
'PMC310725P3'
>>> record.sts[0].unists
'272646'

and similarly for the PROTSIM and SEQUENCE lines.

To parse a file containing more than one UniGene record, use the parse function in Bio.UniGene:

>>> from Bio import UniGene
>>> input = open("unigenerecords.data")
>>> records = UniGene.parse(input)
>>> for record in records:
...     print(record.ID)

9.13  Using a proxy

Normally you won’t have to worry about using a proxy, but if this is an issue on your network here is how to deal with it. Internally, Bio.Entrez uses the standard Python library urllib for accessing the NCBI servers. This will check an environment variable called http_proxy to configure any simple proxy automatically. Unfortunately this module does not support the use of proxies which require authentication.

You may choose to set the http_proxy environment variable once (how you do this will depend on your operating system). Alternatively you can set this within Python at the start of your script, for example:

import os
os.environ["http_proxy"] = "http://proxyhost.example.com:8080"

See the urllib documentation for more details.

9.14  Examples

9.14.1  PubMed and Medline

If you are in the medical field or interested in human issues (and many times even if you are not!), PubMed (http://www.ncbi.nlm.nih.gov/PubMed/) is an excellent source of all kinds of goodies. So like other things, we’d like to be able to grab information from it and use it in Python scripts.

In this example, we will query PubMed for all articles having to do with orchids (see section 2.3 for our motivation). We first check how many of such articles there are:

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
>>> handle = Entrez.egquery(term="orchid")
>>> record = Entrez.read(handle)
>>> for row in record["eGQueryResult"]:
...     if row["DbName"]=="pubmed":
...         print(row["Count"])
463

Now we use the Bio.Entrez.efetch function to download the PubMed IDs of these 463 articles: 

>>> handle = Entrez.esearch(db="pubmed", term="orchid", retmax=463)
>>> record = Entrez.read(handle)
>>> idlist = record["IdList"]
>>> print(idlist)

This returns a Python list containing all of the PubMed IDs of articles related to orchids: 

['18680603', '18665331', '18661158', '18627489', '18627452', '18612381',
'18594007', '18591784', '18589523', '18579475', '18575811', '18575690',
...

Now that we’ve got them, we obviously want to get the corresponding Medline records and extract the information from them. Here, we’ll download the Medline records in the Medline flat-file format, and use the Bio.Medline module to parse them: 

>>> from Bio import Medline
>>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline",
                           retmode="text")
>>> records = Medline.parse(handle)

NOTE - We’ve just done a separate search and fetch here, the NCBI much prefer you to take advantage of their history support in this situation. See Section 9.15.

Keep in mind that records is an iterator, so you can iterate through the records only once. If you want to save the records, you can convert them to a list: 

>>> records = list(records)

Let’s now iterate over the records to print out some information about each record: 

>>> for record in records:
...     print("title:", record.get("TI", "?"))
...     print("authors:", record.get("AU", "?"))
...     print("source:", record.get("SO", "?"))
...     print("")

The output for this looks like: 

title: Sex pheromone mimicry in the early spider orchid (ophrys sphegodes):
patterns of hydrocarbons as the key mechanism for pollination by sexual
deception [In Process Citation]
authors: ['Schiestl FP', 'Ayasse M', 'Paulus HF', 'Lofstedt C', 'Hansson BS',
'Ibarra F', 'Francke W']
source: J Comp Physiol [A] 2000 Jun;186(6):567-74

Especially interesting to note is the list of authors, which is returned as a standard Python list. This makes it easy to manipulate and search using standard Python tools. For instance, we could loop through a whole bunch of entries searching for a particular author with code like the following: 

>>> search_author = "Waits T"

>>> for record in records:
...     if not "AU" in record:
...         continue
...     if search_author in record["AU"]:
...         print("Author %s found: %s" % (search_author, record["SO"]))

Hopefully this section gave you an idea of the power and flexibility of the Entrez and Medline interfaces and how they can be used together.

9.14.2  Searching, downloading, and parsing Entrez Nucleotide records

Here we’ll show a simple example of performing a remote Entrez query. In section 2.3 of the parsing examples, we talked about using NCBI’s Entrez website to search the NCBI nucleotide databases for info on Cypripedioideae, our friends the lady slipper orchids. Now, we’ll look at how to automate that process using a Python script. In this example, we’ll just show how to connect, get the results, and parse them, with the Entrez module doing all of the work.

First, we use EGQuery to find out the number of results we will get before actually downloading them. EGQuery will tell us how many search results were found in each of the databases, but for this example we are only interested in nucleotides: 

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
>>> handle = Entrez.egquery(term="Cypripedioideae")
>>> record = Entrez.read(handle)
>>> for row in record["eGQueryResult"]:
...     if row["DbName"]=="nuccore":
...         print(row["Count"])
814

So, we expect to find 814 Entrez Nucleotide records (this is the number I obtained in 2008; it is likely to increase in the future). If you find some ridiculously high number of hits, you may want to reconsider if you really want to download all of them, which is our next step: 

>>> from Bio import Entrez
>>> handle = Entrez.esearch(db="nucleotide", term="Cypripedioideae", retmax=814)
>>> record = Entrez.read(handle)

Here, record is a Python dictionary containing the search results and some auxiliary information. Just for information, let’s look at what is stored in this dictionary: 

>>> print(record.keys())
[u'Count', u'RetMax', u'IdList', u'TranslationSet', u'RetStart', u'QueryTranslation']

First, let’s check how many results were found: 

>>> print(record["Count"])
'814'

which is the number we expected. The 814 results are stored in record['IdList']: 

>>> len(record["IdList"])
814

Let’s look at the first five results: 

>>> record["IdList"][:5]
['187237168', '187372713', '187372690', '187372688', '187372686']

We can download these records using efetch. While you could download these records one by one, to reduce the load on NCBI’s servers, it is better to fetch a bunch of records at the same time, shown below. However, in this situation you should ideally be using the history feature described later in Section 9.15.

>>> idlist = ",".join(record["IdList"][:5])
>>> print(idlist)
187237168,187372713,187372690,187372688,187372686
>>> handle = Entrez.efetch(db="nucleotide", id=idlist, retmode="xml")
>>> records = Entrez.read(handle)
>>> len(records)
5

Each of these records corresponds to one GenBank record. 

>>> print(records[0].keys())
[u'GBSeq_moltype', u'GBSeq_source', u'GBSeq_sequence',
 u'GBSeq_primary-accession', u'GBSeq_definition', u'GBSeq_accession-version',
 u'GBSeq_topology', u'GBSeq_length', u'GBSeq_feature-table',
 u'GBSeq_create-date', u'GBSeq_other-seqids', u'GBSeq_division',
 u'GBSeq_taxonomy', u'GBSeq_references', u'GBSeq_update-date',
 u'GBSeq_organism', u'GBSeq_locus', u'GBSeq_strandedness']

>>> print(records[0]["GBSeq_primary-accession"])
DQ110336

>>> print(records[0]["GBSeq_other-seqids"])
['gb|DQ110336.1|', 'gi|187237168']

>>> print(records[0]["GBSeq_definition"])
Cypripedium calceolus voucher Davis 03-03 A maturase (matR) gene, partial cds;
mitochondrial

>>> print(records[0]["GBSeq_organism"])
Cypripedium calceolus

You could use this to quickly set up searches – but for heavy usage, see Section 9.15.

9.14.3  Searching, downloading, and parsing GenBank records

The GenBank record format is a very popular method of holding information about sequences, sequence features, and other associated sequence information. The format is a good way to get information from the NCBI databases at http://www.ncbi.nlm.nih.gov/.

In this example we’ll show how to query the NCBI databases,to retrieve the records from the query, and then parse them using Bio.SeqIO - something touched on in Section 5.3.1. For simplicity, this example does not take advantage of the WebEnv history feature – see Section 9.15 for this.

First, we want to make a query and find out the ids of the records to retrieve. Here we’ll do a quick search for one of our favorite organisms, Opuntia (prickly-pear cacti). We can do quick search and get back the GIs (GenBank identifiers) for all of the corresponding records. First we check how many records there are:

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
>>> handle = Entrez.egquery(term="Opuntia AND rpl16")
>>> record = Entrez.read(handle)
>>> for row in record["eGQueryResult"]:
...     if row["DbName"]=="nuccore":
...         print(row["Count"])
...
9

Now we download the list of GenBank identifiers: 

>>> handle = Entrez.esearch(db="nuccore", term="Opuntia AND rpl16")
>>> record = Entrez.read(handle)
>>> gi_list = record["IdList"]
>>> gi_list
['57240072', '57240071', '6273287', '6273291', '6273290', '6273289', '6273286',
'6273285', '6273284']

Now we use these GIs to download the GenBank records - note that with older versions of Biopython you had to supply a comma separated list of GI numbers to Entrez, as of Biopython 1.59 you can pass a list and this is converted for you:

>>> gi_str = ",".join(gi_list)
>>> handle = Entrez.efetch(db="nuccore", id=gi_str, rettype="gb", retmode="text")

If you want to look at the raw GenBank files, you can read from this handle and print out the result:

>>> text = handle.read()
>>> print(text)
LOCUS       AY851612                 892 bp    DNA     linear   PLN 10-APR-2007
DEFINITION  Opuntia subulata rpl16 gene, intron; chloroplast.
ACCESSION   AY851612
VERSION     AY851612.1  GI:57240072
KEYWORDS    .
SOURCE      chloroplast Austrocylindropuntia subulata
  ORGANISM  Austrocylindropuntia subulata
            Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
            Spermatophyta; Magnoliophyta; eudicotyledons; core eudicotyledons;
            Caryophyllales; Cactaceae; Opuntioideae; Austrocylindropuntia.
REFERENCE   1  (bases 1 to 892)
  AUTHORS   Butterworth,C.A. and Wallace,R.S.
...

In this case, we are just getting the raw records. To get the records in a more Python-friendly form, we can use Bio.SeqIO to parse the GenBank data into SeqRecord objects, including SeqFeature objects (see Chapter 5):

>>> from Bio import SeqIO
>>> handle = Entrez.efetch(db="nuccore", id=gi_str, rettype="gb", retmode="text")
>>> records = SeqIO.parse(handle, "gb")

We can now step through the records and look at the information we are interested in: 

>>> for record in records: 
>>> ...    print("%s, length %i, with %i features" \
>>> ...           % (record.name, len(record), len(record.features)))
AY851612, length 892, with 3 features
AY851611, length 881, with 3 features
AF191661, length 895, with 3 features
AF191665, length 902, with 3 features
AF191664, length 899, with 3 features
AF191663, length 899, with 3 features
AF191660, length 893, with 3 features
AF191659, length 894, with 3 features
AF191658, length 896, with 3 features

Using these automated query retrieval functionality is a big plus over doing things by hand. Although the module should obey the NCBI’s max three queries per second rule, the NCBI have other recommendations like avoiding peak hours. See Section 9.1. In particular, please note that for simplicity, this example does not use the WebEnv history feature. You should use this for any non-trivial search and download work, see Section 9.15.

Finally, if plan to repeat your analysis, rather than downloading the files from the NCBI and parsing them immediately (as shown in this example), you should just download the records once and save them to your hard disk, and then parse the local file.

9.14.4  Finding the lineage of an organism

Staying with a plant example, let’s now find the lineage of the Cypripedioideae orchid family. First, we search the Taxonomy database for Cypripedioideae, which yields exactly one NCBI taxonomy identifier: 

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who you are
>>> handle = Entrez.esearch(db="Taxonomy", term="Cypripedioideae")
>>> record = Entrez.read(handle)
>>> record["IdList"]
['158330']
>>> record["IdList"][0]
'158330'

Now, we use efetch to download this entry in the Taxonomy database, and then parse it: 

>>> handle = Entrez.efetch(db="Taxonomy", id="158330", retmode="xml")
>>> records = Entrez.read(handle)

Again, this record stores lots of information: 

>>> records[0].keys()
[u'Lineage', u'Division', u'ParentTaxId', u'PubDate', u'LineageEx',
 u'CreateDate', u'TaxId', u'Rank', u'GeneticCode', u'ScientificName',
 u'MitoGeneticCode', u'UpdateDate']

We can get the lineage directly from this record: 

>>> records[0]["Lineage"]
'cellular organisms; Eukaryota; Viridiplantae; Streptophyta; Streptophytina;
 Embryophyta; Tracheophyta; Euphyllophyta; Spermatophyta; Magnoliophyta;
 Liliopsida; Asparagales; Orchidaceae'

The record data contains much more than just the information shown here - for example look under "LineageEx" instead of "Lineage" and you’ll get the NCBI taxon identifiers of the lineage entries too.

9.15  Using the history and WebEnv

Often you will want to make a series of linked queries. Most typically, running a search, perhaps refining the search, and then retrieving detailed search results. You can do this by making a series of separate calls to Entrez. However, the NCBI prefer you to take advantage of their history support - for example combining ESearch and EFetch.

Another typical use of the history support would be to combine EPost and EFetch. You use EPost to upload a list of identifiers, which starts a new history session. You then download the records with EFetch by referring to the session (instead of the identifiers).

9.15.1  Searching for and downloading sequences using the history

Suppose we want to search and download all the Opuntia rpl16 nucleotide sequences, and store them in a FASTA file. As shown in Section 9.14.3, we can naively combine Bio.Entrez.esearch() to get a list of GI numbers, and then call Bio.Entrez.efetch() to download them all.

However, the approved approach is to run the search with the history feature. Then, we can fetch the results by reference to the search results - which the NCBI can anticipate and cache.

To do this, call Bio.Entrez.esearch() as normal, but with the additional argument of usehistory="y",

>>> from Bio import Entrez
>>> Entrez.email = "history.user@example.com"
>>> search_handle = Entrez.esearch(db="nucleotide",term="Opuntia[orgn] and rpl16",
                                   usehistory="y")
>>> search_results = Entrez.read(search_handle)
>>> search_handle.close()

When you get the XML output back, it will still include the usual search results:

>>> gi_list = search_results["IdList"]
>>> count = int(search_results["Count"])
>>> assert count == len(gi_list)

However, you also get given two additional pieces of information, the WebEnv session cookie, and the QueryKey:

>>> webenv = search_results["WebEnv"]
>>> query_key = search_results["QueryKey"]

Having stored these values in variables session_cookie and query_key we can use them as parameters to Bio.Entrez.efetch() instead of giving the GI numbers as identifiers.

While for small searches you might be OK downloading everything at once, it is better to download in batches. You use the retstart and retmax parameters to specify which range of search results you want returned (starting entry using zero-based counting, and maximum number of results to return). For example,

batch_size = 3
out_handle = open("orchid_rpl16.fasta", "w")
for start in range(0,count,batch_size):
    end = min(count, start+batch_size)
    print("Going to download record %i to %i" % (start+1, end))
    fetch_handle = Entrez.efetch(db="nucleotide", rettype="fasta", retmode="text",
                                 retstart=start, retmax=batch_size,
                                 webenv=webenv, query_key=query_key)
    data = fetch_handle.read()
    fetch_handle.close()
    out_handle.write(data)
out_handle.close()

For illustrative purposes, this example downloaded the FASTA records in batches of three. Unless you are downloading genomes or chromosomes, you would normally pick a larger batch size.

9.15.2  Searching for and downloading abstracts using the history

Here is another history example, searching for papers published in the last year about the Opuntia, and then downloading them into a file in MedLine format:

from Bio import Entrez
Entrez.email = "history.user@example.com"
search_results = Entrez.read(Entrez.esearch(db="pubmed",
                                            term="Opuntia[ORGN]",
                                            reldate=365, datetype="pdat",
                                            usehistory="y"))
count = int(search_results["Count"])
print("Found %i results" % count)

batch_size = 10
out_handle = open("recent_orchid_papers.txt", "w")
for start in range(0,count,batch_size):
    end = min(count, start+batch_size)
    print("Going to download record %i to %i" % (start+1, end))
    fetch_handle = Entrez.efetch(db="pubmed",
                                 rettype="medline", retmode="text",
                                 retstart=start, retmax=batch_size,
                                 webenv=search_results["WebEnv"],
                                 query_key=search_results["QueryKey"])
    data = fetch_handle.read()
    fetch_handle.close()
    out_handle.write(data)
out_handle.close()

At the time of writing, this gave 28 matches - but because this is a date dependent search, this will of course vary. As described in Section 9.12.1 above, you can then use Bio.Medline to parse the saved records.

9.15.3  Searching for citations

Back in Section 9.7 we mentioned ELink can be used to search for citations of a given paper. Unfortunately this only covers journals indexed for PubMed Central (doing it for all the journals in PubMed would mean a lot more work for the NIH). Let’s try this for the Biopython PDB parser paper, PubMed ID 14630660:

>>> from Bio import Entrez
>>> Entrez.email = "A.N.Other@example.com"
>>> pmid = "14630660"
>>> results = Entrez.read(Entrez.elink(dbfrom="pubmed", db="pmc",
...                                    LinkName="pubmed_pmc_refs", from_uid=pmid))
>>> pmc_ids = [link["Id"] for link in results[0]["LinkSetDb"][0]["Link"]]
>>> pmc_ids
['2744707', '2705363', '2682512', ..., '1190160']

Great - eleven articles. But why hasn’t the Biopython application note been found (PubMed ID 19304878)? Well, as you might have guessed from the variable names, there are not actually PubMed IDs, but PubMed Central IDs. Our application note is the third citing paper in that list, PMCID 2682512.

So, what if (like me) you’d rather get back a list of PubMed IDs? Well we can call ELink again to translate them. This becomes a two step process, so by now you should expect to use the history feature to accomplish it (Section 9.15).

But first, taking the more straightforward approach of making a second (separate) call to ELink:

>>> results2 = Entrez.read(Entrez.elink(dbfrom="pmc", db="pubmed", LinkName="pmc_pubmed",
...                                     from_uid=",".join(pmc_ids)))
>>> pubmed_ids = [link["Id"] for link in results2[0]["LinkSetDb"][0]["Link"]]
>>> pubmed_ids
['19698094', '19450287', '19304878', ..., '15985178']

This time you can immediately spot the Biopython application note as the third hit (PubMed ID 19304878).

Now, let’s do that all again but with the history …TODO.

And finally, don’t forget to include your own email address in the Entrez calls.

Chapter 10  Swiss-Prot and ExPASy

10.1  Parsing Swiss-Prot files

Swiss-Prot (http://www.expasy.org/sprot) is a hand-curated database of protein sequences. Biopython can parse the “plain text” Swiss-Prot file format, which is still used for the UniProt Knowledgebase which combined Swiss-Prot, TrEMBL and PIR-PSD. We do not (yet) support the UniProtKB XML file format.

10.1.1  Parsing Swiss-Prot records

In Section 5.3.2, we described how to extract the sequence of a Swiss-Prot record as a SeqRecord object. Alternatively, you can store the Swiss-Prot record in a Bio.SwissProt.Record object, which in fact stores the complete information contained in the Swiss-Prot record. In this Section, we describe how to extract Bio.SwissProt.Record objects from a Swiss-Prot file.

To parse a Swiss-Prot record, we first get a handle to a Swiss-Prot record. There are several ways to do so, depending on where and how the Swiss-Prot record is stored:

  • Open a Swiss-Prot file locally:
    >>> handle = open("myswissprotfile.dat")
  • Open a gzipped Swiss-Prot file: 
    >>> import gzip
>>> handle = gzip.open("myswissprotfile.dat.gz")
  • Open a Swiss-Prot file over the internet: 
    >>> import urllib
>>> handle = urllib.urlopen("http://www.somelocation.org/data/someswissprotfile.dat")
  • Open a Swiss-Prot file over the internet from the ExPASy database (see section 10.5.1): 
    >>> from Bio import ExPASy
>>> handle = ExPASy.get_sprot_raw(myaccessionnumber)

The key point is that for the parser, it doesn’t matter how the handle was created, as long as it points to data in the Swiss-Prot format.

We can use Bio.SeqIO as described in Section 5.3.2 to get file format agnostic SeqRecord objects. Alternatively, we can use Bio.SwissProt get Bio.SwissProt.Record objects, which are a much closer match to the underlying file format.

To read one Swiss-Prot record from the handle, we use the function read(): 

>>> from Bio import SwissProt
>>> record = SwissProt.read(handle)

This function should be used if the handle points to exactly one Swiss-Prot record. It raises a ValueError if no Swiss-Prot record was found, and also if more than one record was found.

We can now print out some information about this record: 

>>> print(record.description)
'RecName: Full=Chalcone synthase 3; EC=2.3.1.74; AltName: Full=Naringenin-chalcone synthase 3;'
>>> for ref in record.references:
...     print("authors:", ref.authors)
...     print("title:", ref.title)
... 
authors: Liew C.F., Lim S.H., Loh C.S., Goh C.J.;
title: "Molecular cloning and sequence analysis of chalcone synthase cDNAs of
Bromheadia finlaysoniana.";
>>> print(record.organism_classification)
['Eukaryota', 'Viridiplantae', 'Streptophyta', 'Embryophyta', ..., 'Bromheadia']

To parse a file that contains more than one Swiss-Prot record, we use the parse function instead. This function allows us to iterate over the records in the file.

For example, let’s parse the full Swiss-Prot database and collect all the descriptions. You can download this from the ExPAYs FTP site as a single gzipped-file uniprot_sprot.dat.gz (about 300MB). This is a compressed file containing a single file, uniprot_sprot.dat (over 1.5GB).

As described at the start of this section, you can use the Python library gzip to open and uncompress a .gz file, like this:

>>> import gzip
>>> handle = gzip.open("uniprot_sprot.dat.gz")

However, uncompressing a large file takes time, and each time you open the file for reading in this way, it has to be decompressed on the fly. So, if you can spare the disk space you’ll save time in the long run if you first decompress the file to disk, to get the uniprot_sprot.dat file inside. Then you can open the file for reading as usual:

>>> handle = open("uniprot_sprot.dat")

As of June 2009, the full Swiss-Prot database downloaded from ExPASy contained 468851 Swiss-Prot records. One concise way to build up a list of the record descriptions is with a list comprehension: 

>>> from Bio import SwissProt
>>> handle = open("uniprot_sprot.dat")
>>> descriptions = [record.description for record in SwissProt.parse(handle)]
>>> len(descriptions)
468851
>>> descriptions[:5]
['RecName: Full=Protein MGF 100-1R;',
 'RecName: Full=Protein MGF 100-1R;',
 'RecName: Full=Protein MGF 100-1R;',
 'RecName: Full=Protein MGF 100-1R;',
 'RecName: Full=Protein MGF 100-2L;']

Or, using a for loop over the record iterator: 

>>> from Bio import SwissProt
>>> descriptions = []
>>> handle = open("uniprot_sprot.dat")
>>> for record in SwissProt.parse(handle):
...     descriptions.append(record.description)
...
>>> len(descriptions)
468851

Because this is such a large input file, either way takes about eleven minutes on my new desktop computer (using the uncompressed uniprot_sprot.dat file as input).

It is equally easy to extract any kind of information you’d like from Swiss-Prot records. To see the members of a Swiss-Prot record, use 

>>> dir(record)
['__doc__', '__init__', '__module__', 'accessions', 'annotation_update',
'comments', 'created', 'cross_references', 'data_class', 'description',
'entry_name', 'features', 'gene_name', 'host_organism', 'keywords',
'molecule_type', 'organelle', 'organism', 'organism_classification',
'references', 'seqinfo', 'sequence', 'sequence_length',
'sequence_update', 'taxonomy_id']

10.1.2  Parsing the Swiss-Prot keyword and category list

Swiss-Prot also distributes a file keywlist.txt, which lists the keywords and categories used in Swiss-Prot. The file contains entries in the following form:

ID   2Fe-2S.
AC   KW-0001
DE   Protein which contains at least one 2Fe-2S iron-sulfur cluster: 2 iron
DE   atoms complexed to 2 inorganic sulfides and 4 sulfur atoms of
DE   cysteines from the protein.
SY   Fe2S2; [2Fe-2S] cluster; [Fe2S2] cluster; Fe2/S2 (inorganic) cluster;
SY   Di-mu-sulfido-diiron; 2 iron, 2 sulfur cluster binding.
GO   GO:0051537; 2 iron, 2 sulfur cluster binding
HI   Ligand: Iron; Iron-sulfur; 2Fe-2S.
HI   Ligand: Metal-binding; 2Fe-2S.
CA   Ligand.
//
ID   3D-structure.
AC   KW-0002
DE   Protein, or part of a protein, whose three-dimensional structure has
DE   been resolved experimentally (for example by X-ray crystallography or
DE   NMR spectroscopy) and whose coordinates are available in the PDB
DE   database. Can also be used for theoretical models.
HI   Technical term: 3D-structure.
CA   Technical term.
//
ID   3Fe-4S.
...

The entries in this file can be parsed by the parse function in the Bio.SwissProt.KeyWList module. Each entry is then stored as a Bio.SwissProt.KeyWList.Record, which is a Python dictionary.

>>> from Bio.SwissProt import KeyWList
>>> handle = open("keywlist.txt")
>>> records = KeyWList.parse(handle)
>>> for record in records:
...     print(record['ID'])
...     print(record['DE'])

This prints 

2Fe-2S.
Protein which contains at least one 2Fe-2S iron-sulfur cluster: 2 iron atoms
complexed to 2 inorganic sulfides and 4 sulfur atoms of cysteines from the
protein.
...

10.2  Parsing Prosite records

Prosite is a database containing protein domains, protein families, functional sites, as well as the patterns and profiles to recognize them. Prosite was developed in parallel with Swiss-Prot. In Biopython, a Prosite record is represented by the Bio.ExPASy.Prosite.Record class, whose members correspond to the different fields in a Prosite record.

In general, a Prosite file can contain more than one Prosite records. For example, the full set of Prosite records, which can be downloaded as a single file (prosite.dat) from the ExPASy FTP site, contains 2073 records (version 20.24 released on 4 December 2007). To parse such a file, we again make use of an iterator:

>>> from Bio.ExPASy import Prosite
>>> handle = open("myprositefile.dat")
>>> records = Prosite.parse(handle)

We can now take the records one at a time and print out some information. For example, using the file containing the complete Prosite database, we’d find 

>>> from Bio.ExPASy import Prosite
>>> handle = open("prosite.dat")
>>> records = Prosite.parse(handle)
>>> record = next(records)
>>> record.accession
'PS00001'
>>> record.name
'ASN_GLYCOSYLATION'
>>> record.pdoc
'PDOC00001'
>>> record = next(records)
>>> record.accession
'PS00004'
>>> record.name
'CAMP_PHOSPHO_SITE'
>>> record.pdoc
'PDOC00004'
>>> record = next(records)
>>> record.accession
'PS00005'
>>> record.name
'PKC_PHOSPHO_SITE'
>>> record.pdoc
'PDOC00005'

and so on. If you’re interested in how many Prosite records there are, you could use 

>>> from Bio.ExPASy import Prosite
>>> handle = open("prosite.dat")
>>> records = Prosite.parse(handle)
>>> n = 0
>>> for record in records: n+=1
...
>>> n
2073

To read exactly one Prosite from the handle, you can use the read function: 

>>> from Bio.ExPASy import Prosite
>>> handle = open("mysingleprositerecord.dat")
>>> record = Prosite.read(handle)

This function raises a ValueError if no Prosite record is found, and also if more than one Prosite record is found.

10.3  Parsing Prosite documentation records

In the Prosite example above, the record.pdoc accession numbers 'PDOC00001', 'PDOC00004', 'PDOC00005' and so on refer to Prosite documentation. The Prosite documentation records are available from ExPASy as individual files, and as one file (prosite.doc) containing all Prosite documentation records.

We use the parser in Bio.ExPASy.Prodoc to parse Prosite documentation records. For example, to create a list of all accession numbers of Prosite documentation record, you can use

>>> from Bio.ExPASy import Prodoc
>>> handle = open("prosite.doc")
>>> records = Prodoc.parse(handle)
>>> accessions = [record.accession for record in records]

Again a read() function is provided to read exactly one Prosite documentation record from the handle.

10.4  Parsing Enzyme records

ExPASy’s Enzyme database is a repository of information on enzyme nomenclature. A typical Enzyme record looks as follows:

ID   3.1.1.34
DE   Lipoprotein lipase.
AN   Clearing factor lipase.
AN   Diacylglycerol lipase.
AN   Diglyceride lipase.
CA   Triacylglycerol + H(2)O = diacylglycerol + a carboxylate.
CC   -!- Hydrolyzes triacylglycerols in chylomicrons and very low-density
CC       lipoproteins (VLDL).
CC   -!- Also hydrolyzes diacylglycerol.
PR   PROSITE; PDOC00110;
DR   P11151, LIPL_BOVIN ;  P11153, LIPL_CAVPO ;  P11602, LIPL_CHICK ;
DR   P55031, LIPL_FELCA ;  P06858, LIPL_HUMAN ;  P11152, LIPL_MOUSE ;
DR   O46647, LIPL_MUSVI ;  P49060, LIPL_PAPAN ;  P49923, LIPL_PIG   ;
DR   Q06000, LIPL_RAT   ;  Q29524, LIPL_SHEEP ;
//

In this example, the first line shows the EC (Enzyme Commission) number of lipoprotein lipase (second line). Alternative names of lipoprotein lipase are "clearing factor lipase", "diacylglycerol lipase", and "diglyceride lipase" (lines 3 through 5). The line starting with "CA" shows the catalytic activity of this enzyme. Comment lines start with "CC". The "PR" line shows references to the Prosite Documentation records, and the "DR" lines show references to Swiss-Prot records. Not of these entries are necessarily present in an Enzyme record.

In Biopython, an Enzyme record is represented by the Bio.ExPASy.Enzyme.Record class. This record derives from a Python dictionary and has keys corresponding to the two-letter codes used in Enzyme files. To read an Enzyme file containing one Enzyme record, use the read function in Bio.ExPASy.Enzyme:

>>> from Bio.ExPASy import Enzyme
>>> handle = open("lipoprotein.txt")
>>> record = Enzyme.read(handle)
>>> record["ID"]
'3.1.1.34'
>>> record["DE"]
'Lipoprotein lipase.'
>>> record["AN"]
['Clearing factor lipase.', 'Diacylglycerol lipase.', 'Diglyceride lipase.']
>>> record["CA"]
'Triacylglycerol + H(2)O = diacylglycerol + a carboxylate.'
>>> record["PR"]
['PDOC00110']

>>> record["CC"]
['Hydrolyzes triacylglycerols in chylomicrons and very low-density lipoproteins
(VLDL).', 'Also hydrolyzes diacylglycerol.']
>>> record["DR"]
[['P11151', 'LIPL_BOVIN'], ['P11153', 'LIPL_CAVPO'], ['P11602', 'LIPL_CHICK'],
['P55031', 'LIPL_FELCA'], ['P06858', 'LIPL_HUMAN'], ['P11152', 'LIPL_MOUSE'],
['O46647', 'LIPL_MUSVI'], ['P49060', 'LIPL_PAPAN'], ['P49923', 'LIPL_PIG'],
['Q06000', 'LIPL_RAT'], ['Q29524', 'LIPL_SHEEP']]

The read function raises a ValueError if no Enzyme record is found, and also if more than one Enzyme record is found.

The full set of Enzyme records can be downloaded as a single file (enzyme.dat) from the ExPASy FTP site, containing 4877 records (release of 3 March 2009). To parse such a file containing multiple Enzyme records, use the parse function in Bio.ExPASy.Enzyme to obtain an iterator:

>>> from Bio.ExPASy import Enzyme
>>> handle = open("enzyme.dat")
>>> records = Enzyme.parse(handle)

We can now iterate over the records one at a time. For example, we can make a list of all EC numbers for which an Enzyme record is available: 

>>> ecnumbers = [record["ID"] for record in records]

10.5  Accessing the ExPASy server

Swiss-Prot, Prosite, and Prosite documentation records can be downloaded from the ExPASy web server at http://www.expasy.org. Six kinds of queries are available from ExPASy:

get_prodoc_entry
To download a Prosite documentation record in HTML format
get_prosite_entry
To download a Prosite record in HTML format
get_prosite_raw
To download a Prosite or Prosite documentation record in raw format
get_sprot_raw
To download a Swiss-Prot record in raw format
sprot_search_ful
To search for a Swiss-Prot record
sprot_search_de
To search for a Swiss-Prot record

To access this web server from a Python script, we use the Bio.ExPASy module.

10.5.1  Retrieving a Swiss-Prot record

Let’s say we are looking at chalcone synthases for Orchids (see section 2.3 for some justification for looking for interesting things about orchids). Chalcone synthase is involved in flavanoid biosynthesis in plants, and flavanoids make lots of cool things like pigment colors and UV protectants.

If you do a search on Swiss-Prot, you can find three orchid proteins for Chalcone Synthase, id numbers O23729, O23730, O23731. Now, let’s write a script which grabs these, and parses out some interesting information.

First, we grab the records, using the get_sprot_raw() function of Bio.ExPASy. This function is very nice since you can feed it an id and get back a handle to a raw text record (no html to mess with!). We can the use Bio.SwissProt.read to pull out the Swiss-Prot record, or Bio.SeqIO.read to get a SeqRecord. The following code accomplishes what I just wrote:

>>> from Bio import ExPASy
>>> from Bio import SwissProt

>>> accessions = ["O23729", "O23730", "O23731"]
>>> records = []

>>> for accession in accessions:
...     handle = ExPASy.get_sprot_raw(accession)
...     record = SwissProt.read(handle)
...     records.append(record)

If the accession number you provided to ExPASy.get_sprot_raw does not exist, then SwissProt.read(handle) will raise a ValueError. You can catch ValueException exceptions to detect invalid accession numbers:

>>> for accession in accessions:
...     handle = ExPASy.get_sprot_raw(accession)
...     try:
...         record = SwissProt.read(handle)
...     except ValueException:
...         print("WARNING: Accession %s not found" % accession)
...     records.append(record)

10.5.2  Searching Swiss-Prot

Now, you may remark that I knew the records’ accession numbers beforehand. Indeed, get_sprot_raw() needs either the entry name or an accession number. When you don’t have them handy, you can use one of the sprot_search_de() or sprot_search_ful() functions.

sprot_search_de() searches in the ID, DE, GN, OS and OG lines; sprot_search_ful() searches in (nearly) all the fields. They are detailed on http://www.expasy.org/cgi-bin/sprot-search-de and http://www.expasy.org/cgi-bin/sprot-search-ful respectively. Note that they don’t search in TrEMBL by default (argument trembl). Note also that they return html pages; however, accession numbers are quite easily extractable:

>>> from Bio import ExPASy
>>> import re

>>> handle = ExPASy.sprot_search_de("Orchid Chalcone Synthase")
>>> # or:
>>> # handle = ExPASy.sprot_search_ful("Orchid and {Chalcone Synthase}")
>>> html_results = handle.read()
>>> if "Number of sequences found" in html_results:
...     ids = re.findall(r'HREF="/uniprot/(\w+)"', html_results)
... else:
...     ids = re.findall(r'href="/cgi-bin/niceprot\.pl\?(\w+)"', html_results)

10.5.3  Retrieving Prosite and Prosite documentation records

Prosite and Prosite documentation records can be retrieved either in HTML format, or in raw format. To parse Prosite and Prosite documentation records with Biopython, you should retrieve the records in raw format. For other purposes, however, you may be interested in these records in HTML format.

To retrieve a Prosite or Prosite documentation record in raw format, use get_prosite_raw(). For example, to download a Prosite record and print it out in raw text format, use

>>> from Bio import ExPASy
>>> handle = ExPASy.get_prosite_raw('PS00001')
>>> text = handle.read()
>>> print(text)

To retrieve a Prosite record and parse it into a Bio.Prosite.Record object, use

>>> from Bio import ExPASy
>>> from Bio import Prosite
>>> handle = ExPASy.get_prosite_raw('PS00001')
>>> record = Prosite.read(handle)

The same function can be used to retrieve a Prosite documentation record and parse it into a Bio.ExPASy.Prodoc.Record object:

>>> from Bio import ExPASy
>>> from Bio.ExPASy import Prodoc
>>> handle = ExPASy.get_prosite_raw('PDOC00001')
>>> record = Prodoc.read(handle)

For non-existing accession numbers, ExPASy.get_prosite_raw returns a handle to an emptry string. When faced with an empty string, Prosite.read and Prodoc.read will raise a ValueError. You can catch these exceptions to detect invalid accession numbers.

The functions get_prosite_entry() and get_prodoc_entry() are used to download Prosite and Prosite documentation records in HTML format. To create a web page showing one Prosite record, you can use

>>> from Bio import ExPASy
>>> handle = ExPASy.get_prosite_entry('PS00001')
>>> html = handle.read()
>>> output = open("myprositerecord.html", "w")
>>> output.write(html)
>>> output.close()

and similarly for a Prosite documentation record:

>>> from Bio import ExPASy
>>> handle = ExPASy.get_prodoc_entry('PDOC00001')
>>> html = handle.read()
>>> output = open("myprodocrecord.html", "w")
>>> output.write(html)
>>> output.close()

For these functions, an invalid accession number returns an error message in HTML format.

10.6  Scanning the Prosite database

ScanProsite allows you to scan protein sequences online against the Prosite database by providing a UniProt or PDB sequence identifier or the sequence itself. For more information about ScanProsite, please see the ScanProsite documentation as well as the documentation for programmatic access of ScanProsite.

You can use Biopython’s Bio.ExPASy.ScanProsite module to scan the Prosite database from Python. This module both helps you to access ScanProsite programmatically, and to parse the results returned by ScanProsite. To scan for Prosite patterns in the following protein sequence:

MEHKEVVLLLLLFLKSGQGEPLDDYVNTQGASLFSVTKKQLGAGSIEECAAKCEEDEEFT
CRAFQYHSKEQQCVIMAENRKSSIIIRMRDVVLFEKKVYLSECKTGNGKNYRGTMSKTKN

you can use the following code:

>>> sequence = "MEHKEVVLLLLLFLKSGQGEPLDDYVNTQGASLFSVTKKQLGAGSIEECAAKCEEDEEFT
CRAFQYHSKEQQCVIMAENRKSSIIIRMRDVVLFEKKVYLSECKTGNGKNYRGTMSKTKN"
>>> from Bio.ExPASy import ScanProsite
>>> handle = ScanProsite.scan(seq=sequence)

By executing handle.read(), you can obtain the search results in raw XML format. Instead, let’s use Bio.ExPASy.ScanProsite.read to parse the raw XML into a Python object:

>>> result = ScanProsite.read(handle)
>>> type(result)
<class 'Bio.ExPASy.ScanProsite.Record'>

A Bio.ExPASy.ScanProsite.Record object is derived from a list, with each element in the list storing one ScanProsite hit. This object also stores the number of hits, as well as the number of search sequences, as returned by ScanProsite. This ScanProsite search resulted in six hits:

>>> result.n_seq
1
>>> result.n_match
6
>>> len(result)
6
>>> result[0]
{'signature_ac': u'PS50948', 'level': u'0', 'stop': 98, 'sequence_ac': u'USERSEQ1', 'start': 16, 'score': u'8.873'}
>>> result[1]
{'start': 37, 'stop': 39, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00005'}
>>> result[2]
{'start': 45, 'stop': 48, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00006'}
>>> result[3]
{'start': 60, 'stop': 62, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00005'}
>>> result[4]
{'start': 80, 'stop': 83, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00004'}
>>> result[5]
{'start': 106, 'stop': 111, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00008'}

Other ScanProsite parameters can be passed as keyword arguments; see the documentation for programmatic access of ScanProsite for more information. As an example, passing lowscore=1 to include matches with low level scores lets use find one additional hit:

>>> handle = ScanProsite.scan(seq=sequence, lowscore=1)
>>> result = ScanProsite.read(handle)
>>> result.n_match
7

Chapter 11  Going 3D: The PDB module

Bio.PDB is a Biopython module that focuses on working with crystal structures of biological macromolecules. Among other things, Bio.PDB includes a PDBParser class that produces a Structure object, which can be used to access the atomic data in the file in a convenient manner. There is limited support for parsing the information contained in the PDB header.

11.1  Reading and writing crystal structure files

11.1.1  Reading a PDB file

First we create a PDBParser object:

>>> from Bio.PDB.PDBParser import PDBParser
>>> p = PDBParser(PERMISSIVE=1)

The PERMISSIVE flag indicates that a number of common problems (see 11.7.1) associated with PDB files will be ignored (but note that some atoms and/or residues will be missing). If the flag is not present a PDBConstructionException will be generated if any problems are detected during the parse operation.

The Structure object is then produced by letting the PDBParser object parse a PDB file (the PDB file in this case is called ’pdb1fat.ent’, ’1fat’ is a user defined name for the structure):

>>> structure_id = "1fat"
>>> filename = "pdb1fat.ent"
>>> s = p.get_structure(structure_id, filename)

You can extract the header and trailer (simple lists of strings) of the PDB file from the PDBParser object with the get_header and get_trailer methods. Note however that many PDB files contain headers with incomplete or erroneous information. Many of the errors have been fixed in the equivalent mmCIF files. Hence, if you are interested in the header information, it is a good idea to extract information from mmCIF files using the MMCIF2Dict tool described below, instead of parsing the PDB header.

Now that is clarified, let’s return to parsing the PDB header. The structure object has an attribute called header which is a Python dictionary that maps header records to their values.

Example:

>>> resolution = structure.header['resolution']
>>> keywords = structure.header['keywords']

The available keys are name, head, deposition_date, release_date, structure_method, resolution, structure_reference (which maps to a list of references), journal_reference, author, and compound (which maps to a dictionary with various information about the crystallized compound).

The dictionary can also be created without creating a Structure object, ie. directly from the PDB file:

>>> file = open(filename, 'r')
>>> header_dict = parse_pdb_header(file)
>>> file.close()

11.1.2  Reading an mmCIF file

Similarly to the case the case of PDB files, first create an MMCIFParser object:

>>> from Bio.PDB.MMCIFParser import MMCIFParser
>>> parser = MMCIFParser()

Then use this parser to create a structure object from the mmCIF file: 

>>> structure = parser.get_structure('1fat', '1fat.cif')

To have some more low level access to an mmCIF file, you can use the MMCIF2Dict class to create a Python dictionary that maps all mmCIF tags in an mmCIF file to their values. If there are multiple values (like in the case of tag _atom_site.Cartn_y, which holds the y coordinates of all atoms), the tag is mapped to a list of values. The dictionary is created from the mmCIF file as follows:

>>> from Bio.PDB.MMCIF2Dict import MMCIF2Dict
>>> mmcif_dict = MMCIF2Dict('1FAT.cif')

Example: get the solvent content from an mmCIF file: 

>>> sc = mmcif_dict['_exptl_crystal.density_percent_sol']

Example: get the list of the y coordinates of all atoms 

>>> y_list = mmcif_dict['_atom_site.Cartn_y']

11.1.3  Reading files in the PDB XML format

That’s not yet supported, but we are definitely planning to support that in the future (it’s not a lot of work). Contact the Biopython developers (biopython-dev@biopython.org) if you need this).

11.1.4  Writing PDB files

Use the PDBIO class for this. It’s easy to write out specific parts of a structure too, of course.

Example: saving a structure

>>> io = PDBIO()
>>> io.set_structure(s)
>>> io.save('out.pdb')

If you want to write out a part of the structure, make use of the Select class (also in PDBIO). Select has four methods:

  • accept_model(model)
  • accept_chain(chain)
  • accept_residue(residue)
  • accept_atom(atom)

By default, every method returns 1 (which means the model/chain/residue/atom is included in the output). By subclassing Select and returning 0 when appropriate you can exclude models, chains, etc. from the output. Cumbersome maybe, but very powerful. The following code only writes out glycine residues:

>>> class GlySelect(Select):
...     def accept_residue(self, residue):
...         if residue.get_name()=='GLY':
...             return True
...         else:
...             return False
... 
>>> io = PDBIO()
>>> io.set_structure(s)
>>> io.save('gly_only.pdb', GlySelect())

If this is all too complicated for you, the Dice module contains a handy extract function that writes out all residues in a chain between a start and end residue.

11.2  Structure representation

The overall layout of a Structure object follows the so-called SMCRA (Structure/Model/Chain/Residue/Atom) architecture:

  • A structure consists of models
  • A model consists of chains
  • A chain consists of residues
  • A residue consists of atoms

This is the way many structural biologists/bioinformaticians think about structure, and provides a simple but efficient way to deal with structure. Additional stuff is essentially added when needed. A UML diagram of the Structure object (forget about the Disordered classes for now) is shown in Fig. 11.1. Such a data structure is not necessarily best suited for the representation of the macromolecular content of a structure, but it is absolutely necessary for a good interpretation of the data present in a file that describes the structure (typically a PDB or MMCIF file). If this hierarchy cannot represent the contents of a structure file, it is fairly certain that the file contains an error or at least does not describe the structure unambiguously. If a SMCRA data structure cannot be generated, there is reason to suspect a problem. Parsing a PDB file can thus be used to detect likely problems. We will give several examples of this in section 11.7.1.

Figure 11.1: UML diagram of SMCRA architecture of the Structure class used to represent a macromolecular structure. Full lines with diamonds denote aggregation, full lines with arrows denote referencing, full lines with triangles denote inheritance and dashed lines with triangles denote interface realization.

Structure, Model, Chain and Residue are all subclasses of the Entity base class. The Atom class only (partly) implements the Entity interface (because an Atom does not have children).

For each Entity subclass, you can extract a child by using a unique id for that child as a key (e.g. you can extract an Atom object from a Residue object by using an atom name string as a key, you can extract a Chain object from a Model object by using its chain identifier as a key).

Disordered atoms and residues are represented by DisorderedAtom and DisorderedResidue classes, which are both subclasses of the DisorderedEntityWrapper base class. They hide the complexity associated with disorder and behave exactly as Atom and Residue objects.

In general, a child Entity object (i.e. Atom, Residue, Chain, Model) can be extracted from its parent (i.e. Residue, Chain, Model, Structure, respectively) by using an id as a key.

>>> child_entity = parent_entity[child_id]

You can also get a list of all child Entities of a parent Entity object. Note that this list is sorted in a specific way (e.g. according to chain identifier for Chain objects in a Model object).

>>> child_list = parent_entity.get_list()

You can also get the parent from a child: 

>>> parent_entity = child_entity.get_parent()

At all levels of the SMCRA hierarchy, you can also extract a full id. The full id is a tuple containing all id’s starting from the top object (Structure) down to the current object. A full id for a Residue object e.g. is something like:

>>> full_id = residue.get_full_id()
>>> print(full_id)
("1abc", 0, "A", ("", 10, "A"))

This corresponds to:

  • The Structure with id "1abc"
  • The Model with id 0
  • The Chain with id "A"
  • The Residue with id (" ", 10, "A").

The Residue id indicates that the residue is not a hetero-residue (nor a water) because it has a blank hetero field, that its sequence identifier is 10 and that its insertion code is "A".

To get the entity’s id, use the get_id method: 

>>> entity.get_id()

You can check if the entity has a child with a given id by using the has_id method: 

>>> entity.has_id(entity_id)

The length of an entity is equal to its number of children: 

>>> nr_children = len(entity)

It is possible to delete, rename, add, etc. child entities from a parent entity, but this does not include any sanity checks (e.g. it is possible to add two residues with the same id to one chain). This really should be done via a nice Decorator class that includes integrity checking, but you can take a look at the code (Entity.py) if you want to use the raw interface.

11.2.1  Structure

The Structure object is at the top of the hierarchy. Its id is a user given string. The Structure contains a number of Model children. Most crystal structures (but not all) contain a single model, while NMR structures typically consist of several models. Disorder in crystal structures of large parts of molecules can also result in several models.

11.2.2  Model

The id of the Model object is an integer, which is derived from the position of the model in the parsed file (they are automatically numbered starting from 0). Crystal structures generally have only one model (with id 0), while NMR files usually have several models. Whereas many PDB parsers assume that there is only one model, the Structure class in Bio.PDB is designed such that it can easily handle PDB files with more than one model.

As an example, to get the first model from a Structure object, use 

>>> first_model = structure[0]

The Model object stores a list of Chain children.

11.2.3  Chain

The id of a Chain object is derived from the chain identifier in the PDB/mmCIF file, and is a single character (typically a letter). Each Chain in a Model object has a unique id. As an example, to get the Chain object with identifier “A” from a Model object, use 

>>> chain_A = model["A"]

The Chain object stores a list of Residue children.

11.2.4  Residue

A residue id is a tuple with three elements:

  • The hetero-field (hetfield): this is
    • 'W' in the case of a water molecule;
    • 'H_' followed by the residue name for other hetero residues (e.g. 'H_GLC' in the case of a glucose molecule);
    • blank for standard amino and nucleic acids.
    This scheme is adopted for reasons described in section 11.4.1.
  • The sequence identifier (resseq), an integer describing the position of the residue in the chain (e.g., 100);
  • The insertion code (icode); a string, e.g. ’A’. The insertion code is sometimes used to preserve a certain desirable residue numbering scheme. A Ser 80 insertion mutant (inserted e.g. between a Thr 80 and an Asn 81 residue) could e.g. have sequence identifiers and insertion codes as follows: Thr 80 A, Ser 80 B, Asn 81. In this way the residue numbering scheme stays in tune with that of the wild type structure.

The id of the above glucose residue would thus be (’H_GLC’, 100, ’A’). If the hetero-flag and insertion code are blank, the sequence identifier alone can be used:

# Full id
>>> residue=chain[(' ', 100, ' ')]
# Shortcut id
>>> residue=chain[100]

The reason for the hetero-flag is that many, many PDB files use the same sequence identifier for an amino acid and a hetero-residue or a water, which would create obvious problems if the hetero-flag was not used.

Unsurprisingly, a Residue object stores a set of Atom children. It also contains a string that specifies the residue name (e.g. “ASN”) and the segment identifier of the residue (well known to X-PLOR users, but not used in the construction of the SMCRA data structure).

Let’s look at some examples. Asn 10 with a blank insertion code would have residue id (’ ’, 10, ’ ’). Water 10 would have residue id (’W’, 10, ’ ’). A glucose molecule (a hetero residue with residue name GLC) with sequence identifier 10 would have residue id (’H_GLC’, 10, ’ ’). In this way, the three residues (with the same insertion code and sequence identifier) can be part of the same chain because their residue id’s are distinct.

In most cases, the hetflag and insertion code fields will be blank, e.g. (’ ’, 10, ’ ’). In these cases, the sequence identifier can be used as a shortcut for the full id:

# use full id
>>> res10 = chain[(' ', 10, ' ')]
# use shortcut
>>> res10 = chain[10]

Each Residue object in a Chain object should have a unique id. However, disordered residues are dealt with in a special way, as described in section 11.3.3.

A Residue object has a number of additional methods:

>>> residue.get_resname()       # returns the residue name, e.g. "ASN"
>>> residue.is_disordered()     # returns 1 if the residue has disordered atoms
>>> residue.get_segid()         # returns the SEGID, e.g. "CHN1"
>>> residue.has_id(name)        # test if a residue has a certain atom

You can use is_aa(residue) to test if a Residue object is an amino acid.

11.2.5  Atom

The Atom object stores the data associated with an atom, and has no children. The id of an atom is its atom name (e.g. “OG” for the side chain oxygen of a Ser residue). An Atom id needs to be unique in a Residue. Again, an exception is made for disordered atoms, as described in section 11.3.2.

The atom id is simply the atom name (eg. ’CA’). In practice, the atom name is created by stripping all spaces from the atom name in the PDB file.

However, in PDB files, a space can be part of an atom name. Often, calcium atoms are called ’CA..’ in order to distinguish them from Cα atoms (which are called ’.CA.’). In cases were stripping the spaces would create problems (ie. two atoms called ’CA’ in the same residue) the spaces are kept.

In a PDB file, an atom name consists of 4 chars, typically with leading and trailing spaces. Often these spaces can be removed for ease of use (e.g. an amino acid C α atom is labeled “.CA.” in a PDB file, where the dots represent spaces). To generate an atom name (and thus an atom id) the spaces are removed, unless this would result in a name collision in a Residue (i.e. two Atom objects with the same atom name and id). In the latter case, the atom name including spaces is tried. This situation can e.g. happen when one residue contains atoms with names “.CA.” and “CA..”, although this is not very likely.

The atomic data stored includes the atom name, the atomic coordinates (including standard deviation if present), the B factor (including anisotropic B factors and standard deviation if present), the altloc specifier and the full atom name including spaces. Less used items like the atom element number or the atomic charge sometimes specified in a PDB file are not stored.

To manipulate the atomic coordinates, use the transform method of the Atom object. Use the set_coord method to specify the atomic coordinates directly.

An Atom object has the following additional methods:

>>> a.get_name()       # atom name (spaces stripped, e.g. "CA")
>>> a.get_id()         # id (equals atom name)
>>> a.get_coord()      # atomic coordinates
>>> a.get_vector()     # atomic coordinates as Vector object
>>> a.get_bfactor()    # isotropic B factor
>>> a.get_occupancy()  # occupancy
>>> a.get_altloc()     # alternative location specifier
>>> a.get_sigatm()     # standard deviation of atomic parameters
>>> a.get_siguij()     # standard deviation of anisotropic B factor
>>> a.get_anisou()     # anisotropic B factor
>>> a.get_fullname()   # atom name (with spaces, e.g. ".CA.")

To represent the atom coordinates, siguij, anisotropic B factor and sigatm Numpy arrays are used.

The get_vector method returns a Vector object representation of the coordinates of the Atom object, allowing you to do vector operations on atomic coordinates. Vector implements the full set of 3D vector operations, matrix multiplication (left and right) and some advanced rotation-related operations as well.

As an example of the capabilities of Bio.PDB’s Vector module, suppose that you would like to find the position of a Gly residue’s Cβ atom, if it had one. Rotating the N atom of the Gly residue along the Cα-C bond over -120 degrees roughly puts it in the position of a virtual Cβ atom. Here’s how to do it, making use of the rotaxis method (which can be used to construct a rotation around a certain axis) of the Vector module:

# get atom coordinates as vectors
>>> n = residue['N'].get_vector() 
>>> c = residue['C'].get_vector() 
>>> ca = residue['CA'].get_vector()
# center at origin
>>> n = n - ca 
>>> c = c - ca 
# find rotation matrix that rotates n 
# -120 degrees along the ca-c vector
>>> rot = rotaxis(-pi * 120.0/180.0, c)
# apply rotation to ca-n vector
>>> cb_at_origin = n.left_multiply(rot)
# put on top of ca atom
>>> cb = cb_at_origin+ca

This example shows that it’s possible to do some quite nontrivial vector operations on atomic data, which can be quite useful. In addition to all the usual vector operations (cross (use **), and dot (use *) product, angle, norm, etc.) and the above mentioned rotaxis function, the Vector module also has methods to rotate (rotmat) or reflect (refmat) one vector on top of another.

11.2.6  Extracting a specific Atom/Residue/Chain/Model from a Structure

These are some examples:

>>> model = structure[0]
>>> chain = model['A']
>>> residue = chain[100]
>>> atom = residue['CA']

Note that you can use a shortcut:

>>> atom = structure[0]['A'][100]['CA']

11.3  Disorder

Bio.PDB can handle both disordered atoms and point mutations (i.e. a Gly and an Ala residue in the same position).

11.3.1  General approach

Disorder should be dealt with from two points of view: the atom and the residue points of view. In general, we have tried to encapsulate all the complexity that arises from disorder. If you just want to loop over all Cα atoms, you do not care that some residues have a disordered side chain. On the other hand it should also be possible to represent disorder completely in the data structure. Therefore, disordered atoms or residues are stored in special objects that behave as if there is no disorder. This is done by only representing a subset of the disordered atoms or residues. Which subset is picked (e.g. which of the two disordered OG side chain atom positions of a Ser residue is used) can be specified by the user.

11.3.2  Disordered atoms

Disordered atoms are represented by ordinary Atom objects, but all Atom objects that represent the same physical atom are stored in a DisorderedAtom object (see Fig. 11.1). Each Atom object in a DisorderedAtom object can be uniquely indexed using its altloc specifier. The DisorderedAtom object forwards all uncaught method calls to the selected Atom object, by default the one that represents the atom with the highest occupancy. The user can of course change the selected Atom object, making use of its altloc specifier. In this way atom disorder is represented correctly without much additional complexity. In other words, if you are not interested in atom disorder, you will not be bothered by it.

Each disordered atom has a characteristic altloc identifier. You can specify that a DisorderedAtom object should behave like the Atom object associated with a specific altloc identifier:

>>> atom.disordered_select('A') # select altloc A atom
>>> print(atom.get_altloc())
"A"
>>> atom.disordered_select('B') # select altloc B atom
>>> print(atom.get_altloc())
"B"

11.3.3  Disordered residues

Common case

The most common case is a residue that contains one or more disordered atoms. This is evidently solved by using DisorderedAtom objects to represent the disordered atoms, and storing the DisorderedAtom object in a Residue object just like ordinary Atom objects. The DisorderedAtom will behave exactly like an ordinary atom (in fact the atom with the highest occupancy) by forwarding all uncaught method calls to one of the Atom objects (the selected Atom object) it contains.

Point mutations

A special case arises when disorder is due to a point mutation, i.e. when two or more point mutants of a polypeptide are present in the crystal. An example of this can be found in PDB structure 1EN2.

Since these residues belong to a different residue type (e.g. let’s say Ser 60 and Cys 60) they should not be stored in a single Residue object as in the common case. In this case, each residue is represented by one Residue object, and both Residue objects are stored in a single DisorderedResidue object (see Fig. 11.1).

The DisorderedResidue object forwards all uncaught methods to the selected Residue object (by default the last Residue object added), and thus behaves like an ordinary residue. Each Residue object in a DisorderedResidue object can be uniquely identified by its residue name. In the above example, residue Ser 60 would have id “SER” in the DisorderedResidue object, while residue Cys 60 would have id “CYS”. The user can select the active Residue object in a DisorderedResidue object via this id.

Example: suppose that a chain has a point mutation at position 10, consisting of a Ser and a Cys residue. Make sure that residue 10 of this chain behaves as the Cys residue. 

>>> residue = chain[10]
>>> residue.disordered_select('CYS')

In addition, you can get a list of all Atom objects (ie. all DisorderedAtom objects are ’unpacked’ to their individual Atom objects) using the get_unpacked_list method of a (Disordered)Residue object.

11.4  Hetero residues

11.4.1  Associated problems

A common problem with hetero residues is that several hetero and non-hetero residues present in the same chain share the same sequence identifier (and insertion code). Therefore, to generate a unique id for each hetero residue, waters and other hetero residues are treated in a different way.

Remember that Residue object have the tuple (hetfield, resseq, icode) as id. The hetfield is blank (“ ”) for amino and nucleic acids, and a string for waters and other hetero residues. The content of the hetfield is explained below.

11.4.2  Water residues

The hetfield string of a water residue consists of the letter “W”. So a typical residue id for a water is (“W”, 1, “ ”).

11.4.3  Other hetero residues

The hetfield string for other hetero residues starts with “H_” followed by the residue name. A glucose molecule e.g. with residue name “GLC” would have hetfield “H_GLC”. Its residue id could e.g. be (“H_GLC”, 1, “ ”).

11.5  Navigating through a Structure object

Parse a PDB file, and extract some Model, Chain, Residue and Atom objects

>>> from Bio.PDB.PDBParser import PDBParser
>>> parser = PDBParser()
>>> structure = parser.get_structure("test", "1fat.pdb")
>>> model = structure[0]
>>> chain = model["A"]
>>> residue = chain[1]
>>> atom = residue["CA"]

Iterating through all atoms of a structure

>>> p = PDBParser()
>>> structure = p.get_structure('X', 'pdb1fat.ent')
>>> for model in structure:
...     for chain in model:
...         for residue in chain:
...             for atom in residue:
...                 print(atom)
...

There is a shortcut if you want to iterate over all atoms in a structure: 

>>> atoms = structure.get_atoms()
>>> for atom in atoms:
...     print(atom)
...

Similarly, to iterate over all atoms in a chain, use 

>>> atoms = chain.get_atoms()
>>> for atom in atoms:
...     print(atom)
...

Iterating over all residues of a model

or if you want to iterate over all residues in a model: 

>>> residues = model.get_residues()
>>> for residue in residues:
...     print(residue)
...

You can also use the Selection.unfold_entities function to get all residues from a structure: 

>>> res_list = Selection.unfold_entities(structure, 'R')

or to get all atoms from a chain: 

>>> atom_list = Selection.unfold_entities(chain, 'A')

Obviously, A=atom, R=residue, C=chain, M=model, S=structure. You can use this to go up in the hierarchy, e.g. to get a list of (unique) Residue or Chain parents from a list of Atoms:

>>> residue_list = Selection.unfold_entities(atom_list, 'R')
>>> chain_list = Selection.unfold_entities(atom_list, 'C')

For more info, see the API documentation.

Extract a hetero residue from a chain (e.g. a glucose (GLC) moiety with resseq 10)

>>> residue_id = ("H_GLC", 10, " ")
>>> residue = chain[residue_id]

Print all hetero residues in chain

>>> for residue in chain.get_list():
...    residue_id = residue.get_id()
...    hetfield = residue_id[0]
...    if hetfield[0]=="H":
...        print(residue_id)
...

Print out the coordinates of all CA atoms in a structure with B factor greater than 50

>>> for model in structure.get_list():
...     for chain in model.get_list():
...         for residue in chain.get_list():
...             if residue.has_id("CA"):
...                 ca = residue["CA"]
...                 if ca.get_bfactor() > 50.0:
...                     print(ca.get_coord())
...

Print out all the residues that contain disordered atoms

>>> for model in structure.get_list():
...     for chain in model.get_list():
...         for residue in chain.get_list():
...             if residue.is_disordered():
...                 resseq = residue.get_id()[1]
...                 resname = residue.get_resname()
...                 model_id = model.get_id()
...                 chain_id = chain.get_id()
...                 print(model_id, chain_id, resname, resseq)
...

Loop over all disordered atoms, and select all atoms with altloc A (if present)

This will make sure that the SMCRA data structure will behave as if only the atoms with altloc A are present.

>>> for model in structure.get_list():
...     for chain in model.get_list():
...         for residue in chain.get_list():
...             if residue.is_disordered():
...                 for atom in residue.get_list():
...                     if atom.is_disordered():
...                         if atom.disordered_has_id("A"):
...                             atom.disordered_select("A")
...

Extracting polypeptides from a Structure object

To extract polypeptides from a structure, construct a list of Polypeptide objects from a Structure object using PolypeptideBuilder as follows:

>>> model_nr = 1
>>> polypeptide_list = build_peptides(structure, model_nr)
>>> for polypeptide in polypeptide_list:
...     print(polypeptide)
...

A Polypeptide object is simply a UserList of Residue objects, and is always created from a single Model (in this case model 1). You can use the resulting Polypeptide object to get the sequence as a Seq object or to get a list of Cα atoms as well. Polypeptides can be built using a C-N or a Cα-Cα distance criterion.

Example:

# Using C-N 
>>> ppb=PPBuilder()
>>> for pp in ppb.build_peptides(structure): 
...     print(pp.get_sequence())
...
# Using CA-CA
>>> ppb=CaPPBuilder()
>>> for pp in ppb.build_peptides(structure): 
...     print(pp.get_sequence())
...

Note that in the above case only model 0 of the structure is considered by PolypeptideBuilder. However, it is possible to use PolypeptideBuilder to build Polypeptide objects from Model and Chain objects as well.

Obtaining the sequence of a structure

The first thing to do is to extract all polypeptides from the structure (as above). The sequence of each polypeptide can then easily be obtained from the Polypeptide objects. The sequence is represented as a Biopython Seq object, and its alphabet is defined by a ProteinAlphabet object.

Example:

>>> seq = polypeptide.get_sequence()
>>> print(seq)
Seq('SNVVE...', <class Bio.Alphabet.ProteinAlphabet>)

11.6  Analyzing structures

11.6.1  Measuring distances

The minus operator for atoms has been overloaded to return the distance between two atoms. 

# Get some atoms
>>> ca1 = residue1['CA']
>>> ca2 = residue2['CA']
# Simply subtract the atoms to get their distance
>>> distance = ca1-ca2

11.6.2  Measuring angles

Use the vector representation of the atomic coordinates, and the calc_angle function from the Vector module: 

>>> vector1 = atom1.get_vector()
>>> vector2 = atom2.get_vector()
>>> vector3 = atom3.get_vector()
>>> angle = calc_angle(vector1, vector2, vector3)

11.6.3  Measuring torsion angles

Use the vector representation of the atomic coordinates, and the calc_dihedral function from the Vector module: 

>>> vector1 = atom1.get_vector()
>>> vector2 = atom2.get_vector()
>>> vector3 = atom3.get_vector()
>>> vector4 = atom4.get_vector()
>>> angle = calc_dihedral(vector1, vector2, vector3, vector4)

11.6.4  Determining atom-atom contacts

Use NeighborSearch to perform neighbor lookup. The neighbor lookup is done using a KD tree module written in C (see Bio.KDTree), making it very fast. It also includes a fast method to find all point pairs within a certain distance of each other.

11.6.5  Superimposing two structures

Use a Superimposer object to superimpose two coordinate sets. This object calculates the rotation and translation matrix that rotates two lists of atoms on top of each other in such a way that their RMSD is minimized. Of course, the two lists need to contain the same number of atoms. The Superimposer object can also apply the rotation/translation to a list of atoms. The rotation and translation are stored as a tuple in the rotran attribute of the Superimposer object (note that the rotation is right multiplying!). The RMSD is stored in the rmsd attribute.

The algorithm used by Superimposer comes from [17, Golub & Van Loan] and makes use of singular value decomposition (this is implemented in the general Bio.SVDSuperimposer module).

Example:

>>> sup = Superimposer()
# Specify the atom lists
# 'fixed' and 'moving' are lists of Atom objects
# The moving atoms will be put on the fixed atoms
>>> sup.set_atoms(fixed, moving)
# Print rotation/translation/rmsd
>>> print(sup.rotran)
>>> print(sup.rms) 
# Apply rotation/translation to the moving atoms
>>> sup.apply(moving)

To superimpose two structures based on their active sites, use the active site atoms to calculate the rotation/translation matrices (as above), and apply these to the whole molecule.

11.6.6  Mapping the residues of two related structures onto each other

First, create an alignment file in FASTA format, then use the StructureAlignment class. This class can also be used for alignments with more than two structures.

11.6.7  Calculating the Half Sphere Exposure

Half Sphere Exposure (HSE) is a new, 2D measure of solvent exposure [20]. Basically, it counts the number of Cα atoms around a residue in the direction of its side chain, and in the opposite direction (within a radius of 13 Å). Despite its simplicity, it outperforms many other measures of solvent exposure.

HSE comes in two flavors: HSEα and HSEβ. The former only uses the Cα atom positions, while the latter uses the Cα and Cβ atom positions. The HSE measure is calculated by the HSExposure class, which can also calculate the contact number. The latter class has methods which return dictionaries that map a Residue object to its corresponding HSEα, HSEβ and contact number values.

Example:

>>> model = structure[0]
>>> hse = HSExposure()
# Calculate HSEalpha
>>> exp_ca = hse.calc_hs_exposure(model, option='CA3')
# Calculate HSEbeta
>>> exp_cb=hse.calc_hs_exposure(model, option='CB')
# Calculate classical coordination number
>>> exp_fs = hse.calc_fs_exposure(model)
# Print HSEalpha for a residue
>>> print(exp_ca[some_residue])

11.6.8  Determining the secondary structure

For this functionality, you need to install DSSP (and obtain a license for it — free for academic use, see http://www.cmbi.kun.nl/gv/dssp/). Then use the DSSP class, which maps Residue objects to their secondary structure (and accessible surface area). The DSSP codes are listed in Table 11.1. Note that DSSP (the program, and thus by consequence the class) cannot handle multiple models!

CodeSecondary structure
Hα-helix
BIsolated β-bridge residue
EStrand
G3-10 helix
IΠ-helix
TTurn
SBend
-Other
Table 11.1: DSSP codes in Bio.PDB.

The DSSP class can also be used to calculate the accessible surface area of a residue. But see also section 11.6.9.

11.6.9  Calculating the residue depth

Residue depth is the average distance of a residue’s atoms from the solvent accessible surface. It’s a fairly new and very powerful parameterization of solvent accessibility. For this functionality, you need to install Michel Sanner’s MSMS program (http://www.scripps.edu/pub/olson-web/people/sanner/html/msms_home.html). Then use the ResidueDepth class. This class behaves as a dictionary which maps Residue objects to corresponding (residue depth, Cα depth) tuples. The Cα depth is the distance of a residue’s Cα atom to the solvent accessible surface.

Example:

>>> model = structure[0]
>>> rd = ResidueDepth(model, pdb_file)
>>> residue_depth, ca_depth=rd[some_residue]

You can also get access to the molecular surface itself (via the get_surface function), in the form of a Numeric Python array with the surface points.

11.7  Common problems in PDB files

It is well known that many PDB files contain semantic errors (not the structures themselves, but their representation in PDB files). Bio.PDB tries to handle this in two ways. The PDBParser object can behave in two ways: a restrictive way and a permissive way, which is the default.

Example:

# Permissive parser
>>> parser = PDBParser(PERMISSIVE=1)
>>> parser = PDBParser() # The same (default)
# Strict parser
>>> strict_parser = PDBParser(PERMISSIVE=0)

In the permissive state (DEFAULT), PDB files that obviously contain errors are “corrected” (i.e. some residues or atoms are left out). These errors include:

  • Multiple residues with the same identifier
  • Multiple atoms with the same identifier (taking into account the altloc identifier)

These errors indicate real problems in the PDB file (for details see [18, Hamelryck and Manderick, 2003]). In the restrictive state, PDB files with errors cause an exception to occur. This is useful to find errors in PDB files.

Some errors however are automatically corrected. Normally each disordered atom should have a non-blank altloc identifier. However, there are many structures that do not follow this convention, and have a blank and a non-blank identifier for two disordered positions of the same atom. This is automatically interpreted in the right way.

Sometimes a structure contains a list of residues belonging to chain A, followed by residues belonging to chain B, and again followed by residues belonging to chain A, i.e. the chains are ’broken’. This is also correctly interpreted.

11.7.1  Examples

The PDBParser/Structure class was tested on about 800 structures (each belonging to a unique SCOP superfamily). This takes about 20 minutes, or on average 1.5 seconds per structure. Parsing the structure of the large ribosomal subunit (1FKK), which contains about 64000 atoms, takes 10 seconds on a 1000 MHz PC.

Three exceptions were generated in cases where an unambiguous data structure could not be built. In all three cases, the likely cause is an error in the PDB file that should be corrected. Generating an exception in these cases is much better than running the chance of incorrectly describing the structure in a data structure.

11.7.1.1  Duplicate residues

One structure contains two amino acid residues in one chain with the same sequence identifier (resseq 3) and icode. Upon inspection it was found that this chain contains the residues Thr A3, …, Gly A202, Leu A3, Glu A204. Clearly, Leu A3 should be Leu A203. A couple of similar situations exist for structure 1FFK (which e.g. contains Gly B64, Met B65, Glu B65, Thr B67, i.e. residue Glu B65 should be Glu B66).

11.7.1.2  Duplicate atoms

Structure 1EJG contains a Ser/Pro point mutation in chain A at position 22. In turn, Ser 22 contains some disordered atoms. As expected, all atoms belonging to Ser 22 have a non-blank altloc specifier (B or C). All atoms of Pro 22 have altloc A, except the N atom which has a blank altloc. This generates an exception, because all atoms belonging to two residues at a point mutation should have non-blank altloc. It turns out that this atom is probably shared by Ser and Pro 22, as Ser 22 misses the N atom. Again, this points to a problem in the file: the N atom should be present in both the Ser and the Pro residue, in both cases associated with a suitable altloc identifier.

11.7.2  Automatic correction

Some errors are quite common and can be easily corrected without much risk of making a wrong interpretation. These cases are listed below.

11.7.2.1  A blank altloc for a disordered atom

Normally each disordered atom should have a non-blank altloc identifier. However, there are many structures that do not follow this convention, and have a blank and a non-blank identifier for two disordered positions of the same atom. This is automatically interpreted in the right way.

11.7.2.2  Broken chains

Sometimes a structure contains a list of residues belonging to chain A, followed by residues belonging to chain B, and again followed by residues belonging to chain A, i.e. the chains are “broken”. This is correctly interpreted.

11.7.3  Fatal errors

Sometimes a PDB file cannot be unambiguously interpreted. Rather than guessing and risking a mistake, an exception is generated, and the user is expected to correct the PDB file. These cases are listed below.

11.7.3.1  Duplicate residues

All residues in a chain should have a unique id. This id is generated based on:

  • The sequence identifier (resseq).
  • The insertion code (icode).
  • The hetfield string (“W” for waters and “H_” followed by the residue name for other hetero residues)
  • The residue names of the residues in the case of point mutations (to store the Residue objects in a DisorderedResidue object).

If this does not lead to a unique id something is quite likely wrong, and an exception is generated.

11.7.3.2  Duplicate atoms

All atoms in a residue should have a unique id. This id is generated based on:

  • The atom name (without spaces, or with spaces if a problem arises).
  • The altloc specifier.

If this does not lead to a unique id something is quite likely wrong, and an exception is generated.

11.8  Accessing the Protein Data Bank

11.8.1  Downloading structures from the Protein Data Bank

Structures can be downloaded from the PDB (Protein Data Bank) by using the retrieve_pdb_file method on a PDBList object. The argument for this method is the PDB identifier of the structure.

>>> pdbl = PDBList()
>>> pdbl.retrieve_pdb_file('1FAT')

The PDBList class can also be used as a command-line tool: 

python PDBList.py 1fat

The downloaded file will be called pdb1fat.ent and stored in the current working directory. Note that the retrieve_pdb_file method also has an optional argument pdir that specifies a specific directory in which to store the downloaded PDB files.

The retrieve_pdb_file method also has some options to specify the compression format used for the download, and the program used for local decompression (default .Z format and gunzip). In addition, the PDB ftp site can be specified upon creation of the PDBList object. By default, the server of the Worldwide Protein Data Bank (ftp://ftp.wwpdb.org/pub/pdb/data/structures/divided/pdb/) is used. See the API documentation for more details. Thanks again to Kristian Rother for donating this module.

11.8.2  Downloading the entire PDB

The following commands will store all PDB files in the /data/pdb directory: 

python PDBList.py all /data/pdb

python PDBList.py all /data/pdb -d

The API method for this is called download_entire_pdb. Adding the -d option will store all files in the same directory. Otherwise, they are sorted into PDB-style subdirectories according to their PDB ID’s. Depending on the traffic, a complete download will take 2-4 days.

11.8.3  Keeping a local copy of the PDB up to date

This can also be done using the PDBList object. One simply creates a PDBList object (specifying the directory where the local copy of the PDB is present) and calls the update_pdb method:

>>> pl = PDBList(pdb='/data/pdb')
>>> pl.update_pdb()

One can of course make a weekly cronjob out of this to keep the local copy automatically up-to-date. The PDB ftp site can also be specified (see API documentation).

PDBList has some additional methods that can be of use. The get_all_obsolete method can be used to get a list of all obsolete PDB entries. The changed_this_week method can be used to obtain the entries that were added, modified or obsoleted during the current week. For more info on the possibilities of PDBList, see the API documentation.

11.9  General questions

11.9.1  How well tested is Bio.PDB?

Pretty well, actually. Bio.PDB has been extensively tested on nearly 5500 structures from the PDB - all structures seemed to be parsed correctly. More details can be found in the Bio.PDB Bioinformatics article. Bio.PDB has been used/is being used in many research projects as a reliable tool. In fact, I’m using Bio.PDB almost daily for research purposes and continue working on improving it and adding new features.

11.9.2  How fast is it?

The PDBParser performance was tested on about 800 structures (each belonging to a unique SCOP superfamily). This takes about 20 minutes, or on average 1.5 seconds per structure. Parsing the structure of the large ribosomal subunit (1FKK), which contains about 64000 atoms, takes 10 seconds on a 1000 MHz PC. In short: it’s more than fast enough for many applications.

11.9.3  Is there support for molecular graphics?

Not directly, mostly since there are quite a few Python based/Python aware solutions already, that can potentially be used with Bio.PDB. My choice is Pymol, BTW (I’ve used this successfully with Bio.PDB, and there will probably be specific PyMol modules in Bio.PDB soon/some day). Python based/aware molecular graphics solutions include:

11.9.4  Who’s using Bio.PDB?

Bio.PDB was used in the construction of DISEMBL, a web server that predicts disordered regions in proteins (http://dis.embl.de/), and COLUMBA, a website that provides annotated protein structures (http://www.columba-db.de/). Bio.PDB has also been used to perform a large scale search for active sites similarities between protein structures in the PDB [19, Hamelryck, 2003], and to develop a new algorithm that identifies linear secondary structure elements [26, Majumdar et al., 2005].

Judging from requests for features and information, Bio.PDB is also used by several LPCs (Large Pharmaceutical Companies :-).

Chapter 12  Bio.PopGen: Population genetics

Bio.PopGen is a Biopython module supporting population genetics, available in Biopython 1.44 onwards.

The medium term objective for the module is to support widely used data formats, applications and databases. This module is currently under intense development and support for new features should appear at a rather fast pace. Unfortunately this might also entail some instability on the API, especially if you are using a development version. APIs that are made available on our official public releases should be much more stable.

12.1  GenePop

GenePop (http://genepop.curtin.edu.au/) is a popular population genetics software package supporting Hardy-Weinberg tests, linkage desiquilibrium, population diferentiation, basic statistics, Fst and migration estimates, among others. GenePop does not supply sequence based statistics as it doesn’t handle sequence data. The GenePop file format is supported by a wide range of other population genetic software applications, thus making it a relevant format in the population genetics field.

Bio.PopGen provides a parser and generator of GenePop file format. Utilities to manipulate the content of a record are also provided. Here is an example on how to read a GenePop file (you can find example GenePop data files in the Test/PopGen directory of Biopython):

from Bio.PopGen import GenePop

handle = open("example.gen")
rec = GenePop.read(handle)
handle.close()

This will read a file called example.gen and parse it. If you do print rec, the record will be output again, in GenePop format.

The most important information in rec will be the loci names and population information (but there is more – use help(GenePop.Record) to check the API documentation). Loci names can be found on rec.loci_list. Population information can be found on rec.populations. Populations is a list with one element per population. Each element is itself a list of individuals, each individual is a pair composed by individual name and a list of alleles (2 per marker), here is an example for rec.populations:

[
    [
        ('Ind1', [(1, 2),    (3, 3), (200, 201)],
        ('Ind2', [(2, None), (3, 3), (None, None)],
    ],
    [
        ('Other1', [(1, 1),  (4, 3), (200, 200)],
    ]
]

So we have two populations, the first with two individuals, the second with only one. The first individual of the first population is called Ind1, allelic information for each of the 3 loci follows. Please note that for any locus, information might be missing (see as an example, Ind2 above).

A few utility functions to manipulate GenePop records are made available, here is an example:

from Bio.PopGen import GenePop

#Imagine that you have loaded rec, as per the code snippet above...

rec.remove_population(pos)
#Removes a population from a record, pos is the population position in
#  rec.populations, remember that it starts on position 0.
#  rec is altered.

rec.remove_locus_by_position(pos)
#Removes a locus by its position, pos is the locus position in
#  rec.loci_list, remember that it starts on position 0.
#  rec is altered.

rec.remove_locus_by_name(name)
#Removes a locus by its name, name is the locus name as in
#  rec.loci_list. If the name doesn't exist the function fails
#  silently.
#  rec is altered.

rec_loci = rec.split_in_loci()
#Splits a record in loci, that is, for each loci, it creates a new
#  record, with a single loci and all populations.
#  The result is returned in a dictionary, being each key the locus name.
#  The value is the GenePop record.
#  rec is not altered.

rec_pops =  rec.split_in_pops(pop_names)
#Splits a record in populations, that is, for each population, it creates
#  a new record, with a single population and all loci.
#  The result is returned in a dictionary, being each key
#  the population name. As population names are not available in GenePop,
#  they are passed in array (pop_names).
#  The value of each dictionary entry is the GenePop record.
#  rec is not altered.

GenePop does not support population names, a limitation which can be cumbersome at times. Functionality to enable population names is currently being planned for Biopython. These extensions won’t break compatibility in any way with the standard format. In the medium term, we would also like to support the GenePop web service.

12.2  Coalescent simulation

A coalescent simulation is a backward model of population genetics with relation to time. A simulation of ancestry is done until the Most Recent Common Ancestor (MRCA) is found. This ancestry relationship starting on the MRCA and ending on the current generation sample is sometimes called a genealogy. Simple cases assume a population of constant size in time, haploidy, no population structure, and simulate the alleles of a single locus under no selection pressure.

Coalescent theory is used in many fields like selection detection, estimation of demographic parameters of real populations or disease gene mapping.

The strategy followed in the Biopython implementation of the coalescent was not to create a new, built-in, simulator from scratch but to use an existing one, SIMCOAL2 (http://cmpg.unibe.ch/software/simcoal2/). SIMCOAL2 allows for, among others, population structure, multiple demographic events, simulation of multiple types of loci (SNPs, sequences, STRs/microsatellites and RFLPs) with recombination, diploidy multiple chromosomes or ascertainment bias. Notably SIMCOAL2 doesn’t support any selection model. We recommend reading SIMCOAL2’s documentation, available in the link above.

The input for SIMCOAL2 is a file specifying the desired demography and genome, the output is a set of files (typically around 1000) with the simulated genomes of a sample of individuals per subpopulation. This set of files can be used in many ways, like to compute confidence intervals where which certain statistics (e.g., Fst or Tajima D) are expected to lie. Real population genetics datasets statistics can then be compared to those confidence intervals.

Biopython coalescent code allows to create demographic scenarios and genomes and to run SIMCOAL2.

12.2.1  Creating scenarios

Creating a scenario involves both creating a demography and a chromosome structure. In many cases (e.g. when doing Approximate Bayesian Computations – ABC) it is important to test many parameter variations (e.g. vary the effective population size, Ne, between 10, 50, 500 and 1000 individuals). The code provided allows for the simulation of scenarios with different demographic parameters very easily.

Below we see how we can create scenarios and then how simulate them.

12.2.1.1  Demography

A few predefined demographies are built-in, all have two shared parameters: sample size (called sample_size on the template, see below for its use) per deme and deme size, i.e. subpopulation size (pop_size). All demographies are available as templates where all parameters can be varied, each template has a system name. The prefedined demographies/templates are:

Single population, constant size
The standard parameters are enough to specify it. Template name: simple.
Single population, bottleneck
As seen on figure 12.2.1.1. The parameters are current population size (pop_size on template ne3 on figure), time of expansion, given as the generation in the past when it occurred (expand_gen), effective population size during bottleneck (ne2), time of contraction (contract_gen) and original size in the remote past (ne3). Template name: bottle.
Island model
The typical island model. The total number of demes is specified by total_demes and the migration rate by mig. Template name island.
Stepping stone model - 1 dimension
The stepping stone model in 1 dimension, extremes disconnected. The total number of demes is total_demes, migration rate is mig. Template name is ssm_1d.
Stepping stone model - 2 dimensions
The stepping stone model in 2 dimensions, extremes disconnected. The parameters are x for the horizontal dimension and y for the vertical (being the total number of demes x times y), migration rate is mig. Template name is ssm_2d.

In our first example, we will generate a template for a single population, constant size model with a sample size of 30 and a deme size of 500. The code for this is:

from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template

generate_simcoal_from_template('simple',
    [(1, [('SNP', [24, 0.0005, 0.0])])],
    [('sample_size', [30]),
    ('pop_size', [100])])

Executing this code snippet will generate a file on the current directory called simple_100_300.par this file can be given as input to SIMCOAL2 to simulate the demography (below we will see how Biopython can take care of calling SIMCOAL2).

This code consists of a single function call, let’s discuss it parameter by parameter.

The first parameter is the template id (from the list above). We are using the id ’simple’ which is the template for a single population of constant size along time.

The second parameter is the chromosome structure. Please ignore it for now, it will be explained in the next section.

The third parameter is a list of all required parameters (recall that the simple model only needs sample_size and pop_size) and possible values (in this case each parameter only has a possible value).

Now, let’s consider an example where we want to generate several island models, and we are interested in varying the number of demes: 10, 50 and 100 with a migration rate of 1%. Sample size and deme size will be the same as before. Here is the code:

from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template

generate_simcoal_from_template('island',
    [(1, [('SNP', [24, 0.0005, 0.0])])],
    [('sample_size', [30]),
    ('pop_size', [100]),
    ('mig', [0.01]),
    ('total_demes', [10, 50, 100])])

In this case, 3 files will be generated: island_100_0.01_100_30.par, island_10_0.01_100_30.par and island_50_0.01_100_30.par. Notice the rule to make file names: template name, followed by parameter values in reverse order.

A few, arguably more esoteric template demographies exist (please check the Bio/PopGen/SimCoal/data directory on Biopython source tree). Furthermore it is possible for the user to create new templates. That functionality will be discussed in a future version of this document.

12.2.1.2  Chromosome structure

We strongly recommend reading SIMCOAL2 documentation to understand the full potential available in modeling chromosome structures. In this subsection we only discuss how to implement chromosome structures using the Biopython interface, not the underlying SIMCOAL2 capabilities.

We will start by implementing a single chromosome, with 24 SNPs with a recombination rate immediately on the right of each locus of 0.0005 and a minimum frequency of the minor allele of 0. This will be specified by the following list (to be passed as second parameter to the function generate_simcoal_from_template):

[(1, [('SNP', [24, 0.0005, 0.0])])]

This is actually the chromosome structure used in the above examples.

The chromosome structure is represented by a list of chromosomes, each chromosome (i.e., each element in the list) is composed by a tuple (a pair): the first element is the number of times the chromosome is to be repeated (as there might be interest in repeating the same chromosome many times). The second element is a list of the actual components of the chromosome. Each element is again a pair, the first member is the locus type and the second element the parameters for that locus type. Confused? Before showing more examples let’s review the example above: We have a list with one element (thus one chromosome), the chromosome is a single instance (therefore not to be repeated), it is composed of 24 SNPs, with a recombination rate of 0.0005 between each consecutive SNP, the minimum frequency of the minor allele is 0.0 (i.e, it can be absent from a certain population).

Let’s see a more complicated example:

[
  (5, [
       ('SNP', [24, 0.0005, 0.0])
      ]
  ),
  (2, [
       ('DNA', [10, 0.0, 0.00005, 0.33]),
       ('RFLP', [1, 0.0, 0.0001]),
       ('MICROSAT', [1, 0.0, 0.001, 0.0, 0.0])
      ]
  )
]

We start by having 5 chromosomes with the same structure as above (i.e., 24 SNPs). We then have 2 chromosomes which have a DNA sequence with 10 nucleotides, 0.0 recombination rate, 0.0005 mutation rate, and a transition rate of 0.33. Then we have an RFLP with 0.0 recombination rate to the next locus and a 0.0001 mutation rate. Finally we have a microsatellite (or STR), with 0.0 recombination rate to the next locus (note, that as this is a single microsatellite which has no loci following, this recombination rate here is irrelevant), with a mutation rate of 0.001, geometric parameter of 0.0 and a range constraint of 0.0 (for information about this parameters please consult the SIMCOAL2 documentation, you can use them to simulate various mutation models, including the typical – for microsatellites – stepwise mutation model among others).

12.2.2  Running SIMCOAL2

We now discuss how to run SIMCOAL2 from inside Biopython. It is required that the binary for SIMCOAL2 is called simcoal2 (or simcoal2.exe on Windows based platforms), please note that the typical name when downloading the program is in the format simcoal2_x_y. As such, when installing SIMCOAL2 you will need to rename of the downloaded executable so that Biopython can find it.

It is possible to run SIMCOAL2 on files that were not generated using the method above (e.g., writing a parameter file by hand), but we will show an example by creating a model using the framework presented above.

from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template
from Bio.PopGen.SimCoal.Controller import SimCoalController


generate_simcoal_from_template('simple',
    [
      (5, [
           ('SNP', [24, 0.0005, 0.0])
          ]
      ),
      (2, [
           ('DNA', [10, 0.0, 0.00005, 0.33]),
           ('RFLP', [1, 0.0, 0.0001]),
           ('MICROSAT', [1, 0.0, 0.001, 0.0, 0.0])
          ]
      )
    ],
    [('sample_size', [30]),
    ('pop_size', [100])])

ctrl = SimCoalController('.')
ctrl.run_simcoal('simple_100_30.par', 50)

The lines of interest are the last two (plus the new import). Firstly a controller for the application is created. The directory where the binary is located has to be specified.

The simulator is then run on the last line: we know, from the rules explained above, that the input file name is simple_100_30.par for the simulation parameter file created. We then specify that we want to run 50 independent simulations, by default Biopython requests a simulation of diploid data, but a third parameter can be added to simulate haploid data (adding as a parameter the string ’0’). SIMCOAL2 will now run (please note that this can take quite a lot of time) and will create a directory with the simulation results. The results can now be analysed (typically studying the data with Arlequin3). In the future Biopython might support reading the Arlequin3 format and thus allowing for the analysis of SIMCOAL2 data inside Biopython.

12.3  Other applications

Here we discuss interfaces and utilities to deal with population genetics’ applications which arguably have a smaller user base.

12.3.1  FDist: Detecting selection and molecular adaptation

FDist is a selection detection application suite based on computing (i.e. simulating) a “neutral” confidence interval based on Fst and heterozygosity. Markers (which can be SNPs, microsatellites, AFLPs among others) which lie outside the “neutral” interval are to be considered as possible candidates for being under selection.

FDist is mainly used when the number of markers is considered enough to estimate an average Fst, but not enough to either have outliers calculated from the dataset directly or, with even more markers for which the relative positions in the genome are known, to use approaches based on, e.g., Extended Haplotype Heterozygosity (EHH).

The typical usage pattern for FDist is as follows:

  1. Import a dataset from an external format into FDist format.
  2. Compute average Fst. This is done by datacal inside FDist.
  3. Simulate “neutral” markers based on the average Fst and expected number of total populations. This is the core operation, done by fdist inside FDist.
  4. Calculate the confidence interval, based on the desired confidence boundaries (typically 95% or 99%). This is done by cplot and is mainly used to plot the interval.
  5. Assess each marker status against the simulation “neutral” confidence interval. Done by pv. This is used to detect the outlier status of each marker against the simulation.

We will now discuss each step with illustrating example code (for this example to work FDist binaries have to be on the executable PATH).

The FDist data format is application specific and is not used at all by other applications, as such you will probably have to convert your data for use with FDist. Biopython can help you do this. Here is an example converting from GenePop format to FDist format (along with imports that will be needed on examples further below):

from Bio.PopGen import GenePop
from Bio.PopGen import FDist
from Bio.PopGen.FDist import Controller
from Bio.PopGen.FDist.Utils import convert_genepop_to_fdist

gp_rec = GenePop.read(open("example.gen"))
fd_rec = convert_genepop_to_fdist(gp_rec)
in_file = open("infile", "w")
in_file.write(str(fd_rec))
in_file.close()

In this code we simply parse a GenePop file and convert it to a FDist record.

Printing an FDist record will generate a string that can be directly saved to a file and supplied to FDist. FDist requires the input file to be called infile, therefore we save the record on a file with that name.

The most important fields on a FDist record are: num_pops, the number of populations; num_loci, the number of loci and loci_data with the marker data itself. Most probably the details of the record are of no interest to the user, as the record only purpose is to be passed to FDist.

The next step is to calculate the average Fst of the dataset (along with the sample size):

ctrl = Controller.FDistController()
fst, samp_size = ctrl.run_datacal()

On the first line we create an object to control the call of FDist suite, this object will be used further on in order to call other suite applications.

On the second line we call the datacal application which computes the average Fst and the sample size. It is worth noting that the Fst computed by datacal is a variation of Weir and Cockerham’s θ.

We can now call the main fdist application in order to simulate neutral markers.

sim_fst = ctrl.run_fdist(npops = 15, nsamples = fd_rec.num_pops, fst = fst,
    sample_size = samp_size, mut = 0, num_sims = 40000)
npops
Number of populations existing in nature. This is really a “guestimate”. Has to be lower than 100.
nsamples
Number of populations sampled, has to be lower than npops.
fst
Average Fst.
sample_size
Average number of individuals sampled on each population.
mut
Mutation model: 0 - Infinite alleles; 1 - Stepwise mutations
num_sims
Number of simulations to perform. Typically a number around 40000 will be OK, but if you get a confidence interval that looks sharp (this can be detected when plotting the confidence interval computed below) the value can be increased (a suggestion would be steps of 10000 simulations).

The confusion in wording between number of samples and sample size stems from the original application.

A file named out.dat will be created with the simulated heterozygosities and Fsts, it will have as many lines as the number of simulations requested.

Note that fdist returns the average Fst that it was capable of simulating, for more details about this issue please read below the paragraph on approximating the desired average Fst.

The next (optional) step is to calculate the confidence interval:

cpl_interval = ctrl.run_cplot(ci=0.99)

You can only call cplot after having run fdist.

This will calculate the confidence intervals (99% in this case) for a previous fdist run. A list of quadruples is returned. The first element represents the heterozygosity, the second the lower bound of Fst confidence interval for that heterozygosity, the third the average and the fourth the upper bound. This can be used to trace the confidence interval contour. This list is also written to a file, out.cpl.

The main purpose of this step is return a set of points which can be easily used to plot a confidence interval. It can be skipped if the objective is only to assess the status of each marker against the simulation, which is the next step...

pv_data = ctrl.run_pv()

You can only call cplot after having run datacal and fdist.

This will use the simulated markers to assess the status of each individual real marker. A list, in the same order than the loci_list that is on the FDist record (which is in the same order that the GenePop record) is returned. Each element in the list is a quadruple, the fundamental member of each quadruple is the last element (regarding the other elements, please refer to the pv documentation – for the sake of simplicity we will not discuss them here) which returns the probability of the simulated Fst being lower than the marker Fst. Higher values would indicate a stronger candidate for positive selection, lower values a candidate for balancing selection, and intermediate values a possible neutral marker. What is “higher”, “lower” or “intermediate” is really a subjective issue, but taking a “confidence interval” approach and considering a 95% confidence interval, “higher” would be between 0.95 and 1.0, “lower” between 0.0 and 0.05 and “intermediate” between 0.05 and 0.95.

12.3.1.1  Approximating the desired average Fst

Fdist tries to approximate the desired average Fst by doing a coalescent simulation using migration rates based on the formula

Nm = 
1 − Fst
4Fst
 

This formula assumes a few premises like an infinite number of populations.

In practice, when the number of populations is low, the mutation model is stepwise and the sample size increases, fdist will not be able to simulate an acceptable approximate average Fst.

To address that, a function is provided to iteratively approach the desired value by running several fdists in sequence. This approach is computationally more intensive than running a single fdist run, but yields good results. The following code runs fdist approximating the desired Fst:

sim_fst = ctrl.run_fdist_force_fst(npops = 15, nsamples = fd_rec.num_pops,
    fst = fst, sample_size = samp_size, mut = 0, num_sims = 40000,
    limit = 0.05)

The only new optional parameter, when comparing with run_fdist, is limit which is the desired maximum error. run_fdist can (and probably should) be safely replaced with run_fdist_force_fst.

12.3.1.2  Final notes

The process to determine the average Fst can be more sophisticated than the one presented here. For more information we refer you to the FDist README file. Biopython’s code can be used to implement more sophisticated approaches.

12.4  Future Developments

The most desired future developments would be the ones you add yourself ;) .

That being said, already existing fully functional code is currently being incorporated in Bio.PopGen, that code covers the applications FDist and SimCoal2, the HapMap and UCSC Table Browser databases and some simple statistics like Fst, or allele counts.

Chapter 13  Phylogenetics with Bio.Phylo

The Bio.Phylo module was introduced in Biopython 1.54. Following the lead of SeqIO and AlignIO, it aims to provide a common way to work with phylogenetic trees independently of the source data format, as well as a consistent API for I/O operations.

Bio.Phylo is described in an open-access journal article [9, Talevich et al., 2012], which you might also find helpful.

13.1  Demo: What’s in a Tree?

To get acquainted with the module, let’s start with a tree that we’ve already constructed, and inspect it a few different ways. Then we’ll colorize the branches, to use a special phyloXML feature, and finally save it.

In a terminal, create a simple Newick file using your favorite text editor:

% cat > simple.dnd <<EOF
> (((A,B),(C,D)),(E,F,G));
> EOF

This tree has no branch lengths, only a topology and labelled terminals. (If you have a real tree file available, you can follow this demo using that instead.)

Launch the Python interpreter of your choice:

% ipython -pylab

For interactive work, launching the IPython interpreter with the -pylab flag enables matplotlib integration, so graphics will pop up automatically. We’ll use that during this demo.

Now, within Python, read the tree file, giving the file name and the name of the format.

>>> from Bio import Phylo
>>> tree = Phylo.read("simple.dnd", "newick")

Printing the tree object as a string gives us a look at the entire object hierarchy.

>>> print(tree)

Tree(weight=1.0, rooted=False, name="")
    Clade(branch_length=1.0)
        Clade(branch_length=1.0)
            Clade(branch_length=1.0)
                Clade(branch_length=1.0, name="A")
                Clade(branch_length=1.0, name="B")
            Clade(branch_length=1.0)
                Clade(branch_length=1.0, name="C")
                Clade(branch_length=1.0, name="D")
        Clade(branch_length=1.0)
            Clade(branch_length=1.0, name="E")
            Clade(branch_length=1.0, name="F")
            Clade(branch_length=1.0, name="G")

The Tree object contains global information about the tree, such as whether it’s rooted or unrooted. It has one root clade, and under that, it’s nested lists of clades all the way down to the tips.

The function draw_ascii creates a simple ASCII-art (plain text) dendrogram. This is a convenient visualization for interactive exploration, in case better graphical tools aren’t available.

>>> Phylo.draw_ascii(tree)
                                                    ________________________ A
                           ________________________|
                          |                        |________________________ B
  ________________________|
 |                        |                         ________________________ C
 |                        |________________________|
_|                                                 |________________________ D
 |
 |                         ________________________ E
 |                        |
 |________________________|________________________ F
                          |
                          |________________________ G

If you have matplotlib or pylab installed, you can create a graphic using the draw function (see Fig. 13.1):

>>> tree.rooted = True
>>> Phylo.draw(tree)

13.1.1  Coloring branches within a tree

The functions draw and draw_graphviz support the display of different colors and branch widths in a tree. As of Biopython 1.59, the color and width attributes are available on the basic Clade object and there’s nothing extra required to use them. Both attributes refer to the branch leading the given clade, and apply recursively, so all descendent branches will also inherit the assigned width and color values during display.

In earlier versions of Biopython, these were special features of PhyloXML trees, and using the attributes required first converting the tree to a subclass of the basic tree object called Phylogeny, from the Bio.Phylo.PhyloXML module.

In Biopython 1.55 and later, this is a convenient tree method:

>>> tree = tree.as_phyloxml()

In Biopython 1.54, you can accomplish the same thing with one extra import:

>>> from Bio.Phylo.PhyloXML import Phylogeny
>>> tree = Phylogeny.from_tree(tree)

Note that the file formats Newick and Nexus don’t support branch colors or widths, so if you use these attributes in Bio.Phylo, you will only be able to save the values in PhyloXML format. (You can still save a tree as Newick or Nexus, but the color and width values will be skipped in the output file.)

Now we can begin assigning colors. First, we’ll color the root clade gray. We can do that by assigning the 24-bit color value as an RGB triple, an HTML-style hex string, or the name of one of the predefined colors.

>>> tree.root.color = (128, 128, 128)

Or:

>>> tree.root.color = "#808080"

Or:

>>> tree.root.color = "gray"

Colors for a clade are treated as cascading down through the entire clade, so when we colorize the root here, it turns the whole tree gray. We can override that by assigning a different color lower down on the tree.

Let’s target the most recent common ancestor (MRCA) of the nodes named “E” and “F”. The common_ancestor method returns a reference to that clade in the original tree, so when we color that clade “salmon”, the color will show up in the original tree.

>>> mrca = tree.common_ancestor({"name": "E"}, {"name": "F"})
>>> mrca.color = "salmon"

If we happened to know exactly where a certain clade is in the tree, in terms of nested list entries, we can jump directly to that position in the tree by indexing it. Here, the index [0,1] refers to the second child of the first child of the root.

>>> tree.clade[0, 1].color = "blue"

Finally, show our work (see Fig. 13.1.1):

>>> Phylo.draw(tree)

Note that a clade’s color includes the branch leading to that clade, as well as its descendents. The common ancestor of E and F turns out to be just under the root, and with this coloring we can see exactly where the root of the tree is.

My, we’ve accomplished a lot! Let’s take a break here and save our work. Call the write function with a file name or handle — here we use standard output, to see what would be written — and the format phyloxml. PhyloXML saves the colors we assigned, so you can open this phyloXML file in another tree viewer like Archaeopteryx, and the colors will show up there, too.

>>> import sys
>>> Phylo.write(tree, sys.stdout, "phyloxml")

<phy:phyloxml xmlns:phy="http://www.phyloxml.org">
  <phy:phylogeny rooted="true">
    <phy:clade>
      <phy:branch_length>1.0</phy:branch_length>
      <phy:color>
        <phy:red>128</phy:red>
        <phy:green>128</phy:green>
        <phy:blue>128</phy:blue>
      </phy:color>
      <phy:clade>
        <phy:branch_length>1.0</phy:branch_length>
        <phy:clade>
          <phy:branch_length>1.0</phy:branch_length>
          <phy:clade>
            <phy:name>A</phy:name>
            ...

The rest of this chapter covers the core functionality of Bio.Phylo in greater detail. For more examples of using Bio.Phylo, see the cookbook page on Biopython.org:

http://biopython.org/wiki/Phylo_cookbook

13.2  I/O functions

Like SeqIO and AlignIO, Phylo handles file input and output through four functions: parse, read, write and convert, all of which support the tree file formats Newick, NEXUS, phyloXML and NeXML, as well as the Comparative Data Analysis Ontology (CDAO).

The read function parses a single tree in the given file and returns it. Careful; it will raise an error if the file contains more than one tree, or no trees.

>>> from Bio import Phylo
>>> tree = Phylo.read("Tests/Nexus/int_node_labels.nwk", "newick")
>>> print(tree)

(Example files are available in the Tests/Nexus/ and Tests/PhyloXML/ directories of the Biopython distribution.)

To handle multiple (or an unknown number of) trees, use the parse function iterates through each of the trees in the given file: 

>>> trees = Phylo.parse("Tests/PhyloXML/phyloxml_examples.xml", "phyloxml")
>>> for tree in trees:
...     print(tree)

Write a tree or iterable of trees back to file with the write function:

>>> trees = list(Phylo.parse("phyloxml_examples.xml", "phyloxml"))
>>> tree1 = trees[0]
>>> others = trees[1:]
>>> Phylo.write(tree1, "tree1.xml", "phyloxml")
1
>>> Phylo.write(others, "other_trees.xml", "phyloxml")
12

Convert files between any of the supported formats with the convert function:

>>> Phylo.convert("tree1.dnd", "newick", "tree1.xml", "nexml")
1
>>> Phylo.convert("other_trees.xml", "phyloxml", "other_trees.nex", 'nexus")
12

To use strings as input or output instead of actual files, use StringIO as you would with SeqIO and AlignIO:

>>> from Bio import Phylo
>>> from StringIO import StringIO
>>> handle = StringIO("(((A,B),(C,D)),(E,F,G));")
>>> tree = Phylo.read(handle, "newick")

13.3  View and export trees

The simplest way to get an overview of a Tree object is to print it:

>>> tree = Phylo.read("Tests/PhyloXML/example.xml", "phyloxml")
>>> print(tree)
Phylogeny(rooted='True', description='phyloXML allows to use either a "branch_length"
attribute...', name='example from Prof. Joe Felsenstein's book "Inferring Phyl...')
    Clade()
        Clade(branch_length='0.06')
            Clade(branch_length='0.102', name='A')
            Clade(branch_length='0.23', name='B')
        Clade(branch_length='0.4', name='C')

This is essentially an outline of the object hierarchy Biopython uses to represent a tree. But more likely, you’d want to see a drawing of the tree. There are three functions to do this.

As we saw in the demo, draw_ascii prints an ascii-art drawing of the tree (a rooted phylogram) to standard output, or an open file handle if given. Not all of the available information about the tree is shown, but it provides a way to quickly view the tree without relying on any external dependencies.

>>> tree = Phylo.read("example.xml", "phyloxml")
>>> Phylo.draw_ascii(tree)
             __________________ A
  __________|
_|          |___________________________________________ B
 |
 |___________________________________________________________________________ C

The draw function draws a more attractive image using the matplotlib library. See the API documentation for details on the arguments it accepts to customize the output.

>>> tree = Phylo.read("example.xml", "phyloxml")
>>> Phylo.draw(tree, branch_labels=lambda c: c.branch_length)

draw_graphviz draws an unrooted cladogram, but requires that you have Graphviz, PyDot or PyGraphviz, NetworkX, and matplotlib (or pylab) installed. Using the same example as above, and the dot program included with Graphviz, let’s draw a rooted tree (see Fig. 13.3):

>>> tree = Phylo.read("example.xml", "phyloxml")
>>> Phylo.draw_graphviz(tree, prog='dot')
>>> import pylab
>>> pylab.show()                    # Displays the tree in an interactive viewer
>>> pylab.savefig('phylo-dot.png')  # Creates a PNG file of the same graphic

(Tip: If you execute IPython with the -pylab option, calling draw_graphviz causes the matplotlib viewer to launch automatically without manually calling show().)

This exports the tree object to a NetworkX graph, uses Graphviz to lay out the nodes, and displays it using matplotlib. There are a number of keyword arguments that can modify the resulting diagram, including most of those accepted by the NetworkX functions networkx.draw and networkx.draw_graphviz.

The display is also affected by the rooted attribute of the given tree object. Rooted trees are shown with a “head” on each branch indicating direction (see Fig. 13.3):

>>> tree = Phylo.read("simple.dnd", "newick")
>>> tree.rooted = True
>>> Phylo.draw_graphiz(tree)

The “prog” argument specifies the Graphviz engine used for layout. The default, twopi, behaves well for any size tree, reliably avoiding crossed branches. The neato program may draw more attractive moderately-sized trees, but sometimes will cross branches (see Fig. 13.3). The dot program may be useful with small trees, but tends to do surprising things with the layout of larger trees.

>>> Phylo.draw_graphviz(tree, prog="neato")

This viewing mode is particularly handy for exploring larger trees, because the matplotlib viewer can zoom in on a selected region, thinning out a cluttered graphic. 

>>> tree = Phylo.read("apaf.xml", "phyloxml")
>>> Phylo.draw_graphviz(tree, prog="neato", node_size=0)

Note that branch lengths are not displayed accurately, because Graphviz ignores them when creating the node layouts. The branch lengths are retained when exporting a tree as a NetworkX graph object (to_networkx), however.

See the Phylo page on the Biopython wiki (http://biopython.org/wiki/Phylo) for descriptions and examples of the more advanced functionality in draw_ascii, draw_graphviz and to_networkx.

13.4  Using Tree and Clade objects

The Tree objects produced by parse and read are containers for recursive sub-trees, attached to the Tree object at the root attribute (whether or not the phylogenic tree is actually considered rooted). A Tree has globally applied information for the phylogeny, such as rootedness, and a reference to a single Clade; a Clade has node- and clade-specific information, such as branch length, and a list of its own descendent Clade instances, attached at the clades attribute.

So there is a distinction between tree and tree.root. In practice, though, you rarely need to worry about it. To smooth over the difference, both Tree and Clade inherit from TreeMixin, which contains the implementations for methods that would be commonly used to search, inspect or modify a tree or any of its clades. This means that almost all of the methods supported by tree are also available on tree.root and any clade below it. (Clade also has a root property, which returns the clade object itself.)

13.4.1  Search and traversal methods

For convenience, we provide a couple of simplified methods that return all external or internal nodes directly as a list:

get_terminals
makes a list of all of this tree’s terminal (leaf) nodes.
get_nonterminals
makes a list of all of this tree’s nonterminal (internal) nodes.

These both wrap a method with full control over tree traversal, find_clades. Two more traversal methods, find_elements and find_any, rely on the same core functionality and accept the same arguments, which we’ll call a “target specification” for lack of a better description. These specify which objects in the tree will be matched and returned during iteration. The first argument can be any of the following types:

  • A TreeElement instance, which tree elements will match by identity — so searching with a Clade instance as the target will find that clade in the tree;
  • A string, which matches tree elements’ string representation — in particular, a clade’s name (added in Biopython 1.56);
  • A class or type, where every tree element of the same type (or sub-type) will be matched;
  • A dictionary where keys are tree element attributes and values are matched to the corresponding attribute of each tree element. This one gets even more elaborate:
    • If an int is given, it matches numerically equal attributes, e.g. 1 will match 1 or 1.0
    • If a boolean is given (True or False), the corresponding attribute value is evaluated as a boolean and checked for the same
    • None matches None
    • If a string is given, the value is treated as a regular expression (which must match the whole string in the corresponding element attribute, not just a prefix). A given string without special regex characters will match string attributes exactly, so if you don’t use regexes, don’t worry about it. For example, in a tree with clade names Foo1, Foo2 and Foo3, tree.find_clades({"name": "Foo1"}) matches Foo1, {"name": "Foo.*"} matches all three clades, and {"name": "Foo"} doesn’t match anything.

    Since floating-point arithmetic can produce some strange behavior, we don’t support matching floats directly. Instead, use the boolean True to match every element with a nonzero value in the specified attribute, then filter on that attribute manually with an inequality (or exact number, if you like living dangerously).

    If the dictionary contains multiple entries, a matching element must match each of the given attribute values — think “and”, not “or”.

  • A function taking a single argument (it will be applied to each element in the tree), returning True or False. For convenience, LookupError, AttributeError and ValueError are silenced, so this provides another safe way to search for floating-point values in the tree, or some more complex characteristic.

After the target, there are two optional keyword arguments:

terminal
— A boolean value to select for or against terminal clades (a.k.a. leaf nodes): True searches for only terminal clades, False for non-terminal (internal) clades, and the default, None, searches both terminal and non-terminal clades, as well as any tree elements lacking the is_terminal method.
order
— Tree traversal order: "preorder" (default) is depth-first search, "postorder" is DFS with child nodes preceding parents, and "level" is breadth-first search.

Finally, the methods accept arbitrary keyword arguments which are treated the same way as a dictionary target specification: keys indicate the name of the element attribute to search for, and the argument value (string, integer, None or boolean) is compared to the value of each attribute found. If no keyword arguments are given, then any TreeElement types are matched. The code for this is generally shorter than passing a dictionary as the target specification: tree.find_clades({"name": "Foo1"}) can be shortened to tree.find_clades(name="Foo1").

(In Biopython 1.56 or later, this can be even shorter: tree.find_clades("Foo1"))

Now that we’ve mastered target specifications, here are the methods used to traverse a tree:

find_clades
Find each clade containing a matching element. That is, find each element as with find_elements, but return the corresponding clade object. (This is usually what you want.)

The result is an iterable through all matching objects, searching depth-first by default. This is not necessarily the same order as the elements appear in the Newick, Nexus or XML source file!

find_elements
Find all tree elements matching the given attributes, and return the matching elements themselves. Simple Newick trees don’t have complex sub-elements, so this behaves the same as find_clades on them. PhyloXML trees often do have complex objects attached to clades, so this method is useful for extracting those.
find_any
Return the first element found by find_elements(), or None. This is also useful for checking whether any matching element exists in the tree, and can be used in a conditional.

Two more methods help navigating between nodes in the tree:

get_path
List the clades directly between the tree root (or current clade) and the given target. Returns a list of all clade objects along this path, ending with the given target, but excluding the root clade.
trace
List of all clade object between two targets in this tree. Excluding start, including finish.

13.4.2  Information methods

These methods provide information about the whole tree (or any clade).

common_ancestor
Find the most recent common ancestor of all the given targets. (This will be a Clade object). If no target is given, returns the root of the current clade (the one this method is called from); if 1 target is given, this returns the target itself. However, if any of the specified targets are not found in the current tree (or clade), an exception is raised.
count_terminals
Counts the number of terminal (leaf) nodes within the tree.
depths
Create a mapping of tree clades to depths. The result is a dictionary where the keys are all of the Clade instances in the tree, and the values are the distance from the root to each clade (including terminals). By default the distance is the cumulative branch length leading to the clade, but with the unit_branch_lengths=True option, only the number of branches (levels in the tree) is counted.
distance
Calculate the sum of the branch lengths between two targets. If only one target is specified, the other is the root of this tree.
total_branch_length
Calculate the sum of all the branch lengths in this tree. This is usually just called the “length” of the tree in phylogenetics, but we use a more explicit name to avoid confusion with Python terminology.

The rest of these methods are boolean checks:

is_bifurcating
True if the tree is strictly bifurcating; i.e. all nodes have either 2 or 0 children (internal or external, respectively). The root may have 3 descendents and still be considered part of a bifurcating tree.
is_monophyletic
Test if all of the given targets comprise a complete subclade — i.e., there exists a clade such that its terminals are the same set as the given targets. The targets should be terminals of the tree. For convenience, this method returns the common ancestor (MCRA) of the targets if they are monophyletic (instead of the value True), and False otherwise.
is_parent_of
True if target is a descendent of this tree — not required to be a direct descendent. To check direct descendents of a clade, simply use list membership testing: if subclade in clade: ...
is_preterminal
True if all direct descendents are terminal; False if any direct descendent is not terminal.

13.4.3  Modification methods

These methods modify the tree in-place. If you want to keep the original tree intact, make a complete copy of the tree first, using Python’s copy module:

tree = Phylo.read('example.xml', 'phyloxml')
import copy
newtree = copy.deepcopy(tree)
collapse
Deletes the target from the tree, relinking its children to its parent.
collapse_all
Collapse all the descendents of this tree, leaving only terminals. Branch lengths are preserved, i.e. the distance to each terminal stays the same. With a target specification (see above), collapses only the internal nodes matching the specification.
ladderize
Sort clades in-place according to the number of terminal nodes. Deepest clades are placed last by default. Use reverse=True to sort clades deepest-to-shallowest.
prune
Prunes a terminal clade from the tree. If taxon is from a bifurcation, the connecting node will be collapsed and its branch length added to remaining terminal node. This might no longer be a meaningful value.
root_with_outgroup
Reroot this tree with the outgroup clade containing the given targets, i.e. the common ancestor of the outgroup. This method is only available on Tree objects, not Clades.

If the outgroup is identical to self.root, no change occurs. If the outgroup clade is terminal (e.g. a single terminal node is given as the outgroup), a new bifurcating root clade is created with a 0-length branch to the given outgroup. Otherwise, the internal node at the base of the outgroup becomes a trifurcating root for the whole tree. If the original root was bifurcating, it is dropped from the tree.

In all cases, the total branch length of the tree stays the same.

root_at_midpoint
Reroot this tree at the calculated midpoint between the two most distant tips of the tree. (This uses root_with_outgroup under the hood.)
split
Generate n (default 2) new descendants. In a species tree, this is a speciation event. New clades have the given branch_length and the same name as this clade’s root plus an integer suffix (counting from 0) — for example, splitting a clade named “A” produces the sub-clades “A0” and “A1”.

See the Phylo page on the Biopython wiki (http://biopython.org/wiki/Phylo) for more examples of using the available methods.

13.4.4  Features of PhyloXML trees

The phyloXML file format includes fields for annotating trees with additional data types and visual cues.

See the PhyloXML page on the Biopython wiki (http://biopython.org/wiki/PhyloXML) for descriptions and examples of using the additional annotation features provided by PhyloXML.

13.5  Running external applications

While Bio.Phylo doesn’t infer trees from alignments itself, there are third-party programs available that do. These are supported through the module Bio.Phylo.Applications, using the same general framework as Bio.Emboss.Applications, Bio.Align.Applications and others.

Biopython 1.58 introduced a wrapper for PhyML (http://www.atgc-montpellier.fr/phyml/). The program accepts an input alignment in phylip-relaxed format (that’s Phylip format, but without the 10-character limit on taxon names) and a variety of options. A quick example:

>>> from Bio import Phylo
>>> from Bio.Phylo.Applications import PhymlCommandline
>>> cmd = PhymlCommandline(input='Tests/Phylip/random.phy')
>>> out_log, err_log = cmd()

This generates a tree file and a stats file with the names [input filename]_phyml_tree.txt and [input filename]_phyml_stats.txt. The tree file is in Newick format:

>>> tree = Phylo.read('Tests/Phylip/random.phy_phyml_tree.txt', 'newick')
>>> Phylo.draw_ascii(tree)

A similar wrapper for RAxML (http://sco.h-its.org/exelixis/software.html) was added in Biopython 1.60, and FastTree (http://www.microbesonline.org/fasttree/) in Biopython 1.62.

Note that some popular Phylip programs, including dnaml and protml, are already available through the EMBOSS wrappers in Bio.Emboss.Applications if you have the Phylip extensions to EMBOSS installed on your system. See Section 6.4 for some examples and clues on how to use programs like these.

13.6  PAML integration

Biopython 1.58 brought support for PAML (http://abacus.gene.ucl.ac.uk/software/paml.html), a suite of programs for phylogenetic analysis by maximum likelihood. Currently the programs codeml, baseml and yn00 are implemented. Due to PAML’s usage of control files rather than command line arguments to control runtime options, usage of this wrapper strays from the format of other application wrappers in Biopython.

A typical workflow would be to initialize a PAML object, specifying an alignment file, a tree file, an output file and a working directory. Next, runtime options are set via the set_options() method or by reading an existing control file. Finally, the program is run via the run() method and the output file is automatically parsed to a results dictionary.

Here is an example of typical usage of codeml: 

>>> from Bio.Phylo.PAML import codeml
>>> cml = codeml.Codeml()
>>> cml.alignment = "Tests/PAML/alignment.phylip"
>>> cml.tree = "Tests/PAML/species.tree"
>>> cml.out_file = "results.out"
>>> cml.working_dir = "./scratch"
>>> cml.set_options(seqtype=1,
...         verbose=0,
...         noisy=0,
...         RateAncestor=0,
...         model=0,
...         NSsites=[0, 1, 2],
...         CodonFreq=2,
...         cleandata=1,
...         fix_alpha=1,
...         kappa=4.54006)
>>> results = cml.run()
>>> ns_sites = results.get("NSsites")
>>> m0 = ns_sites.get(0)
>>> m0_params = m0.get("parameters")
>>> print(m0_params.get("omega"))

Existing output files may be parsed as well using a module’s read() function: 

>>> results = codeml.read("Tests/PAML/Results/codeml/codeml_NSsites_all.out")
>>> print(results.get("lnL max"))

Detailed documentation for this new module currently lives on the Biopython wiki: http://biopython.org/wiki/PAML

13.7  Future plans

Bio.Phylo is under active development. Here are some features we might add in future releases:

New methods
Generally useful functions for operating on Tree or Clade objects appear on the Biopython wiki first, so that casual users can test them and decide if they’re useful before we add them to Bio.Phylo:

http://biopython.org/wiki/Phylo_cookbook

Bio.Nexus port
Much of this module was written during Google Summer of Code 2009, under the auspices of NESCent, as a project to implement Python support for the phyloXML data format (see 13.4.4). Support for Newick and Nexus formats was added by porting part of the existing Bio.Nexus module to the new classes used by Bio.Phylo.

Currently, Bio.Nexus contains some useful features that have not yet been ported to Bio.Phylo classes — notably, calculating a consensus tree. If you find some functionality lacking in Bio.Phylo, try poking throught Bio.Nexus to see if it’s there instead.

We’re open to any suggestions for improving the functionality and usability of this module; just let us know on the mailing list or our bug database.

Finally, if you need additional functionality not yet included in the Phylo module, check if it’s available in another of the high-quality Python libraries for phylogenetics such as DendroPy (http://pythonhosted.org/DendroPy/) or PyCogent (http://pycogent.org/). Since these libraries also support standard file formats for phylogenetic trees, you can easily transfer data between libraries by writing to a temporary file or StringIO object.

Chapter 14  Sequence motif analysis using Bio.motifs

This chapter gives an overview of the functionality of the Bio.motifs package included in Biopython. It is intended for people who are involved in the analysis of sequence motifs, so I’ll assume that you are familiar with basic notions of motif analysis. In case something is unclear, please look at Section 14.10 for some relevant links.

Most of this chapter describes the new Bio.motifs package included in Biopython 1.61 onwards, which is replacing the older Bio.Motif package introduced with Biopython 1.50, which was in turn based on two older former Biopython modules, Bio.AlignAce and Bio.MEME. It provides most of their functionality with a unified motif object implementation.

Speaking of other libraries, if you are reading this you might be interested in TAMO, another python library designed to deal with sequence motifs. It supports more de-novo motif finders, but it is not a part of Biopython and has some restrictions on commercial use.

14.1  Motif objects

Since we are interested in motif analysis, we need to take a look at Motif objects in the first place. For that we need to import the Bio.motifs library: 

>>> from Bio import motifs

and we can start creating our first motif objects. We can either create a Motif object from a list of instances of the motif, or we can obtain a Motif object by parsing a file from a motif database or motif finding software.

14.1.1  Creating a motif from instances

Suppose we have these instances of a DNA motif: 

>>> from Bio.Seq import Seq
>>> instances = [Seq("TACAA"),
...              Seq("TACGC"),
...              Seq("TACAC"),
...              Seq("TACCC"),
...              Seq("AACCC"),
...              Seq("AATGC"),
...              Seq("AATGC"),
...             ]

then we can create a Motif object as follows: 

>>> m = motifs.create(instances)

The instances are saved in an attribute m.instances, which is essentially a Python list with some added functionality, as described below. Printing out the Motif object shows the instances from which it was constructed: 

>>> print(m)
TACAA
TACGC
TACAC
TACCC
AACCC
AATGC
AATGC
<BLANKLINE>

The length of the motif defined as the sequence length, which should be the same for all instances: 

>>> len(m)
5

The Motif object has an attribute .counts containing the counts of each nucleotide at each position. Printing this counts matrix shows it in an easily readable format: 

>>> print(m.counts)
        0      1      2      3      4
A:   3.00   7.00   0.00   2.00   1.00
C:   0.00   0.00   5.00   2.00   6.00
G:   0.00   0.00   0.00   3.00   0.00
T:   4.00   0.00   2.00   0.00   0.00
<BLANKLINE>

You can access these counts as a dictionary: 

>>> m.counts['A']
[3, 7, 0, 2, 1]

but you can also think of it as a 2D array with the nucleotide as the first dimension and the position as the second dimension: 

>>> m.counts['T', 0]
4
>>> m.counts['T', 2]
2
>>> m.counts['T', 3]
0

You can also directly access columns of the counts matrix 

>>> m.counts[:, 3]
{'A': 2, 'C': 2, 'T': 0, 'G': 3}

Instead of the nucleotide itself, you can also use the index of the nucleotide in the sorted letters in the alphabet of the motif: 

>>> m.alphabet
IUPACUnambiguousDNA()
>>> m.alphabet.letters
'GATC'
>>> sorted(m.alphabet.letters)
['A', 'C', 'G', 'T']
>>> m.counts['A',:]
(3, 7, 0, 2, 1)
>>> m.counts[0,:]
(3, 7, 0, 2, 1)

The motif has an associated consensus sequence, defined as the sequence of letters along the positions of the motif for which the largest value in the corresponding columns of the .counts matrix is obtained: 

>>> m.consensus
Seq('TACGC', IUPACUnambiguousDNA())

as well as an anticonsensus sequence, corresponding to the smallest values in the columns of the .counts matrix: 

>>> m.anticonsensus
Seq('GGGTG', IUPACUnambiguousDNA())

You can also ask for a degenerate consensus sequence, in which ambiguous nucleotides are used for positions where there are multiple nucleotides with high counts: 

>>> m.degenerate_consensus
Seq('WACVC', IUPACAmbiguousDNA())

Here, W and R follow the IUPAC nucleotide ambiguity codes: W is either A or T, and V is A, C, or G [10]. The degenerate consensus sequence is constructed following the rules specified by Cavener [11].

We can also get the reverse complement of a motif: 

>>> r = m.reverse_complement()
>>> r.consensus
Seq('GCGTA', IUPACUnambiguousDNA())
>>> r.degenerate_consensus
Seq('GBGTW', IUPACAmbiguousDNA())
>>> print(r)
TTGTA
GCGTA
GTGTA
GGGTA
GGGTT
GCATT
GCATT
<BLANKLINE>

The reverse complement and the degenerate consensus sequence are only defined for DNA motifs.

14.1.2  Creating a sequence logo

If we have internet access, we can create a weblogo: 

>>> m.weblogo("mymotif.png")

We should get our logo saved as a PNG in the specified file.

14.2  Reading motifs

Creating motifs from instances by hand is a bit boring, so it’s useful to have some I/O functions for reading and writing motifs. There are not any really well established standards for storing motifs, but there are a couple of formats that are more used than others.

14.2.1  JASPAR

One of the most popular motif databases is JASPAR. In addition to the motif sequence information, the JASPAR database stores a lot of meta-information for each motif. The module Bio.motifs contains a specialized class jaspar.Motif in which this meta-information is represented as attributes:

  • matrix_id - the unique JASPAR motif ID, e.g. ’MA0004.1’
  • name - the name of the TF, e.g. ’Arnt’
  • collection - the JASPAR collection to which the motif belongs, e.g. ’CORE’
  • tf_class - the structual class of this TF, e.g. ’Zipper-Type’
  • tf_family - the family to which this TF belongs, e.g. ’Helix-Loop-Helix’
  • species - the species to which this TF belongs, may have multiple values, these are specified as taxonomy IDs, e.g. 10090
  • tax_group - the taxonomic supergroup to which this motif belongs, e.g. ’vertebrates’
  • acc - the accesion number of the TF protein, e.g. ’P53762’
  • data_type - the type of data used to construct this motif, e.g. ’SELEX’
  • medline - the Pubmed ID of literature supporting this motif, may be multiple values, e.g. 7592839
  • pazar_id - external reference to the TF in the PAZAR database, e.g. ’TF0000003’
  • comment - free form text containing notes about the construction of the motif

The jaspar.Motif class inherits from the generic Motif class and therefore provides all the facilities of any of the motif formats — reading motifs, writing motifs, scanning sequences for motif instances etc.

JASPAR stores motifs in several different ways including three different flat file formats and as an SQL database. All of these formats facilitate the construction of a counts matrix. However, the amount of meta information described above that is available varies with the format.

The JASPAR sites format

The first of the three flat file formats contains a list of instances. As an example, these are the beginning and ending lines of the JASPAR Arnt.sites file showing known binding sites of the mouse helix-loop-helix transcription factor Arnt. 

>MA0004 ARNT 1
CACGTGatgtcctc
>MA0004 ARNT 2
CACGTGggaggtac
>MA0004 ARNT 3
CACGTGccgcgcgc
...
>MA0004 ARNT 18
AACGTGacagccctcc
>MA0004 ARNT 19
AACGTGcacatcgtcc
>MA0004 ARNT 20
aggaatCGCGTGc

The parts of the sequence in capital letters are the motif instances that were found to align to each other.

We can create a Motif object from these instances as follows: 

>>> from Bio import motifs
>>> arnt = motifs.read(open("Arnt.sites"), "sites")

The instances from which this motif was created is stored in the .instances property: 

>>> print(arnt.instances[:3])
[Seq('CACGTG', IUPACUnambiguousDNA()), Seq('CACGTG', IUPACUnambiguousDNA()), Seq('CACGTG', IUPACUnambiguousDNA())]
>>> for instance in arnt.instances:
...     print(instance)
...
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
AACGTG
AACGTG
AACGTG
AACGTG
CGCGTG

The counts matrix of this motif is automatically calculated from the instances: 

>>> print(arnt.counts)
        0      1      2      3      4      5
A:   4.00  19.00   0.00   0.00   0.00   0.00
C:  16.00   0.00  20.00   0.00   0.00   0.00
G:   0.00   1.00   0.00  20.00   0.00  20.00
T:   0.00   0.00   0.00   0.00  20.00   0.00
<BLANKLINE>

This format does not store any meta information.

The JASPAR pfm format

JASPAR also makes motifs available directly as a count matrix, without the instances from which it was created. This pfm format only stores the counts matrix for a single motif. For example, this is the JASPAR file SRF.pfm containing the counts matrix for the human SRF transcription factor: 

 2 9 0 1 32 3 46 1 43 15 2 2
 1 33 45 45 1 1 0 0 0 1 0 1
39 2 1 0 0 0 0 0 0 0 44 43
 4 2 0 0 13 42 0 45 3 30 0 0

We can create a motif for this count matrix as follows: 

>>> srf = motifs.read(open("SRF.pfm"), "pfm")
>>> print(srf.counts)
        0      1      2      3      4      5      6      7      8      9     10     11
A:   2.00   9.00   0.00   1.00  32.00   3.00  46.00   1.00  43.00  15.00   2.00   2.00
C:   1.00  33.00  45.00  45.00   1.00   1.00   0.00   0.00   0.00   1.00   0.00   1.00
G:  39.00   2.00   1.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00  44.00  43.00
T:   4.00   2.00   0.00   0.00  13.00  42.00   0.00  45.00   3.00  30.00   0.00   0.00
<BLANKLINE>

As this motif was created from the counts matrix directly, it has no instances associated with it: 

>>> print(srf.instances)
None

We can now ask for the consensus sequence of these two motifs: 

>>> print(arnt.counts.consensus)
CACGTG
>>> print(srf.counts.consensus)
GCCCATATATGG

As with the instances file, not meta information is stored in this format.

The JASPAR format jaspar

The jaspar file format allows multiple motifs to be specified in a single file. In this format each of the motif records consist of a header line followed by four lines defining the counts matrix. The header line begins with a > character (similar to the Fasta file format) and is followed by the unique JASPAR matrix ID and the TF name. The following example shows a jaspar formatted file containing the three motifs Arnt, RUNX1 and MEF2A: 

>MA0004.1 Arnt
A  [ 4 19  0  0  0  0 ]
C  [16  0 20  0  0  0 ]
G  [ 0  1  0 20  0 20 ]
T  [ 0  0  0  0 20  0 ]
>MA0002.1 RUNX1
A  [10 12  4  1  2  2  0  0  0  8 13 ]
C  [ 2  2  7  1  0  8  0  0  1  2  2 ]
G  [ 3  1  1  0 23  0 26 26  0  0  4 ]
T  [11 11 14 24  1 16  0  0 25 16  7 ]
>MA0052.1 MEF2A
A  [ 1  0 57  2  9  6 37  2 56  6 ]
C  [50  0  1  1  0  0  0  0  0  0 ]
G  [ 0  0  0  0  0  0  0  0  2 50 ]
T  [ 7 58  0 55 49 52 21 56  0  2 ]

The motifs are read as follows: 

>>> fh = open("jaspar_motifs.txt")
>>> for m in motifs.parse(fh, "jaspar"))
...     print(m)
TF name  Arnt
Matrix ID MA0004.1
Matrix:
        0      1      2      3      4      5
A:   4.00  19.00   0.00   0.00   0.00   0.00
C:  16.00   0.00  20.00   0.00   0.00   0.00
G:   0.00   1.00   0.00  20.00   0.00  20.00
T:   0.00   0.00   0.00   0.00  20.00   0.00



TF name  RUNX1
Matrix ID MA0002.1
Matrix:
        0      1      2      3      4      5      6      7      8      9     10
A:  10.00  12.00   4.00   1.00   2.00   2.00   0.00   0.00   0.00   8.00  13.00
C:   2.00   2.00   7.00   1.00   0.00   8.00   0.00   0.00   1.00   2.00   2.00
G:   3.00   1.00   1.00   0.00  23.00   0.00  26.00  26.00   0.00   0.00   4.00
T:  11.00  11.00  14.00  24.00   1.00  16.00   0.00   0.00  25.00  16.00   7.00



TF name  MEF2A
Matrix ID MA0052.1
Matrix:
        0      1      2      3      4      5      6      7      8      9
A:   1.00   0.00  57.00   2.00   9.00   6.00  37.00   2.00  56.00   6.00
C:  50.00   0.00   1.00   1.00   0.00   0.00   0.00   0.00   0.00   0.00
G:   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   2.00  50.00
T:   7.00  58.00   0.00  55.00  49.00  52.00  21.00  56.00   0.00   2.00

Note that printing a JASPAR motif yields both the counts data and the available meta-information.

Accessing the JASPAR database

In addition to parsing these flat file formats, we can also retrieve motifs from a JASPAR SQL database. Unlike the flat file formats, a JASPAR database allows storing of all possible meta information defined in the JASPAR Motif class. It is beyond the scope of this document to describe how to set up a JASPAR database (please see the main JASPAR website). Motifs are read from a JASPAR database using the Bio.motifs.jaspar.db module. First connect to the JASPAR database using the JASPAR5 class which models the the latest JASPAR schema: 

>>> from Bio.motifs.jaspar.db import JASPAR5
>>> 
>>> JASPAR_DB_HOST = <hostname>
>>> JASPAR_DB_NAME = <db_name>
>>> JASPAR_DB_USER = <user>
>>> JASPAR_DB_PASS = <passord>
>>> 
>>> jdb = JASPAR5(
...     host=JASPAR_DB_HOST,
...     name=JASPAR_DB_NAME,
...     user=JASPAR_DB_USER,
...     password=JASPAR_DB_PASS
... )

Now we can fetch a single motif by its unique JASPAR ID with the fetch_motif_by_id method. Note that a JASPAR ID conists of a base ID and a version number seperated by a decimal point, e.g. ’MA0004.1’. The fetch_motif_by_id method allows you to use either the fully specified ID or just the base ID. If only the base ID is provided, the latest version of the motif is returned. 

>>> arnt = jdb.fetch_motif_by_id("MA0004")

Printing the motif reveals that the JASPAR SQL database stores much more meeta-information than the flat files: 

>>> print(arnt)
TF name Arnt
Matrix ID MA0004.1
Collection CORE
TF class Zipper-Type
TF family Helix-Loop-Helix
Species 10090
Taxonomic group vertebrates
Accession ['P53762']
Data type used SELEX
Medline 7592839
PAZAR ID TF0000003
Comments -
Matrix:
 0      1      2      3      4      5
A:   4.00  19.00   0.00   0.00   0.00   0.00
C:  16.00   0.00  20.00   0.00   0.00   0.00
G:   0.00   1.00   0.00  20.00   0.00  20.00
T:   0.00   0.00   0.00   0.00  20.00   0.00

We can also fetch motifs by name. The name must be an exact match (partial matches or database wildcards are not currently supported). Note that as the name is not guaranteed to be unique, the fetch_motifs_by_name method actually returns a list. 

>>> motifs = jdb.fetch_motifs_by_name("Arnt")
>>> print(motifs[0])
TF name Arnt
Matrix ID MA0004.1
Collection CORE
TF class Zipper-Type
TF family Helix-Loop-Helix
Species 10090
Taxonomic group vertebrates
Accession ['P53762']
Data type used SELEX
Medline 7592839
PAZAR ID TF0000003
Comments -
Matrix:
 0      1      2      3      4      5
A:   4.00  19.00   0.00   0.00   0.00   0.00
C:  16.00   0.00  20.00   0.00   0.00   0.00
G:   0.00   1.00   0.00  20.00   0.00  20.00
T:   0.00   0.00   0.00   0.00  20.00   0.00

The fetch_motifs method allows you to fetch motifs which match a specified set of criteria. These criteria include any of the above described meta information as well as certain matrix properties such as the minimum information content (min_ic in the example below), the minimum length of the matrix or the minimum number of sites used to construct the matrix. Only motifs which pass ALL the specified criteria are returned. Note that selection criteria which correspond to meta information which allow for multiple values may be specified as either a single value or a list of values, e.g. tax_group and tf_family in the example below. 

>>> motifs = jdb.fetch_motifs(
...     collection = 'CORE',
...     tax_group = ['vertebrates', 'insects'],
...     tf_class = 'Winged Helix-Turn-Helix',
...     tf_family = ['Forkhead', 'Ets'],
...     min_ic = 12
... )
>>> for motif in motifs:
...     pass # do something with the motif

Compatibility with Perl TFBS modules

An important thing to note is that the JASPAR Motif class was designed to be compatible with the popular Perl TFBS modules. Therefore some specifics about the choice of defaults for background and pseudocounts as well as how information content is computed and sequences searched for instances is based on this compatibility criteria. These choices are noted in the specific subsections below.

  • Choice of background:
    The Perl TFBS modules appear to allow a choice of custom background probabilities (although the documentation states that uniform background is assumed). However the default is to use a uniform background. Therefore it is recommended that you use a uniform background for computing the position-specific scoring matrix (PSSM). This is the default when using the Biopython motifs module.
  • Choice of pseudocounts:
    By default, the Perl TFBS modules use a pseudocount equal to √N * bg[nucleotide], where N represents the total number of sequences used to construct the matrix. To apply this same pseudocount formula, set the motif pseudocounts attribute using the jaspar.calculate\_pseudcounts() funtion: 
    >>> motif.pseudocounts = motifs.jaspar.calculate_pseudocounts(motif)
    Note that it is possible for the counts matrix to have an unequal number of sequences making up the columns. The pseudocount computation uses the average number of sequences making up the matrix. However, when normalize is called on the counts matrix, each count value in a column is divided by the total number of sequences making up that specific column, not by the average number of sequences. This differs from the Perl TFBS modules because the normalization is not done as a separate step and so the average number of sequences is used throughout the computation of the pssm. Therefore, for matrices with unequal column counts, the pssm computed by the motifs module will differ somewhat from the pssm computed by the Perl TFBS modules.
  • Computation of matrix information content:
    The information content (IC) or specificity of a matrix is computed using the mean method of the PositionSpecificScoringMatrix class. However of note, in the Perl TFBS modules the default behaviour is to compute the IC without first applying pseudocounts, even though by default the PSSMs are computed using pseudocounts as described above.
  • Searching for instances:
    Searching for instances with the Perl TFBS motifs was usually performed using a relative score threshold, i.e. a score in the range 0 to 1. In order to compute the absolute PSSM score corresponding to a relative score one can use the equation: 
    >>> abs_score =  (pssm.max - pssm.min) * rel_score + pssm.min
    To convert the absolute score of an instance back to a relative score, one can use the equation: 
    >>> rel_score = (abs_score - pssm.min) / (pssm.max - pssm.min)
    For example, using the Arnt motif before, let’s search a sequence with a relative score threshold of 0.8. 
    >>> test_seq=Seq("TAAGCGTGCACGCGCAACACGTGCATTA", unambiguous_dna)
>>> arnt.pseudocounts = motifs.jaspar.calculate_pseudocounts(arnt) 
>>> pssm = arnt.pssm()
>>> max_score = pssm.max()
>>> min_score = pssm.min()
>>> abs_score_threshold = (max_score - min_score) * 0.8 + min_score
>>> for position, score in pssm.search(test_seq,
                                       threshold=abs_score_threshold):
...     rel_score = (score - min_score) / (max_score - min_score)
...     print("Position %d: score = %5.3f, rel. score = %5.3f" % (
            position, score, rel_score))
... 
Position 2: score = 5.362, rel. score = 0.801
Position 8: score = 6.112, rel. score = 0.831
Position -20: score = 7.103, rel. score = 0.870
Position 17: score = 10.351, rel. score = 1.000
Position -11: score = 10.351, rel. score = 1.000

14.2.2  MEME

MEME [12] is a tool for discovering motifs in a group of related DNA or protein sequences. It takes as input a group of DNA or protein sequences and outputs as many motifs as requested. Therefore, in contrast to JASPAR files, MEME output files typically contain multiple motifs. This is an example.

At the top of an output file generated by MEME shows some background information about the MEME and the version of MEME used: 

********************************************************************************
MEME - Motif discovery tool
********************************************************************************
MEME version 3.0 (Release date: 2004/08/18 09:07:01)
...

Further down, the input set of training sequences is recapitulated: 

********************************************************************************
TRAINING SET
********************************************************************************
DATAFILE= INO_up800.s
ALPHABET= ACGT
Sequence name            Weight Length  Sequence name            Weight Length
-------------            ------ ------  -------------            ------ ------
CHO1                     1.0000    800  CHO2                     1.0000    800
FAS1                     1.0000    800  FAS2                     1.0000    800
ACC1                     1.0000    800  INO1                     1.0000    800
OPI3                     1.0000    800
********************************************************************************

and the exact command line that was used: 

********************************************************************************
COMMAND LINE SUMMARY
********************************************************************************
This information can also be useful in the event you wish to report a
problem with the MEME software.

command: meme -mod oops -dna -revcomp -nmotifs 2 -bfile yeast.nc.6.freq INO_up800.s
...

Next is detailed information on each motif that was found: 

********************************************************************************
MOTIF  1        width =   12   sites =   7   llr = 95   E-value = 2.0e-001
********************************************************************************
--------------------------------------------------------------------------------
        Motif 1 Description
--------------------------------------------------------------------------------
Simplified        A  :::9:a::::3:
pos.-specific     C  ::a:9:11691a
probability       G  ::::1::94:4:
matrix            T  aa:1::9::11:

To parse this file (stored as meme.dna.oops.txt), use 

>>> handle = open("meme.dna.oops.txt")
>>> record = motifs.parse(handle, "meme")
>>> handle.close()

The motifs.parse command reads the complete file directly, so you can close the file after calling motifs.parse. The header information is stored in attributes: 

>>> record.version
'3.0'
>>> record.datafile
'INO_up800.s'
>>> record.command
'meme -mod oops -dna -revcomp -nmotifs 2 -bfile yeast.nc.6.freq INO_up800.s'
>>> record.alphabet
IUPACUnambiguousDNA()
>>> record.sequences
['CHO1', 'CHO2', 'FAS1', 'FAS2', 'ACC1', 'INO1', 'OPI3']

The record is an object of the Bio.motifs.meme.Record class. The class inherits from list, and you can think of record as a list of Motif objects: 

>>> len(record)
2
>>> motif = record[0]
>>> print(motif.consensus)
TTCACATGCCGC
>>> print(motif.degenerate_consensus)
TTCACATGSCNC

In addition to these generic motif attributes, each motif also stores its specific information as calculated by MEME. For example, 

>>> motif.num_occurrences
7
>>> motif.length
12
>>> evalue = motif.evalue
>>> print("%3.1g" % evalue)
0.2
>>> motif.name
'Motif 1'

In addition to using an index into the record, as we did above, you can also find it by its name: 

>>> motif = record['Motif 1']

Each motif has an attribute .instances with the sequence instances in which the motif was found, providing some information on each instance: 

>>> len(motif.instances)
7
>>> motif.instances[0]
Instance('TTCACATGCCGC', IUPACUnambiguousDNA())
>>> motif.instances[0].motif_name
'Motif 1'
>>> motif.instances[0].sequence_name
'INO1'
>>> motif.instances[0].start
620
>>> motif.instances[0].strand
'-'
>>> motif.instances[0].length
12
>>> pvalue = motif.instances[0].pvalue
>>> print("%5.3g" % pvalue)
1.85e-08

MAST

14.2.3  TRANSFAC

TRANSFAC is a manually curated database of transcription factors, together with their genomic binding sites and DNA binding profiles [27]. While the file format used in the TRANSFAC database is nowadays also used by others, we will refer to it as the TRANSFAC file format.

A minimal file in the TRANSFAC format looks as follows: 

ID  motif1
P0      A      C      G      T
01      1      2      2      0      S
02      2      1      2      0      R
03      3      0      1      1      A
04      0      5      0      0      C
05      5      0      0      0      A
06      0      0      4      1      G
07      0      1      4      0      G
08      0      0      0      5      T
09      0      0      5      0      G
10      0      1      2      2      K
11      0      2      0      3      Y
12      1      0      3      1      G
//

This file shows the frequency matrix of motif motif1 of 12 nucleotides. In general, one file in the TRANSFAC format can contain multiple motifs. For example, this is the contents of the example TRANSFAC file transfac.dat: 

VV  EXAMPLE January 15, 2013
XX
//
ID  motif1
P0      A      C      G      T
01      1      2      2      0      S
02      2      1      2      0      R
03      3      0      1      1      A
...
11      0      2      0      3      Y
12      1      0      3      1      G
//
ID  motif2
P0      A      C      G      T
01      2      1      2      0      R
02      1      2      2      0      S
...
09      0      0      0      5      T
10      0      2      0      3      Y
//

To parse a TRANSFAC file, use 

>>> handle = open("transfac.dat")
>>> record = motifs.parse(handle, "TRANSFAC")
>>> handle.close()

The overall version number, if available, is stored as record.version: 

>>> record.version
'EXAMPLE January 15, 2013'

Each motif in record is in instance of the Bio.motifs.transfac.Motif class, which inherits both from the Bio.motifs.Motif class and from a Python dictionary. The dictionary uses the two-letter keys to store any additional information about the motif: 

>>> motif = record[0]
>>> motif.degenerate_consensus # Using the Bio.motifs.Motif method
Seq('SRACAGGTGKYG', IUPACAmbiguousDNA())
>>> motif['ID'] # Using motif as a dictionary
'motif1'

TRANSFAC files are typically much more elaborate than this example, containing lots of additional information about the motif. Table 14.2.3 lists the two-letter field codes that are commonly found in TRANSFAC files:

Table 14.1: Fields commonly found in TRANSFAC files
ACAccession number
ASAccession numbers, secondary
BAStatistical basis
BFBinding factors
BSFactor binding sites underlying the matrix
CCComments
COCopyright notice
DEShort factor description
DRExternal databases
DTDate created/updated
HCSubfamilies
HPSuperfamilies
IDIdentifier
NAName of the binding factor
OCTaxonomic classification
OSSpecies/Taxon
OVOlder version
PVPreferred version
TYType
XXEmpty line; these are not stored in the Record.

Each motif also has an attribute .references containing the references associated with the motif, using these two-letter keys:

Table 14.2: Fields used to store references in TRANSFAC files
RNReference number
RAReference authors
RLReference data
RTReference title
RXPubMed ID

Printing the motifs writes them out in their native TRANSFAC format: 

>>> print(record)
VV  EXAMPLE January 15, 2013
XX
//
ID  motif1
XX
P0      A      C      G      T
01      1      2      2      0      S
02      2      1      2      0      R
03      3      0      1      1      A
04      0      5      0      0      C
05      5      0      0      0      A
06      0      0      4      1      G
07      0      1      4      0      G
08      0      0      0      5      T
09      0      0      5      0      G
10      0      1      2      2      K
11      0      2      0      3      Y
12      1      0      3      1      G
XX
//
ID  motif2
XX
P0      A      C      G      T
01      2      1      2      0      R
02      1      2      2      0      S
03      0      5      0      0      C
04      3      0      1      1      A
05      0      0      4      1      G
06      5      0      0      0      A
07      0      1      4      0      G
08      0      0      5      0      G
09      0      0      0      5      T
10      0      2      0      3      Y
XX
//
<BLANKLINE>

You can export the motifs in the TRANSFAC format by capturing this output in a string and saving it in a file: 

>>> text = str(record)
>>> handle = open("mytransfacfile.dat", 'w')
>>> handle.write(text)
>>> handle.close()

14.3  Writing motifs

Speaking of exporting, let’s look at export functions in general. We can use the format method to write the motif in the simple JASPAR pfm format: 

>>> print(arnt.format("pfm"))
  4.00  19.00   0.00   0.00   0.00   0.00
 16.00   0.00  20.00   0.00   0.00   0.00
  0.00   1.00   0.00  20.00   0.00  20.00
  0.00   0.00   0.00   0.00  20.00   0.00

Similarly, we can use format to write the motif in the JASPAR jaspar format: 

>>> print(arnt.format("jaspar"))
>MA0004.1  Arnt
A [  4.00  19.00   0.00   0.00   0.00   0.00]
C [ 16.00   0.00  20.00   0.00   0.00   0.00]
G [  0.00   1.00   0.00  20.00   0.00  20.00]
T [  0.00   0.00   0.00   0.00  20.00   0.00]

To write the motif in a TRANSFAC-like matrix format, use 

>>> print(m.format("transfac"))
P0      A      C      G      T
01      3      0      0      4      W
02      7      0      0      0      A
03      0      5      0      2      C
04      2      2      3      0      V
05      1      6      0      0      C
XX
//
<BLANKLINE>

To write out multiple motifs, you can use motifs.write. This function can be used regardless of whether the motifs originated from a TRANSFAC file. For example, 

>>> two_motifs = [arnt, srf]
>>> print(motifs.write(two_motifs, 'transfac'))
P0      A      C      G      T
01      4     16      0      0      C
02     19      0      1      0      A
03      0     20      0      0      C
04      0      0     20      0      G
05      0      0      0     20      T
06      0      0     20      0      G
XX
//
P0      A      C      G      T
01      2      1     39      4      G
02      9     33      2      2      C
03      0     45      1      0      C
04      1     45      0      0      C
05     32      1      0     13      A
06      3      1      0     42      T
07     46      0      0      0      A
08      1      0      0     45      T
09     43      0      0      3      A
10     15      1      0     30      T
11      2      0     44      0      G
12      2      1     43      0      G
XX
//
<BLANKLINE>

Or, to write multiple motifs in the jaspar format: 

>>> two_motifs = [arnt, mef2a]
>>> print(motifs.write(two_motifs, "jaspar"))
>MA0004.1  Arnt
A [  4.00  19.00   0.00   0.00   0.00   0.00]
C [ 16.00   0.00  20.00   0.00   0.00   0.00]
G [  0.00   1.00   0.00  20.00   0.00  20.00]
T [  0.00   0.00   0.00   0.00  20.00   0.00]
>MA0052.1  MEF2A
A [  1.00   0.00  57.00   2.00   9.00   6.00  37.00   2.00  56.00   6.00]
C [ 50.00   0.00   1.00   1.00   0.00   0.00   0.00   0.00   0.00   0.00]
G [  0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   2.00  50.00]
T [  7.00  58.00   0.00  55.00  49.00  52.00  21.00  56.00   0.00   2.00]

14.4  Position-Weight Matrices

The .counts attribute of a Motif object shows how often each nucleotide appeared at each position along the alignment. We can normalize this matrix by dividing by the number of instances in the alignment, resulting in the probability of each nucleotide at each position along the alignment. We refer to these probabilities as the position-weight matrix. However, beware that in the literature this term may also be used to refer to the position-specific scoring matrix, which we discuss below.

Usually, pseudocounts are added to each position before normalizing. This avoids overfitting of the position-weight matrix to the limited number of motif instances in the alignment, and can also prevent probabilities from becoming zero. To add a fixed pseudocount to all nucleotides at all positions, specify a number for the pseudocounts argument: 

>>> pwm = m.counts.normalize(pseudocounts=0.5)
>>> print(pwm)
        0      1      2      3      4
A:   0.39   0.83   0.06   0.28   0.17
C:   0.06   0.06   0.61   0.28   0.72
G:   0.06   0.06   0.06   0.39   0.06
T:   0.50   0.06   0.28   0.06   0.06
<BLANKLINE>

Alternatively, pseudocounts can be a dictionary specifying the pseudocounts for each nucleotide. For example, as the GC content of the human genome is about 40%, you may want to choose the pseudocounts accordingly: 

>>> pwm = m.counts.normalize(pseudocounts={'A':0.6, 'C': 0.4, 'G': 0.4, 'T': 0.6})
>>> print(pwm)
        0      1      2      3      4
A:   0.40   0.84   0.07   0.29   0.18
C:   0.04   0.04   0.60   0.27   0.71
G:   0.04   0.04   0.04   0.38   0.04
T:   0.51   0.07   0.29   0.07   0.07
<BLANKLINE>

The position-weight matrix has its own methods to calculate the consensus, anticonsensus, and degenerate consensus sequences: 

>>> pwm.consensus
Seq('TACGC', IUPACUnambiguousDNA())
>>> pwm.anticonsensus
Seq('GGGTG', IUPACUnambiguousDNA())
>>> pwm.degenerate_consensus
Seq('WACNC', IUPACAmbiguousDNA())

Note that due to the pseudocounts, the degenerate consensus sequence calculated from the position-weight matrix is slightly different from the degenerate consensus sequence calculated from the instances in the motif: 

>>> m.degenerate_consensus
Seq('WACVC', IUPACAmbiguousDNA())

The reverse complement of the position-weight matrix can be calculated directly from the pwm: 

>>> rpwm = pwm.reverse_complement()
>>> print(rpwm)
        0      1      2      3      4
A:   0.07   0.07   0.29   0.07   0.51
C:   0.04   0.38   0.04   0.04   0.04
G:   0.71   0.27   0.60   0.04   0.04
T:   0.18   0.29   0.07   0.84   0.40
<BLANKLINE>

14.5  Position-Specific Scoring Matrices

Using the background distribution and PWM with pseudo-counts added, it’s easy to compute the log-odds ratios, telling us what are the log odds of a particular symbol to be coming from a motif against the background. We can use the .log_odds() method on the position-weight matrix: 

>>> pssm = pwm.log_odds()
>>> print(pssm)
        0      1      2      3      4
A:   0.68   1.76  -1.91   0.21  -0.49
C:  -2.49  -2.49   1.26   0.09   1.51
G:  -2.49  -2.49  -2.49   0.60  -2.49
T:   1.03  -1.91   0.21  -1.91  -1.91
<BLANKLINE>

Here we can see positive values for symbols more frequent in the motif than in the background and negative for symbols more frequent in the background. 0.0 means that it’s equally likely to see a symbol in the background and in the motif.

This assumes that A, C, G, and T are equally likely in the background. To calculate the position-specific scoring matrix against a background with unequal probabilities for A, C, G, T, use the background argument. For example, against a background with a 40% GC content, use 

>>> background = {'A':0.3,'C':0.2,'G':0.2,'T':0.3}
>>> pssm = pwm.log_odds(background)
>>> print(pssm)
        0      1      2      3      4
A:   0.42   1.49  -2.17  -0.05  -0.75
C:  -2.17  -2.17   1.58   0.42   1.83
G:  -2.17  -2.17  -2.17   0.92  -2.17
T:   0.77  -2.17  -0.05  -2.17  -2.17
<BLANKLINE>

The maximum and minimum score obtainable from the PSSM are stored in the .max and .min properties: 

>>> print("%4.2f" % pssm.max)
6.59
>>> print("%4.2f" % pssm.min)
-10.85

The mean and standard deviation of the PSSM scores with respect to a specific background are calculated by the .mean and .std methods. 

>>> mean = pssm.mean(background)
>>> std = pssm.std(background)
>>> print("mean = %0.2f, standard deviation = %0.2f" % (mean, std))
mean = 3.21, standard deviation = 2.59

A uniform background is used if background is not specified. The mean is particularly important, as its value is equal to the Kullback-Leibler divergence or relative entropy, and is a measure for the information content of the motif compared to the background. As in Biopython the base-2 logarithm is used in the calculation of the log-odds scores, the information content has units of bits.

The .reverse_complement, .consensus, .anticonsensus, and .degenerate_consensus methods can be applied directly to PSSM objects.

14.6  Searching for instances

The most frequent use for a motif is to find its instances in some sequence. For the sake of this section, we will use an artificial sequence like this:

>>> test_seq=Seq("TACACTGCATTACAACCCAAGCATTA", m.alphabet)
>>> len(test_seq)
26

14.6.1  Searching for exact matches

The simplest way to find instances, is to look for exact matches of the true instances of the motif: 

>>> for pos, seq in m.instances.search(test_seq):
...     print("%i %s" % (pos, seq))
... 
0 TACAC
10 TACAA
13 AACCC

We can do the same with the reverse complement (to find instances on the complementary strand): 

>>> for pos, seq in r.instances.search(test_seq):
...     print("%i %s" % (pos, seq))
... 
6 GCATT
20 GCATT

14.6.2  Searching for matches using the PSSM score

It’s just as easy to look for positions, giving rise to high log-odds scores against our motif: 

>>> for position, score in pssm.search(test_seq, threshold=3.0):
...     print("Position %d: score = %5.3f" % (position, score))
... 
Position 0: score = 5.622
Position -20: score = 4.601
Position 10: score = 3.037
Position 13: score = 5.738
Position -6: score = 4.601

The negative positions refer to instances of the motif found on the reverse strand of the test sequence, and follow the Python convention on negative indices. Therefore, the instance of the motif at pos is located at test_seq[pos:pos+len(m)] both for positive and for negative values of pos.

You may notice the threshold parameter, here set arbitrarily to 3.0. This is in log2, so we are now looking only for words, which are eight times more likely to occur under the motif model than in the background. The default threshold is 0.0, which selects everything that looks more like the motif than the background.

You can also calculate the scores at all positions along the sequence: 

>>> pssm.calculate(test_seq)
array([  5.62230396,  -5.6796999 ,  -3.43177247,   0.93827754,
        -6.84962511,  -2.04066086, -10.84962463,  -3.65614533,
        -0.03370807,  -3.91102552,   3.03734159,  -2.14918518,
        -0.6016975 ,   5.7381525 ,  -0.50977498,  -3.56422281,
        -8.73414803,  -0.09919716,  -0.6016975 ,  -2.39429784,
       -10.84962463,  -3.65614533], dtype=float32)

In general, this is the fastest way to calculate PSSM scores. The scores returned by pssm.calculate are for the forward strand only. To obtain the scores on the reverse strand, you can take the reverse complement of the PSSM: 

>>> rpssm = pssm.reverse_complement()
>>> rpssm.calculate(test_seq)
array([ -9.43458748,  -3.06172252,  -7.18665981,  -7.76216221,
        -2.04066086,  -4.26466274,   4.60124254,  -4.2480607 ,
        -8.73414803,  -2.26503372,  -6.49598789,  -5.64668512,
        -8.73414803, -10.84962463,  -4.82356262,  -4.82356262,
        -5.64668512,  -8.73414803,  -4.15613794,  -5.6796999 ,
         4.60124254,  -4.2480607 ], dtype=float32)

14.6.3  Selecting a score threshold

If you want to use a less arbitrary way of selecting thresholds, you can explore the distribution of PSSM scores. Since the space for a score distribution grows exponentially with motif length, we are using an approximation with a given precision to keep computation cost manageable: 

>>> distribution = pssm.distribution(background=background, precision=10**4)

The distribution object can be used to determine a number of different thresholds. We can specify the requested false-positive rate (probability of “finding” a motif instance in background generated sequence): 

>>> threshold = distribution.threshold_fpr(0.01)
>>> print("%5.3f" % threshold)
4.009

or the false-negative rate (probability of “not finding” an instance generated from the motif): 

>>> threshold = distribution.threshold_fnr(0.1)
>>> print("%5.3f" % threshold)
-0.510

or a threshold (approximately) satisfying some relation between the false-positive rate and the false-negative rate (fnr/fpr≃ t): 

>>> threshold = distribution.threshold_balanced(1000)
>>> print("%5.3f" % threshold)
6.241

or a threshold satisfying (roughly) the equality between the false-positive rate and the −log of the information content (as used in patser software by Hertz and Stormo): 

>>> threshold = distribution.threshold_patser()
>>> print("%5.3f" % threshold)
0.346

For example, in case of our motif, you can get the threshold giving you exactly the same results (for this sequence) as searching for instances with balanced threshold with rate of 1000. 

>>> threshold = distribution.threshold_fpr(0.01)
>>> print("%5.3f" % threshold)
4.009
>>> for position, score in pssm.search(test_seq, threshold=threshold):
...     print("Position %d: score = %5.3f" % (position, score))
... 
Position 0: score = 5.622
Position -20: score = 4.601
Position 13: score = 5.738
Position -6: score = 4.601

14.7  Each motif object has an associated Position-Specific Scoring Matrix

To facilitate searching for potential TFBSs using PSSMs, both the position-weight matrix and the position-specific scoring matrix are associated with each motif. Using the Arnt motif as an example: 

>>> from Bio import motifs
>>> handle = open("Arnt.sites")
>>> motif = motifs.read(handle, 'sites')
>>> print(motif.counts)
        0      1      2      3      4      5
A:   4.00  19.00   0.00   0.00   0.00   0.00
C:  16.00   0.00  20.00   0.00   0.00   0.00
G:   0.00   1.00   0.00  20.00   0.00  20.00
T:   0.00   0.00   0.00   0.00  20.00   0.00
<BLANKLINE>
>>> print(motif.pwm)
        0      1      2      3      4      5
A:   0.20   0.95   0.00   0.00   0.00   0.00
C:   0.80   0.00   1.00   0.00   0.00   0.00
G:   0.00   0.05   0.00   1.00   0.00   1.00
T:   0.00   0.00   0.00   0.00   1.00   0.00
<BLANKLINE>

>>> print(motif.pssm)
        0      1      2      3      4      5
A:  -0.32   1.93   -inf   -inf   -inf   -inf
C:   1.68   -inf   2.00   -inf   -inf   -inf
G:   -inf  -2.32   -inf   2.00   -inf   2.00
T:   -inf   -inf   -inf   -inf   2.00   -inf
<BLANKLINE>

The negative infinities appear here because the corresponding entry in the frequency matrix is 0, and we are using zero pseudocounts by default: 

>>> for letter in "ACGT":
...     print("%s: %4.2f" % (letter, motif.pseudocounts[letter]))
... 
A: 0.00
C: 0.00
G: 0.00
T: 0.00

If you change the .pseudocounts attribute, the position-frequency matrix and the position-specific scoring matrix are recalculated automatically: 

>>> motif.pseudocounts = 3.0
>>> for letter in "ACGT":
...     print("%s: %4.2f" % (letter, motif.pseudocounts[letter]))
... 
A: 3.00
C: 3.00
G: 3.00
T: 3.00

>>> print(motif.pwm)
        0      1      2      3      4      5
A:   0.22   0.69   0.09   0.09   0.09   0.09
C:   0.59   0.09   0.72   0.09   0.09   0.09
G:   0.09   0.12   0.09   0.72   0.09   0.72
T:   0.09   0.09   0.09   0.09   0.72   0.09
<BLANKLINE>

>>> print(motif.pssm)
        0      1      2      3      4      5
A:  -0.19   1.46  -1.42  -1.42  -1.42  -1.42
C:   1.25  -1.42   1.52  -1.42  -1.42  -1.42
G:  -1.42  -1.00  -1.42   1.52  -1.42   1.52
T:  -1.42  -1.42  -1.42  -1.42   1.52  -1.42
<BLANKLINE>

You can also set the .pseudocounts to a dictionary over the four nucleotides if you want to use different pseudocounts for them. Setting motif.pseudocounts to None resets it to its default value of zero.

The position-specific scoring matrix depends on the background distribution, which is uniform by default: 

>>> for letter in "ACGT":
...     print("%s: %4.2f" % (letter, motif.background[letter]))
... 
A: 0.25
C: 0.25
G: 0.25
T: 0.25

Again, if you modify the background distribution, the position-specific scoring matrix is recalculated: 

>>> motif.background = {'A': 0.2, 'C': 0.3, 'G': 0.3, 'T': 0.2}
>>> print(motif.pssm)
        0      1      2      3      4      5
A:   0.13   1.78  -1.09  -1.09  -1.09  -1.09
C:   0.98  -1.68   1.26  -1.68  -1.68  -1.68
G:  -1.68  -1.26  -1.68   1.26  -1.68   1.26
T:  -1.09  -1.09  -1.09  -1.09   1.85  -1.09
<BLANKLINE>

Setting motif.background to None resets it to a uniform distribution: 

>>> motif.background = None
>>> for letter in "ACGT":
...     print("%s: %4.2f" % (letter, motif.background[letter]))
... 
A: 0.25
C: 0.25
G: 0.25
T: 0.25

If you set motif.background equal to a single value, it will be interpreted as the GC content: 

>>> motif.background = 0.8
>>> for letter in "ACGT":
...     print("%s: %4.2f" % (letter, motif.background[letter]))
... 
A: 0.10
C: 0.40
G: 0.40
T: 0.10

Note that you can now calculate the mean of the PSSM scores over the background against which it was computed: 

>>> print("%f" % motif.pssm.mean(motif.background))
4.703928

as well as its standard deviation: 

>>> print("%f" % motif.pssm.std(motif.background))
3.290900

and its distribution: 

>>> distribution = motif.pssm.distribution(background=motif.background)
>>> threshold = distribution.threshold_fpr(0.01)
>>> print("%f" % threshold)
3.854375

Note that the position-weight matrix and the position-specific scoring matrix are recalculated each time you call motif.pwm or motif.pssm, respectively. If speed is an issue and you want to use the PWM or PSSM repeatedly, you can save them as a variable, as in 

>>> pssm = motif.pssm

14.8  Comparing motifs

Once we have more than one motif, we might want to compare them.

Before we start comparing motifs, I should point out that motif boundaries are usually quite arbitrary. This means we often need to compare motifs of different lengths, so comparison needs to involve some kind of alignment. This means we have to take into account two things:

  • alignment of motifs
  • some function to compare aligned motifs

To align the motifs, we use ungapped alignment of PSSMs and substitute zeros for any missing columns at the beginning and end of the matrices. This means that effectively we are using the background distribution for columns missing from the PSSM. The distance function then returns the minimal distance between motifs, as well as the corresponding offset in their alignment.

To give an exmaple, let us first load another motif, which is similar to our test motif m: 

>>> m_reb1 = motifs.read(open("REB1.pfm"), "pfm")
>>> m_reb1.consensus
Seq('GTTACCCGG', IUPACUnambiguousDNA())
>>> print(m_reb1.counts)
        0      1      2      3      4      5      6      7      8
A:  30.00   0.00   0.00 100.00   0.00   0.00   0.00   0.00  15.00
C:  10.00   0.00   0.00   0.00 100.00 100.00 100.00   0.00  15.00
G:  50.00   0.00   0.00   0.00   0.00   0.00   0.00  60.00  55.00
T:  10.00 100.00 100.00   0.00   0.00   0.00   0.00  40.00  15.00
<BLANKLINE>

To make the motifs comparable, we choose the same values for the pseudocounts and the background distribution as our motif m: 

>>> m_reb1.pseudocounts = {'A':0.6, 'C': 0.4, 'G': 0.4, 'T': 0.6}
>>> m_reb1.background = {'A':0.3,'C':0.2,'G':0.2,'T':0.3}
>>> pssm_reb1 = m_reb1.pssm
>>> print(pssm_reb1)
        0      1      2      3      4      5      6      7      8
A:   0.00  -5.67  -5.67   1.72  -5.67  -5.67  -5.67  -5.67  -0.97
C:  -0.97  -5.67  -5.67  -5.67   2.30   2.30   2.30  -5.67  -0.41
G:   1.30  -5.67  -5.67  -5.67  -5.67  -5.67  -5.67   1.57   1.44
T:  -1.53   1.72   1.72  -5.67  -5.67  -5.67  -5.67   0.41  -0.97
<BLANKLINE>

We’ll compare these motifs using the Pearson correlation. Since we want it to resemble a distance measure, we actually take 1−r, where r is the Pearson correlation coefficient (PCC): 

>>> distance, offset = pssm.dist_pearson(pssm_reb1)
>>> print("distance = %5.3g" % distance)
distance = 0.239
>>> print(offset)
-2

This means that the best PCC between motif m and m_reb1 is obtained with the following alignment: 

m:      bbTACGCbb
m_reb1: GTTACCCGG

where b stands for background distribution. The PCC itself is roughly 1−0.239=0.761.

14.9  De novo motif finding

Currently, Biopython has only limited support for de novo motif finding. Namely, we support running and parsing of AlignAce and MEME. Since the number of motif finding tools is growing rapidly, contributions of new parsers are welcome.

14.9.1  MEME

Let’s assume, you have run MEME on sequences of your choice with your favorite parameters and saved the output in the file meme.out. You can retrieve the motifs reported by MEME by running the following piece of code:

>>> from Bio import motifs
>>> motifsM = motifs.parse(open("meme.out"), "meme")

>>> motifsM
[<Bio.motifs.meme.Motif object at 0xc356b0>]

Besides the most wanted list of motifs, the result object contains more useful information, accessible through properties with self-explanatory names:

  • .alphabet
  • .datafile
  • .sequence_names
  • .version
  • .command

The motifs returned by the MEME Parser can be treated exactly like regular Motif objects (with instances), they also provide some extra functionality, by adding additional information about the instances. 

>>> motifsM[0].consensus
Seq('CTCAATCGTA', IUPACUnambiguousDNA())
>>> motifsM[0].instances[0].sequence_name
'SEQ10;'
>>> motifsM[0].instances[0].start
3
>>> motifsM[0].instances[0].strand
'+'

>>> motifsM[0].instances[0].pvalue
8.71e-07

14.9.2  AlignAce

We can do very similar things with the AlignACE program. Assume, you have your output in the file alignace.out. You can parse your output with the following code:

>>> from Bio import motifs
>>> motifsA = motifs.parse(open("alignace.out"), "alignace")

Again, your motifs behave as they should: 

>>> motifsA[0].consensus
Seq('TCTACGATTGAG', IUPACUnambiguousDNA())

In fact you can even see, that AlignAce found a very similar motif as MEME. It is just a longer version of a reverse complement of the MEME motif: 

>>> motifsM[0].reverse_complement().consensus
Seq('TACGATTGAG', IUPACUnambiguousDNA())

If you have AlignAce installed on the same machine, you can also run it directly from Biopython. A short example of how this can be done is shown below (other parameters can be specified as keyword parameters):

>>> command="/opt/bin/AlignACE"
>>> input_file="test.fa"
>>> from Bio.motifs.applications import AlignAceCommandline
>>> cmd = AlignAceCommandline(cmd=command, input=input_file, gcback=0.6, numcols=10)
>>> stdout, stderr= cmd()

Since AlignAce prints all of its output to standard output, you can get to your motifs by parsing the first part of the result: 

>>> motifs = motifs.parse(stdout, "alignace")

14.10  Useful links

14.11  Obsolete Bio.Motif module

The rest of this chapter above describes the Bio.motifs package included in Biopython 1.61 onwards, which is replacing the older Bio.Motif package introduced with Biopython 1.50, which was in turn based on two older former Biopython modules, Bio.AlignAce and Bio.MEME.

To allow for a smooth transition, the older Bio.Motif package will be maintained in parallel with its replacement Bio.motifs at least two more releases, and at least one year.

14.11.1  Motif objects

Since we are interested in motif analysis, we need to take a look at Motif objects in the first place. For that we need to import the Motif library: 

>>> from Bio import Motif

and we can start creating our first motif objects. Let’s create a DNA motif: 

>>> from Bio.Alphabet import IUPAC
>>> m = Motif.Motif(alphabet=IUPAC.unambiguous_dna)

This is for now just an empty container, so let’s add some sequences to our newly created motif: 

>>> from Bio.Seq import Seq
>>> m.add_instance(Seq("TATAA", m.alphabet))
>>> m.add_instance(Seq("TATTA", m.alphabet))
>>> m.add_instance(Seq("TATAA", m.alphabet))
>>> m.add_instance(Seq("TATAA", m.alphabet))

Now we have a full Motif instance, so we can try to get some basic information about it. Let’s start with length and consensus sequence: 

>>> len(m)
5
>>> m.consensus()
Seq('TATAA', IUPACUnambiguousDNA())

In case of DNA motifs, we can also get a reverse complement of a motif: 

>>> m.reverse_complement().consensus()
Seq('TTATA', IUPACUnambiguousDNA())
>>> for i in m.reverse_complement().instances:
...     print(i)
TTATA
TAATA
TTATA
TTATA

We can also calculate the information content of a motif with a simple call: 

>>> print("%0.2f" % m.ic())
5.27

This gives us a number of bits of information provided by the motif, which tells us how much differs from background.

The most common representation of a motif is a PWM (Position Weight Matrix). It summarizes the probabilities of finding any symbol (in this case nucleotide) in any position of a motif. It can be computed by calling the .pwm() method: 

>>> m.pwm()
[{'A': 0.05, 'C': 0.05, 'T': 0.85, 'G': 0.05}, 
 {'A': 0.85, 'C': 0.05, 'T': 0.05, 'G': 0.05}, 
 {'A': 0.05, 'C': 0.05, 'T': 0.85, 'G': 0.05}, 
 {'A': 0.65, 'C': 0.05, 'T': 0.25, 'G': 0.05}, 
 {'A': 0.85, 'C': 0.05, 'T': 0.05, 'G': 0.05}]

The probabilities in the motif’s PWM are based on the counts in the instances, but we can see, that even though there were no Gs and no Cs in the instances, we still have non-zero probabilities assigned to them. These come from pseudo-counts which are, roughly speaking, a commonly used way to acknowledge the incompleteness of our knowledge and avoid technical problems with calculating logarithms of 0.

We can control the way that pseudo-counts are added with two properties of Motif objects .background is the probability distribution over all symbols in the alphabet that we assume represents background, non-motif sequences (usually based on the GC content of the respective genome). It is by default set to a uniform distribution upon creation of a motif: 

>>> m.background  
{'A': 0.25, 'C': 0.25, 'T': 0.25, 'G': 0.25}

The other parameter is .beta, which states the amount of pseudo-counts we should add to the PWM. By default it is set to 1.0, 

>>> m.beta
1.0

so that the total input of pseudo-counts is equal to that of one instance.

Using the background distribution and pwm with pseudo-counts added, it’s easy to compute the log-odds ratios, telling us what are the log odds of a particular symbol to be coming from a motif against the background. We can use the .log_odds() method:

 >>> m.log_odds() 
[{'A': -2.3219280948873622, 
  'C': -2.3219280948873622, 
  'T': 1.7655347463629771, 
  'G': -2.3219280948873622}, 
 {'A': 1.7655347463629771, 
  'C': -2.3219280948873622, 
  'T': -2.3219280948873622, 
  'G': -2.3219280948873622}, 
 {'A': -2.3219280948873622, 
  'C': -2.3219280948873622, 
  'T': 1.7655347463629771, 
  'G': -2.3219280948873622}, 
 {'A': 1.3785116232537298, 
  'C': -2.3219280948873622, 
  'T': 0.0, 
  'G': -2.3219280948873622}, 
 {'A': 1.7655347463629771, 
  'C': -2.3219280948873622, 
  'T': -2.3219280948873622, 
  'G': -2.3219280948873622}
]

Here we can see positive values for symbols more frequent in the motif than in the background and negative for symbols more frequent in the background. 0.0 means that it’s equally likely to see a symbol in background and in the motif (e.g. ‘T’ in the second-last position).

14.11.1.1  Reading and writing

Creating motifs from instances by hand is a bit boring, so it’s useful to have some I/O functions for reading and writing motifs. There are no really well established standards for storing motifs, but there’s a couple of formats which are more used than others. The most important distinction is whether the motif representation is based on instances or on some version of PWM matrix. On of the most popular motif databases JASPAR stores motifs in both formats, so let’s look at how we can import JASPAR motifs from instances: 

>>> from Bio import Motif
>>> arnt = Motif.read(open("Arnt.sites"), "jaspar-sites")

and from a count matrix: 

>>> srf = Motif.read(open("SRF.pfm"), "jaspar-pfm")

The arnt and srf motifs can both do the same things for us, but they use different internal representations of the motif. We can tell that by inspecting the has_counts and has_instances properties: 

>>> arnt.has_instances
True
>>> srf.has_instances
False
>>> srf.has_counts
True

>>> srf.counts
{'A': [2, 9, 0, 1, 32, 3, 46, 1, 43, 15, 2, 2],
 'C': [1, 33, 45, 45, 1, 1, 0, 0, 0, 1, 0, 1],
 'G': [39, 2, 1, 0, 0, 0, 0, 0, 0, 0, 44, 43],
 'T': [4, 2, 0, 0, 13, 42, 0, 45, 3, 30, 0, 0]}

There are conversion functions, which can help us convert between different representations: 

>>> arnt.make_counts_from_instances()
{'A': [8, 38, 0, 0, 0, 0],
 'C': [32, 0, 40, 0, 0, 0],
 'G': [0, 2, 0, 40, 0, 40],
 'T': [0, 0, 0, 0, 40, 0]}

>>> srf.make_instances_from_counts()
[Seq('GGGAAAAAAAGG', IUPACUnambiguousDNA()),
 Seq('GGCCAAATAAGG', IUPACUnambiguousDNA()),
 Seq('GACCAAATAAGG', IUPACUnambiguousDNA()),
....

The important thing to remember here is that the method make_instances_from_counts() creates fake instances, because usually there are very many possible sets of instances which give rise to the same pwm, and if we have only the count matrix, we cannot reconstruct the original one. This does not make any difference if we are using the PWM as the representation of the motif, but one should be careful with exporting instances from count-based motifs.

Speaking of exporting, let’s look at export functions. We can export to fasta: 

>>> print(m.format("fasta"))
>instance0
TATAA
>instance1
TATTA
>instance2
TATAA
>instance3
TATAA

or to TRANSFAC-like matrix format (used by some motif processing software) 

>>> print(m.format("transfac"))
XX
TY Motif
ID 
BF undef
P0 G A T C
01 0 0 4 0
02 0 4 0 0
03 0 0 4 0
04 0 3 1 0
05 0 4 0 0
XX

Finally, if we have internet access, we can create a weblogo: 

>>> arnt.weblogo("Arnt.png")

We should get our logo saved as a png in the specified file.

14.11.2  Searching for instances

The most frequent use for a motif is to find its instances in some sequence. For the sake of this section, we will use an artificial sequence like this:

test_seq=Seq("TATGATGTAGTATAATATAATTATAA",m.alphabet)

The simplest way to find instances, is to look for exact matches of the true instances of the motif: 

>>> for pos, seq in m.search_instances(test_seq):
...     print(pos, seq.tostring())
... 
10 TATAA
15 TATAA
21 TATAA

We can do the same with the reverse complement (to find instances on the complementary strand): 

>>> for pos, seq in m.reverse_complement().search_instances(test_seq):
...     print(pos, seq.tostring())
... 
12 TAATA
20 TTATA

It’s just as easy to look for positions, giving rise to high log-odds scores against our motif: 

>>> for pos, score in m.search_pwm(test_seq, threshold=5.0):
...     print(pos, score)
... 
10 8.44065060871
-12 7.06213898545
15 8.44065060871
-20 8.44065060871
21 8.44065060871

You may notice the threshold parameter, here set arbitrarily to 5.0. This is in log2, so we are now looking only for words, which are 32 times more likely to occur under the motif model than in the background. The default threshold is 0.0, which selects everything that looks more like the motif than the background.

If you want to use a less arbitrary way of selecting thresholds, you can explore the Motif.score_distribution class implementing an distribution of scores for a given motif. Since the space for a score distribution grows exponentially with motif length, we are using an approximation with a given precision to keep computation cost manageable: 

>>> sd = Motif.score_distribution(m, precision=10**4)

The sd object can be used to determine a number of different thresholds.

We can specify the requested false-positive rate (probability of “finding” a motif instance in background generated sequence): 

>>> sd.threshold_fpr(0.01)
4.3535838726139886

or the false-negative rate (probability of “not finding” an instance generated from the motif): 

>>> sd.threshold_fnr(0.1)
0.26651713652234044

or a threshold (approximately) satisfying some relation between fpr and fnr fnr/fpr≃ t: 

>>> sd.threshold_balanced(1000)
8.4406506087056368

or a threshold satisfying (roughly) the equality between the false-positive rate and the −log of the information content (as used in patser software by Hertz and Stormo).

For example, in case of our motif, you can get the threshold giving you exactly the same results (for this sequence) as searching for instances with balanced threshold with rate of 1000. 

>>> for pos, score in m.search_pwm(test_seq, threshold=sd.threshold_balanced(1000)):
...     print(pos, score)
... 
10 8.44065060871
15 8.44065060871
-20 8.44065060871
21 8.44065060871

14.11.3  Comparing motifs

Once we have more than one motif, we might want to compare them. For that, we have currently three different methods of Bio.Motif objects.

Before we start comparing motifs, I should point out that motif boundaries are usually quite arbitrary. This means, that we often need to compare motifs of different lengths, so comparison needs to involve some kind of alignment. This means, that we have to take into account two things:

  • alignment of motifs
  • some function to compare aligned motifs

In Bio.Motif we have 3 different functions for motif comparison, which are based on the same idea behind motif alignment, but use different functions to compare aligned motifs. Briefly speaking, we are using ungapped alignment of PWMs and substitute the missing columns at the beginning and end of the matrices with background distribution. All three comparison functions are written in such a way, that they can be interpreted as distance measures, however only one (dist_dpq) satisfies the triangle inequality. All of them return the minimal distance and the corresponding offset between motifs.

To show how these functions work, let us first load another motif, which is similar to our test motif m: 

>>> ubx=Motif.read(open("Ubx.pfm"), "jaspar-pfm")
<Bio.Motif.Motif.Motif object at 0xc29b90>
>>> ubx.consensus()
Seq('TAAT', IUPACUnambiguousDNA())

The first function we’ll use to compare these motifs is based on Pearson correlation. Since we want it to resemble a distance measure, we actually take 1−r, where r is the Pearson correlation coefficient (PCC): 

>>> m.dist_pearson(ubx)
(0.41740393308237722, 2)

This means, that the best PCC between motif m and Ubx is obtained with the following alignment: 

bbTAAT
TATAAb

where b stands for background distribution. The PCC itself is roughly 1−0.42=0.58. If we try the reverse complement of the Ubx motif:

>>> m.dist_pearson(ubx.reverse_complement())
(0.25784180151584823, 1)

We can see that the PCC is better (almost 0.75), and the alignment is also different: 

bATTA
TATAA

There are two other functions: dist_dpq, which is a true metric (satisfying traingle inequality) based on the Kullback-Leibler divergence 

>>> m.dist_dpq(ubx.reverse_complement())
(0.49292358382899853, 1)

and the dist_product method, which is based on the product of probabilities which can be interpreted as the probability of independently generating the same instance by both motifs.

>>> m.dist_product(ubx.reverse_complement())
(0.16224587301064275, 1)

14.11.4  De novo motif finding

Currently, Biopython has only limited support for de novo motif finding. Namely, we support running and parsing of AlignAce and MEME. Since the number of motif finding tools is growing rapidly, contributions of new parsers are welcome.

14.11.4.1  MEME

Let’s assume, you have run MEME on sequences of your choice with your favorite parameters and saved the output in the file meme.out. You can retrieve the motifs reported by MEME by running the following piece of code:

>>> motifsM = list(Motif.parse(open("meme.out"), "MEME"))
>>> motifsM
[<Bio.Motif.MEMEMotif.MEMEMotif object at 0xc356b0>]

Besides the most wanted list of motifs, the result object contains more useful information, accessible through properties with self-explanatory names:

  • .alphabet
  • .datafile
  • .sequence_names
  • .version
  • .command

The motifs returned by MEMEParser can be treated exactly like regular Motif objects (with instances), they also provide some extra functionality, by adding additional information about the instances. 

>>> motifsM[0].consensus()
Seq('CTCAATCGTA', IUPACUnambiguousDNA())

>>> motifsM[0].instances[0].pvalue
8.71e-07
>>> motifsM[0].instances[0].sequence_name
'SEQ10;'
>>> motifsM[0].instances[0].start
3
>>> motifsM[0].instances[0].strand
'+'

14.11.4.2  AlignAce

We can do very similar things with AlignACE program. Assume, you have your output in the file alignace.out. You can parse your output with the following code:

>>> motifsA=list(Motif.parse(open("alignace.out"), "AlignAce"))

Again, your motifs behave as they should: 

>>> motifsA[0].consensus()
Seq('TCTACGATTGAG', IUPACUnambiguousDNA())

In fact you can even see, that AlignAce found a very similar motif as MEME, it is just a longer version of a reverse complement of MEME motif: 

>>> motifsM[0].reverse_complement().consensus()
Seq('TACGATTGAG', IUPACUnambiguousDNA())

If you have AlignAce installed on the same machine, you can also run it directly from Biopython. Short example of how this can be done is shown below (other parameters can be specified as keyword parameters):

>>> command="/opt/bin/AlignACE"
>>> input_file="test.fa"
>>> from Bio.Motif.Applications import AlignAceCommandline
>>> cmd = AlignAceCommandline(cmd=command, input=input_file, gcback=0.6, numcols=10)
>>> stdout, stderr= cmd()

Since AlignAce prints all its output to standard output, you can get to your motifs by parsing the first part of the result: 

motifs=list(Motif.parse(stdout,"AlignAce"))

Chapter 15  Cluster analysis

Cluster analysis is the grouping of items into clusters based on the similarity of the items to each other. In bioinformatics, clustering is widely used in gene expression data analysis to find groups of genes with similar gene expression profiles. This may identify functionally related genes, as well as suggest the function of presently unknown genes.

The Biopython module Bio.Cluster provides commonly used clustering algorithms and was designed with the application to gene expression data in mind. However, this module can also be used for cluster analysis of other types of data. Bio.Cluster and the underlying C Clustering Library is described by De Hoon et al. [14].

The following four clustering approaches are implemented in Bio.Cluster:

  • Hierarchical clustering (pairwise centroid-, single-, complete-, and average-linkage);
  • k-means, k-medians, and k-medoids clustering;
  • Self-Organizing Maps;
  • Principal Component Analysis.

Data representation

The data to be clustered are represented by a n × m Numerical Python array data. Within the context of gene expression data clustering, typically the rows correspond to different genes whereas the columns correspond to different experimental conditions. The clustering algorithms in Bio.Cluster can be applied both to rows (genes) and to columns (experiments).

Missing values

Often in microarray experiments, some of the data values are missing, which is indicated by an additional n × m Numerical Python integer array mask. If mask[i,j]==0, then data[i,j] is missing and is ignored in the analysis.

Random number generator

The k-means/medians/medoids clustering algorithms and Self-Organizing Maps (SOMs) include the use of a random number generator. The uniform random number generator in Bio.Cluster is based on the algorithm by L’Ecuyer [25], while random numbers following the binomial distribution are generated using the BTPE algorithm by Kachitvichyanukul and Schmeiser [23]. The random number generator is initialized automatically during its first call. As this random number generator uses a combination of two multiplicative linear congruential generators, two (integer) seeds are needed for initialization, for which we use the system-supplied random number generator rand (in the C standard library). We initialize this generator by calling srand with the epoch time in seconds, and use the first two random numbers generated by rand as seeds for the uniform random number generator in Bio.Cluster.

15.1  Distance functions

In order to cluster items into groups based on their similarity, we should first define what exactly we mean by similar. Bio.Cluster provides eight distance functions, indicated by a single character, to measure similarity, or conversely, distance:

  • 'e': Euclidean distance;
  • 'b': City-block distance.
  • 'c': Pearson correlation coefficient;
  • 'a': Absolute value of the Pearson correlation coefficient;
  • 'u': Uncentered Pearson correlation (equivalent to the cosine of the angle between two data vectors);
  • 'x': Absolute uncentered Pearson correlation;
  • 's': Spearman’s rank correlation;
  • 'k': Kendall’s τ.

The first two are true distance functions that satisfy the triangle inequality:

d⎛
⎜
⎝
u
,
v
⎞
⎟
⎠		≤ d⎛
⎜
⎝
u
,
w
⎞
⎟
⎠		+ d⎛
⎜
⎝
w
,
v
⎞
⎟
⎠		 for all  
u
, 
v
, 
w
,

and are therefore refered to as metrics. In everyday language, this means that the shortest distance between two points is a straight line.

The remaining six distance measures are related to the correlation coefficient, where the distance d is defined in terms of the correlation r by d=1−r. Note that these distance functions are semi-metrics that do not satisfy the triangle inequality. For example, for

u
=⎛
⎝		1,0,−1⎞
⎠		;
v
=⎛
⎝		1,1,0⎞
⎠		;
w
=⎛
⎝		0,1,1⎞
⎠		;

we find a Pearson distance d(u,w) = 1.8660, while d(u,v)+d(v,w) = 1.6340.

Euclidean distance

In Bio.Cluster, we define the Euclidean distance as

d = 
1 
n
 
n
∑
i=1
 ⎛
⎝		xi−yi⎞
⎠		2.

Only those terms are included in the summation for which both xi and yi are present, and the denominator n is chosen accordingly. As the expression data xi and yi are subtracted directly from each other, we should make sure that the expression data are properly normalized when using the Euclidean distance.

City-block distance

The city-block distance, alternatively known as the Manhattan distance, is related to the Euclidean distance. Whereas the Euclidean distance corresponds to the length of the shortest path between two points, the city-block distance is the sum of distances along each dimension. As gene expression data tend to have missing values, in Bio.Cluster we define the city-block distance as the sum of distances divided by the number of dimensions:

d = 
1 
n
 
n
∑
i=1
 ⎪
⎪		xi−yi⎪
⎪		.

This is equal to the distance you would have to walk between two points in a city, where you have to walk along city blocks. As for the Euclidean distance, the expression data are subtracted directly from each other, and we should therefore make sure that they are properly normalized.

The Pearson correlation coefficient

The Pearson correlation coefficient is defined as

r = 
1
n
 
n
∑
i=1
 ⎛
⎜
⎜
⎝
xi −x
σx
 ⎞
⎟
⎟
⎠		⎛
⎜
⎜
⎝
yi −ȳ
σy
 ⎞
⎟
⎟
⎠		,

in which x, ȳ are the sample mean of x and y respectively, and σx, σy are the sample standard deviation of x and y. The Pearson correlation coefficient is a measure for how well a straight line can be fitted to a scatterplot of x and y. If all the points in the scatterplot lie on a straight line, the Pearson correlation coefficient is either +1 or -1, depending on whether the slope of line is positive or negative. If the Pearson correlation coefficient is equal to zero, there is no correlation between x and y.

The Pearson distance is then defined as

dP ≡ 1 − r.

As the Pearson correlation coefficient lies between -1 and 1, the Pearson distance lies between 0 and 2.

Absolute Pearson correlation

By taking the absolute value of the Pearson correlation, we find a number between 0 and 1. If the absolute value is 1, all the points in the scatter plot lie on a straight line with either a positive or a negative slope. If the absolute value is equal to zero, there is no correlation between x and y.

The corresponding distance is defined as

dA ≡ 1 − ⎪
⎪		r⎪
⎪		,

where r is the Pearson correlation coefficient. As the absolute value of the Pearson correlation coefficient lies between 0 and 1, the corresponding distance lies between 0 and 1 as well.

In the context of gene expression experiments, the absolute correlation is equal to 1 if the gene expression profiles of two genes are either exactly the same or exactly opposite. The absolute correlation coefficient should therefore be used with care.

Uncentered correlation (cosine of the angle)

In some cases, it may be preferable to use the uncentered correlation instead of the regular Pearson correlation coefficient. The uncentered correlation is defined as

rU = 
1
n
 
n
∑
i=1
 ⎛
⎜
⎜
⎝
xi
σx(0)
 ⎞
⎟
⎟
⎠		⎛
⎜
⎜
⎝
yi
σy(0)
 ⎞
⎟
⎟
⎠		,

where

     
σx(0) =
 
√
1
n
 
n
∑
i=1
xi2
;  
 
σy(0) =
 
√
1
n
 
n
∑
i=1
yi2
.  
 

This is the same expression as for the regular Pearson correlation coefficient, except that the sample means x, ȳ are set equal to zero. The uncentered correlation may be appropriate if there is a zero reference state. For instance, in the case of gene expression data given in terms of log-ratios, a log-ratio equal to zero corresponds to the green and red signal being equal, which means that the experimental manipulation did not affect the gene expression.

The distance corresponding to the uncentered correlation coefficient is defined as

dU ≡ 1 − rU,

where rU is the uncentered correlation. As the uncentered correlation coefficient lies between -1 and 1, the corresponding distance lies between 0 and 2.

The uncentered correlation is equal to the cosine of the angle of the two data vectors in n-dimensional space, and is often referred to as such.

Absolute uncentered correlation

As for the regular Pearson correlation, we can define a distance measure using the absolute value of the uncentered correlation:

dAU ≡ 1 − ⎪
⎪		rU⎪
⎪		,

where rU is the uncentered correlation coefficient. As the absolute value of the uncentered correlation coefficient lies between 0 and 1, the corresponding distance lies between 0 and 1 as well.

Geometrically, the absolute value of the uncentered correlation is equal to the cosine between the supporting lines of the two data vectors (i.e., the angle without taking the direction of the vectors into consideration).

Spearman rank correlation

The Spearman rank correlation is an example of a non-parametric similarity measure, and tends to be more robust against outliers than the Pearson correlation.

To calculate the Spearman rank correlation, we replace each data value by their rank if we would order the data in each vector by their value. We then calculate the Pearson correlation between the two rank vectors instead of the data vectors.

As in the case of the Pearson correlation, we can define a distance measure corresponding to the Spearman rank correlation as

dS ≡ 1 − rS,

where rS is the Spearman rank correlation.

Kendall’s τ

Kendall’s τ is another example of a non-parametric similarity measure. It is similar to the Spearman rank correlation, but instead of the ranks themselves only the relative ranks are used to calculate τ (see Snedecor & Cochran [29]).

We can define a distance measure corresponding to Kendall’s τ as

dK ≡ 1 − τ.

As Kendall’s τ is always between -1 and 1, the corresponding distance will be between 0 and 2.

Weighting

For most of the distance functions available in Bio.Cluster, a weight vector can be applied. The weight vector contains weights for the items in the data vector. If the weight for item i is wi, then that item is treated as if it occurred wi times in the data. The weight do not have to be integers. For the Spearman rank correlation and Kendall’s τ, weights do not have a well-defined meaning and are therefore not implemented.

Calculating the distance matrix

The distance matrix is a square matrix with all pairwise distances between the items in data, and can be calculated by the function distancematrix in the Bio.Cluster module: 

>>> from Bio.Cluster import distancematrix
>>> matrix = distancematrix(data)

where the following arguments are defined:

  • data (required)
    Array containing the data for the items.
  • mask (default: None)
    Array of integers showing which data are missing. If mask[i,j]==0, then data[i,j] is missing. If mask==None, then all data are present.
  • weight (default: None)
    The weights to be used when calculating distances. If weight==None, then equal weights are assumed.
  • transpose (default: 0)
    Determines if the distances between the rows of data are to be calculated (transpose==0), or between the columns of data (transpose==1).
  • dist (default: 'e', Euclidean distance)
    Defines the distance function to be used (see 15.1).

To save memory, the distance matrix is returned as a list of 1D arrays. The number of columns in each row is equal to the row number. Hence, the first row has zero elements. An example of the return value is 

[array([]),
 array([1.]),
 array([7., 3.]),
 array([4., 2., 6.])]

This corresponds to the distance matrix

⎛
⎜
⎜
⎜
⎝
0174  
1032  
7306  
4260
⎞
⎟
⎟
⎟
⎠		.

15.2  Calculating cluster properties

Calculating the cluster centroids

The centroid of a cluster can be defined either as the mean or as the median of each dimension over all cluster items. The function clustercentroids in Bio.Cluster can be used to calculate either:

>>> from Bio.Cluster import clustercentroids
>>> cdata, cmask = clustercentroids(data)

where the following arguments are defined:

  • data (required)
    Array containing the data for the items.
  • mask (default: None)
    Array of integers showing which data are missing. If mask[i,j]==0, then data[i,j] is missing. If mask==None, then all data are present.
  • clusterid (default: None)
    Vector of integers showing to which cluster each item belongs. If clusterid is None, then all items are assumed to belong to the same cluster.
  • method (default: 'a')
    Specifies whether the arithmetic mean (method=='a') or the median (method=='m') is used to calculate the cluster center.
  • transpose (default: 0)
    Determines if the centroids of the rows of data are to be calculated (transpose==0), or the centroids of the columns of data (transpose==1).

This function returns the tuple (cdata, cmask). The centroid data are stored in the 2D Numerical Python array cdata, with missing data indicated by the 2D Numerical Python integer array cmask. The dimensions of these arrays are (number of clusters, number of columns) if transpose is 0, or (number of rows, number of clusters) if transpose is 1. Each row (if transpose is 0) or column (if transpose is 1) contains the averaged data corresponding to the centroid of each cluster.

Calculating the distance between clusters

Given a distance function between items, we can define the distance between two clusters in several ways. The distance between the arithmetic means of the two clusters is used in pairwise centroid-linkage clustering and in k-means clustering. In k-medoids clustering, the distance between the medians of the two clusters is used instead. The shortest pairwise distance between items of the two clusters is used in pairwise single-linkage clustering, while the longest pairwise distance is used in pairwise maximum-linkage clustering. In pairwise average-linkage clustering, the distance between two clusters is defined as the average over the pairwise distances.

To calculate the distance between two clusters, use 

>>> from Bio.Cluster import clusterdistance
>>> distance = clusterdistance(data)

where the following arguments are defined:

  • data (required)
    Array containing the data for the items.
  • mask (default: None)
    Array of integers showing which data are missing. If mask[i,j]==0, then data[i,j] is missing. If mask==None, then all data are present.
  • weight (default: None)
    The weights to be used when calculating distances. If weight==None, then equal weights are assumed.
  • index1 (default: 0)
    A list containing the indices of the items belonging to the first cluster. A cluster containing only one item i can be represented either as a list [i], or as an integer i.
  • index2 (default: 0)
    A list containing the indices of the items belonging to the second cluster. A cluster containing only one items i can be represented either as a list [i], or as an integer i.
  • method (default: 'a')
    Specifies how the distance between clusters is defined:
    • 'a': Distance between the two cluster centroids (arithmetic mean);
    • 'm': Distance between the two cluster centroids (median);
    • 's': Shortest pairwise distance between items in the two clusters;
    • 'x': Longest pairwise distance between items in the two clusters;
    • 'v': Average over the pairwise distances between items in the two clusters.
  • dist (default: 'e', Euclidean distance)
    Defines the distance function to be used (see 15.1).
  • transpose (default: 0)
    If transpose==0, calculate the distance between the rows of data. If transpose==1, calculate the distance between the columns of data.

15.3  Partitioning algorithms

Partitioning algorithms divide items into k clusters such that the sum of distances over the items to their cluster centers is minimal. The number of clusters k is specified by the user. Three partitioning algorithms are available in Bio.Cluster:

  • k-means clustering
  • k-medians clustering
  • k-medoids clustering

These algorithms differ in how the cluster center is defined. In k-means clustering, the cluster center is defined as the mean data vector averaged over all items in the cluster. Instead of the mean, in k-medians clustering the median is calculated for each dimension in the data vector. Finally, in k-medoids clustering the cluster center is defined as the item which has the smallest sum of distances to the other items in the cluster. This clustering algorithm is suitable for cases in which the distance matrix is known but the original data matrix is not available, for example when clustering proteins based on their structural similarity.

The expectation-maximization (EM) algorithm is used to find this partitioning into k groups. In the initialization of the EM algorithm, we randomly assign items to clusters. To ensure that no empty clusters are produced, we use the binomial distribution to randomly choose the number of items in each cluster to be one or more. We then randomly permute the cluster assignments to items such that each item has an equal probability to be in any cluster. Each cluster is thus guaranteed to contain at least one item.

We then iterate:

  • Calculate the centroid of each cluster, defined as either the mean, the median, or the medoid of the cluster;
  • Calculate the distances of each item to the cluster centers;
  • For each item, determine which cluster centroid is closest;
  • Reassign each item to its closest cluster, or stop the iteration if no further item reassignments take place.

To avoid clusters becoming empty during the iteration, in k-means and k-medians clustering the algorithm keeps track of the number of items in each cluster, and prohibits the last remaining item in a cluster from being reassigned to a different cluster. For k-medoids clustering, such a check is not needed, as the item that functions as the cluster centroid has a zero distance to itself, and will therefore never be closer to a different cluster.

As the initial assignment of items to clusters is done randomly, usually a different clustering solution is found each time the EM algorithm is executed. To find the optimal clustering solution, the k-means algorithm is repeated many times, each time starting from a different initial random clustering. The sum of distances of the items to their cluster center is saved for each run, and the solution with the smallest value of this sum will be returned as the overall clustering solution.

How often the EM algorithm should be run depends on the number of items being clustered. As a rule of thumb, we can consider how often the optimal solution was found; this number is returned by the partitioning algorithms as implemented in this library. If the optimal solution was found many times, it is unlikely that better solutions exist than the one that was found. However, if the optimal solution was found only once, there may well be other solutions with a smaller within-cluster sum of distances. If the number of items is large (more than several hundreds), it may be difficult to find the globally optimal solution.

The EM algorithm terminates when no further reassignments take place. We noticed that for some sets of initial cluster assignments, the EM algorithm fails to converge due to the same clustering solution reappearing periodically after a small number of iteration steps. We therefore check for the occurrence of such periodic solutions during the iteration. After a given number of iteration steps, the current clustering result is saved as a reference. By comparing the clustering result after each subsequent iteration step to the reference state, we can determine if a previously encountered clustering result is found. In such a case, the iteration is halted. If after a given number of iterations the reference state has not yet been encountered, the current clustering solution is saved to be used as the new reference state. Initially, ten iteration steps are executed before resaving the reference state. This number of iteration steps is doubled each time, to ensure that periodic behavior with longer periods can also be detected.

k-means and k-medians

The k-means and k-medians algorithms are implemented as the function kcluster in Bio.Cluster:

>>> from Bio.Cluster import kcluster
>>> clusterid, error, nfound = kcluster(data)

where the following arguments are defined:

  • data (required)
    Array containing the data for the items.
  • nclusters (default: 2)
    The number of clusters k.
  • mask (default: None)
    Array of integers showing which data are missing. If mask[i,j]==0, then data[i,j] is missing. If mask==None, then all data are present.
  • weight (default: None)
    The weights to be used when calculating distances. If weight==None, then equal weights are assumed.
  • transpose (default: 0)
    Determines if rows (transpose is 0) or columns (transpose is 1) are to be clustered.
  • npass (default: 1)
    The number of times the k-means/-medians clustering algorithm is performed, each time with a different (random) initial condition. If initialid is given, the value of npass is ignored and the clustering algorithm is run only once, as it behaves deterministically in that case.
  • method (default: a)
    describes how the center of a cluster is found:
    • method=='a': arithmetic mean (k-means clustering);
    • method=='m': median (k-medians clustering).
    For other values of method, the arithmetic mean is used.
  • dist (default: 'e', Euclidean distance)
    Defines the distance function to be used (see 15.1). Whereas all eight distance measures are accepted by kcluster, from a theoretical viewpoint it is best to use the Euclidean distance for the k-means algorithm, and the city-block distance for k-medians.
  • initialid (default: None)
    Specifies the initial clustering to be used for the EM algorithm. If initialid==None, then a different random initial clustering is used for each of the npass runs of the EM algorithm. If initialid is not None, then it should be equal to a 1D array containing the cluster number (between 0 and nclusters-1) for each item. Each cluster should contain at least one item. With the initial clustering specified, the EM algorithm is deterministic.

This function returns a tuple (clusterid, error, nfound), where clusterid is an integer array containing the number of the cluster to which each row or cluster was assigned, error is the within-cluster sum of distances for the optimal clustering solution, and nfound is the number of times this optimal solution was found.

k-medoids clustering

The kmedoids routine performs k-medoids clustering on a given set of items, using the distance matrix and the number of clusters passed by the user: 

>>> from Bio.Cluster import kmedoids
>>> clusterid, error, nfound = kmedoids(distance)

where the following arguments are defined: , nclusters=2, npass=1, initialid=None)|

  • distance (required)
    The matrix containing the distances between the items; this matrix can be specified in three ways:
    • as a 2D Numerical Python array (in which only the left-lower part of the array will be accessed): 
      distance = array([[0.0, 1.1, 2.3],
                  [1.1, 0.0, 4.5],
                  [2.3, 4.5, 0.0]])
    • as a 1D Numerical Python array containing consecutively the distances in the left-lower part of the distance matrix: 
      distance = array([1.1, 2.3, 4.5])
    • as a list containing the rows of the left-lower part of the distance matrix: 
      distance = [array([]|,
            array([1.1]),
            array([2.3, 4.5])
           ]
    These three expressions correspond to the same distance matrix.
  • nclusters (default: 2)
    The number of clusters k.
  • npass (default: 1)
    The number of times the k-medoids clustering algorithm is performed, each time with a different (random) initial condition. If initialid is given, the value of npass is ignored, as the clustering algorithm behaves deterministically in that case.
  • initialid (default: None)
    Specifies the initial clustering to be used for the EM algorithm. If initialid==None, then a different random initial clustering is used for each of the npass runs of the EM algorithm. If initialid is not None, then it should be equal to a 1D array containing the cluster number (between 0 and nclusters-1) for each item. Each cluster should contain at least one item. With the initial clustering specified, the EM algorithm is deterministic.

This function returns a tuple (clusterid, error, nfound), where clusterid is an array containing the number of the cluster to which each item was assigned, error is the within-cluster sum of distances for the optimal k-medoids clustering solution, and nfound is the number of times the optimal solution was found. Note that the cluster number in clusterid is defined as the item number of the item representing the cluster centroid.

15.4  Hierarchical clustering

Hierarchical clustering methods are inherently different from the k-means clustering method. In hierarchical clustering, the similarity in the expression profile between genes or experimental conditions are represented in the form of a tree structure. This tree structure can be shown graphically by programs such as Treeview and Java Treeview, which has contributed to the popularity of hierarchical clustering in the analysis of gene expression data.

The first step in hierarchical clustering is to calculate the distance matrix, specifying all the distances between the items to be clustered. Next, we create a node by joining the two closest items. Subsequent nodes are created by pairwise joining of items or nodes based on the distance between them, until all items belong to the same node. A tree structure can then be created by retracing which items and nodes were merged. Unlike the EM algorithm, which is used in k-means clustering, the complete process of hierarchical clustering is deterministic.

Several flavors of hierarchical clustering exist, which differ in how the distance between subnodes is defined in terms of their members. In Bio.Cluster, pairwise single, maximum, average, and centroid linkage are available.

  • In pairwise single-linkage clustering, the distance between two nodes is defined as the shortest distance among the pairwise distances between the members of the two nodes.
  • In pairwise maximum-linkage clustering, alternatively known as pairwise complete-linkage clustering, the distance between two nodes is defined as the longest distance among the pairwise distances between the members of the two nodes.
  • In pairwise average-linkage clustering, the distance between two nodes is defined as the average over all pairwise distances between the items of the two nodes.
  • In pairwise centroid-linkage clustering, the distance between two nodes is defined as the distance between their centroids. The centroids are calculated by taking the mean over all the items in a cluster. As the distance from each newly formed node to existing nodes and items need to be calculated at each step, the computing time of pairwise centroid-linkage clustering may be significantly longer than for the other hierarchical clustering methods. Another peculiarity is that (for a distance measure based on the Pearson correlation), the distances do not necessarily increase when going up in the clustering tree, and may even decrease. This is caused by an inconsistency between the centroid calculation and the distance calculation when using the Pearson correlation: Whereas the Pearson correlation effectively normalizes the data for the distance calculation, no such normalization occurs for the centroid calculation.

For pairwise single-, complete-, and average-linkage clustering, the distance between two nodes can be found directly from the distances between the individual items. Therefore, the clustering algorithm does not need access to the original gene expression data, once the distance matrix is known. For pairwise centroid-linkage clustering, however, the centroids of newly formed subnodes can only be calculated from the original data and not from the distance matrix.

The implementation of pairwise single-linkage hierarchical clustering is based on the SLINK algorithm (R. Sibson, 1973), which is much faster and more memory-efficient than a straightforward implementation of pairwise single-linkage clustering. The clustering result produced by this algorithm is identical to the clustering solution found by the conventional single-linkage algorithm. The single-linkage hierarchical clustering algorithm implemented in this library can be used to cluster large gene expression data sets, for which conventional hierarchical clustering algorithms fail due to excessive memory requirements and running time.

Representing a hierarchical clustering solution

The result of hierarchical clustering consists of a tree of nodes, in which each node joins two items or subnodes. Usually, we are not only interested in which items or subnodes are joined at each node, but also in their similarity (or distance) as they are joined. To store one node in the hierarchical clustering tree, we make use of the class Node, which defined in Bio.Cluster. An instance of Node has three attributes:

  • left
  • right
  • distance

Here, left and right are integers referring to the two items or subnodes that are joined at this node, and distance is the distance between them. The items being clustered are numbered from 0 to (number of items − 1), while clusters are numbered from -1 to −(number of items−1). Note that the number of nodes is one less than the number of items.

To create a new Node object, we need to specify left and right; distance is optional.

>>> from Bio.Cluster import Node
>>> Node(2, 3)
(2, 3): 0
>>> Node(2, 3, 0.91)
(2, 3): 0.91

The attributes left, right, and distance of an existing Node object can be modified directly:

>>> node = Node(4, 5)
>>> node.left = 6
>>> node.right = 2
>>> node.distance = 0.73
>>> node
(6, 2): 0.73

An error is raised if left and right are not integers, or if distance cannot be converted to a floating-point value.

The Python class Tree represents a full hierarchical clustering solution. A Tree object can be created from a list of Node objects:

>>> from Bio.Cluster import Node, Tree
>>> nodes = [Node(1, 2, 0.2), Node(0, 3, 0.5), Node(-2, 4, 0.6), Node(-1, -3, 0.9)]
>>> tree = Tree(nodes)
>>> print(tree)
(1, 2): 0.2
(0, 3): 0.5
(-2, 4): 0.6
(-1, -3): 0.9

The Tree initializer checks if the list of nodes is a valid hierarchical clustering result:

>>> nodes = [Node(1, 2, 0.2), Node(0, 2, 0.5)]
>>> Tree(nodes)
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
ValueError: Inconsistent tree

Individual nodes in a Tree object can be accessed using square brackets:

>>> nodes = [Node(1, 2, 0.2), Node(0, -1, 0.5)]
>>> tree = Tree(nodes)
>>> tree[0]
(1, 2): 0.2
>>> tree[1]
(0, -1): 0.5
>>> tree[-1]
(0, -1): 0.5

As a Tree object is read-only, we cannot change individual nodes in a Tree object. However, we can convert the tree to a list of nodes, modify this list, and create a new tree from this list:

>>> tree = Tree([Node(1, 2, 0.1), Node(0, -1, 0.5), Node(-2, 3, 0.9)])
>>> print(tree)
(1, 2): 0.1
(0, -1): 0.5
(-2, 3): 0.9
>>> nodes = tree[:]
>>> nodes[0] = Node(0, 1, 0.2)
>>> nodes[1].left = 2
>>> tree = Tree(nodes)
>>> print(tree)
(0, 1): 0.2
(2, -1): 0.5
(-2, 3): 0.9

This guarantees that any Tree object is always well-formed.

To display a hierarchical clustering solution with visualization programs such as Java Treeview, it is better to scale all node distances such that they are between zero and one. This can be accomplished by calling the scale method on an existing Tree object: 

>>> tree.scale()

This method takes no arguments, and returns None.

After hierarchical clustering, the items can be grouped into k clusters based on the tree structure stored in the Tree object by cutting the tree: 

>>> clusterid = tree.cut(nclusters=1)

where nclusters (defaulting to 1) is the desired number of clusters k. This method ignores the top k−1 linking events in the tree structure, resulting in k separated clusters of items. The number of clusters k should be positive, and less than or equal to the number of items. This method returns an array clusterid containing the number of the cluster to which each item is assigned.

Performing hierarchical clustering

To perform hierarchical clustering, use the treecluster function in Bio.Cluster. 

>>> from Bio.Cluster import treecluster
>>> tree = treecluster(data)

where the following arguments are defined:

  • data
    Array containing the data for the items.
  • mask (default: None)
    Array of integers showing which data are missing. If mask[i,j]==0, then data[i,j] is missing. If mask==None, then all data are present.
  • weight (default: None)
    The weights to be used when calculating distances. If weight==None, then equal weights are assumed.
  • transpose (default: 0)
    Determines if rows (transpose==0) or columns (transpose==1) are to be clustered.
  • method (default: 'm')
    defines the linkage method to be used:
    • method=='s': pairwise single-linkage clustering
    • method=='m': pairwise maximum- (or complete-) linkage clustering
    • method=='c': pairwise centroid-linkage clustering
    • method=='a': pairwise average-linkage clustering
  • dist (default: 'e', Euclidean distance)
    Defines the distance function to be used (see 15.1).

To apply hierarchical clustering on a precalculated distance matrix, specify the distancematrix argument when calling treecluster function instead of the data argument: 

>>> from Bio.Cluster import treecluster
>>> tree = treecluster(distancematrix=distance)

In this case, the following arguments are defined:

  • distancematrix
    The distance matrix, which can be specified in three ways:
    • as a 2D Numerical Python array (in which only the left-lower part of the array will be accessed): 
      distance = array([[0.0, 1.1, 2.3], 
                  [1.1, 0.0, 4.5],
                  [2.3, 4.5, 0.0]])
    • as a 1D Numerical Python array containing consecutively the distances in the left-lower part of the distance matrix: 
      distance = array([1.1, 2.3, 4.5])
    • as a list containing the rows of the left-lower part of the distance matrix: 
      distance = [array([]),
            array([1.1]),
            array([2.3, 4.5])
    These three expressions correspond to the same distance matrix. As treecluster may shuffle the values in the distance matrix as part of the clustering algorithm, be sure to save this array in a different variable before calling treecluster if you need it later.
  • method
    The linkage method to be used:
    • method=='s': pairwise single-linkage clustering
    • method=='m': pairwise maximum- (or complete-) linkage clustering
    • method=='a': pairwise average-linkage clustering
    While pairwise single-, maximum-, and average-linkage clustering can be calculated from the distance matrix alone, pairwise centroid-linkage cannot.

When calling treecluster, either data or distancematrix should be None.

This function returns a Tree object. This object contains (number of items − 1) nodes, where the number of items is the number of rows if rows were clustered, or the number of columns if columns were clustered. Each node describes a pairwise linking event, where the node attributes left and right each contain the number of one item or subnode, and distance the distance between them. Items are numbered from 0 to (number of items − 1), while clusters are numbered -1 to −(number of items−1).

15.5  Self-Organizing Maps

Self-Organizing Maps (SOMs) were invented by Kohonen to describe neural networks (see for instance Kohonen, 1997 [24]). Tamayo (1999) first applied Self-Organizing Maps to gene expression data [30].

SOMs organize items into clusters that are situated in some topology. Usually a rectangular topology is chosen. The clusters generated by SOMs are such that neighboring clusters in the topology are more similar to each other than clusters far from each other in the topology.

The first step to calculate a SOM is to randomly assign a data vector to each cluster in the topology. If rows are being clustered, then the number of elements in each data vector is equal to the number of columns.

An SOM is then generated by taking rows one at a time, and finding which cluster in the topology has the closest data vector. The data vector of that cluster, as well as those of the neighboring clusters, are adjusted using the data vector of the row under consideration. The adjustment is given by

Δ 
x
cell = τ · ⎛
⎜
⎝
x
row − 
x
cell ⎞
⎟
⎠		.

The parameter τ is a parameter that decreases at each iteration step. We have used a simple linear function of the iteration step:

τ = τinit · ⎛
⎜
⎜
⎝		1 − 
i 
n
⎞
⎟
⎟
⎠		,

τinit is the initial value of τ as specified by the user, i is the number of the current iteration step, and n is the total number of iteration steps to be performed. While changes are made rapidly in the beginning of the iteration, at the end of iteration only small changes are made.

All clusters within a radius R are adjusted to the gene under consideration. This radius decreases as the calculation progresses as

R = Rmax · ⎛
⎜
⎜
⎝		1 − 
i 
n
⎞
⎟
⎟
⎠		,

in which the maximum radius is defined as

Rmax = √
Nx2 + Ny2
,

where (Nx, Ny) are the dimensions of the rectangle defining the topology.

The function somcluster implements the complete algorithm to calculate a Self-Organizing Map on a rectangular grid. First it initializes the random number generator. The node data are then initialized using the random number generator. The order in which genes or microarrays are used to modify the SOM is also randomized. The total number of iterations in the SOM algorithm is specified by the user.

To run somcluster, use 

>>> from Bio.Cluster import somcluster
>>> clusterid, celldata = somcluster(data)

where the following arguments are defined:

  • data (required)
    Array containing the data for the items.
  • mask (default: None)
    Array of integers showing which data are missing. If mask[i,j]==0, then data[i,j] is missing. If mask==None, then all data are present.
  • weight (default: None)
    contains the weights to be used when calculating distances. If weight==None, then equal weights are assumed.
  • transpose (default: 0)
    Determines if rows (transpose is 0) or columns (transpose is 1) are to be clustered.
  • nxgrid, nygrid (default: 2, 1)
    The number of cells horizontally and vertically in the rectangular grid on which the Self-Organizing Map is calculated.
  • inittau (default: 0.02)
    The initial value for the parameter τ that is used in the SOM algorithm. The default value for inittau is 0.02, which was used in Michael Eisen’s Cluster/TreeView program.
  • niter (default: 1)
    The number of iterations to be performed.
  • dist (default: 'e', Euclidean distance)
    Defines the distance function to be used (see 15.1).

This function returns the tuple (clusterid, celldata):

  • clusterid:
    An array with two columns, where the number of rows is equal to the number of items that were clustered. Each row contains the x and y coordinates of the cell in the rectangular SOM grid to which the item was assigned.
  • celldata:
    An array with dimensions (nxgrid, nygrid, number of columns) if rows are being clustered, or (nxgrid, nygrid, number of rows) if columns are being clustered. Each element [ix][iy] of this array is a 1D vector containing the gene expression data for the centroid of the cluster in the grid cell with coordinates [ix][iy].

15.6  Principal Component Analysis

Principal Component Analysis (PCA) is a widely used technique for analyzing multivariate data. A practical example of applying Principal Component Analysis to gene expression data is presented by Yeung and Ruzzo (2001) [33].

In essence, PCA is a coordinate transformation in which each row in the data matrix is written as a linear sum over basis vectors called principal components, which are ordered and chosen such that each maximally explains the remaining variance in the data vectors. For example, an n × 3 data matrix can be represented as an ellipsoidal cloud of n points in three dimensional space. The first principal component is the longest axis of the ellipsoid, the second principal component the second longest axis of the ellipsoid, and the third principal component is the shortest axis. Each row in the data matrix can be reconstructed as a suitable linear combination of the principal components. However, in order to reduce the dimensionality of the data, usually only the most important principal components are retained. The remaining variance present in the data is then regarded as unexplained variance.

The principal components can be found by calculating the eigenvectors of the covariance matrix of the data. The corresponding eigenvalues determine how much of the variance present in the data is explained by each principal component.

Before applying principal component analysis, typically the mean is subtracted from each column in the data matrix. In the example above, this effectively centers the ellipsoidal cloud around its centroid in 3D space, with the principal components describing the variation of points in the ellipsoidal cloud with respect to their centroid.

The function pca below first uses the singular value decomposition to calculate the eigenvalues and eigenvectors of the data matrix. The singular value decomposition is implemented as a translation in C of the Algol procedure svd [16], which uses Householder bidiagonalization and a variant of the QR algorithm. The principal components, the coordinates of each data vector along the principal components, and the eigenvalues corresponding to the principal components are then evaluated and returned in decreasing order of the magnitude of the eigenvalue. If data centering is desired, the mean should be subtracted from each column in the data matrix before calling the pca routine.

To apply Principal Component Analysis to a rectangular matrix data, use 

>>> from Bio.Cluster import pca
>>> columnmean, coordinates, components, eigenvalues = pca(data)

This function returns a tuple columnmean, coordinates, components, eigenvalues:

  • columnmean
    Array containing the mean over each column in data.
  • coordinates
    The coordinates of each row in data with respect to the principal components.
  • components
    The principal components.
  • eigenvalues
    The eigenvalues corresponding to each of the principal components.

The original matrix data can be recreated by calculating columnmean + dot(coordinates, components).

15.7  Handling Cluster/TreeView-type files

Cluster/TreeView are GUI-based codes for clustering gene expression data. They were originally written by Michael Eisen while at Stanford University. Bio.Cluster contains functions for reading and writing data files that correspond to the format specified for Cluster/TreeView. In particular, by saving a clustering result in that format, TreeView can be used to visualize the clustering results. We recommend using Alok Saldanha’s http://jtreeview.sourceforge.net/Java TreeView program, which can display hierarchical as well as k-means clustering results.

An object of the class Record contains all information stored in a Cluster/TreeView-type data file. To store the information contained in the data file in a Record object, we first open the file and then read it:

>>> from Bio import Cluster
>>> handle = open("mydatafile.txt")
>>> record = Cluster.read(handle)
>>> handle.close()

This two-step process gives you some flexibility in the source of the data. For example, you can use

>>> import gzip # Python standard library
>>> handle = gzip.open("mydatafile.txt.gz")

to open a gzipped file, or 

>>> import urllib # Python standard library
>>> handle = urllib.urlopen("http://somewhere.org/mydatafile.txt")

to open a file stored on the Internet before calling read.

The read command reads the tab-delimited text file mydatafile.txt containing gene expression data in the format specified for Michael Eisen’s Cluster/TreeView program. For a description of this file format, see the manual to Cluster/TreeView. It is available at Michael Eisen’s lab website and at our website.

A Record object has the following attributes:

  • data
    The data array containing the gene expression data. Genes are stored row-wise, while microarrays are stored column-wise.
  • mask
    This array shows which elements in the data array, if any, are missing. If mask[i,j]==0, then data[i,j] is missing. If no data were found to be missing, mask is set to None.
  • geneid
    This is a list containing a unique description for each gene (i.e., ORF numbers).
  • genename
    This is a list containing a description for each gene (i.e., gene name). If not present in the data file, genename is set to None.
  • gweight
    The weights that are to be used to calculate the distance in expression profile between genes. If not present in the data file, gweight is set to None.
  • gorder
    The preferred order in which genes should be stored in an output file. If not present in the data file, gorder is set to None.
  • expid
    This is a list containing a description of each microarray, e.g. experimental condition.
  • eweight
    The weights that are to be used to calculate the distance in expression profile between microarrays. If not present in the data file, eweight is set to None.
  • eorder
    The preferred order in which microarrays should be stored in an output file. If not present in the data file, eorder is set to None.
  • uniqid
    The string that was used instead of UNIQID in the data file.

After loading a Record object, each of these attributes can be accessed and modified directly. For example, the data can be log-transformed by taking the logarithm of record.data.

Calculating the distance matrix

To calculate the distance matrix between the items stored in the record, use 

>>> matrix = record.distancematrix()

where the following arguments are defined:

  • transpose (default: 0)
    Determines if the distances between the rows of data are to be calculated (transpose==0), or between the columns of data (transpose==1).
  • dist (default: 'e', Euclidean distance)
    Defines the distance function to be used (see 15.1).

This function returns the distance matrix as a list of rows, where the number of columns of each row is equal to the row number (see section 15.1).

Calculating the cluster centroids

To calculate the centroids of clusters of items stored in the record, use 

>>> cdata, cmask = record.clustercentroids()
  • clusterid (default: None)
    Vector of integers showing to which cluster each item belongs. If clusterid is not given, then all items are assumed to belong to the same cluster.
  • method (default: 'a')
    Specifies whether the arithmetic mean (method=='a') or the median (method=='m') is used to calculate the cluster center.
  • transpose (default: 0)
    Determines if the centroids of the rows of data are to be calculated (transpose==0), or the centroids of the columns of data (transpose==1).

This function returns the tuple cdata, cmask; see section 15.2 for a description.

Calculating the distance between clusters

To calculate the distance between clusters of items stored in the record, use 

>>> distance = record.clusterdistance()

where the following arguments are defined:

  • index1 (default: 0)
    A list containing the indices of the items belonging to the first cluster. A cluster containing only one item i can be represented either as a list [i], or as an integer i.
  • index2 (default: 0)
    A list containing the indices of the items belonging to the second cluster. A cluster containing only one item i can be represented either as a list [i], or as an integer i.
  • method (default: 'a')
    Specifies how the distance between clusters is defined:
    • 'a': Distance between the two cluster centroids (arithmetic mean);
    • 'm': Distance between the two cluster centroids (median);
    • 's': Shortest pairwise distance between items in the two clusters;
    • 'x': Longest pairwise distance between items in the two clusters;
    • 'v': Average over the pairwise distances between items in the two clusters.
  • dist (default: 'e', Euclidean distance)
    Defines the distance function to be used (see 15.1).
  • transpose (default: 0)
    If transpose==0, calculate the distance between the rows of data. If transpose==1, calculate the distance between the columns of data.

Performing hierarchical clustering

To perform hierarchical clustering on the items stored in the record, use 

>>> tree = record.treecluster()

where the following arguments are defined:

  • transpose (default: 0)
    Determines if rows (transpose==0) or columns (transpose==1) are to be clustered.
  • method (default: 'm')
    defines the linkage method to be used:
    • method=='s': pairwise single-linkage clustering
    • method=='m': pairwise maximum- (or complete-) linkage clustering
    • method=='c': pairwise centroid-linkage clustering
    • method=='a': pairwise average-linkage clustering
  • dist (default: 'e', Euclidean distance)
    Defines the distance function to be used (see 15.1).
  • transpose
    Determines if genes or microarrays are being clustered. If transpose==0, genes (rows) are being clustered. If transpose==1, microarrays (columns) are clustered.

This function returns a Tree object. This object contains (number of items − 1) nodes, where the number of items is the number of rows if rows were clustered, or the number of columns if columns were clustered. Each node describes a pairwise linking event, where the node attributes left and right each contain the number of one item or subnode, and distance the distance between them. Items are numbered from 0 to (number of items − 1), while clusters are numbered -1 to −(number of items−1).

Performing k-means or k-medians clustering

To perform k-means or k-medians clustering on the items stored in the record, use 

>>> clusterid, error, nfound = record.kcluster()

where the following arguments are defined:

  • nclusters (default: 2)
    The number of clusters k.
  • transpose (default: 0)
    Determines if rows (transpose is 0) or columns (transpose is 1) are to be clustered.
  • npass (default: 1)
    The number of times the k-means/-medians clustering algorithm is performed, each time with a different (random) initial condition. If initialid is given, the value of npass is ignored and the clustering algorithm is run only once, as it behaves deterministically in that case.
  • method (default: a)
    describes how the center of a cluster is found:
    • method=='a': arithmetic mean (k-means clustering);
    • method=='m': median (k-medians clustering).
    For other values of method, the arithmetic mean is used.
  • dist (default: 'e', Euclidean distance)
    Defines the distance function to be used (see 15.1).

This function returns a tuple (clusterid, error, nfound), where clusterid is an integer array containing the number of the cluster to which each row or cluster was assigned, error is the within-cluster sum of distances for the optimal clustering solution, and nfound is the number of times this optimal solution was found.

Calculating a Self-Organizing Map

To calculate a Self-Organizing Map of the items stored in the record, use 

>>> clusterid, celldata = record.somcluster()

where the following arguments are defined:

  • transpose (default: 0)
    Determines if rows (transpose is 0) or columns (transpose is 1) are to be clustered.
  • nxgrid, nygrid (default: 2, 1)
    The number of cells horizontally and vertically in the rectangular grid on which the Self-Organizing Map is calculated.
  • inittau (default: 0.02)
    The initial value for the parameter τ that is used in the SOM algorithm. The default value for inittau is 0.02, which was used in Michael Eisen’s Cluster/TreeView program.
  • niter (default: 1)
    The number of iterations to be performed.
  • dist (default: 'e', Euclidean distance)
    Defines the distance function to be used (see 15.1).

This function returns the tuple (clusterid, celldata):

  • clusterid:
    An array with two columns, where the number of rows is equal to the number of items that were clustered. Each row contains the x and y coordinates of the cell in the rectangular SOM grid to which the item was assigned.
  • celldata:
    An array with dimensions (nxgrid, nygrid, number of columns) if rows are being clustered, or (nxgrid, nygrid, number of rows) if columns are being clustered. Each element [ix][iy] of this array is a 1D vector containing the gene expression data for the centroid of the cluster in the grid cell with coordinates [ix][iy].

Saving the clustering result

To save the clustering result, use 

>>> record.save(jobname, geneclusters, expclusters)

where the following arguments are defined:

  • jobname
    The string jobname is used as the base name for names of the files that are to be saved.
  • geneclusters
    This argument describes the gene (row-wise) clustering result. In case of k-means clustering, this is a 1D array containing the number of the cluster each gene belongs to. It can be calculated using kcluster. In case of hierarchical clustering, geneclusters is a Tree object.
  • expclusters
    This argument describes the (column-wise) clustering result for the experimental conditions. In case of k-means clustering, this is a 1D array containing the number of the cluster each experimental condition belongs to. It can be calculated using kcluster. In case of hierarchical clustering, expclusters is a Tree object.

This method writes the text file jobname.cdt, jobname.gtr, jobname.atr, jobname*.kgg, and/or jobname*.kag for subsequent reading by the Java TreeView program. If geneclusters and expclusters are both None, this method only writes the text file jobname.cdt; this file can subsequently be read into a new Record object.

15.8  Example calculation

This is an example of a hierarchical clustering calculation, using single linkage clustering for genes and maximum linkage clustering for experimental conditions. As the Euclidean distance is being used for gene clustering, it is necessary to scale the node distances genetree such that they are all between zero and one. This is needed for the Java TreeView code to display the tree diagram correctly. To cluster the experimental conditions, the uncentered correlation is being used. No scaling is needed in this case, as the distances in exptree are already between zero and two. The example data cyano.txt can be found in the data subdirectory.

>>> from Bio import Cluster
>>> handle = open("cyano.txt")
>>> record = Cluster.read(handle)
>>> handle.close()
>>> genetree = record.treecluster(method='s')
>>> genetree.scale()
>>> exptree = record.treecluster(dist='u', transpose=1)
>>> record.save("cyano_result", genetree, exptree)

This will create the files cyano_result.cdt, cyano_result.gtr, and cyano_result.atr.

Similarly, we can save a k-means clustering solution:

>>> from Bio import Cluster
>>> handle = open("cyano.txt")
>>> record = Cluster.read(handle)
>>> handle.close()
>>> (geneclusters, error, ifound) = record.kcluster(nclusters=5, npass=1000)
>>> (expclusters, error, ifound) = record.kcluster(nclusters=2, npass=100, transpose=1)
>>> record.save("cyano_result", geneclusters, expclusters)

This will create the files cyano_result_K_G2_A2.cdt, cyano_result_K_G2.kgg, and cyano_result_K_A2.kag.

15.9  Auxiliary functions

median(data) returns the median of the 1D array data.

mean(data) returns the mean of the 1D array data.

version() returns the version number of the underlying C Clustering Library as a string.

Chapter 16  Supervised learning methods

Note the supervised learning methods described in this chapter all require Numerical Python (numpy) to be installed.

16.1  The Logistic Regression Model

16.1.1  Background and Purpose

Logistic regression is a supervised learning approach that attempts to distinguish K classes from each other using a weighted sum of some predictor variables xi. The logistic regression model is used to calculate the weights βi of the predictor variables. In Biopython, the logistic regression model is currently implemented for two classes only (K = 2); the number of predictor variables has no predefined limit.

As an example, let’s try to predict the operon structure in bacteria. An operon is a set of adjacent genes on the same strand of DNA that are transcribed into a single mRNA molecule. Translation of the single mRNA molecule then yields the individual proteins. For Bacillus subtilis, whose data we will be using, the average number of genes in an operon is about 2.4.

As a first step in understanding gene regulation in bacteria, we need to know the operon structure. For about 10% of the genes in Bacillus subtilis, the operon structure is known from experiments. A supervised learning method can be used to predict the operon structure for the remaining 90% of the genes.

For such a supervised learning approach, we need to choose some predictor variables xi that can be measured easily and are somehow related to the operon structure. One predictor variable might be the distance in base pairs between genes. Adjacent genes belonging to the same operon tend to be separated by a relatively short distance, whereas adjacent genes in different operons tend to have a larger space between them to allow for promoter and terminator sequences. Another predictor variable is based on gene expression measurements. By definition, genes belonging to the same operon have equal gene expression profiles, while genes in different operons are expected to have different expression profiles. In practice, the measured expression profiles of genes in the same operon are not quite identical due to the presence of measurement errors. To assess the similarity in the gene expression profiles, we assume that the measurement errors follow a normal distribution and calculate the corresponding log-likelihood score.

We now have two predictor variables that we can use to predict if two adjacent genes on the same strand of DNA belong to the same operon:

  • x1: the number of base pairs between them;
  • x2: their similarity in expression profile.

In a logistic regression model, we use a weighted sum of these two predictors to calculate a joint score S:

S = β0 + β1 x1 + β2 x2.     (16.1)

The logistic regression model gives us appropriate values for the parameters β0, β1, β2 using two sets of example genes:

  • OP: Adjacent genes, on the same strand of DNA, known to belong to the same operon;
  • NOP: Adjacent genes, on the same strand of DNA, known to belong to different operons.

In the logistic regression model, the probability of belonging to a class depends on the score via the logistic function. For the two classes OP and NOP, we can write this as

     
Pr(OP|x1, x2) =
 
exp(β0 + β1 x1 + β2 x2)
1+exp(β0 + β1 x1 + β2 x2)
   
    (16.2)
Pr(NOP|x1, x2) =
 
1
1+exp(β0 + β1 x1 + β2 x2)
   
    (16.3)

Using a set of gene pairs for which it is known whether they belong to the same operon (class OP) or to different operons (class NOP), we can calculate the weights β0, β1, β2 by maximizing the log-likelihood corresponding to the probability functions (16.2) and (16.3).

16.1.2  Training the logistic regression model

Table 16.1: Adjacent gene pairs known to belong to the same operon (class OP) or to different operons (class NOP). Intergene distances are negative if the two genes overlap.
Gene pairIntergene distance (x1)Gene expression score (x2)Class
cotJA — cotJB-53-200.78OP
yesK — yesL117-267.14OP
lplA — lplB57-163.47OP
lplB — lplC16-190.30OP
lplC — lplD11-220.94OP
lplD — yetF85-193.94OP
yfmT — yfmS16-182.71OP
yfmF — yfmE15-180.41OP
citS — citT-26-181.73OP
citM — yflN58-259.87OP
yfiI — yfiJ126-414.53NOP
lipB — yfiQ191-249.57NOP
yfiU — yfiV113-265.28NOP
yfhH — yfhI145-312.99NOP
cotY — cotX154-213.83NOP
yjoB — rapA147-380.85NOP
ptsI — splA93-291.13NOP

Table 16.1 lists some of the Bacillus subtilis gene pairs for which the operon structure is known. Let’s calculate the logistic regression model from these data:

>>> from Bio import LogisticRegression
>>> xs = [[-53, -200.78],
          [117, -267.14],
          [57, -163.47],
          [16, -190.30],
          [11, -220.94],
          [85, -193.94],
          [16, -182.71],
          [15, -180.41],
          [-26, -181.73],
          [58, -259.87],
          [126, -414.53],
          [191, -249.57],
          [113, -265.28],
          [145, -312.99],
          [154, -213.83],
          [147, -380.85],
          [93, -291.13]]
>>> ys = [1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          0,
          0,
          0,
          0,
          0,
          0,
          0]
>>> model = LogisticRegression.train(xs, ys)

Here, xs and ys are the training data: xs contains the predictor variables for each gene pair, and ys specifies if the gene pair belongs to the same operon (1, class OP) or different operons (0, class NOP). The resulting logistic regression model is stored in model, which contains the weights β0, β1, and β2:

>>> model.beta
[8.9830290157144681, -0.035968960444850887, 0.02181395662983519]

Note that β1 is negative, as gene pairs with a shorter intergene distance have a higher probability of belonging to the same operon (class OP). On the other hand, β2 is positive, as gene pairs belonging to the same operon typically have a higher similarity score of their gene expression profiles. The parameter β0 is positive due to the higher prevalence of operon gene pairs than non-operon gene pairs in the training data.

The function train has two optional arguments: update_fn and typecode. The update_fn can be used to specify a callback function, taking as arguments the iteration number and the log-likelihood. With the callback function, we can for example track the progress of the model calculation (which uses a Newton-Raphson iteration to maximize the log-likelihood function of the logistic regression model):

>>> def show_progress(iteration, loglikelihood):
        print("Iteration:", iteration, "Log-likelihood function:", loglikelihood)
>>>
>>> model = LogisticRegression.train(xs, ys, update_fn=show_progress)
Iteration: 0 Log-likelihood function: -11.7835020695
Iteration: 1 Log-likelihood function: -7.15886767672
Iteration: 2 Log-likelihood function: -5.76877209868
Iteration: 3 Log-likelihood function: -5.11362294338
Iteration: 4 Log-likelihood function: -4.74870642433
Iteration: 5 Log-likelihood function: -4.50026077146
Iteration: 6 Log-likelihood function: -4.31127773737
Iteration: 7 Log-likelihood function: -4.16015043396
Iteration: 8 Log-likelihood function: -4.03561719785
Iteration: 9 Log-likelihood function: -3.93073282192
Iteration: 10 Log-likelihood function: -3.84087660929
Iteration: 11 Log-likelihood function: -3.76282560605
Iteration: 12 Log-likelihood function: -3.69425027154
Iteration: 13 Log-likelihood function: -3.6334178602
Iteration: 14 Log-likelihood function: -3.57900855837
Iteration: 15 Log-likelihood function: -3.52999671386
Iteration: 16 Log-likelihood function: -3.48557145163
Iteration: 17 Log-likelihood function: -3.44508206139
Iteration: 18 Log-likelihood function: -3.40799948447
Iteration: 19 Log-likelihood function: -3.3738885624
Iteration: 20 Log-likelihood function: -3.3423876581
Iteration: 21 Log-likelihood function: -3.31319343769
Iteration: 22 Log-likelihood function: -3.2860493346
Iteration: 23 Log-likelihood function: -3.2607366863
Iteration: 24 Log-likelihood function: -3.23706784091
Iteration: 25 Log-likelihood function: -3.21488073614
Iteration: 26 Log-likelihood function: -3.19403459259
Iteration: 27 Log-likelihood function: -3.17440646052
Iteration: 28 Log-likelihood function: -3.15588842703
Iteration: 29 Log-likelihood function: -3.13838533947
Iteration: 30 Log-likelihood function: -3.12181293595
Iteration: 31 Log-likelihood function: -3.10609629966
Iteration: 32 Log-likelihood function: -3.09116857282
Iteration: 33 Log-likelihood function: -3.07696988017
Iteration: 34 Log-likelihood function: -3.06344642288
Iteration: 35 Log-likelihood function: -3.05054971191
Iteration: 36 Log-likelihood function: -3.03823591619
Iteration: 37 Log-likelihood function: -3.02646530573
Iteration: 38 Log-likelihood function: -3.01520177394
Iteration: 39 Log-likelihood function: -3.00441242601
Iteration: 40 Log-likelihood function: -2.99406722296
Iteration: 41 Log-likelihood function: -2.98413867259

The iteration stops once the increase in the log-likelihood function is less than 0.01. If no convergence is reached after 500 iterations, the train function returns with an AssertionError.

The optional keyword typecode can almost always be ignored. This keyword allows the user to choose the type of Numeric matrix to use. In particular, to avoid memory problems for very large problems, it may be necessary to use single-precision floats (Float8, Float16, etc.) rather than double, which is used by default.

16.1.3  Using the logistic regression model for classification

Classification is performed by calling the classify function. Given a logistic regression model and the values for x1 and x2 (e.g. for a gene pair of unknown operon structure), the classify function returns 1 or 0, corresponding to class OP and class NOP, respectively. For example, let’s consider the gene pairs yxcE, yxcD and yxiB, yxiA:

Table 16.2: Adjacent gene pairs of unknown operon status.
Gene pairIntergene distance x1Gene expression score x2
yxcE — yxcD6-173.143442352
yxiB — yxiA309-271.005880394

The logistic regression model classifies yxcE, yxcD as belonging to the same operon (class OP), while yxiB, yxiA are predicted to belong to different operons: 

>>> print("yxcE, yxcD:", LogisticRegression.classify(model, [6, -173.143442352]))
yxcE, yxcD: 1
>>> print("yxiB, yxiA:", LogisticRegression.classify(model, [309, -271.005880394]))
yxiB, yxiA: 0

(which, by the way, agrees with the biological literature).

To find out how confident we can be in these predictions, we can call the calculate function to obtain the probabilities (equations (16.2) and 16.3) for class OP and NOP. For yxcE, yxcD we find 

>>> q, p = LogisticRegression.calculate(model, [6, -173.143442352])
>>> print("class OP: probability =", p, "class NOP: probability =", q)
class OP: probability = 0.993242163503 class NOP: probability = 0.00675783649744

and for yxiB, yxiA 

>>> q, p = LogisticRegression.calculate(model, [309, -271.005880394])
>>> print("class OP: probability =", p, "class NOP: probability =", q)
class OP: probability = 0.000321211251817 class NOP: probability = 0.999678788748

To get some idea of the prediction accuracy of the logistic regression model, we can apply it to the training data: 

>>> for i in range(len(ys)):
        print("True:", ys[i], "Predicted:", LogisticRegression.classify(model, xs[i]))
True: 1 Predicted: 1
True: 1 Predicted: 0
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0

showing that the prediction is correct for all but one of the gene pairs. A more reliable estimate of the prediction accuracy can be found from a leave-one-out analysis, in which the model is recalculated from the training data after removing the gene to be predicted: 

>>> for i in range(len(ys)):
        model = LogisticRegression.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:])
        print("True:", ys[i], "Predicted:", LogisticRegression.classify(model, xs[i]))
True: 1 Predicted: 1
True: 1 Predicted: 0
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 1
True: 0 Predicted: 0
True: 0 Predicted: 0

The leave-one-out analysis shows that the prediction of the logistic regression model is incorrect for only two of the gene pairs, which corresponds to a prediction accuracy of 88%.

16.1.4  Logistic Regression, Linear Discriminant Analysis, and Support Vector Machines

The logistic regression model is similar to linear discriminant analysis. In linear discriminant analysis, the class probabilities also follow equations (16.2) and (16.3). However, instead of estimating the coefficients β directly, we first fit a normal distribution to the predictor variables x. The coefficients β are then calculated from the means and covariances of the normal distribution. If the distribution of x is indeed normal, then we expect linear discriminant analysis to perform better than the logistic regression model. The logistic regression model, on the other hand, is more robust to deviations from normality.

Another similar approach is a support vector machine with a linear kernel. Such an SVM also uses a linear combination of the predictors, but estimates the coefficients β from the predictor variables x near the boundary region between the classes. If the logistic regression model (equations (16.2) and (16.3)) is a good description for x away from the boundary region, we expect the logistic regression model to perform better than an SVM with a linear kernel, as it relies on more data. If not, an SVM with a linear kernel may perform better.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman: The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Springer Series in Statistics, 2001. Chapter 4.4.

16.2  k-Nearest Neighbors

16.2.1  Background and purpose

The k-nearest neighbors method is a supervised learning approach that does not need to fit a model to the data. Instead, data points are classified based on the categories of the k nearest neighbors in the training data set.

In Biopython, the k-nearest neighbors method is available in Bio.kNN. To illustrate the use of the k-nearest neighbor method in Biopython, we will use the same operon data set as in section 16.1.

16.2.2  Initializing a k-nearest neighbors model

Using the data in Table 16.1, we create and initialize a k-nearest neighbors model as follows:

>>> from Bio import kNN
>>> k = 3
>>> model = kNN.train(xs, ys, k)

where xs and ys are the same as in Section 16.1.2. Here, k is the number of neighbors k that will be considered for the classification. For classification into two classes, choosing an odd number for k lets you avoid tied votes. The function name train is a bit of a misnomer, since no model training is done: this function simply stores xs, ys, and k in model.

16.2.3  Using a k-nearest neighbors model for classification

To classify new data using the k-nearest neighbors model, we use the classify function. This function takes a data point (x1,x2) and finds the k-nearest neighbors in the training data set xs. The data point (x1, x2) is then classified based on which category (ys) occurs most among the k neighbors.

For the example of the gene pairs yxcE, yxcD and yxiB, yxiA, we find: 

>>> x = [6, -173.143442352]
>>> print("yxcE, yxcD:", kNN.classify(model, x))
yxcE, yxcD: 1
>>> x = [309, -271.005880394]
>>> print("yxiB, yxiA:", kNN.classify(model, x))
yxiB, yxiA: 0

In agreement with the logistic regression model, yxcE, yxcD are classified as belonging to the same operon (class OP), while yxiB, yxiA are predicted to belong to different operons.

The classify function lets us specify both a distance function and a weight function as optional arguments. The distance function affects which k neighbors are chosen as the nearest neighbors, as these are defined as the neighbors with the smallest distance to the query point (x, y). By default, the Euclidean distance is used. Instead, we could for example use the city-block (Manhattan) distance:

>>> def cityblock(x1, x2):
...    assert len(x1)==2
...    assert len(x2)==2
...    distance = abs(x1[0]-x2[0]) + abs(x1[1]-x2[1])
...    return distance
... 
>>> x = [6, -173.143442352]
>>> print("yxcE, yxcD:", kNN.classify(model, x, distance_fn = cityblock))
yxcE, yxcD: 1

The weight function can be used for weighted voting. For example, we may want to give closer neighbors a higher weight than neighbors that are further away:

>>> def weight(x1, x2):
...    assert len(x1)==2
...    assert len(x2)==2
...    return exp(-abs(x1[0]-x2[0]) - abs(x1[1]-x2[1]))
... 
>>> x = [6, -173.143442352]
>>> print("yxcE, yxcD:", kNN.classify(model, x, weight_fn = weight))
yxcE, yxcD: 1

By default, all neighbors are given an equal weight.

To find out how confident we can be in these predictions, we can call the calculate function, which will calculate the total weight assigned to the classes OP and NOP. For the default weighting scheme, this reduces to the number of neighbors in each category. For yxcE, yxcD, we find 

>>> x = [6, -173.143442352]
>>> weight = kNN.calculate(model, x)
>>> print("class OP: weight =", weight[0], "class NOP: weight =", weight[1])
class OP: weight = 0.0 class NOP: weight = 3.0

which means that all three neighbors of x1, x2 are in the NOP class. As another example, for yesK, yesL we find

>>> x = [117, -267.14]
>>> weight = kNN.calculate(model, x)
>>> print("class OP: weight =", weight[0], "class NOP: weight =", weight[1])
class OP: weight = 2.0 class NOP: weight = 1.0

which means that two neighbors are operon pairs and one neighbor is a non-operon pair.

To get some idea of the prediction accuracy of the k-nearest neighbors approach, we can apply it to the training data: 

>>> for i in range(len(ys)):
        print("True:", ys[i], "Predicted:", kNN.classify(model, xs[i]))
True: 1 Predicted: 1
True: 1 Predicted: 0
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0

showing that the prediction is correct for all but two of the gene pairs. A more reliable estimate of the prediction accuracy can be found from a leave-one-out analysis, in which the model is recalculated from the training data after removing the gene to be predicted: 

>>> for i in range(len(ys)):
        model = kNN.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:])
        print("True:", ys[i], "Predicted:", kNN.classify(model, xs[i]))
True: 1 Predicted: 1
True: 1 Predicted: 0
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 1
True: 1 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 1
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 0
True: 0 Predicted: 1

The leave-one-out analysis shows that k-nearest neighbors model is correct for 13 out of 17 gene pairs, which corresponds to a prediction accuracy of 76%.

16.3  Naïve Bayes

This section will describe the Bio.NaiveBayes module.

16.4  Maximum Entropy

This section will describe the Bio.MaximumEntropy module.

16.5  Markov Models

This section will describe the Bio.MarkovModel and/or Bio.HMM.MarkovModel modules.

Chapter 17  Graphics including GenomeDiagram

The Bio.Graphics module depends on the third party Python library ReportLab. Although focused on producing PDF files, ReportLab can also create encapsulated postscript (EPS) and (SVG) files. In addition to these vector based images, provided certain further dependencies such as the Python Imaging Library (PIL) are installed, ReportLab can also output bitmap images (including JPEG, PNG, GIF, BMP and PICT formats).

17.1  GenomeDiagram

17.1.1  Introduction

The Bio.Graphics.GenomeDiagram module was added to Biopython 1.50, having previously been available as a separate Python module dependent on Biopython. GenomeDiagram is described in the Bioinformatics journal publication by Pritchard et al. (2006) [2], which includes some examples images. There is a PDF copy of the old manual here, http://biopython.org/DIST/docs/GenomeDiagram/userguide.pdf which has some more examples.

As the name might suggest, GenomeDiagram was designed for drawing whole genomes, in particular prokaryotic genomes, either as linear diagrams (optionally broken up into fragments to fit better) or as circular wheel diagrams. Have a look at Figure 2 in Toth et al. (2006) [3] for a good example. It proved also well suited to drawing quite detailed figures for smaller genomes such as phage, plasmids or mitochrondia, for example see Figures 1 and 2 in Van der Auwera et al. (2009) [4] (shown with additional manual editing).

This module is easiest to use if you have your genome loaded as a SeqRecord object containing lots of SeqFeature objects - for example as loaded from a GenBank file (see Chapters 4 and 5).

17.1.2  Diagrams, tracks, feature-sets and features

GenomeDiagram uses a nested set of objects. At the top level, you have a diagram object representing a sequence (or sequence region) along the horizontal axis (or circle). A diagram can contain one or more tracks, shown stacked vertically (or radially on circular diagrams). These will typically all have the same length and represent the same sequence region. You might use one track to show the gene locations, another to show regulatory regions, and a third track to show the GC percentage.

The most commonly used type of track will contain features, bundled together in feature-sets. You might choose to use one feature-set for all your CDS features, and another for tRNA features. This isn’t required - they can all go in the same feature-set, but it makes it easier to update the properties of just selected features (e.g. make all the tRNA features red).

There are two main ways to build up a complete diagram. Firstly, the top down approach where you create a diagram object, and then using its methods add track(s), and use the track methods to add feature-set(s), and use their methods to add the features. Secondly, you can create the individual objects separately (in whatever order suits your code), and then combine them.

17.1.3  A top down example

We’re going to draw a whole genome from a SeqRecord object read in from a GenBank file (see Chapter 5). This example uses the pPCP1 plasmid from Yersinia pestis biovar Microtus, the file is included with the Biopython unit tests under the GenBank folder, or online NC_005816.gb from our website.

from reportlab.lib import colors
from reportlab.lib.units import cm
from Bio.Graphics import GenomeDiagram
from Bio import SeqIO
record = SeqIO.read("NC_005816.gb", "genbank")

We’re using a top down approach, so after loading in our sequence we next create an empty diagram, then add an (empty) track, and to that add an (empty) feature set:

gd_diagram = GenomeDiagram.Diagram("Yersinia pestis biovar Microtus plasmid pPCP1")
gd_track_for_features = gd_diagram.new_track(1, name="Annotated Features")
gd_feature_set = gd_track_for_features.new_set()

Now the fun part - we take each gene SeqFeature object in our SeqRecord, and use it to generate a feature on the diagram. We’re going to color them blue, alternating between a dark blue and a light blue. 

for feature in record.features:
    if feature.type != "gene":
        #Exclude this feature
        continue
    if len(gd_feature_set) % 2 == 0:
        color = colors.blue
    else:
        color = colors.lightblue
    gd_feature_set.add_feature(feature, color=color, label=True)

Now we come to actually making the output file. This happens in two steps, first we call the draw method, which creates all the shapes using ReportLab objects. Then we call the write method which renders these to the requested file format. Note you can output in multiple file formats:

gd_diagram.draw(format="linear", orientation="landscape", pagesize='A4',
                fragments=4, start=0, end=len(record))
gd_diagram.write("plasmid_linear.pdf", "PDF")
gd_diagram.write("plasmid_linear.eps", "EPS")
gd_diagram.write("plasmid_linear.svg", "SVG")

Also, provided you have the dependencies installed, you can also do bitmaps, for example:

gd_diagram.write("plasmid_linear.png", "PNG")

Notice that the fragments argument which we set to four controls how many pieces the genome gets broken up into.

If you want to do a circular figure, then try this:

gd_diagram.draw(format="circular", circular=True, pagesize=(20*cm,20*cm),
                start=0, end=len(record), circle_core=0.7)
gd_diagram.write("plasmid_circular.pdf", "PDF")

These figures are not very exciting, but we’ve only just got started.

17.1.4  A bottom up example

Now let’s produce exactly the same figures, but using the bottom up approach. This means we create the different objects directly (and this can be done in almost any order) and then combine them.

from reportlab.lib import colors
from reportlab.lib.units import cm
from Bio.Graphics import GenomeDiagram
from Bio import SeqIO
record = SeqIO.read("NC_005816.gb", "genbank")

#Create the feature set and its feature objects,
gd_feature_set = GenomeDiagram.FeatureSet()
for feature in record.features:
    if feature.type != "gene":
        #Exclude this feature
        continue
    if len(gd_feature_set) % 2 == 0:
        color = colors.blue
    else:
        color = colors.lightblue
    gd_feature_set.add_feature(feature, color=color, label=True)
#(this for loop is the same as in the previous example)

#Create a track, and a diagram
gd_track_for_features = GenomeDiagram.Track(name="Annotated Features")
gd_diagram = GenomeDiagram.Diagram("Yersinia pestis biovar Microtus plasmid pPCP1")

#Now have to glue the bits together...
gd_track_for_features.add_set(gd_feature_set)
gd_diagram.add_track(gd_track_for_features, 1)

You can now call the draw and write methods as before to produce a linear or circular diagram, using the code at the end of the top-down example above. The figures should be identical.

17.1.5  Features without a SeqFeature

In the above example we used a SeqRecord’s SeqFeature objects to build our diagram (see also Section 4.3). Sometimes you won’t have SeqFeature objects, but just the coordinates for a feature you want to draw. You have to create minimal SeqFeature object, but this is easy:

from Bio.SeqFeature import SeqFeature, FeatureLocation
my_seq_feature = SeqFeature(FeatureLocation(50,100),strand=+1)

For strand, use +1 for the forward strand, -1 for the reverse strand, and None for both. Here is a short self contained example:

from Bio.SeqFeature import SeqFeature, FeatureLocation
from Bio.Graphics import GenomeDiagram
from reportlab.lib.units import cm

gdd = GenomeDiagram.Diagram('Test Diagram')
gdt_features = gdd.new_track(1, greytrack=False)
gds_features = gdt_features.new_set()

#Add three features to show the strand options,
feature = SeqFeature(FeatureLocation(25, 125), strand=+1)
gds_features.add_feature(feature, name="Forward", label=True)
feature = SeqFeature(FeatureLocation(150, 250), strand=None)
gds_features.add_feature(feature, name="Strandless", label=True)
feature = SeqFeature(FeatureLocation(275, 375), strand=-1)
gds_features.add_feature(feature, name="Reverse", label=True)

gdd.draw(format='linear', pagesize=(15*cm,4*cm), fragments=1,
         start=0, end=400)
gdd.write("GD_labels_default.pdf", "pdf")

The top part of the image in the next subsection shows the output (in the default feature color, pale green).

Notice that we have used the name argument here to specify the caption text for these features. This is discussed in more detail next.

17.1.6  Feature captions

Recall we used the following (where feature was a SeqFeature object) to add a feature to the diagram:

gd_feature_set.add_feature(feature, color=color, label=True)

In the example above the SeqFeature annotation was used to pick a sensible caption for the features. By default the following possible entries under the SeqFeature object’s qualifiers dictionary are used: gene, label, name, locus_tag, and product. More simply, you can specify a name directly:

gd_feature_set.add_feature(feature, color=color, label=True, name="My Gene")

In addition to the caption text for each feature’s label, you can also choose the font, position (this defaults to the start of the sigil, you can also choose the middle or at the end) and orientation (for linear diagrams only, where this defaults to rotated by 45 degrees):

#Large font, parallel with the track
gd_feature_set.add_feature(feature, label=True, color="green",
                           label_size=25, label_angle=0)

#Very small font, perpendicular to the track (towards it)
gd_feature_set.add_feature(feature, label=True, color="purple",
                           label_position="end",
                           label_size=4, label_angle=90)

#Small font, perpendicular to the track (away from it)
gd_feature_set.add_feature(feature, label=True, color="blue",
                           label_position="middle",
                           label_size=6, label_angle=-90)

Combining each of these three fragments with the complete example in the previous section should give something like this:

We’ve not shown it here, but you can also set label_color to control the label’s color (used in Section 17.1.9).

You’ll notice the default font is quite small - this makes sense because you will usually be drawing many (small) features on a page, not just a few large ones as shown here.

17.1.7  Feature sigils

The examples above have all just used the default sigil for the feature, a plain box, which was all that was available in the last publicly released standalone version of GenomeDiagram. Arrow sigils were included when GenomeDiagram was added to Biopython 1.50:

#Default uses a BOX sigil
gd_feature_set.add_feature(feature)

#You can make this explicit:
gd_feature_set.add_feature(feature, sigil="BOX")

#Or opt for an arrow:
gd_feature_set.add_feature(feature, sigil="ARROW")

Biopython 1.61 added three more sigils,

#Box with corners cut off (making it an octagon)
gd_feature_set.add_feature(feature, sigil="OCTO")

#Box with jagged edges (useful for showing breaks in contains)
gd_feature_set.add_feature(feature, sigil="JAGGY")

#Arrow which spans the axis with strand used only for direction
gd_feature_set.add_feature(feature, sigil="BIGARROW")

These are shown below. Most sigils fit into a bounding box (as given by the default BOX sigil), either above or below the axis for the forward or reverse strand, or straddling it (double the height) for strand-less features. The BIGARROW sigil is different, always straddling the axis with the direction taken from the feature’s stand.

17.1.8  Arrow sigils

We introduced the arrow sigils in the previous section. There are two additional options to adjust the shapes of the arrows, firstly the thickness of the arrow shaft, given as a proportion of the height of the bounding box:

#Full height shafts, giving pointed boxes:
gd_feature_set.add_feature(feature, sigil="ARROW", color="brown",
                           arrowshaft_height=1.0)
#Or, thin shafts:                      
gd_feature_set.add_feature(feature, sigil="ARROW", color="teal",
                           arrowshaft_height=0.2)
#Or, very thin shafts:
gd_feature_set.add_feature(feature, sigil="ARROW", color="darkgreen",
                           arrowshaft_height=0.1)

The results are shown below:

Secondly, the length of the arrow head - given as a proportion of the height of the bounding box (defaulting to 0.5, or 50%):

#Short arrow heads:
gd_feature_set.add_feature(feature, sigil="ARROW", color="blue",
                           arrowhead_length=0.25)
#Or, longer arrow heads:
gd_feature_set.add_feature(feature, sigil="ARROW", color="orange",
                           arrowhead_length=1)
#Or, very very long arrow heads (i.e. all head, no shaft, so triangles):
gd_feature_set.add_feature(feature, sigil="ARROW", color="red",
                           arrowhead_length=10000)

The results are shown below:

Biopython 1.61 adds a new BIGARROW sigil which always stradles the axis, pointing left for the reverse strand or right otherwise:

#A large arrow straddling the axis:
gd_feature_set.add_feature(feature, sigil="BIGARROW")

All the shaft and arrow head options shown above for the ARROW sigil can be used for the BIGARROW sigil too.

17.1.9  A nice example

Now let’s return to the pPCP1 plasmid from Yersinia pestis biovar Microtus, and the top down approach used in Section 17.1.3, but take advantage of the sigil options we’ve now discussed. This time we’ll use arrows for the genes, and overlay them with strand-less features (as plain boxes) showing the position of some restriction digest sites.

from reportlab.lib import colors
from reportlab.lib.units import cm
from Bio.Graphics import GenomeDiagram
from Bio import SeqIO
from Bio.SeqFeature import SeqFeature, FeatureLocation

record = SeqIO.read("NC_005816.gb", "genbank")

gd_diagram = GenomeDiagram.Diagram(record.id)
gd_track_for_features = gd_diagram.new_track(1, name="Annotated Features")
gd_feature_set = gd_track_for_features.new_set()

for feature in record.features:
    if feature.type != "gene":
        #Exclude this feature
        continue
    if len(gd_feature_set) % 2 == 0:
        color = colors.blue
    else:
        color = colors.lightblue
    gd_feature_set.add_feature(feature, sigil="ARROW",
                               color=color, label=True,
                               label_size = 14, label_angle=0)

#I want to include some strandless features, so for an example
#will use EcoRI recognition sites etc.
for site, name, color in [("GAATTC","EcoRI",colors.green),
                          ("CCCGGG","SmaI",colors.orange),
                          ("AAGCTT","HindIII",colors.red),
                          ("GGATCC","BamHI",colors.purple)]:
    index = 0
    while True:
        index  = record.seq.find(site, start=index)
        if index == -1 : break
        feature = SeqFeature(FeatureLocation(index, index+len(site)))
        gd_feature_set.add_feature(feature, color=color, name=name,
                                   label=True, label_size = 10,
                                   label_color=color)
        index += len(site)

gd_diagram.draw(format="linear", pagesize='A4', fragments=4,
                start=0, end=len(record))
gd_diagram.write("plasmid_linear_nice.pdf", "PDF")
gd_diagram.write("plasmid_linear_nice.eps", "EPS")
gd_diagram.write("plasmid_linear_nice.svg", "SVG")

gd_diagram.draw(format="circular", circular=True, pagesize=(20*cm,20*cm),
                start=0, end=len(record), circle_core = 0.5)
gd_diagram.write("plasmid_circular_nice.pdf", "PDF")
gd_diagram.write("plasmid_circular_nice.eps", "EPS")
gd_diagram.write("plasmid_circular_nice.svg", "SVG")

And the output:

17.1.10  Multiple tracks

All the examples so far have used a single track, but you can have more than one track – for example show the genes on one, and repeat regions on another. In this example we’re going to show three phage genomes side by side to scale, inspired by Figure 6 in Proux et al. (2002) [5]. We’ll need the GenBank files for the following three phage:

  • NC_002703 – Lactococcus phage Tuc2009, complete genome (38347 bp)
  • AF323668 – Bacteriophage bIL285, complete genome (35538 bp)
  • NC_003212 – Listeria innocua Clip11262, complete genome, of which we are focussing only on integrated prophage 5 (similar length).

You can download these using Entrez if you like, see Section 9.6 for more details. For the third record we’ve worked out where the phage is integrated into the genome, and slice the record to extract it (with the features preserved, see Section 4.6), and must also reverse complement to match the orientation of the first two phage (again preserving the features, see Section 4.8):

from Bio import SeqIO

A_rec = SeqIO.read("NC_002703.gbk", "gb")
B_rec = SeqIO.read("AF323668.gbk", "gb")
C_rec = SeqIO.read("NC_003212.gbk", "gb")[2587879:2625807].reverse_complement(name=True)

The figure we are imitating used different colors for different gene functions. One way to do this is to edit the GenBank file to record color preferences for each feature - something Sanger’s Artemis editor does, and which GenomeDiagram should understand. Here however, we’ll just hard code three lists of colors.

Note that the annotation in the GenBank files doesn’t exactly match that shown in Proux et al., they have drawn some unannotated genes.

from reportlab.lib.colors import red, grey, orange, green, brown, blue, lightblue, purple

A_colors = [red]*5 + [grey]*7 + [orange]*2 + [grey]*2 + [orange] + [grey]*11 + [green]*4 \
         + [grey] + [green]*2 + [grey, green] + [brown]*5 + [blue]*4 + [lightblue]*5 \
         + [grey, lightblue] + [purple]*2 + [grey]
B_colors = [red]*6 + [grey]*8 + [orange]*2 + [grey] + [orange] + [grey]*21 + [green]*5 \
         + [grey] + [brown]*4 + [blue]*3 + [lightblue]*3 + [grey]*5 + [purple]*2
C_colors = [grey]*30 + [green]*5 + [brown]*4 + [blue]*2 + [grey, blue] + [lightblue]*2 \
         + [grey]*5

Now to draw them – this time we add three tracks to the diagram, and also notice they are given different start/end values to reflect their different lengths (this requires Biopython 1.59 or later).

from Bio.Graphics import GenomeDiagram

name = "Proux Fig 6"
gd_diagram = GenomeDiagram.Diagram(name)
max_len = 0
for record, gene_colors in zip([A_rec, B_rec, C_rec], [A_colors, B_colors, C_colors]):
    max_len = max(max_len, len(record))
    gd_track_for_features = gd_diagram.new_track(1,
                            name=record.name,
                            greytrack=True,
                            start=0, end=len(record))
    gd_feature_set = gd_track_for_features.new_set()

    i = 0
    for feature in record.features:
        if feature.type != "gene":
            #Exclude this feature                                                                                                   
            continue
        gd_feature_set.add_feature(feature, sigil="ARROW",
                                   color=gene_colors[i], label=True,
                                   name = str(i+1),
                                   label_position="start",
                                   label_size = 6, label_angle=0)
        i+=1

gd_diagram.draw(format="linear", pagesize='A4', fragments=1,
                start=0, end=max_len)
gd_diagram.write(name + ".pdf", "PDF")
gd_diagram.write(name + ".eps", "EPS")
gd_diagram.write(name + ".svg", "SVG")

The result:

I did wonder why in the original manuscript there were no red or orange genes marked in the bottom phage. Another important point is here the phage are shown with different lengths - this is because they are all drawn to the same scale (they are different lengths).

The key difference from the published figure is they have color-coded links between similar proteins – which is what we will do in the next section.

17.1.11  Cross-Links between tracks

Biopython 1.59 added the ability to draw cross links between tracks - both simple linear diagrams as we will show here, but also linear diagrams split into fragments and circular diagrams.

Continuing the example from the previous section inspired by Figure 6 from Proux et al. 2002 [5], we would need a list of cross links between pairs of genes, along with a score or color to use. Realistically you might extract this from a BLAST file computationally, but here I have manually typed them in.

My naming convention continues to refer to the three phage as A, B and C. Here are the links we want to show between A and B, given as a list of tuples (percentage similarity score, gene in A, gene in B).

#Tuc2009 (NC_002703) vs bIL285 (AF323668)
A_vs_B = [
    (99, "Tuc2009_01", "int"),
    (33, "Tuc2009_03", "orf4"),
    (94, "Tuc2009_05", "orf6"),
    (100,"Tuc2009_06", "orf7"),
    (97, "Tuc2009_07", "orf8"),
    (98, "Tuc2009_08", "orf9"),
    (98, "Tuc2009_09", "orf10"),
    (100,"Tuc2009_10", "orf12"),
    (100,"Tuc2009_11", "orf13"),
    (94, "Tuc2009_12", "orf14"),
    (87, "Tuc2009_13", "orf15"),
    (94, "Tuc2009_14", "orf16"),
    (94, "Tuc2009_15", "orf17"),
    (88, "Tuc2009_17", "rusA"),
    (91, "Tuc2009_18", "orf20"),
    (93, "Tuc2009_19", "orf22"),
    (71, "Tuc2009_20", "orf23"),
    (51, "Tuc2009_22", "orf27"),
    (97, "Tuc2009_23", "orf28"),
    (88, "Tuc2009_24", "orf29"),
    (26, "Tuc2009_26", "orf38"),
    (19, "Tuc2009_46", "orf52"),
    (77, "Tuc2009_48", "orf54"),
    (91, "Tuc2009_49", "orf55"),
    (95, "Tuc2009_52", "orf60"), 
]

Likewise for B and C:

#bIL285 (AF323668) vs Listeria innocua prophage 5 (in NC_003212)
B_vs_C = [
    (42, "orf39", "lin2581"),
    (31, "orf40", "lin2580"),
    (49, "orf41", "lin2579"), #terL
    (54, "orf42", "lin2578"), #portal
    (55, "orf43", "lin2577"), #protease
    (33, "orf44", "lin2576"), #mhp
    (51, "orf46", "lin2575"),
    (33, "orf47", "lin2574"),
    (40, "orf48", "lin2573"),
    (25, "orf49", "lin2572"),
    (50, "orf50", "lin2571"),
    (48, "orf51", "lin2570"),
    (24, "orf52", "lin2568"),
    (30, "orf53", "lin2567"),
    (28, "orf54", "lin2566"),
]

For the first and last phage these identifiers are locus tags, for the middle phage there are no locus tags so I’ve used gene names instead. The following little helper function lets us lookup a feature using either a locus tag or gene name:

def get_feature(features, id, tags=["locus_tag", "gene"]):
    """Search list of SeqFeature objects for an identifier under the given tags."""
    for f in features:
        for key in tags:
            #tag may not be present in this feature 
            for x in f.qualifiers.get(key, []):
                if x == id:
                     return f
    raise KeyError(id)

We can now turn those list of identifier pairs into SeqFeature pairs, and thus find their location co-ordinates. We can now add all that code and the following snippet to the previous example (just before the gd_diagram.draw(...) line – see the finished example script Proux_et_al_2002_Figure_6.py included in the Doc/examples folder of the Biopython source code) to add cross links to the figure:

from Bio.Graphics.GenomeDiagram import CrossLink
from reportlab.lib import colors
#Note it might have been clearer to assign the track numbers explicitly...                                                          
for rec_X, tn_X, rec_Y, tn_Y, X_vs_Y in [(A_rec, 3, B_rec, 2, A_vs_B),
                                         (B_rec, 2, C_rec, 1, B_vs_C)]:
    track_X = gd_diagram.tracks[tn_X]
    track_Y = gd_diagram.tracks[tn_Y]
    for score, id_X, id_Y in X_vs_Y:
        feature_X = get_feature(rec_X.features, id_X)
        feature_Y = get_feature(rec_Y.features, id_Y)
        color = colors.linearlyInterpolatedColor(colors.white, colors.firebrick, 0, 100, score)
        link_xy = CrossLink((track_X, feature_X.location.start, feature_X.location.end),
                            (track_Y, feature_Y.location.start, feature_Y.location.end),
                            color, colors.lightgrey)
        gd_diagram.cross_track_links.append(link_xy)

There are several important pieces to this code. First the GenomeDiagram object has a cross_track_links attribute which is just a list of CrossLink objects. Each CrossLink object takes two sets of track-specific co-ordinates (here given as tuples, you can alternatively use a GenomeDiagram.Feature object instead). You can optionally supply a colour, border color, and say if this link should be drawn flipped (useful for showing inversions).

You can also see how we turn the BLAST percentage identity score into a colour, interpolating between white (0%) and a dark red (100%). In this example we don’t have any problems with overlapping cross-links. One way to tackle that is to use transparency in ReportLab, by using colors with their alpha channel set. However, this kind of shaded color scheme combined with overlap transparency would be difficult to interpret. The result:

There is still a lot more that can be done within Biopython to help improve this figure. First of all, the cross links in this case are between proteins which are drawn in a strand specific manor. It can help to add a background region (a feature using the ‘BOX’ sigil) on the feature track to extend the cross link. Also, we could reduce the vertical height of the feature tracks to allocate more to the links instead – one way to do that is to allocate space for empty tracks. Furthermore, in cases like this where there are no large gene overlaps, we can use the axis-straddling BIGARROW sigil, which allows us to further reduce the vertical space needed for the track. These improvements are demonstrated in the example script Proux_et_al_2002_Figure_6.py included in the Doc/examples folder of the Biopython source code. The result:

Beyond that, finishing touches you might want to do manually in a vector image editor include fine tuning the placement of gene labels, and adding other custom annotation such as highlighting particular regions.

Although not really necessary in this example since none of the cross-links overlap, using a transparent color in ReportLab is a very useful technique for superimposing multiple links. However, in this case a shaded color scheme should be avoided.

17.1.12  Further options

You can control the tick marks to show the scale – after all every graph should show its units, and the number of the grey-track labels.

Also, we have only used the FeatureSet so far. GenomeDiagram also has a GraphSet which can be used for show line graphs, bar charts and heat plots (e.g. to show plots of GC% on a track parallel to the features).

These options are not covered here yet, so for now we refer you to the User Guide (PDF) included with the standalone version of GenomeDiagram (but please read the next section first), and the docstrings.

17.1.13  Converting old code

If you have old code written using the standalone version of GenomeDiagram, and you want to switch it over to using the new version included with Biopython then you will have to make a few changes - most importantly to your import statements.

Also, the older version of GenomeDiagram used only the UK spellings of color and center (colour and centre). You will need to change to the American spellings, although for several years the Biopython version of GenomeDiagram supported both.

For example, if you used to have: 

from GenomeDiagram import GDFeatureSet, GDDiagram
gdd = GDDiagram("An example")
...

you could just switch the import statements like this: 

from Bio.Graphics.GenomeDiagram import FeatureSet as GDFeatureSet, Diagram as GDDiagram
gdd = GDDiagram("An example")
...

and hopefully that should be enough. In the long term you might want to switch to the new names, but you would have to change more of your code: 

from Bio.Graphics.GenomeDiagram import FeatureSet, Diagram
gdd = Diagram("An example")
...

or: 

from Bio.Graphics import GenomeDiagram
gdd = GenomeDiagram.Diagram("An example")
...

If you run into difficulties, please ask on the Biopython mailing list for advice. One catch is that we have not included the old module GenomeDiagram.GDUtilities yet. This included a number of GC% related functions, which will probably be merged under Bio.SeqUtils later on.

17.2  Chromosomes

The Bio.Graphics.BasicChromosome module allows drawing of chromosomes. There is an example in Jupe et al. (2012) [6] (open access) using colors to highlight different gene families.

17.2.1  Simple Chromosomes

Here is a very simple example - for which we’ll use Arabidopsis thaliana.

You can skip this bit, but first I downloaded the five sequenced chromosomes from the NCBI’s FTP site ftp://ftp.ncbi.nlm.nih.gov/genomes/Arabidopsis_thaliana and then parsed them with Bio.SeqIO to find out their lengths. You could use the GenBank files for this, but it is faster to use the FASTA files for the whole chromosomes:

from Bio import SeqIO
entries = [("Chr I", "CHR_I/NC_003070.fna"),
           ("Chr II", "CHR_II/NC_003071.fna"),
           ("Chr III", "CHR_III/NC_003074.fna"),
           ("Chr IV", "CHR_IV/NC_003075.fna"),
           ("Chr V", "CHR_V/NC_003076.fna")]
for (name, filename) in entries:
   record = SeqIO.read(filename,"fasta")
   print(name, len(record))

This gave the lengths of the five chromosomes, which we’ll now use in the following short demonstration of the BasicChromosome module:

from reportlab.lib.units import cm
from Bio.Graphics import BasicChromosome

entries = [("Chr I", 30432563),
           ("Chr II", 19705359),
           ("Chr III", 23470805),
           ("Chr IV", 18585042),
           ("Chr V", 26992728)]

max_len = 30432563 #Could compute this
telomere_length = 1000000 #For illustration
         
chr_diagram = BasicChromosome.Organism()
chr_diagram.page_size = (29.7*cm, 21*cm) #A4 landscape

for name, length in entries:
    cur_chromosome = BasicChromosome.Chromosome(name)
    #Set the scale to the MAXIMUM length plus the two telomeres in bp,
    #want the same scale used on all five chromosomes so they can be
    #compared to each other
    cur_chromosome.scale_num = max_len + 2 * telomere_length

    #Add an opening telomere
    start = BasicChromosome.TelomereSegment()
    start.scale = telomere_length
    cur_chromosome.add(start)

    #Add a body - using bp as the scale length here.
    body = BasicChromosome.ChromosomeSegment()
    body.scale = length
    cur_chromosome.add(body)

    #Add a closing telomere
    end = BasicChromosome.TelomereSegment(inverted=True)
    end.scale = telomere_length
    cur_chromosome.add(end)

    #This chromosome is done
    chr_diagram.add(cur_chromosome)

chr_diagram.draw("simple_chrom.pdf", "Arabidopsis thaliana")

This should create a very simple PDF file, shown here:

This example is deliberately short and sweet. The next example shows the location of features of interest.

17.2.2  Annotated Chromosomes

Continuing from the previous example, let’s also show the tRNA genes. We’ll get their locations by parsing the GenBank files for the five Arabidopsis thaliana chromosomes. You’ll need to download these files from the NCBI FTP site ftp://ftp.ncbi.nlm.nih.gov/genomes/Arabidopsis_thaliana, and preserve the subdirectory names or edit the paths below:

from reportlab.lib.units import cm
from Bio import SeqIO
from Bio.Graphics import BasicChromosome

entries = [("Chr I", "CHR_I/NC_003070.gbk"),
           ("Chr II", "CHR_II/NC_003071.gbk"),
           ("Chr III", "CHR_III/NC_003074.gbk"),
           ("Chr IV", "CHR_IV/NC_003075.gbk"),
           ("Chr V", "CHR_V/NC_003076.gbk")]

max_len = 30432563 #Could compute this
telomere_length = 1000000 #For illustration

chr_diagram = BasicChromosome.Organism()
chr_diagram.page_size = (29.7*cm, 21*cm) #A4 landscape

for index, (name, filename) in enumerate(entries):
    record = SeqIO.read(filename,"genbank")
    length = len(record)
    features = [f for f in record.features if f.type=="tRNA"]
    #Record an Artemis style integer color in the feature's qualifiers,
    #1 = Black, 2 = Red, 3 = Green, 4 = blue, 5 =cyan, 6 = purple 
    for f in features: f.qualifiers["color"] = [index+2]

    cur_chromosome = BasicChromosome.Chromosome(name)
    #Set the scale to the MAXIMUM length plus the two telomeres in bp,
    #want the same scale used on all five chromosomes so they can be
    #compared to each other
    cur_chromosome.scale_num = max_len + 2 * telomere_length

    #Add an opening telomere
    start = BasicChromosome.TelomereSegment()
    start.scale = telomere_length
    cur_chromosome.add(start)

    #Add a body - again using bp as the scale length here.
    body = BasicChromosome.AnnotatedChromosomeSegment(length, features)
    body.scale = length
    cur_chromosome.add(body)

    #Add a closing telomere
    end = BasicChromosome.TelomereSegment(inverted=True)
    end.scale = telomere_length
    cur_chromosome.add(end)

    #This chromosome is done
    chr_diagram.add(cur_chromosome)

chr_diagram.draw("tRNA_chrom.pdf", "Arabidopsis thaliana")

It might warn you about the labels being too close together - have a look at the forward strand (right hand side) of Chr I, but it should create a colorful PDF file, shown here:

Chapter 18  Cookbook – Cool things to do with it

Biopython now has two collections of “cookbook” examples – this chapter (which has been included in this tutorial for many years and has gradually grown), and http://biopython.org/wiki/Category:Cookbook which is a user contributed collection on our wiki.

We’re trying to encourage Biopython users to contribute their own examples to the wiki. In addition to helping the community, one direct benefit of sharing an example like this is that you could also get some feedback on the code from other Biopython users and developers - which could help you improve all your Python code.

In the long term, we may end up moving all of the examples in this chapter to the wiki, or elsewhere within the tutorial.

18.1  Working with sequence files

This section shows some more examples of sequence input/output, using the Bio.SeqIO module described in Chapter 5.

18.1.1  Filtering a sequence file

Often you’ll have a large file with many sequences in it (e.g. FASTA file or genes, or a FASTQ or SFF file of reads), a separate shorter list of the IDs for a subset of sequences of interest, and want to make a new sequence file for this subset.

Let’s say the list of IDs is in a simple text file, as the first word on each line. This could be a tabular file where the first column is the ID. Try something like this:

from Bio import SeqIO
input_file = "big_file.sff"
id_file = "short_list.txt"
output_file = "short_list.sff"
wanted = set(line.rstrip("\n").split(None,1)[0] for line in open(id_file))
print("Found %i unique identifiers in %s" % (len(wanted), id_file))
records = (r for r in SeqIO.parse(input_file, "sff") if r.id in wanted)
count = SeqIO.write(records, output_file, "sff")
print("Saved %i records from %s to %s" % (count, input_file, output_file))
if count < len(wanted):
    print("Warning %i IDs not found in %s" % (len(wanted)-count, input_file))

Note that we use a Python set rather than a list, this makes testing membership faster.

18.1.2  Producing randomised genomes

Let’s suppose you are looking at genome sequence, hunting for some sequence feature – maybe extreme local GC% bias, or possible restriction digest sites. Once you’ve got your Python code working on the real genome it may be sensible to try running the same search on randomised versions of the same genome for statistical analysis (after all, any “features” you’ve found could just be there just by chance).

For this discussion, we’ll use the GenBank file for the pPCP1 plasmid from Yersinia pestis biovar Microtus. The file is included with the Biopython unit tests under the GenBank folder, or you can get it from our website, NC_005816.gb. This file contains one and only one record, so we can read it in as a SeqRecord using the Bio.SeqIO.read() function:

>>> from Bio import SeqIO
>>> original_rec = SeqIO.read("NC_005816.gb", "genbank")

So, how can we generate a shuffled versions of the original sequence? I would use the built in Python random module for this, in particular the function random.shuffle – but this works on a Python list. Our sequence is a Seq object, so in order to shuffle it we need to turn it into a list:

>>> import random
>>> nuc_list = list(original_rec.seq)
>>> random.shuffle(nuc_list) #acts in situ!

Now, in order to use Bio.SeqIO to output the shuffled sequence, we need to construct a new SeqRecord with a new Seq object using this shuffled list. In order to do this, we need to turn the list of nucleotides (single letter strings) into a long string – the standard Python way to do this is with the string object’s join method.

>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> shuffled_rec = SeqRecord(Seq("".join(nuc_list), original_rec.seq.alphabet),
...                          id="Shuffled", description="Based on %s" % original_rec.id)

Let’s put all these pieces together to make a complete Python script which generates a single FASTA file containing 30 randomly shuffled versions of the original sequence.

This first version just uses a big for loop and writes out the records one by one (using the SeqRecord’s format method described in Section 5.5.4):

import random
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio import SeqIO

original_rec = SeqIO.read("NC_005816.gb","genbank")

handle = open("shuffled.fasta", "w")
for i in range(30):
    nuc_list = list(original_rec.seq)
    random.shuffle(nuc_list)
    shuffled_rec = SeqRecord(Seq("".join(nuc_list), original_rec.seq.alphabet), \
                             id="Shuffled%i" % (i+1), \
                             description="Based on %s" % original_rec.id)
    handle.write(shuffled_rec.format("fasta"))
handle.close()

Personally I prefer the following version using a function to shuffle the record and a generator expression instead of the for loop:

import random
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio import SeqIO

def make_shuffle_record(record, new_id):
    nuc_list = list(record.seq)
    random.shuffle(nuc_list)
    return SeqRecord(Seq("".join(nuc_list), record.seq.alphabet), \
           id=new_id, description="Based on %s" % original_rec.id)
   
original_rec = SeqIO.read("NC_005816.gb","genbank")
shuffled_recs = (make_shuffle_record(original_rec, "Shuffled%i" % (i+1)) \
                 for i in range(30))
handle = open("shuffled.fasta", "w")
SeqIO.write(shuffled_recs, handle, "fasta")
handle.close()

18.1.3  Translating a FASTA file of CDS entries

Suppose you’ve got an input file of CDS entries for some organism, and you want to generate a new FASTA file containing their protein sequences. i.e. Take each nucleotide sequence from the original file, and translate it. Back in Section 3.9 we saw how to use the Seq object’s translate method, and the optional cds argument which enables correct translation of alternative start codons.

We can combine this with Bio.SeqIO as shown in the reverse complement example in Section 5.5.3. The key point is that for each nucleotide SeqRecord, we need to create a protein SeqRecord - and take care of naming it.

You can write you own function to do this, choosing suitable protein identifiers for your sequences, and the appropriate genetic code. In this example we just use the default table and add a prefix to the identifier:

from Bio.SeqRecord import SeqRecord
def make_protein_record(nuc_record):
    """Returns a new SeqRecord with the translated sequence (default table)."""
    return SeqRecord(seq = nuc_record.seq.translate(cds=True), \
                     id = "trans_" + nuc_record.id, \
                     description = "translation of CDS, using default table")

We can then use this function to turn the input nucleotide records into protein records ready for output. An elegant way and memory efficient way to do this is with a generator expression:

from Bio import SeqIO
proteins = (make_protein_record(nuc_rec) for nuc_rec in \
            SeqIO.parse("coding_sequences.fasta", "fasta"))
SeqIO.write(proteins, "translations.fasta", "fasta")

This should work on any FASTA file of complete coding sequences. If you are working on partial coding sequences, you may prefer to use nuc_record.seq.translate(to_stop=True) in the example above, as this wouldn’t check for a valid start codon etc.

18.1.4  Making the sequences in a FASTA file upper case

Often you’ll get data from collaborators as FASTA files, and sometimes the sequences can be in a mixture of upper and lower case. In some cases this is deliberate (e.g. lower case for poor quality regions), but usually it is not important. You may want to edit the file to make everything consistent (e.g. all upper case), and you can do this easily using the upper() method of the SeqRecord object (added in Biopython 1.55):

from Bio import SeqIO
records = (rec.upper() for rec in SeqIO.parse("mixed.fas", "fasta"))
count = SeqIO.write(records, "upper.fas", "fasta")
print("Converted %i records to upper case" % count)

How does this work? The first line is just importing the Bio.SeqIO module. The second line is the interesting bit – this is a Python generator expression which gives an upper case version of each record parsed from the input file (mixed.fas). In the third line we give this generator expression to the Bio.SeqIO.write() function and it saves the new upper cases records to our output file (upper.fas).

The reason we use a generator expression (rather than a list or list comprehension) is this means only one record is kept in memory at a time. This can be really important if you are dealing with large files with millions of entries.

18.1.5  Sorting a sequence file

Suppose you wanted to sort a sequence file by length (e.g. a set of contigs from an assembly), and you are working with a file format like FASTA or FASTQ which Bio.SeqIO can read, write (and index).

If the file is small enough, you can load it all into memory at once as a list of SeqRecord objects, sort the list, and save it:

from Bio import SeqIO
records = list(SeqIO.parse("ls_orchid.fasta","fasta"))
records.sort(cmp=lambda x,y: cmp(len(x),len(y)))
SeqIO.write(records, "sorted_orchids.fasta", "fasta")

The only clever bit is specifying a comparison function for how to sort the records (here we sort them by length). If you wanted the longest records first, you could flip the comparison or use the reverse argument:

from Bio import SeqIO
records = list(SeqIO.parse("ls_orchid.fasta","fasta"))
records.sort(cmp=lambda x,y: cmp(len(y),len(x)))
SeqIO.write(records, "sorted_orchids.fasta", "fasta")

Now that’s pretty straight forward - but what happens if you have a very large file and you can’t load it all into memory like this? For example, you might have some next-generation sequencing reads to sort by length. This can be solved using the Bio.SeqIO.index() function.

from Bio import SeqIO
#Get the lengths and ids, and sort on length         
len_and_ids = sorted((len(rec), rec.id) for rec in \
                     SeqIO.parse("ls_orchid.fasta","fasta"))
ids = reversed([id for (length, id) in len_and_ids])
del len_and_ids #free this memory
record_index = SeqIO.index("ls_orchid.fasta", "fasta")
records = (record_index[id] for id in ids)
SeqIO.write(records, "sorted.fasta", "fasta")

First we scan through the file once using Bio.SeqIO.parse(), recording the record identifiers and their lengths in a list of tuples. We then sort this list to get them in length order, and discard the lengths. Using this sorted list of identifiers Bio.SeqIO.index() allows us to retrieve the records one by one, and we pass them to Bio.SeqIO.write() for output.

These examples all use Bio.SeqIO to parse the records into SeqRecord objects which are output using Bio.SeqIO.write(). What if you want to sort a file format which Bio.SeqIO.write() doesn’t support, like the plain text SwissProt format? Here is an alternative solution using the get_raw() method added to Bio.SeqIO.index() in Biopython 1.54 (see Section 5.4.2.2).

from Bio import SeqIO
#Get the lengths and ids, and sort on length         
len_and_ids = sorted((len(rec), rec.id) for rec in \
                     SeqIO.parse("ls_orchid.fasta","fasta"))
ids = reversed([id for (length, id) in len_and_ids])
del len_and_ids #free this memory
record_index = SeqIO.index("ls_orchid.fasta", "fasta")
handle = open("sorted.fasta", "w")
for id in ids:
    handle.write(record_index.get_raw(id))
handle.close()

As a bonus, because it doesn’t parse the data into SeqRecord objects a second time it should be faster.

18.1.6  Simple quality filtering for FASTQ files

The FASTQ file format was introduced at Sanger and is now widely used for holding nucleotide sequencing reads together with their quality scores. FASTQ files (and the related QUAL files) are an excellent example of per-letter-annotation, because for each nucleotide in the sequence there is an associated quality score. Any per-letter-annotation is held in a SeqRecord in the letter_annotations dictionary as a list, tuple or string (with the same number of elements as the sequence length).

One common task is taking a large set of sequencing reads and filtering them (or cropping them) based on their quality scores. The following example is very simplistic, but should illustrate the basics of working with quality data in a SeqRecord object. All we are going to do here is read in a file of FASTQ data, and filter it to pick out only those records whose PHRED quality scores are all above some threshold (here 20).

For this example we’ll use some real data downloaded from the ENA sequence read archive, ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz (2MB) which unzips to a 19MB file SRR020192.fastq. This is some Roche 454 GS FLX single end data from virus infected California sea lions (see http://www.ebi.ac.uk/ena/data/view/SRS004476 for details).

First, let’s count the reads:

from Bio import SeqIO
count = 0
for rec in SeqIO.parse("SRR020192.fastq", "fastq"):
    count += 1
print("%i reads" % count)

Now let’s do a simple filtering for a minimum PHRED quality of 20:

from Bio import SeqIO
good_reads = (rec for rec in \
              SeqIO.parse("SRR020192.fastq", "fastq") \
              if min(rec.letter_annotations["phred_quality"]) >= 20)
count = SeqIO.write(good_reads, "good_quality.fastq", "fastq")
print("Saved %i reads" % count)

This pulled out only 14580 reads out of the 41892 present. A more sensible thing to do would be to quality trim the reads, but this is intended as an example only.

FASTQ files can contain millions of entries, so it is best to avoid loading them all into memory at once. This example uses a generator expression, which means only one SeqRecord is created at a time - avoiding any memory limitations.

18.1.7  Trimming off primer sequences

For this example we’re going to pretend that GATGACGGTGT is a 5’ primer sequence we want to look for in some FASTQ formatted read data. As in the example above, we’ll use the SRR020192.fastq file downloaded from the ENA (ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz). The same approach would work with any other supported file format (e.g. FASTA files).

This code uses Bio.SeqIO with a generator expression (to avoid loading all the sequences into memory at once), and the Seq object’s startswith method to see if the read starts with the primer sequence:

from Bio import SeqIO
primer_reads = (rec for rec in \
                SeqIO.parse("SRR020192.fastq", "fastq") \
                if rec.seq.startswith("GATGACGGTGT"))
count = SeqIO.write(primer_reads, "with_primer.fastq", "fastq")
print("Saved %i reads" % count)

That should find 13819 reads from SRR014849.fastq and save them to a new FASTQ file, with_primer.fastq.

Now suppose that instead you wanted to make a FASTQ file containing these reads but with the primer sequence removed? That’s just a small change as we can slice the SeqRecord (see Section 4.6) to remove the first eleven letters (the length of our primer):

from Bio import SeqIO
trimmed_primer_reads = (rec[11:] for rec in \
                        SeqIO.parse("SRR020192.fastq", "fastq") \
                        if rec.seq.startswith("GATGACGGTGT"))
count = SeqIO.write(trimmed_primer_reads, "with_primer_trimmed.fastq", "fastq")
print("Saved %i reads" % count)

Again, that should pull out the 13819 reads from SRR020192.fastq, but this time strip off the first ten characters, and save them to another new FASTQ file, with_primer_trimmed.fastq.

Finally, suppose you want to create a new FASTQ file where these reads have their primer removed, but all the other reads are kept as they were? If we want to still use a generator expression, it is probably clearest to define our own trim function:

from Bio import SeqIO
def trim_primer(record, primer):
    if record.seq.startswith(primer):
        return record[len(primer):]
    else:
        return record

trimmed_reads = (trim_primer(record, "GATGACGGTGT") for record in \
                 SeqIO.parse("SRR020192.fastq", "fastq"))
count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")
print("Saved %i reads" % count)

This takes longer, as this time the output file contains all 41892 reads. Again, we’re used a generator expression to avoid any memory problems. You could alternatively use a generator function rather than a generator expression.

from Bio import SeqIO
def trim_primers(records, primer):
    """Removes perfect primer sequences at start of reads.
    
    This is a generator function, the records argument should
    be a list or iterator returning SeqRecord objects.
    """
    len_primer = len(primer) #cache this for later
    for record in records:
        if record.seq.startswith(primer):
            yield record[len_primer:]
        else:
            yield record

original_reads = SeqIO.parse("SRR020192.fastq", "fastq")
trimmed_reads = trim_primers(original_reads, "GATGACGGTGT")
count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq") 
print("Saved %i reads" % count)

This form is more flexible if you want to do something more complicated where only some of the records are retained – as shown in the next example.

18.1.8  Trimming off adaptor sequences

This is essentially a simple extension to the previous example. We are going to going to pretend GATGACGGTGT is an adaptor sequence in some FASTQ formatted read data, again the SRR020192.fastq file from the NCBI (ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz).

This time however, we will look for the sequence anywhere in the reads, not just at the very beginning:

from Bio import SeqIO

def trim_adaptors(records, adaptor):
    """Trims perfect adaptor sequences.
    
    This is a generator function, the records argument should
    be a list or iterator returning SeqRecord objects.
    """
    len_adaptor = len(adaptor) #cache this for later
    for record in records:
        index = record.seq.find(adaptor)
        if index == -1:
            #adaptor not found, so won't trim
            yield record
        else:
            #trim off the adaptor
            yield record[index+len_adaptor:]

original_reads = SeqIO.parse("SRR020192.fastq", "fastq")
trimmed_reads = trim_adaptors(original_reads, "GATGACGGTGT")
count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq") 
print("Saved %i reads" % count)

Because we are using a FASTQ input file in this example, the SeqRecord objects have per-letter-annotation for the quality scores. By slicing the SeqRecord object the appropriate scores are used on the trimmed records, so we can output them as a FASTQ file too.

Compared to the output of the previous example where we only looked for a primer/adaptor at the start of each read, you may find some of the trimmed reads are quite short after trimming (e.g. if the adaptor was found in the middle rather than near the start). So, let’s add a minimum length requirement as well:

from Bio import SeqIO

def trim_adaptors(records, adaptor, min_len):
    """Trims perfect adaptor sequences, checks read length.
    
    This is a generator function, the records argument should
    be a list or iterator returning SeqRecord objects.
    """
    len_adaptor = len(adaptor) #cache this for later
    for record in records:
        len_record = len(record) #cache this for later
        if len(record) < min_len:
           #Too short to keep
           continue
        index = record.seq.find(adaptor)
        if index == -1:
            #adaptor not found, so won't trim
            yield record
        elif len_record - index - len_adaptor >= min_len:
            #after trimming this will still be long enough
            yield record[index+len_adaptor:]

original_reads = SeqIO.parse("SRR020192.fastq", "fastq")
trimmed_reads = trim_adaptors(original_reads, "GATGACGGTGT", 100)
count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq") 
print("Saved %i reads" % count)

By changing the format names, you could apply this to FASTA files instead. This code also could be extended to do a fuzzy match instead of an exact match (maybe using a pairwise alignment, or taking into account the read quality scores), but that will be much slower.

18.1.9  Converting FASTQ files

Back in Section 5.5.2 we showed how to use Bio.SeqIO to convert between two file formats. Here we’ll go into a little more detail regarding FASTQ files which are used in second generation DNA sequencing. Please refer to Cock et al. (2009) [7] for a longer description. FASTQ files store both the DNA sequence (as a string) and the associated read qualities.

PHRED scores (used in most FASTQ files, and also in QUAL files, ACE files and SFF files) have become a de facto standard for representing the probability of a sequencing error (here denoted by Pe) at a given base using a simple base ten log transformation:

QPHRED = − 10 × log10 ( Pe )     (18.1)

This means a wrong read (Pe = 1) gets a PHRED quality of 0, while a very good read like Pe = 0.00001 gets a PHRED quality of 50. While for raw sequencing data qualities higher than this are rare, with post processing such as read mapping or assembly, qualities of up to about 90 are possible (indeed, the MAQ tool allows for PHRED scores in the range 0 to 93 inclusive).

The FASTQ format has the potential to become a de facto standard for storing the letters and quality scores for a sequencing read in a single plain text file. The only fly in the ointment is that there are at least three versions of the FASTQ format which are incompatible and difficult to distinguish...

  1. The original Sanger FASTQ format uses PHRED qualities encoded with an ASCII offset of 33. The NCBI are using this format in their Short Read Archive. We call this the fastq (or fastq-sanger) format in Bio.SeqIO.
  2. Solexa (later bought by Illumina) introduced their own version using Solexa qualities encoded with an ASCII offset of 64. We call this the fastq-solexa format.
  3. Illumina pipeline 1.3 onwards produces FASTQ files with PHRED qualities (which is more consistent), but encoded with an ASCII offset of 64. We call this the fastq-illumina format.

The Solexa quality scores are defined using a different log transformation:

QSolexa = − 10 × log10 ⎛
⎜
⎜
⎝
Pe
1−Pe
 ⎞
⎟
⎟
⎠		     (18.2)

Given Solexa/Illumina have now moved to using PHRED scores in version 1.3 of their pipeline, the Solexa quality scores will gradually fall out of use. If you equate the error estimates (Pe) these two equations allow conversion between the two scoring systems - and Biopython includes functions to do this in the Bio.SeqIO.QualityIO module, which are called if you use Bio.SeqIO to convert an old Solexa/Illumina file into a standard Sanger FASTQ file:

from Bio import SeqIO
SeqIO.convert("solexa.fastq", "fastq-solexa", "standard.fastq", "fastq")

If you want to convert a new Illumina 1.3+ FASTQ file, all that gets changed is the ASCII offset because although encoded differently the scores are all PHRED qualities:

from Bio import SeqIO
SeqIO.convert("illumina.fastq", "fastq-illumina", "standard.fastq", "fastq")

Note that using Bio.SeqIO.convert() like this is much faster than combining Bio.SeqIO.parse() and Bio.SeqIO.write() because optimised code is used for converting between FASTQ variants (and also for FASTQ to FASTA conversion).

For good quality reads, PHRED and Solexa scores are approximately equal, which means since both the fasta-solexa and fastq-illumina formats use an ASCII offset of 64 the files are almost the same. This was a deliberate design choice by Illumina, meaning applications expecting the old fasta-solexa style files will probably be OK using the newer fastq-illumina files (on good data). Of course, both variants are very different from the original FASTQ standard as used by Sanger, the NCBI, and elsewhere (format name fastq or fastq-sanger).

For more details, see the built in help (also online):

>>> from Bio.SeqIO import QualityIO
>>> help(QualityIO)
...

18.1.10  Converting FASTA and QUAL files into FASTQ files

FASTQ files hold both sequences and their quality strings. FASTA files hold just sequences, while QUAL files hold just the qualities. Therefore a single FASTQ file can be converted to or from paired FASTA and QUAL files.

Going from FASTQ to FASTA is easy:

from Bio import SeqIO
SeqIO.convert("example.fastq", "fastq", "example.fasta", "fasta")

Going from FASTQ to QUAL is also easy:

from Bio import SeqIO
SeqIO.convert("example.fastq", "fastq", "example.qual", "qual")

However, the reverse is a little more tricky. You can use Bio.SeqIO.parse() to iterate over the records in a single file, but in this case we have two input files. There are several strategies possible, but assuming that the two files are really paired the most memory efficient way is to loop over both together. The code is a little fiddly, so we provide a function called PairedFastaQualIterator in the Bio.SeqIO.QualityIO module to do this. This takes two handles (the FASTA file and the QUAL file) and returns a SeqRecord iterator:

from Bio.SeqIO.QualityIO import PairedFastaQualIterator
for record in PairedFastaQualIterator(open("example.fasta"), open("example.qual")):
   print(record)

This function will check that the FASTA and QUAL files are consistent (e.g. the records are in the same order, and have the same sequence length). You can combine this with the Bio.SeqIO.write() function to convert a pair of FASTA and QUAL files into a single FASTQ files:

from Bio import SeqIO
from Bio.SeqIO.QualityIO import PairedFastaQualIterator
handle = open("temp.fastq", "w") #w=write
records = PairedFastaQualIterator(open("example.fasta"), open("example.qual"))
count = SeqIO.write(records, handle, "fastq")
handle.close()
print("Converted %i records" % count)

18.1.11  Indexing a FASTQ file

FASTQ files are often very large, with millions of reads in them. Due to the sheer amount of data, you can’t load all the records into memory at once. This is why the examples above (filtering and trimming) iterate over the file looking at just one SeqRecord at a time.

However, sometimes you can’t use a big loop or an iterator - you may need random access to the reads. Here the Bio.SeqIO.index() function may prove very helpful, as it allows you to access any read in the FASTQ file by its name (see Section 5.4.2).

Again we’ll use the SRR020192.fastq file from the ENA (ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz), although this is actually quite a small FASTQ file with less than 50,000 reads:

>>> from Bio import SeqIO
>>> fq_dict = SeqIO.index("SRR020192.fastq", "fastq")
>>> len(fq_dict)
41892
>>> fq_dict.keys()[:4]
['SRR020192.38240', 'SRR020192.23181', 'SRR020192.40568', 'SRR020192.23186']
>>> fq_dict["SRR020192.23186"].seq
Seq('GTCCCAGTATTCGGATTTGTCTGCCAAAACAATGAAATTGACACAGTTTACAAC...CCG', SingleLetterAlphabet())

When testing this on a FASTQ file with seven million reads, indexing took about a minute, but record access was almost instant.

The example in Section 18.1.5 show how you can use the Bio.SeqIO.index() function to sort a large FASTA file – this could also be used on FASTQ files.

18.1.12  Converting SFF files

If you work with 454 (Roche) sequence data, you will probably have access to the raw data as a Standard Flowgram Format (SFF) file. This contains the sequence reads (called bases) with quality scores and the original flow information.

A common task is to convert from SFF to a pair of FASTA and QUAL files, or to a single FASTQ file. These operations are trivial using the Bio.SeqIO.convert() function (see Section 5.5.2):

>>> from Bio import SeqIO
>>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.fasta", "fasta")
10
>>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.qual", "qual")
10
>>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.fastq", "fastq")
10

Remember the convert function returns the number of records, in this example just ten. This will give you the untrimmed reads, where the leading and trailing poor quality sequence or adaptor will be in lower case. If you want the trimmed reads (using the clipping information recorded within the SFF file) use this:

>>> from Bio import SeqIO
>>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.fasta", "fasta")
10
>>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.qual", "qual")
10
>>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.fastq", "fastq")
10

If you run Linux, you could ask Roche for a copy of their “off instrument” tools (often referred to as the Newbler tools). This offers an alternative way to do SFF to FASTA or QUAL conversion at the command line (but currently FASTQ output is not supported), e.g.

$ sffinfo -seq -notrim E3MFGYR02_random_10_reads.sff > reads.fasta
$ sffinfo -qual -notrim E3MFGYR02_random_10_reads.sff > reads.qual
$ sffinfo -seq -trim E3MFGYR02_random_10_reads.sff > trimmed.fasta
$ sffinfo -qual -trim E3MFGYR02_random_10_reads.sff > trimmed.qual

The way Biopython uses mixed case sequence strings to represent the trimming points deliberately mimics what the Roche tools do.

For more information on the Biopython SFF support, consult the built in help:

>>> from Bio.SeqIO import SffIO
>>> help(SffIO)
...

18.1.13  Identifying open reading frames

A very simplistic first step at identifying possible genes is to look for open reading frames (ORFs). By this we mean look in all six frames for long regions without stop codons – an ORF is just a region of nucleotides with no in frame stop codons.

Of course, to find a gene you would also need to worry about locating a start codon, possible promoters – and in Eukaryotes there are introns to worry about too. However, this approach is still useful in viruses and Prokaryotes.

To show how you might approach this with Biopython, we’ll need a sequence to search, and as an example we’ll again use the bacterial plasmid – although this time we’ll start with a plain FASTA file with no pre-marked genes: NC_005816.fna. This is a bacterial sequence, so we’ll want to use NCBI codon table 11 (see Section 3.9 about translation).

>>> from Bio import SeqIO 
>>> record = SeqIO.read("NC_005816.fna", "fasta")
>>> table = 11
>>> min_pro_len = 100

Here is a neat trick using the Seq object’s split method to get a list of all the possible ORF translations in the six reading frames:

>>> for strand, nuc in [(+1, record.seq), (-1, record.seq.reverse_complement())]:
...     for frame in range(3):
...         length = 3 * ((len(record)-frame) // 3) #Multiple of three
...         for pro in nuc[frame:frame+length].translate(table).split("*"):
...             if len(pro) >= min_pro_len:
...                 print("%s...%s - length %i, strand %i, frame %i" \
...                       % (pro[:30], pro[-3:], len(pro), strand, frame))
GCLMKKSSIVATIITILSGSANAASSQLIP...YRF - length 315, strand 1, frame 0
KSGELRQTPPASSTLHLRLILQRSGVMMEL...NPE - length 285, strand 1, frame 1
GLNCSFFSICNWKFIDYINRLFQIIYLCKN...YYH - length 176, strand 1, frame 1
VKKILYIKALFLCTVIKLRRFIFSVNNMKF...DLP - length 165, strand 1, frame 1
NQIQGVICSPDSGEFMVTFETVMEIKILHK...GVA - length 355, strand 1, frame 2
RRKEHVSKKRRPQKRPRRRRFFHRLRPPDE...PTR - length 128, strand 1, frame 2
TGKQNSCQMSAIWQLRQNTATKTRQNRARI...AIK - length 100, strand 1, frame 2
QGSGYAFPHASILSGIAMSHFYFLVLHAVK...CSD - length 114, strand -1, frame 0
IYSTSEHTGEQVMRTLDEVIASRSPESQTR...FHV - length 111, strand -1, frame 0
WGKLQVIGLSMWMVLFSQRFDDWLNEQEDA...ESK - length 125, strand -1, frame 1
RGIFMSDTMVVNGSGGVPAFLFSGSTLSSY...LLK - length 361, strand -1, frame 1
WDVKTVTGVLHHPFHLTFSLCPEGATQSGR...VKR - length 111, strand -1, frame 1
LSHTVTDFTDQMAQVGLCQCVNVFLDEVTG...KAA - length 107, strand -1, frame 2
RALTGLSAPGIRSQTSCDRLRELRYVPVSL...PLQ - length 119, strand -1, frame 2

Note that here we are counting the frames from the 5’ end (start) of each strand. It is sometimes easier to always count from the 5’ end (start) of the forward strand.

You could easily edit the above loop based code to build up a list of the candidate proteins, or convert this to a list comprehension. Now, one thing this code doesn’t do is keep track of where the proteins are.

You could tackle this in several ways. For example, the following code tracks the locations in terms of the protein counting, and converts back to the parent sequence by multiplying by three, then adjusting for the frame and strand:

from Bio import SeqIO 
record = SeqIO.read("NC_005816.gb","genbank")
table = 11
min_pro_len = 100

def find_orfs_with_trans(seq, trans_table, min_protein_length):
    answer = []
    seq_len = len(seq)
    for strand, nuc in [(+1, seq), (-1, seq.reverse_complement())]:
        for frame in range(3):
            trans = str(nuc[frame:].translate(trans_table))
            trans_len = len(trans)
            aa_start = 0
            aa_end = 0
            while aa_start < trans_len:
                aa_end = trans.find("*", aa_start)
                if aa_end == -1:
                    aa_end = trans_len
                if aa_end-aa_start >= min_protein_length:
                    if strand == 1:
                        start = frame+aa_start*3
                        end = min(seq_len,frame+aa_end*3+3)
                    else:
                        start = seq_len-frame-aa_end*3-3
                        end = seq_len-frame-aa_start*3                        
                    answer.append((start, end, strand,
                                   trans[aa_start:aa_end]))
                aa_start = aa_end+1
    answer.sort()
    return answer

orf_list = find_orfs_with_trans(record.seq, table, min_pro_len)
for start, end, strand, pro in orf_list:
    print("%s...%s - length %i, strand %i, %i:%i" \
          % (pro[:30], pro[-3:], len(pro), strand, start, end))

And the output:

NQIQGVICSPDSGEFMVTFETVMEIKILHK...GVA - length 355, strand 1, 41:1109
WDVKTVTGVLHHPFHLTFSLCPEGATQSGR...VKR - length 111, strand -1, 491:827
KSGELRQTPPASSTLHLRLILQRSGVMMEL...NPE - length 285, strand 1, 1030:1888
RALTGLSAPGIRSQTSCDRLRELRYVPVSL...PLQ - length 119, strand -1, 2830:3190
RRKEHVSKKRRPQKRPRRRRFFHRLRPPDE...PTR - length 128, strand 1, 3470:3857
GLNCSFFSICNWKFIDYINRLFQIIYLCKN...YYH - length 176, strand 1, 4249:4780
RGIFMSDTMVVNGSGGVPAFLFSGSTLSSY...LLK - length 361, strand -1, 4814:5900
VKKILYIKALFLCTVIKLRRFIFSVNNMKF...DLP - length 165, strand 1, 5923:6421
LSHTVTDFTDQMAQVGLCQCVNVFLDEVTG...KAA - length 107, strand -1, 5974:6298
GCLMKKSSIVATIITILSGSANAASSQLIP...YRF - length 315, strand 1, 6654:7602
IYSTSEHTGEQVMRTLDEVIASRSPESQTR...FHV - length 111, strand -1, 7788:8124
WGKLQVIGLSMWMVLFSQRFDDWLNEQEDA...ESK - length 125, strand -1, 8087:8465
TGKQNSCQMSAIWQLRQNTATKTRQNRARI...AIK - length 100, strand 1, 8741:9044
QGSGYAFPHASILSGIAMSHFYFLVLHAVK...CSD - length 114, strand -1, 9264:9609

If you comment out the sort statement, then the protein sequences will be shown in the same order as before, so you can check this is doing the same thing. Here we have sorted them by location to make it easier to compare to the actual annotation in the GenBank file (as visualised in Section 17.1.9).

If however all you want to find are the locations of the open reading frames, then it is a waste of time to translate every possible codon, including doing the reverse complement to search the reverse strand too. All you need to do is search for the possible stop codons (and their reverse complements). Using regular expressions is an obvious approach here (see the Python module re). These are an extremely powerful (but rather complex) way of describing search strings, which are supported in lots of programming languages and also command line tools like grep as well). You can find whole books about this topic!

18.2  Sequence parsing plus simple plots

This section shows some more examples of sequence parsing, using the Bio.SeqIO module described in Chapter 5, plus the Python library matplotlib’s pylab plotting interface (see the matplotlib website for a tutorial). Note that to follow these examples you will need matplotlib installed - but without it you can still try the data parsing bits.

18.2.1  Histogram of sequence lengths

There are lots of times when you might want to visualise the distribution of sequence lengths in a dataset – for example the range of contig sizes in a genome assembly project. In this example we’ll reuse our orchid FASTA file ls_orchid.fasta which has only 94 sequences.

First of all, we will use Bio.SeqIO to parse the FASTA file and compile a list of all the sequence lengths. You could do this with a for loop, but I find a list comprehension more pleasing:

>>> from Bio import SeqIO
>>> sizes = [len(rec) for rec in SeqIO.parse("ls_orchid.fasta", "fasta")]
>>> len(sizes), min(sizes), max(sizes)
(94, 572, 789)
>>> sizes
[740, 753, 748, 744, 733, 718, 730, 704, 740, 709, 700, 726, ..., 592]

Now that we have the lengths of all the genes (as a list of integers), we can use the matplotlib histogram function to display it.

from Bio import SeqIO
sizes = [len(rec) for rec in SeqIO.parse("ls_orchid.fasta", "fasta")]

import pylab
pylab.hist(sizes, bins=20)
pylab.title("%i orchid sequences\nLengths %i to %i" \
            % (len(sizes),min(sizes),max(sizes)))
pylab.xlabel("Sequence length (bp)")
pylab.ylabel("Count")
pylab.show()

That should pop up a new window containing the following graph:

Notice that most of these orchid sequences are about 740 bp long, and there could be two distinct classes of sequence here with a subset of shorter sequences.

Tip: Rather than using pylab.show() to show the plot in a window, you can also use pylab.savefig(...) to save the figure to a file (e.g. as a PNG or PDF).

18.2.2  Plot of sequence GC%

Another easily calculated quantity of a nucleotide sequence is the GC%. You might want to look at the GC% of all the genes in a bacterial genome for example, and investigate any outliers which could have been recently acquired by horizontal gene transfer. Again, for this example we’ll reuse our orchid FASTA file ls_orchid.fasta.

First of all, we will use Bio.SeqIO to parse the FASTA file and compile a list of all the GC percentages. Again, you could do this with a for loop, but I prefer this:

from Bio import SeqIO
from Bio.SeqUtils import GC

gc_values = sorted(GC(rec.seq) for rec in SeqIO.parse("ls_orchid.fasta", "fasta"))

Having read in each sequence and calculated the GC%, we then sorted them into ascending order. Now we’ll take this list of floating point values and plot them with matplotlib:

import pylab
pylab.plot(gc_values)
pylab.title("%i orchid sequences\nGC%% %0.1f to %0.1f" \
            % (len(gc_values),min(gc_values),max(gc_values)))
pylab.xlabel("Genes")
pylab.ylabel("GC%")
pylab.show()

As in the previous example, that should pop up a new window containing a graph:

If you tried this on the full set of genes from one organism, you’d probably get a much smoother plot than this.

18.2.3  Nucleotide dot plots

A dot plot is a way of visually comparing two nucleotide sequences for similarity to each other. A sliding window is used to compare short sub-sequences to each other, often with a mis-match threshold. Here for simplicity we’ll only look for perfect matches (shown in black in the plot below).

To start off, we’ll need two sequences. For the sake of argument, we’ll just take the first two from our orchid FASTA file ls_orchid.fasta:

from Bio import SeqIO
handle = open("ls_orchid.fasta")
record_iterator = SeqIO.parse(handle, "fasta")
rec_one = next(record_iterator)
rec_two = next(record_iterator)
handle.close()

We’re going to show two approaches. Firstly, a simple naive implementation which compares all the window sized sub-sequences to each other to compiles a similarity matrix. You could construct a matrix or array object, but here we just use a list of lists of booleans created with a nested list comprehension:

window = 7
seq_one = str(rec_one.seq).upper()
seq_two = str(rec_two.seq).upper()
data = [[(seq_one[i:i+window] <> seq_two[j:j+window]) \
        for j in range(len(seq_one)-window)] \
       for i in range(len(seq_two)-window)]

Note that we have not checked for reverse complement matches here. Now we’ll use the matplotlib’s pylab.imshow() function to display this data, first requesting the gray color scheme so this is done in black and white:

import pylab
pylab.gray()
pylab.imshow(data)
pylab.xlabel("%s (length %i bp)" % (rec_one.id, len(rec_one)))
pylab.ylabel("%s (length %i bp)" % (rec_two.id, len(rec_two)))
pylab.title("Dot plot using window size %i\n(allowing no mis-matches)" % window)
pylab.show()

That should pop up a new window containing a graph like this:

As you might have expected, these two sequences are very similar with a partial line of window sized matches along the diagonal. There are no off diagonal matches which would be indicative of inversions or other interesting events.

The above code works fine on small examples, but there are two problems applying this to larger sequences, which we will address below. First off all, this brute force approach to the all against all comparisons is very slow. Instead, we’ll compile dictionaries mapping the window sized sub-sequences to their locations, and then take the set intersection to find those sub-sequences found in both sequences. This uses more memory, but is much faster. Secondly, the pylab.imshow() function is limited in the size of matrix it can display. As an alternative, we’ll use the pylab.scatter() function.

We start by creating dictionaries mapping the window-sized sub-sequences to locations: 

window = 7
dict_one = {}
dict_two = {}
for (seq, section_dict) in [(str(rec_one.seq).upper(), dict_one),
                            (str(rec_two.seq).upper(), dict_two)]:
    for i in range(len(seq)-window):
        section = seq[i:i+window]
        try:
            section_dict[section].append(i)
        except KeyError:
            section_dict[section] = [i]
#Now find any sub-sequences found in both sequences
#(Python 2.3 would require slightly different code here)
matches = set(dict_one).intersection(dict_two)
print("%i unique matches" % len(matches))

In order to use the pylab.scatter() we need separate lists for the x and y co-ordinates: 

#Create lists of x and y co-ordinates for scatter plot
x = []
y = []
for section in matches:
    for i in dict_one[section]:
        for j in dict_two[section]:
            x.append(i)
            y.append(j)

We are now ready to draw the revised dot plot as a scatter plot: 

import pylab
pylab.cla() #clear any prior graph
pylab.gray()
pylab.scatter(x,y)
pylab.xlim(0, len(rec_one)-window)
pylab.ylim(0, len(rec_two)-window)
pylab.xlabel("%s (length %i bp)" % (rec_one.id, len(rec_one)))
pylab.ylabel("%s (length %i bp)" % (rec_two.id, len(rec_two)))
pylab.title("Dot plot using window size %i\n(allowing no mis-matches)" % window)
pylab.show()

That should pop up a new window containing a graph like this:

Personally I find this second plot much easier to read! Again note that we have not checked for reverse complement matches here – you could extend this example to do this, and perhaps plot the forward matches in one color and the reverse matches in another.

18.2.4  Plotting the quality scores of sequencing read data

If you are working with second generation sequencing data, you may want to try plotting the quality data. Here is an example using two FASTQ files containing paired end reads, SRR001666_1.fastq for the forward reads, and SRR001666_2.fastq for the reverse reads. These were downloaded from the ENA sequence read archive FTP site (ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_1.fastq.gz and ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_2.fastq.gz), and are from E. coli – see http://www.ebi.ac.uk/ena/data/view/SRR001666 for details.

In the following code the pylab.subplot(...) function is used in order to show the forward and reverse qualities on two subplots, side by side. There is also a little bit of code to only plot the first fifty reads.

import pylab
from Bio import SeqIO
for subfigure in [1,2]:
    filename = "SRR001666_%i.fastq" % subfigure
    pylab.subplot(1, 2, subfigure)
    for i,record in enumerate(SeqIO.parse(filename, "fastq")):
        if i >= 50 : break #trick!
        pylab.plot(record.letter_annotations["phred_quality"])
    pylab.ylim(0,45)
    pylab.ylabel("PHRED quality score")
    pylab.xlabel("Position")
pylab.savefig("SRR001666.png")
print("Done")

You should note that we are using the Bio.SeqIO format name fastq here because the NCBI has saved these reads using the standard Sanger FASTQ format with PHRED scores. However, as you might guess from the read lengths, this data was from an Illumina Genome Analyzer and was probably originally in one of the two Solexa/Illumina FASTQ variant file formats instead.

This example uses the pylab.savefig(...) function instead of pylab.show(...), but as mentioned before both are useful. Here is the result:

18.3  Dealing with alignments

This section can been seen as a follow on to Chapter 6.

18.3.1  Calculating summary information

Once you have an alignment, you are very likely going to want to find out information about it. Instead of trying to have all of the functions that can generate information about an alignment in the alignment object itself, we’ve tried to separate out the functionality into separate classes, which act on the alignment.

Getting ready to calculate summary information about an object is quick to do. Let’s say we’ve got an alignment object called alignment, for example read in using Bio.AlignIO.read(...) as described in Chapter 6. All we need to do to get an object that will calculate summary information is:

from Bio.Align import AlignInfo
summary_align = AlignInfo.SummaryInfo(alignment)

The summary_align object is very useful, and will do the following neat things for you:

  1. Calculate a quick consensus sequence – see section 18.3.2
  2. Get a position specific score matrix for the alignment – see section 18.3.3
  3. Calculate the information content for the alignment – see section 18.3.4
  4. Generate information on substitutions in the alignment – section 18.4 details using this to generate a substitution matrix.

18.3.2  Calculating a quick consensus sequence

The SummaryInfo object, described in section 18.3.1, provides functionality to calculate a quick consensus of an alignment. Assuming we’ve got a SummaryInfo object called summary_align we can calculate a consensus by doing:

consensus = summary_align.dumb_consensus()

As the name suggests, this is a really simple consensus calculator, and will just add up all of the residues at each point in the consensus, and if the most common value is higher than some threshold value will add the common residue to the consensus. If it doesn’t reach the threshold, it adds an ambiguity character to the consensus. The returned consensus object is Seq object whose alphabet is inferred from the alphabets of the sequences making up the consensus. So doing a print consensus would give:

consensus Seq('TATACATNAAAGNAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAAT
...', IUPACAmbiguousDNA())

You can adjust how dumb_consensus works by passing optional parameters:

the threshold
This is the threshold specifying how common a particular residue has to be at a position before it is added. The default is 0.7 (meaning 70%).
the ambiguous character
This is the ambiguity character to use. The default is ’N’.
the consensus alphabet
This is the alphabet to use for the consensus sequence. If an alphabet is not specified than we will try to guess the alphabet based on the alphabets of the sequences in the alignment.

18.3.3  Position Specific Score Matrices

Position specific score matrices (PSSMs) summarize the alignment information in a different way than a consensus, and may be useful for different tasks. Basically, a PSSM is a count matrix. For each column in the alignment, the number of each alphabet letters is counted and totaled. The totals are displayed relative to some representative sequence along the left axis. This sequence may be the consesus sequence, but can also be any sequence in the alignment. For instance for the alignment,

GTATC
AT--C
CTGTC

the PSSM is:

      G A T C
    G 1 1 0 1
    T 0 0 3 0
    A 1 1 0 0
    T 0 0 2 0
    C 0 0 0 3

Let’s assume we’ve got an alignment object called c_align. To get a PSSM with the consensus sequence along the side we first get a summary object and calculate the consensus sequence:

summary_align = AlignInfo.SummaryInfo(c_align)
consensus = summary_align.dumb_consensus()

Now, we want to make the PSSM, but ignore any N ambiguity residues when calculating this:

my_pssm = summary_align.pos_specific_score_matrix(consensus,
                                                  chars_to_ignore = ['N'])

Two notes should be made about this:

  1. To maintain strictness with the alphabets, you can only include characters along the top of the PSSM that are in the alphabet of the alignment object. Gaps are not included along the top axis of the PSSM.
  2. The sequence passed to be displayed along the left side of the axis does not need to be the consensus. For instance, if you wanted to display the second sequence in the alignment along this axis, you would need to do:
    second_seq = alignment.get_seq_by_num(1)
my_pssm = summary_align.pos_specific_score_matrix(second_seq
                                                  chars_to_ignore = ['N'])

The command above returns a PSSM object. To print out the PSSM as shown above, we simply need to do a print(my_pssm), which gives:

    A   C   G   T
T  0.0 0.0 0.0 7.0
A  7.0 0.0 0.0 0.0
T  0.0 0.0 0.0 7.0
A  7.0 0.0 0.0 0.0
C  0.0 7.0 0.0 0.0
A  7.0 0.0 0.0 0.0
T  0.0 0.0 0.0 7.0
T  1.0 0.0 0.0 6.0
...

You can access any element of the PSSM by subscripting like your_pssm[sequence_number][residue_count_name]. For instance, to get the counts for the ’A’ residue in the second element of the above PSSM you would do:

>>> print(my_pssm[1]["A"])
7.0

The structure of the PSSM class hopefully makes it easy both to access elements and to pretty print the matrix.

18.3.4  Information Content

A potentially useful measure of evolutionary conservation is the information content of a sequence.

A useful introduction to information theory targeted towards molecular biologists can be found at http://www.lecb.ncifcrf.gov/~toms/paper/primer/. For our purposes, we will be looking at the information content of a consesus sequence, or a portion of a consensus sequence. We calculate information content at a particular column in a multiple sequence alignment using the following formula:

ICj = 
Na
∑
i=1
 Pij log⎛
⎜
⎜
⎝
Pij
Qi
⎞
⎟
⎟
⎠

where:

  • ICj – The information content for the j-th column in an alignment.
  • Na – The number of letters in the alphabet.
  • Pij – The frequency of a particular letter i in the j-th column (i. e. if G occurred 3 out of 6 times in an aligment column, this would be 0.5)
  • Qi – The expected frequency of a letter i. This is an optional argument, usage of which is left at the user’s discretion. By default, it is automatically assigned to 0.05 = 1/20 for a protein alphabet, and 0.25 = 1/4 for a nucleic acid alphabet. This is for geting the information content without any assumption of prior distributions. When assuming priors, or when using a non-standard alphabet, you should supply the values for Qi.

Well, now that we have an idea what information content is being calculated in Biopython, let’s look at how to get it for a particular region of the alignment.

First, we need to use our alignment to get an alignment summary object, which we’ll assume is called summary_align (see section 18.3.1) for instructions on how to get this. Once we’ve got this object, calculating the information content for a region is as easy as:

info_content = summary_align.information_content(5, 30,
                                                 chars_to_ignore = ['N'])

Wow, that was much easier then the formula above made it look! The variable info_content now contains a float value specifying the information content over the specified region (from 5 to 30 of the alignment). We specifically ignore the ambiguity residue ’N’ when calculating the information content, since this value is not included in our alphabet (so we shouldn’t be interested in looking at it!).

As mentioned above, we can also calculate relative information content by supplying the expected frequencies:

expect_freq = {
    'A' : .3,
    'G' : .2,
    'T' : .3,
    'C' : .2}

The expected should not be passed as a raw dictionary, but instead by passed as a SubsMat.FreqTable object (see section 20.2.2 for more information about FreqTables). The FreqTable object provides a standard for associating the dictionary with an Alphabet, similar to how the Biopython Seq class works.

To create a FreqTable object, from the frequency dictionary you just need to do:

from Bio.Alphabet import IUPAC
from Bio.SubsMat import FreqTable

e_freq_table = FreqTable.FreqTable(expect_freq, FreqTable.FREQ,
                                   IUPAC.unambiguous_dna)

Now that we’ve got that, calculating the relative information content for our region of the alignment is as simple as:

info_content = summary_align.information_content(5, 30,
                                                 e_freq_table = e_freq_table,
                                                 chars_to_ignore = ['N'])

Now, info_content will contain the relative information content over the region in relation to the expected frequencies.

The value return is calculated using base 2 as the logarithm base in the formula above. You can modify this by passing the parameter log_base as the base you want:

info_content = summary_align.information_content(5, 30, log_base = 10,
                                                 chars_to_ignore = ['N'])

Well, now you are ready to calculate information content. If you want to try applying this to some real life problems, it would probably be best to dig into the literature on information content to get an idea of how it is used. Hopefully your digging won’t reveal any mistakes made in coding this function!

18.4  Substitution Matrices

Substitution matrices are an extremely important part of everyday bioinformatics work. They provide the scoring terms for classifying how likely two different residues are to substitute for each other. This is essential in doing sequence comparisons. The book “Biological Sequence Analysis” by Durbin et al. provides a really nice introduction to Substitution Matrices and their uses. Some famous substitution matrices are the PAM and BLOSUM series of matrices.

Biopython provides a ton of common substitution matrices, and also provides functionality for creating your own substitution matrices.

18.4.1  Using common substitution matrices

18.4.2  Creating your own substitution matrix from an alignment

A very cool thing that you can do easily with the substitution matrix classes is to create your own substitution matrix from an alignment. In practice, this is normally done with protein alignments. In this example, we’ll first get a Biopython alignment object and then get a summary object to calculate info about the alignment. The file containing protein.aln (also available online here) contains the Clustalw alignment output.

>>> from Bio import AlignIO
>>> from Bio import Alphabet
>>> from Bio.Alphabet import IUPAC
>>> from Bio.Align import AlignInfo
>>> filename = "protein.aln"
>>> alpha = Alphabet.Gapped(IUPAC.protein)
>>> c_align = AlignIO.read(filename, "clustal", alphabet=alpha)
>>> summary_align = AlignInfo.SummaryInfo(c_align)

Sections 6.4.1 and 18.3.1 contain more information on doing this.

Now that we’ve got our summary_align object, we want to use it to find out the number of times different residues substitute for each other. To make the example more readable, we’ll focus on only amino acids with polar charged side chains. Luckily, this can be done easily when generating a replacement dictionary, by passing in all of the characters that should be ignored. Thus we’ll create a dictionary of replacements for only charged polar amino acids using:

>>> replace_info = summary_align.replacement_dictionary(["G", "A", "V", "L", "I",
...                                                      "M", "P", "F", "W", "S",
...                                                      "T", "N", "Q", "Y", "C"])

This information about amino acid replacements is represented as a python dictionary which will look something like (the order can vary):

{('R', 'R'): 2079.0, ('R', 'H'): 17.0, ('R', 'K'): 103.0, ('R', 'E'): 2.0,
('R', 'D'): 2.0, ('H', 'R'): 0, ('D', 'H'): 15.0, ('K', 'K'): 3218.0,
('K', 'H'): 24.0, ('H', 'K'): 8.0, ('E', 'H'): 15.0, ('H', 'H'): 1235.0,
('H', 'E'): 18.0, ('H', 'D'): 0, ('K', 'D'): 0, ('K', 'E'): 9.0,
('D', 'R'): 48.0, ('E', 'R'): 2.0, ('D', 'K'): 1.0, ('E', 'K'): 45.0,
('K', 'R'): 130.0, ('E', 'D'): 241.0, ('E', 'E'): 3305.0,
('D', 'E'): 270.0, ('D', 'D'): 2360.0}

This information gives us our accepted number of replacements, or how often we expect different things to substitute for each other. It turns out, amazingly enough, that this is all of the information we need to go ahead and create a substitution matrix. First, we use the replacement dictionary information to create an Accepted Replacement Matrix (ARM):

>>> from Bio import SubsMat
>>> my_arm = SubsMat.SeqMat(replace_info)

With this accepted replacement matrix, we can go right ahead and create our log odds matrix (i. e. a standard type Substitution Matrix):

>>> my_lom = SubsMat.make_log_odds_matrix(my_arm)

The log odds matrix you create is customizable with the following optional arguments:

  • exp_freq_table – You can pass a table of expected frequencies for each alphabet. If supplied, this will be used instead of the passed accepted replacement matrix when calculate expected replacments.
  • logbase - The base of the logarithm taken to create the log odd matrix. Defaults to base 10.
  • factor - The factor to multiply each matrix entry by. This defaults to 10, which normally makes the matrix numbers easy to work with.
  • round_digit - The digit to round to in the matrix. This defaults to 0 (i. e. no digits).

Once you’ve got your log odds matrix, you can display it prettily using the function print_mat. Doing this on our created matrix gives:

>>> my_lom.print_mat()
D   2
E  -1   1
H  -5  -4   3
K -10  -5  -4   1
R  -4  -8  -4  -2   2
   D   E   H   K   R

Very nice. Now we’ve got our very own substitution matrix to play with!

18.5  BioSQL – storing sequences in a relational database

BioSQL is a joint effort between the OBF projects (BioPerl, BioJava etc) to support a shared database schema for storing sequence data. In theory, you could load a GenBank file into the database with BioPerl, then using Biopython extract this from the database as a record object with features - and get more or less the same thing as if you had loaded the GenBank file directly as a SeqRecord using Bio.SeqIO (Chapter 5).

Biopython’s BioSQL module is currently documented at http://biopython.org/wiki/BioSQL which is part of our wiki pages.

Chapter 19  The Biopython testing framework

Biopython has a regression testing framework (the file run_tests.py) based on unittest, the standard unit testing framework for Python. Providing comprehensive tests for modules is one of the most important aspects of making sure that the Biopython code is as bug-free as possible before going out. It also tends to be one of the most undervalued aspects of contributing. This chapter is designed to make running the Biopython tests and writing good test code as easy as possible. Ideally, every module that goes into Biopython should have a test (and should also have documentation!). All our developers, and anyone installing Biopython from source, are strongly encouraged to run the unit tests.

19.1  Running the tests

When you download the Biopython source code, or check it out from our source code repository, you should find a subdirectory call Tests. This contains the key script run_tests.py, lots of individual scripts named test_XXX.py, a subdirectory called output and lots of other subdirectories which contain input files for the test suite.

As part of building and installing Biopython you will typically run the full test suite at the command line from the Biopython source top level directory using the following: 

python setup.py test

This is actually equivalent to going to the Tests subdirectory and running: 

python run_tests.py

You’ll often want to run just some of the tests, and this is done like this: 

python run_tests.py test_SeqIO.py test_AlignIO.py

When giving the list of tests, the .py extension is optional, so you can also just type: 

python run_tests.py test_SeqIO test_AlignIO

To run the docstring tests (see section 19.3), you can use 

python run_tests.py doctest

By default, run_tests.py runs all tests, including the docstring tests.

If an individual test is failing, you can also try running it directly, which may give you more information.

Importantly, note that the individual unit tests come in two types:

  • Simple print-and-compare scripts. These unit tests are essentially short example Python programs, which print out various output text. For a test file named test_XXX.py there will be a matching text file called test_XXX under the output subdirectory which contains the expected output. All that the test framework does to is run the script, and check the output agrees.
  • Standard unittest- based tests. These will import unittest and then define unittest.TestCase classes, each with one or more sub-tests as methods starting with test_ which check some specific aspect of the code. These tests should not print any output directly.

Currently, about half of the Biopython tests are unittest-style tests, and half are print-and-compare tests.

Running a simple print-and-compare test directly will usually give lots of output on screen, but does not check the output matches the expected output. If the test is failing with an exception error, it should be very easy to locate where exactly the script is failing. For an example of a print-and-compare test, try: 

python test_SeqIO.py

The unittest-based tests instead show you exactly which sub-section(s) of the test are failing. For example, 

python test_Cluster.py

19.2  Writing tests

Let’s say you want to write some tests for a module called Biospam. This can be a module you wrote, or an existing module that doesn’t have any tests yet. In the examples below, we assume that Biospam is a module that does simple math.

Each Biopython test can have three important files and directories involved with it:

  1. test_Biospam.py – The actual test code for your module.
  2. Biospam [optional]– A directory where any necessary input files will be located. Any output files that will be generated should also be written here (and preferably cleaned up after the tests are done) to prevent clogging up the main Tests directory.
  3. output/Biospam – [for print-and-compare tests only] This file contains the expected output from running test_Biospam.py. This file is not needed for unittest-style tests, since there the validation is done in the test script test_Biospam.py itself.

It’s up to you to decide whether you want to write a print-and-compare test script or a unittest-style test script. The important thing is that you cannot mix these two styles in a single test script. Particularly, don’t use unittest features in a print-and-compare test.

Any script with a test_ prefix in the Tests directory will be found and run by run_tests.py. Below, we show an example test script test_Biospam.py both for a print-and-compare test and for a unittest-based test. If you put this script in the Biopython Tests directory, then run_tests.py will find it and execute the tests contained in it: 

$ python run_tests.py     
test_Ace ... ok
test_AlignIO ... ok
test_BioSQL ... ok
test_BioSQL_SeqIO ... ok
test_Biospam ... ok
test_CAPS ... ok
test_Clustalw ... ok

…

----------------------------------------------------------------------
Ran 107 tests in 86.127 seconds

19.2.1  Writing a print-and-compare test

A print-and-compare style test should be much simpler for beginners or novices to write - essentially it is just an example script using your new module.

Here is what you should do to make a print-and-compare test for the Biospam module.

  1. Write a script called test_Biospam.py
    • This script should live in the Tests directory
    • The script should test all of the important functionality of the module (the more you test the better your test is, of course!).
    • Try to avoid anything which might be platform specific, such as printing floating point numbers without using an explicit formatting string to avoid having too many decimal places (different platforms can give very slightly different values).
  2. If the script requires files to do the testing, these should go in the directory Tests/Biospam (if you just need something generic, like a FASTA sequence file, or a GenBank record, try and use an existing sample input file instead).
  3. Write out the test output and verify the output to be correct.

    There are two ways to do this:

    1. The long way:
      • Run the script and write its output to a file. On UNIX (including Linux and Mac OS X) machines, you would do something like: python test_Biospam.py > test_Biospam which would write the output to the file test_Biospam.
      • Manually look at the file test_Biospam to make sure the output is correct. When you are sure it is all right and there are no bugs, you need to quickly edit the test_Biospam file so that the first line is: ‘test_Biospam’ (no quotes).
      • copy the test_Biospam file to the directory Tests/output
    2. The quick way:
      • Run python run_tests.py -g test_Biospam.py. The regression testing framework is nifty enough that it’ll put the output in the right place in just the way it likes it.
      • Go to the output (which should be in Tests/output/test_Biospam) and double check the output to make sure it is all correct.
  4. Now change to the Tests directory and run the regression tests with python run_tests.py. This will run all of the tests, and you should see your test run (and pass!).
  5. That’s it! Now you’ve got a nice test for your module ready to check in, or submit to Biopython. Congratulations!

As an example, the test_Biospam.py test script to test the addition and multiplication functions in the Biospam module could look as follows:

from __future__ import print_function
from Bio import Biospam

print("2 + 3 =", Biospam.addition(2, 3))
print("9 - 1 =", Biospam.addition(9, -1))
print("2 * 3 =", Biospam.multiplication(2, 3))
print("9 * (- 1) =", Biospam.multiplication(9, -1))

We generate the corresponding output with python run_tests.py -g test_Biospam.py, and check the output file output/test_Biospam:

test_Biospam
2 + 3 = 5
9 - 1 = 8
2 * 3 = 6
9 * (- 1) = -9

Often, the difficulty with larger print-and-compare tests is to keep track which line in the output corresponds to which command in the test script. For this purpose, it is important to print out some markers to help you match lines in the input script with the generated output.

19.2.2  Writing a unittest-based test

We want all the modules in Biopython to have unit tests, and a simple print-and-compare test is better than no test at all. However, although there is a steeper learning curve, using the unittest framework gives a more structured result, and if there is a test failure this can clearly pinpoint which part of the test is going wrong. The sub-tests can also be run individually which is helpful for testing or debugging.

The unittest-framework has been included with Python since version 2.1, and is documented in the Python Library Reference (which I know you are keeping under your pillow, as recommended). There is also online documentaion for unittest. If you are familiar with the unittest system (or something similar like the nose test framework), you shouldn’t have any trouble. You may find looking at the existing example within Biopython helpful too.

Here’s a minimal unittest-style test script for Biospam, which you can copy and paste to get started:

import unittest
from Bio import Biospam

class BiospamTestAddition(unittest.TestCase):

    def test_addition1(self):
        result = Biospam.addition(2, 3)
        self.assertEqual(result, 5)

    def test_addition2(self):
        result = Biospam.addition(9, -1)
        self.assertEqual(result, 8)

class BiospamTestDivision(unittest.TestCase):

    def test_division1(self):
        result = Biospam.division(3.0, 2.0)
        self.assertAlmostEqual(result, 1.5)

    def test_division2(self):
        result = Biospam.division(10.0, -2.0)
        self.assertAlmostEqual(result, -5.0)


if __name__ == "__main__":
    runner = unittest.TextTestRunner(verbosity = 2)
    unittest.main(testRunner=runner)

In the division tests, we use assertAlmostEqual instead of assertEqual to avoid tests failing due to roundoff errors; see the unittest chapter in the Python documentation for details and for other functionality available in unittest (online reference).

These are the key points of unittest-based tests:

  • Test cases are stored in classes that derive from unittest.TestCase and cover one basic aspect of your code
  • You can use methods setUp and tearDown for any repeated code which should be run before and after each test method. For example, the setUp method might be used to create an instance of the object you are testing, or open a file handle. The tearDown should do any “tidying up”, for example closing the file handle.
  • The tests are prefixed with test_ and each test should cover one specific part of what you are trying to test. You can have as many tests as you want in a class.
  • At the end of the test script, you can use 
    if __name__ == "__main__":
    runner = unittest.TextTestRunner(verbosity = 2)
    unittest.main(testRunner=runner)
    to execute the tests when the script is run by itself (rather than imported from run_tests.py). If you run this script, then you’ll see something like the following:
    $ python test_BiospamMyModule.py
test_addition1 (__main__.TestAddition) ... ok
test_addition2 (__main__.TestAddition) ... ok
test_division1 (__main__.TestDivision) ... ok
test_division2 (__main__.TestDivision) ... ok

----------------------------------------------------------------------
Ran 4 tests in 0.059s

OK
  • To indicate more clearly what each test is doing, you can add docstrings to each test. These are shown when running the tests, which can be useful information if a test is failing.
    import unittest
from Bio import Biospam

class BiospamTestAddition(unittest.TestCase):

    def test_addition1(self):
        """An addition test"""
        result = Biospam.addition(2, 3)
        self.assertEqual(result, 5)

    def test_addition2(self):
        """A second addition test"""
        result = Biospam.addition(9, -1)
        self.assertEqual(result, 8)

class BiospamTestDivision(unittest.TestCase):

    def test_division1(self):
        """Now let's check division"""
        result = Biospam.division(3.0, 2.0)
        self.assertAlmostEqual(result, 1.5)

    def test_division2(self):
        """A second division test"""
        result = Biospam.division(10.0, -2.0)
        self.assertAlmostEqual(result, -5.0)


if __name__ == "__main__":
    runner = unittest.TextTestRunner(verbosity = 2)
    unittest.main(testRunner=runner)

    Running the script will now show you:

    $ python test_BiospamMyModule.py
An addition test ... ok
A second addition test ... ok
Now let's check division ... ok
A second division test ... ok

----------------------------------------------------------------------
Ran 4 tests in 0.001s

OK

If your module contains docstring tests (see section 19.3), you may want to include those in the tests to be run. You can do so as follows by modifying the code under if __name__ == "__main__": to look like this:

if __name__ == "__main__":
    unittest_suite = unittest.TestLoader().loadTestsFromName("test_Biospam")
    doctest_suite = doctest.DocTestSuite(Biospam)
    suite = unittest.TestSuite((unittest_suite, doctest_suite))
    runner = unittest.TextTestRunner(sys.stdout, verbosity = 2)
    runner.run(suite)

This is only relevant if you want to run the docstring tests when you execute python test_Biospam.py; with python run_tests.py, the docstring tests are run automatically (assuming they are included in the list of docstring tests in run_tests.py, see the section below).

19.3  Writing doctests

Python modules, classes and functions support built in documentation using docstrings. The doctest framework (included with Python) allows the developer to embed working examples in the docstrings, and have these examples automatically tested.

Currently only a small part of Biopython includes doctests. The run_tests.py script takes care of running the doctests. For this purpose, at the top of the run_tests.py script is a manually compiled list of modules to test, which allows us to skip modules with optional external dependencies which may not be installed (e.g. the Reportlab and NumPy libraries). So, if you’ve added some doctests to the docstrings in a Biopython module, in order to have them included in the Biopython test suite, you must update run_tests.py to include your module. Currently, the relevant part of run_tests.py looks as follows:

# This is the list of modules containing docstring tests.
# If you develop docstring tests for other modules, please add
# those modules here.
DOCTEST_MODULES = ["Bio.Seq",
                   "Bio.SeqRecord",
                   "Bio.SeqIO",
                   "...",
                  ]
#Silently ignore any doctests for modules requiring numpy!
try:
    import numpy
    DOCTEST_MODULES.extend(["Bio.Statistics.lowess"])
except ImportError:
    pass

Note that we regard doctests primarily as documentation, so you should stick to typical usage. Generally complicated examples dealing with error conditions and the like would be best left to a dedicated unit test.

Note that if you want to write doctests involving file parsing, defining the file location complicates matters. Ideally use relative paths assuming the code will be run from the Tests directory, see the Bio.SeqIO doctests for an example of this.

To run the docstring tests only, use 

$ python run_tests.py doctest

Chapter 20  Advanced

20.1  Parser Design

Many of the older Biopython parsers were built around an event-oriented design that includes Scanner and Consumer objects.

Scanners take input from a data source and analyze it line by line, sending off an event whenever it recognizes some information in the data. For example, if the data includes information about an organism name, the scanner may generate an organism_name event whenever it encounters a line containing the name.

Consumers are objects that receive the events generated by Scanners. Following the previous example, the consumer receives the organism_name event, and the processes it in whatever manner necessary in the current application.

This is a very flexible framework, which is advantageous if you want to be able to parse a file format into more than one representation. For example, the Bio.GenBank module uses this to construct either SeqRecord objects or file-format-specific record objects.

More recently, many of the parsers added for Bio.SeqIO and Bio.AlignIO take a much simpler approach, but only generate a single object representation (SeqRecord and MultipleSeqAlignment objects respectively). In some cases the Bio.SeqIO parsers actually wrap another Biopython parser - for example, the Bio.SwissProt parser produces SwissProt format specific record objects, which get converted into SeqRecord objects.

20.2  Substitution Matrices

20.2.1  SubsMat

This module provides a class and a few routines for generating substitution matrices, similar to BLOSUM or PAM matrices, but based on user-provided data. Additionally, you may select a matrix from MatrixInfo.py, a collection of established substitution matrices. The SeqMat class derives from a dictionary: 

class SeqMat(dict)

The dictionary is of the form {(i1,j1):n1, (i1,j2):n2,...,(ik,jk):nk} where i, j are alphabet letters, and n is a value.

  1. Attributes
    1. self.alphabet: a class as defined in Bio.Alphabet
    2. self.ab_list: a list of the alphabet’s letters, sorted. Needed mainly for internal purposes
  2. Methods
    1. __init__(self,data=None,alphabet=None, mat_name='', build_later=0):
      1. data: can be either a dictionary, or another SeqMat instance.
      2. alphabet: a Bio.Alphabet instance. If not provided, construct an alphabet from data.
      3. mat_name: matrix name, such as "BLOSUM62" or "PAM250"
      4. build_later: default false. If true, user may supply only alphabet and empty dictionary, if intending to build the matrix later. this skips the sanity check of alphabet size vs. matrix size.
    2. entropy(self,obs_freq_mat)
      1. obs_freq_mat: an observed frequency matrix. Returns the matrix’s entropy, based on the frequency in obs_freq_mat. The matrix instance should be LO or SUBS. 
    3. sum(self)
      Calculates the sum of values for each letter in the matrix’s alphabet, and returns it as a dictionary of the form {i1: s1, i2: s2,...,in:sn}, where:
      • i: an alphabet letter;
      • s: sum of all values in a half-matrix for that letter;
      • n: number of letters in alphabet. 
    4. print_mat(self,f,format="%4d",bottomformat="%4s",alphabet=None)

      prints the matrix to file handle f. format is the format field for the matrix values; bottomformat is the format field for the bottom row, containing matrix letters. Example output for a 3-letter alphabet matrix:

      A 23
B 12 34
C 7  22  27
  A   B   C

      The alphabet optional argument is a string of all characters in the alphabet. If supplied, the order of letters along the axes is taken from the string, rather than by alphabetical order.

  3. Usage

    The following section is laid out in the order by which most people wish to generate a log-odds matrix. Of course, interim matrices can be generated and investigated. Most people just want a log-odds matrix, that’s all.

    1. Generating an Accepted Replacement Matrix

      Initially, you should generate an accepted replacement matrix (ARM) from your data. The values in ARM are the counted number of replacements according to your data. The data could be a set of pairs or multiple alignments. So for instance if Alanine was replaced by Cysteine 10 times, and Cysteine by Alanine 12 times, the corresponding ARM entries would be:

      ('A','C'): 10, ('C','A'): 12

      as order doesn’t matter, user can already provide only one entry:

      ('A','C'): 22

      A SeqMat instance may be initialized with either a full (first method of counting: 10, 12) or half (the latter method, 22) matrices. A full protein alphabet matrix would be of the size 20x20 = 400. A half matrix of that alphabet would be 20x20/2 + 20/2 = 210. That is because same-letter entries don’t change. (The matrix diagonal). Given an alphabet size of N:

      1. Full matrix size:N*N
      2. Half matrix size: N(N+1)/2

      The SeqMat constructor automatically generates a half-matrix, if a full matrix is passed. If a half matrix is passed, letters in the key should be provided in alphabetical order: (’A’,’C’) and not (’C’,A’).

      At this point, if all you wish to do is generate a log-odds matrix, please go to the section titled Example of Use. The following text describes the nitty-gritty of internal functions, to be used by people who wish to investigate their nucleotide/amino-acid frequency data more thoroughly.

    2. Generating the observed frequency matrix (OFM)

      Use: 

      OFM = SubsMat._build_obs_freq_mat(ARM)

      The OFM is generated from the ARM, only instead of replacement counts, it contains replacement frequencies.

    3. Generating an expected frequency matrix (EFM)

      Use:

      EFM = SubsMat._build_exp_freq_mat(OFM,exp_freq_table)
      1. exp_freq_table: should be a FreqTable instance. See section 20.2.2 for detailed information on FreqTable. Briefly, the expected frequency table has the frequencies of appearance for each member of the alphabet. It is implemented as a dictionary with the alphabet letters as keys, and each letter’s frequency as a value. Values sum to 1.

      The expected frequency table can (and generally should) be generated from the observed frequency matrix. So in most cases you will generate exp_freq_table using:

      >>> exp_freq_table = SubsMat._exp_freq_table_from_obs_freq(OFM)
>>> EFM = SubsMat._build_exp_freq_mat(OFM, exp_freq_table)

      But you can supply your own exp_freq_table, if you wish

    4. Generating a substitution frequency matrix (SFM)

      Use:

      SFM = SubsMat._build_subs_mat(OFM,EFM)

      Accepts an OFM, EFM. Provides the division product of the corresponding values.

    5. Generating a log-odds matrix (LOM)

      Use: 

      LOM=SubsMat._build_log_odds_mat(SFM[,logbase=10,factor=10.0,round_digit=1])
      1. Accepts an SFM.
      2. logbase: base of the logarithm used to generate the log-odds values.
      3. factor: factor used to multiply the log-odds values. Each entry is generated by log(LOM[key])*factor And rounded to the round_digit place after the decimal point, if required.
  4. Example of use

    As most people would want to generate a log-odds matrix, with minimum hassle, SubsMat provides one function which does it all:

    make_log_odds_matrix(acc_rep_mat,exp_freq_table=None,logbase=10,
                      factor=10.0,round_digit=0):
    1. acc_rep_mat: user provided accepted replacements matrix
    2. exp_freq_table: expected frequencies table. Used if provided, if not, generated from the acc_rep_mat.
    3. logbase: base of logarithm for the log-odds matrix. Default base 10.
    4. round_digit: number after decimal digit to which result should be rounded. Default zero.

20.2.2  FreqTable

FreqTable.FreqTable(UserDict.UserDict)
  1. Attributes:
    1. alphabet: A Bio.Alphabet instance.
    2. data: frequency dictionary
    3. count: count dictionary (in case counts are provided).
  2. Functions:
    1. read_count(f): read a count file from stream f. Then convert to frequencies
    2. read_freq(f): read a frequency data file from stream f. Of course, we then don’t have the counts, but it is usually the letter frquencies which are interesting.
  3. Example of use: The expected count of the residues in the database is sitting in a file, whitespace delimited, in the following format (example given for a 3-letter alphabet):
    A   35
B   65
C   100

    And will be read using the FreqTable.read_count(file_handle) function.

    An equivalent frequency file:

    A  0.175
B  0.325
C  0.5

    Conversely, the residue frequencies or counts can be passed as a dictionary. Example of a count dictionary (3-letter alphabet):

    {'A': 35, 'B': 65, 'C': 100}

    Which means that an expected data count would give a 0.5 frequency for ’C’, a 0.325 probability of ’B’ and a 0.175 probability of ’A’ out of 200 total, sum of A, B and C)

    A frequency dictionary for the same data would be:

    {'A': 0.175, 'B': 0.325, 'C': 0.5}

    Summing up to 1.

    When passing a dictionary as an argument, you should indicate whether it is a count or a frequency dictionary. Therefore the FreqTable class constructor requires two arguments: the dictionary itself, and FreqTable.COUNT or FreqTable.FREQ indicating counts or frequencies, respectively.

    Read expected counts. readCount will already generate the frequencies Any one of the following may be done to geerate the frequency table (ftab):

    >>> from SubsMat import *
>>> ftab = FreqTable.FreqTable(my_frequency_dictionary, FreqTable.FREQ)
>>> ftab = FreqTable.FreqTable(my_count_dictionary, FreqTable.COUNT)
>>> ftab = FreqTable.read_count(open('myCountFile'))
>>> ftab = FreqTable.read_frequency(open('myFrequencyFile'))

Chapter 21  Where to go from here – contributing to Biopython

21.1  Bug Reports + Feature Requests

Getting feedback on the Biopython modules is very important to us. Open-source projects like this benefit greatly from feedback, bug-reports (and patches!) from a wide variety of contributors.

The main forums for discussing feature requests and potential bugs are the Biopython mailing lists:

Additionally, if you think you’ve found a new bug, you can submit it to our issue tracker at https://github.com/biopython/biopython/issues (this has replaced the older tracker hosted at http://redmine.open-bio.org/projects/biopython). This way, it won’t get buried in anyone’s Inbox and forgotten about.

21.2  Mailing lists and helping newcomers

We encourage all our uses to sign up to the main Biopython mailing list. Once you’ve got the hang of an area of Biopython, we’d encourage you to help answer questions from beginners. After all, you were a beginner once.

21.3  Contributing Documentation

We’re happy to take feedback or contributions - either via a bug-report or on the Mailing List. While reading this tutorial, perhaps you noticed some topics you were interested in which were missing, or not clearly explained. There is also Biopython’s built in documentation (the docstrings, these are also online), where again, you may be able to help fill in any blanks.

21.4  Contributing cookbook examples

As explained in Chapter 18, Biopython now has a wiki collection of user contributed “cookbook” examples, http://biopython.org/wiki/Category:Cookbook – maybe you can add to this?

21.5  Maintaining a distribution for a platform

We currently provide source code archives (suitable for any OS, if you have the right build tools installed), and Windows Installers which are just click and run. This covers all the major operating systems.

Most major Linux distributions have volunteers who take these source code releases, and compile them into packages for Linux users to easily install (taking care of dependencies etc). This is really great and we are of course very grateful. If you would like to contribute to this work, please find out more about how your Linux distribution handles this.

Below are some tips for certain platforms to maybe get people started with helping out:

Windows
– Windows products typically have a nice graphical installer that installs all of the essential components in the right place. We use Distutils to create a installer of this type fairly easily.

You must first make sure you have a C compiler on your Windows computer, and that you can compile and install things (this is the hard bit - see the Biopython installation instructions for info on how to do this).

Once you are setup with a C compiler, making the installer just requires doing:

python setup.py bdist_wininst

Now you’ve got a Windows installer. Congrats! At the moment we have no trouble shipping installers built on 32 bit windows. If anyone would like to look into supporting 64 bit Windows that would be great.

RPMs
– RPMs are pretty popular package systems on some Linux platforms. There is lots of documentation on RPMs available at http://www.rpm.org to help you get started with them. To create an RPM for your platform is really easy. You just need to be able to build the package from source (having a C compiler that works is thus essential) – see the Biopython installation instructions for more info on this.

To make the RPM, you just need to do:

python setup.py bdist_rpm

This will create an RPM for your specific platform and a source RPM in the directory dist. This RPM should be good and ready to go, so this is all you need to do! Nice and easy.

Macintosh
– Since Apple moved to Mac OS X, things have become much easier on the Mac. We generally treat it as just another Unix variant, and installing Biopython from source is just as easy as on Linux. The easiest way to get all the GCC compilers etc installed is to install Apple’s X-Code. We might be able to provide click and run installers for Mac OS X, but to date there hasn’t been any demand.

Once you’ve got a package, please test it on your system to make sure it installs everything in a good way and seems to work properly. Once you feel good about it, send it off to one of the Biopython developers (write to our main mailing list at biopython@biopython.org if you’re not sure who to send it to) and you’ve done it. Thanks!

21.6  Contributing Unit Tests

Even if you don’t have any new functionality to add to Biopython, but you want to write some code, please consider extending our unit test coverage. We’ve devoted all of Chapter 19 to this topic.

21.7  Contributing Code

There are no barriers to joining Biopython code development other than an interest in creating biology-related code in Python. The best place to express an interest is on the Biopython mailing lists – just let us know you are interested in coding and what kind of stuff you want to work on. Normally, we try to have some discussion on modules before coding them, since that helps generate good ideas – then just feel free to jump right in and start coding!

The main Biopython release tries to be fairly uniform and interworkable, to make it easier for users. You can read about some of (fairly informal) coding style guidelines we try to use in Biopython in the contributing documentation at http://biopython.org/wiki/Contributing. We also try to add code to the distribution along with tests (see Chapter 19 for more info on the regression testing framework) and documentation, so that everything can stay as workable and well documented as possible (including docstrings). This is, of course, the most ideal situation, under many situations you’ll be able to find other people on the list who will be willing to help add documentation or more tests for your code once you make it available. So, to end this paragraph like the last, feel free to start working!

Please note that to make a code contribution you must have the legal right to contribute it and license it under the Biopython license. If you wrote it all yourself, and it is not based on any other code, this shouldn’t be a problem. However, there are issues if you want to contribute a derivative work - for example something based on GPL or LPGL licenced code would not be compatible with our license. If you have any queries on this, please discuss the issue on the biopython-dev mailing list.

Another point of concern for any additions to Biopython regards any build time or run time dependencies. Generally speaking, writing code to interact with a standalone tool (like BLAST, EMBOSS or ClustalW) doesn’t present a big problem. However, any dependency on another library - even a Python library (especially one needed in order to compile and install Biopython like NumPy) would need further discussion.

Additionally, if you have code that you don’t think fits in the distribution, but that you want to make available, we maintain Script Central (http://biopython.org/wiki/Scriptcentral) which has pointers to freely available code in Python for bioinformatics.

Hopefully this documentation has got you excited enough about Biopython to try it out (and most importantly, contribute!). Thanks for reading all the way through!

Chapter 22  Appendix: Useful stuff about Python

If you haven’t spent a lot of time programming in Python, many questions and problems that come up in using Biopython are often related to Python itself. This section tries to present some ideas and code that come up often (at least for us!) while using the Biopython libraries. If you have any suggestions for useful pointers that could go here, please contribute!

22.1  What the heck is a handle?

Handles are mentioned quite frequently throughout this documentation, and are also fairly confusing (at least to me!). Basically, you can think of a handle as being a “wrapper” around text information.

Handles provide (at least) two benefits over plain text information:

  1. They provide a standard way to deal with information stored in different ways. The text information can be in a file, or in a string stored in memory, or the output from a command line program, or at some remote website, but the handle provides a common way of dealing with information in all of these formats.
  2. They allow text information to be read incrementally, instead of all at once. This is really important when you are dealing with huge text files which would use up all of your memory if you had to load them all.

Handles can deal with text information that is being read (e. g. reading from a file) or written (e. g. writing information to a file). In the case of a “read” handle, commonly used functions are read(), which reads the entire text information from the handle, and readline(), which reads information one line at a time. For “write” handles, the function write() is regularly used.

The most common usage for handles is reading information from a file, which is done using the built-in Python function open. Here, we open a handle to the file m_cold.fasta (also available online here):

>>> handle = open("m_cold.fasta", "r")
>>> handle.readline()
">gi|8332116|gb|BE037100.1|BE037100 MP14H09 MP Mesembryanthemum ...\n"

Handles are regularly used in Biopython for passing information to parsers. For example, since Biopython 1.54 the main functions in Bio.SeqIO and Bio.AlignIO have allowed you to use a filename instead of a handle:

from Bio import SeqIO
for record in SeqIO.parse("m_cold.fasta", "fasta"):
    print(record.id, len(record))

On older versions of Biopython you had to use a handle, e.g.

from Bio import SeqIO
handle = open("m_cold.fasta", "r")
for record in SeqIO.parse(handle, "fasta"):
    print(record.id, len(record))
handle.close()

This pattern is still useful - for example suppose you have a gzip compressed FASTA file you want to parse:

import gzip
from Bio import SeqIO
handle = gzip.open("m_cold.fasta.gz")
for record in SeqIO.parse(handle, "fasta"):
    print(record.id, len(record))
handle.close()

See Section 5.2 for more examples like this, including reading bzip2 compressed files.

22.1.1  Creating a handle from a string

One useful thing is to be able to turn information contained in a string into a handle. The following example shows how to do this using cStringIO from the Python standard library:

>>> my_info = 'A string\n with multiple lines.'
>>> print(my_info)
A string
 with multiple lines.
>>> from StringIO import StringIO
>>> my_info_handle = StringIO(my_info)
>>> first_line = my_info_handle.readline()
>>> print(first_line)
A string
<BLANKLINE>
>>> second_line = my_info_handle.readline()
>>> print(second_line)
 with multiple lines.

References

[1]
Peter J. A. Cock, Tiago Antao, Jeffrey T. Chang, Brad A. Chapman, Cymon J. Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski, Michiel J. L. de Hoon: “Biopython: freely available Python tools for computational molecular biology and bioinformatics”. Bioinformatics 25 (11), 1422–1423 (2009). doi:10.1093/bioinformatics/btp163,
[2]
Leighton Pritchard, Jennifer A. White, Paul R.J. Birch, Ian K. Toth: “GenomeDiagram: a python package for the visualization of large-scale genomic data”. Bioinformatics 22 (5): 616–617 (2006). doi:10.1093/bioinformatics/btk021,
[3]
Ian K. Toth, Leighton Pritchard, Paul R. J. Birch: “Comparative genomics reveals what makes an enterobacterial plant pathogen”. Annual Review of Phytopathology 44: 305–336 (2006). doi:10.1146/annurev.phyto.44.070505.143444,
[4]
Géraldine A. van der Auwera, Jaroslaw E. Król, Haruo Suzuki, Brian Foster, Rob van Houdt, Celeste J. Brown, Max Mergeay, Eva M. Top: “Plasmids captured in C. metallidurans CH34: defining the PromA family of broad-host-range plasmids”. Antonie van Leeuwenhoek 96 (2): 193–204 (2009). doi:10.1007/s10482-009-9316-9
[5]
Caroline Proux, Douwe van Sinderen, Juan Suarez, Pilar Garcia, Victor Ladero, Gerald F. Fitzgerald, Frank Desiere, Harald Brüssow: “The dilemma of phage taxonomy illustrated by comparative genomics of Sfi21-Like Siphoviridae in lactic acid bacteria”. Journal of Bacteriology 184 (21): 6026–6036 (2002). http://dx.doi.org/10.1128/JB.184.21.6026-6036.2002
[6]
Florian Jupe, Leighton Pritchard, Graham J. Etherington, Katrin MacKenzie, Peter JA Cock, Frank Wright, Sanjeev Kumar Sharma1, Dan Bolser, Glenn J Bryan, Jonathan DG Jones, Ingo Hein: “Identification and localisation of the NB-LRR gene family within the potato genome”. BMC Genomics 13: 75 (2012). http://dx.doi.org/10.1186/1471-2164-13-75
[7]
Peter J. A. Cock, Christopher J. Fields, Naohisa Goto, Michael L. Heuer, Peter M. Rice: “The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants”. Nucleic Acids Research 38 (6): 1767–1771 (2010). doi:10.1093/nar/gkp1137
[8]
Patrick O. Brown, David Botstein: “Exploring the new world of the genome with DNA microarrays”. Nature Genetics 21 (Supplement 1), 33–37 (1999). doi:10.1038/4462
[9]
Eric Talevich, Brandon M. Invergo, Peter J.A. Cock, Brad A. Chapman: “Bio.Phylo: A unified toolkit for processing, analyzing and visualizing phylogenetic trees in Biopython”. BMC Bioinformatics 13: 209 (2012). doi:10.1186/1471-2105-13-209
[10]
Athel Cornish-Bowden: “Nomenclature for incompletely specified bases in nucleic acid sequences: Recommendations 1984.” Nucleic Acids Research 13 (9): 3021–3030 (1985). doi:10.1093/nar/13.9.3021
[11]
Douglas R. Cavener: “Comparison of the consensus sequence flanking translational start sites in Drosophila and vertebrates.” Nucleic Acids Research 15 (4): 1353–1361 (1987). doi:10.1093/nar/15.4.1353
[12]
Timothy L. Bailey and Charles Elkan: “Fitting a mixture model by expectation maximization to discover motifs in biopolymers”, Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology 28–36. AAAI Press, Menlo Park, California (1994).
[13]
Brad Chapman and Jeff Chang: “Biopython: Python tools for computational biology”. ACM SIGBIO Newsletter 20 (2): 15–19 (August 2000).
[14]
Michiel J. L. de Hoon, Seiya Imoto, John Nolan, Satoru Miyano: “Open source clustering software”. Bioinformatics 20 (9): 1453–1454 (2004). doi:10.1093/bioinformatics/bth078
[15]
Michiel B. Eisen, Paul T. Spellman, Patrick O. Brown, David Botstein: “Cluster analysis and display of genome-wide expression patterns”. Proceedings of the National Academy of Science USA 95 (25): 14863–14868 (1998). doi:10.1073/pnas.96.19.10943-c
[16]
Gene H. Golub, Christian Reinsch: “Singular value decomposition and least squares solutions”. In Handbook for Automatic Computation, 2, (Linear Algebra) (J. H. Wilkinson and C. Reinsch, eds), 134–151. New York: Springer-Verlag (1971).
[17]
Gene H. Golub, Charles F. Van Loan: Matrix computations, 2nd edition (1989).
[18]
Thomas Hamelryck and Bernard Manderick: 11PDB parser and structure class implemented in Python”. Bioinformatics, 19 (17): 2308–2310 (2003) doi: 10.1093/bioinformatics/btg299.
[19]
Thomas Hamelryck: “Efficient identification of side-chain patterns using a multidimensional index tree”. Proteins 51 (1): 96–108 (2003). doi:10.1002/prot.10338
[20]
Thomas Hamelryck: “An amino acid has two sides; A new 2D measure provides a different view of solvent exposure”. Proteins 59 (1): 29–48 (2005). doi:10.1002/prot.20379.
[21]
John A. Hartiga. Clustering algorithms. New York: Wiley (1975).
[22]
Anil L. Jain, Richard C. Dubes: Algorithms for clustering data. Englewood Cliffs, N.J.: Prentice Hall (1988).
[23]
Voratas Kachitvichyanukul, Bruce W. Schmeiser: Binomial Random Variate Generation. Communications of the ACM 31 (2): 216–222 (1988). doi:10.1145/42372.42381
[24]
Teuvo Kohonen: “Self-organizing maps”, 2nd Edition. Berlin; New York: Springer-Verlag (1997).
[25]
Pierre L’Ecuyer: “Efficient and Portable Combined Random Number Generators.” Communications of the ACM 31 (6): 742–749,774 (1988). doi:10.1145/62959.62969
[26]
Indraneel Majumdar, S. Sri Krishna, Nick V. Grishin: “PALSSE: A program to delineate linear secondary structural elements from protein structures.” BMC Bioinformatics, 6: 202 (2005). doi:10.1186/1471-2105-6-202.
[27]
V. Matys, E. Fricke, R. Geffers, E. Gössling, M. Haubrock, R. Hehl, K. Hornischer, D. Karas, A.E. Kel, O.V. Kel-Margoulis, D.U. Kloos, S. Land, B. Lewicki-Potapov, H. Michael, R. Münch, I. Reuter, S. Rotert, H. Saxel, M. Scheer, S. Thiele, E. Wingender E: “TRANSFAC: transcriptional regulation, from patterns to profiles.” Nucleic Acids Research 31 (1): 374–378 (2003). doi:10.1093/nar/gkg108
[28]
Robin Sibson: “SLINK: An optimally efficient algorithm for the single-link cluster method”. The Computer Journal 16 (1): 30–34 (1973). doi:10.1093/comjnl/16.1.30
[29]
George W. Snedecor, William G. Cochran: Statistical methods. Ames, Iowa: Iowa State University Press (1989).
[30]
Pablo Tamayo, Donna Slonim, Jill Mesirov, Qing Zhu, Sutisak Kitareewan, Ethan Dmitrovsky, Eric S. Lander, Todd R. Golub: “Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation”. Proceedings of the National Academy of Science USA 96 (6): 2907–2912 (1999). doi:10.1073/pnas.96.6.2907
[31]
Robert C. Tryon, Daniel E. Bailey: Cluster analysis. New York: McGraw-Hill (1970).
[32]
John W. Tukey: “Exploratory data analysis”. Reading, Mass.: Addison-Wesley Pub. Co. (1977).
[33]
Ka Yee Yeung, Walter L. Ruzzo: “Principal Component Analysis for clustering gene expression data”. Bioinformatics 17 (9): 763–774 (2001). doi:10.1093/bioinformatics/17.9.763
[34]
Alok Saldanha: “Java Treeview—extensible visualization of microarray data”. Bioinformatics 20 (17): 3246–3248 (2004). http://dx.doi.org/10.1093/bioinformatics/bth349
This document was translated from LATEX by HEVEA.
biopython-1.63/Doc/Tutorial.tex0000644000175000017500000313301012250104543016103 0ustar tratra00000000000000% This is the main LaTeX file which is used to produce the Biopython % Tutorial documentation. % % If you just want to read the documentation, you can pick up ready-to-go % copies in both pdf and html format from: % % http://biopython.org/DIST/docs/tutorial/Tutorial.html % http://biopython.org/DIST/docs/tutorial/Tutorial.pdf % % If you want to typeset the documentation, you'll need a standard TeX/LaTeX % distribution (I use teTeX, which works great for me on Unix platforms). % Additionally, you need HeVeA (or at least hevea.sty), which can be % found at: % % http://pauillac.inria.fr/~maranget/hevea/index.html % % You will also need the pictures included in the document, some of % which are UMLish diagrams created by Dia % (http://www.lysator.liu.se/~alla/dia/dia.html). % These diagrams are available from Biopython git in the original dia % format, which you can easily save as .png format using Dia itself. % They are also checked in as the png files, so if you make % modifications to the original dia files, the png files should also be % changed. % % Once you're all set, you should be able to generate pdf by running: % % pdflatex Tutorial.tex (to generate the first draft) % pdflatex Tutorial.tex (to get the cross references right) % pdflatex Tutorial.tex (to get the table of contents right) % % To generate the html, you'll need HeVeA installed. You should be % able to just run: % % hevea -fix Tutorial.tex % % However, on older versions of hevea you may first need to remove the % Tutorial.aux file generated by LaTeX, then run hevea twice to get % the references right. % % If you want to typeset this and have problems, please report them % at biopython-dev@biopython.org, and we'll try to get things resolved. We % always love to have people interested in the documentation! \documentclass{report} \usepackage{url} \usepackage{fullpage} \usepackage{hevea} \usepackage{graphicx} % make everything have section numbers \setcounter{secnumdepth}{4} % Make links between references \usepackage{hyperref} \newif\ifpdf \ifx\pdfoutput\undefined \pdffalse \else \pdfoutput=1 \pdftrue \fi \ifpdf \hypersetup{colorlinks=true, hyperindex=true, citecolor=red, urlcolor=blue} \fi \begin{document} \begin{htmlonly} \title{Biopython Tutorial and Cookbook} \end{htmlonly} \begin{latexonly} \title{ %Hack to get the logo on the PDF front page: \includegraphics[width=\textwidth]{images/biopython.jpg}\\ %Hack to get some white space using a blank line: ~\\ Biopython Tutorial and Cookbook} \end{latexonly} \author{Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck, \\ Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek Wilczy\'{n}ski} \date{Last Update -- 1 December 2013 (Biopython 1.63)} %Hack to get the logo at the start of the HTML front page: %(hopefully this isn't going to be too wide for most people) \begin{rawhtml}

[Biopython Logo]

\end{rawhtml} \maketitle \tableofcontents \chapter{Introduction} \label{chapter:introduction} \section{What is Biopython?} The Biopython Project is an international association of developers of freely available Python (\url{http://www.python.org}) tools for computational molecular biology. Python is an object oriented, interpreted, flexible language that is becoming increasingly popular for scientific computing. Python is easy to learn, has a very clear syntax and can easily be extended with modules written in C, C++ or FORTRAN. The Biopython web site (\url{http://www.biopython.org}) provides an online resource for modules, scripts, and web links for developers of Python-based software for bioinformatics use and research. Basically, the goal of Biopython is to make it as easy as possible to use Python for bioinformatics by creating high-quality, reusable modules and classes. Biopython features include parsers for various Bioinformatics file formats (BLAST, Clustalw, FASTA, Genbank,...), access to online services (NCBI, Expasy,...), interfaces to common and not-so-common programs (Clustalw, DSSP, MSMS...), a standard sequence class, various clustering modules, a KD tree data structure etc. and even documentation. Basically, we just like to program in Python and want to make it as easy as possible to use Python for bioinformatics by creating high-quality, reusable modules and scripts. \section{What can I find in the Biopython package} The main Biopython releases have lots of functionality, including: \begin{itemize} \item The ability to parse bioinformatics files into Python utilizable data structures, including support for the following formats: \begin{itemize} \item Blast output -- both from standalone and WWW Blast \item Clustalw \item FASTA \item GenBank \item PubMed and Medline \item ExPASy files, like Enzyme and Prosite \item SCOP, including `dom' and `lin' files \item UniGene \item SwissProt \end{itemize} \item Files in the supported formats can be iterated over record by record or indexed and accessed via a Dictionary interface. \item Code to deal with popular on-line bioinformatics destinations such as: \begin{itemize} \item NCBI -- Blast, Entrez and PubMed services \item ExPASy -- Swiss-Prot and Prosite entries, as well as Prosite searches \end{itemize} \item Interfaces to common bioinformatics programs such as: \begin{itemize} \item Standalone Blast from NCBI \item Clustalw alignment program \item EMBOSS command line tools \end{itemize} \item A standard sequence class that deals with sequences, ids on sequences, and sequence features. \item Tools for performing common operations on sequences, such as translation, transcription and weight calculations. \item Code to perform classification of data using k Nearest Neighbors, Naive Bayes or Support Vector Machines. \item Code for dealing with alignments, including a standard way to create and deal with substitution matrices. \item Code making it easy to split up parallelizable tasks into separate processes. \item GUI-based programs to do basic sequence manipulations, translations, BLASTing, etc. \item Extensive documentation and help with using the modules, including this file, on-line wiki documentation, the web site, and the mailing list. \item Integration with BioSQL, a sequence database schema also supported by the BioPerl and BioJava projects. \end{itemize} We hope this gives you plenty of reasons to download and start using Biopython! \section{Installing Biopython} All of the installation information for Biopython was separated from this document to make it easier to keep updated. The short version is go to our downloads page (\url{http://biopython.org/wiki/Download}), download and install the listed dependencies, then download and install Biopython. Biopython runs on many platforms (Windows, Mac, and on the various flavors of Linux and Unix). For Windows we provide pre-compiled click-and-run installers, while for Unix and other operating systems you must install from source as described in the included README file. This is usually as simple as the standard commands: \begin{verbatim} python setup.py build python setup.py test sudo python setup.py install \end{verbatim} \noindent (You can in fact skip the build and test, and go straight to the install -- but its better to make sure everything seems to be working.) The longer version of our installation instructions covers installation of Python, Biopython dependencies and Biopython itself. It is available in PDF (\url{http://biopython.org/DIST/docs/install/Installation.pdf}) and HTML formats (\url{http://biopython.org/DIST/docs/install/Installation.html}). \section{Frequently Asked Questions (FAQ)} \begin{enumerate} \item \emph{How do I cite Biopython in a scientific publication?} \\ Please cite our application note \cite[Cock \textit{et al.}, 2009]{cock2009} as the main Biopython reference. In addition, please cite any publications from the following list if appropriate, in particular as a reference for specific modules within Biopython (more information can be found on our website): \begin{itemize} \item For the official project announcement: \cite[Chapman and Chang, 2000]{chapman2000}; \item For \verb+Bio.PDB+: \cite[Hamelryck and Manderick, 2003]{hamelryck2003a}; \item For \verb+Bio.Cluster+: \cite[De Hoon \textit{et al.}, 2004]{dehoon2004}; \item For \verb+Bio.Graphics.GenomeDiagram+: \cite[Pritchard \textit{et al.}, 2006]{pritchard2006}; \item For \verb+Bio.Phylo+ and \verb+Bio.Phylo.PAML+: \cite[Talevich \textit{et al.}, 2012]{talevich2012}; \item For the FASTQ file format as supported in Biopython, BioPerl, BioRuby, BioJava, and EMBOSS: \cite[Cock \textit{et al.}, 2010]{cock2010}. \end{itemize} \item \emph{How should I capitalize ``Biopython''? Is ``BioPython'' OK?} \\ The correct capitalization is ``Biopython'', not ``BioPython'' (even though that would have matched BioPerl, BioJava and BioRuby). \item \emph{What is going wrong with my print commands?} \\ This tutorial now uses the Python 3 style print \emph{function}. As of Biopython 1.62, we support both Python 2 and Python 3. The most obvious language difference is the print \emph{statement} in Python 2 became a print \emph{function} in Python 3. For example, this will only work under Python 2: \begin{verbatim} >>> print "Hello World!" Hello World! \end{verbatim} If you try that on Python 3 you'll get a \verb|SyntaxError|. Under Python 3 you must write: %doctest \begin{verbatim} >>> print("Hello World!") Hello World! \end{verbatim} Surprisingly that will also work on Python 2 -- but only for simple examples printing one thing. In general you need to add this magic line to the start of your Python scripts to use the print function under Python 2.6 and 2.7: \begin{verbatim} from __future__ import print_function \end{verbatim} If you forget to add this magic import, under Python 2 you'll see extra brackets produced by trying to use the print function when Python 2 is interpretting it as a print statement and a tuple. \item \emph{How do I find out what version of Biopython I have installed?} \\ Use this: \begin{verbatim} >>> import Bio >>> print(Bio.__version__) ... \end{verbatim} If the ``\verb|import Bio|'' line fails, Biopython is not installed. If the second line fails, your version is very out of date. If the version string ends with a plus, you don't have an official release, but a snapshot of the in development code. \item \emph{Where is the latest version of this document?}\\ If you download a Biopython source code archive, it will include the relevant version in both HTML and PDF formats. The latest published version of this document (updated at each release) is online: \begin{itemize} \item \url{http://biopython.org/DIST/docs/tutorial/Tutorial.html} \item \url{http://biopython.org/DIST/docs/tutorial/Tutorial.pdf} \end{itemize} If you are using the very latest unreleased code from our repository you can find copies of the in-progress tutorial here: \begin{itemize} \item \url{http://biopython.org/DIST/docs/tutorial/Tutorial-dev.html} \item \url{http://biopython.org/DIST/docs/tutorial/Tutorial-dev.pdf} \end{itemize} \item \emph{Why is the} \verb|Seq| \emph{object missing the upper \& lower methods described in this Tutorial?} \\ You need Biopython 1.53 or later. Alternatively, use \verb|str(my_seq).upper()| to get an upper case string. If you need a Seq object, try \verb|Seq(str(my_seq).upper())| but be careful about blindly re-using the same alphabet. \item \emph{Why doesn't the} \verb|Seq| \emph{object translation method support the} \verb|cds| \emph{option described in this Tutorial?} \\ You need Biopython 1.51 or later. \item \emph{What file formats do} \verb|Bio.SeqIO| \emph{and} \verb|Bio.AlignIO| \emph{read and write?} \\ Check the built in docstrings (\texttt{from Bio import SeqIO}, then \texttt{help(SeqIO)}), or see \url{http://biopython.org/wiki/SeqIO} and \url{http://biopython.org/wiki/AlignIO} on the wiki for the latest listing. \item \emph{Why won't the } \verb|Bio.SeqIO| \emph{and} \verb|Bio.AlignIO| \emph{functions} \verb|parse|\emph{,} \verb|read| \emph{and} \verb|write| \emph{take filenames? They insist on handles!} \\ You need Biopython 1.54 or later, or just use handles explicitly (see Section~\ref{sec:appendix-handles}). It is especially important to remember to close output handles explicitly after writing your data. \item \emph{Why won't the } \verb|Bio.SeqIO.write()| \emph{and} \verb|Bio.AlignIO.write()| \emph{functions accept a single record or alignment? They insist on a list or iterator!} \\ You need Biopython 1.54 or later, or just wrap the item with \verb|[...]| to create a list of one element. \item \emph{Why doesn't} \verb|str(...)| \emph{give me the full sequence of a} \verb|Seq| \emph{object?} \\ You need Biopython 1.45 or later. Alternatively, rather than \verb|str(my_seq)|, use \verb|my_seq.tostring()| (which will also work on recent versions of Biopython). \item \emph{Why doesn't} \verb|Bio.Blast| \emph{work with the latest plain text NCBI blast output?} \\ The NCBI keep tweaking the plain text output from the BLAST tools, and keeping our parser up to date is/was an ongoing struggle. If you aren't using the latest version of Biopython, you could try upgrading. However, we (and the NCBI) recommend you use the XML output instead, which is designed to be read by a computer program. \item \emph{Why doesn't} \verb|Bio.Entrez.parse()| \emph{work? The module imports fine but there is no parse function!} \\ You need Biopython 1.52 or later. \item \emph{Why has my script using} \verb|Bio.Entrez.efetch()| \emph{stopped working?} \\ This could be due to NCBI changes in February 2012 introducing EFetch 2.0. First, they changed the default return modes - you probably want to add \verb|retmode="text"| to your call. Second, they are now stricter about how to provide a list of IDs -- Biopython 1.59 onwards turns a list into a comma separated string automatically. \item \emph{Why doesn't} \verb|Bio.Blast.NCBIWWW.qblast()| \emph{give the same results as the NCBI BLAST website?} \\ You need to specify the same options -- the NCBI often adjust the default settings on the website, and they do not match the QBLAST defaults anymore. Check things like the gap penalties and expectation threshold. \item \emph{Why doesn't} \verb|Bio.Blast.NCBIXML.read()| \emph{work? The module imports but there is no read function!} \\ You need Biopython 1.50 or later. Or, use \texttt{next(Bio.Blast.NCBIXML.parse(...))} instead. \item \emph{Why doesn't my} \verb|SeqRecord| \emph{object have a} \verb|letter_annotations| \emph{attribute?} \\ Per-letter-annotation support was added in Biopython 1.50. \item \emph{Why can't I slice my} \verb|SeqRecord| \emph{to get a sub-record?} \\ You need Biopython 1.50 or later. \item \emph{Why can't I add} \verb|SeqRecord| \emph{objects together?} \\ You need Biopython 1.53 or later. \item \emph{Why doesn't} \verb|Bio.SeqIO.convert()| \emph{or} \verb|Bio.AlignIO.convert()| \emph{work? The modules import fine but there is no convert function!} \\ You need Biopython 1.52 or later. Alternatively, combine the \verb|parse| and \verb|write| functions as described in this tutorial (see Sections~\ref{sec:SeqIO-conversion} and~\ref{sec:converting-alignments}). \item \emph{Why doesn't} \verb|Bio.SeqIO.index()| \emph{work? The module imports fine but there is no index function!} \\ You need Biopython 1.52 or later. \item \emph{Why doesn't} \verb|Bio.SeqIO.index_db()| \emph{work? The module imports fine but there is no \texttt{index\_db} function!} \\ You need Biopython 1.57 or later (and a Python with SQLite3 support). \item \emph{Where is the} \verb|MultipleSeqAlignment| \emph{object? The} \verb|Bio.Align| \emph{module imports fine but this class isn't there!} \\ You need Biopython 1.54 or later. Alternatively, the older \verb|Bio.Align.Generic.Alignment| class supports some of its functionality, but using this is now discouraged. \item \emph{Why can't I run command line tools directly from the application wrappers?} \\ You need Biopython 1.55 or later. Alternatively, use the Python \verb|subprocess| module directly. \item \emph{I looked in a directory for code, but I couldn't find the code that does something. Where's it hidden?} \\ One thing to know is that we put code in \verb|__init__.py| files. If you are not used to looking for code in this file this can be confusing. The reason we do this is to make the imports easier for users. For instance, instead of having to do a ``repetitive'' import like \verb|from Bio.GenBank import GenBank|, you can just use \verb|from Bio import GenBank|. \item \emph{Why does the code from CVS seem out of date?} \\ In late September 2009, just after the release of Biopython 1.52, we switched from using CVS to git, a distributed version control system. The old CVS server will remain available as a static and read only backup, but if you want to grab the latest code, you'll need to use git instead. See our website for more details. \end{enumerate} \noindent For more general questions, the Python FAQ pages \url{http://www.python.org/doc/faq/} may be useful. \chapter{Quick Start -- What can you do with Biopython?} \label{chapter:quick-start} This section is designed to get you started quickly with Biopython, and to give a general overview of what is available and how to use it. All of the examples in this section assume that you have some general working knowledge of Python, and that you have successfully installed Biopython on your system. If you think you need to brush up on your Python, the main Python web site provides quite a bit of free documentation to get started with (\url{http://www.python.org/doc/}). Since much biological work on the computer involves connecting with databases on the internet, some of the examples will also require a working internet connection in order to run. Now that that is all out of the way, let's get into what we can do with Biopython. \section{General overview of what Biopython provides} As mentioned in the introduction, Biopython is a set of libraries to provide the ability to deal with ``things'' of interest to biologists working on the computer. In general this means that you will need to have at least some programming experience (in Python, of course!) or at least an interest in learning to program. Biopython's job is to make your job easier as a programmer by supplying reusable libraries so that you can focus on answering your specific question of interest, instead of focusing on the internals of parsing a particular file format (of course, if you want to help by writing a parser that doesn't exist and contributing it to Biopython, please go ahead!). So Biopython's job is to make you happy! One thing to note about Biopython is that it often provides multiple ways of ``doing the same thing.'' Things have improved in recent releases, but this can still be frustrating as in Python there should ideally be one right way to do something. However, this can also be a real benefit because it gives you lots of flexibility and control over the libraries. The tutorial helps to show you the common or easy ways to do things so that you can just make things work. To learn more about the alternative possibilities, look in the Cookbook (Chapter~\ref{chapter:cookbook}, this has some cools tricks and tips), the Advanced section (Chapter~\ref{chapter:advanced}), the built in ``docstrings'' (via the Python help command, or the \href{http://biopython.org/DIST/docs/api/}{API documentation}) or ultimately the code itself. \section{Working with sequences} \label{sec:sequences} Disputably (of course!), the central object in bioinformatics is the sequence. Thus, we'll start with a quick introduction to the Biopython mechanisms for dealing with sequences, the \verb|Seq| object, which we'll discuss in more detail in Chapter~\ref{chapter:Bio.Seq}. Most of the time when we think about sequences we have in my mind a string of letters like `\verb|AGTACACTGGT|'. You can create such \verb|Seq| object with this sequence as follows - the ``\verb+>>>+'' represents the Python prompt followed by what you would type in: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> my_seq = Seq("AGTACACTGGT") >>> my_seq Seq('AGTACACTGGT', Alphabet()) >>> print(my_seq) AGTACACTGGT >>> my_seq.alphabet Alphabet() \end{verbatim} What we have here is a sequence object with a \emph{generic} alphabet - reflecting the fact we have \emph{not} specified if this is a DNA or protein sequence (okay, a protein with a lot of Alanines, Glycines, Cysteines and Threonines!). We'll talk more about alphabets in Chapter~\ref{chapter:Bio.Seq}. In addition to having an alphabet, the \verb|Seq| object differs from the Python string in the methods it supports. You can't do this with a plain string: %cont-doctest \begin{verbatim} >>> my_seq Seq('AGTACACTGGT', Alphabet()) >>> my_seq.complement() Seq('TCATGTGACCA', Alphabet()) >>> my_seq.reverse_complement() Seq('ACCAGTGTACT', Alphabet()) \end{verbatim} The next most important class is the \verb|SeqRecord| or Sequence Record. This holds a sequence (as a \verb|Seq| object) with additional annotation including an identifier, name and description. The \verb|Bio.SeqIO| module for reading and writing sequence file formats works with \verb|SeqRecord| objects, which will be introduced below and covered in more detail by Chapter~\ref{chapter:Bio.SeqIO}. This covers the basic features and uses of the Biopython sequence class. Now that you've got some idea of what it is like to interact with the Biopython libraries, it's time to delve into the fun, fun world of dealing with biological file formats! \section{A usage example} \label{sec:orchids} Before we jump right into parsers and everything else to do with Biopython, let's set up an example to motivate everything we do and make life more interesting. After all, if there wasn't any biology in this tutorial, why would you want you read it? Since I love plants, I think we're just going to have to have a plant based example (sorry to all the fans of other organisms out there!). Having just completed a recent trip to our local greenhouse, we've suddenly developed an incredible obsession with Lady Slipper Orchids (if you wonder why, have a look at some \href{http://www.flickr.com/search/?q=lady+slipper+orchid&s=int&z=t}{Lady Slipper Orchids photos on Flickr}, or try a \href{http://images.google.com/images?q=lady%20slipper%20orchid}{Google Image Search}). Of course, orchids are not only beautiful to look at, they are also extremely interesting for people studying evolution and systematics. So let's suppose we're thinking about writing a funding proposal to do a molecular study of Lady Slipper evolution, and would like to see what kind of research has already been done and how we can add to that. % Brad's links to Millicent Orchids are dead now (March 2007) % http://www.millicentorchids.com/greenhouse/images/papesq01.jpg % http://www.millicentorchids.com/greenhouse/indexphoto.htm After a little bit of reading up we discover that the Lady Slipper Orchids are in the Orchidaceae family and the Cypripedioideae sub-family and are made up of 5 genera: \emph{Cypripedium}, \emph{Paphiopedilum}, \emph{Phragmipedium}, \emph{Selenipedium} and \emph{Mexipedium}. That gives us enough to get started delving for more information. So, let's look at how the Biopython tools can help us. We'll start with sequence parsing in Section~\ref{sec:sequence-parsing}, but the orchids will be back later on as well - for example we'll search PubMed for papers about orchids and extract sequence data from GenBank in Chapter~\ref{chapter:entrez}, extract data from Swiss-Prot from certain orchid proteins in Chapter~\ref{chapter:swiss_prot}, and work with ClustalW multiple sequence alignments of orchid proteins in Section~\ref{sec:align_clustal}. \section{Parsing sequence file formats} \label{sec:sequence-parsing} A large part of much bioinformatics work involves dealing with the many types of file formats designed to hold biological data. These files are loaded with interesting biological data, and a special challenge is parsing these files into a format so that you can manipulate them with some kind of programming language. However the task of parsing these files can be frustrated by the fact that the formats can change quite regularly, and that formats may contain small subtleties which can break even the most well designed parsers. We are now going to briefly introduce the \verb|Bio.SeqIO| module -- you can find out more in Chapter~\ref{chapter:Bio.SeqIO}. We'll start with an online search for our friends, the lady slipper orchids. To keep this introduction simple, we're just using the NCBI website by hand. Let's just take a look through the nucleotide databases at NCBI, using an Entrez online search (\url{http://www.ncbi.nlm.nih.gov:80/entrez/query.fcgi?db=Nucleotide}) for everything mentioning the text Cypripedioideae (this is the subfamily of lady slipper orchids). When this tutorial was originally written, this search gave us only 94 hits, which we saved as a FASTA formatted text file and as a GenBank formatted text file (files \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta} and \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{\tt ls\_orchid.gbk}, also included with the Biopython source code under {\tt docs/tutorial/examples/}). % The GenBank version is actually new - it was created by filtering out the original 94 hits from the % latest search of over 400 hits. If you run the search today, you'll get hundreds of results! When following the tutorial, if you want to see the same list of genes, just download the two files above or copy them from \verb|docs/examples/| in the Biopython source code. In Section~\ref{sec:connecting-with-biological-databases} we will look at how to do a search like this from within Python. \subsection{Simple FASTA parsing example} \label{sec:fasta-parsing} If you open the lady slipper orchids FASTA file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta} in your favourite text editor, you'll see that the file starts like this: \begin{verbatim} >gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG ... \end{verbatim} It contains 94 records, each has a line starting with ``\verb+>+'' (greater-than symbol) followed by the sequence on one or more lines. Now try this in Python: \begin{verbatim} from Bio import SeqIO for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"): print(seq_record.id) print(repr(seq_record.seq)) print(len(seq_record)) \end{verbatim} \noindent You should get something like this on your screen: \begin{verbatim} gi|2765658|emb|Z78533.1|CIZ78533 Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', SingleLetterAlphabet()) 740 ... gi|2765564|emb|Z78439.1|PBZ78439 Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', SingleLetterAlphabet()) 592 \end{verbatim} Notice that the FASTA format does not specify the alphabet, so \verb|Bio.SeqIO| has defaulted to the rather generic \verb|SingleLetterAlphabet()| rather than something DNA specific. \subsection{Simple GenBank parsing example} Now let's load the GenBank file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{\tt ls\_orchid.gbk} instead - notice that the code to do this is almost identical to the snippet used above for the FASTA file - the only difference is we change the filename and the format string: \begin{verbatim} from Bio import SeqIO for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"): print(seq_record.id) print(repr(seq_record.seq)) print(len(seq_record)) \end{verbatim} \noindent This should give: \begin{verbatim} Z78533.1 Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', IUPACAmbiguousDNA()) 740 ... Z78439.1 Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', IUPACAmbiguousDNA()) 592 \end{verbatim} This time \verb|Bio.SeqIO| has been able to choose a sensible alphabet, IUPAC Ambiguous DNA. You'll also notice that a shorter string has been used as the \verb|seq_record.id| in this case. \subsection{I love parsing -- please don't stop talking about it!} Biopython has a lot of parsers, and each has its own little special niches based on the sequence format it is parsing and all of that. Chapter~\ref{chapter:Bio.SeqIO} covers \verb|Bio.SeqIO| in more detail, while Chapter~\ref{chapter:Bio.AlignIO} introduces \verb|Bio.AlignIO| for sequence alignments. While the most popular file formats have parsers integrated into \verb|Bio.SeqIO| and/or \verb|Bio.AlignIO|, for some of the rarer and unloved file formats there is either no parser at all, or an old parser which has not been linked in yet. Please also check the wiki pages \url{http://biopython.org/wiki/SeqIO} and \url{http://biopython.org/wiki/AlignIO} for the latest information, or ask on the mailing list. The wiki pages should include an up to date list of supported file types, and some additional examples. The next place to look for information about specific parsers and how to do cool things with them is in the Cookbook (Chapter~\ref{chapter:cookbook} of this Tutorial). If you don't find the information you are looking for, please consider helping out your poor overworked documentors and submitting a cookbook entry about it! (once you figure out how to do it, that is!) \section{Connecting with biological databases} \label{sec:connecting-with-biological-databases} One of the very common things that you need to do in bioinformatics is extract information from biological databases. It can be quite tedious to access these databases manually, especially if you have a lot of repetitive work to do. Biopython attempts to save you time and energy by making some on-line databases available from Python scripts. Currently, Biopython has code to extract information from the following databases: \begin{itemize} \item \href{http://www.ncbi.nlm.nih.gov/Entrez/}{Entrez} (and \href{http://www.ncbi.nlm.nih.gov/PubMed/}{PubMed}) from the NCBI -- See Chapter~\ref{chapter:entrez}. \item \href{http://www.expasy.org/}{ExPASy} -- See Chapter~\ref{chapter:swiss_prot}. \item \href{http://scop.mrc-lmb.cam.ac.uk/scop/}{SCOP} -- See the \verb|Bio.SCOP.search()| function. \end{itemize} The code in these modules basically makes it easy to write Python code that interact with the CGI scripts on these pages, so that you can get results in an easy to deal with format. In some cases, the results can be tightly integrated with the Biopython parsers to make it even easier to extract information. \section{What to do next} Now that you've made it this far, you hopefully have a good understanding of the basics of Biopython and are ready to start using it for doing useful work. The best thing to do now is finish reading this tutorial, and then if you want start snooping around in the source code, and looking at the automatically generated documentation. Once you get a picture of what you want to do, and what libraries in Biopython will do it, you should take a peak at the Cookbook (Chapter~\ref{chapter:cookbook}), which may have example code to do something similar to what you want to do. If you know what you want to do, but can't figure out how to do it, please feel free to post questions to the main Biopython list (see \url{http://biopython.org/wiki/Mailing_lists}). This will not only help us answer your question, it will also allow us to improve the documentation so it can help the next person do what you want to do. Enjoy the code! \chapter{Sequence objects} \label{chapter:Bio.Seq} Biological sequences are arguably the central object in Bioinformatics, and in this chapter we'll introduce the Biopython mechanism for dealing with sequences, the \verb|Seq| object. Chapter~\ref{chapter:SeqRecord} will introduce the related \verb|SeqRecord| object, which combines the sequence information with any annotation, used again in Chapter~\ref{chapter:Bio.SeqIO} for Sequence Input/Output. Sequences are essentially strings of letters like \verb|AGTACACTGGT|, which seems very natural since this is the most common way that sequences are seen in biological file formats. There are two important differences between \verb|Seq| objects and standard Python strings. First of all, they have different methods. Although the \verb|Seq| object supports many of the same methods as a plain string, its \verb|translate()| method differs by doing biological translation, and there are also additional biologically relevant methods like \verb|reverse_complement()|. Secondly, the \verb|Seq| object has an important attribute, \verb|alphabet|, which is an object describing what the individual characters making up the sequence string ``mean'', and how they should be interpreted. For example, is \verb|AGTACACTGGT| a DNA sequence, or just a protein sequence that happens to be rich in Alanines, Glycines, Cysteines and Threonines? \section{Sequences and Alphabets} The alphabet object is perhaps the important thing that makes the \verb|Seq| object more than just a string. The currently available alphabets for Biopython are defined in the \verb|Bio.Alphabet| module. We'll use the IUPAC alphabets (\url{http://www.chem.qmw.ac.uk/iupac/}) here to deal with some of our favorite objects: DNA, RNA and Proteins. \verb|Bio.Alphabet.IUPAC| provides basic definitions for proteins, DNA and RNA, but additionally provides the ability to extend and customize the basic definitions. For instance, for proteins, there is a basic IUPACProtein class, but there is an additional ExtendedIUPACProtein class providing for the additional elements ``U'' (or ``Sec'' for selenocysteine) and ``O'' (or ``Pyl'' for pyrrolysine), plus the ambiguous symbols ``B'' (or ``Asx'' for asparagine or aspartic acid), ``Z'' (or ``Glx'' for glutamine or glutamic acid), ``J'' (or ``Xle'' for leucine isoleucine) and ``X'' (or ``Xxx'' for an unknown amino acid). For DNA you've got choices of IUPACUnambiguousDNA, which provides for just the basic letters, IUPACAmbiguousDNA (which provides for ambiguity letters for every possible situation) and ExtendedIUPACDNA, which allows letters for modified bases. Similarly, RNA can be represented by IUPACAmbiguousRNA or IUPACUnambiguousRNA. The advantages of having an alphabet class are two fold. First, this gives an idea of the type of information the Seq object contains. Secondly, this provides a means of constraining the information, as a means of type checking. Now that we know what we are dealing with, let's look at how to utilize this class to do interesting work. You can create an ambiguous sequence with the default generic alphabet like this: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> my_seq = Seq("AGTACACTGGT") >>> my_seq Seq('AGTACACTGGT', Alphabet()) >>> my_seq.alphabet Alphabet() \end{verbatim} However, where possible you should specify the alphabet explicitly when creating your sequence objects - in this case an unambiguous DNA alphabet object: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> my_seq = Seq("AGTACACTGGT", IUPAC.unambiguous_dna) >>> my_seq Seq('AGTACACTGGT', IUPACUnambiguousDNA()) >>> my_seq.alphabet IUPACUnambiguousDNA() \end{verbatim} Unless of course, this really is an amino acid sequence: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> my_prot = Seq("AGTACACTGGT", IUPAC.protein) >>> my_prot Seq('AGTACACTGGT', IUPACProtein()) >>> my_prot.alphabet IUPACProtein() \end{verbatim} \section{Sequences act like strings} In many ways, we can deal with Seq objects as if they were normal Python strings, for example getting the length, or iterating over the elements: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> my_seq = Seq("GATCG", IUPAC.unambiguous_dna) >>> for index, letter in enumerate(my_seq): ... print("%i %s" % (index, letter)) 0 G 1 A 2 T 3 C 4 G >>> print(len(my_seq)) 5 \end{verbatim} You can access elements of the sequence in the same way as for strings (but remember, Python counts from zero!): %cont-doctest \begin{verbatim} >>> print(my_seq[0]) #first letter G >>> print(my_seq[2]) #third letter T >>> print(my_seq[-1]) #last letter G \end{verbatim} The \verb|Seq| object has a \verb|.count()| method, just like a string. Note that this means that like a Python string, this gives a \emph{non-overlapping} count: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> "AAAA".count("AA") 2 >>> Seq("AAAA").count("AA") 2 \end{verbatim} \noindent For some biological uses, you may actually want an overlapping count (i.e. $3$ in this trivial example). When searching for single letters, this makes no difference: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> my_seq = Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPAC.unambiguous_dna) >>> len(my_seq) 32 >>> my_seq.count("G") 9 >>> 100 * float(my_seq.count("G") + my_seq.count("C")) / len(my_seq) 46.875 \end{verbatim} While you could use the above snippet of code to calculate a GC\%, note that the \verb|Bio.SeqUtils| module has several GC functions already built. For example: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> from Bio.SeqUtils import GC >>> my_seq = Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPAC.unambiguous_dna) >>> GC(my_seq) 46.875 \end{verbatim} \noindent Note that using the \verb|Bio.SeqUtils.GC()| function should automatically cope with mixed case sequences and the ambiguous nucleotide S which means G or C. Also note that just like a normal Python string, the \verb|Seq| object is in some ways ``read-only''. If you need to edit your sequence, for example simulating a point mutation, look at the Section~\ref{sec:mutable-seq} below which talks about the \verb|MutableSeq| object. \section{Slicing a sequence} A more complicated example, let's get a slice of the sequence: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC", IUPAC.unambiguous_dna) >>> my_seq[4:12] Seq('GATGGGCC', IUPACUnambiguousDNA()) \end{verbatim} Two things are interesting to note. First, this follows the normal conventions for Python strings. So the first element of the sequence is 0 (which is normal for computer science, but not so normal for biology). When you do a slice the first item is included (i.e.~4 in this case) and the last is excluded (12 in this case), which is the way things work in Python, but of course not necessarily the way everyone in the world would expect. The main goal is to stay consistent with what Python does. The second thing to notice is that the slice is performed on the sequence data string, but the new object produced is another \verb|Seq| object which retains the alphabet information from the original \verb|Seq| object. Also like a Python string, you can do slices with a start, stop and \emph{stride} (the step size, which defaults to one). For example, we can get the first, second and third codon positions of this DNA sequence: %cont-doctest \begin{verbatim} >>> my_seq[0::3] Seq('GCTGTAGTAAG', IUPACUnambiguousDNA()) >>> my_seq[1::3] Seq('AGGCATGCATC', IUPACUnambiguousDNA()) >>> my_seq[2::3] Seq('TAGCTAAGAC', IUPACUnambiguousDNA()) \end{verbatim} Another stride trick you might have seen with a Python string is the use of a -1 stride to reverse the string. You can do this with a \verb|Seq| object too: %cont-doctest \begin{verbatim} >>> my_seq[::-1] Seq('CGCTAAAAGCTAGGATATATCCGGGTAGCTAG', IUPACUnambiguousDNA()) \end{verbatim} \section{Turning Seq objects into strings} \label{sec:seq-to-string} If you really do just need a plain string, for example to write to a file, or insert into a database, then this is very easy to get: %cont-doctest \begin{verbatim} >>> str(my_seq) 'GATCGATGGGCCTATATAGGATCGAAAATCGC' \end{verbatim} Since calling \verb|str()| on a \verb|Seq| object returns the full sequence as a string, you often don't actually have to do this conversion explicitly. Python does this automatically in the print function (and the print statement under Python 2): %cont-doctest \begin{verbatim} >>> print(my_seq) GATCGATGGGCCTATATAGGATCGAAAATCGC \end{verbatim} You can also use the \verb|Seq| object directly with a \verb|%s| placeholder when using the Python string formatting or interpolation operator (\verb|%|): %cont-doctest \begin{verbatim} >>> fasta_format_string = ">Name\n%s\n" % my_seq >>> print(fasta_format_string) >Name GATCGATGGGCCTATATAGGATCGAAAATCGC \end{verbatim} \noindent This line of code constructs a simple FASTA format record (without worrying about line wrapping). Section~\ref{sec:SeqRecord-format} describes a neat way to get a FASTA formatted string from a \verb|SeqRecord| object, while the more general topic of reading and writing FASTA format sequence files is covered in Chapter~\ref{chapter:Bio.SeqIO}. \emph{NOTE:} If you are using Biopython 1.44 or older, using \verb|str(my_seq)| will give just a truncated representation. Instead use \verb|my_seq.tostring()| (which is still available in the current Biopython releases for backwards compatibility): %cont-doctest \begin{verbatim} >>> my_seq.tostring() 'GATCGATGGGCCTATATAGGATCGAAAATCGC' \end{verbatim} \section{Concatenating or adding sequences} Naturally, you can in principle add any two Seq objects together - just like you can with Python strings to concatenate them. However, you can't add sequences with incompatible alphabets, such as a protein sequence and a DNA sequence: %doctest \begin{verbatim} >>> from Bio.Alphabet import IUPAC >>> from Bio.Seq import Seq >>> protein_seq = Seq("EVRNAK", IUPAC.protein) >>> dna_seq = Seq("ACGT", IUPAC.unambiguous_dna) >>> protein_seq + dna_seq Traceback (most recent call last): ... TypeError: Incompatible alphabets IUPACProtein() and IUPACUnambiguousDNA() \end{verbatim} If you \emph{really} wanted to do this, you'd have to first give both sequences generic alphabets: %cont-doctest \begin{verbatim} >>> from Bio.Alphabet import generic_alphabet >>> protein_seq.alphabet = generic_alphabet >>> dna_seq.alphabet = generic_alphabet >>> protein_seq + dna_seq Seq('EVRNAKACGT', Alphabet()) \end{verbatim} Here is an example of adding a generic nucleotide sequence to an unambiguous IUPAC DNA sequence, resulting in an ambiguous nucleotide sequence: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import generic_nucleotide >>> from Bio.Alphabet import IUPAC >>> nuc_seq = Seq("GATCGATGC", generic_nucleotide) >>> dna_seq = Seq("ACGT", IUPAC.unambiguous_dna) >>> nuc_seq Seq('GATCGATGC', NucleotideAlphabet()) >>> dna_seq Seq('ACGT', IUPACUnambiguousDNA()) >>> nuc_seq + dna_seq Seq('GATCGATGCACGT', NucleotideAlphabet()) \end{verbatim} \section{Changing case} Python strings have very useful \verb|upper| and \verb|lower| methods for changing the case. As of Biopython 1.53, the \verb|Seq| object gained similar methods which are alphabet aware. For example, %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import generic_dna >>> dna_seq = Seq("acgtACGT", generic_dna) >>> dna_seq Seq('acgtACGT', DNAAlphabet()) >>> dna_seq.upper() Seq('ACGTACGT', DNAAlphabet()) >>> dna_seq.lower() Seq('acgtacgt', DNAAlphabet()) \end{verbatim} These are useful for doing case insensitive matching: %cont-doctest \begin{verbatim} >>> "GTAC" in dna_seq False >>> "GTAC" in dna_seq.upper() True \end{verbatim} Note that strictly speaking the IUPAC alphabets are for upper case sequences only, thus: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> dna_seq = Seq("ACGT", IUPAC.unambiguous_dna) >>> dna_seq Seq('ACGT', IUPACUnambiguousDNA()) >>> dna_seq.lower() Seq('acgt', DNAAlphabet()) \end{verbatim} \section{Nucleotide sequences and (reverse) complements} \label{sec:seq-reverse-complement} For nucleotide sequences, you can easily obtain the complement or reverse complement of a \verb|Seq| object using its built-in methods: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC", IUPAC.unambiguous_dna) >>> my_seq Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPACUnambiguousDNA()) >>> my_seq.complement() Seq('CTAGCTACCCGGATATATCCTAGCTTTTAGCG', IUPACUnambiguousDNA()) >>> my_seq.reverse_complement() Seq('GCGATTTTCGATCCTATATAGGCCCATCGATC', IUPACUnambiguousDNA()) \end{verbatim} As mentioned earlier, an easy way to just reverse a \verb|Seq| object (or a Python string) is slice it with -1 step: %cont-doctest \begin{verbatim} >>> my_seq[::-1] Seq('CGCTAAAAGCTAGGATATATCCGGGTAGCTAG', IUPACUnambiguousDNA()) \end{verbatim} In all of these operations, the alphabet property is maintained. This is very useful in case you accidentally end up trying to do something weird like take the (reverse)complement of a protein sequence: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> protein_seq = Seq("EVRNAK", IUPAC.protein) >>> protein_seq.complement() Traceback (most recent call last): ... ValueError: Proteins do not have complements! \end{verbatim} The example in Section~\ref{sec:SeqIO-reverse-complement} combines the \verb|Seq| object's reverse complement method with \verb|Bio.SeqIO| for sequence input/output. \section{Transcription} Before talking about transcription, I want to try and clarify the strand issue. Consider the following (made up) stretch of double stranded DNA which encodes a short peptide: \begin{tabular}{rcl} \\ & {\small DNA coding strand (aka Crick strand, strand $+1$)} & \\ 5' & \texttt{ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG} & 3' \\ & \texttt{|||||||||||||||||||||||||||||||||||||||} & \\ 3' & \texttt{TACCGGTAACATTACCCGGCGACTTTCCCACGGGCTATC} & 5' \\ & {\small DNA template strand (aka Watson strand, strand $-1$)} & \\ \\ & {\LARGE $|$} &\\ & Transcription & \\ & {\LARGE $\downarrow$} &\\ \\ 5' & \texttt{AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG} & 3' \\ & {\small Single stranded messenger RNA} & \\ \\ \end{tabular} The actual biological transcription process works from the template strand, doing a reverse complement (TCAG $\rightarrow$ CUGA) to give the mRNA. However, in Biopython and bioinformatics in general, we typically work directly with the coding strand because this means we can get the mRNA sequence just by switching T $\rightarrow$ U. Now let's actually get down to doing a transcription in Biopython. First, let's create \verb|Seq| objects for the coding and template DNA strands: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG", IUPAC.unambiguous_dna) >>> coding_dna Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA()) >>> template_dna = coding_dna.reverse_complement() >>> template_dna Seq('CTATCGGGCACCCTTTCAGCGGCCCATTACAATGGCCAT', IUPACUnambiguousDNA()) \end{verbatim} \noindent These should match the figure above - remember by convention nucleotide sequences are normally read from the 5' to 3' direction, while in the figure the template strand is shown reversed. Now let's transcribe the coding strand into the corresponding mRNA, using the \verb|Seq| object's built in \verb|transcribe| method: %cont-doctest \begin{verbatim} >>> coding_dna Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA()) >>> messenger_rna = coding_dna.transcribe() >>> messenger_rna Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA()) \end{verbatim} \noindent As you can see, all this does is switch T $\rightarrow$ U, and adjust the alphabet. If you do want to do a true biological transcription starting with the template strand, then this becomes a two-step process: %cont-doctest \begin{verbatim} >>> template_dna.reverse_complement().transcribe() Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA()) \end{verbatim} The \verb|Seq| object also includes a back-transcription method for going from the mRNA to the coding strand of the DNA. Again, this is a simple U $\rightarrow$ T substitution and associated change of alphabet: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> messenger_rna = Seq("AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG", IUPAC.unambiguous_rna) >>> messenger_rna Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA()) >>> messenger_rna.back_transcribe() Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA()) \end{verbatim} \emph{Note:} The \verb|Seq| object's \verb|transcribe| and \verb|back_transcribe| methods were added in Biopython 1.49. For older releases you would have to use the \verb|Bio.Seq| module's functions instead, see Section~\ref{sec:seq-module-functions}. \section{Translation} \label{sec:translation} Sticking with the same example discussed in the transcription section above, now let's translate this mRNA into the corresponding protein sequence - again taking advantage of one of the \verb|Seq| object's biological methods: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> messenger_rna = Seq("AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG", IUPAC.unambiguous_rna) >>> messenger_rna Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA()) >>> messenger_rna.translate() Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*')) \end{verbatim} You can also translate directly from the coding strand DNA sequence: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG", IUPAC.unambiguous_dna) >>> coding_dna Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA()) >>> coding_dna.translate() Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*')) \end{verbatim} You should notice in the above protein sequences that in addition to the end stop character, there is an internal stop as well. This was a deliberate choice of example, as it gives an excuse to talk about some optional arguments, including different translation tables (Genetic Codes). The translation tables available in Biopython are based on those \href{http://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi}{from the NCBI} (see the next section of this tutorial). By default, translation will use the \emph{standard} genetic code (NCBI table id 1). Suppose we are dealing with a mitochondrial sequence. We need to tell the translation function to use the relevant genetic code instead: %cont-doctest \begin{verbatim} >>> coding_dna.translate(table="Vertebrate Mitochondrial") Seq('MAIVMGRWKGAR*', HasStopCodon(IUPACProtein(), '*')) \end{verbatim} You can also specify the table using the NCBI table number which is shorter, and often included in the feature annotation of GenBank files: %cont-doctest \begin{verbatim} >>> coding_dna.translate(table=2) Seq('MAIVMGRWKGAR*', HasStopCodon(IUPACProtein(), '*')) \end{verbatim} Now, you may want to translate the nucleotides up to the first in frame stop codon, and then stop (as happens in nature): %cont-doctest \begin{verbatim} >>> coding_dna.translate() Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*')) >>> coding_dna.translate(to_stop=True) Seq('MAIVMGR', IUPACProtein()) >>> coding_dna.translate(table=2) Seq('MAIVMGRWKGAR*', HasStopCodon(IUPACProtein(), '*')) >>> coding_dna.translate(table=2, to_stop=True) Seq('MAIVMGRWKGAR', IUPACProtein()) \end{verbatim} \noindent Notice that when you use the \verb|to_stop| argument, the stop codon itself is not translated - and the stop symbol is not included at the end of your protein sequence. You can even specify the stop symbol if you don't like the default asterisk: %cont-doctest \begin{verbatim} >>> coding_dna.translate(table=2, stop_symbol="@") Seq('MAIVMGRWKGAR@', HasStopCodon(IUPACProtein(), '@')) \end{verbatim} Now, suppose you have a complete coding sequence CDS, which is to say a nucleotide sequence (e.g. mRNA -- after any splicing) which is a whole number of codons (i.e. the length is a multiple of three), commences with a start codon, ends with a stop codon, and has no internal in-frame stop codons. In general, given a complete CDS, the default translate method will do what you want (perhaps with the \verb|to_stop| option). However, what if your sequence uses a non-standard start codon? This happens a lot in bacteria -- for example the gene yaaX in \texttt{E. coli} K12: %TODO - handle line wrapping in doctest? \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import generic_dna >>> gene = Seq("GTGAAAAAGATGCAATCTATCGTACTCGCACTTTCCCTGGTTCTGGTCGCTCCCATGGCA" + \ ... "GCACAGGCTGCGGAAATTACGTTAGTCCCGTCAGTAAAATTACAGATAGGCGATCGTGAT" + \ ... "AATCGTGGCTATTACTGGGATGGAGGTCACTGGCGCGACCACGGCTGGTGGAAACAACAT" + \ ... "TATGAATGGCGAGGCAATCGCTGGCACCTACACGGACCGCCGCCACCGCCGCGCCACCAT" + \ ... "AAGAAAGCTCCTCATGATCATCACGGCGGTCATGGTCCAGGCAAACATCACCGCTAA", ... generic_dna) >>> gene.translate(table="Bacterial") Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HR*', HasStopCodon(ExtendedIUPACProtein(), '*') >>> gene.translate(table="Bacterial", to_stop=True) Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HHR', ExtendedIUPACProtein()) \end{verbatim} \noindent In the bacterial genetic code \texttt{GTG} is a valid start codon, and while it does \emph{normally} encode Valine, if used as a start codon it should be translated as methionine. This happens if you tell Biopython your sequence is a complete CDS: %TODO - handle line wrapping in doctest? \begin{verbatim} >>> gene.translate(table="Bacterial", cds=True) Seq('MKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HHR', ExtendedIUPACProtein()) \end{verbatim} In addition to telling Biopython to translate an alternative start codon as methionine, using this option also makes sure your sequence really is a valid CDS (you'll get an exception if not). The example in Section~\ref{sec:SeqIO-translate} combines the \verb|Seq| object's translate method with \verb|Bio.SeqIO| for sequence input/output. \section{Translation Tables} In the previous sections we talked about the \verb|Seq| object translation method (and mentioned the equivalent function in the \verb|Bio.Seq| module -- see Section~\ref{sec:seq-module-functions}). Internally these use codon table objects derived from the NCBI information at \url{ftp://ftp.ncbi.nlm.nih.gov/entrez/misc/data/gc.prt}, also shown on \url{http://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi} in a much more readable layout. As before, let's just focus on two choices: the Standard translation table, and the translation table for Vertebrate Mitochondrial DNA. %doctest \begin{verbatim} >>> from Bio.Data import CodonTable >>> standard_table = CodonTable.unambiguous_dna_by_name["Standard"] >>> mito_table = CodonTable.unambiguous_dna_by_name["Vertebrate Mitochondrial"] \end{verbatim} Alternatively, these tables are labeled with ID numbers 1 and 2, respectively: %cont-doctest \begin{verbatim} >>> from Bio.Data import CodonTable >>> standard_table = CodonTable.unambiguous_dna_by_id[1] >>> mito_table = CodonTable.unambiguous_dna_by_id[2] \end{verbatim} You can compare the actual tables visually by printing them: %TODO - handle automatically in doctest? \begin{verbatim} >>> print(standard_table) Table 1 Standard, SGC0 | T | C | A | G | --+---------+---------+---------+---------+-- T | TTT F | TCT S | TAT Y | TGT C | T T | TTC F | TCC S | TAC Y | TGC C | C T | TTA L | TCA S | TAA Stop| TGA Stop| A T | TTG L(s)| TCG S | TAG Stop| TGG W | G --+---------+---------+---------+---------+-- C | CTT L | CCT P | CAT H | CGT R | T C | CTC L | CCC P | CAC H | CGC R | C C | CTA L | CCA P | CAA Q | CGA R | A C | CTG L(s)| CCG P | CAG Q | CGG R | G --+---------+---------+---------+---------+-- A | ATT I | ACT T | AAT N | AGT S | T A | ATC I | ACC T | AAC N | AGC S | C A | ATA I | ACA T | AAA K | AGA R | A A | ATG M(s)| ACG T | AAG K | AGG R | G --+---------+---------+---------+---------+-- G | GTT V | GCT A | GAT D | GGT G | T G | GTC V | GCC A | GAC D | GGC G | C G | GTA V | GCA A | GAA E | GGA G | A G | GTG V | GCG A | GAG E | GGG G | G --+---------+---------+---------+---------+-- \end{verbatim} \noindent and: \begin{verbatim} >>> print(mito_table) Table 2 Vertebrate Mitochondrial, SGC1 | T | C | A | G | --+---------+---------+---------+---------+-- T | TTT F | TCT S | TAT Y | TGT C | T T | TTC F | TCC S | TAC Y | TGC C | C T | TTA L | TCA S | TAA Stop| TGA W | A T | TTG L | TCG S | TAG Stop| TGG W | G --+---------+---------+---------+---------+-- C | CTT L | CCT P | CAT H | CGT R | T C | CTC L | CCC P | CAC H | CGC R | C C | CTA L | CCA P | CAA Q | CGA R | A C | CTG L | CCG P | CAG Q | CGG R | G --+---------+---------+---------+---------+-- A | ATT I(s)| ACT T | AAT N | AGT S | T A | ATC I(s)| ACC T | AAC N | AGC S | C A | ATA M(s)| ACA T | AAA K | AGA Stop| A A | ATG M(s)| ACG T | AAG K | AGG Stop| G --+---------+---------+---------+---------+-- G | GTT V | GCT A | GAT D | GGT G | T G | GTC V | GCC A | GAC D | GGC G | C G | GTA V | GCA A | GAA E | GGA G | A G | GTG V(s)| GCG A | GAG E | GGG G | G --+---------+---------+---------+---------+-- \end{verbatim} You may find these following properties useful -- for example if you are trying to do your own gene finding: %cont-doctest \begin{verbatim} >>> mito_table.stop_codons ['TAA', 'TAG', 'AGA', 'AGG'] >>> mito_table.start_codons ['ATT', 'ATC', 'ATA', 'ATG', 'GTG'] >>> mito_table.forward_table["ACG"] 'T' \end{verbatim} \section{Comparing Seq objects} \label{sec:seq-comparison} Sequence comparison is actually a very complicated topic, and there is no easy way to decide if two sequences are equal. The basic problem is the meaning of the letters in a sequence are context dependent - the letter ``A'' could be part of a DNA, RNA or protein sequence. Biopython uses alphabet objects as part of each \verb|Seq| object to try and capture this information - so comparing two \verb|Seq| objects means considering both the sequence strings \emph{and} the alphabets. For example, you might argue that the two DNA \verb|Seq| objects \texttt{Seq("ACGT", IUPAC.unambiguous\_dna)} and \texttt{Seq("ACGT", IUPAC.ambiguous\_dna)} should be equal, even though they do have different alphabets. Depending on the context this could be important. This gets worse -- suppose you think \texttt{Seq("ACGT", IUPAC.unambiguous\_dna)} and \texttt{Seq("ACGT")} (i.e. the default generic alphabet) should be equal. Then, logically, \texttt{Seq("ACGT", IUPAC.protein)} and \texttt{Seq("ACGT")} should also be equal. Now, in logic if $A=B$ and $B=C$, by transitivity we expect $A=C$. So for logical consistency we'd require \texttt{Seq("ACGT", IUPAC.unambiguous\_dna)} and \texttt{Seq("ACGT", IUPAC.protein)} to be equal -- which most people would agree is just not right. This transitivity problem would also have implications for using \verb|Seq| objects as Python dictionary keys. %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> seq1 = Seq("ACGT", IUPAC.unambiguous_dna) >>> seq2 = Seq("ACGT", IUPAC.unambiguous_dna) \end{verbatim} So, what does Biopython do? Well, the equality test is the default for Python objects -- it tests to see if they are the same object in memory. This is a very strict test: %TODO - silence FutureWarning from this doctest? \begin{verbatim} >>> seq1 == seq2 False >>> seq1 == seq1 True \end{verbatim} If you actually want to do this, you can be more explicit by using the Python \verb|id| function, %cont-doctest \begin{verbatim} >>> id(seq1) == id(seq2) False >>> id(seq1) == id(seq1) True \end{verbatim} Now, in every day use, your sequences will probably all have the same alphabet, or at least all be the same type of sequence (all DNA, all RNA, or all protein). What you probably want is to just compare the sequences as strings -- so do this explicitly: %cont-doctest \begin{verbatim} >>> str(seq1) == str(seq2) True >>> str(seq1) == str(seq1) True \end{verbatim} \noindent As an extension to this, while you can use a Python dictionary with \verb|Seq| objects as keys, it is generally more useful to use the sequence a string for the key. See also Section~\ref{sec:seq-to-string}. \section{MutableSeq objects} \label{sec:mutable-seq} Just like the normal Python string, the \verb|Seq| object is ``read only'', or in Python terminology, immutable. Apart from wanting the \verb|Seq| object to act like a string, this is also a useful default since in many biological applications you want to ensure you are not changing your sequence data: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.Alphabet import IUPAC >>> my_seq = Seq("GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA", IUPAC.unambiguous_dna) \end{verbatim} Observe what happens if you try to edit the sequence: %TODO - This is not a doctest as Python 2.4 output omits the object name. \begin{verbatim} >>> my_seq[5] = "G" Traceback (most recent call last): ... TypeError: 'Seq' object does not support item assignment \end{verbatim} However, you can convert it into a mutable sequence (a \verb|MutableSeq| object) and do pretty much anything you want with it: %cont-doctest \begin{verbatim} >>> mutable_seq = my_seq.tomutable() >>> mutable_seq MutableSeq('GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA()) \end{verbatim} Alternatively, you can create a \verb|MutableSeq| object directly from a string: %doctest \begin{verbatim} >>> from Bio.Seq import MutableSeq >>> from Bio.Alphabet import IUPAC >>> mutable_seq = MutableSeq("GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA", IUPAC.unambiguous_dna) \end{verbatim} Either way will give you a sequence object which can be changed: %cont-doctest \begin{verbatim} >>> mutable_seq MutableSeq('GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA()) >>> mutable_seq[5] = "C" >>> mutable_seq MutableSeq('GCCATCGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA()) >>> mutable_seq.remove("T") >>> mutable_seq MutableSeq('GCCACGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA()) >>> mutable_seq.reverse() >>> mutable_seq MutableSeq('AGCCCGTGGGAAAGTCGCCGGGTAATGCACCG', IUPACUnambiguousDNA()) \end{verbatim} Do note that unlike the \verb|Seq| object, the \verb|MutableSeq| object's methods like \verb|reverse_complement()| and \verb|reverse()| act in-situ! An important technical difference between mutable and immutable objects in Python means that you can't use a \verb|MutableSeq| object as a dictionary key, but you can use a Python string or a \verb|Seq| object in this way. Once you have finished editing your a \verb|MutableSeq| object, it's easy to get back to a read-only \verb|Seq| object should you need to: %cont-doctest \begin{verbatim} >>> new_seq = mutable_seq.toseq() >>> new_seq Seq('AGCCCGTGGGAAAGTCGCCGGGTAATGCACCG', IUPACUnambiguousDNA()) \end{verbatim} You can also get a string from a \verb|MutableSeq| object just like from a \verb|Seq| object (Section~\ref{sec:seq-to-string}). \section{UnknownSeq objects} The \verb|UnknownSeq| object is a subclass of the basic \verb|Seq| object and its purpose is to represent a sequence where we know the length, but not the actual letters making it up. You could of course use a normal \verb|Seq| object in this situation, but it wastes rather a lot of memory to hold a string of a million ``N'' characters when you could just store a single letter ``N'' and the desired length as an integer. %doctest \begin{verbatim} >>> from Bio.Seq import UnknownSeq >>> unk = UnknownSeq(20) >>> unk UnknownSeq(20, alphabet = Alphabet(), character = '?') >>> print(unk) ???????????????????? >>> len(unk) 20 \end{verbatim} You can of course specify an alphabet, meaning for nucleotide sequences the letter defaults to ``N'' and for proteins ``X'', rather than just ``?''. %cont-doctest \begin{verbatim} >>> from Bio.Seq import UnknownSeq >>> from Bio.Alphabet import IUPAC >>> unk_dna = UnknownSeq(20, alphabet=IUPAC.ambiguous_dna) >>> unk_dna UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N') >>> print(unk_dna) NNNNNNNNNNNNNNNNNNNN \end{verbatim} You can use all the usual \verb|Seq| object methods too, note these give back memory saving \verb|UnknownSeq| objects where appropriate as you might expect: %cont-doctest \begin{verbatim} >>> unk_dna UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N') >>> unk_dna.complement() UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N') >>> unk_dna.reverse_complement() UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N') >>> unk_dna.transcribe() UnknownSeq(20, alphabet = IUPACAmbiguousRNA(), character = 'N') >>> unk_protein = unk_dna.translate() >>> unk_protein UnknownSeq(6, alphabet = ProteinAlphabet(), character = 'X') >>> print(unk_protein) XXXXXX >>> len(unk_protein) 6 \end{verbatim} You may be able to find a use for the \verb|UnknownSeq| object in your own code, but it is more likely that you will first come across them in a \verb|SeqRecord| object created by \verb|Bio.SeqIO| (see Chapter~\ref{chapter:Bio.SeqIO}). Some sequence file formats don't always include the actual sequence, for example GenBank and EMBL files may include a list of features but for the sequence just present the contig information. Alternatively, the QUAL files used in sequencing work hold quality scores but they \emph{never} contain a sequence -- instead there is a partner FASTA file which \emph{does} have the sequence. \section{Working with strings directly} \label{sec:seq-module-functions} To close this chapter, for those you who \emph{really} don't want to use the sequence objects (or who prefer a functional programming style to an object orientated one), there are module level functions in \verb|Bio.Seq| will accept plain Python strings, \verb|Seq| objects (including \verb|UnknownSeq| objects) or \verb|MutableSeq| objects: %doctest \begin{verbatim} >>> from Bio.Seq import reverse_complement, transcribe, back_transcribe, translate >>> my_string = "GCTGTTATGGGTCGTTGGAAGGGTGGTCGTGCTGCTGGTTAG" >>> reverse_complement(my_string) 'CTAACCAGCAGCACGACCACCCTTCCAACGACCCATAACAGC' >>> transcribe(my_string) 'GCUGUUAUGGGUCGUUGGAAGGGUGGUCGUGCUGCUGGUUAG' >>> back_transcribe(my_string) 'GCTGTTATGGGTCGTTGGAAGGGTGGTCGTGCTGCTGGTTAG' >>> translate(my_string) 'AVMGRWKGGRAAG*' \end{verbatim} \noindent You are, however, encouraged to work with \verb|Seq| objects by default. \chapter{Sequence annotation objects} \label{chapter:SeqRecord} Chapter~\ref{chapter:Bio.Seq} introduced the sequence classes. Immediately ``above'' the \verb|Seq| class is the Sequence Record or \verb|SeqRecord| class, defined in the \verb|Bio.SeqRecord| module. This class allows higher level features such as identifiers and features (as \verb|SeqFeature| objects) to be associated with the sequence, and is used throughout the sequence input/output interface \verb|Bio.SeqIO| described fully in Chapter~\ref{chapter:Bio.SeqIO}. If you are only going to be working with simple data like FASTA files, you can probably skip this chapter for now. If on the other hand you are going to be using richly annotated sequence data, say from GenBank or EMBL files, this information is quite important. While this chapter should cover most things to do with the \verb|SeqRecord| and \verb|SeqFeature| objects in this chapter, you may also want to read the \verb|SeqRecord| wiki page (\url{http://biopython.org/wiki/SeqRecord}), and the built in documentation (also online -- \href{http://biopython.org/DIST/docs/api/Bio.SeqRecord.SeqRecord-class.html}{SeqRecord} and \href{http://biopython.org/DIST/docs/api/Bio.SeqFeature.SeqFeature-class.html}{SeqFeature}): \begin{verbatim} >>> from Bio.SeqRecord import SeqRecord >>> help(SeqRecord) ... \end{verbatim} \section{The SeqRecord object} \label{sec:SeqRecord} The \verb|SeqRecord| (Sequence Record) class is defined in the \verb|Bio.SeqRecord| module. This class allows higher level features such as identifiers and features to be associated with a sequence (see Chapter~\ref{chapter:Bio.Seq}), and is the basic data type for the \verb|Bio.SeqIO| sequence input/output interface (see Chapter~\ref{chapter:Bio.SeqIO}). The \verb|SeqRecord| class itself is quite simple, and offers the following information as attributes: \begin{description} \item[.seq] -- The sequence itself, typically a \verb|Seq| object. \item[.id] -- The primary ID used to identify the sequence -- a string. In most cases this is something like an accession number. \item[.name] -- A ``common'' name/id for the sequence -- a string. In some cases this will be the same as the accession number, but it could also be a clone name. I think of this as being analogous to the LOCUS id in a GenBank record. \item[.description] -- A human readable description or expressive name for the sequence -- a string. \item[.letter\_annotations] -- Holds per-letter-annotations using a (restricted) dictionary of additional information about the letters in the sequence. The keys are the name of the information, and the information is contained in the value as a Python sequence (i.e. a list, tuple or string) with the same length as the sequence itself. This is often used for quality scores (e.g. Section~\ref{sec:FASTQ-filtering-example}) or secondary structure information (e.g. from Stockholm/PFAM alignment files). \item[.annotations] -- A dictionary of additional information about the sequence. The keys are the name of the information, and the information is contained in the value. This allows the addition of more ``unstructured'' information to the sequence. \item[.features] -- A list of \verb|SeqFeature| objects with more structured information about the features on a sequence (e.g. position of genes on a genome, or domains on a protein sequence). The structure of sequence features is described below in Section~\ref{sec:seq_features}. \item[.dbxrefs] - A list of database cross-references as strings. \end{description} \section{Creating a SeqRecord} Using a \verb|SeqRecord| object is not very complicated, since all of the information is presented as attributes of the class. Usually you won't create a \verb|SeqRecord| ``by hand'', but instead use \verb|Bio.SeqIO| to read in a sequence file for you (see Chapter~\ref{chapter:Bio.SeqIO} and the examples below). However, creating \verb|SeqRecord| can be quite simple. \subsection{SeqRecord objects from scratch} To create a \verb|SeqRecord| at a minimum you just need a \verb|Seq| object: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> simple_seq = Seq("GATC") >>> from Bio.SeqRecord import SeqRecord >>> simple_seq_r = SeqRecord(simple_seq) \end{verbatim} Additionally, you can also pass the id, name and description to the initialization function, but if not they will be set as strings indicating they are unknown, and can be modified subsequently: %cont-doctest \begin{verbatim} >>> simple_seq_r.id '' >>> simple_seq_r.id = "AC12345" >>> simple_seq_r.description = "Made up sequence I wish I could write a paper about" >>> print(simple_seq_r.description) Made up sequence I wish I could write a paper about >>> simple_seq_r.seq Seq('GATC', Alphabet()) \end{verbatim} Including an identifier is very important if you want to output your \verb|SeqRecord| to a file. You would normally include this when creating the object: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> simple_seq = Seq("GATC") >>> from Bio.SeqRecord import SeqRecord >>> simple_seq_r = SeqRecord(simple_seq, id="AC12345") \end{verbatim} As mentioned above, the \verb|SeqRecord| has an dictionary attribute \verb|annotations|. This is used for any miscellaneous annotations that doesn't fit under one of the other more specific attributes. Adding annotations is easy, and just involves dealing directly with the annotation dictionary: %cont-doctest \begin{verbatim} >>> simple_seq_r.annotations["evidence"] = "None. I just made it up." >>> print(simple_seq_r.annotations) {'evidence': 'None. I just made it up.'} >>> print(simple_seq_r.annotations["evidence"]) None. I just made it up. \end{verbatim} Working with per-letter-annotations is similar, \verb|letter_annotations| is a dictionary like attribute which will let you assign any Python sequence (i.e. a string, list or tuple) which has the same length as the sequence: %cont-doctest \begin{verbatim} >>> simple_seq_r.letter_annotations["phred_quality"] = [40, 40, 38, 30] >>> print(simple_seq_r.letter_annotations) {'phred_quality': [40, 40, 38, 30]} >>> print(simple_seq_r.letter_annotations["phred_quality"]) [40, 40, 38, 30] \end{verbatim} The \verb|dbxrefs| and \verb|features| attributes are just Python lists, and should be used to store strings and \verb|SeqFeature| objects (discussed later in this chapter) respectively. %TODO - Update this to show passing in the annotations etc to __init__ ? \subsection{SeqRecord objects from FASTA files} This example uses a fairly large FASTA file containing the whole sequence for \textit{Yersinia pestis biovar Microtus} str. 91001 plasmid pPCP1, originally downloaded from the NCBI. This file is included with the Biopython unit tests under the GenBank folder, or online \href{http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.fna}{\texttt{NC\_005816.fna}} from our website. The file starts like this - and you can check there is only one record present (i.e. only one line starting with a greater than symbol): \begin{verbatim} >gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus ... pPCP1, complete sequence TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGGGGGTAATCTGCTCTCC ... \end{verbatim} Back in Chapter~\ref{chapter:quick-start} you will have seen the function \verb|Bio.SeqIO.parse(...)| used to loop over all the records in a file as \verb|SeqRecord| objects. The \verb|Bio.SeqIO| module has a sister function for use on files which contain just one record which we'll use here (see Chapter~\ref{chapter:Bio.SeqIO} for details): %TODO - line wrapping for doctest? \begin{verbatim} >>> from Bio import SeqIO >>> record = SeqIO.read("NC_005816.fna", "fasta") >>> record SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', SingleLetterAlphabet()), id='gi|45478711|ref|NC_005816.1|', name='gi|45478711|ref|NC_005816.1|', description='gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus ... sequence', dbxrefs=[]) \end{verbatim} Now, let's have a look at the key attributes of this \verb|SeqRecord| individually -- starting with the \verb|seq| attribute which gives you a \verb|Seq| object: \begin{verbatim} >>> record.seq Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', SingleLetterAlphabet()) \end{verbatim} \noindent Here \verb|Bio.SeqIO| has defaulted to a generic alphabet, rather than guessing that this is DNA. If you know in advance what kind of sequence your FASTA file contains, you can tell \verb|Bio.SeqIO| which alphabet to use (see Chapter~\ref{chapter:Bio.SeqIO}). Next, the identifiers and description: \begin{verbatim} >>> record.id 'gi|45478711|ref|NC_005816.1|' >>> record.name 'gi|45478711|ref|NC_005816.1|' >>> record.description 'gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus ... pPCP1, complete sequence' \end{verbatim} As you can see above, the first word of the FASTA record's title line (after removing the greater than symbol) is used for both the \verb|id| and \verb|name| attributes. The whole title line (after removing the greater than symbol) is used for the record description. This is deliberate, partly for backwards compatibility reasons, but it also makes sense if you have a FASTA file like this: \begin{verbatim} >Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1 TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGGGGGTAATCTGCTCTCC ... \end{verbatim} Note that none of the other annotation attributes get populated when reading a FASTA file: \begin{verbatim} >>> record.dbxrefs [] >>> record.annotations {} >>> record.letter_annotations {} >>> record.features [] \end{verbatim} In this case our example FASTA file was from the NCBI, and they have a fairly well defined set of conventions for formatting their FASTA lines. This means it would be possible to parse this information and extract the GI number and accession for example. However, FASTA files from other sources vary, so this isn't possible in general. \subsection{SeqRecord objects from GenBank files} As in the previous example, we're going to look at the whole sequence for \textit{Yersinia pestis biovar Microtus} str. 91001 plasmid pPCP1, originally downloaded from the NCBI, but this time as a GenBank file. Again, this file is included with the Biopython unit tests under the GenBank folder, or online \href{http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.gb}{\texttt{NC\_005816.gb}} from our website. This file contains a single record (i.e. only one LOCUS line) and starts: \begin{verbatim} LOCUS NC_005816 9609 bp DNA circular BCT 21-JUL-2008 DEFINITION Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence. ACCESSION NC_005816 VERSION NC_005816.1 GI:45478711 PROJECT GenomeProject:10638 ... \end{verbatim} Again, we'll use \verb|Bio.SeqIO| to read this file in, and the code is almost identical to that for used above for the FASTA file (see Chapter~\ref{chapter:Bio.SeqIO} for details): \begin{verbatim} >>> from Bio import SeqIO >>> record = SeqIO.read("NC_005816.gb", "genbank") >>> record SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816', description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.', dbxrefs=['Project:10638']) \end{verbatim} You should be able to spot some differences already! But taking the attributes individually, the sequence string is the same as before, but this time \verb|Bio.SeqIO| has been able to automatically assign a more specific alphabet (see Chapter~\ref{chapter:Bio.SeqIO} for details): \begin{verbatim} >>> record.seq Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', IUPACAmbiguousDNA()) \end{verbatim} The \verb|name| comes from the LOCUS line, while the \verb|id| includes the version suffix. The description comes from the DEFINITION line: \begin{verbatim} >>> record.id 'NC_005816.1' >>> record.name 'NC_005816' >>> record.description 'Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.' \end{verbatim} GenBank files don't have any per-letter annotations: \begin{verbatim} >>> record.letter_annotations {} \end{verbatim} Most of the annotations information gets recorded in the \verb|annotations| dictionary, for example: \begin{verbatim} >>> len(record.annotations) 11 >>> record.annotations["source"] 'Yersinia pestis biovar Microtus str. 91001' \end{verbatim} The \verb|dbxrefs| list gets populated from any PROJECT or DBLINK lines: \begin{verbatim} >>> record.dbxrefs ['Project:10638'] \end{verbatim} Finally, and perhaps most interestingly, all the entries in the features table (e.g. the genes or CDS features) get recorded as \verb|SeqFeature| objects in the \verb|features| list. \begin{verbatim} >>> len(record.features) 29 \end{verbatim} \noindent We'll talk about \verb|SeqFeature| objects next, in Section~\ref{sec:seq_features}. \section{Feature, location and position objects} \label{sec:seq_features} \subsection{SeqFeature objects} Sequence features are an essential part of describing a sequence. Once you get beyond the sequence itself, you need some way to organize and easily get at the more ``abstract'' information that is known about the sequence. While it is probably impossible to develop a general sequence feature class that will cover everything, the Biopython \verb|SeqFeature| class attempts to encapsulate as much of the information about the sequence as possible. The design is heavily based on the GenBank/EMBL feature tables, so if you understand how they look, you'll probably have an easier time grasping the structure of the Biopython classes. The key idea about each \verb|SeqFeature| object is to describe a region on a parent sequence, typically a \verb|SeqRecord| object. That region is described with a location object, typically a range between two positions (see Section~\ref{sec:locations} below). The \verb|SeqFeature| class has a number of attributes, so first we'll list them and their general features, and then later in the chapter work through examples to show how this applies to a real life example. The attributes of a SeqFeature are: \begin{description} \item[.type] -- This is a textual description of the type of feature (for instance, this will be something like `CDS' or `gene'). \item[.location] -- The location of the \verb|SeqFeature| on the sequence that you are dealing with, see Section~\ref{sec:locations} below. The \verb|SeqFeature| delegates much of its functionality to the location object, and includes a number of shortcut attributes for properties of the location: \begin{description} \item[.ref] -- shorthand for \verb|.location.ref| -- any (different) reference sequence the location is referring to. Usually just None. \item[.ref\_db] -- shorthand for \verb|.location.ref_db| -- specifies the database any identifier in \verb|.ref| refers to. Usually just None. \item[.strand] -- shorthand for \verb|.location.strand| -- the strand on the sequence that the feature is located on. For double stranded nucleotide sequence this may either be $1$ for the top strand, $-1$ for the bottom strand, $0$ if the strand is important but is unknown, or \texttt{None} if it doesn't matter. This is None for proteins, or single stranded sequences. \end{description} \item[.qualifiers] -- This is a Python dictionary of additional information about the feature. The key is some kind of terse one-word description of what the information contained in the value is about, and the value is the actual information. For example, a common key for a qualifier might be ``evidence'' and the value might be ``computational (non-experimental).'' This is just a way to let the person who is looking at the feature know that it has not be experimentally (i.~e.~in a wet lab) confirmed. Note that other the value will be a list of strings (even when there is only one string). This is a reflection of the feature tables in GenBank/EMBL files. \item[.sub\_features] -- This used to be used to represent features with complicated locations like `joins' in GenBank/EMBL files. This has been deprecated with the introduction of the \verb|CompoundLocation| object, and should now be ignored. \end{description} \subsection{Positions and locations} \label{sec:locations} The key idea about each \verb|SeqFeature| object is to describe a region on a parent sequence, for which we use a location object, typically describing a range between two positions. Two try to clarify the terminology we're using: \begin{description} \item[position] -- This refers to a single position on a sequence, which may be fuzzy or not. For instance, 5, 20, \verb|<100| and \verb|>200| are all positions. \item[location] -- A location is region of sequence bounded by some positions. For instance 5..20 (i.~e.~5 to 20) is a location. \end{description} I just mention this because sometimes I get confused between the two. \subsubsection{FeatureLocation object} Unless you work with eukaryotic genes, most \verb|SeqFeature| locations are extremely simple - you just need start and end coordinates and a strand. That's essentially all the basic \verb|FeatureLocation| object does. %TODO -- add example here In practise of course, things can be more complicated. First of all we have to handle compound locations made up of several regions. Secondly, the positions themselves may be fuzzy (inexact). \subsubsection{CompoundLocation object} Biopython 1.62 introduced the \verb|CompoundLocation| as part of a restructuring of how complex locations made up of multiple regions are represented. The main usage is for handling `join' locations in EMBL/GenBank files. %TODO -- add example here \subsubsection{Fuzzy Positions} So far we've only used simple positions. One complication in dealing with feature locations comes in the positions themselves. In biology many times things aren't entirely certain (as much as us wet lab biologists try to make them certain!). For instance, you might do a dinucleotide priming experiment and discover that the start of mRNA transcript starts at one of two sites. This is very useful information, but the complication comes in how to represent this as a position. To help us deal with this, we have the concept of fuzzy positions. Basically there are several types of fuzzy positions, so we have five classes do deal with them: \begin{description} \item[ExactPosition] -- As its name suggests, this class represents a position which is specified as exact along the sequence. This is represented as just a number, and you can get the position by looking at the \verb|position| attribute of the object. \item[BeforePosition] -- This class represents a fuzzy position that occurs prior to some specified site. In GenBank/EMBL notation, this is represented as something like \verb|`<13'|, signifying that the real position is located somewhere less than 13. To get the specified upper boundary, look at the \verb|position| attribute of the object. \item[AfterPosition] -- Contrary to \verb|BeforePosition|, this class represents a position that occurs after some specified site. This is represented in GenBank as \verb|`>13'|, and like \verb|BeforePosition|, you get the boundary number by looking at the \verb|position| attribute of the object. \item[WithinPosition] -- Occasionally used for GenBank/EMBL locations, this class models a position which occurs somewhere between two specified nucleotides. In GenBank/EMBL notation, this would be represented as `(1.5)', to represent that the position is somewhere within the range 1 to 5. To get the information in this class you have to look at two attributes. The \verb|position| attribute specifies the lower boundary of the range we are looking at, so in our example case this would be one. The \verb|extension| attribute specifies the range to the higher boundary, so in this case it would be 4. So \verb|object.position| is the lower boundary and \verb|object.position + object.extension| is the upper boundary. \item[OneOfPosition] -- Occasionally used for GenBank/EMBL locations, this class deals with a position where several possible values exist, for instance you could use this if the start codon was unclear and there where two candidates for the start of the gene. Alternatively, that might be handled explicitly as two related gene features. \item[UnknownPosition] -- This class deals with a position of unknown location. This is not used in GenBank/EMBL, but corresponds to the `?' feature coordinate used in UniProt. \end{description} Here's an example where we create a location with fuzzy end points: %doctest \begin{verbatim} >>> from Bio import SeqFeature >>> start_pos = SeqFeature.AfterPosition(5) >>> end_pos = SeqFeature.BetweenPosition(9, left=8, right=9) >>> my_location = SeqFeature.FeatureLocation(start_pos, end_pos) \end{verbatim} Note that the details of some of the fuzzy-locations changed in Biopython 1.59, in particular for BetweenPosition and WithinPosition you must now make it explicit which integer position should be used for slicing etc. For a start position this is generally the lower (left) value, while for an end position this would generally be the higher (right) value. If you print out a \verb|FeatureLocation| object, you can get a nice representation of the information: %cont-doctest \begin{verbatim} >>> print(my_location) [>5:(8^9)] \end{verbatim} We can access the fuzzy start and end positions using the start and end attributes of the location: %cont-doctest \begin{verbatim} >>> my_location.start AfterPosition(5) >>> print(my_location.start) >5 >>> my_location.end BetweenPosition(9, left=8, right=9) >>> print(my_location.end) (8^9) \end{verbatim} If you don't want to deal with fuzzy positions and just want numbers, they are actually subclasses of integers so should work like integers: %cont-doctest \begin{verbatim} >>> int(my_location.start) 5 >>> int(my_location.end) 9 \end{verbatim} For compatibility with older versions of Biopython you can ask for the \verb|nofuzzy_start| and \verb|nofuzzy_end| attributes of the location which are plain integers: %cont-doctest \begin{verbatim} >>> my_location.nofuzzy_start 5 >>> my_location.nofuzzy_end 9 \end{verbatim} Notice that this just gives you back the position attributes of the fuzzy locations. Similarly, to make it easy to create a position without worrying about fuzzy positions, you can just pass in numbers to the \verb|FeaturePosition| constructors, and you'll get back out \verb|ExactPosition| objects: %cont-doctest \begin{verbatim} >>> exact_location = SeqFeature.FeatureLocation(5, 9) >>> print(exact_location) [5:9] >>> exact_location.start ExactPosition(5) >>> int(exact_location.start) 5 >>> exact_location.nofuzzy_start 5 \end{verbatim} That is most of the nitty gritty about dealing with fuzzy positions in Biopython. It has been designed so that dealing with fuzziness is not that much more complicated than dealing with exact positions, and hopefully you find that true! \subsubsection{Location testing} You can use the Python keyword \verb|in| with a \verb|SeqFeature| or location object to see if the base/residue for a parent coordinate is within the feature/location or not. For example, suppose you have a SNP of interest and you want to know which features this SNP is within, and lets suppose this SNP is at index 4350 (Python counting!). Here is a simple brute force solution where we just check all the features one by one in a loop: %doctest ../Tests/GenBank \begin{verbatim} >>> from Bio import SeqIO >>> my_snp = 4350 >>> record = SeqIO.read("NC_005816.gb", "genbank") >>> for feature in record.features: ... if my_snp in feature: ... print("%s %s" % (feature.type, feature.qualifiers.get('db_xref'))) ... source ['taxon:229193'] gene ['GeneID:2767712'] CDS ['GI:45478716', 'GeneID:2767712'] \end{verbatim} Note that gene and CDS features from GenBank or EMBL files defined with joins are the union of the exons -- they do not cover any introns. %TODO - Add join example \subsection{Sequence described by a feature or location} A \verb|SeqFeature| or location object doesn't directly contain a sequence, instead the location (see Section~\ref{sec:locations}) describes how to get this from the parent sequence. For example consider a (short) gene sequence with location 5:18 on the reverse strand, which in GenBank/EMBL notation using 1-based counting would be \texttt{complement(6..18)}, like this: %doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.SeqFeature import SeqFeature, FeatureLocation >>> example_parent = Seq("ACCGAGACGGCAAAGGCTAGCATAGGTATGAGACTTCCTTCCTGCCAGTGCTGAGGAACTGGGAGCCTAC") >>> example_feature = SeqFeature(FeatureLocation(5, 18), type="gene", strand=-1) \end{verbatim} You could take the parent sequence, slice it to extract 5:18, and then take the reverse complement. If you are using Biopython 1.59 or later, the feature location's start and end are integer like so this works: %cont-doctest \begin{verbatim} >>> feature_seq = example_parent[example_feature.location.start:example_feature.location.end].reverse_complement() >>> print(feature_seq) AGCCTTTGCCGTC \end{verbatim} This is a simple example so this isn't too bad -- however once you have to deal with compound features (joins) this is rather messy. Instead, the \verb|SeqFeature| object has an \verb|extract| method to take care of all this: %cont-doctest \begin{verbatim} >>> feature_seq = example_feature.extract(example_parent) >>> print(feature_seq) AGCCTTTGCCGTC \end{verbatim} The length of a \verb|SeqFeature| or location matches that of the region of sequence it describes. %cont-doctest \begin{verbatim} >>> print(example_feature.extract(example_parent)) AGCCTTTGCCGTC >>> print(len(example_feature.extract(example_parent))) 13 >>> print(len(example_feature)) 13 >>> print(len(example_feature.location)) 13 \end{verbatim} For simple \verb|FeatureLocation| objects the length is just the difference between the start and end positions. However, for a \verb|CompoundLocation| the length is the sum of the constituent regions. \section{References} Another common annotation related to a sequence is a reference to a journal or other published work dealing with the sequence. We have a fairly simple way of representing a Reference in Biopython -- we have a \verb|Bio.SeqFeature.Reference| class that stores the relevant information about a reference as attributes of an object. The attributes include things that you would expect to see in a reference like \verb|journal|, \verb|title| and \verb|authors|. Additionally, it also can hold the \verb|medline_id| and \verb|pubmed_id| and a \verb|comment| about the reference. These are all accessed simply as attributes of the object. A reference also has a \verb|location| object so that it can specify a particular location on the sequence that the reference refers to. For instance, you might have a journal that is dealing with a particular gene located on a BAC, and want to specify that it only refers to this position exactly. The \verb|location| is a potentially fuzzy location, as described in section~\ref{sec:locations}. Any reference objects are stored as a list in the \verb|SeqRecord| object's \verb|annotations| dictionary under the key ``references''. That's all there is too it. References are meant to be easy to deal with, and hopefully general enough to cover lots of usage cases. \section{The format method} \label{sec:SeqRecord-format} The \verb|format()| method of the \verb|SeqRecord| class gives a string containing your record formatted using one of the output file formats supported by \verb|Bio.SeqIO|, such as FASTA: \begin{verbatim} from Bio.Seq import Seq from Bio.SeqRecord import SeqRecord from Bio.Alphabet import generic_protein record = SeqRecord(Seq("MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD" \ +"GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK" \ +"NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM" \ +"SSAC", generic_protein), id="gi|14150838|gb|AAK54648.1|AF376133_1", description="chalcone synthase [Cucumis sativus]") print(record.format("fasta")) \end{verbatim} \noindent which should give: \begin{verbatim} >gi|14150838|gb|AAK54648.1|AF376133_1 chalcone synthase [Cucumis sativus] MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM SSAC \end{verbatim} This \verb|format| method takes a single mandatory argument, a lower case string which is supported by \verb|Bio.SeqIO| as an output format (see Chapter~\ref{chapter:Bio.SeqIO}). However, some of the file formats \verb|Bio.SeqIO| can write to \emph{require} more than one record (typically the case for multiple sequence alignment formats), and thus won't work via this \verb|format()| method. See also Section~\ref{sec:Bio.SeqIO-and-StringIO}. \section{Slicing a SeqRecord} \label{sec:SeqRecord-slicing} You can slice a \verb|SeqRecord|, to give you a new \verb|SeqRecord| covering just part of the sequence. What is important here is that any per-letter annotations are also sliced, and any features which fall completely within the new sequence are preserved (with their locations adjusted). For example, taking the same GenBank file used earlier: %doctest ../Tests/GenBank \begin{verbatim} >>> from Bio import SeqIO >>> record = SeqIO.read("NC_005816.gb", "genbank") \end{verbatim} %TODO - support line wrapping in doctest \begin{verbatim} >>> record SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816', description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.', dbxrefs=['Project:10638']) \end{verbatim} %cont-doctest \begin{verbatim} >>> len(record) 9609 >>> len(record.features) 41 \end{verbatim} For this example we're going to focus in on the \verb|pim| gene, \verb|YP_pPCP05|. If you have a look at the GenBank file directly you'll find this gene/CDS has location string \texttt{4343..4780}, or in Python counting \texttt{4342:4780}. From looking at the file you can work out that these are the twelfth and thirteenth entries in the file, so in Python zero-based counting they are entries $11$ and $12$ in the \texttt{features} list: %cont-doctest \begin{verbatim} >>> print(record.features[20]) type: gene location: [4342:4780](+) qualifiers: Key: db_xref, Value: ['GeneID:2767712'] Key: gene, Value: ['pim'] Key: locus_tag, Value: ['YP_pPCP05'] \end{verbatim} %This one is truncated so can't use for doctest \begin{verbatim} >>> print(record.features[21]) type: CDS location: [4342:4780](+) qualifiers: Key: codon_start, Value: ['1'] Key: db_xref, Value: ['GI:45478716', 'GeneID:2767712'] Key: gene, Value: ['pim'] Key: locus_tag, Value: ['YP_pPCP05'] Key: note, Value: ['similar to many previously sequenced pesticin immunity ...'] Key: product, Value: ['pesticin immunity protein'] Key: protein_id, Value: ['NP_995571.1'] Key: transl_table, Value: ['11'] Key: translation, Value: ['MGGGMISKLFCLALIFLSSSGLAEKNTYTAKDILQNLELNTFGNSLSH...'] \end{verbatim} Let's slice this parent record from 4300 to 4800 (enough to include the \verb|pim| gene/CDS), and see how many features we get: %cont-doctest \begin{verbatim} >>> sub_record = record[4300:4800] \end{verbatim} %TODO - Line wrapping for doctest? \begin{verbatim} >>> sub_record SeqRecord(seq=Seq('ATAAATAGATTATTCCAAATAATTTATTTATGTAAGAACAGGATGGGAGGGGGA...TTA', IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816', description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.', dbxrefs=[]) \end{verbatim} %cont-doctest \begin{verbatim} >>> len(sub_record) 500 >>> len(sub_record.features) 2 \end{verbatim} Our sub-record just has two features, the gene and CDS entries for \verb|YP_pPCP05|: %cont-doctest \begin{verbatim} >>> print(sub_record.features[0]) type: gene location: [42:480](+) qualifiers: Key: db_xref, Value: ['GeneID:2767712'] Key: gene, Value: ['pim'] Key: locus_tag, Value: ['YP_pPCP05'] \end{verbatim} \begin{verbatim} >>> print(sub_record.features[20]) type: CDS location: [42:480](+) qualifiers: Key: codon_start, Value: ['1'] Key: db_xref, Value: ['GI:45478716', 'GeneID:2767712'] Key: gene, Value: ['pim'] Key: locus_tag, Value: ['YP_pPCP05'] Key: note, Value: ['similar to many previously sequenced pesticin immunity ...'] Key: product, Value: ['pesticin immunity protein'] Key: protein_id, Value: ['NP_995571.1'] Key: transl_table, Value: ['11'] Key: translation, Value: ['MGGGMISKLFCLALIFLSSSGLAEKNTYTAKDILQNLELNTFGNSLSH...'] \end{verbatim} \noindent Notice that their locations have been adjusted to reflect the new parent sequence! While Biopython has done something sensible and hopefully intuitive with the features (and any per-letter annotation), for the other annotation it is impossible to know if this still applies to the sub-sequence or not. To avoid guessing, the \texttt{annotations} and \texttt{dbxrefs} are omitted from the sub-record, and it is up to you to transfer any relevant information as appropriate. %cont-doctest \begin{verbatim} >>> sub_record.annotations {} >>> sub_record.dbxrefs [] \end{verbatim} The same point could be made about the record \texttt{id}, \texttt{name} and \texttt{description}, but for practicality these are preserved: %cont-doctest \begin{verbatim} >>> sub_record.id 'NC_005816.1' >>> sub_record.name 'NC_005816' >>> sub_record.description 'Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.' \end{verbatim} \noindent This illustrates the problem nicely though, our new sub-record is \emph{not} the complete sequence of the plasmid, so the description is wrong! Let's fix this and then view the sub-record as a reduced GenBank file using the \texttt{format} method described above in Section~\ref{sec:SeqRecord-format}: \begin{verbatim} >>> sub_record.description = "Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, partial." >>> print(sub_record.format("genbank")) ... \end{verbatim} See Sections~\ref{sec:FASTQ-slicing-off-primer} and~\ref{sec:FASTQ-slicing-off-adaptor} for some FASTQ examples where the per-letter annotations (the read quality scores) are also sliced. \section{Adding SeqRecord objects} \label{sec:SeqRecord-addition} You can add \verb|SeqRecord| objects together, giving a new \verb|SeqRecord|. What is important here is that any common per-letter annotations are also added, all the features are preserved (with their locations adjusted), and any other common annotation is also kept (like the id, name and description). For an example with per-letter annotation, we'll use the first record in a FASTQ file. Chapter~\ref{chapter:Bio.SeqIO} will explain the \verb|SeqIO| functions: %doctest ../Tests/Quality \begin{verbatim} >>> from Bio import SeqIO >>> record = next(SeqIO.parse("example.fastq", "fastq")) >>> len(record) 25 >>> print(record.seq) CCCTTCTTGTCTTCAGCGTTTCTCC \end{verbatim} %TODO - doctest wrapping \begin{verbatim} >>> print(record.letter_annotations["phred_quality"]) [26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26, 26, 26, 26, 26, 26, 26, 23, 23] \end{verbatim} \noindent Let's suppose this was Roche 454 data, and that from other information you think the \texttt{TTT} should be only \texttt{TT}. We can make a new edited record by first slicing the \verb|SeqRecord| before and after the ``extra'' third \texttt{T}: %cont-doctest \begin{verbatim} >>> left = record[:20] >>> print(left.seq) CCCTTCTTGTCTTCAGCGTT >>> print(left.letter_annotations["phred_quality"]) [26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26, 26, 26, 26] >>> right = record[21:] >>> print(right.seq) CTCC >>> print(right.letter_annotations["phred_quality"]) [26, 26, 23, 23] \end{verbatim} \noindent Now add the two parts together: %cont-doctest \begin{verbatim} >>> edited = left + right >>> len(edited) 24 >>> print(edited.seq) CCCTTCTTGTCTTCAGCGTTCTCC \end{verbatim} \begin{verbatim} >>> print(edited.letter_annotations["phred_quality"]) [26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26, 26, 26, 26, 26, 26, 23, 23] \end{verbatim} \noindent Easy and intuitive? We hope so! You can make this shorter with just: %cont-doctest \begin{verbatim} >>> edited = record[:20] + record[21:] \end{verbatim} Now, for an example with features, we'll use a GenBank file. Suppose you have a circular genome: %doctest ../Tests/GenBank \begin{verbatim} >>> from Bio import SeqIO >>> record = SeqIO.read("NC_005816.gb", "genbank") \end{verbatim} %TODO - doctest wrapping \begin{verbatim} >>> record SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816', description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.', dbxrefs=['Project:10638']) \end{verbatim} %cont-doctest \begin{verbatim} >>> len(record) 9609 >>> len(record.features) 41 >>> record.dbxrefs ['Project:58037'] \end{verbatim} %TODO - doctest wrapping \begin{verbatim} >>> record.annotations.keys() ['comment', 'sequence_version', 'source', 'taxonomy', 'keywords', 'references', 'accessions', 'data_file_division', 'date', 'organism', 'gi'] \end{verbatim} You can shift the origin like this: %cont-doctest \begin{verbatim} >>> shifted = record[2000:] + record[:2000] \end{verbatim} %TODO - doctest wrapping \begin{verbatim} >>> shifted SeqRecord(seq=Seq('GATACGCAGTCATATTTTTTACACAATTCTCTAATCCCGACAAGGTCGTAGGTC...GGA', IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816', description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.', dbxrefs=[]) \end{verbatim} %cont-doctest \begin{verbatim} >>> len(shifted) 9609 \end{verbatim} Note that this isn't perfect in that some annotation like the database cross references and one of the features (the source feature) have been lost: %cont-doctest \begin{verbatim} >>> len(shifted.features) 40 >>> shifted.dbxrefs [] >>> shifted.annotations.keys() [] \end{verbatim} This is because the \verb|SeqRecord| slicing step is cautious in what annotation it preserves (erroneously propagating annotation can cause major problems). If you want to keep the database cross references or the annotations dictionary, this must be done explicitly: \begin{verbatim} >>> shifted.dbxrefs = record.dbxrefs[:] >>> shifted.annotations = record.annotations.copy() >>> shifted.dbxrefs ['Project:10638'] >>> shifted.annotations.keys() ['comment', 'sequence_version', 'source', 'taxonomy', 'keywords', 'references', 'accessions', 'data_file_division', 'date', 'organism', 'gi'] \end{verbatim} Also note that in an example like this, you should probably change the record identifiers since the NCBI references refer to the \emph{original} unmodified sequence. \section{Reverse-complementing SeqRecord objects} \label{sec:SeqRecord-reverse-complement} One of the new features in Biopython 1.57 was the \verb|SeqRecord| object's \verb|reverse_complement| method. This tries to balance easy of use with worries about what to do with the annotation in the reverse complemented record. For the sequence, this uses the Seq object's reverse complement method. Any features are transferred with the location and strand recalculated. Likewise any per-letter-annotation is also copied but reversed (which makes sense for typical examples like quality scores). However, transfer of most annotation is problematical. For instance, if the record ID was an accession, that accession should not really apply to the reverse complemented sequence, and transferring the identifier by default could easily cause subtle data corruption in downstream analysis. Therefore by default, the \verb|SeqRecord|'s id, name, description, annotations and database cross references are all \emph{not} transferred by default. The \verb|SeqRecord| object's \verb|reverse_complement| method takes a number of optional arguments corresponding to properties of the record. Setting these arguments to \verb|True| means copy the old values, while \verb|False| means drop the old values and use the default value. You can alternatively provide the new desired value instead. Consider this example record: %doctest ../Tests/GenBank \begin{verbatim} >>> from Bio import SeqIO >>> record = SeqIO.read("NC_005816.gb", "genbank") >>> print("%s %i %i %i %i" % (record.id, len(record), len(record.features), len(record.dbxrefs), len(record.annotations))) NC_005816.1 9609 41 1 11 \end{verbatim} Here we take the reverse complement and specify a new identifier -- but notice how most of the annotation is dropped (but not the features): %cont-doctest \begin{verbatim} >>> rc = record.reverse_complement(id="TESTING") >>> print("%s %i %i %i %i" % (rc.id, len(rc), len(rc.features), len(rc.dbxrefs), len(rc.annotations))) TESTING 9609 41 0 0 \end{verbatim} \chapter{Sequence Input/Output} \label{chapter:Bio.SeqIO} In this chapter we'll discuss in more detail the \verb|Bio.SeqIO| module, which was briefly introduced in Chapter~\ref{chapter:quick-start} and also used in Chapter~\ref{chapter:SeqRecord}. This aims to provide a simple interface for working with assorted sequence file formats in a uniform way. See also the \verb|Bio.SeqIO| wiki page (\url{http://biopython.org/wiki/SeqIO}), and the built in documentation (also \href{http://biopython.org/DIST/docs/api/Bio.SeqIO-module.html}{online}): \begin{verbatim} >>> from Bio import SeqIO >>> help(SeqIO) ... \end{verbatim} The ``catch'' is that you have to work with \verb|SeqRecord| objects (see Chapter~\ref{chapter:SeqRecord}), which contain a \verb|Seq| object (see Chapter~\ref{chapter:Bio.Seq}) plus annotation like an identifier and description. \section{Parsing or Reading Sequences} \label{sec:Bio.SeqIO-input} The workhorse function \verb|Bio.SeqIO.parse()| is used to read in sequence data as SeqRecord objects. This function expects two arguments: \begin{enumerate} \item The first argument is a {\it handle} to read the data from, or a filename. A handle is typically a file opened for reading, but could be the output from a command line program, or data downloaded from the internet (see Section~\ref{sec:SeqIO_Online}). See Section~\ref{sec:appendix-handles} for more about handles. \item The second argument is a lower case string specifying sequence format -- we don't try and guess the file format for you! See \url{http://biopython.org/wiki/SeqIO} for a full listing of supported formats. \end{enumerate} \noindent There is an optional argument \verb|alphabet| to specify the alphabet to be used. This is useful for file formats like FASTA where otherwise \verb|Bio.SeqIO| will default to a generic alphabet. The \verb|Bio.SeqIO.parse()| function returns an {\it iterator} which gives \verb|SeqRecord| objects. Iterators are typically used in a for loop as shown below. Sometimes you'll find yourself dealing with files which contain only a single record. For this situation use the function \verb|Bio.SeqIO.read()| which takes the same arguments. Provided there is one and only one record in the file, this is returned as a \verb|SeqRecord| object. Otherwise an exception is raised. \subsection{Reading Sequence Files} In general \verb|Bio.SeqIO.parse()| is used to read in sequence files as \verb|SeqRecord| objects, and is typically used with a for loop like this: \begin{verbatim} from Bio import SeqIO for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"): print(seq_record.id) print(repr(seq_record.seq)) print(len(seq_record)) \end{verbatim} The above example is repeated from the introduction in Section~\ref{sec:sequence-parsing}, and will load the orchid DNA sequences in the FASTA format file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{ls\_orchid.fasta}. If instead you wanted to load a GenBank format file like \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{ls\_orchid.gbk} then all you need to do is change the filename and the format string: \begin{verbatim} from Bio import SeqIO for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"): print(seq_record.id) print(seq_record.seq) print(len(seq_record)) \end{verbatim} Similarly, if you wanted to read in a file in another file format, then assuming \verb|Bio.SeqIO.parse()| supports it you would just need to change the format string as appropriate, for example ``swiss'' for SwissProt files or ``embl'' for EMBL text files. There is a full listing on the wiki page (\url{http://biopython.org/wiki/SeqIO}) and in the built in documentation (also \href{http://biopython.org/DIST/docs/api/Bio.SeqIO-module.html}{online}). Another very common way to use a Python iterator is within a list comprehension (or a generator expression). For example, if all you wanted to extract from the file was a list of the record identifiers we can easily do this with the following list comprehension: \begin{verbatim} >>> from Bio import SeqIO >>> identifiers = [seq_record.id for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank")] >>> identifiers ['Z78533.1', 'Z78532.1', 'Z78531.1', 'Z78530.1', 'Z78529.1', 'Z78527.1', ..., 'Z78439.1'] \end{verbatim} \noindent There are more examples using \verb|SeqIO.parse()| in a list comprehension like this in Section~\ref{seq:sequence-parsing-plus-pylab} (e.g. for plotting sequence lengths or GC\%). \subsection{Iterating over the records in a sequence file} In the above examples, we have usually used a for loop to iterate over all the records one by one. You can use the for loop with all sorts of Python objects (including lists, tuples and strings) which support the iteration interface. The object returned by \verb|Bio.SeqIO| is actually an iterator which returns \verb|SeqRecord| objects. You get to see each record in turn, but once and only once. The plus point is that an iterator can save you memory when dealing with large files. Instead of using a for loop, can also use the \verb|next()| function on an iterator to step through the entries, like this: \begin{verbatim} from Bio import SeqIO record_iterator = SeqIO.parse("ls_orchid.fasta", "fasta") first_record = next(record_iterator) print(first_record.id) print(first_record.description) second_record = next(record_iterator) print(second_record.id) print(second_record.description) \end{verbatim} Note that if you try to use \verb|next()| and there are no more results, you'll get the special \verb|StopIteration| exception. One special case to consider is when your sequence files have multiple records, but you only want the first one. In this situation the following code is very concise: \begin{verbatim} from Bio import SeqIO first_record = next(SeqIO.parse("ls_orchid.gbk", "genbank")) \end{verbatim} A word of warning here -- using the \verb|next()| function like this will silently ignore any additional records in the file. If your files have {\it one and only one} record, like some of the online examples later in this chapter, or a GenBank file for a single chromosome, then use the new \verb|Bio.SeqIO.read()| function instead. This will check there are no extra unexpected records present. \subsection{Getting a list of the records in a sequence file} In the previous section we talked about the fact that \verb|Bio.SeqIO.parse()| gives you a \verb|SeqRecord| iterator, and that you get the records one by one. Very often you need to be able to access the records in any order. The Python \verb|list| data type is perfect for this, and we can turn the record iterator into a list of \verb|SeqRecord| objects using the built-in Python function \verb|list()| like so: \begin{verbatim} from Bio import SeqIO records = list(SeqIO.parse("ls_orchid.gbk", "genbank")) print("Found %i records" % len(records)) print("The last record") last_record = records[-1] #using Python's list tricks print(last_record.id) print(repr(last_record.seq)) print(len(last_record)) print("The first record") first_record = records[0] #remember, Python counts from zero print(first_record.id) print(repr(first_record.seq)) print(len(first_record)) \end{verbatim} \noindent Giving: \begin{verbatim} Found 94 records The last record Z78439.1 Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', IUPACAmbiguousDNA()) 592 The first record Z78533.1 Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', IUPACAmbiguousDNA()) 740 \end{verbatim} You can of course still use a for loop with a list of \verb|SeqRecord| objects. Using a list is much more flexible than an iterator (for example, you can determine the number of records from the length of the list), but does need more memory because it will hold all the records in memory at once. \subsection{Extracting data} The \verb|SeqRecord| object and its annotation structures are described more fully in Chapter~\ref{chapter:SeqRecord}. As an example of how annotations are stored, we'll look at the output from parsing the first record in the GenBank file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{ls\_orchid.gbk}. \begin{verbatim} from Bio import SeqIO record_iterator = SeqIO.parse("ls_orchid.gbk", "genbank") first_record = next(record_iterator) print(first_record) \end{verbatim} \noindent That should give something like this: \begin{verbatim} ID: Z78533.1 Name: Z78533 Description: C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA. Number of features: 5 /sequence_version=1 /source=Cypripedium irapeanum /taxonomy=['Eukaryota', 'Viridiplantae', 'Streptophyta', ..., 'Cypripedium'] /keywords=['5.8S ribosomal RNA', '5.8S rRNA gene', ..., 'ITS1', 'ITS2'] /references=[...] /accessions=['Z78533'] /data_file_division=PLN /date=30-NOV-2006 /organism=Cypripedium irapeanum /gi=2765658 Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', IUPACAmbiguousDNA()) \end{verbatim} This gives a human readable summary of most of the annotation data for the \verb|SeqRecord|. For this example we're going to use the \verb|.annotations| attribute which is just a Python dictionary. The contents of this annotations dictionary were shown when we printed the record above. You can also print them out directly: \begin{verbatim} print(first_record.annotations) \end{verbatim} \noindent Like any Python dictionary, you can easily get a list of the keys: \begin{verbatim} print(first_record.annotations.keys()) \end{verbatim} \noindent or values: \begin{verbatim} print(first_record.annotations.values()) \end{verbatim} In general, the annotation values are strings, or lists of strings. One special case is any references in the file get stored as reference objects. Suppose you wanted to extract a list of the species from the \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{ls\_orchid.gbk} GenBank file. The information we want, \emph{Cypripedium irapeanum}, is held in the annotations dictionary under `source' and `organism', which we can access like this: \begin{verbatim} >>> print(first_record.annotations["source"]) Cypripedium irapeanum \end{verbatim} \noindent or: \begin{verbatim} >>> print(first_record.annotations["organism"]) Cypripedium irapeanum \end{verbatim} In general, `organism' is used for the scientific name (in Latin, e.g. \textit{Arabidopsis thaliana}), while `source' will often be the common name (e.g. thale cress). In this example, as is often the case, the two fields are identical. Now let's go through all the records, building up a list of the species each orchid sequence is from: \begin{verbatim} from Bio import SeqIO all_species = [] for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"): all_species.append(seq_record.annotations["organism"]) print(all_species) \end{verbatim} Another way of writing this code is to use a list comprehension: \begin{verbatim} from Bio import SeqIO all_species = [seq_record.annotations["organism"] for seq_record in \ SeqIO.parse("ls_orchid.gbk", "genbank")] print(all_species) \end{verbatim} \noindent In either case, the result is: % Try and keep this example output line short enough to fit on one page of PDF output: \begin{verbatim} ['Cypripedium irapeanum', 'Cypripedium californicum', ..., 'Paphiopedilum barbatum'] \end{verbatim} Great. That was pretty easy because GenBank files are annotated in a standardised way. Now, let's suppose you wanted to extract a list of the species from a FASTA file, rather than the GenBank file. The bad news is you will have to write some code to extract the data you want from the record's description line - if the information is in the file in the first place! Our example FASTA format file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{ls\_orchid.fasta} starts like this: \begin{verbatim} >gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG ... \end{verbatim} You can check by hand, but for every record the species name is in the description line as the second word. This means if we break up each record's \verb|.description| at the spaces, then the species is there as field number one (field zero is the record identifier). That means we can do this: \begin{verbatim} from Bio import SeqIO all_species = [] for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"): all_species.append(seq_record.description.split()[1]) print(all_species) \end{verbatim} \noindent This gives: \begin{verbatim} ['C.irapeanum', 'C.californicum', 'C.fasciculatum', 'C.margaritaceum', ..., 'P.barbatum'] \end{verbatim} The concise alternative using list comprehensions would be: \begin{verbatim} from Bio import SeqIO all_species == [seq_record.description.split()[1] for seq_record in \ SeqIO.parse("ls_orchid.fasta", "fasta")] print(all_species) \end{verbatim} In general, extracting information from the FASTA description line is not very nice. If you can get your sequences in a well annotated file format like GenBank or EMBL, then this sort of annotation information is much easier to deal with. \section{Parsing sequences from compressed files} \label{sec:SeqIO_compressed} In the previous section, we looked at parsing sequence data from a file. Instead of using a filename, you can give \verb|Bio.SeqIO| a handle (see Section~\ref{sec:appendix-handles}), and in this section we'll use handles to parse sequence from compressed files. As you'll have seen above, we can use \verb|Bio.SeqIO.read()| or \verb|Bio.SeqIO.parse()| with a filename - for instance this quick example calculates the total length of the sequences in a multiple record GenBank file using a generator expression: %doctest examples \begin{verbatim} >>> from Bio import SeqIO >>> print(sum(len(r) for r in SeqIO.parse("ls_orchid.gbk", "gb"))) 67518 \end{verbatim} \noindent Here we use a file handle instead, using the \verb|with| statement to close the handle automatically: %doctest examples \begin{verbatim} >>> from Bio import SeqIO >>> with open("ls_orchid.gbk") as handle: ... print(sum(len(r) for r in SeqIO.parse(handle, "gb"))) 67518 \end{verbatim} \noindent Or, the old fashioned way where you manually close the handle: %doctest examples \begin{verbatim} >>> from Bio import SeqIO >>> handle = open("ls_orchid.gbk") >>> print(sum(len(r) for r in SeqIO.parse(handle, "gb"))) 67518 >>> handle.close() \end{verbatim} Now, suppose we have a gzip compressed file instead? These are very commonly used on Linux. We can use Python's \verb|gzip| module to open the compressed file for reading - which gives us a handle object: %This doctest fails on Python 3, http://bugs.python.org/issue13989 \begin{verbatim} >>> import gzip >>> from Bio import SeqIO >>> handle = gzip.open("ls_orchid.gbk.gz", "r") >>> print(sum(len(r) for r in SeqIO.parse(handle, "gb"))) 67518 >>> handle.close() \end{verbatim} Similarly if we had a bzip2 compressed file (sadly the function name isn't quite as consistent): %This doctest fails on Python 3 \begin{verbatim} >>> import bz2 >>> from Bio import SeqIO >>> handle = bz2.BZ2File("ls_orchid.gbk.bz2", "r") >>> print(sum(len(r) for r in SeqIO.parse(handle, "gb"))) 67518 >>> handle.close() \end{verbatim} \noindent If you are using Python 2.7 or later, the \verb|with|-version works for gzip and bz2 as well. Unfortunately this is broken on older versions of Python (\href{http://bugs.python.org/issue3860}{Issue 3860}) and you'd get an \verb|AttributeError| about \verb|__exit__| being missing. There is a gzip (GNU Zip) variant called BGZF (Blocked GNU Zip Format), which can be treated like an ordinary gzip file for reading, but has advantages for random access later which we'll talk about later in Section~\ref{sec:SeqIO-index-bgzf}. \section{Parsing sequences from the net} \label{sec:SeqIO_Online} In the previous sections, we looked at parsing sequence data from a file (using a filename or handle), and from compressed files (using a handle). Here we'll use \verb|Bio.SeqIO| with another type of handle, a network connection, to download and parse sequences from the internet. Note that just because you \emph{can} download sequence data and parse it into a \verb|SeqRecord| object in one go doesn't mean this is a good idea. In general, you should probably download sequences \emph{once} and save them to a file for reuse. \subsection{Parsing GenBank records from the net} \label{sec:SeqIO_GenBank_Online} Section~\ref{sec:efetch} talks about the Entrez EFetch interface in more detail, but for now let's just connect to the NCBI and get a few \textit{Opuntia} (prickly-pear) sequences from GenBank using their GI numbers. First of all, let's fetch just one record. If you don't care about the annotations and features downloading a FASTA file is a good choice as these are compact. Now remember, when you expect the handle to contain one and only one record, use the \verb|Bio.SeqIO.read()| function: \begin{verbatim} from Bio import Entrez from Bio import SeqIO Entrez.email = "A.N.Other@example.com" handle = Entrez.efetch(db="nucleotide", rettype="fasta", retmode="text", id="6273291") seq_record = SeqIO.read(handle, "fasta") handle.close() print("%s with %i features" % (seq_record.id, len(seq_record.features))) \end{verbatim} \noindent Expected output: \begin{verbatim} gi|6273291|gb|AF191665.1|AF191665 with 0 features \end{verbatim} The NCBI will also let you ask for the file in other formats, in particular as a GenBank file. Until Easter 2009, the Entrez EFetch API let you use ``genbank'' as the return type, however the NCBI now insist on using the official return types of ``gb'' (or ``gp'' for proteins) as described on \href{http://www.ncbi.nlm.nih.gov/entrez/query/static/efetchseq_help.html} {EFetch for Sequence and other Molecular Biology Databases}. As a result, in Biopython 1.50 onwards, we support ``gb'' as an alias for ``genbank'' in \verb|Bio.SeqIO|. \begin{verbatim} from Bio import Entrez from Bio import SeqIO Entrez.email = "A.N.Other@example.com" handle = Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", id="6273291") seq_record = SeqIO.read(handle, "gb") #using "gb" as an alias for "genbank" handle.close() print("%s with %i features" % (seq_record.id, len(seq_record.features))) \end{verbatim} \noindent The expected output of this example is: \begin{verbatim} AF191665.1 with 3 features \end{verbatim} \noindent Notice this time we have three features. Now let's fetch several records. This time the handle contains multiple records, so we must use the \verb|Bio.SeqIO.parse()| function: \begin{verbatim} from Bio import Entrez from Bio import SeqIO Entrez.email = "A.N.Other@example.com" handle = Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", \ id="6273291,6273290,6273289") for seq_record in SeqIO.parse(handle, "gb"): print seq_record.id, seq_record.description[:50] + "..." print "Sequence length %i," % len(seq_record), print "%i features," % len(seq_record.features), print "from: %s" % seq_record.annotations["source"] handle.close() \end{verbatim} \noindent That should give the following output: \begin{verbatim} AF191665.1 Opuntia marenae rpl16 gene; chloroplast gene for c... Sequence length 902, 3 features, from: chloroplast Opuntia marenae AF191664.1 Opuntia clavata rpl16 gene; chloroplast gene for c... Sequence length 899, 3 features, from: chloroplast Grusonia clavata AF191663.1 Opuntia bradtiana rpl16 gene; chloroplast gene for... Sequence length 899, 3 features, from: chloroplast Opuntia bradtianaa \end{verbatim} See Chapter~\ref{chapter:entrez} for more about the \verb|Bio.Entrez| module, and make sure to read about the NCBI guidelines for using Entrez (Section~\ref{sec:entrez-guidelines}). \subsection{Parsing SwissProt sequences from the net} \label{sec:SeqIO_ExPASy_and_SwissProt} Now let's use a handle to download a SwissProt file from ExPASy, something covered in more depth in Chapter~\ref{chapter:swiss_prot}. As mentioned above, when you expect the handle to contain one and only one record, use the \verb|Bio.SeqIO.read()| function: \begin{verbatim} from Bio import ExPASy from Bio import SeqIO handle = ExPASy.get_sprot_raw("O23729") seq_record = SeqIO.read(handle, "swiss") handle.close() print(seq_record.id) print(seq_record.name) print(seq_record.description) print(repr(seq_record.seq)) print("Length %i" % len(seq_record)) print(seq_record.annotations["keywords"]) \end{verbatim} \noindent Assuming your network connection is OK, you should get back: \begin{verbatim} O23729 CHS3_BROFI RecName: Full=Chalcone synthase 3; EC=2.3.1.74; AltName: Full=Naringenin-chalcone synthase 3; Seq('MAPAMEEIRQAQRAEGPAAVLAIGTSTPPNALYQADYPDYYFRITKSEHLTELK...GAE', ProteinAlphabet()) Length 394 ['Acyltransferase', 'Flavonoid biosynthesis', 'Transferase'] \end{verbatim} \section{Sequence files as Dictionaries} We're now going to introduce three related functions in the \verb|Bio.SeqIO| module which allow dictionary like random access to a multi-sequence file. There is a trade off here between flexibility and memory usage. In summary: \begin{itemize} \item \verb|Bio.SeqIO.to_dict()| is the most flexible but also the most memory demanding option (see Section~\ref{SeqIO:to_dict}). This is basically a helper function to build a normal Python \verb|dictionary| with each entry held as a \verb|SeqRecord| object in memory, allowing you to modify the records. \item \verb|Bio.SeqIO.index()| is a useful middle ground, acting like a read only dictionary and parsing sequences into \verb|SeqRecord| objects on demand (see Section~\ref{sec:SeqIO-index}). \item \verb|Bio.SeqIO.index_db()| also acts like a read only dictionary but stores the identifiers and file offsets in a file on disk (as an SQLite3 database), meaning it has very low memory requirements (see Section~\ref{sec:SeqIO-index-db}), but will be a little bit slower. \end{itemize} See the discussion for an broad overview (Section~\ref{sec:SeqIO-indexing-discussion}). \subsection{Sequence files as Dictionaries -- In memory} \label{SeqIO:to_dict} The next thing that we'll do with our ubiquitous orchid files is to show how to index them and access them like a database using the Python \verb|dictionary| data type (like a hash in Perl). This is very useful for moderately large files where you only need to access certain elements of the file, and makes for a nice quick 'n dirty database. For dealing with larger files where memory becomes a problem, see Section~\ref{sec:SeqIO-index} below. You can use the function \verb|Bio.SeqIO.to_dict()| to make a SeqRecord dictionary (in memory). By default this will use each record's identifier (i.e. the \verb|.id| attribute) as the key. Let's try this using our GenBank file: %doctest examples \begin{verbatim} >>> from Bio import SeqIO >>> orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.gbk", "genbank")) \end{verbatim} There is just one required argument for \verb|Bio.SeqIO.to_dict()|, a list or generator giving \verb|SeqRecord| objects. Here we have just used the output from the \verb|SeqIO.parse| function. As the name suggests, this returns a Python dictionary. Since this variable \verb|orchid_dict| is an ordinary Python dictionary, we can look at all of the keys we have available: %cont-doctest \begin{verbatim} >>> len(orchid_dict) 94 \end{verbatim} %Can't use following for doctest due to abbreviation \begin{verbatim} >>> orchid_dict.keys() ['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1'] \end{verbatim} If you really want to, you can even look at all the records at once: \begin{verbatim} >>> orchid_dict.values() #lots of output! ... \end{verbatim} We can access a single \verb|SeqRecord| object via the keys and manipulate the object as normal: %cont-doctest \begin{verbatim} >>> seq_record = orchid_dict["Z78475.1"] >>> print(seq_record.description) P.supardii 5.8S rRNA gene and ITS1 and ITS2 DNA. >>> print(repr(seq_record.seq)) Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGT', IUPACAmbiguousDNA()) \end{verbatim} So, it is very easy to create an in memory ``database'' of our GenBank records. Next we'll try this for the FASTA file instead. Note that those of you with prior Python experience should all be able to construct a dictionary like this ``by hand''. However, typical dictionary construction methods will not deal with the case of repeated keys very nicely. Using the \verb|Bio.SeqIO.to_dict()| will explicitly check for duplicate keys, and raise an exception if any are found. \subsubsection{Specifying the dictionary keys} \label{seq:seqio-todict-functionkey} Using the same code as above, but for the FASTA file instead: \begin{verbatim} from Bio import SeqIO orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.fasta", "fasta")) print(orchid_dict.keys()) \end{verbatim} \noindent This time the keys are: \begin{verbatim} ['gi|2765596|emb|Z78471.1|PDZ78471', 'gi|2765646|emb|Z78521.1|CCZ78521', ... ..., 'gi|2765613|emb|Z78488.1|PTZ78488', 'gi|2765583|emb|Z78458.1|PHZ78458'] \end{verbatim} You should recognise these strings from when we parsed the FASTA file earlier in Section~\ref{sec:fasta-parsing}. Suppose you would rather have something else as the keys - like the accession numbers. This brings us nicely to \verb|SeqIO.to_dict()|'s optional argument \verb|key_function|, which lets you define what to use as the dictionary key for your records. First you must write your own function to return the key you want (as a string) when given a \verb|SeqRecord| object. In general, the details of function will depend on the sort of input records you are dealing with. But for our orchids, we can just split up the record's identifier using the ``pipe'' character (the vertical line) and return the fourth entry (field three): \begin{verbatim} def get_accession(record): """"Given a SeqRecord, return the accession number as a string. e.g. "gi|2765613|emb|Z78488.1|PTZ78488" -> "Z78488.1" """ parts = record.id.split("|") assert len(parts) == 5 and parts[0] == "gi" and parts[2] == "emb" return parts[3] \end{verbatim} \noindent Then we can give this function to the \verb|SeqIO.to_dict()| function to use in building the dictionary: \begin{verbatim} from Bio import SeqIO orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.fasta", "fasta"), key_function=get_accession) print(orchid_dict.keys()) \end{verbatim} \noindent Finally, as desired, the new dictionary keys: \begin{verbatim} >>> print(orchid_dict.keys()) ['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1'] \end{verbatim} \noindent Not too complicated, I hope! \subsubsection{Indexing a dictionary using the SEGUID checksum} To give another example of working with dictionaries of \verb|SeqRecord| objects, we'll use the SEGUID checksum function. This is a relatively recent checksum, and collisions should be very rare (i.e. two different sequences with the same checksum), an improvement on the CRC64 checksum. Once again, working with the orchids GenBank file: \begin{verbatim} from Bio import SeqIO from Bio.SeqUtils.CheckSum import seguid for record in SeqIO.parse("ls_orchid.gbk", "genbank"): print(record.id, seguid(record.seq)) \end{verbatim} \noindent This should give: \begin{verbatim} Z78533.1 JUEoWn6DPhgZ9nAyowsgtoD9TTo Z78532.1 MN/s0q9zDoCVEEc+k/IFwCNF2pY ... Z78439.1 H+JfaShya/4yyAj7IbMqgNkxdxQ \end{verbatim} Now, recall the \verb|Bio.SeqIO.to_dict()| function's \verb|key_function| argument expects a function which turns a \verb|SeqRecord| into a string. We can't use the \verb|seguid()| function directly because it expects to be given a \verb|Seq| object (or a string). However, we can use Python's \verb|lambda| feature to create a ``one off'' function to give to \verb|Bio.SeqIO.to_dict()| instead: %doctest examples \begin{verbatim} >>> from Bio import SeqIO >>> from Bio.SeqUtils.CheckSum import seguid >>> seguid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.gbk", "genbank"), ... lambda rec : seguid(rec.seq)) >>> record = seguid_dict["MN/s0q9zDoCVEEc+k/IFwCNF2pY"] >>> print(record.id) Z78532.1 >>> print(record.description) C.californicum 5.8S rRNA gene and ITS1 and ITS2 DNA. \end{verbatim} \noindent That should have retrieved the record {\tt Z78532.1}, the second entry in the file. \subsection{Sequence files as Dictionaries -- Indexed files} %\subsection{Indexing really large files} \label{sec:SeqIO-index} As the previous couple of examples tried to illustrate, using \verb|Bio.SeqIO.to_dict()| is very flexible. However, because it holds everything in memory, the size of file you can work with is limited by your computer's RAM. In general, this will only work on small to medium files. For larger files you should consider \verb|Bio.SeqIO.index()|, which works a little differently. Although it still returns a dictionary like object, this does \emph{not} keep \emph{everything} in memory. Instead, it just records where each record is within the file -- when you ask for a particular record, it then parses it on demand. As an example, let's use the same GenBank file as before: %doctest examples \begin{verbatim} >>> from Bio import SeqIO >>> orchid_dict = SeqIO.index("ls_orchid.gbk", "genbank") >>> len(orchid_dict) 94 \end{verbatim} %Following is abbr. \begin{verbatim} >>> orchid_dict.keys() ['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1'] \end{verbatim} %cont-doctest \begin{verbatim} >>> seq_record = orchid_dict["Z78475.1"] >>> print(seq_record.description) P.supardii 5.8S rRNA gene and ITS1 and ITS2 DNA. >>> seq_record.seq Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGT', IUPACAmbiguousDNA()) \end{verbatim} \noindent Note that \verb|Bio.SeqIO.index()| won't take a handle, but only a filename. There are good reasons for this, but it is a little technical. The second argument is the file format (a lower case string as used in the other \verb|Bio.SeqIO| functions). You can use many other simple file formats, including FASTA and FASTQ files (see the example in Section~\ref{sec:fastq-indexing}). However, alignment formats like PHYLIP or Clustal are not supported. Finally as an optional argument you can supply an alphabet, or a key function. Here is the same example using the FASTA file - all we change is the filename and the format name: \begin{verbatim} >>> from Bio import SeqIO >>> orchid_dict = SeqIO.index("ls_orchid.fasta", "fasta") >>> len(orchid_dict) 94 >>> orchid_dict.keys() ['gi|2765596|emb|Z78471.1|PDZ78471', 'gi|2765646|emb|Z78521.1|CCZ78521', ... ..., 'gi|2765613|emb|Z78488.1|PTZ78488', 'gi|2765583|emb|Z78458.1|PHZ78458'] \end{verbatim} \subsubsection{Specifying the dictionary keys} \label{seq:seqio-index-functionkey} Suppose you want to use the same keys as before? Much like with the \verb|Bio.SeqIO.to_dict()| example in Section~\ref{seq:seqio-todict-functionkey}, you'll need to write a tiny function to map from the FASTA identifier (as a string) to the key you want: \begin{verbatim} def get_acc(identifier): """"Given a SeqRecord identifier string, return the accession number as a string. e.g. "gi|2765613|emb|Z78488.1|PTZ78488" -> "Z78488.1" """ parts = identifier.split("|") assert len(parts) == 5 and parts[0] == "gi" and parts[2] == "emb" return parts[3] \end{verbatim} \noindent Then we can give this function to the \verb|Bio.SeqIO.index()| function to use in building the dictionary: \begin{verbatim} >>> from Bio import SeqIO >>> orchid_dict = SeqIO.index("ls_orchid.fasta", "fasta", key_function=get_acc) >>> print(orchid_dict.keys()) ['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1'] \end{verbatim} \noindent Easy when you know how? \subsubsection{Getting the raw data for a record} \label{sec:seqio-index-getraw} The dictionary-like object from \verb|Bio.SeqIO.index()| gives you each entry as a \verb|SeqRecord| object. However, it is sometimes useful to be able to get the original raw data straight from the file. For this use the \verb|get_raw()| method which takes a single argument (the record identifier) and returns a string (extracted from the file without modification). A motivating example is extracting a subset of a records from a large file where either \verb|Bio.SeqIO.write()| does not (yet) support the output file format (e.g. the plain text SwissProt file format) or where you need to preserve the text exactly (e.g. GenBank or EMBL output from Biopython does not yet preserve every last bit of annotation). Let's suppose you have download the whole of UniProt in the plain text SwissPort file format from their FTP site (\url{ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.dat.gz}) and uncompressed it as the file \verb|uniprot_sprot.dat|, and you want to extract just a few records from it: \begin{verbatim} >>> from Bio import SeqIO >>> uniprot = SeqIO.index("uniprot_sprot.dat", "swiss") >>> handle = open("selected.dat", "w") >>> for acc in ["P33487", "P19801", "P13689", "Q8JZQ5", "Q9TRC7"]: ... handle.write(uniprot.get_raw(acc)) >>> handle.close() \end{verbatim} There is a longer example in Section~\ref{sec:SeqIO-sort} using the \verb|SeqIO.index()| function to sort a large sequence file (without loading everything into memory at once). \subsection{Sequence files as Dictionaries -- Database indexed files} \label{sec:SeqIO-index-db} Biopython 1.57 introduced an alternative, \verb|Bio.SeqIO.index_db()|, which can work on even extremely large files since it stores the record information as a file on disk (using an SQLite3 database) rather than in memory. Also, you can index multiple files together (providing all the record identifiers are unique). The \verb|Bio.SeqIO.index()| function takes three required arguments: \begin{itemize} \item Index filename, we suggest using something ending \texttt{.idx}. This index file is actually an SQLite3 database. \item List of sequence filenames to index (or a single filename) \item File format (lower case string as used in the rest of the \verb|SeqIO| module). \end{itemize} As an example, consider the GenBank flat file releases from the NCBI FTP site, \url{ftp://ftp.ncbi.nih.gov/genbank/}, which are gzip compressed GenBank files. As of GenBank release $182$, there are $16$ files making up the viral sequences, \texttt{gbvrl1.seq}, \ldots, \texttt{gbvrl16.seq}, containing in total almost one million records. You can index them like this: \begin{verbatim} >>> from Bio import SeqIO >>> files = ["gbvrl%i.seq" % (i+1) for i in range(16)] >>> gb_vrl = SeqIO.index_db("gbvrl.idx", files, "genbank") >>> print("%i sequences indexed" % len(gb_vrl)) 958086 sequences indexed \end{verbatim} That takes about two minutes to run on my machine. If you rerun it then the index file (here \texttt{gbvrl.idx}) is reloaded in under a second. You can use the index as a read only Python dictionary - without having to worry about which file the sequence comes from, e.g. \begin{verbatim} >>> print(gb_vrl["GQ333173.1"].description) HIV-1 isolate F12279A1 from Uganda gag protein (gag) gene, partial cds. \end{verbatim} \subsubsection{Getting the raw data for a record} Just as with the \verb|Bio.SeqIO.index()| function discussed above in Section~\ref{sec:seqio-index-getraw}, the dictionary like object also lets you get at the raw text of each record: \begin{verbatim} >>> print(gb_vrl.get_raw("GQ333173.1")) LOCUS GQ333173 459 bp DNA linear VRL 21-OCT-2009 DEFINITION HIV-1 isolate F12279A1 from Uganda gag protein (gag) gene, partial cds. ACCESSION GQ333173 ... // \end{verbatim} \subsection{Indexing compressed files} \label{sec:SeqIO-index-bgzf} Very often when you are indexing a sequence file it can be quite large -- so you may want to compress it on disk. Unfortunately efficient random access is difficult with the more common file formats like gzip and bzip2. In this setting, BGZF (Blocked GNU Zip Format) can be very helpful. This is a variant of gzip (and can be decompressed using standard gzip tools) popularised by the BAM file format, \href{http://samtools.sourceforge.net/}{samtools}, and \href{http://samtools.sourceforge.net/tabix.shtml}{tabix}. To create a BGZF compressed file you can use the command line tool \verb|bgzip| which comes with samtools. In our examples we use a filename extension \verb|*.bgz|, so they can be distinguished from normal gzipped files (named \verb|*.gz|). You can also use the \verb|Bio.bgzf| module to read and write BGZF files from within Python. The \verb|Bio.SeqIO.index()| and \verb|Bio.SeqIO.index_db()| can both be used with BGZF compressed files. For example, if you started with an uncompressed GenBank file: %doctest examples \begin{verbatim} >>> from Bio import SeqIO >>> orchid_dict = SeqIO.index("ls_orchid.gbk", "genbank") >>> len(orchid_dict) 94 \end{verbatim} You could compress this (while keeping the original file) at the command line using the following command -- but don't worry, the compressed file is already included with the other example files: \begin{verbatim} $ bgzip -c ls_orchid.gbk > ls_orchid.gbk.bgz \end{verbatim} You can use the compressed file in exactly the same way: %doctest examples \begin{verbatim} >>> from Bio import SeqIO >>> orchid_dict = SeqIO.index("ls_orchid.gbk.bgz", "genbank") >>> len(orchid_dict) 94 \end{verbatim} \noindent or: %Don't use doctest as would have to clean up the *.idx file \begin{verbatim} >>> from Bio import SeqIO >>> orchid_dict = SeqIO.index_db("ls_orchid.gbk.bgz.idx", "ls_orchid.gbk.bgz", "genbank") >>> len(orchid_dict) 94 \end{verbatim} The \verb|SeqIO| indexing automatically detects the BGZF compression. Note that you can't use the same index file for the uncompressed and compressed files. \subsection{Discussion} \label{sec:SeqIO-indexing-discussion} So, which of these methods should you use and why? It depends on what you are trying to do (and how much data you are dealing with). However, in general picking \verb|Bio.SeqIO.index()| is a good starting point. If you are dealing with millions of records, multiple files, or repeated analyses, then look at \verb|Bio.SeqIO.index_db()|. Reasons to choose \verb|Bio.SeqIO.to_dict()| over either \verb|Bio.SeqIO.index()| or \verb|Bio.SeqIO.index_db()| boil down to a need for flexibility despite its high memory needs. The advantage of storing the \verb|SeqRecord| objects in memory is they can be changed, added to, or removed at will. In addition to the downside of high memory consumption, indexing can also take longer because all the records must be fully parsed. Both \verb|Bio.SeqIO.index()| and \verb|Bio.SeqIO.index_db()| only parse records on demand. When indexing, they scan the file once looking for the start of each record and do as little work as possible to extract the identifier. Reasons to choose \verb|Bio.SeqIO.index()| over \verb|Bio.SeqIO.index_db()| include: \begin{itemize} \item Faster to build the index (more noticeable in simple file formats) \item Slightly faster access as SeqRecord objects (but the difference is only really noticeable for simple to parse file formats). \item Can use any immutable Python object as the dictionary keys (e.g. a tuple of strings, or a frozen set) not just strings. \item Don't need to worry about the index database being out of date if the sequence file being indexed has changed. \end{itemize} Reasons to choose \verb|Bio.SeqIO.index_db()| over \verb|Bio.SeqIO.index()| include: \begin{itemize} \item Not memory limited -- this is already important with files from second generation sequencing where 10s of millions of sequences are common, and using \verb|Bio.SeqIO.index()| can require more than 4GB of RAM and therefore a 64bit version of Python. \item Because the index is kept on disk, it can be reused. Although building the index database file takes longer, if you have a script which will be rerun on the same datafiles in future, this could save time in the long run. \item Indexing multiple files together \item The \verb|get_raw()| method can be much faster, since for most file formats the length of each record is stored as well as its offset. \end{itemize} \section{Writing Sequence Files} We've talked about using \verb|Bio.SeqIO.parse()| for sequence input (reading files), and now we'll look at \verb|Bio.SeqIO.write()| which is for sequence output (writing files). This is a function taking three arguments: some \verb|SeqRecord| objects, a handle or filename to write to, and a sequence format. Here is an example, where we start by creating a few \verb|SeqRecord| objects the hard way (by hand, rather than by loading them from a file): \begin{verbatim} from Bio.Seq import Seq from Bio.SeqRecord import SeqRecord from Bio.Alphabet import generic_protein rec1 = SeqRecord(Seq("MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD" \ +"GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK" \ +"NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM" \ +"SSAC", generic_protein), id="gi|14150838|gb|AAK54648.1|AF376133_1", description="chalcone synthase [Cucumis sativus]") rec2 = SeqRecord(Seq("YPDYYFRITNREHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDARQ" \ +"DMVVVEIPKLGKEAAVKAIKEWGQ", generic_protein), id="gi|13919613|gb|AAK33142.1|", description="chalcone synthase [Fragaria vesca subsp. bracteata]") rec3 = SeqRecord(Seq("MVTVEEFRRAQCAEGPATVMAIGTATPSNCVDQSTYPDYYFRITNSEHKVELKEKFKRMC" \ +"EKSMIKKRYMHLTEEILKENPNICAYMAPSLDARQDIVVVEVPKLGKEAAQKAIKEWGQP" \ +"KSKITHLVFCTTSGVDMPGCDYQLTKLLGLRPSVKRFMMYQQGCFAGGTVLRMAKDLAEN" \ +"NKGARVLVVCSEITAVTFRGPNDTHLDSLVGQALFGDGAAAVIIGSDPIPEVERPLFELV" \ +"SAAQTLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLVEAFQPLGISDWNSLFW" \ +"IAHPGGPAILDQVELKLGLKQEKLKATRKVLSNYGNMSSACVLFILDEMRKASAKEGLGT" \ +"TGEGLEWGVLFGFGPGLTVETVVLHSVAT", generic_protein), id="gi|13925890|gb|AAK49457.1|", description="chalcone synthase [Nicotiana tabacum]") my_records = [rec1, rec2, rec3] \end{verbatim} \noindent Now we have a list of \verb|SeqRecord| objects, we'll write them to a FASTA format file: \begin{verbatim} from Bio import SeqIO SeqIO.write(my_records, "my_example.faa", "fasta") \end{verbatim} \noindent And if you open this file in your favourite text editor it should look like this: \begin{verbatim} >gi|14150838|gb|AAK54648.1|AF376133_1 chalcone synthase [Cucumis sativus] MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM SSAC >gi|13919613|gb|AAK33142.1| chalcone synthase [Fragaria vesca subsp. bracteata] YPDYYFRITNREHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDARQ DMVVVEIPKLGKEAAVKAIKEWGQ >gi|13925890|gb|AAK49457.1| chalcone synthase [Nicotiana tabacum] MVTVEEFRRAQCAEGPATVMAIGTATPSNCVDQSTYPDYYFRITNSEHKVELKEKFKRMC EKSMIKKRYMHLTEEILKENPNICAYMAPSLDARQDIVVVEVPKLGKEAAQKAIKEWGQP KSKITHLVFCTTSGVDMPGCDYQLTKLLGLRPSVKRFMMYQQGCFAGGTVLRMAKDLAEN NKGARVLVVCSEITAVTFRGPNDTHLDSLVGQALFGDGAAAVIIGSDPIPEVERPLFELV SAAQTLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLVEAFQPLGISDWNSLFW IAHPGGPAILDQVELKLGLKQEKLKATRKVLSNYGNMSSACVLFILDEMRKASAKEGLGT TGEGLEWGVLFGFGPGLTVETVVLHSVAT \end{verbatim} Suppose you wanted to know how many records the \verb|Bio.SeqIO.write()| function wrote to the handle? If your records were in a list you could just use \verb|len(my_records)|, however you can't do that when your records come from a generator/iterator. The \verb|Bio.SeqIO.write()| function returns the number of \verb|SeqRecord| objects written to the file. \emph{Note} - If you tell the \verb|Bio.SeqIO.write()| function to write to a file that already exists, the old file will be overwritten without any warning. \subsection{Round trips} Some people like their parsers to be ``round-tripable'', meaning if you read in a file and write it back out again it is unchanged. This requires that the parser must extract enough information to reproduce the original file \emph{exactly}. \verb|Bio.SeqIO| does \emph{not} aim to do this. As a trivial example, any line wrapping of the sequence data in FASTA files is allowed. An identical \verb|SeqRecord| would be given from parsing the following two examples which differ only in their line breaks: \begin{verbatim} >YAL068C-7235.2170 Putative promoter sequence TACGAGAATAATTTCTCATCATCCAGCTTTAACACAAAATTCGCACAGTTTTCGTTAAGA GAACTTAACATTTTCTTATGACGTAAATGAAGTTTATATATAAATTTCCTTTTTATTGGA >YAL068C-7235.2170 Putative promoter sequence TACGAGAATAATTTCTCATCATCCAGCTTTAACACAAAATTCGCA CAGTTTTCGTTAAGAGAACTTAACATTTTCTTATGACGTAAATGA AGTTTATATATAAATTTCCTTTTTATTGGA \end{verbatim} To make a round-tripable FASTA parser you would need to keep track of where the sequence line breaks occurred, and this extra information is usually pointless. Instead Biopython uses a default line wrapping of $60$ characters on output. The same problem with white space applies in many other file formats too. Another issue in some cases is that Biopython does not (yet) preserve every last bit of annotation (e.g. GenBank and EMBL). Occasionally preserving the original layout (with any quirks it may have) is important. See Section~\ref{sec:seqio-index-getraw} about the \verb|get_raw()| method of the \verb|Bio.SeqIO.index()| dictionary-like object for one potential solution. \subsection{Converting between sequence file formats} \label{sec:SeqIO-conversion} In previous example we used a list of \verb|SeqRecord| objects as input to the \verb|Bio.SeqIO.write()| function, but it will also accept a \verb|SeqRecord| iterator like we get from \verb|Bio.SeqIO.parse()| -- this lets us do file conversion by combining these two functions. For this example we'll read in the GenBank format file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{ls\_orchid.gbk} and write it out in FASTA format: \begin{verbatim} from Bio import SeqIO records = SeqIO.parse("ls_orchid.gbk", "genbank") count = SeqIO.write(records, "my_example.fasta", "fasta") print("Converted %i records" % count) \end{verbatim} Still, that is a little bit complicated. So, because file conversion is such a common task, there is a helper function letting you replace that with just: \begin{verbatim} from Bio import SeqIO count = SeqIO.convert("ls_orchid.gbk", "genbank", "my_example.fasta", "fasta") print("Converted %i records" % count) \end{verbatim} The \verb|Bio.SeqIO.convert()| function will take handles \emph{or} filenames. Watch out though -- if the output file already exists, it will overwrite it! To find out more, see the built in help: \begin{verbatim} >>> from Bio import SeqIO >>> help(SeqIO.convert) ... \end{verbatim} In principle, just by changing the filenames and the format names, this code could be used to convert between any file formats available in Biopython. However, writing some formats requires information (e.g. quality scores) which other files formats don't contain. For example, while you can turn a FASTQ file into a FASTA file, you can't do the reverse. See also Sections~\ref{sec:SeqIO-fastq-conversion} and~\ref{sec:SeqIO-fasta-qual-conversion} in the cookbook chapter which looks at inter-converting between different FASTQ formats. Finally, as an added incentive for using the \verb|Bio.SeqIO.convert()| function (on top of the fact your code will be shorter), doing it this way may also be faster! The reason for this is the convert function can take advantage of several file format specific optimisations and tricks. \subsection{Converting a file of sequences to their reverse complements} \label{sec:SeqIO-reverse-complement} Suppose you had a file of nucleotide sequences, and you wanted to turn it into a file containing their reverse complement sequences. This time a little bit of work is required to transform the \verb|SeqRecord| objects we get from our input file into something suitable for saving to our output file. To start with, we'll use \verb|Bio.SeqIO.parse()| to load some nucleotide sequences from a file, then print out their reverse complements using the \verb|Seq| object's built in \verb|.reverse_complement()| method (see Section~\ref{sec:seq-reverse-complement}): \begin{verbatim} >>> from Bio import SeqIO >>> for record in SeqIO.parse("ls_orchid.gbk", "genbank"): ... print(record.id) ... print(record.seq.reverse_complement()) \end{verbatim} Now, if we want to save these reverse complements to a file, we'll need to make \verb|SeqRecord| objects. We can use the \verb|SeqRecord| object's built in \verb|.reverse_complement()| method (see Section~\ref{sec:SeqRecord-reverse-complement}) but we must decide how to name our new records. This is an excellent place to demonstrate the power of list comprehensions which make a list in memory: %doctest examples \begin{verbatim} >>> from Bio import SeqIO >>> records = [rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \ ... for rec in SeqIO.parse("ls_orchid.fasta", "fasta")] >>> len(records) 94 \end{verbatim} \noindent Now list comprehensions have a nice trick up their sleeves, you can add a conditional statement: %cont-doctest examples \begin{verbatim} >>> records = [rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \ ... for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if len(rec)<700] >>> len(records) 18 \end{verbatim} That would create an in memory list of reverse complement records where the sequence length was under 700 base pairs. However, we can do exactly the same with a generator expression - but with the advantage that this does not create a list of all the records in memory at once: %cont-doctest examples \begin{verbatim} >>> records = (rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \ ... for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if len(rec)<700) \end{verbatim} As a complete example: %not a doctest as would have to remove the output file \begin{verbatim} >>> from Bio import SeqIO >>> records = (rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \ ... for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if len(rec)<700) >>> SeqIO.write(records, "rev_comp.fasta", "fasta") 18 \end{verbatim} There is a related example in Section~\ref{sec:SeqIO-translate}, translating each record in a FASTA file from nucleotides to amino acids. \subsection{Getting your SeqRecord objects as formatted strings} \label{sec:Bio.SeqIO-and-StringIO} Suppose that you don't really want to write your records to a file or handle -- instead you want a string containing the records in a particular file format. The \verb|Bio.SeqIO| interface is based on handles, but Python has a useful built in module which provides a string based handle. For an example of how you might use this, let's load in a bunch of \verb|SeqRecord| objects from our orchids GenBank file, and create a string containing the records in FASTA format: \begin{verbatim} from Bio import SeqIO from StringIO import StringIO records = SeqIO.parse("ls_orchid.gbk", "genbank") out_handle = StringIO() SeqIO.write(records, out_handle, "fasta") fasta_data = out_handle.getvalue() print(fasta_data) \end{verbatim} This isn't entirely straightforward the first time you see it! On the bright side, for the special case where you would like a string containing a \emph{single} record in a particular file format, use the the \verb|SeqRecord| class' \verb|format()| method (see Section~\ref{sec:SeqRecord-format}). Note that although we don't encourage it, you \emph{can} use the \verb|format()| method to write to a file, for example something like this: \begin{verbatim} from Bio import SeqIO out_handle = open("ls_orchid_long.tab", "w") for record in SeqIO.parse("ls_orchid.gbk", "genbank"): if len(record) > 100: out_handle.write(record.format("tab")) out_handle.close() \end{verbatim} \noindent While this style of code will work for a simple sequential file format like FASTA or the simple tab separated format used here, it will \emph{not} work for more complex or interlaced file formats. This is why we still recommend using \verb|Bio.SeqIO.write()|, as in the following example: \begin{verbatim} from Bio import SeqIO records = (rec for rec in SeqIO.parse("ls_orchid.gbk", "genbank") if len(rec) > 100) SeqIO.write(records, "ls_orchid.tab", "tab") \end{verbatim} \noindent Making a single call to \verb|SeqIO.write(...)| is also much quicker than multiple calls to the \verb|SeqRecord.format(...)| method. \chapter{Multiple Sequence Alignment objects} \label{chapter:Bio.AlignIO} This chapter is about Multiple Sequence Alignments, by which we mean a collection of multiple sequences which have been aligned together -- usually with the insertion of gap characters, and addition of leading or trailing gaps -- such that all the sequence strings are the same length. Such an alignment can be regarded as a matrix of letters, where each row is held as a \verb|SeqRecord| object internally. We will introduce the \verb|MultipleSeqAlignment| object which holds this kind of data, and the \verb|Bio.AlignIO| module for reading and writing them as various file formats (following the design of the \verb|Bio.SeqIO| module from the previous chapter). Note that both \verb|Bio.SeqIO| and \verb|Bio.AlignIO| can read and write sequence alignment files. The appropriate choice will depend largely on what you want to do with the data. The final part of this chapter is about our command line wrappers for common multiple sequence alignment tools like ClustalW and MUSCLE. \section{Parsing or Reading Sequence Alignments} We have two functions for reading in sequence alignments, \verb|Bio.AlignIO.read()| and \verb|Bio.AlignIO.parse()| which following the convention introduced in \verb|Bio.SeqIO| are for files containing one or multiple alignments respectively. Using \verb|Bio.AlignIO.parse()| will return an {\it iterator} which gives \verb|MultipleSeqAlignment| objects. Iterators are typically used in a for loop. Examples of situations where you will have multiple different alignments include resampled alignments from the PHYLIP tool \verb|seqboot|, or multiple pairwise alignments from the EMBOSS tools \verb|water| or \verb|needle|, or Bill Pearson's FASTA tools. However, in many situations you will be dealing with files which contain only a single alignment. In this case, you should use the \verb|Bio.AlignIO.read()| function which returns a single \verb|MultipleSeqAlignment| object. Both functions expect two mandatory arguments: \begin{enumerate} \item The first argument is a {\it handle} to read the data from, typically an open file (see Section~\ref{sec:appendix-handles}), or a filename. \item The second argument is a lower case string specifying the alignment format. As in \verb|Bio.SeqIO| we don't try and guess the file format for you! See \url{http://biopython.org/wiki/AlignIO} for a full listing of supported formats. \end{enumerate} \noindent There is also an optional \verb|seq_count| argument which is discussed in Section~\ref{sec:AlignIO-count-argument} below for dealing with ambiguous file formats which may contain more than one alignment. A further optional \verb|alphabet| argument allowing you to specify the expected alphabet. This can be useful as many alignment file formats do not explicitly label the sequences as RNA, DNA or protein -- which means \verb|Bio.AlignIO| will default to using a generic alphabet. \subsection{Single Alignments} As an example, consider the following annotation rich protein alignment in the PFAM or Stockholm file format: \begin{verbatim} # STOCKHOLM 1.0 #=GS COATB_BPIKE/30-81 AC P03620.1 #=GS COATB_BPIKE/30-81 DR PDB; 1ifl ; 1-52; #=GS Q9T0Q8_BPIKE/1-52 AC Q9T0Q8.1 #=GS COATB_BPI22/32-83 AC P15416.1 #=GS COATB_BPM13/24-72 AC P69541.1 #=GS COATB_BPM13/24-72 DR PDB; 2cpb ; 1-49; #=GS COATB_BPM13/24-72 DR PDB; 2cps ; 1-49; #=GS COATB_BPZJ2/1-49 AC P03618.1 #=GS Q9T0Q9_BPFD/1-49 AC Q9T0Q9.1 #=GS Q9T0Q9_BPFD/1-49 DR PDB; 1nh4 A; 1-49; #=GS COATB_BPIF1/22-73 AC P03619.2 #=GS COATB_BPIF1/22-73 DR PDB; 1ifk ; 1-50; COATB_BPIKE/30-81 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA #=GR COATB_BPIKE/30-81 SS -HHHHHHHHHHHHHH--HHHHHHHH--HHHHHHHHHHHHHHHHHHHHH---- Q9T0Q8_BPIKE/1-52 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA COATB_BPI22/32-83 DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPM13/24-72 AEGDDP...AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA #=GR COATB_BPM13/24-72 SS ---S-T...CHCHHHHCCCCTCCCTTCHHHHHHHHHHHHHHHHHHHHCTT-- COATB_BPZJ2/1-49 AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA Q9T0Q9_BPFD/1-49 AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA #=GR Q9T0Q9_BPFD/1-49 SS ------...-HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH-- COATB_BPIF1/22-73 FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA #=GR COATB_BPIF1/22-73 SS XX-HHHH--HHHHHH--HHHHHHH--HHHHHHHHHHHHHHHHHHHHHHH--- #=GC SS_cons XHHHHHHHHHHHHHHHCHHHHHHHHCHHHHHHHHHHHHHHHHHHHHHHHC-- #=GC seq_cons AEssss...AptAhDSLpspAT-hIu.sWshVsslVsAsluIKLFKKFsSKA // \end{verbatim} This is the seed alignment for the Phage\_Coat\_Gp8 (PF05371) PFAM entry, downloaded from a now out of date release of PFAM from \url{http://pfam.sanger.ac.uk/}. We can load this file as follows (assuming it has been saved to disk as ``PF05371\_seed.sth'' in the current working directory): %doctest examples \begin{verbatim} >>> from Bio import AlignIO >>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm") \end{verbatim} \noindent This code will print out a summary of the alignment: %cont-doctest \begin{verbatim} >>> print(alignment) SingleLetterAlphabet() alignment with 7 rows and 52 columns AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRL...SKA COATB_BPIKE/30-81 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKL...SRA Q9T0Q8_BPIKE/1-52 DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRL...SKA COATB_BPI22/32-83 AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49 FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73 \end{verbatim} You'll notice in the above output the sequences have been truncated. We could instead write our own code to format this as we please by iterating over the rows as \verb|SeqRecord| objects: %doctest examples \begin{verbatim} >>> from Bio import AlignIO >>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm") >>> print("Alignment length %i" % alignment.get_alignment_length()) Alignment length 52 >>> for record in alignment: ... print("%s - %s" % (record.seq, record.id)) AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA - COATB_BPIKE/30-81 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA - Q9T0Q8_BPIKE/1-52 DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA - COATB_BPI22/32-83 AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - COATB_BPM13/24-72 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA - COATB_BPZJ2/1-49 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - Q9T0Q9_BPFD/1-49 FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA - COATB_BPIF1/22-73 \end{verbatim} You could also use the alignment object's \verb|format| method to show it in a particular file format -- see Section~\ref{sec:alignment-format-method} for details. Did you notice in the raw file above that several of the sequences include database cross-references to the PDB and the associated known secondary structure? Try this: %cont-doctest \begin{verbatim} >>> for record in alignment: ... if record.dbxrefs: ... print("%s %s" % (record.id, record.dbxrefs)) COATB_BPIKE/30-81 ['PDB; 1ifl ; 1-52;'] COATB_BPM13/24-72 ['PDB; 2cpb ; 1-49;', 'PDB; 2cps ; 1-49;'] Q9T0Q9_BPFD/1-49 ['PDB; 1nh4 A; 1-49;'] COATB_BPIF1/22-73 ['PDB; 1ifk ; 1-50;'] \end{verbatim} \noindent To have a look at all the sequence annotation, try this: \begin{verbatim} >>> for record in alignment: ... print(record) \end{verbatim} Sanger provide a nice web interface at \url{http://pfam.sanger.ac.uk/family?acc=PF05371} which will actually let you download this alignment in several other formats. This is what the file looks like in the FASTA file format: \begin{verbatim} >COATB_BPIKE/30-81 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA >Q9T0Q8_BPIKE/1-52 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA >COATB_BPI22/32-83 DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA >COATB_BPM13/24-72 AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA >COATB_BPZJ2/1-49 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA >Q9T0Q9_BPFD/1-49 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA >COATB_BPIF1/22-73 FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA \end{verbatim} \noindent Note the website should have an option about showing gaps as periods (dots) or dashes, we've shown dashes above. Assuming you download and save this as file ``PF05371\_seed.faa'' then you can load it with almost exactly the same code: \begin{verbatim} from Bio import AlignIO alignment = AlignIO.read("PF05371_seed.faa", "fasta") print(alignment) \end{verbatim} All that has changed in this code is the filename and the format string. You'll get the same output as before, the sequences and record identifiers are the same. However, as you should expect, if you check each \verb|SeqRecord| there is no annotation nor database cross-references because these are not included in the FASTA file format. Note that rather than using the Sanger website, you could have used \verb|Bio.AlignIO| to convert the original Stockholm format file into a FASTA file yourself (see below). With any supported file format, you can load an alignment in exactly the same way just by changing the format string. For example, use ``phylip'' for PHYLIP files, ``nexus'' for NEXUS files or ``emboss'' for the alignments output by the EMBOSS tools. There is a full listing on the wiki page (\url{http://biopython.org/wiki/AlignIO}) and in the built in documentation (also \href{http://biopython.org/DIST/docs/api/Bio.AlignIO-module.html}{online}): \begin{verbatim} >>> from Bio import AlignIO >>> help(AlignIO) ... \end{verbatim} \subsection{Multiple Alignments} The previous section focused on reading files containing a single alignment. In general however, files can contain more than one alignment, and to read these files we must use the \verb|Bio.AlignIO.parse()| function. Suppose you have a small alignment in PHYLIP format: \begin{verbatim} 5 6 Alpha AACAAC Beta AACCCC Gamma ACCAAC Delta CCACCA Epsilon CCAAAC \end{verbatim} If you wanted to bootstrap a phylogenetic tree using the PHYLIP tools, one of the steps would be to create a set of many resampled alignments using the tool \verb|bootseq|. This would give output something like this, which has been abbreviated for conciseness: \begin{verbatim} 5 6 Alpha AAACCA Beta AAACCC Gamma ACCCCA Delta CCCAAC Epsilon CCCAAA 5 6 Alpha AAACAA Beta AAACCC Gamma ACCCAA Delta CCCACC Epsilon CCCAAA 5 6 Alpha AAAAAC Beta AAACCC Gamma AACAAC Delta CCCCCA Epsilon CCCAAC ... 5 6 Alpha AAAACC Beta ACCCCC Gamma AAAACC Delta CCCCAA Epsilon CAAACC \end{verbatim} If you wanted to read this in using \verb|Bio.AlignIO| you could use: %TODO - Replace the print blank line with print()? \begin{verbatim} from Bio import AlignIO alignments = AlignIO.parse("resampled.phy", "phylip") for alignment in alignments: print(alignment) print("") \end{verbatim} \noindent This would give the following output, again abbreviated for display: \begin{verbatim} SingleLetterAlphabet() alignment with 5 rows and 6 columns AAACCA Alpha AAACCC Beta ACCCCA Gamma CCCAAC Delta CCCAAA Epsilon SingleLetterAlphabet() alignment with 5 rows and 6 columns AAACAA Alpha AAACCC Beta ACCCAA Gamma CCCACC Delta CCCAAA Epsilon SingleLetterAlphabet() alignment with 5 rows and 6 columns AAAAAC Alpha AAACCC Beta AACAAC Gamma CCCCCA Delta CCCAAC Epsilon ... SingleLetterAlphabet() alignment with 5 rows and 6 columns AAAACC Alpha ACCCCC Beta AAAACC Gamma CCCCAA Delta CAAACC Epsilon \end{verbatim} As with the function \verb|Bio.SeqIO.parse()|, using \verb|Bio.AlignIO.parse()| returns an iterator. If you want to keep all the alignments in memory at once, which will allow you to access them in any order, then turn the iterator into a list: \begin{verbatim} from Bio import AlignIO alignments = list(AlignIO.parse("resampled.phy", "phylip")) last_align = alignments[-1] first_align = alignments[0] \end{verbatim} \subsection{Ambiguous Alignments} \label{sec:AlignIO-count-argument} Many alignment file formats can explicitly store more than one alignment, and the division between each alignment is clear. However, when a general sequence file format has been used there is no such block structure. The most common such situation is when alignments have been saved in the FASTA file format. For example consider the following: \begin{verbatim} >Alpha ACTACGACTAGCTCAG--G >Beta ACTACCGCTAGCTCAGAAG >Gamma ACTACGGCTAGCACAGAAG >Alpha ACTACGACTAGCTCAGG-- >Beta ACTACCGCTAGCTCAGAAG >Gamma ACTACGGCTAGCACAGAAG \end{verbatim} \noindent This could be a single alignment containing six sequences (with repeated identifiers). Or, judging from the identifiers, this is probably two different alignments each with three sequences, which happen to all have the same length. What about this next example? \begin{verbatim} >Alpha ACTACGACTAGCTCAG--G >Beta ACTACCGCTAGCTCAGAAG >Alpha ACTACGACTAGCTCAGG-- >Gamma ACTACGGCTAGCACAGAAG >Alpha ACTACGACTAGCTCAGG-- >Delta ACTACGGCTAGCACAGAAG \end{verbatim} \noindent Again, this could be a single alignment with six sequences. However this time based on the identifiers we might guess this is three pairwise alignments which by chance have all got the same lengths. This final example is similar: \begin{verbatim} >Alpha ACTACGACTAGCTCAG--G >XXX ACTACCGCTAGCTCAGAAG >Alpha ACTACGACTAGCTCAGG >YYY ACTACGGCAAGCACAGG >Alpha --ACTACGAC--TAGCTCAGG >ZZZ GGACTACGACAATAGCTCAGG \end{verbatim} \noindent In this third example, because of the differing lengths, this cannot be treated as a single alignment containing all six records. However, it could be three pairwise alignments. Clearly trying to store more than one alignment in a FASTA file is not ideal. However, if you are forced to deal with these as input files \verb|Bio.AlignIO| can cope with the most common situation where all the alignments have the same number of records. One example of this is a collection of pairwise alignments, which can be produced by the EMBOSS tools \verb|needle| and \verb|water| -- although in this situation, \verb|Bio.AlignIO| should be able to understand their native output using ``emboss'' as the format string. To interpret these FASTA examples as several separate alignments, we can use \verb|Bio.AlignIO.parse()| with the optional \verb|seq_count| argument which specifies how many sequences are expected in each alignment (in these examples, 3, 2 and 2 respectively). For example, using the third example as the input data: %TODO - Replace the print blank line with print()? \begin{verbatim} for alignment in AlignIO.parse(handle, "fasta", seq_count=2): print("Alignment length %i" % alignment.get_alignment_length()) for record in alignment: print("%s - %s" % (record.seq, record.id)) print("") \end{verbatim} \noindent giving: \begin{verbatim} Alignment length 19 ACTACGACTAGCTCAG--G - Alpha ACTACCGCTAGCTCAGAAG - XXX Alignment length 17 ACTACGACTAGCTCAGG - Alpha ACTACGGCAAGCACAGG - YYY Alignment length 21 --ACTACGAC--TAGCTCAGG - Alpha GGACTACGACAATAGCTCAGG - ZZZ \end{verbatim} Using \verb|Bio.AlignIO.read()| or \verb|Bio.AlignIO.parse()| without the \verb|seq_count| argument would give a single alignment containing all six records for the first two examples. For the third example, an exception would be raised because the lengths differ preventing them being turned into a single alignment. If the file format itself has a block structure allowing \verb|Bio.AlignIO| to determine the number of sequences in each alignment directly, then the \verb|seq_count| argument is not needed. If it is supplied, and doesn't agree with the file contents, an error is raised. Note that this optional \verb|seq_count| argument assumes each alignment in the file has the same number of sequences. Hypothetically you may come across stranger situations, for example a FASTA file containing several alignments each with a different number of sequences -- although I would love to hear of a real world example of this. Assuming you cannot get the data in a nicer file format, there is no straight forward way to deal with this using \verb|Bio.AlignIO|. In this case, you could consider reading in the sequences themselves using \verb|Bio.SeqIO| and batching them together to create the alignments as appropriate. \section{Writing Alignments} We've talked about using \verb|Bio.AlignIO.read()| and \verb|Bio.AlignIO.parse()| for alignment input (reading files), and now we'll look at \verb|Bio.AlignIO.write()| which is for alignment output (writing files). This is a function taking three arguments: some \verb|MultipleSeqAlignment| objects (or for backwards compatibility the obsolete \verb|Alignment| objects), a handle or filename to write to, and a sequence format. Here is an example, where we start by creating a few \verb|MultipleSeqAlignment| objects the hard way (by hand, rather than by loading them from a file). Note we create some \verb|SeqRecord| objects to construct the alignment from. \begin{verbatim} from Bio.Alphabet import generic_dna from Bio.Seq import Seq from Bio.SeqRecord import SeqRecord from Bio.Align import MultipleSeqAlignment align1 = MultipleSeqAlignment([ SeqRecord(Seq("ACTGCTAGCTAG", generic_dna), id="Alpha"), SeqRecord(Seq("ACT-CTAGCTAG", generic_dna), id="Beta"), SeqRecord(Seq("ACTGCTAGDTAG", generic_dna), id="Gamma"), ]) align2 = MultipleSeqAlignment([ SeqRecord(Seq("GTCAGC-AG", generic_dna), id="Delta"), SeqRecord(Seq("GACAGCTAG", generic_dna), id="Epsilon"), SeqRecord(Seq("GTCAGCTAG", generic_dna), id="Zeta"), ]) align3 = MultipleSeqAlignment([ SeqRecord(Seq("ACTAGTACAGCTG", generic_dna), id="Eta"), SeqRecord(Seq("ACTAGTACAGCT-", generic_dna), id="Theta"), SeqRecord(Seq("-CTACTACAGGTG", generic_dna), id="Iota"), ]) my_alignments = [align1, align2, align3] \end{verbatim} \noindent Now we have a list of \verb|Alignment| objects, we'll write them to a PHYLIP format file: \begin{verbatim} from Bio import AlignIO AlignIO.write(my_alignments, "my_example.phy", "phylip") \end{verbatim} \noindent And if you open this file in your favourite text editor it should look like this: \begin{verbatim} 3 12 Alpha ACTGCTAGCT AG Beta ACT-CTAGCT AG Gamma ACTGCTAGDT AG 3 9 Delta GTCAGC-AG Epislon GACAGCTAG Zeta GTCAGCTAG 3 13 Eta ACTAGTACAG CTG Theta ACTAGTACAG CT- Iota -CTACTACAG GTG \end{verbatim} Its more common to want to load an existing alignment, and save that, perhaps after some simple manipulation like removing certain rows or columns. Suppose you wanted to know how many alignments the \verb|Bio.AlignIO.write()| function wrote to the handle? If your alignments were in a list like the example above, you could just use \verb|len(my_alignments)|, however you can't do that when your records come from a generator/iterator. Therefore the \verb|Bio.AlignIO.write()| function returns the number of alignments written to the file. \emph{Note} - If you tell the \verb|Bio.AlignIO.write()| function to write to a file that already exists, the old file will be overwritten without any warning. \subsection{Converting between sequence alignment file formats} \label{sec:converting-alignments} Converting between sequence alignment file formats with \verb|Bio.AlignIO| works in the same way as converting between sequence file formats with \verb|Bio.SeqIO| (Section~\ref{sec:SeqIO-conversion}). We load generally the alignment(s) using \verb|Bio.AlignIO.parse()| and then save them using the \verb|Bio.AlignIO.write()| -- or just use the \verb|Bio.AlignIO.convert()| helper function. For this example, we'll load the PFAM/Stockholm format file used earlier and save it as a Clustal W format file: \begin{verbatim} from Bio import AlignIO count = AlignIO.convert("PF05371_seed.sth", "stockholm", "PF05371_seed.aln", "clustal") print("Converted %i alignments" % count) \end{verbatim} Or, using \verb|Bio.AlignIO.parse()| and \verb|Bio.AlignIO.write()|: \begin{verbatim} from Bio import AlignIO alignments = AlignIO.parse("PF05371_seed.sth", "stockholm") count = AlignIO.write(alignments, "PF05371_seed.aln", "clustal") print("Converted %i alignments" % count) \end{verbatim} The \verb|Bio.AlignIO.write()| function expects to be given multiple alignment objects. In the example above we gave it the alignment iterator returned by \verb|Bio.AlignIO.parse()|. In this case, we know there is only one alignment in the file so we could have used \verb|Bio.AlignIO.read()| instead, but notice we have to pass this alignment to \verb|Bio.AlignIO.write()| as a single element list: \begin{verbatim} from Bio import AlignIO alignment = AlignIO.read("PF05371_seed.sth", "stockholm") AlignIO.write([alignment], "PF05371_seed.aln", "clustal") \end{verbatim} Either way, you should end up with the same new Clustal W format file ``PF05371\_seed.aln'' with the following content: \begin{verbatim} CLUSTAL X (1.81) multiple sequence alignment COATB_BPIKE/30-81 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSS Q9T0Q8_BPIKE/1-52 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVS COATB_BPI22/32-83 DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSS COATB_BPM13/24-72 AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTS COATB_BPZJ2/1-49 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFAS Q9T0Q9_BPFD/1-49 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTS COATB_BPIF1/22-73 FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVS COATB_BPIKE/30-81 KA Q9T0Q8_BPIKE/1-52 RA COATB_BPI22/32-83 KA COATB_BPM13/24-72 KA COATB_BPZJ2/1-49 KA Q9T0Q9_BPFD/1-49 KA COATB_BPIF1/22-73 RA \end{verbatim} Alternatively, you could make a PHYLIP format file which we'll name ``PF05371\_seed.phy'': \begin{verbatim} from Bio import AlignIO AlignIO.convert("PF05371_seed.sth", "stockholm", "PF05371_seed.phy", "phylip") \end{verbatim} This time the output looks like this: \begin{verbatim} 7 52 COATB_BPIK AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS Q9T0Q8_BPI AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS COATB_BPI2 DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS COATB_BPM1 AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS COATB_BPZJ AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS Q9T0Q9_BPF AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS COATB_BPIF FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS KA RA KA KA KA KA RA \end{verbatim} One of the big handicaps of the PHYLIP alignment file format is that the sequence identifiers are strictly truncated at ten characters. In this example, as you can see the resulting names are still unique - but they are not very readable. In this particular case, there is no clear way to compress the identifiers, but for the sake of argument you may want to assign your own names or numbering system. This following bit of code manipulates the record identifiers before saving the output: \begin{verbatim} from Bio import AlignIO alignment = AlignIO.read("PF05371_seed.sth", "stockholm") name_mapping = {} for i, record in enumerate(alignment): name_mapping[i] = record.id record.id = "seq%i" % i print(name_mapping) AlignIO.write([alignment], "PF05371_seed.phy", "phylip") \end{verbatim} \noindent This code used a Python dictionary to record a simple mapping from the new sequence system to the original identifier: \begin{verbatim} {0: 'COATB_BPIKE/30-81', 1: 'Q9T0Q8_BPIKE/1-52', 2: 'COATB_BPI22/32-83', ...} \end{verbatim} \noindent Here is the new PHYLIP format output: \begin{verbatim} 7 52 seq0 AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS seq1 AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS seq2 DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS seq3 AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS seq4 AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS seq5 AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS seq6 FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS KA RA KA KA KA KA RA \end{verbatim} \noindent In general, because of the identifier limitation, working with PHYLIP file formats shouldn't be your first choice. Using the PFAM/Stockholm format on the other hand allows you to record a lot of additional annotation too. \subsection{Getting your alignment objects as formatted strings} \label{sec:alignment-format-method} The \verb|Bio.AlignIO| interface is based on handles, which means if you want to get your alignment(s) into a string in a particular file format you need to do a little bit more work (see below). However, you will probably prefer to take advantage of the alignment object's \verb|format()| method. This takes a single mandatory argument, a lower case string which is supported by \verb|Bio.AlignIO| as an output format. For example: \begin{verbatim} from Bio import AlignIO alignment = AlignIO.read("PF05371_seed.sth", "stockholm") print(alignment.format("clustal")) \end{verbatim} As described in Section~\ref{sec:SeqRecord-format}), the \verb|SeqRecord| object has a similar method using output formats supported by \verb|Bio.SeqIO|. Internally the \verb|format()| method is using the \verb|StringIO| string based handle and calling \verb|Bio.AlignIO.write()|. You can do this in your own code if for example you are using an older version of Biopython: \begin{verbatim} from Bio import AlignIO from StringIO import StringIO alignments = AlignIO.parse("PF05371_seed.sth", "stockholm") out_handle = StringIO() AlignIO.write(alignments, out_handle, "clustal") clustal_data = out_handle.getvalue() print(clustal_data) \end{verbatim} \section{Manipulating Alignments} \label{sec:manipulating-alignments} Now that we've covered loading and saving alignments, we'll look at what else you can do with them. \subsection{Slicing alignments} First of all, in some senses the alignment objects act like a Python \verb|list| of \verb|SeqRecord| objects (the rows). With this model in mind hopefully the actions of \verb|len()| (the number of rows) and iteration (each row as a \verb|SeqRecord|) make sense: %doctest examples \begin{verbatim} >>> from Bio import AlignIO >>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm") >>> print("Number of rows: %i" % len(alignment)) Number of rows: 7 >>> for record in alignment: ... print("%s - %s" % (record.seq, record.id)) AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA - COATB_BPIKE/30-81 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA - Q9T0Q8_BPIKE/1-52 DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA - COATB_BPI22/32-83 AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - COATB_BPM13/24-72 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA - COATB_BPZJ2/1-49 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - Q9T0Q9_BPFD/1-49 FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA - COATB_BPIF1/22-73 \end{verbatim} You can also use the list-like \verb|append| and \verb|extend| methods to add more rows to the alignment (as \verb|SeqRecord| objects). Keeping the list metaphor in mind, simple slicing of the alignment should also make sense - it selects some of the rows giving back another alignment object: %cont-doctest \begin{verbatim} >>> print(alignment) SingleLetterAlphabet() alignment with 7 rows and 52 columns AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRL...SKA COATB_BPIKE/30-81 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKL...SRA Q9T0Q8_BPIKE/1-52 DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRL...SKA COATB_BPI22/32-83 AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49 FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73 >>> print(alignment[3:7]) SingleLetterAlphabet() alignment with 4 rows and 52 columns AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49 FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73 \end{verbatim} What if you wanted to select by column? Those of you who have used the NumPy matrix or array objects won't be surprised at this - you use a double index. %cont-doctest \begin{verbatim} >>> print(alignment[2, 6]) T \end{verbatim} \noindent Using two integer indices pulls out a single letter, short hand for this: %cont-doctest \begin{verbatim} >>> print(alignment[2].seq[6]) T \end{verbatim} You can pull out a single column as a string like this: %cont-doctest \begin{verbatim} >>> print(alignment[:, 6]) TTT---T \end{verbatim} You can also select a range of columns. For example, to pick out those same three rows we extracted earlier, but take just their first six columns: %cont-doctest \begin{verbatim} >>> print(alignment[3:6, :6]) SingleLetterAlphabet() alignment with 3 rows and 6 columns AEGDDP COATB_BPM13/24-72 AEGDDP COATB_BPZJ2/1-49 AEGDDP Q9T0Q9_BPFD/1-49 \end{verbatim} Leaving the first index as \verb|:| means take all the rows: %cont-doctest \begin{verbatim} >>> print(alignment[:, :6]) SingleLetterAlphabet() alignment with 7 rows and 6 columns AEPNAA COATB_BPIKE/30-81 AEPNAA Q9T0Q8_BPIKE/1-52 DGTSTA COATB_BPI22/32-83 AEGDDP COATB_BPM13/24-72 AEGDDP COATB_BPZJ2/1-49 AEGDDP Q9T0Q9_BPFD/1-49 FAADDA COATB_BPIF1/22-73 \end{verbatim} This brings us to a neat way to remove a section. Notice columns 7, 8 and 9 which are gaps in three of the seven sequences: %cont-doctest \begin{verbatim} >>> print(alignment[:, 6:9]) SingleLetterAlphabet() alignment with 7 rows and 3 columns TNY COATB_BPIKE/30-81 TNY Q9T0Q8_BPIKE/1-52 TSY COATB_BPI22/32-83 --- COATB_BPM13/24-72 --- COATB_BPZJ2/1-49 --- Q9T0Q9_BPFD/1-49 TSQ COATB_BPIF1/22-73 \end{verbatim} \noindent Again, you can slice to get everything after the ninth column: %cont-doctest \begin{verbatim} >>> print(alignment[:, 9:]) SingleLetterAlphabet() alignment with 7 rows and 43 columns ATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81 ATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52 ATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83 AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72 AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49 AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49 AKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73 \end{verbatim} \noindent Now, the interesting thing is that addition of alignment objects works by column. This lets you do this as a way to remove a block of columns: %cont-doctest \begin{verbatim} >>> edited = alignment[:, :6] + alignment[:, 9:] >>> print(edited) SingleLetterAlphabet() alignment with 7 rows and 49 columns AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81 AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52 DGTSTAATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83 AEGDDPAKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72 AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49 AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49 FAADDAAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73 \end{verbatim} Another common use of alignment addition would be to combine alignments for several different genes into a meta-alignment. Watch out though - the identifiers need to match up (see Section~\ref{sec:SeqRecord-addition} for how adding \verb|SeqRecord| objects works). You may find it helpful to first sort the alignment rows alphabetically by id: %cont-doctest \begin{verbatim} >>> edited.sort() >>> print(edited) SingleLetterAlphabet() alignment with 7 rows and 49 columns DGTSTAATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83 FAADDAAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73 AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81 AEGDDPAKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72 AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49 AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52 AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49 \end{verbatim} \noindent Note that you can only add two alignments together if they have the same number of rows. \subsection{Alignments as arrays} Depending on what you are doing, it can be more useful to turn the alignment object into an array of letters -- and you can do this with NumPy: %This example fails under PyPy 2.0, https://bugs.pypy.org/issue1546 %doctest examples lib:numpy \begin{verbatim} >>> import numpy as np >>> from Bio import AlignIO >>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm") >>> align_array = np.array([list(rec) for rec in alignment], np.character) >>> align_array.shape (7, 52) \end{verbatim} If you will be working heavily with the columns, you can tell NumPy to store the array by column (as in Fortran) rather then its default of by row (as in C): \begin{verbatim} >>> align_array = np.array([list(rec) for rec in alignment], np.character, order="F") \end{verbatim} Note that this leaves the original Biopython alignment object and the NumPy array in memory as separate objects - editing one will not update the other! \section{Alignment Tools} \label{sec:alignment-tools} There are \emph{lots} of algorithms out there for aligning sequences, both pairwise alignments and multiple sequence alignments. These calculations are relatively slow, and you generally wouldn't want to write such an algorithm in Python. Instead, you can use Biopython to invoke a command line tool on your behalf. Normally you would: \begin{enumerate} \item Prepare an input file of your unaligned sequences, typically this will be a FASTA file which you might create using \verb|Bio.SeqIO| (see Chapter~\ref{chapter:Bio.SeqIO}). \item Call the command line tool to process this input file, typically via one of Biopython's command line wrappers (which we'll discuss here). \item Read the output from the tool, i.e. your aligned sequences, typically using \verb|Bio.AlignIO| (see earlier in this chapter). \end{enumerate} All the command line wrappers we're going to talk about in this chapter follow the same style. You create a command line object specifying the options (e.g. the input filename and the output filename), then invoke this command line via a Python operating system call (e.g. using the \texttt{subprocess} module). Most of these wrappers are defined in the \verb|Bio.Align.Applications| module: \begin{verbatim} >>> import Bio.Align.Applications >>> dir(Bio.Align.Applications) ... ['ClustalwCommandline', 'DialignCommandline', 'MafftCommandline', 'MuscleCommandline', 'PrankCommandline', 'ProbconsCommandline', 'TCoffeeCommandline' ...] \end{verbatim} \noindent (Ignore the entries starting with an underscore -- these have special meaning in Python.) The module \verb|Bio.Emboss.Applications| has wrappers for some of the \href{http://emboss.sourceforge.net/}{EMBOSS suite}, including \texttt{needle} and \texttt{water}, which are described below in Section~\ref{seq:emboss-needle-water}, and wrappers for the EMBOSS packaged versions of the PHYLIP tools (which EMBOSS refer to as one of their EMBASSY packages - third party tools with an EMBOSS style interface). We won't explore all these alignment tools here in the section, just a sample, but the same principles apply. \subsection{ClustalW} \label{sec:align_clustal} ClustalW is a popular command line tool for multiple sequence alignment (there is also a graphical interface called ClustalX). Biopython's \verb|Bio.Align.Applications| module has a wrapper for this alignment tool (and several others). Before trying to use ClustalW from within Python, you should first try running the ClustalW tool yourself by hand at the command line, to familiarise yourself the other options. You'll find the Biopython wrapper is very faithful to the actual command line API: \begin{verbatim} >>> from Bio.Align.Applications import ClustalwCommandline >>> help(ClustalwCommandline) ... \end{verbatim} For the most basic usage, all you need is to have a FASTA input file, such as \href{http://biopython.org/DIST/docs/tutorial/examples/opuntia.fasta}{opuntia.fasta} (available online or in the Doc/examples subdirectory of the Biopython source code). This is a small FASTA file containing seven prickly-pear DNA sequences (from the cactus family \textit{Opuntia}). By default ClustalW will generate an alignment and guide tree file with names based on the input FASTA file, in this case \texttt{opuntia.aln} and \texttt{opuntia.dnd}, but you can override this or make it explicit: %doctest \begin{verbatim} >>> from Bio.Align.Applications import ClustalwCommandline >>> cline = ClustalwCommandline("clustalw2", infile="opuntia.fasta") >>> print(cline) clustalw2 -infile=opuntia.fasta \end{verbatim} Notice here we have given the executable name as \texttt{clustalw2}, indicating we have version two installed, which has a different filename to version one (\texttt{clustalw}, the default). Fortunately both versions support the same set of arguments at the command line (and indeed, should be functionally identical). You may find that even though you have ClustalW installed, the above command doesn't work -- you may get a message about ``command not found'' (especially on Windows). This indicated that the ClustalW executable is not on your PATH (an environment variable, a list of directories to be searched). You can either update your PATH setting to include the location of your copy of ClustalW tools (how you do this will depend on your OS), or simply type in the full path of the tool. For example: %doctest \begin{verbatim} >>> import os >>> from Bio.Align.Applications import ClustalwCommandline >>> clustalw_exe = r"C:\Program Files\new clustal\clustalw2.exe" >>> clustalw_cline = ClustalwCommandline(clustalw_exe, infile="opuntia.fasta") \end{verbatim} %Don't run it in the doctest \begin{verbatim} >>> assert os.path.isfile(clustalw_exe), "Clustal W executable missing" >>> stdout, stderr = clustalw_cline() \end{verbatim} \noindent Remember, in Python strings \verb|\n| and \verb|\t| are by default interpreted as a new line and a tab -- which is why we're put a letter ``r'' at the start for a raw string that isn't translated in this way. This is generally good practice when specifying a Windows style file name. Internally this uses the \verb|subprocess| module which is now the recommended way to run another program in Python. This replaces older options like the \verb|os.system()| and the \verb|os.popen*| functions. Now, at this point it helps to know about how command line tools ``work''. When you run a tool at the command line, it will often print text output directly to screen. This text can be captured or redirected, via two ``pipes'', called standard output (the normal results) and standard error (for error messages and debug messages). There is also standard input, which is any text fed into the tool. These names get shortened to stdin, stdout and stderr. When the tool finishes, it has a return code (an integer), which by convention is zero for success. When you run the command line tool like this via the Biopython wrapper, it will wait for it to finish, and check the return code. If this is non zero (indicating an error), an exception is raised. The wrapper then returns two strings, stdout and stderr. In the case of ClustalW, when run at the command line all the important output is written directly to the output files. Everything normally printed to screen while you wait (via stdout or stderr) is boring and can be ignored (assuming it worked). What we care about are the two output files, the alignment and the guide tree. We didn't tell ClustalW what filenames to use, but it defaults to picking names based on the input file. In this case the output should be in the file \verb|opuntia.aln|. You should be able to work out how to read in the alignment using \verb|Bio.AlignIO| by now: \begin{verbatim} >>> from Bio import AlignIO >>> align = AlignIO.read("opuntia.aln", "clustal") >>> print(align) SingleLetterAlphabet() alignment with 7 rows and 906 columns TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191 TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191 TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191 TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191 TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191 \end{verbatim} In case you are interested (and this is an aside from the main thrust of this chapter), the \texttt{opuntia.dnd} file ClustalW creates is just a standard Newick tree file, and \verb|Bio.Phylo| can parse these: \begin{verbatim} >>> from Bio import Phylo >>> tree = Phylo.read("opuntia.dnd", "newick") >>> Phylo.draw_ascii(tree) _______________ gi|6273291|gb|AF191665.1|AF191665 __________________________| | | ______ gi|6273290|gb|AF191664.1|AF191664 | |__| | |_____ gi|6273289|gb|AF191663.1|AF191663 | _|_________________ gi|6273287|gb|AF191661.1|AF191661 | |__________ gi|6273286|gb|AF191660.1|AF191660 | | __ gi|6273285|gb|AF191659.1|AF191659 |___| | gi|6273284|gb|AF191658.1|AF191658 \end{verbatim} \noindent Chapter \ref{sec:Phylo} covers Biopython's support for phylogenetic trees in more depth. \subsection{MUSCLE} MUSCLE is a more recent multiple sequence alignment tool than ClustalW, and Biopython also has a wrapper for it under the \verb|Bio.Align.Applications| module. As before, we recommend you try using MUSCLE from the command line before trying it from within Python, as the Biopython wrapper is very faithful to the actual command line API: \begin{verbatim} >>> from Bio.Align.Applications import MuscleCommandline >>> help(MuscleCommandline) ... \end{verbatim} For the most basic usage, all you need is to have a FASTA input file, such as \href{http://biopython.org/DIST/docs/tutorial/examples/opuntia.fasta}{opuntia.fasta} (available online or in the Doc/examples subdirectory of the Biopython source code). You can then tell MUSCLE to read in this FASTA file, and write the alignment to an output file: %doctest \begin{verbatim} >>> from Bio.Align.Applications import MuscleCommandline >>> cline = MuscleCommandline(input="opuntia.fasta", out="opuntia.txt") >>> print(cline) muscle -in opuntia.fasta -out opuntia.txt \end{verbatim} Note that MUSCLE uses ``-in'' and ``-out'' but in Biopython we have to use ``input'' and ``out'' as the keyword arguments or property names. This is because ``in'' is a reserved word in Python. By default MUSCLE will output the alignment as a FASTA file (using gapped sequences). The \verb|Bio.AlignIO| module should be able to read this alignment using \texttt{format="fasta"}. You can also ask for ClustalW-like output: %doctest \begin{verbatim} >>> from Bio.Align.Applications import MuscleCommandline >>> cline = MuscleCommandline(input="opuntia.fasta", out="opuntia.aln", clw=True) >>> print(cline) muscle -in opuntia.fasta -out opuntia.aln -clw \end{verbatim} Or, strict ClustalW output where the original ClustalW header line is used for maximum compatibility: %doctest \begin{verbatim} >>> from Bio.Align.Applications import MuscleCommandline >>> cline = MuscleCommandline(input="opuntia.fasta", out="opuntia.aln", clwstrict=True) >>> print(cline) muscle -in opuntia.fasta -out opuntia.aln -clwstrict \end{verbatim} \noindent The \verb|Bio.AlignIO| module should be able to read these alignments using \texttt{format="clustal"}. MUSCLE can also output in GCG MSF format (using the \texttt{msf} argument), but Biopython can't currently parse that, or using HTML which would give a human readable web page (not suitable for parsing). You can also set the other optional parameters, for example the maximum number of iterations. See the built in help for details. You would then run MUSCLE command line string as described above for ClustalW, and parse the output using \verb|Bio.AlignIO| to get an alignment object. \subsection{MUSCLE using stdout} Using a MUSCLE command line as in the examples above will write the alignment to a file. This means there will be no important information written to the standard out (stdout) or standard error (stderr) handles. However, by default MUSCLE will write the alignment to standard output (stdout). We can take advantage of this to avoid having a temporary output file! For example: %doctest \begin{verbatim} >>> from Bio.Align.Applications import MuscleCommandline >>> muscle_cline = MuscleCommandline(input="opuntia.fasta") >>> print(muscle_cline) muscle -in opuntia.fasta \end{verbatim} If we run this via the wrapper, we get back the output as a string. In order to parse this we can use \verb|StringIO| to turn it into a handle. Remember that MUSCLE defaults to using FASTA as the output format: \begin{verbatim} >>> from Bio.Align.Applications import MuscleCommandline >>> muscle_cline = MuscleCommandline(input="opuntia.fasta") >>> stdout, stderr = muscle_cline() >>> from StringIO import StringIO >>> from Bio import AlignIO >>> align = AlignIO.read(StringIO(stdout), "fasta") >>> print(align) SingleLetterAlphabet() alignment with 7 rows and 906 columns TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191663 TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191665 TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191664 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191661 TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191660 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191659 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191658 \end{verbatim} The above approach is fairly simple, but if you are dealing with very large output text the fact that all of stdout and stderr is loaded into memory as a string can be a potential drawback. Using the \verb|subprocess| module we can work directly with handles instead: \begin{verbatim} >>> import subprocess >>> from Bio.Align.Applications import MuscleCommandline >>> muscle_cline = MuscleCommandline(input="opuntia.fasta") >>> child = subprocess.Popen(str(muscle_cline), ... stdout=subprocess.PIPE, ... stderr=subprocess.PIPE, ... shell=(sys.platform!="win32")) >>> from Bio import AlignIO >>> align = AlignIO.read(child.stdout, "fasta") >>> print(align) SingleLetterAlphabet() alignment with 7 rows and 906 columns TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191663 TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191665 TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191664 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191661 TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191660 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191659 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191658 \end{verbatim} \subsection{MUSCLE using stdin and stdout} We don't actually \emph{need} to have our FASTA input sequences prepared in a file, because by default MUSCLE will read in the input sequence from standard input! Note this is a bit more advanced and fiddly, so don't bother with this technique unless you need to. First, we'll need some unaligned sequences in memory as \verb|SeqRecord| objects. For this demonstration I'm going to use a filtered version of the original FASTA file (using a generator expression), taking just six of the seven sequences: %doctest \begin{verbatim} >>> from Bio import SeqIO >>> records = (r for r in SeqIO.parse("opuntia.fasta", "fasta") if len(r) < 900) \end{verbatim} Then we create the MUSCLE command line, leaving the input and output to their defaults (stdin and stdout). I'm also going to ask for strict ClustalW format as for the output. %doctest \begin{verbatim} >>> from Bio.Align.Applications import MuscleCommandline >>> muscle_cline = MuscleCommandline(clwstrict=True) >>> print(muscle_cline) muscle -clwstrict \end{verbatim} Now for the fiddly bits using the \verb|subprocess| module, stdin and stdout: \begin{verbatim} >>> import subprocess >>> import sys >>> child = subprocess.Popen(str(cline), ... stdin=subprocess.PIPE, ... stdout=subprocess.PIPE, ... stderr=subprocess.PIPE, ... shell=(sys.platform!="win32")) \end{verbatim} That should start MUSCLE, but it will be sitting waiting for its FASTA input sequences, which we must supply via its stdin handle: \begin{verbatim} >>> SeqIO.write(records, child.stdin, "fasta") 6 >>> child.stdin.close() \end{verbatim} After writing the six sequences to the handle, MUSCLE will still be waiting to see if that is all the FASTA sequences or not -- so we must signal that this is all the input data by closing the handle. At that point MUSCLE should start to run, and we can ask for the output: \begin{verbatim} >>> from Bio import AlignIO >>> align = AlignIO.read(child.stdout, "clustal") >>> print(align) SingleLetterAlphabet() alignment with 6 rows and 900 columns TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF19166 TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF19166 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF19166 TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF19166 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF19165 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF19165 \end{verbatim} Wow! There we are with a new alignment of just the six records, without having created a temporary FASTA input file, or a temporary alignment output file. However, a word of caution: Dealing with errors with this style of calling external programs is much more complicated. It also becomes far harder to diagnose problems, because you can't try running MUSCLE manually outside of Biopython (because you don't have the input file to supply). There can also be subtle cross platform issues (e.g. Windows versus Linux), and how you run your script can have an impact (e.g. at the command line, from IDLE or an IDE, or as a GUI script). These are all generic Python issues though, and not specific to Biopython. If you find working directly with \texttt{subprocess} like this scary, there is an alternative. If you execute the tool with \texttt{muscle\_cline()} you can supply any standard input as a big string, \texttt{muscle\_cline(stdin=...)}. So, provided your data isn't very big, you can prepare the FASTA input in memory as a string using \texttt{StringIO} (see Section~\ref{sec:appendix-handles}): %doctest \begin{verbatim} >>> from Bio import SeqIO >>> records = (r for r in SeqIO.parse("opuntia.fasta", "fasta") if len(r) < 900) >>> from StringIO import StringIO >>> handle = StringIO() >>> SeqIO.write(records, handle, "fasta") 6 >>> data = handle.getvalue() \end{verbatim} \noindent You can then run the tool and parse the alignment as follows: %not a doctest as can't assume the MUSCLE binary is present \begin{verbatim} >>> stdout, stderr = muscle_cline(stdin=data) >>> from Bio import AlignIO >>> align = AlignIO.read(StringIO(stdout), "clustal") >>> print(align) SingleLetterAlphabet() alignment with 6 rows and 900 columns TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF19166 TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF19166 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF19166 TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF19166 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF19165 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF19165 \end{verbatim} You might find this easier, but it does require more memory (RAM) for the strings used for the input FASTA and output Clustal formatted data. \subsection{EMBOSS needle and water} \label{seq:emboss-needle-water} The \href{http://emboss.sourceforge.net/}{EMBOSS} suite includes the \texttt{water} and \texttt{needle} tools for Smith-Waterman algorithm local alignment, and Needleman-Wunsch global alignment. The tools share the same style interface, so switching between the two is trivial -- we'll just use \texttt{needle} here. Suppose you want to do a global pairwise alignment between two sequences, prepared in FASTA format as follows: \begin{verbatim} >HBA_HUMAN MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHG KKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTP AVHASLDKFLASVSTVLTSKYR \end{verbatim} \noindent in a file \texttt{alpha.fasta}, and secondly in a file \texttt{beta.fasta}: \begin{verbatim} >HBB_HUMAN MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPK VKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFG KEFTPPVQAAYQKVVAGVANALAHKYH \end{verbatim} Let's start by creating a complete \texttt{needle} command line object in one go: %doctest \begin{verbatim} >>> from Bio.Emboss.Applications import NeedleCommandline >>> needle_cline = NeedleCommandline(asequence="alpha.faa", bsequence="beta.faa", ... gapopen=10, gapextend=0.5, outfile="needle.txt") >>> print(needle_cline) needle -outfile=needle.txt -asequence=alpha.faa -bsequence=beta.faa -gapopen=10 -gapextend=0.5 \end{verbatim} Why not try running this by hand at the command prompt? You should see it does a pairwise comparison and records the output in the file \texttt{needle.txt} (in the default EMBOSS alignment file format). Even if you have EMBOSS installed, running this command may not work -- you might get a message about ``command not found'' (especially on Windows). This probably means that the EMBOSS tools are not on your PATH environment variable. You can either update your PATH setting, or simply tell Biopython the full path to the tool, for example: %doctest \begin{verbatim} >>> from Bio.Emboss.Applications import NeedleCommandline >>> needle_cline = NeedleCommandline(r"C:\EMBOSS\needle.exe", ... asequence="alpha.faa", bsequence="beta.faa", ... gapopen=10, gapextend=0.5, outfile="needle.txt") \end{verbatim} \noindent Remember in Python that for a default string \verb|\n| or \verb|\t| means a new line or a tab -- which is why we're put a letter ``r'' at the start for a raw string. At this point it might help to try running the EMBOSS tools yourself by hand at the command line, to familiarise yourself the other options and compare them to the Biopython help text: \begin{verbatim} >>> from Bio.Emboss.Applications import NeedleCommandline >>> help(NeedleCommandline) ... \end{verbatim} Note that you can also specify (or change or look at) the settings like this: %doctest \begin{verbatim} >>> from Bio.Emboss.Applications import NeedleCommandline >>> needle_cline = NeedleCommandline() >>> needle_cline.asequence="alpha.faa" >>> needle_cline.bsequence="beta.faa" >>> needle_cline.gapopen=10 >>> needle_cline.gapextend=0.5 >>> needle_cline.outfile="needle.txt" >>> print(needle_cline) needle -outfile=needle.txt -asequence=alpha.faa -bsequence=beta.faa -gapopen=10 -gapextend=0.5 >>> print(needle_cline.outfile) needle.txt \end{verbatim} Next we want to use Python to run this command for us. As explained above, for full control, we recommend you use the built in Python \texttt{subprocess} module, but for simple usage the wrapper object usually suffices: \begin{verbatim} >>> stdout, stderr = needle_cline() >>> print(stdout + stderr) Needleman-Wunsch global alignment of two sequences \end{verbatim} Next we can load the output file with \verb|Bio.AlignIO| as discussed earlier in this chapter, as the \texttt{emboss} format: \begin{verbatim} >>> from Bio import AlignIO >>> align = AlignIO.read("needle.txt", "emboss") >>> print(align) SingleLetterAlphabet() alignment with 2 rows and 149 columns MV-LSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTY...KYR HBA_HUMAN MVHLTPEEKSAVTALWGKV--NVDEVGGEALGRLLVVYPWTQRF...KYH HBB_HUMAN \end{verbatim} In this example, we told EMBOSS to write the output to a file, but you \emph{can} tell it to write the output to stdout instead (useful if you don't want a temporary output file to get rid of -- use \texttt{stdout=True} rather than the \texttt{outfile} argument), and also to read \emph{one} of the one of the inputs from stdin (e.g. \texttt{asequence="stdin"}, much like in the MUSCLE example in the section above). This has only scratched the surface of what you can do with \texttt{needle} and \texttt{water}. One useful trick is that the second file can contain multiple sequences (say five), and then EMBOSS will do five pairwise alignments. Note - Biopython includes its own pairwise alignment code in the \verb|Bio.pairwise2| module (written in C for speed, but with a pure Python fallback available too). This doesn't work with alignment objects, so we have not covered it within this chapter. See the module's docstring (built in help) for details. \chapter{BLAST} \label{chapter:blast} Hey, everybody loves BLAST right? I mean, geez, how can get it get any easier to do comparisons between one of your sequences and every other sequence in the known world? But, of course, this section isn't about how cool BLAST is, since we already know that. It is about the problem with BLAST -- it can be really difficult to deal with the volume of data generated by large runs, and to automate BLAST runs in general. Fortunately, the Biopython folks know this only too well, so they've developed lots of tools for dealing with BLAST and making things much easier. This section details how to use these tools and do useful things with them. Dealing with BLAST can be split up into two steps, both of which can be done from within Biopython. Firstly, running BLAST for your query sequence(s), and getting some output. Secondly, parsing the BLAST output in Python for further analysis. Your first introduction to running BLAST was probably via the NCBI web-service. In fact, there are lots of ways you can run BLAST, which can be categorised several ways. The most important distinction is running BLAST locally (on your own machine), and running BLAST remotely (on another machine, typically the NCBI servers). We're going to start this chapter by invoking the NCBI online BLAST service from within a Python script. \emph{NOTE}: The following Chapter~\ref{chapter:searchio} describes \verb|Bio.SearchIO|, an \emph{experimental} module in Biopython. We intend this to ultimately replace the older \verb|Bio.Blast| module, as it provides a more general framework handling other related sequence searching tools as well. However, until that is declared stable, for production code please continue to use the \verb|Bio.Blast| module for dealing with NCBI BLAST. \section{Running BLAST over the Internet} \label{sec:running-www-blast} We use the function \verb|qblast()| in the \verb|Bio.Blast.NCBIWWW| module call the online version of BLAST. This has three non-optional arguments: \begin{itemize} \item The first argument is the blast program to use for the search, as a lower case string. The options and descriptions of the programs are available at \url{http://www.ncbi.nlm.nih.gov/BLAST/blast_program.shtml}. Currently \verb|qblast| only works with blastn, blastp, blastx, tblast and tblastx. \item The second argument specifies the databases to search against. Again, the options for this are available on the NCBI web pages at \url{http://www.ncbi.nlm.nih.gov/BLAST/blast_databases.shtml}. \item The third argument is a string containing your query sequence. This can either be the sequence itself, the sequence in fasta format, or an identifier like a GI number. \end{itemize} The \verb|qblast| function also take a number of other option arguments which are basically analogous to the different parameters you can set on the BLAST web page. We'll just highlight a few of them here: \begin{itemize} \item The \verb|qblast| function can return the BLAST results in various formats, which you can choose with the optional \verb|format_type| keyword: \verb|"HTML"|, \verb|"Text"|, \verb|"ASN.1"|, or \verb|"XML"|. The default is \verb|"XML"|, as that is the format expected by the parser, described in section~\ref{sec:parsing-blast} below. \item The argument \verb|expect| sets the expectation or e-value threshold. \end{itemize} For more about the optional BLAST arguments, we refer you to the NCBI's own documentation, or that built into Biopython: \begin{verbatim} >>> from Bio.Blast import NCBIWWW >>> help(NCBIWWW.qblast) ... \end{verbatim} Note that the default settings on the NCBI BLAST website are not quite the same as the defaults on QBLAST. If you get different results, you'll need to check the parameters (e.g. the expectation value threshold and the gap values). For example, if you have a nucleotide sequence you want to search against the nucleotide database (nt) using BLASTN, and you know the GI number of your query sequence, you can use: \begin{verbatim} >>> from Bio.Blast import NCBIWWW >>> result_handle = NCBIWWW.qblast("blastn", "nt", "8332116") \end{verbatim} Alternatively, if we have our query sequence already in a FASTA formatted file, we just need to open the file and read in this record as a string, and use that as the query argument: \begin{verbatim} >>> from Bio.Blast import NCBIWWW >>> fasta_string = open("m_cold.fasta").read() >>> result_handle = NCBIWWW.qblast("blastn", "nt", fasta_string) \end{verbatim} We could also have read in the FASTA file as a \verb|SeqRecord| and then supplied just the sequence itself: \begin{verbatim} >>> from Bio.Blast import NCBIWWW >>> from Bio import SeqIO >>> record = SeqIO.read("m_cold.fasta", format="fasta") >>> result_handle = NCBIWWW.qblast("blastn", "nt", record.seq) \end{verbatim} Supplying just the sequence means that BLAST will assign an identifier for your sequence automatically. You might prefer to use the \verb|SeqRecord| object's format method to make a fasta string (which will include the existing identifier): \begin{verbatim} >>> from Bio.Blast import NCBIWWW >>> from Bio import SeqIO >>> record = SeqIO.read("m_cold.fasta", format="fasta") >>> result_handle = NCBIWWW.qblast("blastn", "nt", record.format("fasta")) \end{verbatim} This approach makes more sense if you have your sequence(s) in a non-FASTA file format which you can extract using \verb|Bio.SeqIO| (see Chapter~\ref{chapter:Bio.SeqIO}). Whatever arguments you give the \verb|qblast()| function, you should get back your results in a handle object (by default in XML format). The next step would be to parse the XML output into Python objects representing the search results (Section~\ref{sec:parsing-blast}), but you might want to save a local copy of the output file first. I find this especially useful when debugging my code that extracts info from the BLAST results (because re-running the online search is slow and wastes the NCBI computer time). \label{sec:saving-blast-output} We need to be a bit careful since we can use \verb|result_handle.read()| to read the BLAST output only once -- calling \verb|result_handle.read()| again returns an empty string. \begin{verbatim} >>> save_file = open("my_blast.xml", "w") >>> save_file.write(result_handle.read()) >>> save_file.close() >>> result_handle.close() \end{verbatim} After doing this, the results are in the file \verb|my_blast.xml| and the original handle has had all its data extracted (so we closed it). However, the \verb|parse| function of the BLAST parser (described in~\ref{sec:parsing-blast}) takes a file-handle-like object, so we can just open the saved file for input: \begin{verbatim} >>> result_handle = open("my_blast.xml") \end{verbatim} Now that we've got the BLAST results back into a handle again, we are ready to do something with them, so this leads us right into the parsing section (see Section~\ref{sec:parsing-blast} below). You may want to jump ahead to that now \ldots. \section{Running BLAST locally} \label{sec:running-local-blast} \subsection{Introduction} Running BLAST locally (as opposed to over the internet, see Section~\ref{sec:running-www-blast}) has at least major two advantages: \begin{itemize} \item Local BLAST may be faster than BLAST over the internet; \item Local BLAST allows you to make your own database to search for sequences against. \end{itemize} Dealing with proprietary or unpublished sequence data can be another reason to run BLAST locally. You may not be allowed to redistribute the sequences, so submitting them to the NCBI as a BLAST query would not be an option. Unfortunately, there are some major drawbacks too -- installing all the bits and getting it setup right takes some effort: \begin{itemize} \item Local BLAST requires command line tools to be installed. \item Local BLAST requires (large) BLAST databases to be setup (and potentially kept up to date). \end{itemize} \noindent To further confuse matters there are several different BLAST packages available, and there are also other tools which can produce imitation BLAST output files, such as BLAT. \subsection{Standalone NCBI BLAST+} The ``new'' \href{http://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs&DOC_TYPE=Download} {NCBI BLAST+} suite was released in 2009. This replaces the old NCBI ``legacy'' BLAST package (see below). This section will show briefly how to use these tools from within Python. If you have already read or tried the alignment tool examples in Section~\ref{sec:alignment-tools} this should all seem quite straightforward. First, we construct a command line string (as you would type in at the command line prompt if running standalone BLAST by hand). Then we can execute this command from within Python. For example, taking a FASTA file of gene nucleotide sequences, you might want to run a BLASTX (translation) search against the non-redundant (NR) protein database. Assuming you (or your systems administrator) has downloaded and installed the NR database, you might run: \begin{verbatim} blastx -query opuntia.fasta -db nr -out opuntia.xml -evalue 0.001 -outfmt 5 \end{verbatim} This should run BLASTX against the NR database, using an expectation cut-off value of $0.001$ and produce XML output to the specified file (which we can then parse). On my computer this takes about six minutes - a good reason to save the output to a file so you and repeat any analysis as needed. From within Biopython we can use the NCBI BLASTX wrapper from the \verb|Bio.Blast.Applications| module to build the command line string, and run it: \begin{verbatim} >>> from Bio.Blast.Applications import NcbiblastxCommandline >>> help(NcbiblastxCommandline) ... >>> blastx_cline = NcbiblastxCommandline(query="opuntia.fasta", db="nr", evalue=0.001, ... outfmt=5, out="opuntia.xml") >>> blastx_cline NcbiblastxCommandline(cmd='blastx', out='opuntia.xml', outfmt=5, query='opuntia.fasta', db='nr', evalue=0.001) >>> print(blastx_cline) blastx -out opuntia.xml -outfmt 5 -query opuntia.fasta -db nr -evalue 0.001 >>> stdout, stderr = blastx_cline() \end{verbatim} In this example there shouldn't be any output from BLASTX to the terminal, so stdout and stderr should be empty. You may want to check the output file \verb|opuntia.xml| has been created. As you may recall from earlier examples in the tutorial, the \verb|opuntia.fasta| contains seven sequences, so the BLAST XML output should contain multiple results. Therefore use \verb|Bio.Blast.NCBIXML.parse()| to parse it as described below in Section~\ref{sec:parsing-blast}. \subsection{Other versions of BLAST} NCBI BLAST+ (written in C++) was first released in 2009 as a replacement for the original NCBI ``legacy'' BLAST (written in C) which is no longer being updated. There were a lot of changes -- the old version had a single core command line tool \verb|blastall| which covered multiple different BLAST search types (which are now separate commands in BLAST+), and all the command line options were renamed. Biopython's wrappers for the NCBI ``legacy'' BLAST tools have been deprecated and will be removed in a future release. To try to avoid confusion, we do not cover calling these old tools from Biopython in this tutorial. You may also come across \href{http://blast.wustl.edu/}{Washington University BLAST} (WU-BLAST), and its successor, \href{http://blast.advbiocomp.com}{Advanced Biocomputing BLAST} (AB-BLAST, released in 2009, not free/open source). These packages include the command line tools \verb|wu-blastall| and \verb|ab-blastall|, which mimicked \verb|blastall| from the NCBI ``legacy'' BLAST stuie. Biopython does not currently provide wrappers for calling these tools, but should be able to parse any NCBI compatible output from them. \section{Parsing BLAST output} \label{sec:parsing-blast} As mentioned above, BLAST can generate output in various formats, such as XML, HTML, and plain text. Originally, Biopython had parsers for BLAST plain text and HTML output, as these were the only output formats offered at the time. Unfortunately, the BLAST output in these formats kept changing, each time breaking the Biopython parsers. Our HTML BLAST parser has been removed, but the plain text BLAST parser is still available (see Section~\ref{sec:parsing-blast-deprecated}). Use it at your own risk, it may or may not work, depending on which BLAST version you're using. As keeping up with changes in BLAST became a hopeless endeavor, especially with users running different BLAST versions, we now recommend to parse the output in XML format, which can be generated by recent versions of BLAST. Not only is the XML output more stable than the plain text and HTML output, it is also much easier to parse automatically, making Biopython a whole lot more stable. You can get BLAST output in XML format in various ways. For the parser, it doesn't matter how the output was generated, as long as it is in the XML format. \begin{itemize} \item You can use Biopython to run BLAST over the internet, as described in section~\ref{sec:running-www-blast}. \item You can use Biopython to run BLAST locally, as described in section~\ref{sec:running-local-blast}. \item You can do the BLAST search yourself on the NCBI site through your web browser, and then save the results. You need to choose XML as the format in which to receive the results, and save the final BLAST page you get (you know, the one with all of the interesting results!) to a file. \item You can also run BLAST locally without using Biopython, and save the output in a file. Again, you need to choose XML as the format in which to receive the results. \end{itemize} The important point is that you do not have to use Biopython scripts to fetch the data in order to be able to parse it. Doing things in one of these ways, you then need to get a handle to the results. In Python, a handle is just a nice general way of describing input to any info source so that the info can be retrieved using \verb|read()| and \verb|readline()| functions (see Section~{sec:appendix-handles}). If you followed the code above for interacting with BLAST through a script, then you already have \verb|result_handle|, the handle to the BLAST results. For example, using a GI number to do an online search: \begin{verbatim} >>> from Bio.Blast import NCBIWWW >>> result_handle = NCBIWWW.qblast("blastn", "nt", "8332116") \end{verbatim} If instead you ran BLAST some other way, and have the BLAST output (in XML format) in the file \verb|my_blast.xml|, all you need to do is to open the file for reading: \begin{verbatim} >>> result_handle = open("my_blast.xml") \end{verbatim} Now that we've got a handle, we are ready to parse the output. The code to parse it is really quite small. If you expect a single BLAST result (i.e. you used a single query): \begin{verbatim} >>> from Bio.Blast import NCBIXML >>> blast_record = NCBIXML.read(result_handle) \end{verbatim} \noindent or, if you have lots of results (i.e. multiple query sequences): \begin{verbatim} >>> from Bio.Blast import NCBIXML >>> blast_records = NCBIXML.parse(result_handle) \end{verbatim} Just like \verb|Bio.SeqIO| and \verb|Bio.AlignIO| (see Chapters~\ref{chapter:Bio.SeqIO} and~\ref{chapter:Bio.AlignIO}), we have a pair of input functions, \verb|read| and \verb|parse|, where \verb|read| is for when you have exactly one object, and \verb|parse| is an iterator for when you can have lots of objects -- but instead of getting \verb|SeqRecord| or \verb|MultipleSeqAlignment| objects, we get BLAST record objects. To be able to handle the situation where the BLAST file may be huge, containing thousands of results, \verb|NCBIXML.parse()| returns an iterator. In plain English, an iterator allows you to step through the BLAST output, retrieving BLAST records one by one for each BLAST search result: \begin{verbatim} >>> from Bio.Blast import NCBIXML >>> blast_records = NCBIXML.parse(result_handle) >>> blast_record = next(blast_records) # ... do something with blast_record >>> blast_record = next(blast_records) # ... do something with blast_record >>> blast_record = next(blast_records) # ... do something with blast_record >>> blast_record = next(blast_records) Traceback (most recent call last): File "", line 1, in StopIteration # No further records \end{verbatim} Or, you can use a \verb|for|-loop: \begin{verbatim} >>> for blast_record in blast_records: ... # Do something with blast_record \end{verbatim} Note though that you can step through the BLAST records only once. Usually, from each BLAST record you would save the information that you are interested in. If you want to save all returned BLAST records, you can convert the iterator into a list: \begin{verbatim} >>> blast_records = list(blast_records) \end{verbatim} Now you can access each BLAST record in the list with an index as usual. If your BLAST file is huge though, you may run into memory problems trying to save them all in a list. Usually, you'll be running one BLAST search at a time. Then, all you need to do is to pick up the first (and only) BLAST record in \verb|blast_records|: \begin{verbatim} >>> from Bio.Blast import NCBIXML >>> blast_records = NCBIXML.parse(result_handle) >>> blast_record = next(blast_records) \end{verbatim} \noindent or more elegantly: \begin{verbatim} >>> from Bio.Blast import NCBIXML >>> blast_record = NCBIXML.read(result_handle) \end{verbatim} I guess by now you're wondering what is in a BLAST record. \section{The BLAST record class} A BLAST Record contains everything you might ever want to extract from the BLAST output. Right now we'll just show an example of how to get some info out of the BLAST report, but if you want something in particular that is not described here, look at the info on the record class in detail, and take a gander into the code or automatically generated documentation -- the docstrings have lots of good info about what is stored in each piece of information. To continue with our example, let's just print out some summary info about all hits in our blast report greater than a particular threshold. The following code does this: \begin{verbatim} >>> E_VALUE_THRESH = 0.04 >>> for alignment in blast_record.alignments: ... for hsp in alignment.hsps: ... if hsp.expect < E_VALUE_THRESH: ... print('****Alignment****') ... print('sequence:', alignment.title) ... print('length:', alignment.length) ... print('e value:', hsp.expect) ... print(hsp.query[0:75] + '...') ... print(hsp.match[0:75] + '...') ... print(hsp.sbjct[0:75] + '...') \end{verbatim} This will print out summary reports like the following: \begin{verbatim} ****Alignment**** sequence: >gb|AF283004.1|AF283004 Arabidopsis thaliana cold acclimation protein WCOR413-like protein alpha form mRNA, complete cds length: 783 e value: 0.034 tacttgttgatattggatcgaacaaactggagaaccaacatgctcacgtcacttttagtcccttacatattcctc... ||||||||| | ||||||||||| || |||| || || |||||||| |||||| | | |||||||| ||| ||... tacttgttggtgttggatcgaaccaattggaagacgaatatgctcacatcacttctcattccttacatcttcttc... \end{verbatim} Basically, you can do anything you want to with the info in the BLAST report once you have parsed it. This will, of course, depend on what you want to use it for, but hopefully this helps you get started on doing what you need to do! An important consideration for extracting information from a BLAST report is the type of objects that the information is stored in. In Biopython, the parsers return \verb|Record| objects, either \verb|Blast| or \verb|PSIBlast| depending on what you are parsing. These objects are defined in \verb|Bio.Blast.Record| and are quite complete. Here are my attempts at UML class diagrams for the \verb|Blast| and \verb|PSIBlast| record classes. If you are good at UML and see mistakes/improvements that can be made, please let me know. The Blast class diagram is shown in Figure~\ref{fig:blastrecord}. \begin{htmlonly} \label{fig:blastrecord} \imgsrc[width=650, height=750]{images/BlastRecord.png} \end{htmlonly} \begin{latexonly} \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/BlastRecord.png} \caption{Class diagram for the Blast Record class representing all of the info in a BLAST report} \label{fig:blastrecord} \end{figure} \end{latexonly} The PSIBlast record object is similar, but has support for the rounds that are used in the iteration steps of PSIBlast. The class diagram for PSIBlast is shown in Figure~\ref{fig:psiblastrecord}. \begin{htmlonly} \label{fig:psiblastrecord} \imgsrc[width=650, height=750]{images/PSIBlastRecord.png} \end{htmlonly} \begin{latexonly} \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/PSIBlastRecord.png} \caption{Class diagram for the PSIBlast Record class.} \label{fig:psiblastrecord} \end{figure} \end{latexonly} \section{Deprecated BLAST parsers} \label{sec:parsing-blast-deprecated} Older versions of Biopython had parsers for BLAST output in plain text or HTML format. Over the years, we discovered that it is very hard to maintain these parsers in working order. Basically, any small change to the BLAST output in newly released BLAST versions tends to cause the plain text and HTML parsers to break. We therefore recommend parsing BLAST output in XML format, as described in section~\ref{sec:parsing-blast}. Depending on which BLAST versions or programs you're using, our plain text BLAST parser may or may not work. Use it at your own risk! \subsection{Parsing plain-text BLAST output} The plain text BLAST parser is located in \verb|Bio.Blast.NCBIStandalone|. As with the XML parser, we need to have a handle object that we can pass to the parser. The handle must implement the \verb|readline()| method and do this properly. The common ways to get such a handle are to either use the provided \verb|blastall| or \verb|blastpgp| functions to run the local blast, or to run a local blast via the command line, and then do something like the following: \begin{verbatim} >>> result_handle = open("my_file_of_blast_output.txt") \end{verbatim} Well, now that we've got a handle (which we'll call \verb|result_handle|), we are ready to parse it. This can be done with the following code: \begin{verbatim} >>> from Bio.Blast import NCBIStandalone >>> blast_parser = NCBIStandalone.BlastParser() >>> blast_record = blast_parser.parse(result_handle) \end{verbatim} This will parse the BLAST report into a Blast Record class (either a Blast or a PSIBlast record, depending on what you are parsing) so that you can extract the information from it. In our case, let's just use print out a quick summary of all of the alignments greater than some threshold value. \begin{verbatim} >>> E_VALUE_THRESH = 0.04 >>> for alignment in blast_record.alignments: ... for hsp in alignment.hsps: ... if hsp.expect < E_VALUE_THRESH: ... print('****Alignment****') ... print('sequence:', alignment.title) ... print('length:', alignment.length) ... print('e value:', hsp.expect) ... print(hsp.query[0:75] + '...') ... print(hsp.match[0:75] + '...') ... print(hsp.sbjct[0:75] + '...') \end{verbatim} If you also read the section~\ref{sec:parsing-blast} on parsing BLAST XML output, you'll notice that the above code is identical to what is found in that section. Once you parse something into a record class you can deal with it independent of the format of the original BLAST info you were parsing. Pretty snazzy! Sure, parsing one record is great, but I've got a BLAST file with tons of records -- how can I parse them all? Well, fear not, the answer lies in the very next section. \subsection{Parsing a plain-text BLAST file full of BLAST runs} We can do this using the blast iterator. To set up an iterator, we first set up a parser, to parse our blast reports in Blast Record objects: \begin{verbatim} >>> from Bio.Blast import NCBIStandalone >>> blast_parser = NCBIStandalone.BlastParser() \end{verbatim} Then we will assume we have a handle to a bunch of blast records, which we'll call \verb|result_handle|. Getting a handle is described in full detail above in the blast parsing sections. Now that we've got a parser and a handle, we are ready to set up the iterator with the following command: \begin{verbatim} >>> blast_iterator = NCBIStandalone.Iterator(result_handle, blast_parser) \end{verbatim} The second option, the parser, is optional. If we don't supply a parser, then the iterator will just return the raw BLAST reports one at a time. Now that we've got an iterator, we start retrieving blast records (generated by our parser) using \verb|next()|: \begin{verbatim} >>> blast_record = next(blast_iterator) \end{verbatim} Each call to next will return a new record that we can deal with. Now we can iterate through this records and generate our old favorite, a nice little blast report: \begin{verbatim} >>> for blast_record in blast_iterator: ... E_VALUE_THRESH = 0.04 ... for alignment in blast_record.alignments: ... for hsp in alignment.hsps: ... if hsp.expect < E_VALUE_THRESH: ... print('****Alignment****') ... print('sequence:', alignment.title) ... print('length:', alignment.length) ... print('e value:', hsp.expect) ... if len(hsp.query) > 75: ... dots = '...' ... else: ... dots = '' ... print(hsp.query[0:75] + dots) ... print(hsp.match[0:75] + dots) ... print(hsp.sbjct[0:75] + dots) \end{verbatim} %Notice that \verb|next(b_iterator)| will return \verb|None| when it runs out of records to parse, so it is easy to iterate through the entire file with a while loop that checks for the existence of a record. The iterator allows you to deal with huge blast records without any memory problems, since things are read in one at a time. I have parsed tremendously huge files without any problems using this. %TODO - Remove this or update it for BLAST XML parser \subsection{Finding a bad record somewhere in a huge plain-text BLAST file} One really ugly problem that happens to me is that I'll be parsing a huge blast file for a while, and the parser will bomb out with a ValueError. This is a serious problem, since you can't tell if the ValueError is due to a parser problem, or a problem with the BLAST. To make it even worse, you have no idea where the parse failed, so you can't just ignore the error, since this could be ignoring an important data point. We used to have to make a little script to get around this problem, but the \verb|Bio.Blast| module now includes a \verb|BlastErrorParser| which really helps make this easier. The \verb|BlastErrorParser| works very similar to the regular \verb|BlastParser|, but it adds an extra layer of work by catching ValueErrors that are generated by the parser, and attempting to diagnose the errors. Let's take a look at using this parser -- first we define the file we are going to parse and the file to write the problem reports to: \begin{verbatim} >>> import os >>> blast_file = os.path.join(os.getcwd(), "blast_out", "big_blast.out") >>> error_file = os.path.join(os.getcwd(), "blast_out", "big_blast.problems") \end{verbatim} Now we want to get a \verb|BlastErrorParser|: \begin{verbatim} >>> from Bio.Blast import NCBIStandalone >>> error_handle = open(error_file, "w") >>> blast_error_parser = NCBIStandalone.BlastErrorParser(error_handle) \end{verbatim} Notice that the parser take an optional argument of a handle. If a handle is passed, then the parser will write any blast records which generate a ValueError to this handle. Otherwise, these records will not be recorded. Now we can use the \verb|BlastErrorParser| just like a regular blast parser. Specifically, we might want to make an iterator that goes through our blast records one at a time and parses them with the error parser: \begin{verbatim} >>> result_handle = open(blast_file) >>> iterator = NCBIStandalone.Iterator(result_handle, blast_error_parser) \end{verbatim} We can read these records one a time, but now we can catch and deal with errors that are due to problems with Blast (and not with the parser itself): \begin{verbatim} >>> try: ... next_record = next(iterator) ... except NCBIStandalone.LowQualityBlastError as info: ... print("LowQualityBlastError detected in id %s" % info[1]) \end{verbatim} The \verb|next()| functionality is normally called indirectly via a \verb|for|-loop. Right now the \verb|BlastErrorParser| can generate the following errors: \begin{itemize} \item \verb|ValueError| -- This is the same error generated by the regular BlastParser, and is due to the parser not being able to parse a specific file. This is normally either due to a bug in the parser, or some kind of discrepancy between the version of BLAST you are using and the versions the parser is able to handle. \item \verb|LowQualityBlastError| -- When BLASTing a sequence that is of really bad quality (for example, a short sequence that is basically a stretch of one nucleotide), it seems that Blast ends up masking out the entire sequence and ending up with nothing to parse. In this case it will produce a truncated report that causes the parser to generate a ValueError. \verb|LowQualityBlastError| is reported in these cases. This error returns an info item with the following information: \begin{itemize} \item \verb|item[0]| -- The error message \item \verb|item[1]| -- The id of the input record that caused the error. This is really useful if you want to record all of the records that are causing problems. \end{itemize} \end{itemize} As mentioned, with each error generated, the BlastErrorParser will write the offending record to the specified \verb|error_handle|. You can then go ahead and look and these and deal with them as you see fit. Either you will be able to debug the parser with a single blast report, or will find out problems in your blast runs. Either way, it will definitely be a useful experience! Hopefully the \verb|BlastErrorParser| will make it much easier to debug and deal with large Blast files. \section{Dealing with PSI-BLAST} You can run the standalone version of PSI-BLAST (the legacy NCBI command line tool \verb|blastpgp|, or its replacement \verb|psiblast|) using the wrappers in \verb|Bio.Blast.Applications| module. At the time of writing, the NCBI do not appear to support tools running a PSI-BLAST search via the internet. Note that the \verb|Bio.Blast.NCBIXML| parser can read the XML output from current versions of PSI-BLAST, but information like which sequences in each iteration is new or reused isn't present in the XML file. If you care about this information you may have more joy with the plain text output and the \verb|PSIBlastParser| in \verb|Bio.Blast.NCBIStandalone|. \section{Dealing with RPS-BLAST} You can run the standalone version of RPS-BLAST (either the legacy NCBI command line tool \verb|rpsblast|, or its replacement with the same name) using the wrappers in \verb|Bio.Blast.Applications| module. At the time of writing, the NCBI do not appear to support tools running an RPS-BLAST search via the internet. You can use the \verb|Bio.Blast.NCBIXML| parser to read the XML output from current versions of RPS-BLAST. \chapter{BLAST and other sequence search tools (\textit{experimental code})} \label{chapter:searchio} \emph{WARNING}: This chapter of the Tutorial describes an \emph{experimental} module in Biopython. It is being included in Biopython and documented here in the tutorial in a pre-final state to allow a period of feedback and refinement before we declare it stable. Until then the details will probably change, and any scripts using the current \verb|Bio.SearchIO| would need to be updated. Please keep this in mind! For stable code working with NCBI BLAST, please continue to use Bio.Blast described in the preceding Chapter~\ref{chapter:blast}. Biological sequence identification is an integral part of bioinformatics. Several tools are available for this, each with their own algorithms and approaches, such as BLAST (arguably the most popular), FASTA, HMMER, and many more. In general, these tools usually use your sequence to search a database of potential matches. With the growing number of known sequences (hence the growing number of potential matches), interpreting the results becomes increasingly hard as there could be hundreds or even thousands of potential matches. Naturally, manual interpretation of these searches' results is out of the question. Moreover, you often need to work with several sequence search tools, each with its own statistics, conventions, and output format. Imagine how daunting it would be when you need to work with multiple sequences using multiple search tools. We know this too well ourselves, which is why we created the \verb|Bio.SearchIO| submodule in Biopython. \verb|Bio.SearchIO| allows you to extract information from your search results in a convenient way, while also dealing with the different standards and conventions used by different search tools. The name \verb|SearchIO| is a homage to BioPerl's module of the same name. In this chapter, we'll go through the main features of \verb|Bio.SearchIO| to show what it can do for you. We'll use two popular search tools along the way: BLAST and BLAT. They are used merely for illustrative purposes, and you should be able to adapt the workflow to any other search tools supported by \verb|Bio.SearchIO| in a breeze. You're very welcome to follow along with the search output files we'll be using. The BLAST output file can be downloaded \href{http://biopython.org/SRC/Tests/Tutorial/my_blast.xml}{here}, and the BLAT output file \href{http://biopython.org/SRC/Tests/Tutorial/my_blat.psl}{here}. Both output files were generated using this sequence: \begin{verbatim} >mystery_seq CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG \end{verbatim} The BLAST result is an XML file generated using \verb|blastn| against the NCBI \verb|refseq_rna| database. For BLAT, the sequence database was the February 2009 \verb|hg19| human genome draft and the output format is PSL. We'll start from an introduction to the \verb|Bio.SearchIO| object model. The model is the representation of your search results, thus it is core to \verb|Bio.SearchIO| itself. After that, we'll check out the main functions in \verb|Bio.SearchIO| that you may often use. Now that we're all set, let's go to the first step: introducing the core object model. \section{The SearchIO object model} \label{sec:searchio-model} Despite the wildly differing output styles among many sequence search tools, it turns out that their underlying concept is similar: \begin{itemize} \item The output file may contain results from one or more search queries. \item In each search query, you will see one or more hits from the given search database. \item In each database hit, you will see one or more regions containing the actual sequence alignment between your query sequence and the database sequence. \item Some programs like BLAT or Exonerate may further split these regions into several alignment fragments (or blocks in BLAT and possibly exons in exonerate). This is not something you always see, as programs like BLAST and HMMER do not do this. \end{itemize} Realizing this generality, we decided use it as base for creating the \verb|Bio.SearchIO| object model. The object model consists of a nested hierarchy of Python objects, each one representing one concept outlined above. These objects are: \begin{itemize} \item \verb|QueryResult|, to represent a single search query. \item \verb|Hit|, to represent a single database hit. \verb|Hit| objects are contained within \verb|QueryResult| and in each \verb|QueryResult| there is zero or more \verb|Hit| objects. \item \verb|HSP| (short for high-scoring pair), to represent region(s) of significant alignments between query and hit sequences. \verb|HSP| objects are contained within \verb|Hit| objects and each \verb|Hit| has one or more \verb|HSP| objects. \item \verb|HSPFragment|, to represent a single contiguous alignment between query and hit sequences. \verb|HSPFragment| objects are contained within \verb|HSP| objects. Most sequence search tools like BLAST and HMMER unify \verb|HSP| and \verb|HSPFragment| objects as each \verb|HSP| will only have a single \verb|HSPFragment|. However there are tools like BLAT and Exonerate that produce \verb|HSP| containing multiple \verb|HSPFragment|. Don't worry if this seems a tad confusing now, we'll elaborate more on these two objects later on. \end{itemize} These four objects are the ones you will interact with when you use \verb|Bio.SearchIO|. They are created using one of the main \verb|Bio.SearchIO| methods: \verb|read|, \verb|parse|, \verb|index|, or \verb|index_db|. The details of these methods are provided in later sections. For this section, we'll only be using read and parse. These functions behave similarly to their \verb|Bio.SeqIO| and \verb|Bio.AlignIO| counterparts: \begin{itemize} \item \verb|read| is used for search output files with a single query and returns a \verb|QueryResult| object \item \verb|parse| is used for search output files with multiple queries and returns a generator that yields \verb|QueryResult| objects \end{itemize} With that settled, let's start probing each \verb|Bio.SearchIO| object, beginning with \verb|QueryResult|. \subsection{QueryResult} \label{sec:searchio-qresult} The QueryResult object represents a single search query and contains zero or more Hit objects. Let's see what it looks like using the BLAST file we have: %doctest ../Doc/examples \begin{verbatim} >>> from Bio import SearchIO >>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml') >>> print(blast_qresult) Program: blastn (2.2.27+) Query: 42291 (61) mystery_seq Target: refseq_rna Hits: ---- ----- ---------------------------------------------------------- # # HSP ID + description ---- ----- ---------------------------------------------------------- 0 1 gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 52... 1 1 gi|301171311|ref|NR_035856.1| Pan troglodytes microRNA... 2 1 gi|270133242|ref|NR_032573.1| Macaca mulatta microRNA ... 3 2 gi|301171322|ref|NR_035857.1| Pan troglodytes microRNA... 4 1 gi|301171267|ref|NR_035851.1| Pan troglodytes microRNA... 5 2 gi|262205330|ref|NR_030198.1| Homo sapiens microRNA 52... 6 1 gi|262205302|ref|NR_030191.1| Homo sapiens microRNA 51... 7 1 gi|301171259|ref|NR_035850.1| Pan troglodytes microRNA... 8 1 gi|262205451|ref|NR_030222.1| Homo sapiens microRNA 51... 9 2 gi|301171447|ref|NR_035871.1| Pan troglodytes microRNA... 10 1 gi|301171276|ref|NR_035852.1| Pan troglodytes microRNA... 11 1 gi|262205290|ref|NR_030188.1| Homo sapiens microRNA 51... 12 1 gi|301171354|ref|NR_035860.1| Pan troglodytes microRNA... 13 1 gi|262205281|ref|NR_030186.1| Homo sapiens microRNA 52... 14 2 gi|262205298|ref|NR_030190.1| Homo sapiens microRNA 52... 15 1 gi|301171394|ref|NR_035865.1| Pan troglodytes microRNA... 16 1 gi|262205429|ref|NR_030218.1| Homo sapiens microRNA 51... 17 1 gi|262205423|ref|NR_030217.1| Homo sapiens microRNA 52... 18 1 gi|301171401|ref|NR_035866.1| Pan troglodytes microRNA... 19 1 gi|270133247|ref|NR_032574.1| Macaca mulatta microRNA ... 20 1 gi|262205309|ref|NR_030193.1| Homo sapiens microRNA 52... 21 2 gi|270132717|ref|NR_032716.1| Macaca mulatta microRNA ... 22 2 gi|301171437|ref|NR_035870.1| Pan troglodytes microRNA... 23 2 gi|270133306|ref|NR_032587.1| Macaca mulatta microRNA ... 24 2 gi|301171428|ref|NR_035869.1| Pan troglodytes microRNA... 25 1 gi|301171211|ref|NR_035845.1| Pan troglodytes microRNA... 26 2 gi|301171153|ref|NR_035838.1| Pan troglodytes microRNA... 27 2 gi|301171146|ref|NR_035837.1| Pan troglodytes microRNA... 28 2 gi|270133254|ref|NR_032575.1| Macaca mulatta microRNA ... 29 2 gi|262205445|ref|NR_030221.1| Homo sapiens microRNA 51... ~~~ 97 1 gi|356517317|ref|XM_003527287.1| PREDICTED: Glycine ma... 98 1 gi|297814701|ref|XM_002875188.1| Arabidopsis lyrata su... 99 1 gi|397513516|ref|XM_003827011.1| PREDICTED: Pan panisc... \end{verbatim} We've just begun to scratch the surface of the object model, but you can see that there's already some useful information. By invoking \verb|print| on the \verb|QueryResult| object, you can see: \begin{itemize} \item The program name and version (blastn version 2.2.27+) \item The query ID, description, and its sequence length (ID is 42291, description is `mystery\_seq', and it is 61 nucleotides long) \item The target database to search against (refseq\_rna) \item A quick overview of the resulting hits. For our query sequence, there are 100 potential hits (numbered 0--99 in the table). For each hit, we can also see how many HSPs it contains, its ID, and a snippet of its description. Notice here that \verb|Bio.SearchIO| truncates the hit table overview, by showing only hits numbered 0--29, and then 97--99. \end{itemize} Now let's check our BLAT results using the same procedure as above: %cont-doctest \begin{verbatim} >>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl') >>> print(blat_qresult) Program: blat () Query: mystery_seq (61) Target: Hits: ---- ----- ---------------------------------------------------------- # # HSP ID + description ---- ----- ---------------------------------------------------------- 0 17 chr19 \end{verbatim} You'll immediately notice that there are some differences. Some of these are caused by the way PSL format stores its details, as you'll see. The rest are caused by the genuine program and target database differences between our BLAST and BLAT searches: \begin{itemize} \item The program name and version. \verb|Bio.SearchIO| knows that the program is BLAT, but in the output file there is no information regarding the program version so it defaults to `'. \item The query ID, description, and its sequence length. Notice here that these details are slightly different from the ones we saw in BLAST. The ID is `mystery\_seq' instead of 42991, there is no known description, but the query length is still 61. This is actually a difference introduced by the file formats themselves. BLAST sometimes creates its own query IDs and uses your original ID as the sequence description. \item The target database is not known, as it is not stated in the BLAT output file. \item And finally, the list of hits we have is completely different. Here, we see that our query sequence only hits the `chr19' database entry, but in it we see 17 HSP regions. This should not be surprising however, given that we are using a different program, each with its own target database. \end{itemize} All the details you saw when invoking the \verb|print| method can be accessed individually using Python's object attribute access notation (a.k.a. the dot notation). There are also other format-specific attributes that you can access using the same method. %cont-doctest \begin{verbatim} >>> print("%s %s" % (blast_qresult.program, blast_qresult.version)) blastn 2.2.27+ >>> print("%s %s" % (blat_qresult.program, blat_qresult.version)) blat >>> blast_qresult.param_evalue_threshold # blast-xml specific 10.0 \end{verbatim} For a complete list of accessible attributes, you can check each format-specific documentation. Here are the ones \href{http://biopython.org/DIST/docs/api/Bio.SearchIO.BlastIO-module.html}{for BLAST} and for \href{http://biopython.org/DIST/docs/api/Bio.SearchIO.BlatIO-module.html}{BLAT}. Having looked at using \verb|print| on \verb|QueryResult| objects, let's drill down deeper. What exactly is a \verb|QueryResult|? In terms of Python objects, \verb|QueryResult| is a hybrid between a list and a dictionary. In other words, it is a container object with all the convenient features of lists and dictionaries. Like Python lists and dictionaries, \verb|QueryResult| objects are iterable. Each iteration returns a \verb|Hit| object: \begin{verbatim} >>> for hit in blast_qresult: ... hit Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps) Hit(id='gi|301171311|ref|NR_035856.1|', query_id='42291', 1 hsps) Hit(id='gi|270133242|ref|NR_032573.1|', query_id='42291', 1 hsps) Hit(id='gi|301171322|ref|NR_035857.1|', query_id='42291', 2 hsps) Hit(id='gi|301171267|ref|NR_035851.1|', query_id='42291', 1 hsps) ... \end{verbatim} To check how many items (hits) a \verb|QueryResult| has, you can simply invoke Python's \verb|len| method: %cont-doctest \begin{verbatim} >>> len(blast_qresult) 100 >>> len(blat_qresult) 1 \end{verbatim} Like Python lists, you can retrieve items (hits) from a \verb|QueryResult| using the slice notation: %cont-doctest \begin{verbatim} >>> blast_qresult[0] # retrieves the top hit Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps) >>> blast_qresult[-1] # retrieves the last hit Hit(id='gi|397513516|ref|XM_003827011.1|', query_id='42291', 1 hsps) \end{verbatim} To retrieve multiple hits, you can slice \verb|QueryResult| objects using the slice notation as well. In this case, the slice will return a new \verb|QueryResult| object containing only the sliced hits: %cont-doctest \begin{verbatim} >>> blast_slice = blast_qresult[:3] # slices the first three hits >>> print(blast_slice) Program: blastn (2.2.27+) Query: 42291 (61) mystery_seq Target: refseq_rna Hits: ---- ----- ---------------------------------------------------------- # # HSP ID + description ---- ----- ---------------------------------------------------------- 0 1 gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 52... 1 1 gi|301171311|ref|NR_035856.1| Pan troglodytes microRNA... 2 1 gi|270133242|ref|NR_032573.1| Macaca mulatta microRNA ... \end{verbatim} Like Python dictionaries, you can also retrieve hits using the hit's ID. This is particularly useful if you know a given hit ID exists within a search query results: %cont-doctest \begin{verbatim} >>> blast_qresult['gi|262205317|ref|NR_030195.1|'] Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps) \end{verbatim} You can also get a full list of \verb|Hit| objects using \verb|hits| and a full list of \verb|Hit| IDs using \verb|hit_keys|: \begin{verbatim} >>> blast_qresult.hits [...] # list of all hits >>> blast_qresult.hit_keys [...] # list of all hit IDs \end{verbatim} What if you just want to check whether a particular hit is present in the query results? You can do a simple Python membership test using the \verb|in| keyword: %cont-doctest \begin{verbatim} >>> 'gi|262205317|ref|NR_030195.1|' in blast_qresult True >>> 'gi|262205317|ref|NR_030194.1|' in blast_qresult False \end{verbatim} Sometimes, knowing whether a hit is present is not enough; you also want to know the rank of the hit. Here, the \verb|index| method comes to the rescue: %cont-doctest \begin{verbatim} >>> blast_qresult.index('gi|301171437|ref|NR_035870.1|') 22 \end{verbatim} Remember that we're using Python's indexing style here, which is zero-based. This means our hit above is ranked at no. 23, not 22. Also, note that the hit rank you see here is based on the native hit ordering present in the original search output file. Different search tools may order these hits based on different criteria. If the native hit ordering doesn't suit your taste, you can use the \verb|sort| method of the \verb|QueryResult| object. It is very similar to Python's \verb|list.sort| method, with the addition of an option to create a new sorted \verb|QueryResult| object or not. Here is an example of using \verb|QueryResult.sort| to sort the hits based on each hit's full sequence length. For this particular sort, we'll set the \verb|in_place| flag to \verb|False| so that sorting will return a new \verb|QueryResult| object and leave our initial object unsorted. We'll also set the \verb|reverse| flag to \verb|True| so that we sort in descending order. %cont-doctest \begin{verbatim} >>> for hit in blast_qresult[:5]: # id and sequence length of the first five hits ... print("%s %i" % (hit.id, hit.seq_len)) ... gi|262205317|ref|NR_030195.1| 61 gi|301171311|ref|NR_035856.1| 60 gi|270133242|ref|NR_032573.1| 85 gi|301171322|ref|NR_035857.1| 86 gi|301171267|ref|NR_035851.1| 80 >>> sort_key = lambda hit: hit.seq_len >>> sorted_qresult = blast_qresult.sort(key=sort_key, reverse=True, in_place=False) >>> for hit in sorted_qresult[:5]: ... print("%s %i" % (hit.id, hit.seq_len)) ... gi|397513516|ref|XM_003827011.1| 6002 gi|390332045|ref|XM_776818.2| 4082 gi|390332043|ref|XM_003723358.1| 4079 gi|356517317|ref|XM_003527287.1| 3251 gi|356543101|ref|XM_003539954.1| 2936 \end{verbatim} The advantage of having the \verb|in_place| flag here is that we're preserving the native ordering, so we may use it again later. You should note that this is not the default behavior of \verb|QueryResult.sort|, however, which is why we needed to set the \verb|in_place| flag to \verb|True| explicitly. At this point, you've known enough about \verb|QueryResult| objects to make it work for you. But before we go on to the next object in the \verb|Bio.SearchIO| model, let's take a look at two more sets of methods that could make it even easier to work with \verb|QueryResult| objects: the \verb|filter| and \verb|map| methods. If you're familiar with Python's list comprehensions, generator expressions or the built in \verb|filter| and \verb|map| functions, you'll know how useful they are for working with list-like objects (if you're not, check them out!). You can use these built in methods to manipulate \verb|QueryResult| objects, but you'll end up with regular Python lists and lose the ability to do more interesting manipulations. That's why, \verb|QueryResult| objects provide its own flavor of \verb|filter| and \verb|map| methods. Analogous to \verb|filter|, there are \verb|hit_filter| and \verb|hsp_filter| methods. As their name implies, these methods filter its \verb|QueryResult| object either on its \verb|Hit| objects or \verb|HSP| objects. Similarly, analogous to \verb|map|, \verb|QueryResult| objects also provide the \verb|hit_map| and \verb|hsp_map| methods. These methods apply a given function to all hits or HSPs in a \verb|QueryResult| object, respectively. Let's see these methods in action, beginning with \verb|hit_filter|. This method accepts a callback function that checks whether a given \verb|Hit| object passes the condition you set or not. In other words, the function must accept as its argument a single \verb|Hit| object and returns \verb|True| or \verb|False|. Here is an example of using \verb|hit_filter| to filter out \verb|Hit| objects that only have one HSP: %cont-doctest \begin{verbatim} >>> filter_func = lambda hit: len(hit.hsps) > 1 # the callback function >>> len(blast_qresult) # no. of hits before filtering 100 >>> filtered_qresult = blast_qresult.hit_filter(filter_func) >>> len(filtered_qresult) # no. of hits after filtering 37 >>> for hit in filtered_qresult[:5]: # quick check for the hit lengths ... print("%s %i" % (hit.id, len(hit.hsps))) gi|301171322|ref|NR_035857.1| 2 gi|262205330|ref|NR_030198.1| 2 gi|301171447|ref|NR_035871.1| 2 gi|262205298|ref|NR_030190.1| 2 gi|270132717|ref|NR_032716.1| 2 \end{verbatim} \verb|hsp_filter| works the same as \verb|hit_filter|, only instead of looking at the \verb|Hit| objects, it performs filtering on the \verb|HSP| objects in each hits. As for the \verb|map| methods, they too accept a callback function as their arguments. However, instead of returning \verb|True| or \verb|False|, the callback function must return the modified \verb|Hit| or \verb|HSP| object (depending on whether you're using \verb|hit_map| or \verb|hsp_map|). Let's see an example where we're using \verb|hit_map| to rename the hit IDs: %cont-doctest \begin{verbatim} >>> def map_func(hit): ... hit.id = hit.id.split('|')[3] # renames 'gi|301171322|ref|NR_035857.1|' to 'NR_035857.1' ... return hit ... >>> mapped_qresult = blast_qresult.hit_map(map_func) >>> for hit in mapped_qresult[:5]: ... print(hit.id) NR_030195.1 NR_035856.1 NR_032573.1 NR_035857.1 NR_035851.1 \end{verbatim} Again, \verb|hsp_map| works the same as \verb|hit_map|, but on \verb|HSP| objects instead of \verb|Hit| objects. \subsection{Hit} \label{sec:searchio-hit} \verb|Hit| objects represent all query results from a single database entry. They are the second-level container in the \verb|Bio.SearchIO| object hierarchy. You've seen that they are contained by \verb|QueryResult| objects, but they themselves contain \verb|HSP| objects. Let's see what they look like, beginning with our BLAST search: %doctest ../Doc/examples \begin{verbatim} >>> from Bio import SearchIO >>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml') >>> blast_hit = blast_qresult[3] # fourth hit from the query result >>> print(blast_hit) Query: 42291 mystery_seq Hit: gi|301171322|ref|NR_035857.1| (86) Pan troglodytes microRNA mir-520c (MIR520C), microRNA HSPs: ---- -------- --------- ------ --------------- --------------------- # E-value Bit score Span Query range Hit range ---- -------- --------- ------ --------------- --------------------- 0 8.9e-20 100.47 60 [1:61] [13:73] 1 3.3e-06 55.39 60 [0:60] [13:73] \end{verbatim} You see that we've got the essentials covered here: \begin{itemize} \item The query ID and description is present. A hit is always tied to a query, so we want to keep track of the originating query as well. These values can be accessed from a hit using the \verb|query_id| and \verb|query_description| attributes. \item We also have the unique hit ID, description, and full sequence lengths. They can be accessed using \verb|id|, \verb|description|, and \verb|seq_len|, respectively. \item Finally, there's a table containing quick information about the HSPs this hit contains. In each row, we've got the important HSP details listed: the HSP index, its e-value, its bit score, its span (the alignment length including gaps), its query coordinates, and its hit coordinates. \end{itemize} Now let's contrast this with the BLAT search. Remember that in the BLAT search we had one hit with 17 HSPs. %cont-doctest \begin{verbatim} >>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl') >>> blat_hit = blat_qresult[0] # the only hit >>> print(blat_hit) Query: mystery_seq Hit: chr19 (59128983) HSPs: ---- -------- --------- ------ --------------- --------------------- # E-value Bit score Span Query range Hit range ---- -------- --------- ------ --------------- --------------------- 0 ? ? ? [0:61] [54204480:54204541] 1 ? ? ? [0:61] [54233104:54264463] 2 ? ? ? [0:61] [54254477:54260071] 3 ? ? ? [1:61] [54210720:54210780] 4 ? ? ? [0:60] [54198476:54198536] 5 ? ? ? [0:61] [54265610:54265671] 6 ? ? ? [0:61] [54238143:54240175] 7 ? ? ? [0:60] [54189735:54189795] 8 ? ? ? [0:61] [54185425:54185486] 9 ? ? ? [0:60] [54197657:54197717] 10 ? ? ? [0:61] [54255662:54255723] 11 ? ? ? [0:61] [54201651:54201712] 12 ? ? ? [8:60] [54206009:54206061] 13 ? ? ? [10:61] [54178987:54179038] 14 ? ? ? [8:61] [54212018:54212071] 15 ? ? ? [8:51] [54234278:54234321] 16 ? ? ? [8:61] [54238143:54238196] \end{verbatim} Here, we've got a similar level of detail as with the BLAST hit we saw earlier. There are some differences worth explaining, though: \begin{itemize} \item The e-value and bit score column values. As BLAT HSPs do not have e-values and bit scores, the display defaults to `?'. \item What about the span column? The span values is meant to display the complete alignment length, which consists of all residues and any gaps that may be present. The PSL format do not have this information readily available and \verb|Bio.SearchIO| does not attempt to try guess what it is, so we get a `?' similar to the e-value and bit score columns. \end{itemize} In terms of Python objects, \verb|Hit| behaves almost the same as Python lists, but contain \verb|HSP| objects exclusively. If you're familiar with lists, you should encounter no difficulties working with the \verb|Hit| object. Just like Python lists, \verb|Hit| objects are iterable, and each iteration returns one \verb|HSP| object it contains: %cont-doctest \begin{verbatim} >>> for hsp in blast_hit: ... hsp HSP(hit_id='gi|301171322|ref|NR_035857.1|', query_id='42291', 1 fragments) HSP(hit_id='gi|301171322|ref|NR_035857.1|', query_id='42291', 1 fragments) \end{verbatim} You can invoke \verb|len| on a \verb|Hit| to see how many \verb|HSP| objects it has: %cont-doctest \begin{verbatim} >>> len(blast_hit) 2 >>> len(blat_hit) 17 \end{verbatim} You can use the slice notation on \verb|Hit| objects, whether to retrieve single \verb|HSP| or multiple \verb|HSP| objects. Like \verb|QueryResult|, if you slice for multiple \verb|HSP|, a new \verb|Hit| object will be returned containing only the sliced \verb|HSP| objects: %cont-doctest \begin{verbatim} >>> blat_hit[0] # retrieve single items HSP(hit_id='chr19', query_id='mystery_seq', 1 fragments) >>> sliced_hit = blat_hit[4:9] # retrieve multiple items >>> len(sliced_hit) 5 >>> print(sliced_hit) Query: mystery_seq Hit: chr19 (59128983) HSPs: ---- -------- --------- ------ --------------- --------------------- # E-value Bit score Span Query range Hit range ---- -------- --------- ------ --------------- --------------------- 0 ? ? ? [0:60] [54198476:54198536] 1 ? ? ? [0:61] [54265610:54265671] 2 ? ? ? [0:61] [54238143:54240175] 3 ? ? ? [0:60] [54189735:54189795] 4 ? ? ? [0:61] [54185425:54185486] \end{verbatim} You can also sort the \verb|HSP| inside a \verb|Hit|, using the exact same arguments like the sort method you saw in the \verb|QueryResult| object. Finally, there are also the \verb|filter| and \verb|map| methods you can use on \verb|Hit| objects. Unlike in the \verb|QueryResult| object, \verb|Hit| objects only have one variant of \verb|filter| (\verb|Hit.filter|) and one variant of \verb|map| (\verb|Hit.map|). Both of \verb|Hit.filter| and \verb|Hit.map| work on the \verb|HSP| objects a \verb|Hit| has. \subsection{HSP} \label{sec:searchio-hsp} \verb|HSP| (high-scoring pair) represents region(s) in the hit sequence that contains significant alignment(s) to the query sequence. It contains the actual match between your query sequence and a database entry. As this match is determined by the sequence search tool's algorithms, the \verb|HSP| object contains the bulk of the statistics computed by the search tool. This also makes the distinction between \verb|HSP| objects from different search tools more apparent compared to the differences you've seen in \verb|QueryResult| or \verb|Hit| objects. Let's see some examples from our BLAST and BLAT searches. We'll look at the BLAST HSP first: %doctest ../Doc/examples \begin{verbatim} >>> from Bio import SearchIO >>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml') >>> blast_hsp = blast_qresult[0][0] # first hit, first hsp >>> print(blast_hsp) Query: 42291 mystery_seq Hit: gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 520b (MIR520... Query range: [0:61] (1) Hit range: [0:61] (1) Quick stats: evalue 4.9e-23; bitscore 111.29 Fragments: 1 (61 columns) Query - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Hit - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG \end{verbatim} Just like \verb|QueryResult| and \verb|Hit|, invoking \verb|print| on an \verb|HSP| shows its general details: \begin{itemize} \item There are the query and hit IDs and descriptions. We need these to identify our \verb|HSP|. \item We've also got the matching range of the query and hit sequences. The slice notation we're using here is an indication that the range is displayed using Python's indexing style (zero-based, half open). The number inside the parenthesis denotes the strand. In this case, both sequences have the plus strand. \item Some quick statistics are available: the e-value and bitscore. \item There is information about the HSP fragments. Ignore this for now; it will be explained later on. \item And finally, we have the query and hit sequence alignment itself. \end{itemize} These details can be accessed on their own using the dot notation, just like in \verb|QueryResult| and \verb|Hit|: %cont-doctest \begin{verbatim} >>> blast_hsp.query_range (0, 61) \end{verbatim} %hack! since float display may be different across versions \begin{verbatim} >>> blast_hsp.evalue 4.91307e-23 \end{verbatim} They're not the only attributes available, though. \verb|HSP| objects come with a default set of properties that makes it easy to probe their various details. Here are some examples: %cont-doctest \begin{verbatim} >>> blast_hsp.hit_start # start coordinate of the hit sequence 0 >>> blast_hsp.query_span # how many residues in the query sequence 61 >>> blast_hsp.aln_span # how long the alignment is 61 \end{verbatim} Check out the \verb|HSP| \href{http://biopython.org/DIST/docs/api/Bio.SearchIO._model.hsp-module.html}{documentation} for a full list of these predefined properties. Furthermore, each sequence search tool usually computes its own statistics / details for its \verb|HSP| objects. For example, an XML BLAST search also outputs the number of gaps and identical residues. These attributes can be accessed like so: %cont-doctest \begin{verbatim} >>> blast_hsp.gap_num # number of gaps 0 >>> blast_hsp.ident_num # number of identical residues 61 \end{verbatim} These details are format-specific; they may not be present in other formats. To see which details are available for a given sequence search tool, you should check the format's documentation in \verb|Bio.SearchIO|. Alternatively, you may also use \verb|.__dict__.keys()| for a quick list of what's available: \begin{verbatim} >>> blast_hsp.__dict__.keys() ['bitscore', 'evalue', 'ident_num', 'gap_num', 'bitscore_raw', 'pos_num', '_items'] \end{verbatim} Finally, you may have noticed that the \verb|query| and \verb|hit| attributes of our HSP are not just regular strings: %cont-doctest \begin{verbatim} >>> blast_hsp.query SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='42291', name='aligned query sequence', description='mystery_seq', dbxrefs=[]) >>> blast_hsp.hit SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='gi|262205317|ref|NR_030195.1|', name='aligned hit sequence', description='Homo sapiens microRNA 520b (MIR520B), microRNA', dbxrefs=[]) \end{verbatim} They are \verb|SeqRecord| objects you saw earlier in Section~\ref{chapter:SeqRecord}! This means that you can do all sorts of interesting things you can do with \verb|SeqRecord| objects on \verb|HSP.query| and/or \verb|HSP.hit|. It should not surprise you now that the \verb|HSP| object has an \verb|alignment| property which is a \verb|MultipleSeqAlignment| object: %cont-doctest \begin{verbatim} >>> print(blast_hsp.aln) DNAAlphabet() alignment with 2 rows and 61 columns CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAG...GGG 42291 CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAG...GGG gi|262205317|ref|NR_030195.1| \end{verbatim} Having probed the BLAST HSP, let's now take a look at HSPs from our BLAT results for a different kind of HSP. As usual, we'll begin by invoking \verb|print| on it: %cont-doctest \begin{verbatim} >>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl') >>> blat_hsp = blat_qresult[0][0] # first hit, first hsp >>> print(blat_hsp) Query: mystery_seq Hit: chr19 Query range: [0:61] (1) Hit range: [54204480:54204541] (1) Quick stats: evalue ?; bitscore ? Fragments: 1 (? columns) \end{verbatim} Some of the outputs you may have already guessed. We have the query and hit IDs and descriptions and the sequence coordinates. Values for evalue and bitscore is `?' as BLAT HSPs do not have these attributes. But The biggest difference here is that you don't see any sequence alignments displayed. If you look closer, PSL formats themselves do not have any hit or query sequences, so \verb|Bio.SearchIO| won't create any sequence or alignment objects. What happens if you try to access \verb|HSP.query|, \verb|HSP.hit|, or \verb|HSP.aln|? You'll get the default values for these attributes, which is \verb|None|: %cont-doctest \begin{verbatim} >>> blat_hsp.hit is None True >>> blat_hsp.query is None True >>> blat_hsp.aln is None True \end{verbatim} This does not affect other attributes, though. For example, you can still access the length of the query or hit alignment. Despite not displaying any attributes, the PSL format still have this information so \verb|Bio.SearchIO| can extract them: %cont-doctest \begin{verbatim} >>> blat_hsp.query_span # length of query match 61 >>> blat_hsp.hit_span # length of hit match 61 \end{verbatim} Other format-specific attributes are still present as well: %cont-doctest \begin{verbatim} >>> blat_hsp.score # PSL score 61 >>> blat_hsp.mismatch_num # the mismatch column 0 \end{verbatim} So far so good? Things get more interesting when you look at another `variant' of HSP present in our BLAT results. You might recall that in BLAT searches, sometimes we get our results separated into `blocks'. These blocks are essentially alignment fragments that may have some intervening sequence between them. Let's take a look at a BLAT HSP that contains multiple blocks to see how \verb|Bio.SearchIO| deals with this: %cont-doctest \begin{verbatim} >>> blat_hsp2 = blat_qresult[0][1] # first hit, second hsp >>> print(blat_hsp2) Query: mystery_seq Hit: chr19 Query range: [0:61] (1) Hit range: [54233104:54264463] (1) Quick stats: evalue ?; bitscore ? Fragments: --- -------------- ---------------------- ---------------------- # Span Query range Hit range --- -------------- ---------------------- ---------------------- 0 ? [0:18] [54233104:54233122] 1 ? [18:61] [54264420:54264463] \end{verbatim} What's happening here? We still some essential details covered: the IDs and descriptions, the coordinates, and the quick statistics are similar to what you've seen before. But the fragments detail is all different. Instead of showing `Fragments: 1', we now have a table with two data rows. This is how \verb|Bio.SearchIO| deals with HSPs having multiple fragments. As mentioned before, an HSP alignment may be separated by intervening sequences into fragments. The intervening sequences are not part of the query-hit match, so they should not be considered part of query nor hit sequence. However, they do affect how we deal with sequence coordinates, so we can't ignore them. Take a look at the hit coordinate of the HSP above. In the \verb|Hit range:| field, we see that the coordinate is \verb|[54233104:54264463]|. But looking at the table rows, we see that not the entire region spanned by this coordinate matches our query. Specifically, the intervening region spans from \verb|54233122| to \verb|54264420|. Why then, is the query coordinates seem to be contiguous, you ask? This is perfectly fine. In this case it means that the query match is contiguous (no intervening regions), while the hit match is not. All these attributes are accessible from the HSP directly, by the way: %cont-doctest \begin{verbatim} >>> blat_hsp2.hit_range # hit start and end coordinates of the entire HSP (54233104, 54264463) >>> blat_hsp2.hit_range_all # hit start and end coordinates of each fragment [(54233104, 54233122), (54264420, 54264463)] >>> blat_hsp2.hit_span # hit span of the entire HSP 31359 >>> blat_hsp2.hit_span_all # hit span of each fragment [18, 43] >>> blat_hsp2.hit_inter_ranges # start and end coordinates of intervening regions in the hit sequence [(54233122, 54264420)] >>> blat_hsp2.hit_inter_spans # span of intervening regions in the hit sequence [31298] \end{verbatim} Most of these attributes are not readily available from the PSL file we have, but \verb|Bio.SearchIO| calculates them for you on the fly when you parse the PSL file. All it needs are the start and end coordinates of each fragment. What about the \verb|query|, \verb|hit|, and \verb|aln| attributes? If the HSP has multiple fragments, you won't be able to use these attributes as they only fetch single \verb|SeqRecord| or \verb|MultipleSeqAlignment| objects. However, you can use their \verb|*_all| counterparts: \verb|query_all|, \verb|hit_all|, and \verb|aln_all|. These properties will return a list containing \verb|SeqRecord| or \verb|MultipleSeqAlignment| objects from each of the HSP fragment. There are other attributes that behave similarly, i.e. they only work for HSPs with one fragment. Check out the \verb|HSP| \href{http://biopython.org/DIST/docs/api/Bio.SearchIO._model.hsp-module.html}{documentation} for a full list. Finally, to check whether you have multiple fragments or not, you can use the \verb|is_fragmented| property like so: %cont-doctest \begin{verbatim} >>> blat_hsp2.is_fragmented # BLAT HSP with 2 fragments True >>> blat_hsp.is_fragmented # BLAT HSP from earlier, with one fragment False \end{verbatim} Before we move on, you should also know that we can use the slice notation on \verb|HSP| objects, just like \verb|QueryResult| or \verb|Hit| objects. When you use this notation, you'll get an \verb|HSPFragment| object in return, the last component of the object model. \subsection{HSPFragment} \label{sec:searchio-hspfragment} \verb|HSPFragment| represents a single, contiguous match between the query and hit sequences. You could consider it the core of the object model and search result, since it is the presence of these fragments that determine whether your search have results or not. In most cases, you don't have to deal with \verb|HSPFragment| objects directly since not that many sequence search tools fragment their HSPs. When you do have to deal with them, what you should remember is that \verb|HSPFragment| objects were written with to be as compact as possible. In most cases, they only contain attributes directly related to sequences: strands, reading frames, alphabets, coordinates, the sequences themselves, and their IDs and descriptions. These attributes are readily shown when you invoke \verb|print| on an \verb|HSPFragment|. Here's an example, taken from our BLAST search: %doctest ../Doc/examples \begin{verbatim} >>> from Bio import SearchIO >>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml') >>> blast_frag = blast_qresult[0][0][0] # first hit, first hsp, first fragment >>> print(blast_frag) Query: 42291 mystery_seq Hit: gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 520b (MIR520... Query range: [0:61] (1) Hit range: [0:61] (1) Fragments: 1 (61 columns) Query - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Hit - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG \end{verbatim} At this level, the BLAT fragment looks quite similar to the BLAST fragment, save for the query and hit sequences which are not present: %cont-doctest \begin{verbatim} >>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl') >>> blat_frag = blat_qresult[0][0][0] # first hit, first hsp, first fragment >>> print(blat_frag) Query: mystery_seq Hit: chr19 Query range: [0:61] (1) Hit range: [54204480:54204541] (1) Fragments: 1 (? columns) \end{verbatim} In all cases, these attributes are accessible using our favorite dot notation. Some examples: %cont-doctest \begin{verbatim} >>> blast_frag.query_start # query start coordinate 0 >>> blast_frag.hit_strand # hit sequence strand 1 >>> blast_frag.hit # hit sequence, as a SeqRecord object SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='gi|262205317|ref|NR_030195.1|', name='aligned hit sequence', description='Homo sapiens microRNA 520b (MIR520B), microRNA', dbxrefs=[]) \end{verbatim} \section{A note about standards and conventions} \label{sec:searchio-standards} Before we move on to the main functions, there is something you ought to know about the standards \verb|Bio.SearchIO| uses. If you've worked with multiple sequence search tools, you might have had to deal with the many different ways each program deals with things like sequence coordinates. It might not have been a pleasant experience as these search tools usually have their own standards. For example, one tools might use one-based coordinates, while the other uses zero-based coordinates. Or, one program might reverse the start and end coordinates if the strand is minus, while others don't. In short, these often creates unnecessary mess must be dealt with. We realize this problem ourselves and we intend to address it in \verb|Bio.SearchIO|. After all, one of the goals of \verb|Bio.SearchIO| is to create a common, easy to use interface to deal with various search output files. This means creating standards that extend beyond the object model you just saw. Now, you might complain, "Not another standard!". Well, eventually we have to choose one convention or the other, so this is necessary. Plus, we're not creating something entirely new here; just adopting a standard we think is best for a Python programmer (it is Biopython, after all). There are three implicit standards that you can expect when working with \verb|Bio.SearchIO|: \begin{itemize} \item The first one pertains to sequence coordinates. In \verb|Bio.SearchIO|, all sequence coordinates follows Python's coordinate style: zero-based and half open. For example, if in a BLAST XML output file the start and end coordinates of an HSP are 10 and 28, they would become 9 and 28 in \verb|Bio.SearchIO|. The start coordinate becomes 9 because Python indices start from zero, while the end coordinate remains 28 as Python slices omit the last item in an interval. \item The second is on sequence coordinate orders. In \verb|Bio.SearchIO|, start coordinates are always less than or equal to end coordinates. This isn't always the case with all sequence search tools, as some of them have larger start coordinates when the sequence strand is minus. \item The last one is on strand and reading frame values. For strands, there are only four valid choices: \verb|1| (plus strand), \verb|-1| (minus strand), \verb|0| (protein sequences), and \verb|None| (no strand). For reading frames, the valid choices are integers from \verb|-3| to \verb|3| and \verb|None|. \end{itemize} Note that these standards only exist in \verb|Bio.SearchIO| objects. If you write \verb|Bio.SearchIO| objects into an output format, \verb|Bio.SearchIO| will use the format's standard for the output. It does not force its standard over to your output file. \section{Reading search output files} \label{sec:searchio-input} There are two functions you can use for reading search output files into \verb|Bio.SearchIO| objects: \verb|read| and \verb|parse|. They're essentially similar to \verb|read| and \verb|parse| functions in other submodules like \verb|Bio.SeqIO| or \verb|Bio.AlignIO|. In both cases, you need to supply the search output file name and the file format name, both as Python strings. You can check the documentation for a list of format names \verb|Bio.SearchIO| recognizes. \verb|Bio.SearchIO.read| is used for reading search output files with only one query and returns a \verb|QueryResult| object. You've seen \verb|read| used in our previous examples. What you haven't seen is that \verb|read| may also accept additional keyword arguments, depending on the file format. Here are some examples. In the first one, we use \verb|read| just like previously to read a BLAST tabular output file. In the second one, we use a keyword argument to modify so it parses the BLAST tabular variant with comments in it: %doctest ../Tests/Blast \begin{verbatim} >>> from Bio import SearchIO >>> qresult = SearchIO.read('tab_2226_tblastn_003.txt', 'blast-tab') >>> qresult QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits) >>> qresult2 = SearchIO.read('tab_2226_tblastn_007.txt', 'blast-tab', comments=True) >>> qresult2 QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits) \end{verbatim} These keyword arguments differs among file formats. Check the format documentation to see if it has keyword arguments that modifies its parser's behavior. As for the \verb|Bio.SearchIO.parse|, it is used for reading search output files with any number of queries. The function returns a generator object that yields a \verb|QueryResult| object in each iteration. Like \verb|Bio.SearchIO.read|, it also accepts format-specific keyword arguments: %doctest ../Tests/Blast \begin{verbatim} >>> from Bio import SearchIO >>> qresults = SearchIO.parse('tab_2226_tblastn_001.txt', 'blast-tab') >>> for qresult in qresults: ... print(qresult.id) gi|16080617|ref|NP_391444.1| gi|11464971:4-101 >>> qresults2 = SearchIO.parse('tab_2226_tblastn_005.txt', 'blast-tab', comments=True) >>> for qresult in qresults2: ... print(qresult.id) random_s00 gi|16080617|ref|NP_391444.1| gi|11464971:4-101 \end{verbatim} \section{Dealing with large search output files with indexing} \label{sec:searchio-index} Sometimes, you're handed a search output file containing hundreds or thousands of queries that you need to parse. You can of course use \verb|Bio.SearchIO.parse| for this file, but that would be grossly inefficient if you need to access only a few of the queries. This is because \verb|parse| will parse all queries it sees before it fetches your query of interest. In this case, the ideal choice would be to index the file using \verb|Bio.SearchIO.index| or \verb|Bio.SearchIO.index_db|. If the names sound familiar, it's because you've seen them before in Section~\ref{sec:SeqIO-index}. These functions also behave similarly to their \verb|Bio.SeqIO| counterparts, with the addition of format-specific keyword arguments. Here are some examples. You can use \verb|index| with just the filename and format name: %doctest ../Tests/Blast \begin{verbatim} >>> from Bio import SearchIO >>> idx = SearchIO.index('tab_2226_tblastn_001.txt', 'blast-tab') >>> sorted(idx.keys()) ['gi|11464971:4-101', 'gi|16080617|ref|NP_391444.1|'] >>> idx['gi|16080617|ref|NP_391444.1|'] QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits) \end{verbatim} Or also with the format-specific keyword argument: %cont-doctest \begin{verbatim} >>> idx = SearchIO.index('tab_2226_tblastn_005.txt', 'blast-tab', comments=True) >>> sorted(idx.keys()) ['gi|11464971:4-101', 'gi|16080617|ref|NP_391444.1|', 'random_s00'] >>> idx['gi|16080617|ref|NP_391444.1|'] QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits) \end{verbatim} Or with the \verb|key_function| argument, as in \verb|Bio.SeqIO|: %cont-doctest \begin{verbatim} >>> key_function = lambda id: id.upper() # capitalizes the keys >>> idx = SearchIO.index('tab_2226_tblastn_001.txt', 'blast-tab', key_function=key_function) >>> sorted(idx.keys()) ['GI|11464971:4-101', 'GI|16080617|REF|NP_391444.1|'] >>> idx['GI|16080617|REF|NP_391444.1|'] QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits) \end{verbatim} \verb|Bio.SearchIO.index_db| works like as \verb|index|, only it writes the query offsets into an SQLite database file. \section{Writing and converting search output files} \label{sec:searchio-write} It is occasionally useful to be able to manipulate search results from an output file and write it again to a new file. \verb|Bio.SearchIO| provides a \verb|write| function that lets you do exactly this. It takes as its arguments an iterable returning \verb|QueryResult| objects, the output filename to write to, the format name to write to, and optionally some format-specific keyword arguments. It returns a four-item tuple, which denotes the number or \verb|QueryResult|, \verb|Hit|, \verb|HSP|, and \verb|HSPFragment| objects that were written. \begin{verbatim} >>> from Bio import SearchIO >>> qresults = SearchIO.parse('mirna.xml', 'blast-xml') # read XML file >>> SearchIO.write(qresults, 'results.tab', 'blast-tab') # write to tabular file (3, 239, 277, 277) \end{verbatim} You should note different file formats require different attributes of the \verb|QueryResult|, \verb|Hit|, \verb|HSP| and \verb|HSPFragment| objects. If these attributes are not present, writing won't work. In other words, you can't always write to the output format that you want. For example, if you read a BLAST XML file, you wouldn't be able to write the results to a PSL file as PSL files require attributes not calculated by BLAST (e.g. the number of repeat matches). You can always set these attributes manually, if you really want to write to PSL, though. Like \verb|read|, \verb|parse|, \verb|index|, and \verb|index_db|, \verb|write| also accepts format-specific keyword arguments. Check out the documentation for a complete list of formats \verb|Bio.SearchIO| can write to and their arguments. Finally, \verb|Bio.SearchIO| also provides a \verb|convert| function, which is simply a shortcut for \verb|Bio.SearchIO.parse| and \verb|Bio.SearchIO.write|. Using the convert function, our example above would be: \begin{verbatim} >>> from Bio import SearchIO >>> SearchIO.convert('mirna.xml', 'blast-xml', 'results.tab', 'blast-tab') (3, 239, 277, 277) \end{verbatim} As \verb|convert| uses \verb|write|, it is only limited to format conversions that have all the required attributes. Here, the BLAST XML file provides all the default values a BLAST tabular file requires, so it works just fine. However, other format conversions are less likely to work since you need to manually assign the required attributes first. \chapter{Accessing NCBI's Entrez databases} \label{chapter:entrez} Entrez (\url{http://www.ncbi.nlm.nih.gov/Entrez}) is a data retrieval system that provides users access to NCBI's databases such as PubMed, GenBank, GEO, and many others. You can access Entrez from a web browser to manually enter queries, or you can use Biopython's \verb+Bio.Entrez+ module for programmatic access to Entrez. The latter allows you for example to search PubMed or download GenBank records from within a Python script. The \verb+Bio.Entrez+ module makes use of the Entrez Programming Utilities (also known as EUtils), consisting of eight tools that are described in detail on NCBI's page at \url{http://www.ncbi.nlm.nih.gov/entrez/utils/}. Each of these tools corresponds to one Python function in the \verb+Bio.Entrez+ module, as described in the sections below. This module makes sure that the correct URL is used for the queries, and that not more than one request is made every three seconds, as required by NCBI. The output returned by the Entrez Programming Utilities is typically in XML format. To parse such output, you have several options: \begin{enumerate} \item Use \verb+Bio.Entrez+'s parser to parse the XML output into a Python object; \item Use the DOM (Document Object Model) parser in Python's standard library; \item Use the SAX (Simple API for XML) parser in Python's standard library; \item Read the XML output as raw text, and parse it by string searching and manipulation. \end{enumerate} For the DOM and SAX parsers, see the Python documentation. The parser in \verb+Bio.Entrez+ is discussed below. NCBI uses DTD (Document Type Definition) files to describe the structure of the information contained in XML files. Most of the DTD files used by NCBI are included in the Biopython distribution. The \verb+Bio.Entrez+ parser makes use of the DTD files when parsing an XML file returned by NCBI Entrez. Occasionally, you may find that the DTD file associated with a specific XML file is missing in the Biopython distribution. In particular, this may happen when NCBI updates its DTD files. If this happens, \verb+Entrez.read+ will show a warning message with the name and URL of the missing DTD file. The parser will proceed to access the missing DTD file through the internet, allowing the parsing of the XML file to continue. However, the parser is much faster if the DTD file is available locally. For this purpose, please download the DTD file from the URL in the warning message and place it in the directory \verb+...site-packages/Bio/Entrez/DTDs+, containing the other DTD files. If you don't have write access to this directory, you can also place the DTD file in \verb+~/.biopython/Bio/Entrez/DTDs+, where \verb+~+ represents your home directory. Since this directory is read before the directory \verb+...site-packages/Bio/Entrez/DTDs+, you can also put newer versions of DTD files there if the ones in \verb+...site-packages/Bio/Entrez/DTDs+ become outdated. Alternatively, if you installed Biopython from source, you can add the DTD file to the source code's \verb+Bio/Entrez/DTDs+ directory, and reinstall Biopython. This will install the new DTD file in the correct location together with the other DTD files. The Entrez Programming Utilities can also generate output in other formats, such as the Fasta or GenBank file formats for sequence databases, or the MedLine format for the literature database, discussed in Section~\ref{sec:entrez-specialized-parsers}. \section{Entrez Guidelines} \label{sec:entrez-guidelines} Before using Biopython to access the NCBI's online resources (via \verb|Bio.Entrez| or some of the other modules), please read the \href{http://www.ncbi.nlm.nih.gov/books/NBK25497/#chapter2.Usage_Guidelines_and_Requiremen}{NCBI's Entrez User Requirements}. If the NCBI finds you are abusing their systems, they can and will ban your access! To paraphrase: \begin{itemize} \item For any series of more than 100 requests, do this at weekends or outside USA peak times. This is up to you to obey. \item Use the \url{http://eutils.ncbi.nlm.nih.gov} address, not the standard NCBI Web address. Biopython uses this web address. \item Make no more than three requests every seconds (relaxed from at most one request every three seconds in early 2009). This is automatically enforced by Biopython. \item Use the optional email parameter so the NCBI can contact you if there is a problem. You can either explicitly set this as a parameter with each call to Entrez (e.g. include {\tt email="A.N.Other@example.com"} in the argument list), or you can set a global email address: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" \end{verbatim} {\tt Bio.Entrez} will then use this email address with each call to Entrez. The {\tt example.com} address is a reserved domain name specifically for documentation (RFC 2606). Please DO NOT use a random email -- it's better not to give an email at all. The email parameter will be mandatory from June 1, 2010. In case of excessive usage, NCBI will attempt to contact a user at the e-mail address provided prior to blocking access to the E-utilities. \item If you are using Biopython within some larger software suite, use the tool parameter to specify this. You can either explicitly set the tool name as a parameter with each call to Entrez (e.g. include {\tt tool="MyLocalScript"} in the argument list), or you can set a global tool name: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.tool = "MyLocalScript" \end{verbatim} The tool parameter will default to Biopython. \item For large queries, the NCBI also recommend using their session history feature (the WebEnv session cookie string, see Section~\ref{sec:entrez-webenv}). This is only slightly more complicated. \end{itemize} In conclusion, be sensible with your usage levels. If you plan to download lots of data, consider other options. For example, if you want easy access to all the human genes, consider fetching each chromosome by FTP as a GenBank file, and importing these into your own BioSQL database (see Section~\ref{sec:BioSQL}). \section{EInfo: Obtaining information about the Entrez databases} \label{sec:entrez-einfo} EInfo provides field index term counts, last update, and available links for each of NCBI's databases. In addition, you can use EInfo to obtain a list of all database names accessible through the Entrez utilities: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> handle = Entrez.einfo() >>> result = handle.read() \end{verbatim} The variable \verb+result+ now contains a list of databases in XML format: \begin{verbatim} >>> print(result) pubmed protein nucleotide nuccore nucgss nucest structure genome books cancerchromosomes cdd gap domains gene genomeprj gensat geo gds homologene journals mesh ncbisearch nlmcatalog omia omim pmc popset probe proteinclusters pcassay pccompound pcsubstance snp taxonomy toolkit unigene unists \end{verbatim} Since this is a fairly simple XML file, we could extract the information it contains simply by string searching. Using \verb+Bio.Entrez+'s parser instead, we can directly parse this XML file into a Python object: \begin{verbatim} >>> from Bio import Entrez >>> handle = Entrez.einfo() >>> record = Entrez.read(handle) \end{verbatim} Now \verb+record+ is a dictionary with exactly one key: \begin{verbatim} >>> record.keys() [u'DbList'] \end{verbatim} The values stored in this key is the list of database names shown in the XML above: \begin{verbatim} >>> record["DbList"] ['pubmed', 'protein', 'nucleotide', 'nuccore', 'nucgss', 'nucest', 'structure', 'genome', 'books', 'cancerchromosomes', 'cdd', 'gap', 'domains', 'gene', 'genomeprj', 'gensat', 'geo', 'gds', 'homologene', 'journals', 'mesh', 'ncbisearch', 'nlmcatalog', 'omia', 'omim', 'pmc', 'popset', 'probe', 'proteinclusters', 'pcassay', 'pccompound', 'pcsubstance', 'snp', 'taxonomy', 'toolkit', 'unigene', 'unists'] \end{verbatim} For each of these databases, we can use EInfo again to obtain more information: \begin{verbatim} >>> handle = Entrez.einfo(db="pubmed") >>> record = Entrez.read(handle) >>> record["DbInfo"]["Description"] 'PubMed bibliographic record' >>> record["DbInfo"]["Count"] '17989604' >>> record["DbInfo"]["LastUpdate"] '2008/05/24 06:45' \end{verbatim} Try \verb+record["DbInfo"].keys()+ for other information stored in this record. One of the most useful is a list of possible search fields for use with ESearch: \begin{verbatim} >>> for field in record["DbInfo"]["FieldList"]: ... print("%(Name)s, %(FullName)s, %(Description)s" % field) ALL, All Fields, All terms from all searchable fields UID, UID, Unique number assigned to publication FILT, Filter, Limits the records TITL, Title, Words in title of publication WORD, Text Word, Free text associated with publication MESH, MeSH Terms, Medical Subject Headings assigned to publication MAJR, MeSH Major Topic, MeSH terms of major importance to publication AUTH, Author, Author(s) of publication JOUR, Journal, Journal abbreviation of publication AFFL, Affiliation, Author's institutional affiliation and address ... \end{verbatim} That's a long list, but indirectly this tells you that for the PubMed database, you can do things like \texttt{Jones[AUTH]} to search the author field, or \texttt{Sanger[AFFL]} to restrict to authors at the Sanger Centre. This can be very handy - especially if you are not so familiar with a particular database. \section{ESearch: Searching the Entrez databases} \label{sec:entrez-esearch} To search any of these databases, we use \verb+Bio.Entrez.esearch()+. For example, let's search in PubMed for publications related to Biopython: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> handle = Entrez.esearch(db="pubmed", term="biopython") >>> record = Entrez.read(handle) >>> record["IdList"] ['19304878', '18606172', '16403221', '16377612', '14871861', '14630660', '12230038'] \end{verbatim} In this output, you see seven PubMed IDs (including 19304878 which is the PMID for the Biopython application note), which can be retrieved by EFetch (see section \ref{sec:efetch}). You can also use ESearch to search GenBank. Here we'll do a quick search for the \emph{matK} gene in \emph{Cypripedioideae} orchids (see Section~\ref{sec:entrez-einfo} about EInfo for one way to find out which fields you can search in each Entrez database): \begin{verbatim} >>> handle = Entrez.esearch(db="nucleotide", term="Cypripedioideae[Orgn] AND matK[Gene]") >>> record = Entrez.read(handle) >>> record["Count"] '25' >>> record["IdList"] ['126789333', '37222967', '37222966', '37222965', ..., '61585492'] \end{verbatim} \noindent Each of the IDs (126789333, 37222967, 37222966, \ldots) is a GenBank identifier. See section~\ref{sec:efetch} for information on how to actually download these GenBank records. Note that instead of a species name like \texttt{Cypripedioideae[Orgn]}, you can restrict the search using an NCBI taxon identifier, here this would be \texttt{txid158330[Orgn]}. This isn't currently documented on the ESearch help page - the NCBI explained this in reply to an email query. You can often deduce the search term formatting by playing with the Entrez web interface. For example, including \texttt{complete[prop]} in a genome search restricts to just completed genomes. As a final example, let's get a list of computational journal titles: \begin{verbatim} >>> handle = Entrez.esearch(db="journals", term="computational") >>> record = Entrez.read(handle) >>> record["Count"] '16' >>> record["IdList"] ['30367', '33843', '33823', '32989', '33190', '33009', '31986', '34502', '8799', '22857', '32675', '20258', '33859', '32534', '32357', '32249'] \end{verbatim} Again, we could use EFetch to obtain more information for each of these journal IDs. ESearch has many useful options --- see the \href{http://www.ncbi.nlm.nih.gov/entrez/query/static/esearch\_help.html}{ESearch help page} for more information. \section{EPost: Uploading a list of identifiers} EPost uploads a list of UIs for use in subsequent search strategies; see the \href{http://www.ncbi.nlm.nih.gov/entrez/query/static/epost\_help.html}{EPost help page} for more information. It is available from Biopython through the \verb+Bio.Entrez.epost()+ function. To give an example of when this is useful, suppose you have a long list of IDs you want to download using EFetch (maybe sequences, maybe citations -- anything). When you make a request with EFetch your list of IDs, the database etc, are all turned into a long URL sent to the server. If your list of IDs is long, this URL gets long, and long URLs can break (e.g. some proxies don't cope well). Instead, you can break this up into two steps, first uploading the list of IDs using EPost (this uses an ``HTML post'' internally, rather than an ``HTML get'', getting round the long URL problem). With the history support, you can then refer to this long list of IDs, and download the associated data with EFetch. Let's look at a simple example to see how EPost works -- uploading some PubMed identifiers: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> id_list = ["19304878", "18606172", "16403221", "16377612", "14871861", "14630660"] >>> print(Entrez.epost("pubmed", id=",".join(id_list)).read()) 1 NCID_01_206841095_130.14.22.101_9001_1242061629 \end{verbatim} \noindent The returned XML includes two important strings, \verb|QueryKey| and \verb|WebEnv| which together define your history session. You would extract these values for use with another Entrez call such as EFetch: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> id_list = ["19304878", "18606172", "16403221", "16377612", "14871861", "14630660"] >>> search_results = Entrez.read(Entrez.epost("pubmed", id=",".join(id_list))) >>> webenv = search_results["WebEnv"] >>> query_key = search_results["QueryKey"] \end{verbatim} \noindent Section~\ref{sec:entrez-webenv} shows how to use the history feature. \section{ESummary: Retrieving summaries from primary IDs} ESummary retrieves document summaries from a list of primary IDs (see the \href{http://www.ncbi.nlm.nih.gov/entrez/query/static/esummary\_help.html}{ESummary help page} for more information). In Biopython, ESummary is available as \verb+Bio.Entrez.esummary()+. Using the search result above, we can for example find out more about the journal with ID 30367: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> handle = Entrez.esummary(db="journals", id="30367") >>> record = Entrez.read(handle) >>> record[0]["Id"] '30367' >>> record[0]["Title"] 'Computational biology and chemistry' >>> record[0]["Publisher"] 'Pergamon,' \end{verbatim} \section{EFetch: Downloading full records from Entrez} \label{sec:efetch} EFetch is what you use when you want to retrieve a full record from Entrez. This covers several possible databases, as described on the main \href{http://eutils.ncbi.nlm.nih.gov/entrez/query/static/efetch_help.html}{EFetch Help page}. For most of their databases, the NCBI support several different file formats. Requesting a specific file format from Entrez using \verb|Bio.Entrez.efetch()| requires specifying the \verb|rettype| and/or \verb|retmode| optional arguments. The different combinations are described for each database type on the pages linked to on \href{http://www.ncbi.nlm.nih.gov/entrez/query/static/efetch_help.html}{NCBI efetch webpage} (e.g. \href{http://eutils.ncbi.nlm.nih.gov/corehtml/query/static/efetchlit_help.html}{literature}, \href{http://eutils.ncbi.nlm.nih.gov/corehtml/query/static/efetchseq_help.html}{sequences} and \href{http://eutils.ncbi.nlm.nih.gov/corehtml/query/static/efetchtax_help.html}{taxonomy}). One common usage is downloading sequences in the FASTA or GenBank/GenPept plain text formats (which can then be parsed with \verb|Bio.SeqIO|, see Sections~\ref{sec:SeqIO_GenBank_Online} and~\ref{sec:efetch}). From the \emph{Cypripedioideae} example above, we can download GenBank record 186972394 using \verb+Bio.Entrez.efetch+: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> handle = Entrez.efetch(db="nucleotide", id="186972394", rettype="gb", retmode="text") >>> print(handle.read()) LOCUS EU490707 1302 bp DNA linear PLN 05-MAY-2008 DEFINITION Selenipedium aequinoctiale maturase K (matK) gene, partial cds; chloroplast. ACCESSION EU490707 VERSION EU490707.1 GI:186972394 KEYWORDS . SOURCE chloroplast Selenipedium aequinoctiale ORGANISM Selenipedium aequinoctiale Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta; Spermatophyta; Magnoliophyta; Liliopsida; Asparagales; Orchidaceae; Cypripedioideae; Selenipedium. REFERENCE 1 (bases 1 to 1302) AUTHORS Neubig,K.M., Whitten,W.M., Carlsward,B.S., Blanco,M.A., Endara,C.L., Williams,N.H. and Moore,M.J. TITLE Phylogenetic utility of ycf1 in orchids JOURNAL Unpublished REFERENCE 2 (bases 1 to 1302) AUTHORS Neubig,K.M., Whitten,W.M., Carlsward,B.S., Blanco,M.A., Endara,C.L., Williams,N.H. and Moore,M.J. TITLE Direct Submission JOURNAL Submitted (14-FEB-2008) Department of Botany, University of Florida, 220 Bartram Hall, Gainesville, FL 32611-8526, USA FEATURES Location/Qualifiers source 1..1302 /organism="Selenipedium aequinoctiale" /organelle="plastid:chloroplast" /mol_type="genomic DNA" /specimen_voucher="FLAS:Blanco 2475" /db_xref="taxon:256374" gene <1..>1302 /gene="matK" CDS <1..>1302 /gene="matK" /codon_start=1 /transl_table=11 /product="maturase K" /protein_id="ACC99456.1" /db_xref="GI:186972395" /translation="IFYEPVEIFGYDNKSSLVLVKRLITRMYQQNFLISSVNDSNQKG FWGHKHFFSSHFSSQMVSEGFGVILEIPFSSQLVSSLEEKKIPKYQNLRSIHSIFPFL EDKFLHLNYVSDLLIPHPIHLEILVQILQCRIKDVPSLHLLRLLFHEYHNLNSLITSK KFIYAFSKRKKRFLWLLYNSYVYECEYLFQFLRKQSSYLRSTSSGVFLERTHLYVKIE HLLVVCCNSFQRILCFLKDPFMHYVRYQGKAILASKGTLILMKKWKFHLVNFWQSYFH FWSQPYRIHIKQLSNYSFSFLGYFSSVLENHLVVRNQMLENSFIINLLTKKFDTIAPV ISLIGSLSKAQFCTVLGHPISKPIWTDFSDSDILDRFCRICRNLCRYHSGSSKKQVLY RIKYILRLSCARTLARKHKSTVRTFMRRLGSGLLEEFFMEEE" ORIGIN 1 attttttacg aacctgtgga aatttttggt tatgacaata aatctagttt agtacttgtg 61 aaacgtttaa ttactcgaat gtatcaacag aattttttga tttcttcggt taatgattct 121 aaccaaaaag gattttgggg gcacaagcat tttttttctt ctcatttttc ttctcaaatg 181 gtatcagaag gttttggagt cattctggaa attccattct cgtcgcaatt agtatcttct 241 cttgaagaaa aaaaaatacc aaaatatcag aatttacgat ctattcattc aatatttccc 301 tttttagaag acaaattttt acatttgaat tatgtgtcag atctactaat accccatccc 361 atccatctgg aaatcttggt tcaaatcctt caatgccgga tcaaggatgt tccttctttg 421 catttattgc gattgctttt ccacgaatat cataatttga atagtctcat tacttcaaag 481 aaattcattt acgccttttc aaaaagaaag aaaagattcc tttggttact atataattct 541 tatgtatatg aatgcgaata tctattccag tttcttcgta aacagtcttc ttatttacga 601 tcaacatctt ctggagtctt tcttgagcga acacatttat atgtaaaaat agaacatctt 661 ctagtagtgt gttgtaattc ttttcagagg atcctatgct ttctcaagga tcctttcatg 721 cattatgttc gatatcaagg aaaagcaatt ctggcttcaa agggaactct tattctgatg 781 aagaaatgga aatttcatct tgtgaatttt tggcaatctt attttcactt ttggtctcaa 841 ccgtatagga ttcatataaa gcaattatcc aactattcct tctcttttct ggggtatttt 901 tcaagtgtac tagaaaatca tttggtagta agaaatcaaa tgctagagaa ttcatttata 961 ataaatcttc tgactaagaa attcgatacc atagccccag ttatttctct tattggatca 1021 ttgtcgaaag ctcaattttg tactgtattg ggtcatccta ttagtaaacc gatctggacc 1081 gatttctcgg attctgatat tcttgatcga ttttgccgga tatgtagaaa tctttgtcgt 1141 tatcacagcg gatcctcaaa aaaacaggtt ttgtatcgta taaaatatat acttcgactt 1201 tcgtgtgcta gaactttggc acggaaacat aaaagtacag tacgcacttt tatgcgaaga 1261 ttaggttcgg gattattaga agaattcttt atggaagaag aa // \end{verbatim} The arguments \verb+rettype="gb"+ and \verb+retmode="text"+ let us download this record in the GenBank format. Note that until Easter 2009, the Entrez EFetch API let you use ``genbank'' as the return type, however the NCBI now insist on using the official return types of ``gb'' or ``gbwithparts'' (or ``gp'' for proteins) as described on online. Also not that until Feb 2012, the Entrez EFetch API would default to returning plain text files, but now defaults to XML. Alternatively, you could for example use \verb+rettype="fasta"+ to get the Fasta-format; see the \href{http://www.ncbi.nlm.nih.gov/entrez/query/static/efetchseq\_help.html}{EFetch Sequences Help page} for other options. Remember -- the available formats depend on which database you are downloading from - see the main \href{http://eutils.ncbi.nlm.nih.gov/entrez/query/static/efetch\_help.html}{EFetch Help page}. If you fetch the record in one of the formats accepted by \verb+Bio.SeqIO+ (see Chapter~\ref{chapter:Bio.SeqIO}), you could directly parse it into a \verb+SeqRecord+: \begin{verbatim} >>> from Bio import Entrez, SeqIO >>> handle = Entrez.efetch(db="nucleotide", id="186972394", rettype="gb", retmode="text") >>> record = SeqIO.read(handle, "genbank") >>> handle.close() >>> print(record) ID: EU490707.1 Name: EU490707 Description: Selenipedium aequinoctiale maturase K (matK) gene, partial cds; chloroplast. Number of features: 3 ... Seq('ATTTTTTACGAACCTGTGGAAATTTTTGGTTATGACAATAAATCTAGTTTAGTA...GAA', IUPACAmbiguousDNA()) \end{verbatim} Note that a more typical use would be to save the sequence data to a local file, and \emph{then} parse it with \verb|Bio.SeqIO|. This can save you having to re-download the same file repeatedly while working on your script, and places less load on the NCBI's servers. For example: \begin{verbatim} import os from Bio import SeqIO from Bio import Entrez Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are filename = "gi_186972394.gbk" if not os.path.isfile(filename): # Downloading... net_handle = Entrez.efetch(db="nucleotide",id="186972394",rettype="gb", retmode="text") out_handle = open(filename, "w") out_handle.write(net_handle.read()) out_handle.close() net_handle.close() print("Saved") print("Parsing...") record = SeqIO.read(filename, "genbank") print(record) \end{verbatim} To get the output in XML format, which you can parse using the \verb+Bio.Entrez.read()+ function, use \verb+retmode="xml"+: \begin{verbatim} >>> from Bio import Entrez >>> handle = Entrez.efetch(db="nucleotide", id="186972394", retmode="xml") >>> record = Entrez.read(handle) >>> handle.close() >>> record[0]["GBSeq_definition"] 'Selenipedium aequinoctiale maturase K (matK) gene, partial cds; chloroplast' >>> record[0]["GBSeq_source"] 'chloroplast Selenipedium aequinoctiale' \end{verbatim} So, that dealt with sequences. For examples of parsing file formats specific to the other databases (e.g. the \verb+MEDLINE+ format used in PubMed), see Section~\ref{sec:entrez-specialized-parsers}. If you want to perform a search with \verb|Bio.Entrez.esearch()|, and then download the records with \verb|Bio.Entrez.efetch()|, you should use the WebEnv history feature -- see Section~\ref{sec:entrez-webenv}. \section{ELink: Searching for related items in NCBI Entrez} \label{sec:elink} ELink, available from Biopython as \verb+Bio.Entrez.elink()+, can be used to find related items in the NCBI Entrez databases. For example, you can us this to find nucleotide entries for an entry in the gene database, and other cool stuff. Let's use ELink to find articles related to the Biopython application note published in \textit{Bioinformatics} in 2009. The PubMed ID of this article is 19304878: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" >>> pmid = "19304878" >>> record = Entrez.read(Entrez.elink(dbfrom="pubmed", id=pmid)) \end{verbatim} The \verb+record+ variable consists of a Python list, one for each database in which we searched. Since we specified only one PubMed ID to search for, \verb+record+ contains only one item. This item is a dictionary containing information about our search term, as well as all the related items that were found: \begin{verbatim} >>> record[0]["DbFrom"] 'pubmed' >>> record[0]["IdList"] ['19304878'] \end{verbatim} The \verb+"LinkSetDb"+ key contains the search results, stored as a list consisting of one item for each target database. In our search results, we only find hits in the PubMed database (although sub-divided into categories): \begin{verbatim} >>> len(record[0]["LinkSetDb"]) 5 >>> for linksetdb in record[0]["LinkSetDb"]: ... print(linksetdb["DbTo"], linksetdb["LinkName"], len(linksetdb["Link"])) ... pubmed pubmed_pubmed 110 pubmed pubmed_pubmed_combined 6 pubmed pubmed_pubmed_five 6 pubmed pubmed_pubmed_reviews 5 pubmed pubmed_pubmed_reviews_five 5 \end{verbatim} The actual search results are stored as under the \verb+"Link"+ key. In total, 110 items were found under standard search. Let's now at the first search result: \begin{verbatim} >>> record[0]["LinkSetDb"][0]["Link"][0] {u'Id': '19304878'} \end{verbatim} \noindent This is the article we searched for, which doesn't help us much, so let's look at the second search result: \begin{verbatim} >>> record[0]["LinkSetDb"][0]["Link"][1] {u'Id': '14630660'} \end{verbatim} \noindent This paper, with PubMed ID 14630660, is about the Biopython PDB parser. We can use a loop to print out all PubMed IDs: \begin{verbatim} >>> for link in record[0]["LinkSetDb"][0]["Link"]: ... print(link["Id"]) 19304878 14630660 18689808 17121776 16377612 12368254 ...... \end{verbatim} Now that was nice, but personally I am often more interested to find out if a paper has been cited. Well, ELink can do that too -- at least for journals in Pubmed Central (see Section~\ref{sec:elink-citations}). For help on ELink, see the \href{http://www.ncbi.nlm.nih.gov/entrez/query/static/elink\_help.html}{ELink help page}. There is an entire sub-page just for the \href{http://eutils.ncbi.nlm.nih.gov/corehtml/query/static/entrezlinks.html}{link names}, describing how different databases can be cross referenced. \section{EGQuery: Global Query - counts for search terms} EGQuery provides counts for a search term in each of the Entrez databases (i.e. a global query). This is particularly useful to find out how many items your search terms would find in each database without actually performing lots of separate searches with ESearch (see the example in \ref{subsec:entrez_example_genbank} below). In this example, we use \verb+Bio.Entrez.egquery()+ to obtain the counts for ``Biopython'': \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> handle = Entrez.egquery(term="biopython") >>> record = Entrez.read(handle) >>> for row in record["eGQueryResult"]: ... print(row["DbName"], row["Count"]) ... pubmed 6 pmc 62 journals 0 ... \end{verbatim} See the \href{http://www.ncbi.nlm.nih.gov/entrez/query/static/egquery\_help.html}{EGQuery help page} for more information. \section{ESpell: Obtaining spelling suggestions} ESpell retrieves spelling suggestions. In this example, we use \verb+Bio.Entrez.espell()+ to obtain the correct spelling of Biopython: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> handle = Entrez.espell(term="biopythooon") >>> record = Entrez.read(handle) >>> record["Query"] 'biopythooon' >>> record["CorrectedQuery"] 'biopython' \end{verbatim} See the \href{http://www.ncbi.nlm.nih.gov/entrez/query/static/espell\_help.html}{ESpell help page} for more information. The main use of this is for GUI tools to provide automatic suggestions for search terms. \section{Parsing huge Entrez XML files} The \verb+Entrez.read+ function reads the entire XML file returned by Entrez into a single Python object, which is kept in memory. To parse Entrez XML files too large to fit in memory, you can use the function \verb+Entrez.parse+. This is a generator function that reads records in the XML file one by one. This function is only useful if the XML file reflects a Python list object (in other words, if \verb+Entrez.read+ on a computer with infinite memory resources would return a Python list). For example, you can download the entire Entrez Gene database for a given organism as a file from NCBI's ftp site. These files can be very large. As an example, on September 4, 2009, the file \verb+Homo_sapiens.ags.gz+, containing the Entrez Gene database for human, had a size of 116576 kB. This file, which is in the \verb+ASN+ format, can be converted into an XML file using NCBI's \verb+gene2xml+ program (see NCBI's ftp site for more information): \begin{verbatim} gene2xml -b T -i Homo_sapiens.ags -o Homo_sapiens.xml \end{verbatim} The resulting XML file has a size of 6.1 GB. Attempting \verb+Entrez.read+ on this file will result in a \verb+MemoryError+ on many computers. The XML file \verb+Homo_sapiens.xml+ consists of a list of Entrez gene records, each corresponding to one Entrez gene in human. \verb+Entrez.parse+ retrieves these gene records one by one. You can then print out or store the relevant information in each record by iterating over the records. For example, this script iterates over the Entrez gene records and prints out the gene numbers and names for all current genes: \begin{verbatim} >>> from Bio import Entrez >>> handle = open("Homo_sapiens.xml") >>> records = Entrez.parse(handle) >>> for record in records: ... status = record['Entrezgene_track-info']['Gene-track']['Gene-track_status'] ... if status.attributes['value']=='discontinued': ... continue ... geneid = record['Entrezgene_track-info']['Gene-track']['Gene-track_geneid'] ... genename = record['Entrezgene_gene']['Gene-ref']['Gene-ref_locus'] ... print(geneid, genename) \end{verbatim} This will print: \begin{verbatim} 1 A1BG 2 A2M 3 A2MP 8 AA 9 NAT1 10 NAT2 11 AACP 12 SERPINA3 13 AADAC 14 AAMP 15 AANAT 16 AARS 17 AAVS1 ... \end{verbatim} \section{Handling errors} Three things can go wrong when parsing an XML file: \begin{itemize} \item The file may not be an XML file to begin with; \item The file may end prematurely or otherwise be corrupted; \item The file may be correct XML, but contain items that are not represented in the associated DTD. \end{itemize} The first case occurs if, for example, you try to parse a Fasta file as if it were an XML file: \begin{verbatim} >>> from Bio import Entrez >>> handle = open("NC_005816.fna") # a Fasta file >>> record = Entrez.read(handle) Traceback (most recent call last): File "", line 1, in File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/__init__.py", line 257, in read record = handler.read(handle) File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 164, in read raise NotXMLError(e) Bio.Entrez.Parser.NotXMLError: Failed to parse the XML data (syntax error: line 1, column 0). Please make sure that the input data are in XML format. \end{verbatim} Here, the parser didn't find the \verb| pubmed protein nucleotide nuccore nucgss nucest structure genome books cancerchromosomes cdd \end{verbatim} which will generate the following traceback: \begin{verbatim} >>> Entrez.read(handle) Traceback (most recent call last): File "", line 1, in File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/__init__.py", line 257, in read record = handler.read(handle) File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 160, in read raise CorruptedXMLError(e) Bio.Entrez.Parser.CorruptedXMLError: Failed to parse the XML data (no element found: line 16, column 0). Please make sure that the input data are not corrupted. >>> \end{verbatim} Note that the error message tells you at what point in the XML file the error was detected. The third type of error occurs if the XML file contains tags that do not have a description in the corresponding DTD file. This is an example of such an XML file: \begin{verbatim} pubmed PubMed PubMed bibliographic record 20161961 2010/09/10 04:52 ... PubDate 4 string EPubDate ... \end{verbatim} In this file, for some reason the tag \verb|| (and several others) are not listed in the DTD file \verb|eInfo_020511.dtd|, which is specified on the second line as the DTD for this XML file. By default, the parser will stop and raise a ValidationError if it cannot find some tag in the DTD: \begin{verbatim} >>> from Bio import Entrez >>> handle = open("einfo3.xml") >>> record = Entrez.read(handle) Traceback (most recent call last): File "", line 1, in File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/__init__.py", line 257, in read record = handler.read(handle) File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 154, in read self.parser.ParseFile(handle) File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 246, in startElementHandler raise ValidationError(name) Bio.Entrez.Parser.ValidationError: Failed to find tag 'DocsumList' in the DTD. To skip all tags that are not represented in the DTD, please call Bio.Entrez.read or Bio.Entrez.parse with validate=False. \end{verbatim} Optionally, you can instruct the parser to skip such tags instead of raising a ValidationError. This is done by calling \verb|Entrez.read| or \verb|Entrez.parse| with the argument \verb|validate| equal to False: \begin{verbatim} >>> from Bio import Entrez >>> handle = open("einfo3.xml") >>> record = Entrez.read(handle, validate=False) >>> \end{verbatim} Of course, the information contained in the XML tags that are not in the DTD are not present in the record returned by \verb|Entrez.read|. \section{Specialized parsers} \label{sec:entrez-specialized-parsers} The \verb|Bio.Entrez.read()| function can parse most (if not all) XML output returned by Entrez. Entrez typically allows you to retrieve records in other formats, which may have some advantages compared to the XML format in terms of readability (or download size). To request a specific file format from Entrez using \verb|Bio.Entrez.efetch()| requires specifying the \verb|rettype| and/or \verb|retmode| optional arguments. The different combinations are described for each database type on the \href{http://www.ncbi.nlm.nih.gov/entrez/query/static/efetch_help.html}{NCBI efetch webpage}. One obvious case is you may prefer to download sequences in the FASTA or GenBank/GenPept plain text formats (which can then be parsed with \verb|Bio.SeqIO|, see Sections~\ref{sec:SeqIO_GenBank_Online} and~\ref{sec:efetch}). For the literature databases, Biopython contains a parser for the \verb+MEDLINE+ format used in PubMed. \subsection{Parsing Medline records} \label{subsec:entrez-and-medline} You can find the Medline parser in \verb+Bio.Medline+. Suppose we want to parse the file \verb+pubmed_result1.txt+, containing one Medline record. You can find this file in Biopython's \verb+Tests\Medline+ directory. The file looks like this: \begin{verbatim} PMID- 12230038 OWN - NLM STAT- MEDLINE DA - 20020916 DCOM- 20030606 LR - 20041117 PUBM- Print IS - 1467-5463 (Print) VI - 3 IP - 3 DP - 2002 Sep TI - The Bio* toolkits--a brief overview. PG - 296-302 AB - Bioinformatics research is often difficult to do with commercial software. The Open Source BioPerl, BioPython and Biojava projects provide toolkits with ... \end{verbatim} We first open the file and then parse it: %doctest ../Tests/Medline \begin{verbatim} >>> from Bio import Medline >>> input = open("pubmed_result1.txt") >>> record = Medline.read(input) \end{verbatim} The \verb+record+ now contains the Medline record as a Python dictionary: %cont-doctest \begin{verbatim} >>> record["PMID"] '12230038' \end{verbatim} %TODO - doctest wrapping? \begin{verbatim} >>> record["AB"] 'Bioinformatics research is often difficult to do with commercial software. The Open Source BioPerl, BioPython and Biojava projects provide toolkits with multiple functionality that make it easier to create customised pipelines or analysis. This review briefly compares the quirks of the underlying languages and the functionality, documentation, utility and relative advantages of the Bio counterparts, particularly from the point of view of the beginning biologist programmer.' \end{verbatim} The key names used in a Medline record can be rather obscure; use \begin{verbatim} >>> help(record) \end{verbatim} for a brief summary. To parse a file containing multiple Medline records, you can use the \verb+parse+ function instead: %doctest ../Tests/Medline \begin{verbatim} >>> from Bio import Medline >>> input = open("pubmed_result2.txt") >>> records = Medline.parse(input) >>> for record in records: ... print(record["TI"]) A high level interface to SCOP and ASTRAL implemented in python. GenomeDiagram: a python package for the visualization of large-scale genomic data. Open source clustering software. PDB file parser and structure class implemented in Python. \end{verbatim} Instead of parsing Medline records stored in files, you can also parse Medline records downloaded by \verb+Bio.Entrez.efetch+. For example, let's look at all Medline records in PubMed related to Biopython: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> handle = Entrez.esearch(db="pubmed", term="biopython") >>> record = Entrez.read(handle) >>> record["IdList"] ['19304878', '18606172', '16403221', '16377612', '14871861', '14630660', '12230038'] \end{verbatim} We now use \verb+Bio.Entrez.efetch+ to download these Medline records: \begin{verbatim} >>> idlist = record["IdList"] >>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline", retmode="text") \end{verbatim} Here, we specify \verb+rettype="medline", retmode="text"+ to obtain the Medline records in plain-text Medline format. Now we use \verb+Bio.Medline+ to parse these records: \begin{verbatim} >>> from Bio import Medline >>> records = Medline.parse(handle) >>> for record in records: ... print(record["AU"]) ['Cock PJ', 'Antao T', 'Chang JT', 'Chapman BA', 'Cox CJ', 'Dalke A', ..., 'de Hoon MJ'] ['Munteanu CR', 'Gonzalez-Diaz H', 'Magalhaes AL'] ['Casbon JA', 'Crooks GE', 'Saqi MA'] ['Pritchard L', 'White JA', 'Birch PR', 'Toth IK'] ['de Hoon MJ', 'Imoto S', 'Nolan J', 'Miyano S'] ['Hamelryck T', 'Manderick B'] ['Mangalam H'] \end{verbatim} For comparison, here we show an example using the XML format: \begin{verbatim} >>> idlist = record["IdList"] >>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline", retmode="xml") >>> records = Entrez.read(handle) >>> for record in records: ... print(record["MedlineCitation"]["Article"]["ArticleTitle"]) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices. A high level interface to SCOP and ASTRAL implemented in python. GenomeDiagram: a python package for the visualization of large-scale genomic data. Open source clustering software. PDB file parser and structure class implemented in Python. The Bio* toolkits--a brief overview. \end{verbatim} Note that in both of these examples, for simplicity we have naively combined ESearch and EFetch. In this situation, the NCBI would expect you to use their history feature, as illustrated in Section~\ref{sec:entrez-webenv}. \subsection{Parsing GEO records} GEO (\href{http://www.ncbi.nlm.nih.gov/geo/}{Gene Expression Omnibus}) is a data repository of high-throughput gene expression and hybridization array data. The \verb|Bio.Geo| module can be used to parse GEO-formatted data. The following code fragment shows how to parse the example GEO file \verb|GSE16.txt| into a record and print the record: \begin{verbatim} >>> from Bio import Geo >>> handle = open("GSE16.txt") >>> records = Geo.parse(handle) >>> for record in records: ... print(record) \end{verbatim} You can search the ``gds'' database (GEO datasets) with ESearch: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> handle = Entrez.esearch(db="gds", term="GSE16") >>> record = Entrez.read(handle) >>> record["Count"] 2 >>> record["IdList"] ['200000016', '100000028'] \end{verbatim} From the Entrez website, UID ``200000016'' is GDS16 while the other hit ``100000028'' is for the associated platform, GPL28. Unfortunately, at the time of writing the NCBI don't seem to support downloading GEO files using Entrez (not as XML, nor in the \textit{Simple Omnibus Format in Text} (SOFT) format). However, it is actually pretty straight forward to download the GEO files by FTP from \url{ftp://ftp.ncbi.nih.gov/pub/geo/} instead. In this case you might want \url{ftp://ftp.ncbi.nih.gov/pub/geo/DATA/SOFT/by_series/GSE16/GSE16_family.soft.gz} (a compressed file, see the Python module gzip). \subsection{Parsing UniGene records} UniGene is an NCBI database of the transcriptome, with each UniGene record showing the set of transcripts that are associated with a particular gene in a specific organism. A typical UniGene record looks like this: \begin{verbatim} ID Hs.2 TITLE N-acetyltransferase 2 (arylamine N-acetyltransferase) GENE NAT2 CYTOBAND 8p22 GENE_ID 10 LOCUSLINK 10 HOMOL YES EXPRESS bone| connective tissue| intestine| liver| liver tumor| normal| soft tissue/muscle tissue tumor| adult RESTR_EXPR adult CHROMOSOME 8 STS ACC=PMC310725P3 UNISTS=272646 STS ACC=WIAF-2120 UNISTS=44576 STS ACC=G59899 UNISTS=137181 ... STS ACC=GDB:187676 UNISTS=155563 PROTSIM ORG=10090; PROTGI=6754794; PROTID=NP_035004.1; PCT=76.55; ALN=288 PROTSIM ORG=9796; PROTGI=149742490; PROTID=XP_001487907.1; PCT=79.66; ALN=288 PROTSIM ORG=9986; PROTGI=126722851; PROTID=NP_001075655.1; PCT=76.90; ALN=288 ... PROTSIM ORG=9598; PROTGI=114619004; PROTID=XP_519631.2; PCT=98.28; ALN=288 SCOUNT 38 SEQUENCE ACC=BC067218.1; NID=g45501306; PID=g45501307; SEQTYPE=mRNA SEQUENCE ACC=NM_000015.2; NID=g116295259; PID=g116295260; SEQTYPE=mRNA SEQUENCE ACC=D90042.1; NID=g219415; PID=g219416; SEQTYPE=mRNA SEQUENCE ACC=D90040.1; NID=g219411; PID=g219412; SEQTYPE=mRNA SEQUENCE ACC=BC015878.1; NID=g16198419; PID=g16198420; SEQTYPE=mRNA SEQUENCE ACC=CR407631.1; NID=g47115198; PID=g47115199; SEQTYPE=mRNA SEQUENCE ACC=BG569293.1; NID=g13576946; CLONE=IMAGE:4722596; END=5'; LID=6989; SEQTYPE=EST; TRACE=44157214 ... SEQUENCE ACC=AU099534.1; NID=g13550663; CLONE=HSI08034; END=5'; LID=8800; SEQTYPE=EST // \end{verbatim} This particular record shows the set of transcripts (shown in the \verb+SEQUENCE+ lines) that originate from the human gene NAT2, encoding en N-acetyltransferase. The \verb+PROTSIM+ lines show proteins with significant similarity to NAT2, whereas the \verb+STS+ lines show the corresponding sequence-tagged sites in the genome. To parse UniGene files, use the \verb+Bio.UniGene+ module: \begin{verbatim} >>> from Bio import UniGene >>> input = open("myunigenefile.data") >>> record = UniGene.read(input) \end{verbatim} The \verb+record+ returned by \verb+UniGene.read+ is a Python object with attributes corresponding to the fields in the UniGene record. For example, \begin{verbatim} >>> record.ID "Hs.2" >>> record.title "N-acetyltransferase 2 (arylamine N-acetyltransferase)" \end{verbatim} The \verb+EXPRESS+ and \verb+RESTR_EXPR+ lines are stored as Python lists of strings: \begin{verbatim} ['bone', 'connective tissue', 'intestine', 'liver', 'liver tumor', 'normal', 'soft tissue/muscle tissue tumor', 'adult'] \end{verbatim} Specialized objects are returned for the \verb+STS+, \verb+PROTSIM+, and \verb+SEQUENCE+ lines, storing the keys shown in each line as attributes: \begin{verbatim} >>> record.sts[0].acc 'PMC310725P3' >>> record.sts[0].unists '272646' \end{verbatim} and similarly for the \verb+PROTSIM+ and \verb+SEQUENCE+ lines. To parse a file containing more than one UniGene record, use the \verb+parse+ function in \verb+Bio.UniGene+: \begin{verbatim} >>> from Bio import UniGene >>> input = open("unigenerecords.data") >>> records = UniGene.parse(input) >>> for record in records: ... print(record.ID) \end{verbatim} \section{Using a proxy} Normally you won't have to worry about using a proxy, but if this is an issue on your network here is how to deal with it. Internally, \verb|Bio.Entrez| uses the standard Python library \verb|urllib| for accessing the NCBI servers. This will check an environment variable called \verb|http_proxy| to configure any simple proxy automatically. Unfortunately this module does not support the use of proxies which require authentication. You may choose to set the \verb|http_proxy| environment variable once (how you do this will depend on your operating system). Alternatively you can set this within Python at the start of your script, for example: \begin{verbatim} import os os.environ["http_proxy"] = "http://proxyhost.example.com:8080" \end{verbatim} \noindent See the \href{http://www.python.org/doc/lib/module-urllib.html} {urllib documentation} for more details. \section{Examples} \label{sec:entrez_examples} \subsection{PubMed and Medline} \label{subsec:pub_med} If you are in the medical field or interested in human issues (and many times even if you are not!), PubMed (\url{http://www.ncbi.nlm.nih.gov/PubMed/}) is an excellent source of all kinds of goodies. So like other things, we'd like to be able to grab information from it and use it in Python scripts. In this example, we will query PubMed for all articles having to do with orchids (see section~\ref{sec:orchids} for our motivation). We first check how many of such articles there are: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> handle = Entrez.egquery(term="orchid") >>> record = Entrez.read(handle) >>> for row in record["eGQueryResult"]: ... if row["DbName"]=="pubmed": ... print(row["Count"]) 463 \end{verbatim} Now we use the \verb+Bio.Entrez.efetch+ function to download the PubMed IDs of these 463 articles: \begin{verbatim} >>> handle = Entrez.esearch(db="pubmed", term="orchid", retmax=463) >>> record = Entrez.read(handle) >>> idlist = record["IdList"] >>> print(idlist) \end{verbatim} This returns a Python list containing all of the PubMed IDs of articles related to orchids: \begin{verbatim} ['18680603', '18665331', '18661158', '18627489', '18627452', '18612381', '18594007', '18591784', '18589523', '18579475', '18575811', '18575690', ... \end{verbatim} Now that we've got them, we obviously want to get the corresponding Medline records and extract the information from them. Here, we'll download the Medline records in the Medline flat-file format, and use the \verb+Bio.Medline+ module to parse them: \begin{verbatim} >>> from Bio import Medline >>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline", retmode="text") >>> records = Medline.parse(handle) \end{verbatim} NOTE - We've just done a separate search and fetch here, the NCBI much prefer you to take advantage of their history support in this situation. See Section~\ref{sec:entrez-webenv}. Keep in mind that \verb+records+ is an iterator, so you can iterate through the records only once. If you want to save the records, you can convert them to a list: \begin{verbatim} >>> records = list(records) \end{verbatim} Let's now iterate over the records to print out some information about each record: %TODO - Replace the print blank line with print()? \begin{verbatim} >>> for record in records: ... print("title:", record.get("TI", "?")) ... print("authors:", record.get("AU", "?")) ... print("source:", record.get("SO", "?")) ... print("") \end{verbatim} The output for this looks like: \begin{verbatim} title: Sex pheromone mimicry in the early spider orchid (ophrys sphegodes): patterns of hydrocarbons as the key mechanism for pollination by sexual deception [In Process Citation] authors: ['Schiestl FP', 'Ayasse M', 'Paulus HF', 'Lofstedt C', 'Hansson BS', 'Ibarra F', 'Francke W'] source: J Comp Physiol [A] 2000 Jun;186(6):567-74 \end{verbatim} Especially interesting to note is the list of authors, which is returned as a standard Python list. This makes it easy to manipulate and search using standard Python tools. For instance, we could loop through a whole bunch of entries searching for a particular author with code like the following: \begin{verbatim} >>> search_author = "Waits T" >>> for record in records: ... if not "AU" in record: ... continue ... if search_author in record["AU"]: ... print("Author %s found: %s" % (search_author, record["SO"])) \end{verbatim} Hopefully this section gave you an idea of the power and flexibility of the Entrez and Medline interfaces and how they can be used together. \subsection{Searching, downloading, and parsing Entrez Nucleotide records} \label{subsec:entrez_example_genbank} Here we'll show a simple example of performing a remote Entrez query. In section~\ref{sec:orchids} of the parsing examples, we talked about using NCBI's Entrez website to search the NCBI nucleotide databases for info on Cypripedioideae, our friends the lady slipper orchids. Now, we'll look at how to automate that process using a Python script. In this example, we'll just show how to connect, get the results, and parse them, with the Entrez module doing all of the work. First, we use EGQuery to find out the number of results we will get before actually downloading them. EGQuery will tell us how many search results were found in each of the databases, but for this example we are only interested in nucleotides: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> handle = Entrez.egquery(term="Cypripedioideae") >>> record = Entrez.read(handle) >>> for row in record["eGQueryResult"]: ... if row["DbName"]=="nuccore": ... print(row["Count"]) 814 \end{verbatim} So, we expect to find 814 Entrez Nucleotide records (this is the number I obtained in 2008; it is likely to increase in the future). If you find some ridiculously high number of hits, you may want to reconsider if you really want to download all of them, which is our next step: \begin{verbatim} >>> from Bio import Entrez >>> handle = Entrez.esearch(db="nucleotide", term="Cypripedioideae", retmax=814) >>> record = Entrez.read(handle) \end{verbatim} Here, \verb+record+ is a Python dictionary containing the search results and some auxiliary information. Just for information, let's look at what is stored in this dictionary: \begin{verbatim} >>> print(record.keys()) [u'Count', u'RetMax', u'IdList', u'TranslationSet', u'RetStart', u'QueryTranslation'] \end{verbatim} First, let's check how many results were found: \begin{verbatim} >>> print(record["Count"]) '814' \end{verbatim} which is the number we expected. The 814 results are stored in \verb+record['IdList']+: \begin{verbatim} >>> len(record["IdList"]) 814 \end{verbatim} Let's look at the first five results: \begin{verbatim} >>> record["IdList"][:5] ['187237168', '187372713', '187372690', '187372688', '187372686'] \end{verbatim} \label{sec:entrez-batched-efetch} We can download these records using \verb+efetch+. While you could download these records one by one, to reduce the load on NCBI's servers, it is better to fetch a bunch of records at the same time, shown below. However, in this situation you should ideally be using the history feature described later in Section~\ref{sec:entrez-webenv}. \begin{verbatim} >>> idlist = ",".join(record["IdList"][:5]) >>> print(idlist) 187237168,187372713,187372690,187372688,187372686 >>> handle = Entrez.efetch(db="nucleotide", id=idlist, retmode="xml") >>> records = Entrez.read(handle) >>> len(records) 5 \end{verbatim} Each of these records corresponds to one GenBank record. \begin{verbatim} >>> print(records[0].keys()) [u'GBSeq_moltype', u'GBSeq_source', u'GBSeq_sequence', u'GBSeq_primary-accession', u'GBSeq_definition', u'GBSeq_accession-version', u'GBSeq_topology', u'GBSeq_length', u'GBSeq_feature-table', u'GBSeq_create-date', u'GBSeq_other-seqids', u'GBSeq_division', u'GBSeq_taxonomy', u'GBSeq_references', u'GBSeq_update-date', u'GBSeq_organism', u'GBSeq_locus', u'GBSeq_strandedness'] >>> print(records[0]["GBSeq_primary-accession"]) DQ110336 >>> print(records[0]["GBSeq_other-seqids"]) ['gb|DQ110336.1|', 'gi|187237168'] >>> print(records[0]["GBSeq_definition"]) Cypripedium calceolus voucher Davis 03-03 A maturase (matR) gene, partial cds; mitochondrial >>> print(records[0]["GBSeq_organism"]) Cypripedium calceolus \end{verbatim} You could use this to quickly set up searches -- but for heavy usage, see Section~\ref{sec:entrez-webenv}. \subsection{Searching, downloading, and parsing GenBank records} \label{sec:entrez-search-fetch-genbank} The GenBank record format is a very popular method of holding information about sequences, sequence features, and other associated sequence information. The format is a good way to get information from the NCBI databases at \url{http://www.ncbi.nlm.nih.gov/}. In this example we'll show how to query the NCBI databases,to retrieve the records from the query, and then parse them using \verb+Bio.SeqIO+ - something touched on in Section~\ref{sec:SeqIO_GenBank_Online}. For simplicity, this example \emph{does not} take advantage of the WebEnv history feature -- see Section~\ref{sec:entrez-webenv} for this. First, we want to make a query and find out the ids of the records to retrieve. Here we'll do a quick search for one of our favorite organisms, \emph{Opuntia} (prickly-pear cacti). We can do quick search and get back the GIs (GenBank identifiers) for all of the corresponding records. First we check how many records there are: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> handle = Entrez.egquery(term="Opuntia AND rpl16") >>> record = Entrez.read(handle) >>> for row in record["eGQueryResult"]: ... if row["DbName"]=="nuccore": ... print(row["Count"]) ... 9 \end{verbatim} Now we download the list of GenBank identifiers: \begin{verbatim} >>> handle = Entrez.esearch(db="nuccore", term="Opuntia AND rpl16") >>> record = Entrez.read(handle) >>> gi_list = record["IdList"] >>> gi_list ['57240072', '57240071', '6273287', '6273291', '6273290', '6273289', '6273286', '6273285', '6273284'] \end{verbatim} Now we use these GIs to download the GenBank records - note that with older versions of Biopython you had to supply a comma separated list of GI numbers to Entrez, as of Biopython 1.59 you can pass a list and this is converted for you: \begin{verbatim} >>> gi_str = ",".join(gi_list) >>> handle = Entrez.efetch(db="nuccore", id=gi_str, rettype="gb", retmode="text") \end{verbatim} If you want to look at the raw GenBank files, you can read from this handle and print out the result: \begin{verbatim} >>> text = handle.read() >>> print(text) LOCUS AY851612 892 bp DNA linear PLN 10-APR-2007 DEFINITION Opuntia subulata rpl16 gene, intron; chloroplast. ACCESSION AY851612 VERSION AY851612.1 GI:57240072 KEYWORDS . SOURCE chloroplast Austrocylindropuntia subulata ORGANISM Austrocylindropuntia subulata Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta; Spermatophyta; Magnoliophyta; eudicotyledons; core eudicotyledons; Caryophyllales; Cactaceae; Opuntioideae; Austrocylindropuntia. REFERENCE 1 (bases 1 to 892) AUTHORS Butterworth,C.A. and Wallace,R.S. ... \end{verbatim} In this case, we are just getting the raw records. To get the records in a more Python-friendly form, we can use \verb+Bio.SeqIO+ to parse the GenBank data into \verb|SeqRecord| objects, including \verb|SeqFeature| objects (see Chapter~\ref{chapter:Bio.SeqIO}): \begin{verbatim} >>> from Bio import SeqIO >>> handle = Entrez.efetch(db="nuccore", id=gi_str, rettype="gb", retmode="text") >>> records = SeqIO.parse(handle, "gb") \end{verbatim} \noindent We can now step through the records and look at the information we are interested in: \begin{verbatim} >>> for record in records: >>> ... print("%s, length %i, with %i features" \ >>> ... % (record.name, len(record), len(record.features))) AY851612, length 892, with 3 features AY851611, length 881, with 3 features AF191661, length 895, with 3 features AF191665, length 902, with 3 features AF191664, length 899, with 3 features AF191663, length 899, with 3 features AF191660, length 893, with 3 features AF191659, length 894, with 3 features AF191658, length 896, with 3 features \end{verbatim} Using these automated query retrieval functionality is a big plus over doing things by hand. Although the module should obey the NCBI's max three queries per second rule, the NCBI have other recommendations like avoiding peak hours. See Section~\ref{sec:entrez-guidelines}. In particular, please note that for simplicity, this example does not use the WebEnv history feature. You should use this for any non-trivial search and download work, see Section~\ref{sec:entrez-webenv}. Finally, if plan to repeat your analysis, rather than downloading the files from the NCBI and parsing them immediately (as shown in this example), you should just download the records \emph{once} and save them to your hard disk, and then parse the local file. \subsection{Finding the lineage of an organism} Staying with a plant example, let's now find the lineage of the Cypripedioideae orchid family. First, we search the Taxonomy database for Cypripedioideae, which yields exactly one NCBI taxonomy identifier: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" # Always tell NCBI who you are >>> handle = Entrez.esearch(db="Taxonomy", term="Cypripedioideae") >>> record = Entrez.read(handle) >>> record["IdList"] ['158330'] >>> record["IdList"][0] '158330' \end{verbatim} Now, we use \verb+efetch+ to download this entry in the Taxonomy database, and then parse it: \begin{verbatim} >>> handle = Entrez.efetch(db="Taxonomy", id="158330", retmode="xml") >>> records = Entrez.read(handle) \end{verbatim} Again, this record stores lots of information: \begin{verbatim} >>> records[0].keys() [u'Lineage', u'Division', u'ParentTaxId', u'PubDate', u'LineageEx', u'CreateDate', u'TaxId', u'Rank', u'GeneticCode', u'ScientificName', u'MitoGeneticCode', u'UpdateDate'] \end{verbatim} We can get the lineage directly from this record: \begin{verbatim} >>> records[0]["Lineage"] 'cellular organisms; Eukaryota; Viridiplantae; Streptophyta; Streptophytina; Embryophyta; Tracheophyta; Euphyllophyta; Spermatophyta; Magnoliophyta; Liliopsida; Asparagales; Orchidaceae' \end{verbatim} The record data contains much more than just the information shown here - for example look under \texttt{"LineageEx"} instead of \texttt{"Lineage"} and you'll get the NCBI taxon identifiers of the lineage entries too. \section{Using the history and WebEnv} \label{sec:entrez-webenv} Often you will want to make a series of linked queries. Most typically, running a search, perhaps refining the search, and then retrieving detailed search results. You \emph{can} do this by making a series of separate calls to Entrez. However, the NCBI prefer you to take advantage of their history support - for example combining ESearch and EFetch. Another typical use of the history support would be to combine EPost and EFetch. You use EPost to upload a list of identifiers, which starts a new history session. You then download the records with EFetch by referring to the session (instead of the identifiers). \subsection{Searching for and downloading sequences using the history} Suppose we want to search and download all the \textit{Opuntia} rpl16 nucleotide sequences, and store them in a FASTA file. As shown in Section~\ref{sec:entrez-search-fetch-genbank}, we can naively combine \verb|Bio.Entrez.esearch()| to get a list of GI numbers, and then call \verb|Bio.Entrez.efetch()| to download them all. However, the approved approach is to run the search with the history feature. Then, we can fetch the results by reference to the search results - which the NCBI can anticipate and cache. To do this, call \verb|Bio.Entrez.esearch()| as normal, but with the additional argument of \verb|usehistory="y"|, \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "history.user@example.com" >>> search_handle = Entrez.esearch(db="nucleotide",term="Opuntia[orgn] and rpl16", usehistory="y") >>> search_results = Entrez.read(search_handle) >>> search_handle.close() \end{verbatim} \noindent When you get the XML output back, it will still include the usual search results: \begin{verbatim} >>> gi_list = search_results["IdList"] >>> count = int(search_results["Count"]) >>> assert count == len(gi_list) \end{verbatim} \noindent However, you also get given two additional pieces of information, the {\tt WebEnv} session cookie, and the {\tt QueryKey}: \begin{verbatim} >>> webenv = search_results["WebEnv"] >>> query_key = search_results["QueryKey"] \end{verbatim} Having stored these values in variables {\tt session\_cookie} and {\tt query\_key} we can use them as parameters to \verb|Bio.Entrez.efetch()| instead of giving the GI numbers as identifiers. While for small searches you might be OK downloading everything at once, it is better to download in batches. You use the {\tt retstart} and {\tt retmax} parameters to specify which range of search results you want returned (starting entry using zero-based counting, and maximum number of results to return). For example, \begin{verbatim} batch_size = 3 out_handle = open("orchid_rpl16.fasta", "w") for start in range(0,count,batch_size): end = min(count, start+batch_size) print("Going to download record %i to %i" % (start+1, end)) fetch_handle = Entrez.efetch(db="nucleotide", rettype="fasta", retmode="text", retstart=start, retmax=batch_size, webenv=webenv, query_key=query_key) data = fetch_handle.read() fetch_handle.close() out_handle.write(data) out_handle.close() \end{verbatim} \noindent For illustrative purposes, this example downloaded the FASTA records in batches of three. Unless you are downloading genomes or chromosomes, you would normally pick a larger batch size. \subsection{Searching for and downloading abstracts using the history} Here is another history example, searching for papers published in the last year about the \textit{Opuntia}, and then downloading them into a file in MedLine format: \begin{verbatim} from Bio import Entrez Entrez.email = "history.user@example.com" search_results = Entrez.read(Entrez.esearch(db="pubmed", term="Opuntia[ORGN]", reldate=365, datetype="pdat", usehistory="y")) count = int(search_results["Count"]) print("Found %i results" % count) batch_size = 10 out_handle = open("recent_orchid_papers.txt", "w") for start in range(0,count,batch_size): end = min(count, start+batch_size) print("Going to download record %i to %i" % (start+1, end)) fetch_handle = Entrez.efetch(db="pubmed", rettype="medline", retmode="text", retstart=start, retmax=batch_size, webenv=search_results["WebEnv"], query_key=search_results["QueryKey"]) data = fetch_handle.read() fetch_handle.close() out_handle.write(data) out_handle.close() \end{verbatim} \noindent At the time of writing, this gave 28 matches - but because this is a date dependent search, this will of course vary. As described in Section~\ref{subsec:entrez-and-medline} above, you can then use \verb|Bio.Medline| to parse the saved records. \subsection{Searching for citations} \label{sec:elink-citations} Back in Section~\ref{sec:elink} we mentioned ELink can be used to search for citations of a given paper. Unfortunately this only covers journals indexed for PubMed Central (doing it for all the journals in PubMed would mean a lot more work for the NIH). Let's try this for the Biopython PDB parser paper, PubMed ID 14630660: \begin{verbatim} >>> from Bio import Entrez >>> Entrez.email = "A.N.Other@example.com" >>> pmid = "14630660" >>> results = Entrez.read(Entrez.elink(dbfrom="pubmed", db="pmc", ... LinkName="pubmed_pmc_refs", from_uid=pmid)) >>> pmc_ids = [link["Id"] for link in results[0]["LinkSetDb"][0]["Link"]] >>> pmc_ids ['2744707', '2705363', '2682512', ..., '1190160'] \end{verbatim} Great - eleven articles. But why hasn't the Biopython application note been found (PubMed ID 19304878)? Well, as you might have guessed from the variable names, there are not actually PubMed IDs, but PubMed Central IDs. Our application note is the third citing paper in that list, PMCID 2682512. So, what if (like me) you'd rather get back a list of PubMed IDs? Well we can call ELink again to translate them. This becomes a two step process, so by now you should expect to use the history feature to accomplish it (Section~\ref{sec:entrez-webenv}). But first, taking the more straightforward approach of making a second (separate) call to ELink: \begin{verbatim} >>> results2 = Entrez.read(Entrez.elink(dbfrom="pmc", db="pubmed", LinkName="pmc_pubmed", ... from_uid=",".join(pmc_ids))) >>> pubmed_ids = [link["Id"] for link in results2[0]["LinkSetDb"][0]["Link"]] >>> pubmed_ids ['19698094', '19450287', '19304878', ..., '15985178'] \end{verbatim} \noindent This time you can immediately spot the Biopython application note as the third hit (PubMed ID 19304878). Now, let's do that all again but with the history \ldots \textit{TODO}. And finally, don't forget to include your \emph{own} email address in the Entrez calls. \chapter{Swiss-Prot and ExPASy} \label{chapter:swiss_prot} \section{Parsing Swiss-Prot files} Swiss-Prot (\url{http://www.expasy.org/sprot}) is a hand-curated database of protein sequences. Biopython can parse the ``plain text'' Swiss-Prot file format, which is still used for the UniProt Knowledgebase which combined Swiss-Prot, TrEMBL and PIR-PSD. We do not (yet) support the UniProtKB XML file format. \subsection{Parsing Swiss-Prot records} In Section~\ref{sec:SeqIO_ExPASy_and_SwissProt}, we described how to extract the sequence of a Swiss-Prot record as a \verb|SeqRecord| object. Alternatively, you can store the Swiss-Prot record in a \verb|Bio.SwissProt.Record| object, which in fact stores the complete information contained in the Swiss-Prot record. In this Section, we describe how to extract \verb|Bio.SwissProt.Record| objects from a Swiss-Prot file. To parse a Swiss-Prot record, we first get a handle to a Swiss-Prot record. There are several ways to do so, depending on where and how the Swiss-Prot record is stored: \begin{itemize} \item Open a Swiss-Prot file locally: \newline \verb|>>> handle = open("myswissprotfile.dat")| \item Open a gzipped Swiss-Prot file: \begin{verbatim} >>> import gzip >>> handle = gzip.open("myswissprotfile.dat.gz") \end{verbatim} \item Open a Swiss-Prot file over the internet: \begin{verbatim} >>> import urllib >>> handle = urllib.urlopen("http://www.somelocation.org/data/someswissprotfile.dat") \end{verbatim} \item Open a Swiss-Prot file over the internet from the ExPASy database (see section \ref{subsec:expasy_swissprot}): \begin{verbatim} >>> from Bio import ExPASy >>> handle = ExPASy.get_sprot_raw(myaccessionnumber) \end{verbatim} \end{itemize} The key point is that for the parser, it doesn't matter how the handle was created, as long as it points to data in the Swiss-Prot format. We can use \verb+Bio.SeqIO+ as described in Section~\ref{sec:SeqIO_ExPASy_and_SwissProt} to get file format agnostic \verb|SeqRecord| objects. Alternatively, we can use \verb+Bio.SwissProt+ get \verb|Bio.SwissProt.Record| objects, which are a much closer match to the underlying file format. To read one Swiss-Prot record from the handle, we use the function \verb|read()|: \begin{verbatim} >>> from Bio import SwissProt >>> record = SwissProt.read(handle) \end{verbatim} This function should be used if the handle points to exactly one Swiss-Prot record. It raises a \verb|ValueError| if no Swiss-Prot record was found, and also if more than one record was found. We can now print out some information about this record: %TODO - Check the single quotes when printing record.description here: \begin{verbatim} >>> print(record.description) 'RecName: Full=Chalcone synthase 3; EC=2.3.1.74; AltName: Full=Naringenin-chalcone synthase 3;' >>> for ref in record.references: ... print("authors:", ref.authors) ... print("title:", ref.title) ... authors: Liew C.F., Lim S.H., Loh C.S., Goh C.J.; title: "Molecular cloning and sequence analysis of chalcone synthase cDNAs of Bromheadia finlaysoniana."; >>> print(record.organism_classification) ['Eukaryota', 'Viridiplantae', 'Streptophyta', 'Embryophyta', ..., 'Bromheadia'] \end{verbatim} To parse a file that contains more than one Swiss-Prot record, we use the \verb|parse| function instead. This function allows us to iterate over the records in the file. For example, let's parse the full Swiss-Prot database and collect all the descriptions. You can download this from the \href{ftp://ftp.expasy.org/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.dat.gz}{ExPAYs FTP site} as a single gzipped-file \verb|uniprot_sprot.dat.gz| (about 300MB). This is a compressed file containing a single file, \verb|uniprot_sprot.dat| (over 1.5GB). As described at the start of this section, you can use the Python library \verb|gzip| to open and uncompress a \texttt{.gz} file, like this: \begin{verbatim} >>> import gzip >>> handle = gzip.open("uniprot_sprot.dat.gz") \end{verbatim} However, uncompressing a large file takes time, and each time you open the file for reading in this way, it has to be decompressed on the fly. So, if you can spare the disk space you'll save time in the long run if you first decompress the file to disk, to get the \verb|uniprot_sprot.dat| file inside. Then you can open the file for reading as usual: \begin{verbatim} >>> handle = open("uniprot_sprot.dat") \end{verbatim} As of June 2009, the full Swiss-Prot database downloaded from ExPASy contained 468851 Swiss-Prot records. One concise way to build up a list of the record descriptions is with a list comprehension: \begin{verbatim} >>> from Bio import SwissProt >>> handle = open("uniprot_sprot.dat") >>> descriptions = [record.description for record in SwissProt.parse(handle)] >>> len(descriptions) 468851 >>> descriptions[:5] ['RecName: Full=Protein MGF 100-1R;', 'RecName: Full=Protein MGF 100-1R;', 'RecName: Full=Protein MGF 100-1R;', 'RecName: Full=Protein MGF 100-1R;', 'RecName: Full=Protein MGF 100-2L;'] \end{verbatim} Or, using a for loop over the record iterator: \begin{verbatim} >>> from Bio import SwissProt >>> descriptions = [] >>> handle = open("uniprot_sprot.dat") >>> for record in SwissProt.parse(handle): ... descriptions.append(record.description) ... >>> len(descriptions) 468851 \end{verbatim} Because this is such a large input file, either way takes about eleven minutes on my new desktop computer (using the uncompressed \verb|uniprot_sprot.dat| file as input). It is equally easy to extract any kind of information you'd like from Swiss-Prot records. To see the members of a Swiss-Prot record, use \begin{verbatim} >>> dir(record) ['__doc__', '__init__', '__module__', 'accessions', 'annotation_update', 'comments', 'created', 'cross_references', 'data_class', 'description', 'entry_name', 'features', 'gene_name', 'host_organism', 'keywords', 'molecule_type', 'organelle', 'organism', 'organism_classification', 'references', 'seqinfo', 'sequence', 'sequence_length', 'sequence_update', 'taxonomy_id'] \end{verbatim} \subsection{Parsing the Swiss-Prot keyword and category list} Swiss-Prot also distributes a file \verb+keywlist.txt+, which lists the keywords and categories used in Swiss-Prot. The file contains entries in the following form: \begin{verbatim} ID 2Fe-2S. AC KW-0001 DE Protein which contains at least one 2Fe-2S iron-sulfur cluster: 2 iron DE atoms complexed to 2 inorganic sulfides and 4 sulfur atoms of DE cysteines from the protein. SY Fe2S2; [2Fe-2S] cluster; [Fe2S2] cluster; Fe2/S2 (inorganic) cluster; SY Di-mu-sulfido-diiron; 2 iron, 2 sulfur cluster binding. GO GO:0051537; 2 iron, 2 sulfur cluster binding HI Ligand: Iron; Iron-sulfur; 2Fe-2S. HI Ligand: Metal-binding; 2Fe-2S. CA Ligand. // ID 3D-structure. AC KW-0002 DE Protein, or part of a protein, whose three-dimensional structure has DE been resolved experimentally (for example by X-ray crystallography or DE NMR spectroscopy) and whose coordinates are available in the PDB DE database. Can also be used for theoretical models. HI Technical term: 3D-structure. CA Technical term. // ID 3Fe-4S. ... \end{verbatim} The entries in this file can be parsed by the \verb+parse+ function in the \verb+Bio.SwissProt.KeyWList+ module. Each entry is then stored as a \verb+Bio.SwissProt.KeyWList.Record+, which is a Python dictionary. \begin{verbatim} >>> from Bio.SwissProt import KeyWList >>> handle = open("keywlist.txt") >>> records = KeyWList.parse(handle) >>> for record in records: ... print(record['ID']) ... print(record['DE']) \end{verbatim} This prints \begin{verbatim} 2Fe-2S. Protein which contains at least one 2Fe-2S iron-sulfur cluster: 2 iron atoms complexed to 2 inorganic sulfides and 4 sulfur atoms of cysteines from the protein. ... \end{verbatim} \section{Parsing Prosite records} Prosite is a database containing protein domains, protein families, functional sites, as well as the patterns and profiles to recognize them. Prosite was developed in parallel with Swiss-Prot. In Biopython, a Prosite record is represented by the \verb|Bio.ExPASy.Prosite.Record| class, whose members correspond to the different fields in a Prosite record. In general, a Prosite file can contain more than one Prosite records. For example, the full set of Prosite records, which can be downloaded as a single file (\verb|prosite.dat|) from the \href{ftp://ftp.expasy.org/databases/prosite/prosite.dat}{ExPASy FTP site}, contains 2073 records (version 20.24 released on 4 December 2007). To parse such a file, we again make use of an iterator: \begin{verbatim} >>> from Bio.ExPASy import Prosite >>> handle = open("myprositefile.dat") >>> records = Prosite.parse(handle) \end{verbatim} We can now take the records one at a time and print out some information. For example, using the file containing the complete Prosite database, we'd find \begin{verbatim} >>> from Bio.ExPASy import Prosite >>> handle = open("prosite.dat") >>> records = Prosite.parse(handle) >>> record = next(records) >>> record.accession 'PS00001' >>> record.name 'ASN_GLYCOSYLATION' >>> record.pdoc 'PDOC00001' >>> record = next(records) >>> record.accession 'PS00004' >>> record.name 'CAMP_PHOSPHO_SITE' >>> record.pdoc 'PDOC00004' >>> record = next(records) >>> record.accession 'PS00005' >>> record.name 'PKC_PHOSPHO_SITE' >>> record.pdoc 'PDOC00005' \end{verbatim} and so on. If you're interested in how many Prosite records there are, you could use \begin{verbatim} >>> from Bio.ExPASy import Prosite >>> handle = open("prosite.dat") >>> records = Prosite.parse(handle) >>> n = 0 >>> for record in records: n+=1 ... >>> n 2073 \end{verbatim} To read exactly one Prosite from the handle, you can use the \verb|read| function: \begin{verbatim} >>> from Bio.ExPASy import Prosite >>> handle = open("mysingleprositerecord.dat") >>> record = Prosite.read(handle) \end{verbatim} This function raises a ValueError if no Prosite record is found, and also if more than one Prosite record is found. \section{Parsing Prosite documentation records} In the Prosite example above, the \verb|record.pdoc| accession numbers \verb|'PDOC00001'|, \verb|'PDOC00004'|, \verb|'PDOC00005'| and so on refer to Prosite documentation. The Prosite documentation records are available from ExPASy as individual files, and as one file (\verb|prosite.doc|) containing all Prosite documentation records. We use the parser in \verb|Bio.ExPASy.Prodoc| to parse Prosite documentation records. For example, to create a list of all accession numbers of Prosite documentation record, you can use \begin{verbatim} >>> from Bio.ExPASy import Prodoc >>> handle = open("prosite.doc") >>> records = Prodoc.parse(handle) >>> accessions = [record.accession for record in records] \end{verbatim} Again a \verb|read()| function is provided to read exactly one Prosite documentation record from the handle. \section{Parsing Enzyme records} ExPASy's Enzyme database is a repository of information on enzyme nomenclature. A typical Enzyme record looks as follows: \begin{verbatim} ID 3.1.1.34 DE Lipoprotein lipase. AN Clearing factor lipase. AN Diacylglycerol lipase. AN Diglyceride lipase. CA Triacylglycerol + H(2)O = diacylglycerol + a carboxylate. CC -!- Hydrolyzes triacylglycerols in chylomicrons and very low-density CC lipoproteins (VLDL). CC -!- Also hydrolyzes diacylglycerol. PR PROSITE; PDOC00110; DR P11151, LIPL_BOVIN ; P11153, LIPL_CAVPO ; P11602, LIPL_CHICK ; DR P55031, LIPL_FELCA ; P06858, LIPL_HUMAN ; P11152, LIPL_MOUSE ; DR O46647, LIPL_MUSVI ; P49060, LIPL_PAPAN ; P49923, LIPL_PIG ; DR Q06000, LIPL_RAT ; Q29524, LIPL_SHEEP ; // \end{verbatim} In this example, the first line shows the EC (Enzyme Commission) number of lipoprotein lipase (second line). Alternative names of lipoprotein lipase are "clearing factor lipase", "diacylglycerol lipase", and "diglyceride lipase" (lines 3 through 5). The line starting with "CA" shows the catalytic activity of this enzyme. Comment lines start with "CC". The "PR" line shows references to the Prosite Documentation records, and the "DR" lines show references to Swiss-Prot records. Not of these entries are necessarily present in an Enzyme record. In Biopython, an Enzyme record is represented by the \verb|Bio.ExPASy.Enzyme.Record| class. This record derives from a Python dictionary and has keys corresponding to the two-letter codes used in Enzyme files. To read an Enzyme file containing one Enzyme record, use the \verb+read+ function in \verb|Bio.ExPASy.Enzyme|: %doctest ../Tests/Enzymes \begin{verbatim} >>> from Bio.ExPASy import Enzyme >>> handle = open("lipoprotein.txt") >>> record = Enzyme.read(handle) >>> record["ID"] '3.1.1.34' >>> record["DE"] 'Lipoprotein lipase.' >>> record["AN"] ['Clearing factor lipase.', 'Diacylglycerol lipase.', 'Diglyceride lipase.'] >>> record["CA"] 'Triacylglycerol + H(2)O = diacylglycerol + a carboxylate.' >>> record["PR"] ['PDOC00110'] \end{verbatim} %TODO - line wrapping? \begin{verbatim} >>> record["CC"] ['Hydrolyzes triacylglycerols in chylomicrons and very low-density lipoproteins (VLDL).', 'Also hydrolyzes diacylglycerol.'] >>> record["DR"] [['P11151', 'LIPL_BOVIN'], ['P11153', 'LIPL_CAVPO'], ['P11602', 'LIPL_CHICK'], ['P55031', 'LIPL_FELCA'], ['P06858', 'LIPL_HUMAN'], ['P11152', 'LIPL_MOUSE'], ['O46647', 'LIPL_MUSVI'], ['P49060', 'LIPL_PAPAN'], ['P49923', 'LIPL_PIG'], ['Q06000', 'LIPL_RAT'], ['Q29524', 'LIPL_SHEEP']] \end{verbatim} The \verb+read+ function raises a ValueError if no Enzyme record is found, and also if more than one Enzyme record is found. The full set of Enzyme records can be downloaded as a single file (\verb|enzyme.dat|) from the \href{ftp://ftp.expasy.org/databases/enzyme/enzyme.dat}{ExPASy FTP site}, containing 4877 records (release of 3 March 2009). To parse such a file containing multiple Enzyme records, use the \verb+parse+ function in \verb+Bio.ExPASy.Enzyme+ to obtain an iterator: \begin{verbatim} >>> from Bio.ExPASy import Enzyme >>> handle = open("enzyme.dat") >>> records = Enzyme.parse(handle) \end{verbatim} We can now iterate over the records one at a time. For example, we can make a list of all EC numbers for which an Enzyme record is available: \begin{verbatim} >>> ecnumbers = [record["ID"] for record in records] \end{verbatim} \section{Accessing the ExPASy server} Swiss-Prot, Prosite, and Prosite documentation records can be downloaded from the ExPASy web server at \url{http://www.expasy.org}. Six kinds of queries are available from ExPASy: \begin{description} \item[get\_prodoc\_entry]To download a Prosite documentation record in HTML format \item[get\_prosite\_entry]To download a Prosite record in HTML format \item[get\_prosite\_raw]To download a Prosite or Prosite documentation record in raw format \item[get\_sprot\_raw]To download a Swiss-Prot record in raw format \item[sprot\_search\_ful]To search for a Swiss-Prot record \item[sprot\_search\_de]To search for a Swiss-Prot record \end{description} To access this web server from a Python script, we use the \verb|Bio.ExPASy| module. \subsection{Retrieving a Swiss-Prot record} \label{subsec:expasy_swissprot} Let's say we are looking at chalcone synthases for Orchids (see section~\ref{sec:orchids} for some justification for looking for interesting things about orchids). Chalcone synthase is involved in flavanoid biosynthesis in plants, and flavanoids make lots of cool things like pigment colors and UV protectants. If you do a search on Swiss-Prot, you can find three orchid proteins for Chalcone Synthase, id numbers O23729, O23730, O23731. Now, let's write a script which grabs these, and parses out some interesting information. First, we grab the records, using the \verb|get_sprot_raw()| function of \verb|Bio.ExPASy|. This function is very nice since you can feed it an id and get back a handle to a raw text record (no html to mess with!). We can the use \verb|Bio.SwissProt.read| to pull out the Swiss-Prot record, or \verb|Bio.SeqIO.read| to get a SeqRecord. The following code accomplishes what I just wrote: \begin{verbatim} >>> from Bio import ExPASy >>> from Bio import SwissProt >>> accessions = ["O23729", "O23730", "O23731"] >>> records = [] >>> for accession in accessions: ... handle = ExPASy.get_sprot_raw(accession) ... record = SwissProt.read(handle) ... records.append(record) \end{verbatim} If the accession number you provided to \verb|ExPASy.get_sprot_raw| does not exist, then \verb|SwissProt.read(handle)| will raise a \verb|ValueError|. You can catch \verb|ValueException| exceptions to detect invalid accession numbers: \begin{verbatim} >>> for accession in accessions: ... handle = ExPASy.get_sprot_raw(accession) ... try: ... record = SwissProt.read(handle) ... except ValueException: ... print("WARNING: Accession %s not found" % accession) ... records.append(record) \end{verbatim} \subsection{Searching Swiss-Prot} Now, you may remark that I knew the records' accession numbers beforehand. Indeed, \verb|get_sprot_raw()| needs either the entry name or an accession number. When you don't have them handy, you can use one of the \verb|sprot_search_de()| or \verb|sprot_search_ful()| functions. \verb|sprot_search_de()| searches in the ID, DE, GN, OS and OG lines; \verb|sprot_search_ful()| searches in (nearly) all the fields. They are detailed on \url{http://www.expasy.org/cgi-bin/sprot-search-de} and \url{http://www.expasy.org/cgi-bin/sprot-search-ful} respectively. Note that they don't search in TrEMBL by default (argument \verb|trembl|). Note also that they return html pages; however, accession numbers are quite easily extractable: \begin{verbatim} >>> from Bio import ExPASy >>> import re >>> handle = ExPASy.sprot_search_de("Orchid Chalcone Synthase") >>> # or: >>> # handle = ExPASy.sprot_search_ful("Orchid and {Chalcone Synthase}") >>> html_results = handle.read() >>> if "Number of sequences found" in html_results: ... ids = re.findall(r'HREF="/uniprot/(\w+)"', html_results) ... else: ... ids = re.findall(r'href="/cgi-bin/niceprot\.pl\?(\w+)"', html_results) \end{verbatim} \subsection{Retrieving Prosite and Prosite documentation records} Prosite and Prosite documentation records can be retrieved either in HTML format, or in raw format. To parse Prosite and Prosite documentation records with Biopython, you should retrieve the records in raw format. For other purposes, however, you may be interested in these records in HTML format. To retrieve a Prosite or Prosite documentation record in raw format, use \verb|get_prosite_raw()|. For example, to download a Prosite record and print it out in raw text format, use \begin{verbatim} >>> from Bio import ExPASy >>> handle = ExPASy.get_prosite_raw('PS00001') >>> text = handle.read() >>> print(text) \end{verbatim} To retrieve a Prosite record and parse it into a \verb|Bio.Prosite.Record| object, use \begin{verbatim} >>> from Bio import ExPASy >>> from Bio import Prosite >>> handle = ExPASy.get_prosite_raw('PS00001') >>> record = Prosite.read(handle) \end{verbatim} The same function can be used to retrieve a Prosite documentation record and parse it into a \verb|Bio.ExPASy.Prodoc.Record| object: \begin{verbatim} >>> from Bio import ExPASy >>> from Bio.ExPASy import Prodoc >>> handle = ExPASy.get_prosite_raw('PDOC00001') >>> record = Prodoc.read(handle) \end{verbatim} For non-existing accession numbers, \verb|ExPASy.get_prosite_raw| returns a handle to an emptry string. When faced with an empty string, \verb|Prosite.read| and \verb|Prodoc.read| will raise a ValueError. You can catch these exceptions to detect invalid accession numbers. The functions \verb|get_prosite_entry()| and \verb|get_prodoc_entry()| are used to download Prosite and Prosite documentation records in HTML format. To create a web page showing one Prosite record, you can use \begin{verbatim} >>> from Bio import ExPASy >>> handle = ExPASy.get_prosite_entry('PS00001') >>> html = handle.read() >>> output = open("myprositerecord.html", "w") >>> output.write(html) >>> output.close() \end{verbatim} and similarly for a Prosite documentation record: \begin{verbatim} >>> from Bio import ExPASy >>> handle = ExPASy.get_prodoc_entry('PDOC00001') >>> html = handle.read() >>> output = open("myprodocrecord.html", "w") >>> output.write(html) >>> output.close() \end{verbatim} For these functions, an invalid accession number returns an error message in HTML format. \section{Scanning the Prosite database} \href{http://www.expasy.org/tools/scanprosite/}{ScanProsite} allows you to scan protein sequences online against the Prosite database by providing a UniProt or PDB sequence identifier or the sequence itself. For more information about ScanProsite, please see the \href{http://www.expasy.org/tools/scanprosite/scanprosite-doc.html}{ScanProsite documentation} as well as the \href{http://www.expasy.org/tools/scanprosite/ScanPrositeREST.html}{documentation for programmatic access of ScanProsite}. You can use Biopython's \verb+Bio.ExPASy.ScanProsite+ module to scan the Prosite database from Python. This module both helps you to access ScanProsite programmatically, and to parse the results returned by ScanProsite. To scan for Prosite patterns in the following protein sequence: \begin{verbatim} MEHKEVVLLLLLFLKSGQGEPLDDYVNTQGASLFSVTKKQLGAGSIEECAAKCEEDEEFT CRAFQYHSKEQQCVIMAENRKSSIIIRMRDVVLFEKKVYLSECKTGNGKNYRGTMSKTKN \end{verbatim} you can use the following code: \begin{verbatim} >>> sequence = "MEHKEVVLLLLLFLKSGQGEPLDDYVNTQGASLFSVTKKQLGAGSIEECAAKCEEDEEFT CRAFQYHSKEQQCVIMAENRKSSIIIRMRDVVLFEKKVYLSECKTGNGKNYRGTMSKTKN" >>> from Bio.ExPASy import ScanProsite >>> handle = ScanProsite.scan(seq=sequence) \end{verbatim} By executing \verb+handle.read()+, you can obtain the search results in raw XML format. Instead, let's use \verb+Bio.ExPASy.ScanProsite.read+ to parse the raw XML into a Python object: \begin{verbatim} >>> result = ScanProsite.read(handle) >>> type(result) \end{verbatim} A \verb+Bio.ExPASy.ScanProsite.Record+ object is derived from a list, with each element in the list storing one ScanProsite hit. This object also stores the number of hits, as well as the number of search sequences, as returned by ScanProsite. This ScanProsite search resulted in six hits: \begin{verbatim} >>> result.n_seq 1 >>> result.n_match 6 >>> len(result) 6 >>> result[0] {'signature_ac': u'PS50948', 'level': u'0', 'stop': 98, 'sequence_ac': u'USERSEQ1', 'start': 16, 'score': u'8.873'} >>> result[1] {'start': 37, 'stop': 39, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00005'} >>> result[2] {'start': 45, 'stop': 48, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00006'} >>> result[3] {'start': 60, 'stop': 62, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00005'} >>> result[4] {'start': 80, 'stop': 83, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00004'} >>> result[5] {'start': 106, 'stop': 111, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00008'} \end{verbatim} Other ScanProsite parameters can be passed as keyword arguments; see the \href{http://www.expasy.org/tools/scanprosite/ScanPrositeREST.html}{documentation for programmatic access of ScanProsite} for more information. As an example, passing \verb+lowscore=1+ to include matches with low level scores lets use find one additional hit: \begin{verbatim} >>> handle = ScanProsite.scan(seq=sequence, lowscore=1) >>> result = ScanProsite.read(handle) >>> result.n_match 7 \end{verbatim} \chapter{Going 3D: The PDB module} Bio.PDB is a Biopython module that focuses on working with crystal structures of biological macromolecules. Among other things, Bio.PDB includes a PDBParser class that produces a Structure object, which can be used to access the atomic data in the file in a convenient manner. There is limited support for parsing the information contained in the PDB header. %Note the \verb|Bio.PDB| module requires Numerical Python (numpy) to be installed. \section{Reading and writing crystal structure files} \subsection{Reading a PDB file} First we create a \texttt{PDBParser} object: \begin{verbatim} >>> from Bio.PDB.PDBParser import PDBParser >>> p = PDBParser(PERMISSIVE=1) \end{verbatim} The {\tt PERMISSIVE} flag indicates that a number of common problems (see \ref{problem structures}) associated with PDB files will be ignored (but note that some atoms and/or residues will be missing). If the flag is not present a {\tt PDBConstructionException} will be generated if any problems are detected during the parse operation. The Structure object is then produced by letting the \texttt{PDBParser} object parse a PDB file (the PDB file in this case is called 'pdb1fat.ent', '1fat' is a user defined name for the structure): \begin{verbatim} >>> structure_id = "1fat" >>> filename = "pdb1fat.ent" >>> s = p.get_structure(structure_id, filename) \end{verbatim} You can extract the header and trailer (simple lists of strings) of the PDB file from the PDBParser object with the {\tt get\_header} and {\tt get\_trailer} methods. Note however that many PDB files contain headers with incomplete or erroneous information. Many of the errors have been fixed in the equivalent mmCIF files. \emph{Hence, if you are interested in the header information, it is a good idea to extract information from mmCIF files using the} \texttt{\emph{MMCIF2Dict}} \emph{tool described below, instead of parsing the PDB header. } Now that is clarified, let's return to parsing the PDB header. The structure object has an attribute called \texttt{header} which is a Python dictionary that maps header records to their values. Example: \begin{verbatim} >>> resolution = structure.header['resolution'] >>> keywords = structure.header['keywords'] \end{verbatim} The available keys are \verb+name+, \verb+head+, \verb+deposition_date+, \verb+release_date+, \verb+structure_method+, \verb+resolution+, \verb+structure_reference+ (which maps to a list of references), \verb+journal_reference+, \verb+author+, and \verb+compound+ (which maps to a dictionary with various information about the crystallized compound). The dictionary can also be created without creating a \texttt{Structure} object, ie. directly from the PDB file: \begin{verbatim} >>> file = open(filename, 'r') >>> header_dict = parse_pdb_header(file) >>> file.close() \end{verbatim} \subsection{Reading an mmCIF file} Similarly to the case the case of PDB files, first create an \texttt{MMCIFParser} object: \begin{verbatim} >>> from Bio.PDB.MMCIFParser import MMCIFParser >>> parser = MMCIFParser() \end{verbatim} Then use this parser to create a structure object from the mmCIF file: \begin{verbatim} >>> structure = parser.get_structure('1fat', '1fat.cif') \end{verbatim} To have some more low level access to an mmCIF file, you can use the \verb+MMCIF2Dict+ class to create a Python dictionary that maps all mmCIF tags in an mmCIF file to their values. If there are multiple values (like in the case of tag \verb+_atom_site.Cartn_y+, which holds the $y$ coordinates of all atoms), the tag is mapped to a list of values. The dictionary is created from the mmCIF file as follows: \begin{verbatim} >>> from Bio.PDB.MMCIF2Dict import MMCIF2Dict >>> mmcif_dict = MMCIF2Dict('1FAT.cif') \end{verbatim} Example: get the solvent content from an mmCIF file: \begin{verbatim} >>> sc = mmcif_dict['_exptl_crystal.density_percent_sol'] \end{verbatim} Example: get the list of the $y$ coordinates of all atoms \begin{verbatim} >>> y_list = mmcif_dict['_atom_site.Cartn_y'] \end{verbatim} \subsection{Reading files in the PDB XML format} That's not yet supported, but we are definitely planning to support that in the future (it's not a lot of work). Contact the Biopython developers (\mailto{biopython-dev@biopython.org}) if you need this). \subsection{Writing PDB files} Use the PDBIO class for this. It's easy to write out specific parts of a structure too, of course. Example: saving a structure \begin{verbatim} >>> io = PDBIO() >>> io.set_structure(s) >>> io.save('out.pdb') \end{verbatim} If you want to write out a part of the structure, make use of the \texttt{Select} class (also in \texttt{PDBIO}). Select has four methods: \begin{itemize} \item \verb+accept_model(model)+ \item \verb+accept_chain(chain)+ \item \verb+accept_residue(residue)+ \item \verb+accept_atom(atom)+ \end{itemize} By default, every method returns 1 (which means the model/\-chain/\-residue/\-atom is included in the output). By subclassing \texttt{Select} and returning 0 when appropriate you can exclude models, chains, etc. from the output. Cumbersome maybe, but very powerful. The following code only writes out glycine residues: \begin{verbatim} >>> class GlySelect(Select): ... def accept_residue(self, residue): ... if residue.get_name()=='GLY': ... return True ... else: ... return False ... >>> io = PDBIO() >>> io.set_structure(s) >>> io.save('gly_only.pdb', GlySelect()) \end{verbatim} If this is all too complicated for you, the \texttt{Dice} module contains a handy \texttt{extract} function that writes out all residues in a chain between a start and end residue. \section{Structure representation} The overall layout of a \texttt{Structure} object follows the so-called SMCRA (Structure/Model/Chain/Residue/Atom) architecture: \begin{itemize} \item A structure consists of models \item A model consists of chains \item A chain consists of residues \item A residue consists of atoms \end{itemize} This is the way many structural biologists/bioinformaticians think about structure, and provides a simple but efficient way to deal with structure. Additional stuff is essentially added when needed. A UML diagram of the \texttt{Structure} object (forget about the \texttt{Disordered} classes for now) is shown in Fig. \ref{fig:smcra}. Such a data structure is not necessarily best suited for the representation of the macromolecular content of a structure, but it is absolutely necessary for a good interpretation of the data present in a file that describes the structure (typically a PDB or MMCIF file). If this hierarchy cannot represent the contents of a structure file, it is fairly certain that the file contains an error or at least does not describe the structure unambiguously. If a SMCRA data structure cannot be generated, there is reason to suspect a problem. Parsing a PDB file can thus be used to detect likely problems. We will give several examples of this in section \ref{problem structures}. \begin{figure}[htbp] \begin{htmlonly} \imgsrc[width=650, height=750]{images/smcra.png} \end{htmlonly} \begin{latexonly} \centering \includegraphics[width=0.8\textwidth]{images/smcra.png} \end{latexonly} \caption{UML diagram of SMCRA architecture of the \texttt{Structure} class used to represent a macromolecular structure. Full lines with diamonds denote aggregation, full lines with arrows denote referencing, full lines with triangles denote inheritance and dashed lines with triangles denote interface realization.} \label{fig:smcra} \end{figure} Structure, Model, Chain and Residue are all subclasses of the Entity base class. The Atom class only (partly) implements the Entity interface (because an Atom does not have children). For each Entity subclass, you can extract a child by using a unique id for that child as a key (e.g. you can extract an Atom object from a Residue object by using an atom name string as a key, you can extract a Chain object from a Model object by using its chain identifier as a key). Disordered atoms and residues are represented by DisorderedAtom and DisorderedResidue classes, which are both subclasses of the DisorderedEntityWrapper base class. They hide the complexity associated with disorder and behave exactly as Atom and Residue objects. In general, a child Entity object (i.e. Atom, Residue, Chain, Model) can be extracted from its parent (i.e. Residue, Chain, Model, Structure, respectively) by using an id as a key. \begin{verbatim} >>> child_entity = parent_entity[child_id] \end{verbatim} You can also get a list of all child Entities of a parent Entity object. Note that this list is sorted in a specific way (e.g. according to chain identifier for Chain objects in a Model object). \begin{verbatim} >>> child_list = parent_entity.get_list() \end{verbatim} You can also get the parent from a child: \begin{verbatim} >>> parent_entity = child_entity.get_parent() \end{verbatim} At all levels of the SMCRA hierarchy, you can also extract a \emph{full id}. The full id is a tuple containing all id's starting from the top object (Structure) down to the current object. A full id for a Residue object e.g. is something like: \begin{verbatim} >>> full_id = residue.get_full_id() >>> print(full_id) ("1abc", 0, "A", ("", 10, "A")) \end{verbatim} This corresponds to: \begin{itemize} \item The Structure with id \char`\"{}1abc\char`\"{} \item The Model with id 0 \item The Chain with id \char`\"{}A\char`\"{} \item The Residue with id (\char`\"{} \char`\"{}, 10, \char`\"{}A\char`\"{}). \end{itemize} The Residue id indicates that the residue is not a hetero-residue (nor a water) because it has a blank hetero field, that its sequence identifier is 10 and that its insertion code is \char`\"{}A\char`\"{}. To get the entity's id, use the \verb+get_id+ method: \begin{verbatim} >>> entity.get_id() \end{verbatim} You can check if the entity has a child with a given id by using the \verb+has_id+ method: \begin{verbatim} >>> entity.has_id(entity_id) \end{verbatim} The length of an entity is equal to its number of children: \begin{verbatim} >>> nr_children = len(entity) \end{verbatim} It is possible to delete, rename, add, etc. child entities from a parent entity, but this does not include any sanity checks (e.g. it is possible to add two residues with the same id to one chain). This really should be done via a nice Decorator class that includes integrity checking, but you can take a look at the code (Entity.py) if you want to use the raw interface. \subsection{Structure} The Structure object is at the top of the hierarchy. Its id is a user given string. The Structure contains a number of Model children. Most crystal structures (but not all) contain a single model, while NMR structures typically consist of several models. Disorder in crystal structures of large parts of molecules can also result in several models. \subsection{Model} The id of the Model object is an integer, which is derived from the position of the model in the parsed file (they are automatically numbered starting from 0). Crystal structures generally have only one model (with id 0), while NMR files usually have several models. Whereas many PDB parsers assume that there is only one model, the \verb+Structure+ class in \verb+Bio.PDB+ is designed such that it can easily handle PDB files with more than one model. As an example, to get the first model from a Structure object, use \begin{verbatim} >>> first_model = structure[0] \end{verbatim} The Model object stores a list of Chain children. \subsection{Chain} The id of a Chain object is derived from the chain identifier in the PDB/mmCIF file, and is a single character (typically a letter). Each Chain in a Model object has a unique id. As an example, to get the Chain object with identifier ``A'' from a Model object, use \begin{verbatim} >>> chain_A = model["A"] \end{verbatim} The Chain object stores a list of Residue children. \subsection{Residue} A residue id is a tuple with three elements: \begin{itemize} \item The \textbf{hetero-field} (hetfield): this is \begin{itemize} \item \verb+'W'+ in the case of a water molecule; \item \verb+'H_'+ followed by the residue name for other hetero residues (e.g. \verb+'H_GLC'+ in the case of a glucose molecule); \item blank for standard amino and nucleic acids. \end{itemize} This scheme is adopted for reasons described in section \ref{hetero problems}. \item The \textbf{sequence identifier} (resseq), an integer describing the position of the residue in the chain (e.g., 100); \item The \textbf{insertion code} (icode); a string, e.g. 'A'. The insertion code is sometimes used to preserve a certain desirable residue numbering scheme. A Ser 80 insertion mutant (inserted e.g. between a Thr 80 and an Asn 81 residue) could e.g. have sequence identifiers and insertion codes as follows: Thr 80 A, Ser 80 B, Asn 81. In this way the residue numbering scheme stays in tune with that of the wild type structure. \end{itemize} The id of the above glucose residue would thus be \texttt{('H\_GLC', 100, 'A')}. If the hetero-flag and insertion code are blank, the sequence identifier alone can be used: \begin{verbatim} # Full id >>> residue=chain[(' ', 100, ' ')] # Shortcut id >>> residue=chain[100] \end{verbatim} The reason for the hetero-flag is that many, many PDB files use the same sequence identifier for an amino acid and a hetero-residue or a water, which would create obvious problems if the hetero-flag was not used. Unsurprisingly, a Residue object stores a set of Atom children. It also contains a string that specifies the residue name (e.g. ``ASN'') and the segment identifier of the residue (well known to X-PLOR users, but not used in the construction of the SMCRA data structure). Let's look at some examples. Asn 10 with a blank insertion code would have residue id {\tt (' ', 10, ' ')}. Water 10 would have residue id {\tt ('W', 10, ' ')}. A glucose molecule (a hetero residue with residue name GLC) with sequence identifier 10 would have residue id {\tt ('H\_GLC', 10, ' ')}. In this way, the three residues (with the same insertion code and sequence identifier) can be part of the same chain because their residue id's are distinct. In most cases, the hetflag and insertion code fields will be blank, e.g. {\tt (' ', 10, ' ')}. In these cases, the sequence identifier can be used as a shortcut for the full id: \begin{verbatim} # use full id >>> res10 = chain[(' ', 10, ' ')] # use shortcut >>> res10 = chain[10] \end{verbatim} Each Residue object in a Chain object should have a unique id. However, disordered residues are dealt with in a special way, as described in section \ref{point mutations}. A Residue object has a number of additional methods: \begin{verbatim} >>> residue.get_resname() # returns the residue name, e.g. "ASN" >>> residue.is_disordered() # returns 1 if the residue has disordered atoms >>> residue.get_segid() # returns the SEGID, e.g. "CHN1" >>> residue.has_id(name) # test if a residue has a certain atom \end{verbatim} You can use \texttt{is\_aa(residue)} to test if a Residue object is an amino acid. \subsection{Atom} The Atom object stores the data associated with an atom, and has no children. The id of an atom is its atom name (e.g. ``OG'' for the side chain oxygen of a Ser residue). An Atom id needs to be unique in a Residue. Again, an exception is made for disordered atoms, as described in section \ref{disordered atoms}. The atom id is simply the atom name (eg. \texttt{'CA'}). In practice, the atom name is created by stripping all spaces from the atom name in the PDB file. However, in PDB files, a space can be part of an atom name. Often, calcium atoms are called \texttt{'CA..'} in order to distinguish them from C$\alpha$ atoms (which are called \texttt{'.CA.'}). In cases were stripping the spaces would create problems (ie. two atoms called \texttt{'CA'} in the same residue) the spaces are kept. In a PDB file, an atom name consists of 4 chars, typically with leading and trailing spaces. Often these spaces can be removed for ease of use (e.g. an amino acid C\( \alpha \) atom is labeled ``.CA.'' in a PDB file, where the dots represent spaces). To generate an atom name (and thus an atom id) the spaces are removed, unless this would result in a name collision in a Residue (i.e. two Atom objects with the same atom name and id). In the latter case, the atom name including spaces is tried. This situation can e.g. happen when one residue contains atoms with names ``.CA.'' and ``CA..'', although this is not very likely. The atomic data stored includes the atom name, the atomic coordinates (including standard deviation if present), the B factor (including anisotropic B factors and standard deviation if present), the altloc specifier and the full atom name including spaces. Less used items like the atom element number or the atomic charge sometimes specified in a PDB file are not stored. To manipulate the atomic coordinates, use the \texttt{transform} method of the \texttt{Atom} object. Use the \texttt{set\_coord} method to specify the atomic coordinates directly. An Atom object has the following additional methods: \begin{verbatim} >>> a.get_name() # atom name (spaces stripped, e.g. "CA") >>> a.get_id() # id (equals atom name) >>> a.get_coord() # atomic coordinates >>> a.get_vector() # atomic coordinates as Vector object >>> a.get_bfactor() # isotropic B factor >>> a.get_occupancy() # occupancy >>> a.get_altloc() # alternative location specifier >>> a.get_sigatm() # standard deviation of atomic parameters >>> a.get_siguij() # standard deviation of anisotropic B factor >>> a.get_anisou() # anisotropic B factor >>> a.get_fullname() # atom name (with spaces, e.g. ".CA.") \end{verbatim} To represent the atom coordinates, siguij, anisotropic B factor and sigatm Numpy arrays are used. The \texttt{get\_vector} method returns a \texttt{Vector} object representation of the coordinates of the \texttt{Atom} object, allowing you to do vector operations on atomic coordinates. \texttt{Vector} implements the full set of 3D vector operations, matrix multiplication (left and right) and some advanced rotation-related operations as well. As an example of the capabilities of Bio.PDB's \texttt{Vector} module, suppose that you would like to find the position of a Gly residue's C$\beta$ atom, if it had one. Rotating the N atom of the Gly residue along the C$\alpha$-C bond over -120 degrees roughly puts it in the position of a virtual C$\beta$ atom. Here's how to do it, making use of the \texttt{rotaxis} method (which can be used to construct a rotation around a certain axis) of the \texttt{Vector} module: \begin{verbatim} # get atom coordinates as vectors >>> n = residue['N'].get_vector() >>> c = residue['C'].get_vector() >>> ca = residue['CA'].get_vector() # center at origin >>> n = n - ca >>> c = c - ca # find rotation matrix that rotates n # -120 degrees along the ca-c vector >>> rot = rotaxis(-pi * 120.0/180.0, c) # apply rotation to ca-n vector >>> cb_at_origin = n.left_multiply(rot) # put on top of ca atom >>> cb = cb_at_origin+ca \end{verbatim} This example shows that it's possible to do some quite nontrivial vector operations on atomic data, which can be quite useful. In addition to all the usual vector operations (cross (use \texttt{{*}{*}}), and dot (use \texttt{{*}}) product, angle, norm, etc.) and the above mentioned \texttt{rotaxis} function, the \texttt{Vector} module also has methods to rotate (\texttt{rotmat}) or reflect (\texttt{refmat}) one vector on top of another. \subsection{Extracting a specific \texttt{Atom/\-Residue/\-Chain/\-Model} from a Structure} These are some examples: \begin{verbatim} >>> model = structure[0] >>> chain = model['A'] >>> residue = chain[100] >>> atom = residue['CA'] \end{verbatim} Note that you can use a shortcut: \begin{verbatim} >>> atom = structure[0]['A'][100]['CA'] \end{verbatim} \section{Disorder} Bio.PDB can handle both disordered atoms and point mutations (i.e. a Gly and an Ala residue in the same position). \subsection{General approach\label{disorder problems}} Disorder should be dealt with from two points of view: the atom and the residue points of view. In general, we have tried to encapsulate all the complexity that arises from disorder. If you just want to loop over all C$\alpha$ atoms, you do not care that some residues have a disordered side chain. On the other hand it should also be possible to represent disorder completely in the data structure. Therefore, disordered atoms or residues are stored in special objects that behave as if there is no disorder. This is done by only representing a subset of the disordered atoms or residues. Which subset is picked (e.g. which of the two disordered OG side chain atom positions of a Ser residue is used) can be specified by the user. \subsection{Disordered atoms\label{disordered atoms}} Disordered atoms are represented by ordinary \texttt{Atom} objects, but all \texttt{Atom} objects that represent the same physical atom are stored in a \texttt{Disordered\-Atom} object (see Fig. \ref{fig:smcra}). Each \texttt{Atom} object in a \texttt{Disordered\-Atom} object can be uniquely indexed using its altloc specifier. The \texttt{Disordered\-Atom} object forwards all uncaught method calls to the selected Atom object, by default the one that represents the atom with the highest occupancy. The user can of course change the selected \texttt{Atom} object, making use of its altloc specifier. In this way atom disorder is represented correctly without much additional complexity. In other words, if you are not interested in atom disorder, you will not be bothered by it. Each disordered atom has a characteristic altloc identifier. You can specify that a \texttt{Disordered\-Atom} object should behave like the \texttt{Atom} object associated with a specific altloc identifier: \begin{verbatim} >>> atom.disordered_select('A') # select altloc A atom >>> print(atom.get_altloc()) "A" >>> atom.disordered_select('B') # select altloc B atom >>> print(atom.get_altloc()) "B" \end{verbatim} \subsection{Disordered residues} \subsubsection*{Common case} The most common case is a residue that contains one or more disordered atoms. This is evidently solved by using DisorderedAtom objects to represent the disordered atoms, and storing the DisorderedAtom object in a Residue object just like ordinary Atom objects. The DisorderedAtom will behave exactly like an ordinary atom (in fact the atom with the highest occupancy) by forwarding all uncaught method calls to one of the Atom objects (the selected Atom object) it contains. \subsubsection*{Point mutations\label{point mutations}} A special case arises when disorder is due to a point mutation, i.e. when two or more point mutants of a polypeptide are present in the crystal. An example of this can be found in PDB structure 1EN2. Since these residues belong to a different residue type (e.g. let's say Ser 60 and Cys 60) they should not be stored in a single \texttt{Residue} object as in the common case. In this case, each residue is represented by one \texttt{Residue} object, and both \texttt{Residue} objects are stored in a single \texttt{Disordered\-Residue} object (see Fig. \ref{fig:smcra}). The \texttt{Dis\-ordered\-Residue} object forwards all un\-caught methods to the selected \texttt{Residue} object (by default the last \texttt{Residue} object added), and thus behaves like an ordinary residue. Each \texttt{Residue} object in a \texttt{Disordered\-Residue} object can be uniquely identified by its residue name. In the above example, residue Ser 60 would have id ``SER'' in the \texttt{Disordered\-Residue} object, while residue Cys 60 would have id ``CYS''. The user can select the active \texttt{Residue} object in a \texttt{Disordered\-Residue} object via this id. Example: suppose that a chain has a point mutation at position 10, consisting of a Ser and a Cys residue. Make sure that residue 10 of this chain behaves as the Cys residue. \begin{verbatim} >>> residue = chain[10] >>> residue.disordered_select('CYS') \end{verbatim} In addition, you can get a list of all \texttt{Atom} objects (ie. all \texttt{DisorderedAtom} objects are 'unpacked' to their individual \texttt{Atom} objects) using the \texttt{get\_unpacked\_list} method of a \texttt{(Disordered)\-Residue} object. \section{Hetero residues} \subsection{Associated problems\label{hetero problems}} A common problem with hetero residues is that several hetero and non-hetero residues present in the same chain share the same sequence identifier (and insertion code). Therefore, to generate a unique id for each hetero residue, waters and other hetero residues are treated in a different way. Remember that Residue object have the tuple (hetfield, resseq, icode) as id. The hetfield is blank (`` '') for amino and nucleic acids, and a string for waters and other hetero residues. The content of the hetfield is explained below. \subsection{Water residues} The hetfield string of a water residue consists of the letter ``W''. So a typical residue id for a water is (``W'', 1, `` ''). \subsection{Other hetero residues} The hetfield string for other hetero residues starts with ``H\_'' followed by the residue name. A glucose molecule e.g. with residue name ``GLC'' would have hetfield ``H\_GLC''. Its residue id could e.g. be (``H\_GLC'', 1, `` ''). \section{Navigating through a Structure object} \subsubsection*{Parse a PDB file, and extract some Model, Chain, Residue and Atom objects} \begin{verbatim} >>> from Bio.PDB.PDBParser import PDBParser >>> parser = PDBParser() >>> structure = parser.get_structure("test", "1fat.pdb") >>> model = structure[0] >>> chain = model["A"] >>> residue = chain[1] >>> atom = residue["CA"] \end{verbatim} \subsubsection*{Iterating through all atoms of a structure} \begin{verbatim} >>> p = PDBParser() >>> structure = p.get_structure('X', 'pdb1fat.ent') >>> for model in structure: ... for chain in model: ... for residue in chain: ... for atom in residue: ... print(atom) ... \end{verbatim} There is a shortcut if you want to iterate over all atoms in a structure: \begin{verbatim} >>> atoms = structure.get_atoms() >>> for atom in atoms: ... print(atom) ... \end{verbatim} Similarly, to iterate over all atoms in a chain, use \begin{verbatim} >>> atoms = chain.get_atoms() >>> for atom in atoms: ... print(atom) ... \end{verbatim} \subsubsection*{Iterating over all residues of a model} or if you want to iterate over all residues in a model: \begin{verbatim} >>> residues = model.get_residues() >>> for residue in residues: ... print(residue) ... \end{verbatim} You can also use the \verb+Selection.unfold_entities+ function to get all residues from a structure: \begin{verbatim} >>> res_list = Selection.unfold_entities(structure, 'R') \end{verbatim} or to get all atoms from a chain: \begin{verbatim} >>> atom_list = Selection.unfold_entities(chain, 'A') \end{verbatim} Obviously, \verb+A=atom, R=residue, C=chain, M=model, S=structure+. You can use this to go up in the hierarchy, e.g. to get a list of (unique) \verb+Residue+ or \verb+Chain+ parents from a list of \verb+Atoms+: \begin{verbatim} >>> residue_list = Selection.unfold_entities(atom_list, 'R') >>> chain_list = Selection.unfold_entities(atom_list, 'C') \end{verbatim} For more info, see the API documentation. \subsubsection*{Extract a hetero residue from a chain (e.g. a glucose (GLC) moiety with resseq 10)} \begin{verbatim} >>> residue_id = ("H_GLC", 10, " ") >>> residue = chain[residue_id] \end{verbatim} \subsubsection*{Print all hetero residues in chain} \begin{verbatim} >>> for residue in chain.get_list(): ... residue_id = residue.get_id() ... hetfield = residue_id[0] ... if hetfield[0]=="H": ... print(residue_id) ... \end{verbatim} \subsubsection*{Print out the coordinates of all CA atoms in a structure with B factor greater than 50} \begin{verbatim} >>> for model in structure.get_list(): ... for chain in model.get_list(): ... for residue in chain.get_list(): ... if residue.has_id("CA"): ... ca = residue["CA"] ... if ca.get_bfactor() > 50.0: ... print(ca.get_coord()) ... \end{verbatim} \subsubsection*{Print out all the residues that contain disordered atoms} \begin{verbatim} >>> for model in structure.get_list(): ... for chain in model.get_list(): ... for residue in chain.get_list(): ... if residue.is_disordered(): ... resseq = residue.get_id()[1] ... resname = residue.get_resname() ... model_id = model.get_id() ... chain_id = chain.get_id() ... print(model_id, chain_id, resname, resseq) ... \end{verbatim} \subsubsection*{Loop over all disordered atoms, and select all atoms with altloc A (if present)} This will make sure that the SMCRA data structure will behave as if only the atoms with altloc A are present. \begin{verbatim} >>> for model in structure.get_list(): ... for chain in model.get_list(): ... for residue in chain.get_list(): ... if residue.is_disordered(): ... for atom in residue.get_list(): ... if atom.is_disordered(): ... if atom.disordered_has_id("A"): ... atom.disordered_select("A") ... \end{verbatim} \subsubsection*{Extracting polypeptides from a \texttt{Structure} object\label{subsubsec:extracting_polypeptides}} To extract polypeptides from a structure, construct a list of \texttt{Polypeptide} objects from a \texttt{Structure} object using \texttt{PolypeptideBuilder} as follows: \begin{verbatim} >>> model_nr = 1 >>> polypeptide_list = build_peptides(structure, model_nr) >>> for polypeptide in polypeptide_list: ... print(polypeptide) ... \end{verbatim} A Polypeptide object is simply a UserList of Residue objects, and is always created from a single Model (in this case model 1). You can use the resulting \texttt{Polypeptide} object to get the sequence as a \texttt{Seq} object or to get a list of C$\alpha$ atoms as well. Polypeptides can be built using a C-N or a C$\alpha$-C$\alpha$ distance criterion. Example: \begin{verbatim} # Using C-N >>> ppb=PPBuilder() >>> for pp in ppb.build_peptides(structure): ... print(pp.get_sequence()) ... # Using CA-CA >>> ppb=CaPPBuilder() >>> for pp in ppb.build_peptides(structure): ... print(pp.get_sequence()) ... \end{verbatim} Note that in the above case only model 0 of the structure is considered by \texttt{PolypeptideBuilder}. However, it is possible to use \texttt{PolypeptideBuilder} to build \texttt{Polypeptide} objects from \texttt{Model} and \texttt{Chain} objects as well. \subsubsection*{Obtaining the sequence of a structure} The first thing to do is to extract all polypeptides from the structure (as above). The sequence of each polypeptide can then easily be obtained from the \texttt{Polypeptide} objects. The sequence is represented as a Biopython \texttt{Seq} object, and its alphabet is defined by a \texttt{ProteinAlphabet} object. Example: \begin{verbatim} >>> seq = polypeptide.get_sequence() >>> print(seq) Seq('SNVVE...', ) \end{verbatim} \section{Analyzing structures} \subsection{Measuring distances} The minus operator for atoms has been overloaded to return the distance between two atoms. \begin{verbatim} # Get some atoms >>> ca1 = residue1['CA'] >>> ca2 = residue2['CA'] # Simply subtract the atoms to get their distance >>> distance = ca1-ca2 \end{verbatim} \subsection{Measuring angles} Use the vector representation of the atomic coordinates, and the \texttt{calc\_angle} function from the \texttt{Vector} module: \begin{verbatim} >>> vector1 = atom1.get_vector() >>> vector2 = atom2.get_vector() >>> vector3 = atom3.get_vector() >>> angle = calc_angle(vector1, vector2, vector3) \end{verbatim} \subsection{Measuring torsion angles} Use the vector representation of the atomic coordinates, and the \texttt{calc\_dihedral} function from the \texttt{Vector} module: \begin{verbatim} >>> vector1 = atom1.get_vector() >>> vector2 = atom2.get_vector() >>> vector3 = atom3.get_vector() >>> vector4 = atom4.get_vector() >>> angle = calc_dihedral(vector1, vector2, vector3, vector4) \end{verbatim} \subsection{Determining atom-atom contacts} Use \texttt{NeighborSearch} to perform neighbor lookup. The neighbor lookup is done using a KD tree module written in C (see \texttt{Bio.KDTree}), making it very fast. It also includes a fast method to find all point pairs within a certain distance of each other. \subsection{Superimposing two structures} Use a \texttt{Superimposer} object to superimpose two coordinate sets. This object calculates the rotation and translation matrix that rotates two lists of atoms on top of each other in such a way that their RMSD is minimized. Of course, the two lists need to contain the same number of atoms. The \texttt{Superimposer} object can also apply the rotation/translation to a list of atoms. The rotation and translation are stored as a tuple in the \texttt{rotran} attribute of the \texttt{Superimposer} object (note that the rotation is right multiplying!). The RMSD is stored in the \texttt{rmsd} attribute. The algorithm used by \texttt{Superimposer} comes from \cite[Golub \& Van Loan]{golub1989} and makes use of singular value decomposition (this is implemented in the general \texttt{Bio.SVDSuperimposer} module). Example: \begin{verbatim} >>> sup = Superimposer() # Specify the atom lists # 'fixed' and 'moving' are lists of Atom objects # The moving atoms will be put on the fixed atoms >>> sup.set_atoms(fixed, moving) # Print rotation/translation/rmsd >>> print(sup.rotran) >>> print(sup.rms) # Apply rotation/translation to the moving atoms >>> sup.apply(moving) \end{verbatim} To superimpose two structures based on their active sites, use the active site atoms to calculate the rotation/translation matrices (as above), and apply these to the whole molecule. \subsection{Mapping the residues of two related structures onto each other} First, create an alignment file in FASTA format, then use the \texttt{StructureAlignment} class. This class can also be used for alignments with more than two structures. \subsection{Calculating the Half Sphere Exposure} Half Sphere Exposure (HSE) is a new, 2D measure of solvent exposure \cite{hamelryck2005}. Basically, it counts the number of C$\alpha$ atoms around a residue in the direction of its side chain, and in the opposite direction (within a radius of $13 \AA$). Despite its simplicity, it outperforms many other measures of solvent exposure. HSE comes in two flavors: HSE$\alpha$ and HSE$\beta$. The former only uses the C$\alpha$ atom positions, while the latter uses the C$\alpha$ and C$\beta$ atom positions. The HSE measure is calculated by the \texttt{HSExposure} class, which can also calculate the contact number. The latter class has methods which return dictionaries that map a \texttt{Residue} object to its corresponding HSE$\alpha$, HSE$\beta$ and contact number values. Example: \begin{verbatim} >>> model = structure[0] >>> hse = HSExposure() # Calculate HSEalpha >>> exp_ca = hse.calc_hs_exposure(model, option='CA3') # Calculate HSEbeta >>> exp_cb=hse.calc_hs_exposure(model, option='CB') # Calculate classical coordination number >>> exp_fs = hse.calc_fs_exposure(model) # Print HSEalpha for a residue >>> print(exp_ca[some_residue]) \end{verbatim} \subsection{Determining the secondary structure} For this functionality, you need to install DSSP (and obtain a license for it --- free for academic use, see \url{http://www.cmbi.kun.nl/gv/dssp/}). Then use the \texttt{DSSP} class, which maps \texttt{Residue} objects to their secondary structure (and accessible surface area). The DSSP codes are listed in Table \ref{cap:DSSP-codes}. Note that DSSP (the program, and thus by consequence the class) cannot handle multiple models! \begin{table} \begin{tabular}{|c|c|} \hline Code& Secondary structure \\ \hline \hline H& $\alpha$-helix \\ \hline B& Isolated $\beta$-bridge residue \\ \hline E& Strand \\ \hline G& 3-10 helix \\ \hline I& $\Pi$-helix \\ \hline T& Turn\\ \hline S& Bend \\ \hline -& Other\\ \hline \end{tabular} \caption{\label{cap:DSSP-codes}DSSP codes in Bio.PDB.} \end{table} The \texttt{DSSP} class can also be used to calculate the accessible surface area of a residue. But see also section \ref{subsec:residue_depth}. \subsection{Calculating the residue depth\label{subsec:residue_depth}} Residue depth is the average distance of a residue's atoms from the solvent accessible surface. It's a fairly new and very powerful parameterization of solvent accessibility. For this functionality, you need to install Michel Sanner's MSMS program (\url{http://www.scripps.edu/pub/olson-web/people/sanner/html/msms_home.html}). Then use the \texttt{ResidueDepth} class. This class behaves as a dictionary which maps \texttt{Residue} objects to corresponding (residue depth, C$\alpha$ depth) tuples. The C$\alpha$ depth is the distance of a residue's C$\alpha$ atom to the solvent accessible surface. Example: \begin{verbatim} >>> model = structure[0] >>> rd = ResidueDepth(model, pdb_file) >>> residue_depth, ca_depth=rd[some_residue] \end{verbatim} You can also get access to the molecular surface itself (via the \texttt{get\_surface} function), in the form of a Numeric Python array with the surface points. \section{Common problems in PDB files} It is well known that many PDB files contain semantic errors (not the structures themselves, but their representation in PDB files). Bio.PDB tries to handle this in two ways. The PDBParser object can behave in two ways: a restrictive way and a permissive way, which is the default. Example: \begin{verbatim} # Permissive parser >>> parser = PDBParser(PERMISSIVE=1) >>> parser = PDBParser() # The same (default) # Strict parser >>> strict_parser = PDBParser(PERMISSIVE=0) \end{verbatim} In the permissive state (DEFAULT), PDB files that obviously contain errors are ``corrected'' (i.e. some residues or atoms are left out). These errors include: \begin{itemize} \item Multiple residues with the same identifier \item Multiple atoms with the same identifier (taking into account the altloc identifier) \end{itemize} These errors indicate real problems in the PDB file (for details see \cite[Hamelryck and Manderick, 2003]{hamelryck2003a}). In the restrictive state, PDB files with errors cause an exception to occur. This is useful to find errors in PDB files. Some errors however are automatically corrected. Normally each disordered atom should have a non-blank altloc identifier. However, there are many structures that do not follow this convention, and have a blank and a non-blank identifier for two disordered positions of the same atom. This is automatically interpreted in the right way. Sometimes a structure contains a list of residues belonging to chain A, followed by residues belonging to chain B, and again followed by residues belonging to chain A, i.e. the chains are 'broken'. This is also correctly interpreted. \subsection{Examples\label{problem structures}} The PDBParser/Structure class was tested on about 800 structures (each belonging to a unique SCOP superfamily). This takes about 20 minutes, or on average 1.5 seconds per structure. Parsing the structure of the large ribosomal subunit (1FKK), which contains about 64000 atoms, takes 10 seconds on a 1000 MHz PC. Three exceptions were generated in cases where an unambiguous data structure could not be built. In all three cases, the likely cause is an error in the PDB file that should be corrected. Generating an exception in these cases is much better than running the chance of incorrectly describing the structure in a data structure. \subsubsection{Duplicate residues} One structure contains two amino acid residues in one chain with the same sequence identifier (resseq 3) and icode. Upon inspection it was found that this chain contains the residues Thr A3, \ldots, Gly A202, Leu A3, Glu A204. Clearly, Leu A3 should be Leu A203. A couple of similar situations exist for structure 1FFK (which e.g. contains Gly B64, Met B65, Glu B65, Thr B67, i.e. residue Glu B65 should be Glu B66). \subsubsection{Duplicate atoms} Structure 1EJG contains a Ser/Pro point mutation in chain A at position 22. In turn, Ser 22 contains some disordered atoms. As expected, all atoms belonging to Ser 22 have a non-blank altloc specifier (B or C). All atoms of Pro 22 have altloc A, except the N atom which has a blank altloc. This generates an exception, because all atoms belonging to two residues at a point mutation should have non-blank altloc. It turns out that this atom is probably shared by Ser and Pro 22, as Ser 22 misses the N atom. Again, this points to a problem in the file: the N atom should be present in both the Ser and the Pro residue, in both cases associated with a suitable altloc identifier. \subsection{Automatic correction} Some errors are quite common and can be easily corrected without much risk of making a wrong interpretation. These cases are listed below. \subsubsection{A blank altloc for a disordered atom} Normally each disordered atom should have a non-blank altloc identifier. However, there are many structures that do not follow this convention, and have a blank and a non-blank identifier for two disordered positions of the same atom. This is automatically interpreted in the right way. \subsubsection{Broken chains} Sometimes a structure contains a list of residues belonging to chain A, followed by residues belonging to chain B, and again followed by residues belonging to chain A, i.e. the chains are ``broken''. This is correctly interpreted. \subsection{Fatal errors} Sometimes a PDB file cannot be unambiguously interpreted. Rather than guessing and risking a mistake, an exception is generated, and the user is expected to correct the PDB file. These cases are listed below. \subsubsection{Duplicate residues} All residues in a chain should have a unique id. This id is generated based on: \begin{itemize} \item The sequence identifier (resseq). \item The insertion code (icode). \item The hetfield string (``W'' for waters and ``H\_'' followed by the residue name for other hetero residues) \item The residue names of the residues in the case of point mutations (to store the Residue objects in a DisorderedResidue object). \end{itemize} If this does not lead to a unique id something is quite likely wrong, and an exception is generated. \subsubsection{Duplicate atoms} All atoms in a residue should have a unique id. This id is generated based on: \begin{itemize} \item The atom name (without spaces, or with spaces if a problem arises). \item The altloc specifier. \end{itemize} If this does not lead to a unique id something is quite likely wrong, and an exception is generated. \section{Accessing the Protein Data Bank} \subsection{Downloading structures from the Protein Data Bank} Structures can be downloaded from the PDB (Protein Data Bank) by using the \texttt{retrieve\_pdb\_file} method on a \texttt{PDBList} object. The argument for this method is the PDB identifier of the structure. \begin{verbatim} >>> pdbl = PDBList() >>> pdbl.retrieve_pdb_file('1FAT') \end{verbatim} The \texttt{PDBList} class can also be used as a command-line tool: \begin{verbatim} python PDBList.py 1fat \end{verbatim} The downloaded file will be called \texttt{pdb1fat.ent} and stored in the current working directory. Note that the \texttt{retrieve\_pdb\_file} method also has an optional argument \texttt{pdir} that specifies a specific directory in which to store the downloaded PDB files. The \texttt{retrieve\_pdb\_file} method also has some options to specify the compression format used for the download, and the program used for local decompression (default \texttt{.Z} format and \texttt{gunzip}). In addition, the PDB ftp site can be specified upon creation of the \texttt{PDBList} object. By default, the server of the Worldwide Protein Data Bank (\url{ftp://ftp.wwpdb.org/pub/pdb/data/structures/divided/pdb/}) is used. See the API documentation for more details. Thanks again to Kristian Rother for donating this module. \subsection{Downloading the entire PDB} The following commands will store all PDB files in the \texttt{/data/pdb} directory: \begin{verbatim} python PDBList.py all /data/pdb python PDBList.py all /data/pdb -d \end{verbatim} \noindent The API method for this is called \texttt{download\_entire\_pdb}. Adding the \texttt{-d} option will store all files in the same directory. Otherwise, they are sorted into PDB-style subdirectories according to their PDB ID's. Depending on the traffic, a complete download will take 2-4 days. \subsection{Keeping a local copy of the PDB up to date} This can also be done using the \texttt{PDBList} object. One simply creates a \texttt{PDBList} object (specifying the directory where the local copy of the PDB is present) and calls the \texttt{update\_pdb} method: \begin{verbatim} >>> pl = PDBList(pdb='/data/pdb') >>> pl.update_pdb() \end{verbatim} One can of course make a weekly \texttt{cronjob} out of this to keep the local copy automatically up-to-date. The PDB ftp site can also be specified (see API documentation). \texttt{PDBList} has some additional methods that can be of use. The \texttt{get\_all\_obsolete} method can be used to get a list of all obsolete PDB entries. The \texttt{changed\_this\_week} method can be used to obtain the entries that were added, modified or obsoleted during the current week. For more info on the possibilities of \texttt{PDBList}, see the API documentation. \section{General questions} \subsection{How well tested is Bio.PDB?} Pretty well, actually. Bio.PDB has been extensively tested on nearly 5500 structures from the PDB - all structures seemed to be parsed correctly. More details can be found in the Bio.PDB Bioinformatics article. Bio.PDB has been used/is being used in many research projects as a reliable tool. In fact, I'm using Bio.PDB almost daily for research purposes and continue working on improving it and adding new features. \subsection{How fast is it?} The \texttt{PDBParser} performance was tested on about 800 structures (each belonging to a unique SCOP superfamily). This takes about 20 minutes, or on average 1.5 seconds per structure. Parsing the structure of the large ribosomal subunit (1FKK), which contains about 64000 atoms, takes 10 seconds on a 1000 MHz PC. In short: it's more than fast enough for many applications. \subsection{Is there support for molecular graphics?} Not directly, mostly since there are quite a few Python based/Python aware solutions already, that can potentially be used with Bio.PDB. My choice is Pymol, BTW (I've used this successfully with Bio.PDB, and there will probably be specific PyMol modules in Bio.PDB soon/some day). Python based/aware molecular graphics solutions include: \begin{itemize} \item PyMol: \url{http://pymol.sourceforge.net/} \item Chimera: \url{http://www.cgl.ucsf.edu/chimera/} \item PMV: \url{http://www.scripps.edu/~sanner/python/} \item Coot: \url{http://www.ysbl.york.ac.uk/~emsley/coot/} \item CCP4mg: \url{http://www.ysbl.york.ac.uk/~lizp/molgraphics.html} \item mmLib: \url{http://pymmlib.sourceforge.net/} \item VMD: \url{http://www.ks.uiuc.edu/Research/vmd/} \item MMTK: \url{http://starship.python.net/crew/hinsen/MMTK/} \end{itemize} \subsection{Who's using Bio.PDB?} Bio.PDB was used in the construction of DISEMBL, a web server that predicts disordered regions in proteins (\url{http://dis.embl.de/}), and COLUMBA, a website that provides annotated protein structures (\url{http://www.columba-db.de/}). Bio.PDB has also been used to perform a large scale search for active sites similarities between protein structures in the PDB \cite[Hamelryck, 2003]{hamelryck2003b}, and to develop a new algorithm that identifies linear secondary structure elements \cite[Majumdar \textit{et al.}, 2005]{majumdar2005}. Judging from requests for features and information, Bio.PDB is also used by several LPCs (Large Pharmaceutical Companies :-). % Perhaps this section should be updated for other Biopython modules, and % moved to the Biopython FAQ above. \chapter{Bio.PopGen: Population genetics} Bio.PopGen is a Biopython module supporting population genetics, available in Biopython 1.44 onwards. The medium term objective for the module is to support widely used data formats, applications and databases. This module is currently under intense development and support for new features should appear at a rather fast pace. Unfortunately this might also entail some instability on the API, especially if you are using a development version. APIs that are made available on our official public releases should be much more stable. \section{GenePop} GenePop (\url{http://genepop.curtin.edu.au/}) is a popular population genetics software package supporting Hardy-Weinberg tests, linkage desiquilibrium, population diferentiation, basic statistics, $F_{st}$ and migration estimates, among others. GenePop does not supply sequence based statistics as it doesn't handle sequence data. The GenePop file format is supported by a wide range of other population genetic software applications, thus making it a relevant format in the population genetics field. Bio.PopGen provides a parser and generator of GenePop file format. Utilities to manipulate the content of a record are also provided. Here is an example on how to read a GenePop file (you can find example GenePop data files in the Test/PopGen directory of Biopython): \begin{verbatim} from Bio.PopGen import GenePop handle = open("example.gen") rec = GenePop.read(handle) handle.close() \end{verbatim} This will read a file called example.gen and parse it. If you do print rec, the record will be output again, in GenePop format. The most important information in rec will be the loci names and population information (but there is more -- use help(GenePop.Record) to check the API documentation). Loci names can be found on rec.loci\_list. Population information can be found on rec.populations. Populations is a list with one element per population. Each element is itself a list of individuals, each individual is a pair composed by individual name and a list of alleles (2 per marker), here is an example for rec.populations: \begin{verbatim} [ [ ('Ind1', [(1, 2), (3, 3), (200, 201)], ('Ind2', [(2, None), (3, 3), (None, None)], ], [ ('Other1', [(1, 1), (4, 3), (200, 200)], ] ] \end{verbatim} So we have two populations, the first with two individuals, the second with only one. The first individual of the first population is called Ind1, allelic information for each of the 3 loci follows. Please note that for any locus, information might be missing (see as an example, Ind2 above). A few utility functions to manipulate GenePop records are made available, here is an example: \begin{verbatim} from Bio.PopGen import GenePop #Imagine that you have loaded rec, as per the code snippet above... rec.remove_population(pos) #Removes a population from a record, pos is the population position in # rec.populations, remember that it starts on position 0. # rec is altered. rec.remove_locus_by_position(pos) #Removes a locus by its position, pos is the locus position in # rec.loci_list, remember that it starts on position 0. # rec is altered. rec.remove_locus_by_name(name) #Removes a locus by its name, name is the locus name as in # rec.loci_list. If the name doesn't exist the function fails # silently. # rec is altered. rec_loci = rec.split_in_loci() #Splits a record in loci, that is, for each loci, it creates a new # record, with a single loci and all populations. # The result is returned in a dictionary, being each key the locus name. # The value is the GenePop record. # rec is not altered. rec_pops = rec.split_in_pops(pop_names) #Splits a record in populations, that is, for each population, it creates # a new record, with a single population and all loci. # The result is returned in a dictionary, being each key # the population name. As population names are not available in GenePop, # they are passed in array (pop_names). # The value of each dictionary entry is the GenePop record. # rec is not altered. \end{verbatim} GenePop does not support population names, a limitation which can be cumbersome at times. Functionality to enable population names is currently being planned for Biopython. These extensions won't break compatibility in any way with the standard format. In the medium term, we would also like to support the GenePop web service. \section{Coalescent simulation} A coalescent simulation is a backward model of population genetics with relation to time. A simulation of ancestry is done until the Most Recent Common Ancestor (MRCA) is found. This ancestry relationship starting on the MRCA and ending on the current generation sample is sometimes called a genealogy. Simple cases assume a population of constant size in time, haploidy, no population structure, and simulate the alleles of a single locus under no selection pressure. Coalescent theory is used in many fields like selection detection, estimation of demographic parameters of real populations or disease gene mapping. The strategy followed in the Biopython implementation of the coalescent was not to create a new, built-in, simulator from scratch but to use an existing one, SIMCOAL2 (\url{http://cmpg.unibe.ch/software/simcoal2/}). SIMCOAL2 allows for, among others, population structure, multiple demographic events, simulation of multiple types of loci (SNPs, sequences, STRs/microsatellites and RFLPs) with recombination, diploidy multiple chromosomes or ascertainment bias. Notably SIMCOAL2 doesn't support any selection model. We recommend reading SIMCOAL2's documentation, available in the link above. The input for SIMCOAL2 is a file specifying the desired demography and genome, the output is a set of files (typically around 1000) with the simulated genomes of a sample of individuals per subpopulation. This set of files can be used in many ways, like to compute confidence intervals where which certain statistics (e.g., $F_{st}$ or Tajima D) are expected to lie. Real population genetics datasets statistics can then be compared to those confidence intervals. Biopython coalescent code allows to create demographic scenarios and genomes and to run SIMCOAL2. \subsection{Creating scenarios} Creating a scenario involves both creating a demography and a chromosome structure. In many cases (e.g. when doing Approximate Bayesian Computations -- ABC) it is important to test many parameter variations (e.g. vary the effective population size, $N_e$, between 10, 50, 500 and 1000 individuals). The code provided allows for the simulation of scenarios with different demographic parameters very easily. Below we see how we can create scenarios and then how simulate them. \subsubsection{Demography} A few predefined demographies are built-in, all have two shared parameters: sample size (called sample\_size on the template, see below for its use) per deme and deme size, i.e. subpopulation size (pop\_size). All demographies are available as templates where all parameters can be varied, each template has a system name. The prefedined demographies/templates are: \begin{description} \item[Single population, constant size] The standard parameters are enough to specify it. Template name: simple. \item[Single population, bottleneck] As seen on figure \ref{fig:bottle}. The parameters are current population size (pop\_size on template ne3 on figure), time of expansion, given as the generation in the past when it occurred (expand\_gen), effective population size during bottleneck (ne2), time of contraction (contract\_gen) and original size in the remote past (ne3). Template name: bottle. \item[Island model] The typical island model. The total number of demes is specified by total\_demes and the migration rate by mig. Template name island. \item[Stepping stone model - 1 dimension] The stepping stone model in 1 dimension, extremes disconnected. The total number of demes is total\_demes, migration rate is mig. Template name is ssm\_1d. \item[Stepping stone model - 2 dimensions] The stepping stone model in 2 dimensions, extremes disconnected. The parameters are x for the horizontal dimension and y for the vertical (being the total number of demes x times y), migration rate is mig. Template name is ssm\_2d. \end{description} \begin{htmlonly} \label{fig:bottle} \imgsrc{images/bottle.png} \end{htmlonly} \begin{latexonly} \begin{figure}[htbp] \centering \includegraphics{images/bottle.png} \caption{A bottleneck} \label{fig:bottle} \end{figure} \end{latexonly} In our first example, we will generate a template for a single population, constant size model with a sample size of 30 and a deme size of 500. The code for this is: \begin{verbatim} from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template generate_simcoal_from_template('simple', [(1, [('SNP', [24, 0.0005, 0.0])])], [('sample_size', [30]), ('pop_size', [100])]) \end{verbatim} Executing this code snippet will generate a file on the current directory called simple\_100\_300.par this file can be given as input to SIMCOAL2 to simulate the demography (below we will see how Biopython can take care of calling SIMCOAL2). This code consists of a single function call, let's discuss it parameter by parameter. The first parameter is the template id (from the list above). We are using the id 'simple' which is the template for a single population of constant size along time. The second parameter is the chromosome structure. Please ignore it for now, it will be explained in the next section. The third parameter is a list of all required parameters (recall that the simple model only needs sample\_size and pop\_size) and possible values (in this case each parameter only has a possible value). Now, let's consider an example where we want to generate several island models, and we are interested in varying the number of demes: 10, 50 and 100 with a migration rate of 1\%. Sample size and deme size will be the same as before. Here is the code: \begin{verbatim} from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template generate_simcoal_from_template('island', [(1, [('SNP', [24, 0.0005, 0.0])])], [('sample_size', [30]), ('pop_size', [100]), ('mig', [0.01]), ('total_demes', [10, 50, 100])]) \end{verbatim} In this case, 3 files will be generated: island\_100\_0.01\_100\_30.par, island\_10\_0.01\_100\_30.par and island\_50\_0.01\_100\_30.par. Notice the rule to make file names: template name, followed by parameter values in reverse order. A few, arguably more esoteric template demographies exist (please check the Bio/PopGen/SimCoal/data directory on Biopython source tree). Furthermore it is possible for the user to create new templates. That functionality will be discussed in a future version of this document. \subsubsection{Chromosome structure} We strongly recommend reading SIMCOAL2 documentation to understand the full potential available in modeling chromosome structures. In this subsection we only discuss how to implement chromosome structures using the Biopython interface, not the underlying SIMCOAL2 capabilities. We will start by implementing a single chromosome, with 24 SNPs with a recombination rate immediately on the right of each locus of 0.0005 and a minimum frequency of the minor allele of 0. This will be specified by the following list (to be passed as second parameter to the function generate\_simcoal\_from\_template): \begin{verbatim} [(1, [('SNP', [24, 0.0005, 0.0])])] \end{verbatim} This is actually the chromosome structure used in the above examples. The chromosome structure is represented by a list of chromosomes, each chromosome (i.e., each element in the list) is composed by a tuple (a pair): the first element is the number of times the chromosome is to be repeated (as there might be interest in repeating the same chromosome many times). The second element is a list of the actual components of the chromosome. Each element is again a pair, the first member is the locus type and the second element the parameters for that locus type. Confused? Before showing more examples let's review the example above: We have a list with one element (thus one chromosome), the chromosome is a single instance (therefore not to be repeated), it is composed of 24 SNPs, with a recombination rate of 0.0005 between each consecutive SNP, the minimum frequency of the minor allele is 0.0 (i.e, it can be absent from a certain population). Let's see a more complicated example: \begin{verbatim} [ (5, [ ('SNP', [24, 0.0005, 0.0]) ] ), (2, [ ('DNA', [10, 0.0, 0.00005, 0.33]), ('RFLP', [1, 0.0, 0.0001]), ('MICROSAT', [1, 0.0, 0.001, 0.0, 0.0]) ] ) ] \end{verbatim} We start by having 5 chromosomes with the same structure as above (i.e., 24 SNPs). We then have 2 chromosomes which have a DNA sequence with 10 nucleotides, 0.0 recombination rate, 0.0005 mutation rate, and a transition rate of 0.33. Then we have an RFLP with 0.0 recombination rate to the next locus and a 0.0001 mutation rate. Finally we have a microsatellite (or STR), with 0.0 recombination rate to the next locus (note, that as this is a single microsatellite which has no loci following, this recombination rate here is irrelevant), with a mutation rate of 0.001, geometric parameter of 0.0 and a range constraint of 0.0 (for information about this parameters please consult the SIMCOAL2 documentation, you can use them to simulate various mutation models, including the typical -- for microsatellites -- stepwise mutation model among others). \subsection{Running SIMCOAL2} We now discuss how to run SIMCOAL2 from inside Biopython. It is required that the binary for SIMCOAL2 is called simcoal2 (or simcoal2.exe on Windows based platforms), please note that the typical name when downloading the program is in the format simcoal2\_x\_y. As such, when installing SIMCOAL2 you will need to rename of the downloaded executable so that Biopython can find it. It is possible to run SIMCOAL2 on files that were not generated using the method above (e.g., writing a parameter file by hand), but we will show an example by creating a model using the framework presented above. \begin{verbatim} from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template from Bio.PopGen.SimCoal.Controller import SimCoalController generate_simcoal_from_template('simple', [ (5, [ ('SNP', [24, 0.0005, 0.0]) ] ), (2, [ ('DNA', [10, 0.0, 0.00005, 0.33]), ('RFLP', [1, 0.0, 0.0001]), ('MICROSAT', [1, 0.0, 0.001, 0.0, 0.0]) ] ) ], [('sample_size', [30]), ('pop_size', [100])]) ctrl = SimCoalController('.') ctrl.run_simcoal('simple_100_30.par', 50) \end{verbatim} The lines of interest are the last two (plus the new import). Firstly a controller for the application is created. The directory where the binary is located has to be specified. The simulator is then run on the last line: we know, from the rules explained above, that the input file name is simple\_100\_30.par for the simulation parameter file created. We then specify that we want to run 50 independent simulations, by default Biopython requests a simulation of diploid data, but a third parameter can be added to simulate haploid data (adding as a parameter the string '0'). SIMCOAL2 will now run (please note that this can take quite a lot of time) and will create a directory with the simulation results. The results can now be analysed (typically studying the data with Arlequin3). In the future Biopython might support reading the Arlequin3 format and thus allowing for the analysis of SIMCOAL2 data inside Biopython. \section{Other applications} Here we discuss interfaces and utilities to deal with population genetics' applications which arguably have a smaller user base. \subsection{FDist: Detecting selection and molecular adaptation} FDist is a selection detection application suite based on computing (i.e. simulating) a ``neutral'' confidence interval based on $F_{st}$ and heterozygosity. Markers (which can be SNPs, microsatellites, AFLPs among others) which lie outside the ``neutral'' interval are to be considered as possible candidates for being under selection. FDist is mainly used when the number of markers is considered enough to estimate an average $F_{st}$, but not enough to either have outliers calculated from the dataset directly or, with even more markers for which the relative positions in the genome are known, to use approaches based on, e.g., Extended Haplotype Heterozygosity (EHH). The typical usage pattern for FDist is as follows: \begin{enumerate} \item Import a dataset from an external format into FDist format. \item Compute average $F_{st}$. This is done by datacal inside FDist. \item Simulate ``neutral'' markers based on the average $F_{st}$ and expected number of total populations. This is the core operation, done by fdist inside FDist. \item Calculate the confidence interval, based on the desired confidence boundaries (typically 95\% or 99\%). This is done by cplot and is mainly used to plot the interval. \item Assess each marker status against the simulation ``neutral'' confidence interval. Done by pv. This is used to detect the outlier status of each marker against the simulation. \end{enumerate} We will now discuss each step with illustrating example code (for this example to work FDist binaries have to be on the executable PATH). The FDist data format is application specific and is not used at all by other applications, as such you will probably have to convert your data for use with FDist. Biopython can help you do this. Here is an example converting from GenePop format to FDist format (along with imports that will be needed on examples further below): \begin{verbatim} from Bio.PopGen import GenePop from Bio.PopGen import FDist from Bio.PopGen.FDist import Controller from Bio.PopGen.FDist.Utils import convert_genepop_to_fdist gp_rec = GenePop.read(open("example.gen")) fd_rec = convert_genepop_to_fdist(gp_rec) in_file = open("infile", "w") in_file.write(str(fd_rec)) in_file.close() \end{verbatim} In this code we simply parse a GenePop file and convert it to a FDist record. Printing an FDist record will generate a string that can be directly saved to a file and supplied to FDist. FDist requires the input file to be called infile, therefore we save the record on a file with that name. The most important fields on a FDist record are: num\_pops, the number of populations; num\_loci, the number of loci and loci\_data with the marker data itself. Most probably the details of the record are of no interest to the user, as the record only purpose is to be passed to FDist. The next step is to calculate the average $F_{st}$ of the dataset (along with the sample size): \begin{verbatim} ctrl = Controller.FDistController() fst, samp_size = ctrl.run_datacal() \end{verbatim} On the first line we create an object to control the call of FDist suite, this object will be used further on in order to call other suite applications. On the second line we call the datacal application which computes the average $F_{st}$ and the sample size. It is worth noting that the $F_{st}$ computed by datacal is a \emph{variation} of Weir and Cockerham's $\theta$. We can now call the main fdist application in order to simulate neutral markers. \begin{verbatim} sim_fst = ctrl.run_fdist(npops = 15, nsamples = fd_rec.num_pops, fst = fst, sample_size = samp_size, mut = 0, num_sims = 40000) \end{verbatim} \begin{description} \item[npops] Number of populations existing in nature. This is really a ``guestimate''. Has to be lower than 100. \item[nsamples] Number of populations sampled, has to be lower than npops. \item[fst] Average $F_{st}$. \item[sample\_size] Average number of individuals sampled on each population. \item[mut] Mutation model: 0 - Infinite alleles; 1 - Stepwise mutations \item[num\_sims] Number of simulations to perform. Typically a number around 40000 will be OK, but if you get a confidence interval that looks sharp (this can be detected when plotting the confidence interval computed below) the value can be increased (a suggestion would be steps of 10000 simulations). \end{description} The confusion in wording between number of samples and sample size stems from the original application. A file named out.dat will be created with the simulated heterozygosities and $F_{st}$s, it will have as many lines as the number of simulations requested. Note that fdist returns the average $F_{st}$ that it was \emph{capable} of simulating, for more details about this issue please read below the paragraph on approximating the desired average $F_{st}$. The next (optional) step is to calculate the confidence interval: \begin{verbatim} cpl_interval = ctrl.run_cplot(ci=0.99) \end{verbatim} You can only call cplot after having run fdist. This will calculate the confidence intervals (99\% in this case) for a previous fdist run. A list of quadruples is returned. The first element represents the heterozygosity, the second the lower bound of $F_{st}$ confidence interval for that heterozygosity, the third the average and the fourth the upper bound. This can be used to trace the confidence interval contour. This list is also written to a file, out.cpl. The main purpose of this step is return a set of points which can be easily used to plot a confidence interval. It can be skipped if the objective is only to assess the status of each marker against the simulation, which is the next step... \begin{verbatim} pv_data = ctrl.run_pv() \end{verbatim} You can only call cplot after having run datacal and fdist. This will use the simulated markers to assess the status of each individual real marker. A list, in the same order than the loci\_list that is on the FDist record (which is in the same order that the GenePop record) is returned. Each element in the list is a quadruple, the fundamental member of each quadruple is the last element (regarding the other elements, please refer to the pv documentation -- for the sake of simplicity we will not discuss them here) which returns the probability of the simulated $F_{st}$ being lower than the marker $F_{st}$. Higher values would indicate a stronger candidate for positive selection, lower values a candidate for balancing selection, and intermediate values a possible neutral marker. What is ``higher'', ``lower'' or ``intermediate'' is really a subjective issue, but taking a ``confidence interval'' approach and considering a 95\% confidence interval, ``higher'' would be between 0.95 and 1.0, ``lower'' between 0.0 and 0.05 and ``intermediate'' between 0.05 and 0.95. \subsubsection{Approximating the desired average $F_{st}$} Fdist tries to approximate the desired average $F_{st}$ by doing a coalescent simulation using migration rates based on the formula \[ N_{m} = \frac{1 - F_{st}}{4F_{st}} \] This formula assumes a few premises like an infinite number of populations. In practice, when the number of populations is low, the mutation model is stepwise and the sample size increases, fdist will not be able to simulate an acceptable approximate average $F_{st}$. To address that, a function is provided to iteratively approach the desired value by running several fdists in sequence. This approach is computationally more intensive than running a single fdist run, but yields good results. The following code runs fdist approximating the desired $F_{st}$: \begin{verbatim} sim_fst = ctrl.run_fdist_force_fst(npops = 15, nsamples = fd_rec.num_pops, fst = fst, sample_size = samp_size, mut = 0, num_sims = 40000, limit = 0.05) \end{verbatim} The only new optional parameter, when comparing with run\_fdist, is limit which is the desired maximum error. run\_fdist can (and probably should) be safely replaced with run\_fdist\_force\_fst. \subsubsection{Final notes} The process to determine the average $F_{st}$ can be more sophisticated than the one presented here. For more information we refer you to the FDist README file. Biopython's code can be used to implement more sophisticated approaches. \section{Future Developments} The most desired future developments would be the ones you add yourself ;) . That being said, already existing fully functional code is currently being incorporated in Bio.PopGen, that code covers the applications FDist and SimCoal2, the HapMap and UCSC Table Browser databases and some simple statistics like $F_{st}$, or allele counts. \chapter{Phylogenetics with Bio.Phylo} \label{sec:Phylo} The Bio.Phylo module was introduced in Biopython 1.54. Following the lead of SeqIO and AlignIO, it aims to provide a common way to work with phylogenetic trees independently of the source data format, as well as a consistent API for I/O operations. Bio.Phylo is described in an open-access journal article \cite[Talevich \textit{et al.}, 2012]{talevich2012}, which you might also find helpful. \section{Demo: What's in a Tree?} To get acquainted with the module, let's start with a tree that we've already constructed, and inspect it a few different ways. Then we'll colorize the branches, to use a special phyloXML feature, and finally save it. In a terminal, create a simple Newick file using your favorite text editor: \begin{verbatim} % cat > simple.dnd < (((A,B),(C,D)),(E,F,G)); > EOF \end{verbatim} This tree has no branch lengths, only a topology and labelled terminals. (If you have a real tree file available, you can follow this demo using that instead.) Launch the Python interpreter of your choice: \begin{verbatim} % ipython -pylab \end{verbatim} For interactive work, launching the IPython interpreter with the \verb|-pylab| flag enables \textbf{matplotlib} integration, so graphics will pop up automatically. We'll use that during this demo. Now, within Python, read the tree file, giving the file name and the name of the format. \begin{verbatim} >>> from Bio import Phylo >>> tree = Phylo.read("simple.dnd", "newick") \end{verbatim} Printing the tree object as a string gives us a look at the entire object hierarchy. \begin{verbatim} >>> print(tree) Tree(weight=1.0, rooted=False, name="") Clade(branch_length=1.0) Clade(branch_length=1.0) Clade(branch_length=1.0) Clade(branch_length=1.0, name="A") Clade(branch_length=1.0, name="B") Clade(branch_length=1.0) Clade(branch_length=1.0, name="C") Clade(branch_length=1.0, name="D") Clade(branch_length=1.0) Clade(branch_length=1.0, name="E") Clade(branch_length=1.0, name="F") Clade(branch_length=1.0, name="G") \end{verbatim} The \texttt{Tree} object contains global information about the tree, such as whether it's rooted or unrooted. It has one root clade, and under that, it's nested lists of clades all the way down to the tips. The function \verb|draw_ascii| creates a simple ASCII-art (plain text) dendrogram. This is a convenient visualization for interactive exploration, in case better graphical tools aren't available. \begin{verbatim} >>> Phylo.draw_ascii(tree) ________________________ A ________________________| | |________________________ B ________________________| | | ________________________ C | |________________________| _| |________________________ D | | ________________________ E | | |________________________|________________________ F | |________________________ G \end{verbatim} If you have \textbf{matplotlib} or \textbf{pylab} installed, you can create a graphic using the \verb|draw| function (see Fig. \ref{fig:phylo-simple-draw}): \begin{verbatim} >>> tree.rooted = True >>> Phylo.draw(tree) \end{verbatim} \begin{htmlonly} \imgsrc[width=666, height=530]{images/phylo-simple-draw.png} \label{fig:phylo-simple-draw} \end{htmlonly} \begin{latexonly} \begin{figure}[htb] \centering \includegraphics[width=0.7\textwidth]{images/phylo-simple-draw.png} \caption{A rooted tree drawn with {\tt Phylo.draw}.} \label{fig:phylo-simple-draw} \end{figure} \end{latexonly} \subsection{Coloring branches within a tree} The functions \verb|draw| and \verb|draw_graphviz| support the display of different colors and branch widths in a tree. As of Biopython 1.59, the \verb|color| and \verb|width| attributes are available on the basic Clade object and there's nothing extra required to use them. Both attributes refer to the branch leading the given clade, and apply recursively, so all descendent branches will also inherit the assigned width and color values during display. In earlier versions of Biopython, these were special features of PhyloXML trees, and using the attributes required first converting the tree to a subclass of the basic tree object called Phylogeny, from the Bio.Phylo.PhyloXML module. In Biopython 1.55 and later, this is a convenient tree method: \begin{verbatim} >>> tree = tree.as_phyloxml() \end{verbatim} In Biopython 1.54, you can accomplish the same thing with one extra import: \begin{verbatim} >>> from Bio.Phylo.PhyloXML import Phylogeny >>> tree = Phylogeny.from_tree(tree) \end{verbatim} Note that the file formats Newick and Nexus don't support branch colors or widths, so if you use these attributes in Bio.Phylo, you will only be able to save the values in PhyloXML format. (You can still save a tree as Newick or Nexus, but the color and width values will be skipped in the output file.) Now we can begin assigning colors. First, we'll color the root clade gray. We can do that by assigning the 24-bit color value as an RGB triple, an HTML-style hex string, or the name of one of the predefined colors. \begin{verbatim} >>> tree.root.color = (128, 128, 128) \end{verbatim} Or: \begin{verbatim} >>> tree.root.color = "#808080" \end{verbatim} Or: \begin{verbatim} >>> tree.root.color = "gray" \end{verbatim} Colors for a clade are treated as cascading down through the entire clade, so when we colorize the root here, it turns the whole tree gray. We can override that by assigning a different color lower down on the tree. Let's target the most recent common ancestor (MRCA) of the nodes named ``E'' and ``F''. The \verb|common_ancestor| method returns a reference to that clade in the original tree, so when we color that clade ``salmon'', the color will show up in the original tree. \begin{verbatim} >>> mrca = tree.common_ancestor({"name": "E"}, {"name": "F"}) >>> mrca.color = "salmon" \end{verbatim} If we happened to know exactly where a certain clade is in the tree, in terms of nested list entries, we can jump directly to that position in the tree by indexing it. Here, the index \verb|[0,1]| refers to the second child of the first child of the root. \begin{verbatim} >>> tree.clade[0, 1].color = "blue" \end{verbatim} Finally, show our work (see Fig. \ref{fig:phylo-color-draw}): \begin{verbatim} >>> Phylo.draw(tree) \end{verbatim} \begin{htmlonly} \imgsrc[width=666, height=530]{images/phylo-color-draw.png} \label{fig:phylo-color-draw} \end{htmlonly} \begin{latexonly} \begin{figure}[htb] \centering \includegraphics[width=0.7\textwidth]{images/phylo-color-draw.png} \caption{A colorized tree drawn with {\tt Phylo.draw}.} \label{fig:phylo-color-draw} \end{figure} \end{latexonly} Note that a clade's color includes the branch leading to that clade, as well as its descendents. The common ancestor of E and F turns out to be just under the root, and with this coloring we can see exactly where the root of the tree is. My, we've accomplished a lot! Let's take a break here and save our work. Call the \texttt{write} function with a file name or handle --- here we use standard output, to see what would be written --- and the format \texttt{phyloxml}. PhyloXML saves the colors we assigned, so you can open this phyloXML file in another tree viewer like Archaeopteryx, and the colors will show up there, too. \begin{verbatim} >>> import sys >>> Phylo.write(tree, sys.stdout, "phyloxml") 1.0 128 128 128 1.0 1.0 A ... \end{verbatim} The rest of this chapter covers the core functionality of Bio.Phylo in greater detail. For more examples of using Bio.Phylo, see the cookbook page on Biopython.org: \url{http://biopython.org/wiki/Phylo_cookbook} \section{I/O functions} Like SeqIO and AlignIO, Phylo handles file input and output through four functions: \verb|parse|, \verb|read|, \verb|write| and \verb|convert|, all of which support the tree file formats Newick, NEXUS, phyloXML and NeXML, as well as the Comparative Data Analysis Ontology (CDAO). The \verb|read| function parses a single tree in the given file and returns it. Careful; it will raise an error if the file contains more than one tree, or no trees. \begin{verbatim} >>> from Bio import Phylo >>> tree = Phylo.read("Tests/Nexus/int_node_labels.nwk", "newick") >>> print(tree) \end{verbatim} (Example files are available in the \texttt{Tests/Nexus/} and \texttt{Tests/PhyloXML/} directories of the Biopython distribution.) To handle multiple (or an unknown number of) trees, use the \verb|parse| function iterates through each of the trees in the given file: \begin{verbatim} >>> trees = Phylo.parse("Tests/PhyloXML/phyloxml_examples.xml", "phyloxml") >>> for tree in trees: ... print(tree) \end{verbatim} Write a tree or iterable of trees back to file with the \verb|write| function: \begin{verbatim} >>> trees = list(Phylo.parse("phyloxml_examples.xml", "phyloxml")) >>> tree1 = trees[0] >>> others = trees[1:] >>> Phylo.write(tree1, "tree1.xml", "phyloxml") 1 >>> Phylo.write(others, "other_trees.xml", "phyloxml") 12 \end{verbatim} Convert files between any of the supported formats with the \verb|convert| function: \begin{verbatim} >>> Phylo.convert("tree1.dnd", "newick", "tree1.xml", "nexml") 1 >>> Phylo.convert("other_trees.xml", "phyloxml", "other_trees.nex", 'nexus") 12 \end{verbatim} To use strings as input or output instead of actual files, use \verb|StringIO| as you would with SeqIO and AlignIO: %doctest \begin{verbatim} >>> from Bio import Phylo >>> from StringIO import StringIO >>> handle = StringIO("(((A,B),(C,D)),(E,F,G));") >>> tree = Phylo.read(handle, "newick") \end{verbatim} \section{View and export trees} The simplest way to get an overview of a \verb|Tree| object is to \verb|print| it: %TODO - make this into a doctest? \begin{verbatim} >>> tree = Phylo.read("Tests/PhyloXML/example.xml", "phyloxml") >>> print(tree) Phylogeny(rooted='True', description='phyloXML allows to use either a "branch_length" attribute...', name='example from Prof. Joe Felsenstein's book "Inferring Phyl...') Clade() Clade(branch_length='0.06') Clade(branch_length='0.102', name='A') Clade(branch_length='0.23', name='B') Clade(branch_length='0.4', name='C') \end{verbatim} This is essentially an outline of the object hierarchy Biopython uses to represent a tree. But more likely, you'd want to see a drawing of the tree. There are three functions to do this. As we saw in the demo, \verb|draw_ascii| prints an ascii-art drawing of the tree (a rooted phylogram) to standard output, or an open file handle if given. Not all of the available information about the tree is shown, but it provides a way to quickly view the tree without relying on any external dependencies. \begin{verbatim} >>> tree = Phylo.read("example.xml", "phyloxml") >>> Phylo.draw_ascii(tree) __________________ A __________| _| |___________________________________________ B | |___________________________________________________________________________ C \end{verbatim} The \verb|draw| function draws a more attractive image using the matplotlib library. See the API documentation for details on the arguments it accepts to customize the output. \begin{verbatim} >>> tree = Phylo.read("example.xml", "phyloxml") >>> Phylo.draw(tree, branch_labels=lambda c: c.branch_length) \end{verbatim} \begin{htmlonly} \imgsrc[width=701, height=465]{images/phylo-draw-example.png} \label{fig:phylo-draw-example} \end{htmlonly} \begin{latexonly} \begin{figure}[tbp] \centering \includegraphics[width=0.7\textwidth]{images/phylo-draw-example.png} \caption{A simple rooted tree plotted with the {\tt draw} function.} \label{fig:phylo-draw-example} \end{figure} \end{latexonly} \verb|draw_graphviz| draws an unrooted cladogram, but requires that you have Graphviz, PyDot or PyGraphviz, NetworkX, and matplotlib (or pylab) installed. Using the same example as above, and the \verb|dot| program included with Graphviz, let's draw a rooted tree (see Fig.~\ref{fig:phylo-dot}): \begin{verbatim} >>> tree = Phylo.read("example.xml", "phyloxml") >>> Phylo.draw_graphviz(tree, prog='dot') >>> import pylab >>> pylab.show() # Displays the tree in an interactive viewer >>> pylab.savefig('phylo-dot.png') # Creates a PNG file of the same graphic \end{verbatim} \begin{htmlonly} \imgsrc[width=400, height=312]{images/phylo-dot.png} \label{fig:phylo-dot} \end{htmlonly} \begin{latexonly} \begin{figure}[tbp] \centering \includegraphics[width=0.5\textwidth]{images/phylo-dot.png} \caption{A simple rooted tree drawn with {\tt draw\_graphviz}, using {\tt dot} for node layout.} \label{fig:phylo-dot} \end{figure} \end{latexonly} (Tip: If you execute IPython with the \verb|-pylab| option, calling \verb|draw_graphviz| causes the matplotlib viewer to launch automatically without manually calling \verb|show()|.) This exports the tree object to a NetworkX graph, uses Graphviz to lay out the nodes, and displays it using matplotlib. There are a number of keyword arguments that can modify the resulting diagram, including most of those accepted by the NetworkX functions \verb|networkx.draw| and \verb|networkx.draw_graphviz|. The display is also affected by the \verb|rooted| attribute of the given tree object. Rooted trees are shown with a ``head'' on each branch indicating direction (see Fig.~\ref{fig:phylo-rooted}): \begin{verbatim} >>> tree = Phylo.read("simple.dnd", "newick") >>> tree.rooted = True >>> Phylo.draw_graphiz(tree) \end{verbatim} \begin{htmlonly} \imgsrc[width=432, height=368]{images/phylo-rooted.png} \label{fig:phylo-rooted} \end{htmlonly} The ``prog'' argument specifies the Graphviz engine used for layout. The default, \verb|twopi|, behaves well for any size tree, reliably avoiding crossed branches. The \verb|neato| program may draw more attractive moderately-sized trees, but sometimes will cross branches (see Fig.~\ref{fig:phylo-color}). The \verb|dot| program may be useful with small trees, but tends to do surprising things with the layout of larger trees. \begin{latexonly} \begin{figure}[ptb] \centering \includegraphics[width=0.45\textwidth]{images/phylo-rooted.png} \caption{A rooted tree drawn with {\tt draw\_graphviz}. Since we rooted the tree, we can see the root must be along the branch with two directional ``heads''. } \label{fig:phylo-rooted} \end{figure} \begin{figure}[ptb] \centering \includegraphics[width=0.5\textwidth]{images/phylo-color.png} \caption{A colorized tree drawn with {\tt draw\_graphviz}, using {\tt neato} for node layout.} \label{fig:phylo-color} \end{figure} \end{latexonly} \begin{verbatim} >>> Phylo.draw_graphviz(tree, prog="neato") \end{verbatim} \begin{htmlonly} \imgsrc[width=499, height=348]{images/phylo-color.png} \label{fig:phylo-color} \end{htmlonly} This viewing mode is particularly handy for exploring larger trees, because the matplotlib viewer can zoom in on a selected region, thinning out a cluttered graphic. \begin{latexonly} %Can't reference unless inside a figure (See Figs.~\ref{fig:phylo-apaf}, \ref{fig:phylo-apaf-zoom}). \end{latexonly} \begin{verbatim} >>> tree = Phylo.read("apaf.xml", "phyloxml") >>> Phylo.draw_graphviz(tree, prog="neato", node_size=0) \end{verbatim} \begin{htmlonly} \imgsrc[width=519, height=400]{images/phylo-apaf.png} \label{fig:phylo-apaf} \imgsrc[width=506, height=400]{images/phylo-apaf-zoom.png} \label{fig:phylo-apaf-zoom} \end{htmlonly} \begin{latexonly} \begin{figure}[tbp] \centering \includegraphics[width=0.65\textwidth]{images/phylo-apaf.png} \\ \caption{A larger tree, using {\tt neato} for layout.} \label{fig:phylo-apaf} \end{figure} \begin{figure}[btp] \centering \includegraphics[width=0.6\textwidth]{images/phylo-apaf-zoom.png} \caption{A zoomed-in portion of the same tree as Fig.~\ref{fig:phylo-apaf}.} \label{fig:phylo-apaf-zoom} \end{figure} \end{latexonly} Note that branch lengths are not displayed accurately, because Graphviz ignores them when creating the node layouts. The branch lengths are retained when exporting a tree as a NetworkX graph object (\verb|to_networkx|), however. See the Phylo page on the Biopython wiki (\url{http://biopython.org/wiki/Phylo}) for descriptions and examples of the more advanced functionality in \verb|draw_ascii|, \verb|draw_graphviz| and \verb|to_networkx|. \section{Using Tree and Clade objects} The \verb|Tree| objects produced by \verb|parse| and \verb|read| are containers for recursive sub-trees, attached to the \verb|Tree| object at the \verb|root| attribute (whether or not the phylogenic tree is actually considered rooted). A \verb|Tree| has globally applied information for the phylogeny, such as rootedness, and a reference to a single \verb|Clade|; a \verb|Clade| has node- and clade-specific information, such as branch length, and a list of its own descendent \verb|Clade| instances, attached at the \verb|clades| attribute. So there is a distinction between \verb|tree| and \verb|tree.root|. In practice, though, you rarely need to worry about it. To smooth over the difference, both \verb|Tree| and \verb|Clade| inherit from \verb|TreeMixin|, which contains the implementations for methods that would be commonly used to search, inspect or modify a tree or any of its clades. This means that almost all of the methods supported by \verb|tree| are also available on \verb|tree.root| and any clade below it. (\verb|Clade| also has a \verb|root| property, which returns the clade object itself.) % TODO - use dia to make a class diagram like images/smcra.png \subsection{Search and traversal methods} For convenience, we provide a couple of simplified methods that return all external or internal nodes directly as a list: \begin{description} \item[\texttt{get\_terminals}] makes a list of all of this tree's terminal (leaf) nodes. \item[\texttt{get\_nonterminals}] makes a list of all of this tree's nonterminal (internal) nodes. \end{description} These both wrap a method with full control over tree traversal, \verb|find_clades|. Two more traversal methods, \verb|find_elements| and \verb|find_any|, rely on the same core functionality and accept the same arguments, which we'll call a ``target specification'' for lack of a better description. These specify which objects in the tree will be matched and returned during iteration. The first argument can be any of the following types: \begin{itemize} \item A \textbf{TreeElement instance}, which tree elements will match by identity --- so searching with a Clade instance as the target will find that clade in the tree; \item A \textbf{string}, which matches tree elements' string representation --- in particular, a clade's \verb|name| \textit{(added in Biopython 1.56)}; \item A \textbf{class} or \textbf{type}, where every tree element of the same type (or sub-type) will be matched; \item A \textbf{dictionary} where keys are tree element attributes and values are matched to the corresponding attribute of each tree element. This one gets even more elaborate: \begin{itemize} \item If an \texttt{int} is given, it matches numerically equal attributes, e.g. 1 will match 1 or 1.0 \item If a boolean is given (True or False), the corresponding attribute value is evaluated as a boolean and checked for the same \item \texttt{None} matches \texttt{None} \item If a string is given, the value is treated as a regular expression (which must match the whole string in the corresponding element attribute, not just a prefix). A given string without special regex characters will match string attributes exactly, so if you don't use regexes, don't worry about it. For example, in a tree with clade names Foo1, Foo2 and Foo3, \verb|tree.find_clades({"name": "Foo1"})| matches Foo1, \verb|{"name": "Foo.*"}| matches all three clades, and \verb|{"name": "Foo"}| doesn't match anything. \end{itemize} Since floating-point arithmetic can produce some strange behavior, we don't support matching \texttt{float}s directly. Instead, use the boolean \texttt{True} to match every element with a nonzero value in the specified attribute, then filter on that attribute manually with an inequality (or exact number, if you like living dangerously). If the dictionary contains multiple entries, a matching element must match each of the given attribute values --- think ``and'', not ``or''. \item A \textbf{function} taking a single argument (it will be applied to each element in the tree), returning True or False. For convenience, LookupError, AttributeError and ValueError are silenced, so this provides another safe way to search for floating-point values in the tree, or some more complex characteristic. \end{itemize} After the target, there are two optional keyword arguments: \begin{description} \item[terminal] --- A boolean value to select for or against terminal clades (a.k.a. leaf nodes): True searches for only terminal clades, False for non-terminal (internal) clades, and the default, None, searches both terminal and non-terminal clades, as well as any tree elements lacking the \verb|is_terminal| method. \item[order] --- Tree traversal order: \texttt{"preorder"} (default) is depth-first search, \texttt{"postorder"} is DFS with child nodes preceding parents, and \texttt{"level"} is breadth-first search. \end{description} Finally, the methods accept arbitrary keyword arguments which are treated the same way as a dictionary target specification: keys indicate the name of the element attribute to search for, and the argument value (string, integer, None or boolean) is compared to the value of each attribute found. If no keyword arguments are given, then any TreeElement types are matched. The code for this is generally shorter than passing a dictionary as the target specification: \verb|tree.find_clades({"name": "Foo1"})| can be shortened to \verb|tree.find_clades(name="Foo1")|. (In Biopython 1.56 or later, this can be even shorter: \verb|tree.find_clades("Foo1")|) % TODO -- introduce these / segue and clean up the descriptions Now that we've mastered target specifications, here are the methods used to traverse a tree: \begin{description} \item[\texttt{find\_clades}] Find each clade containing a matching element. That is, find each element as with \verb|find_elements|, but return the corresponding clade object. (This is usually what you want.) The result is an iterable through all matching objects, searching depth-first by default. This is not necessarily the same order as the elements appear in the Newick, Nexus or XML source file! \item[\texttt{find\_elements}] Find all tree elements matching the given attributes, and return the matching elements themselves. Simple Newick trees don't have complex sub-elements, so this behaves the same as \verb|find_clades| on them. PhyloXML trees often do have complex objects attached to clades, so this method is useful for extracting those. \item[\texttt{find\_any}] Return the first element found by \verb|find_elements()|, or None. This is also useful for checking whether any matching element exists in the tree, and can be used in a conditional. \end{description} Two more methods help navigating between nodes in the tree: \begin{description} \item[\texttt{get\_path}] List the clades directly between the tree root (or current clade) and the given target. Returns a list of all clade objects along this path, ending with the given target, but excluding the root clade. \item[\texttt{trace}] List of all clade object between two targets in this tree. Excluding start, including finish. \end{description} \subsection{Information methods} These methods provide information about the whole tree (or any clade). \begin{description} \item[\texttt{common\_ancestor}] Find the most recent common ancestor of all the given targets. (This will be a Clade object). If no target is given, returns the root of the current clade (the one this method is called from); if 1 target is given, this returns the target itself. However, if any of the specified targets are not found in the current tree (or clade), an exception is raised. \item[\texttt{count\_terminals}] Counts the number of terminal (leaf) nodes within the tree. \item[\texttt{depths}] Create a mapping of tree clades to depths. The result is a dictionary where the keys are all of the Clade instances in the tree, and the values are the distance from the root to each clade (including terminals). By default the distance is the cumulative branch length leading to the clade, but with the \verb|unit_branch_lengths=True| option, only the number of branches (levels in the tree) is counted. \item[\texttt{distance}] Calculate the sum of the branch lengths between two targets. If only one target is specified, the other is the root of this tree. \item[\texttt{total\_branch\_length}] Calculate the sum of all the branch lengths in this tree. This is usually just called the ``length'' of the tree in phylogenetics, but we use a more explicit name to avoid confusion with Python terminology. \end{description} The rest of these methods are boolean checks: \begin{description} \item[\texttt{is\_bifurcating}] True if the tree is strictly bifurcating; i.e. all nodes have either 2 or 0 children (internal or external, respectively). The root may have 3 descendents and still be considered part of a bifurcating tree. \item[\texttt{is\_monophyletic}] Test if all of the given targets comprise a complete subclade --- i.e., there exists a clade such that its terminals are the same set as the given targets. The targets should be terminals of the tree. For convenience, this method returns the common ancestor (MCRA) of the targets if they are monophyletic (instead of the value \verb|True|), and \verb|False| otherwise. \item[\texttt{is\_parent\_of}] True if target is a descendent of this tree --- not required to be a direct descendent. To check direct descendents of a clade, simply use list membership testing: \verb|if subclade in clade: ...| \item[\texttt{is\_preterminal}] True if all direct descendents are terminal; False if any direct descendent is not terminal. \end{description} \subsection{Modification methods} These methods modify the tree in-place. If you want to keep the original tree intact, make a complete copy of the tree first, using Python's \texttt{copy} module: \begin{verbatim} tree = Phylo.read('example.xml', 'phyloxml') import copy newtree = copy.deepcopy(tree) \end{verbatim} \begin{description} \item[\texttt{collapse}] Deletes the target from the tree, relinking its children to its parent. \item[\texttt{collapse\_all}] Collapse all the descendents of this tree, leaving only terminals. Branch lengths are preserved, i.e. the distance to each terminal stays the same. With a target specification (see above), collapses only the internal nodes matching the specification. \item[\texttt{ladderize}] Sort clades in-place according to the number of terminal nodes. Deepest clades are placed last by default. Use \verb|reverse=True| to sort clades deepest-to-shallowest. \item[\texttt{prune}] Prunes a terminal clade from the tree. If taxon is from a bifurcation, the connecting node will be collapsed and its branch length added to remaining terminal node. This might no longer be a meaningful value. \item[\texttt{root\_with\_outgroup}] Reroot this tree with the outgroup clade containing the given targets, i.e. the common ancestor of the outgroup. This method is only available on Tree objects, not Clades. If the outgroup is identical to self.root, no change occurs. If the outgroup clade is terminal (e.g. a single terminal node is given as the outgroup), a new bifurcating root clade is created with a 0-length branch to the given outgroup. Otherwise, the internal node at the base of the outgroup becomes a trifurcating root for the whole tree. If the original root was bifurcating, it is dropped from the tree. In all cases, the total branch length of the tree stays the same. \item[\texttt{root\_at\_midpoint}] Reroot this tree at the calculated midpoint between the two most distant tips of the tree. (This uses \verb|root_with_outgroup| under the hood.) \item[\texttt{split}] Generate \textit{n} (default 2) new descendants. In a species tree, this is a speciation event. New clades have the given \verb|branch_length| and the same name as this clade's root plus an integer suffix (counting from 0) --- for example, splitting a clade named ``A'' produces the sub-clades ``A0'' and ``A1''. \end{description} See the Phylo page on the Biopython wiki (\url{http://biopython.org/wiki/Phylo}) for more examples of using the available methods. \subsection{Features of PhyloXML trees} \label{sec:PhyloXML} The phyloXML file format includes fields for annotating trees with additional data types and visual cues. % TODO % annotations -- see wiki and epydoc pages % - on wiki, discuss each class See the PhyloXML page on the Biopython wiki (\url{http://biopython.org/wiki/PhyloXML}) for descriptions and examples of using the additional annotation features provided by PhyloXML. % The object hierarchy still looks and behaves similarly: % \begin{verbatim} % >>> print(tree) % Phylogeny(rooted=True, name="") % Clade(branch_length=1.0) % Clade(branch_length=1.0) % Clade(branch_length=1.0) % Clade(branch_length=1.0, name="A") % Clade(branch_length=1.0, name="B") % Clade(branch_length=1.0) % Clade(branch_length=1.0, name="C") % Clade(branch_length=1.0, name="D") % Clade(branch_length=1.0) % Clade(branch_length=1.0, name="E") % Clade(branch_length=1.0, name="F") % Clade(branch_length=1.0, name="G") % \end{verbatim} \section{Running external applications} \label{sec:PhyloApps} While Bio.Phylo doesn't infer trees from alignments itself, there are third-party programs available that do. These are supported through the module \texttt{Bio.Phylo.Applications}, using the same general framework as \texttt{Bio.Emboss.Applications}, \texttt{Bio.Align.Applications} and others. Biopython 1.58 introduced a wrapper for PhyML (\url{http://www.atgc-montpellier.fr/phyml/}). The program accepts an input alignment in \texttt{phylip-relaxed} format (that's Phylip format, but without the 10-character limit on taxon names) and a variety of options. A quick example: \begin{verbatim} >>> from Bio import Phylo >>> from Bio.Phylo.Applications import PhymlCommandline >>> cmd = PhymlCommandline(input='Tests/Phylip/random.phy') >>> out_log, err_log = cmd() \end{verbatim} This generates a tree file and a stats file with the names [\textit{input~filename}]\verb|_phyml_tree.txt| and [\textit{input~filename}]\verb|_phyml_stats.txt|. The tree file is in Newick format: \begin{verbatim} >>> tree = Phylo.read('Tests/Phylip/random.phy_phyml_tree.txt', 'newick') >>> Phylo.draw_ascii(tree) \end{verbatim} A similar wrapper for RAxML (\url{http://sco.h-its.org/exelixis/software.html}) was added in Biopython 1.60, and FastTree (\url{http://www.microbesonline.org/fasttree/}) in Biopython 1.62. Note that some popular Phylip programs, including \texttt{dnaml} and \texttt{protml}, are already available through the EMBOSS wrappers in \texttt{Bio.Emboss.Applications} if you have the Phylip extensions to EMBOSS installed on your system. See Section~\ref{sec:alignment-tools} for some examples and clues on how to use programs like these. \section{PAML integration} \label{sec:PhyloPAML} Biopython 1.58 brought support for PAML (\url{http://abacus.gene.ucl.ac.uk/software/paml.html}), a suite of programs for phylogenetic analysis by maximum likelihood. Currently the programs codeml, baseml and yn00 are implemented. Due to PAML's usage of control files rather than command line arguments to control runtime options, usage of this wrapper strays from the format of other application wrappers in Biopython. A typical workflow would be to initialize a PAML object, specifying an alignment file, a tree file, an output file and a working directory. Next, runtime options are set via the \texttt{set\_options()} method or by reading an existing control file. Finally, the program is run via the \texttt{run()} method and the output file is automatically parsed to a results dictionary. Here is an example of typical usage of codeml: \begin{verbatim} >>> from Bio.Phylo.PAML import codeml >>> cml = codeml.Codeml() >>> cml.alignment = "Tests/PAML/alignment.phylip" >>> cml.tree = "Tests/PAML/species.tree" >>> cml.out_file = "results.out" >>> cml.working_dir = "./scratch" >>> cml.set_options(seqtype=1, ... verbose=0, ... noisy=0, ... RateAncestor=0, ... model=0, ... NSsites=[0, 1, 2], ... CodonFreq=2, ... cleandata=1, ... fix_alpha=1, ... kappa=4.54006) >>> results = cml.run() >>> ns_sites = results.get("NSsites") >>> m0 = ns_sites.get(0) >>> m0_params = m0.get("parameters") >>> print(m0_params.get("omega")) \end{verbatim} Existing output files may be parsed as well using a module's \texttt{read()} function: \begin{verbatim} >>> results = codeml.read("Tests/PAML/Results/codeml/codeml_NSsites_all.out") >>> print(results.get("lnL max")) \end{verbatim} Detailed documentation for this new module currently lives on the Biopython wiki: \url{http://biopython.org/wiki/PAML} \section{Future plans} \label{sec:PhyloFuture} Bio.Phylo is under active development. Here are some features we might add in future releases: \begin{description} \item[New methods] Generally useful functions for operating on Tree or Clade objects appear on the Biopython wiki first, so that casual users can test them and decide if they're useful before we add them to Bio.Phylo: \url{http://biopython.org/wiki/Phylo_cookbook} \item[Bio.Nexus port] Much of this module was written during Google Summer of Code 2009, under the auspices of NESCent, as a project to implement Python support for the phyloXML data format (see \ref{sec:PhyloXML}). Support for Newick and Nexus formats was added by porting part of the existing Bio.Nexus module to the new classes used by Bio.Phylo. Currently, Bio.Nexus contains some useful features that have not yet been ported to Bio.Phylo classes --- notably, calculating a consensus tree. If you find some functionality lacking in Bio.Phylo, try poking throught Bio.Nexus to see if it's there instead. \end{description} We're open to any suggestions for improving the functionality and usability of this module; just let us know on the mailing list or our bug database. Finally, if you need additional functionality not yet included in the Phylo module, check if it's available in another of the high-quality Python libraries for phylogenetics such as DendroPy (\url{http://pythonhosted.org/DendroPy/}) or PyCogent (\url{http://pycogent.org/}). Since these libraries also support standard file formats for phylogenetic trees, you can easily transfer data between libraries by writing to a temporary file or StringIO object. \chapter{Sequence motif analysis using Bio.motifs} This chapter gives an overview of the functionality of the \verb|Bio.motifs| package included in Biopython. It is intended for people who are involved in the analysis of sequence motifs, so I'll assume that you are familiar with basic notions of motif analysis. In case something is unclear, please look at Section~\ref{sec:links} for some relevant links. Most of this chapter describes the new \verb|Bio.motifs| package included in Biopython 1.61 onwards, which is replacing the older \verb|Bio.Motif| package introduced with Biopython 1.50, which was in turn based on two older former Biopython modules, \verb|Bio.AlignAce| and \verb|Bio.MEME|. It provides most of their functionality with a unified motif object implementation. Speaking of other libraries, if you are reading this you might be interested in \href{http://fraenkel.mit.edu/TAMO/}{TAMO}, another python library designed to deal with sequence motifs. It supports more \emph{de-novo} motif finders, but it is not a part of Biopython and has some restrictions on commercial use. \section{Motif objects} \label{sec:object} Since we are interested in motif analysis, we need to take a look at \verb|Motif| objects in the first place. For that we need to import the Bio.motifs library: %doctest ../Tests/Motif \begin{verbatim} >>> from Bio import motifs \end{verbatim} and we can start creating our first motif objects. We can either create a \verb+Motif+ object from a list of instances of the motif, or we can obtain a \verb+Motif+ object by parsing a file from a motif database or motif finding software. \subsection{Creating a motif from instances} Suppose we have these instances of a DNA motif: %cont-doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> instances = [Seq("TACAA"), ... Seq("TACGC"), ... Seq("TACAC"), ... Seq("TACCC"), ... Seq("AACCC"), ... Seq("AATGC"), ... Seq("AATGC"), ... ] \end{verbatim} then we can create a Motif object as follows: %cont-doctest \begin{verbatim} >>> m = motifs.create(instances) \end{verbatim} The instances are saved in an attribute \verb+m.instances+, which is essentially a Python list with some added functionality, as described below. Printing out the Motif object shows the instances from which it was constructed: %cont-doctest \begin{verbatim} >>> print(m) TACAA TACGC TACAC TACCC AACCC AATGC AATGC \end{verbatim} The length of the motif defined as the sequence length, which should be the same for all instances: %cont-doctest \begin{verbatim} >>> len(m) 5 \end{verbatim} The Motif object has an attribute \verb+.counts+ containing the counts of each nucleotide at each position. Printing this counts matrix shows it in an easily readable format: %cont-doctest \begin{verbatim} >>> print(m.counts) 0 1 2 3 4 A: 3.00 7.00 0.00 2.00 1.00 C: 0.00 0.00 5.00 2.00 6.00 G: 0.00 0.00 0.00 3.00 0.00 T: 4.00 0.00 2.00 0.00 0.00 \end{verbatim} You can access these counts as a dictionary: %cont-doctest \begin{verbatim} >>> m.counts['A'] [3, 7, 0, 2, 1] \end{verbatim} but you can also think of it as a 2D array with the nucleotide as the first dimension and the position as the second dimension: %cont-doctest \begin{verbatim} >>> m.counts['T', 0] 4 >>> m.counts['T', 2] 2 >>> m.counts['T', 3] 0 \end{verbatim} You can also directly access columns of the counts matrix %Don't doctest this as dictionary order is platform dependent: \begin{verbatim} >>> m.counts[:, 3] {'A': 2, 'C': 2, 'T': 0, 'G': 3} \end{verbatim} Instead of the nucleotide itself, you can also use the index of the nucleotide in the sorted letters in the alphabet of the motif: %cont-doctest \begin{verbatim} >>> m.alphabet IUPACUnambiguousDNA() >>> m.alphabet.letters 'GATC' >>> sorted(m.alphabet.letters) ['A', 'C', 'G', 'T'] >>> m.counts['A',:] (3, 7, 0, 2, 1) >>> m.counts[0,:] (3, 7, 0, 2, 1) \end{verbatim} The motif has an associated consensus sequence, defined as the sequence of letters along the positions of the motif for which the largest value in the corresponding columns of the \verb+.counts+ matrix is obtained: %cont-doctest \begin{verbatim} >>> m.consensus Seq('TACGC', IUPACUnambiguousDNA()) \end{verbatim} as well as an anticonsensus sequence, corresponding to the smallest values in the columns of the \verb+.counts+ matrix: %cont-doctest \begin{verbatim} >>> m.anticonsensus Seq('GGGTG', IUPACUnambiguousDNA()) \end{verbatim} You can also ask for a degenerate consensus sequence, in which ambiguous nucleotides are used for positions where there are multiple nucleotides with high counts: %cont-doctest \begin{verbatim} >>> m.degenerate_consensus Seq('WACVC', IUPACAmbiguousDNA()) \end{verbatim} Here, W and R follow the IUPAC nucleotide ambiguity codes: W is either A or T, and V is A, C, or G \cite{cornish1985}. The degenerate consensus sequence is constructed following the rules specified by Cavener \cite{cavener1987}. We can also get the reverse complement of a motif: %cont-doctest \begin{verbatim} >>> r = m.reverse_complement() >>> r.consensus Seq('GCGTA', IUPACUnambiguousDNA()) >>> r.degenerate_consensus Seq('GBGTW', IUPACAmbiguousDNA()) >>> print(r) TTGTA GCGTA GTGTA GGGTA GGGTT GCATT GCATT \end{verbatim} The reverse complement and the degenerate consensus sequence are only defined for DNA motifs. \subsection{Creating a sequence logo} If we have internet access, we can create a \href{http://weblogo.berkeley.edu}{weblogo}: \begin{verbatim} >>> m.weblogo("mymotif.png") \end{verbatim} We should get our logo saved as a PNG in the specified file. \section{Reading motifs} \label{sec:io} Creating motifs from instances by hand is a bit boring, so it's useful to have some I/O functions for reading and writing motifs. There are not any really well established standards for storing motifs, but there are a couple of formats that are more used than others. \subsection{JASPAR} One of the most popular motif databases is \href{http://jaspar.genereg.net}{JASPAR}. In addition to the motif sequence information, the JASPAR database stores a lot of meta-information for each motif. The module \verb+Bio.motifs+ contains a specialized class \verb+jaspar.Motif+ in which this meta-information is represented as attributes: \begin{itemize} \item \verb+matrix_id+ - the unique JASPAR motif ID, e.g. 'MA0004.1' \item \verb+name+ - the name of the TF, e.g. 'Arnt' \item \verb+collection+ - the JASPAR collection to which the motif belongs, e.g. 'CORE' \item \verb+tf_class+ - the structual class of this TF, e.g. 'Zipper-Type' \item \verb+tf_family+ - the family to which this TF belongs, e.g. 'Helix-Loop-Helix' \item \verb+species+ - the species to which this TF belongs, may have multiple values, these are specified as taxonomy IDs, e.g. 10090 \item \verb+tax_group+ - the taxonomic supergroup to which this motif belongs, e.g. 'vertebrates' \item \verb+acc+ - the accesion number of the TF protein, e.g. 'P53762' \item \verb+data_type+ - the type of data used to construct this motif, e.g. 'SELEX' \item \verb+medline+ - the Pubmed ID of literature supporting this motif, may be multiple values, e.g. 7592839 \item \verb+pazar_id+ - external reference to the TF in the \href{http://pazar.info}{PAZAR} database, e.g. 'TF0000003' \item \verb+comment+ - free form text containing notes about the construction of the motif \end{itemize} The \verb+jaspar.Motif+ class inherits from the generic \verb+Motif+ class and therefore provides all the facilities of any of the motif formats --- reading motifs, writing motifs, scanning sequences for motif instances etc. JASPAR stores motifs in several different ways including three different flat file formats and as an SQL database. All of these formats facilitate the construction of a counts matrix. However, the amount of meta information described above that is available varies with the format. \subsubsection*{The JASPAR \texttt{sites} format} The first of the three flat file formats contains a list of instances. As an example, these are the beginning and ending lines of the JASPAR \verb+Arnt.sites+ file showing known binding sites of the mouse helix-loop-helix transcription factor Arnt. \begin{verbatim} >MA0004 ARNT 1 CACGTGatgtcctc >MA0004 ARNT 2 CACGTGggaggtac >MA0004 ARNT 3 CACGTGccgcgcgc ... >MA0004 ARNT 18 AACGTGacagccctcc >MA0004 ARNT 19 AACGTGcacatcgtcc >MA0004 ARNT 20 aggaatCGCGTGc \end{verbatim} The parts of the sequence in capital letters are the motif instances that were found to align to each other. We can create a \verb+Motif+ object from these instances as follows: %cont-doctest \begin{verbatim} >>> from Bio import motifs >>> arnt = motifs.read(open("Arnt.sites"), "sites") \end{verbatim} The instances from which this motif was created is stored in the \verb+.instances+ property: %cont-doctest \begin{verbatim} >>> print(arnt.instances[:3]) [Seq('CACGTG', IUPACUnambiguousDNA()), Seq('CACGTG', IUPACUnambiguousDNA()), Seq('CACGTG', IUPACUnambiguousDNA())] >>> for instance in arnt.instances: ... print(instance) ... CACGTG CACGTG CACGTG CACGTG CACGTG CACGTG CACGTG CACGTG CACGTG CACGTG CACGTG CACGTG CACGTG CACGTG CACGTG AACGTG AACGTG AACGTG AACGTG CGCGTG \end{verbatim} The counts matrix of this motif is automatically calculated from the instances: %cont-doctest \begin{verbatim} >>> print(arnt.counts) 0 1 2 3 4 5 A: 4.00 19.00 0.00 0.00 0.00 0.00 C: 16.00 0.00 20.00 0.00 0.00 0.00 G: 0.00 1.00 0.00 20.00 0.00 20.00 T: 0.00 0.00 0.00 0.00 20.00 0.00 \end{verbatim} This format does not store any meta information. \subsubsection*{The JASPAR \texttt{pfm} format} JASPAR also makes motifs available directly as a count matrix, without the instances from which it was created. This \verb+pfm+ format only stores the counts matrix for a single motif. For example, this is the JASPAR file \verb+SRF.pfm+ containing the counts matrix for the human SRF transcription factor: \begin{verbatim} 2 9 0 1 32 3 46 1 43 15 2 2 1 33 45 45 1 1 0 0 0 1 0 1 39 2 1 0 0 0 0 0 0 0 44 43 4 2 0 0 13 42 0 45 3 30 0 0 \end{verbatim} We can create a motif for this count matrix as follows: %cont-doctest \begin{verbatim} >>> srf = motifs.read(open("SRF.pfm"), "pfm") >>> print(srf.counts) 0 1 2 3 4 5 6 7 8 9 10 11 A: 2.00 9.00 0.00 1.00 32.00 3.00 46.00 1.00 43.00 15.00 2.00 2.00 C: 1.00 33.00 45.00 45.00 1.00 1.00 0.00 0.00 0.00 1.00 0.00 1.00 G: 39.00 2.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 44.00 43.00 T: 4.00 2.00 0.00 0.00 13.00 42.00 0.00 45.00 3.00 30.00 0.00 0.00 \end{verbatim} As this motif was created from the counts matrix directly, it has no instances associated with it: %cont-doctest \begin{verbatim} >>> print(srf.instances) None \end{verbatim} We can now ask for the consensus sequence of these two motifs: %cont-doctest \begin{verbatim} >>> print(arnt.counts.consensus) CACGTG >>> print(srf.counts.consensus) GCCCATATATGG \end{verbatim} As with the instances file, not meta information is stored in this format. \subsubsection*{The JASPAR format \texttt{jaspar}} The \verb+jaspar+ file format allows multiple motifs to be specified in a single file. In this format each of the motif records consist of a header line followed by four lines defining the counts matrix. The header line begins with a \verb+>+ character (similar to the Fasta file format) and is followed by the unique JASPAR matrix ID and the TF name. The following example shows a \verb+jaspar+ formatted file containing the three motifs Arnt, RUNX1 and MEF2A: \begin{verbatim} >MA0004.1 Arnt A [ 4 19 0 0 0 0 ] C [16 0 20 0 0 0 ] G [ 0 1 0 20 0 20 ] T [ 0 0 0 0 20 0 ] >MA0002.1 RUNX1 A [10 12 4 1 2 2 0 0 0 8 13 ] C [ 2 2 7 1 0 8 0 0 1 2 2 ] G [ 3 1 1 0 23 0 26 26 0 0 4 ] T [11 11 14 24 1 16 0 0 25 16 7 ] >MA0052.1 MEF2A A [ 1 0 57 2 9 6 37 2 56 6 ] C [50 0 1 1 0 0 0 0 0 0 ] G [ 0 0 0 0 0 0 0 0 2 50 ] T [ 7 58 0 55 49 52 21 56 0 2 ] \end{verbatim} The motifs are read as follows: \begin{verbatim} >>> fh = open("jaspar_motifs.txt") >>> for m in motifs.parse(fh, "jaspar")) ... print(m) TF name Arnt Matrix ID MA0004.1 Matrix: 0 1 2 3 4 5 A: 4.00 19.00 0.00 0.00 0.00 0.00 C: 16.00 0.00 20.00 0.00 0.00 0.00 G: 0.00 1.00 0.00 20.00 0.00 20.00 T: 0.00 0.00 0.00 0.00 20.00 0.00 TF name RUNX1 Matrix ID MA0002.1 Matrix: 0 1 2 3 4 5 6 7 8 9 10 A: 10.00 12.00 4.00 1.00 2.00 2.00 0.00 0.00 0.00 8.00 13.00 C: 2.00 2.00 7.00 1.00 0.00 8.00 0.00 0.00 1.00 2.00 2.00 G: 3.00 1.00 1.00 0.00 23.00 0.00 26.00 26.00 0.00 0.00 4.00 T: 11.00 11.00 14.00 24.00 1.00 16.00 0.00 0.00 25.00 16.00 7.00 TF name MEF2A Matrix ID MA0052.1 Matrix: 0 1 2 3 4 5 6 7 8 9 A: 1.00 0.00 57.00 2.00 9.00 6.00 37.00 2.00 56.00 6.00 C: 50.00 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 G: 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 50.00 T: 7.00 58.00 0.00 55.00 49.00 52.00 21.00 56.00 0.00 2.00 \end{verbatim} Note that printing a JASPAR motif yields both the counts data and the available meta-information. \subsubsection*{Accessing the JASPAR database} In addition to parsing these flat file formats, we can also retrieve motifs from a JASPAR SQL database. Unlike the flat file formats, a JASPAR database allows storing of all possible meta information defined in the JASPAR \verb+Motif+ class. It is beyond the scope of this document to describe how to set up a JASPAR database (please see the main \href{http://jaspar.genereg.net}{JASPAR} website). Motifs are read from a JASPAR database using the \verb+Bio.motifs.jaspar.db+ module. First connect to the JASPAR database using the JASPAR5 class which models the the latest JASPAR schema: \begin{verbatim} >>> from Bio.motifs.jaspar.db import JASPAR5 >>> >>> JASPAR_DB_HOST = >>> JASPAR_DB_NAME = >>> JASPAR_DB_USER = >>> JASPAR_DB_PASS = >>> >>> jdb = JASPAR5( ... host=JASPAR_DB_HOST, ... name=JASPAR_DB_NAME, ... user=JASPAR_DB_USER, ... password=JASPAR_DB_PASS ... ) \end{verbatim} Now we can fetch a single motif by its unique JASPAR ID with the \verb+fetch_motif_by_id+ method. Note that a JASPAR ID conists of a base ID and a version number seperated by a decimal point, e.g. 'MA0004.1'. The \verb+fetch_motif_by_id+ method allows you to use either the fully specified ID or just the base ID. If only the base ID is provided, the latest version of the motif is returned. \begin{verbatim} >>> arnt = jdb.fetch_motif_by_id("MA0004") \end{verbatim} Printing the motif reveals that the JASPAR SQL database stores much more meeta-information than the flat files: \begin{verbatim} >>> print(arnt) TF name Arnt Matrix ID MA0004.1 Collection CORE TF class Zipper-Type TF family Helix-Loop-Helix Species 10090 Taxonomic group vertebrates Accession ['P53762'] Data type used SELEX Medline 7592839 PAZAR ID TF0000003 Comments - Matrix: 0 1 2 3 4 5 A: 4.00 19.00 0.00 0.00 0.00 0.00 C: 16.00 0.00 20.00 0.00 0.00 0.00 G: 0.00 1.00 0.00 20.00 0.00 20.00 T: 0.00 0.00 0.00 0.00 20.00 0.00 \end{verbatim} We can also fetch motifs by name. The name must be an exact match (partial matches or database wildcards are not currently supported). Note that as the name is not guaranteed to be unique, the \verb+fetch_motifs_by_name+ method actually returns a list. \begin{verbatim} >>> motifs = jdb.fetch_motifs_by_name("Arnt") >>> print(motifs[0]) TF name Arnt Matrix ID MA0004.1 Collection CORE TF class Zipper-Type TF family Helix-Loop-Helix Species 10090 Taxonomic group vertebrates Accession ['P53762'] Data type used SELEX Medline 7592839 PAZAR ID TF0000003 Comments - Matrix: 0 1 2 3 4 5 A: 4.00 19.00 0.00 0.00 0.00 0.00 C: 16.00 0.00 20.00 0.00 0.00 0.00 G: 0.00 1.00 0.00 20.00 0.00 20.00 T: 0.00 0.00 0.00 0.00 20.00 0.00 \end{verbatim} The \verb+fetch_motifs+ method allows you to fetch motifs which match a specified set of criteria. These criteria include any of the above described meta information as well as certain matrix properties such as the minimum information content (\verb+min_ic+ in the example below), the minimum length of the matrix or the minimum number of sites used to construct the matrix. Only motifs which pass ALL the specified criteria are returned. Note that selection criteria which correspond to meta information which allow for multiple values may be specified as either a single value or a list of values, e.g. \verb+tax_group+ and \verb+tf_family+ in the example below. \begin{verbatim} >>> motifs = jdb.fetch_motifs( ... collection = 'CORE', ... tax_group = ['vertebrates', 'insects'], ... tf_class = 'Winged Helix-Turn-Helix', ... tf_family = ['Forkhead', 'Ets'], ... min_ic = 12 ... ) >>> for motif in motifs: ... pass # do something with the motif \end{verbatim} \subsubsection*{Compatibility with Perl TFBS modules} An important thing to note is that the JASPAR \verb+Motif+ class was designed to be compatible with the popular \href{http://tfbs.genereg.net/}{Perl TFBS modules}. Therefore some specifics about the choice of defaults for background and pseudocounts as well as how information content is computed and sequences searched for instances is based on this compatibility criteria. These choices are noted in the specific subsections below. \begin{itemize} \item{\bf Choice of background:} \\ The Perl \verb+TFBS+ modules appear to allow a choice of custom background probabilities (although the documentation states that uniform background is assumed). However the default is to use a uniform background. Therefore it is recommended that you use a uniform background for computing the position-specific scoring matrix (PSSM). This is the default when using the Biopython \verb+motifs+ module. \item{\bf Choice of pseudocounts:} \\ By default, the Perl \verb+TFBS+ modules use a pseudocount equal to $\sqrt{N} * \textrm{bg}[\textrm{nucleotide}]$, where $N$ represents the total number of sequences used to construct the matrix. To apply this same pseudocount formula, set the motif \verb+pseudocounts+ attribute using the \verb+jaspar.calculate\_pseudcounts()+ funtion: \begin{verbatim} >>> motif.pseudocounts = motifs.jaspar.calculate_pseudocounts(motif) \end{verbatim} Note that it is possible for the counts matrix to have an unequal number of sequences making up the columns. The pseudocount computation uses the average number of sequences making up the matrix. However, when \verb+normalize+ is called on the counts matrix, each count value in a column is divided by the total number of sequences making up that specific column, not by the average number of sequences. This differs from the Perl \verb+TFBS+ modules because the normalization is not done as a separate step and so the average number of sequences is used throughout the computation of the pssm. Therefore, for matrices with unequal column counts, the pssm computed by the \verb+motifs+ module will differ somewhat from the pssm computed by the Perl \verb+TFBS+ modules. \item{\bf Computation of matrix information content:} \\ The information content (IC) or specificity of a matrix is computed using the \verb+mean+ method of the \verb+PositionSpecificScoringMatrix+ class. However of note, in the Perl \verb+TFBS+ modules the default behaviour is to compute the IC without first applying pseudocounts, even though by default the PSSMs are computed using pseudocounts as described above. \item{\bf Searching for instances:} \\ Searching for instances with the Perl \verb+TFBS+ motifs was usually performed using a relative score threshold, i.e. a score in the range 0 to 1. In order to compute the absolute PSSM score corresponding to a relative score one can use the equation: \begin{verbatim} >>> abs_score = (pssm.max - pssm.min) * rel_score + pssm.min \end{verbatim} To convert the absolute score of an instance back to a relative score, one can use the equation: \begin{verbatim} >>> rel_score = (abs_score - pssm.min) / (pssm.max - pssm.min) \end{verbatim} For example, using the Arnt motif before, let's search a sequence with a relative score threshold of 0.8. %TODO - Check missing ... lines, make into a doctest? \begin{verbatim} >>> test_seq=Seq("TAAGCGTGCACGCGCAACACGTGCATTA", unambiguous_dna) >>> arnt.pseudocounts = motifs.jaspar.calculate_pseudocounts(arnt) >>> pssm = arnt.pssm() >>> max_score = pssm.max() >>> min_score = pssm.min() >>> abs_score_threshold = (max_score - min_score) * 0.8 + min_score >>> for position, score in pssm.search(test_seq, threshold=abs_score_threshold): ... rel_score = (score - min_score) / (max_score - min_score) ... print("Position %d: score = %5.3f, rel. score = %5.3f" % ( position, score, rel_score)) ... Position 2: score = 5.362, rel. score = 0.801 Position 8: score = 6.112, rel. score = 0.831 Position -20: score = 7.103, rel. score = 0.870 Position 17: score = 10.351, rel. score = 1.000 Position -11: score = 10.351, rel. score = 1.000 \end{verbatim} \end{itemize} \subsection{MEME} MEME \cite{bailey1994} is a tool for discovering motifs in a group of related DNA or protein sequences. It takes as input a group of DNA or protein sequences and outputs as many motifs as requested. Therefore, in contrast to JASPAR files, MEME output files typically contain multiple motifs. This is an example. At the top of an output file generated by MEME shows some background information about the MEME and the version of MEME used: \begin{verbatim} ******************************************************************************** MEME - Motif discovery tool ******************************************************************************** MEME version 3.0 (Release date: 2004/08/18 09:07:01) ... \end{verbatim} Further down, the input set of training sequences is recapitulated: \begin{verbatim} ******************************************************************************** TRAINING SET ******************************************************************************** DATAFILE= INO_up800.s ALPHABET= ACGT Sequence name Weight Length Sequence name Weight Length ------------- ------ ------ ------------- ------ ------ CHO1 1.0000 800 CHO2 1.0000 800 FAS1 1.0000 800 FAS2 1.0000 800 ACC1 1.0000 800 INO1 1.0000 800 OPI3 1.0000 800 ******************************************************************************** \end{verbatim} and the exact command line that was used: \begin{verbatim} ******************************************************************************** COMMAND LINE SUMMARY ******************************************************************************** This information can also be useful in the event you wish to report a problem with the MEME software. command: meme -mod oops -dna -revcomp -nmotifs 2 -bfile yeast.nc.6.freq INO_up800.s ... \end{verbatim} Next is detailed information on each motif that was found: \begin{verbatim} ******************************************************************************** MOTIF 1 width = 12 sites = 7 llr = 95 E-value = 2.0e-001 ******************************************************************************** -------------------------------------------------------------------------------- Motif 1 Description -------------------------------------------------------------------------------- Simplified A :::9:a::::3: pos.-specific C ::a:9:11691a probability G ::::1::94:4: matrix T aa:1::9::11: \end{verbatim} To parse this file (stored as \verb+meme.dna.oops.txt+), use %cont-doctest \begin{verbatim} >>> handle = open("meme.dna.oops.txt") >>> record = motifs.parse(handle, "meme") >>> handle.close() \end{verbatim} The \verb+motifs.parse+ command reads the complete file directly, so you can close the file after calling \verb+motifs.parse+. The header information is stored in attributes: %cont-doctest \begin{verbatim} >>> record.version '3.0' >>> record.datafile 'INO_up800.s' >>> record.command 'meme -mod oops -dna -revcomp -nmotifs 2 -bfile yeast.nc.6.freq INO_up800.s' >>> record.alphabet IUPACUnambiguousDNA() >>> record.sequences ['CHO1', 'CHO2', 'FAS1', 'FAS2', 'ACC1', 'INO1', 'OPI3'] \end{verbatim} The record is an object of the \verb+Bio.motifs.meme.Record+ class. The class inherits from list, and you can think of \verb+record+ as a list of Motif objects: %cont-doctest \begin{verbatim} >>> len(record) 2 >>> motif = record[0] >>> print(motif.consensus) TTCACATGCCGC >>> print(motif.degenerate_consensus) TTCACATGSCNC \end{verbatim} In addition to these generic motif attributes, each motif also stores its specific information as calculated by MEME. For example, %cont-doctest \begin{verbatim} >>> motif.num_occurrences 7 >>> motif.length 12 >>> evalue = motif.evalue >>> print("%3.1g" % evalue) 0.2 >>> motif.name 'Motif 1' \end{verbatim} In addition to using an index into the record, as we did above, you can also find it by its name: %cont-doctest \begin{verbatim} >>> motif = record['Motif 1'] \end{verbatim} Each motif has an attribute \verb+.instances+ with the sequence instances in which the motif was found, providing some information on each instance: %cont-doctest \begin{verbatim} >>> len(motif.instances) 7 >>> motif.instances[0] Instance('TTCACATGCCGC', IUPACUnambiguousDNA()) >>> motif.instances[0].motif_name 'Motif 1' >>> motif.instances[0].sequence_name 'INO1' >>> motif.instances[0].start 620 >>> motif.instances[0].strand '-' >>> motif.instances[0].length 12 >>> pvalue = motif.instances[0].pvalue >>> print("%5.3g" % pvalue) 1.85e-08 \end{verbatim} \subsubsection*{MAST} \subsection{TRANSFAC} TRANSFAC is a manually curated database of transcription factors, together with their genomic binding sites and DNA binding profiles \cite{matys2003}. While the file format used in the TRANSFAC database is nowadays also used by others, we will refer to it as the TRANSFAC file format. A minimal file in the TRANSFAC format looks as follows: \begin{verbatim} ID motif1 P0 A C G T 01 1 2 2 0 S 02 2 1 2 0 R 03 3 0 1 1 A 04 0 5 0 0 C 05 5 0 0 0 A 06 0 0 4 1 G 07 0 1 4 0 G 08 0 0 0 5 T 09 0 0 5 0 G 10 0 1 2 2 K 11 0 2 0 3 Y 12 1 0 3 1 G // \end{verbatim} This file shows the frequency matrix of motif \verb+motif1+ of 12 nucleotides. In general, one file in the TRANSFAC format can contain multiple motifs. For example, this is the contents of the example TRANSFAC file \verb+transfac.dat+: \begin{verbatim} VV EXAMPLE January 15, 2013 XX // ID motif1 P0 A C G T 01 1 2 2 0 S 02 2 1 2 0 R 03 3 0 1 1 A ... 11 0 2 0 3 Y 12 1 0 3 1 G // ID motif2 P0 A C G T 01 2 1 2 0 R 02 1 2 2 0 S ... 09 0 0 0 5 T 10 0 2 0 3 Y // \end{verbatim} To parse a TRANSFAC file, use %cont-doctest \begin{verbatim} >>> handle = open("transfac.dat") >>> record = motifs.parse(handle, "TRANSFAC") >>> handle.close() \end{verbatim} The overall version number, if available, is stored as \verb+record.version+: %cont-doctest \begin{verbatim} >>> record.version 'EXAMPLE January 15, 2013' \end{verbatim} Each motif in \verb+record+ is in instance of the \verb+Bio.motifs.transfac.Motif+ class, which inherits both from the \verb+Bio.motifs.Motif+ class and from a Python dictionary. The dictionary uses the two-letter keys to store any additional information about the motif: %cont-doctest \begin{verbatim} >>> motif = record[0] >>> motif.degenerate_consensus # Using the Bio.motifs.Motif method Seq('SRACAGGTGKYG', IUPACAmbiguousDNA()) >>> motif['ID'] # Using motif as a dictionary 'motif1' \end{verbatim} TRANSFAC files are typically much more elaborate than this example, containing lots of additional information about the motif. Table \ref{table:transfaccodes} lists the two-letter field codes that are commonly found in TRANSFAC files: \begin{table}[h] \label{table:transfaccodes} \begin{center} \caption{Fields commonly found in TRANSFAC files} \begin{tabular}{|l|l||} \verb+AC+ & Accession number \\ \verb+AS+ & Accession numbers, secondary \\ \verb+BA+ & Statistical basis \\ \verb+BF+ & Binding factors \\ \verb+BS+ & Factor binding sites underlying the matrix \\ \verb+CC+ & Comments \\ \verb+CO+ & Copyright notice \\ \verb+DE+ & Short factor description \\ \verb+DR+ & External databases \\ \verb+DT+ & Date created/updated \\ \verb+HC+ & Subfamilies \\ \verb+HP+ & Superfamilies \\ \verb+ID+ & Identifier \\ \verb+NA+ & Name of the binding factor \\ \verb+OC+ & Taxonomic classification \\ \verb+OS+ & Species/Taxon \\ \verb+OV+ & Older version \\ \verb+PV+ & Preferred version \\ \verb+TY+ & Type \\ \verb+XX+ & Empty line; these are not stored in the Record. \\ \end{tabular} \end{center} \end{table} Each motif also has an attribute \verb+.references+ containing the references associated with the motif, using these two-letter keys: \begin{table}[h] \begin{center} \caption{Fields used to store references in TRANSFAC files} \begin{tabular}{|l|l||} \verb+RN+ & Reference number \\ \verb+RA+ & Reference authors \\ \verb+RL+ & Reference data \\ \verb+RT+ & Reference title \\ \verb+RX+ & PubMed ID \\ \end{tabular} \end{center} \end{table} Printing the motifs writes them out in their native TRANSFAC format: %cont-doctest \begin{verbatim} >>> print(record) VV EXAMPLE January 15, 2013 XX // ID motif1 XX P0 A C G T 01 1 2 2 0 S 02 2 1 2 0 R 03 3 0 1 1 A 04 0 5 0 0 C 05 5 0 0 0 A 06 0 0 4 1 G 07 0 1 4 0 G 08 0 0 0 5 T 09 0 0 5 0 G 10 0 1 2 2 K 11 0 2 0 3 Y 12 1 0 3 1 G XX // ID motif2 XX P0 A C G T 01 2 1 2 0 R 02 1 2 2 0 S 03 0 5 0 0 C 04 3 0 1 1 A 05 0 0 4 1 G 06 5 0 0 0 A 07 0 1 4 0 G 08 0 0 5 0 G 09 0 0 0 5 T 10 0 2 0 3 Y XX // \end{verbatim} You can export the motifs in the TRANSFAC format by capturing this output in a string and saving it in a file: \begin{verbatim} >>> text = str(record) >>> handle = open("mytransfacfile.dat", 'w') >>> handle.write(text) >>> handle.close() \end{verbatim} \section{Writing motifs} Speaking of exporting, let's look at export functions in general. We can use the \verb+format+ method to write the motif in the simple JASPAR \verb+pfm+ format: %the tabs in the output confuse doctest; don't test \begin{verbatim} >>> print(arnt.format("pfm")) 4.00 19.00 0.00 0.00 0.00 0.00 16.00 0.00 20.00 0.00 0.00 0.00 0.00 1.00 0.00 20.00 0.00 20.00 0.00 0.00 0.00 0.00 20.00 0.00 \end{verbatim} Similarly, we can use \verb+format+ to write the motif in the JASPAR \verb+jaspar+ format: \begin{verbatim} >>> print(arnt.format("jaspar")) >MA0004.1 Arnt A [ 4.00 19.00 0.00 0.00 0.00 0.00] C [ 16.00 0.00 20.00 0.00 0.00 0.00] G [ 0.00 1.00 0.00 20.00 0.00 20.00] T [ 0.00 0.00 0.00 0.00 20.00 0.00] \end{verbatim} To write the motif in a TRANSFAC-like matrix format, use %cont-doctest \begin{verbatim} >>> print(m.format("transfac")) P0 A C G T 01 3 0 0 4 W 02 7 0 0 0 A 03 0 5 0 2 C 04 2 2 3 0 V 05 1 6 0 0 C XX // \end{verbatim} To write out multiple motifs, you can use \verb+motifs.write+. This function can be used regardless of whether the motifs originated from a TRANSFAC file. For example, %cont-doctest \begin{verbatim} >>> two_motifs = [arnt, srf] >>> print(motifs.write(two_motifs, 'transfac')) P0 A C G T 01 4 16 0 0 C 02 19 0 1 0 A 03 0 20 0 0 C 04 0 0 20 0 G 05 0 0 0 20 T 06 0 0 20 0 G XX // P0 A C G T 01 2 1 39 4 G 02 9 33 2 2 C 03 0 45 1 0 C 04 1 45 0 0 C 05 32 1 0 13 A 06 3 1 0 42 T 07 46 0 0 0 A 08 1 0 0 45 T 09 43 0 0 3 A 10 15 1 0 30 T 11 2 0 44 0 G 12 2 1 43 0 G XX // \end{verbatim} Or, to write multiple motifs in the \verb+jaspar+ format: \begin{verbatim} >>> two_motifs = [arnt, mef2a] >>> print(motifs.write(two_motifs, "jaspar")) >MA0004.1 Arnt A [ 4.00 19.00 0.00 0.00 0.00 0.00] C [ 16.00 0.00 20.00 0.00 0.00 0.00] G [ 0.00 1.00 0.00 20.00 0.00 20.00] T [ 0.00 0.00 0.00 0.00 20.00 0.00] >MA0052.1 MEF2A A [ 1.00 0.00 57.00 2.00 9.00 6.00 37.00 2.00 56.00 6.00] C [ 50.00 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00] G [ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 50.00] T [ 7.00 58.00 0.00 55.00 49.00 52.00 21.00 56.00 0.00 2.00] \end{verbatim} \section{Position-Weight Matrices} The \verb+.counts+ attribute of a Motif object shows how often each nucleotide appeared at each position along the alignment. We can normalize this matrix by dividing by the number of instances in the alignment, resulting in the probability of each nucleotide at each position along the alignment. We refer to these probabilities as the position-weight matrix. However, beware that in the literature this term may also be used to refer to the position-specific scoring matrix, which we discuss below. Usually, pseudocounts are added to each position before normalizing. This avoids overfitting of the position-weight matrix to the limited number of motif instances in the alignment, and can also prevent probabilities from becoming zero. To add a fixed pseudocount to all nucleotides at all positions, specify a number for the \verb+pseudocounts+ argument: %cont-doctest \begin{verbatim} >>> pwm = m.counts.normalize(pseudocounts=0.5) >>> print(pwm) 0 1 2 3 4 A: 0.39 0.83 0.06 0.28 0.17 C: 0.06 0.06 0.61 0.28 0.72 G: 0.06 0.06 0.06 0.39 0.06 T: 0.50 0.06 0.28 0.06 0.06 \end{verbatim} Alternatively, \verb+pseudocounts+ can be a dictionary specifying the pseudocounts for each nucleotide. For example, as the GC content of the human genome is about 40\%, you may want to choose the pseudocounts accordingly: %cont-doctest \begin{verbatim} >>> pwm = m.counts.normalize(pseudocounts={'A':0.6, 'C': 0.4, 'G': 0.4, 'T': 0.6}) >>> print(pwm) 0 1 2 3 4 A: 0.40 0.84 0.07 0.29 0.18 C: 0.04 0.04 0.60 0.27 0.71 G: 0.04 0.04 0.04 0.38 0.04 T: 0.51 0.07 0.29 0.07 0.07 \end{verbatim} The position-weight matrix has its own methods to calculate the consensus, anticonsensus, and degenerate consensus sequences: %cont-doctest \begin{verbatim} >>> pwm.consensus Seq('TACGC', IUPACUnambiguousDNA()) >>> pwm.anticonsensus Seq('GGGTG', IUPACUnambiguousDNA()) >>> pwm.degenerate_consensus Seq('WACNC', IUPACAmbiguousDNA()) \end{verbatim} Note that due to the pseudocounts, the degenerate consensus sequence calculated from the position-weight matrix is slightly different from the degenerate consensus sequence calculated from the instances in the motif: %cont-doctest \begin{verbatim} >>> m.degenerate_consensus Seq('WACVC', IUPACAmbiguousDNA()) \end{verbatim} The reverse complement of the position-weight matrix can be calculated directly from the \verb+pwm+: %cont-doctest \begin{verbatim} >>> rpwm = pwm.reverse_complement() >>> print(rpwm) 0 1 2 3 4 A: 0.07 0.07 0.29 0.07 0.51 C: 0.04 0.38 0.04 0.04 0.04 G: 0.71 0.27 0.60 0.04 0.04 T: 0.18 0.29 0.07 0.84 0.40 \end{verbatim} \section{Position-Specific Scoring Matrices} Using the background distribution and PWM with pseudo-counts added, it's easy to compute the log-odds ratios, telling us what are the log odds of a particular symbol to be coming from a motif against the background. We can use the \verb|.log_odds()| method on the position-weight matrix: %cont-doctest \begin{verbatim} >>> pssm = pwm.log_odds() >>> print(pssm) 0 1 2 3 4 A: 0.68 1.76 -1.91 0.21 -0.49 C: -2.49 -2.49 1.26 0.09 1.51 G: -2.49 -2.49 -2.49 0.60 -2.49 T: 1.03 -1.91 0.21 -1.91 -1.91 \end{verbatim} Here we can see positive values for symbols more frequent in the motif than in the background and negative for symbols more frequent in the background. $0.0$ means that it's equally likely to see a symbol in the background and in the motif. This assumes that A, C, G, and T are equally likely in the background. To calculate the position-specific scoring matrix against a background with unequal probabilities for A, C, G, T, use the \verb+background+ argument. For example, against a background with a 40\% GC content, use %cont-doctest \begin{verbatim} >>> background = {'A':0.3,'C':0.2,'G':0.2,'T':0.3} >>> pssm = pwm.log_odds(background) >>> print(pssm) 0 1 2 3 4 A: 0.42 1.49 -2.17 -0.05 -0.75 C: -2.17 -2.17 1.58 0.42 1.83 G: -2.17 -2.17 -2.17 0.92 -2.17 T: 0.77 -2.17 -0.05 -2.17 -2.17 \end{verbatim} The maximum and minimum score obtainable from the PSSM are stored in the \verb+.max+ and \verb+.min+ properties: %cont-doctest \begin{verbatim} >>> print("%4.2f" % pssm.max) 6.59 >>> print("%4.2f" % pssm.min) -10.85 \end{verbatim} The mean and standard deviation of the PSSM scores with respect to a specific background are calculated by the \verb+.mean+ and \verb+.std+ methods. %cont-doctest \begin{verbatim} >>> mean = pssm.mean(background) >>> std = pssm.std(background) >>> print("mean = %0.2f, standard deviation = %0.2f" % (mean, std)) mean = 3.21, standard deviation = 2.59 \end{verbatim} A uniform background is used if \verb+background+ is not specified. The mean is particularly important, as its value is equal to the Kullback-Leibler divergence or relative entropy, and is a measure for the information content of the motif compared to the background. As in Biopython the base-2 logarithm is used in the calculation of the log-odds scores, the information content has units of bits. The \verb+.reverse_complement+, \verb+.consensus+, \verb+.anticonsensus+, and \verb+.degenerate_consensus+ methods can be applied directly to PSSM objects. \section{Searching for instances} \label{sec:search} The most frequent use for a motif is to find its instances in some sequence. For the sake of this section, we will use an artificial sequence like this: %cont-doctest \begin{verbatim} >>> test_seq=Seq("TACACTGCATTACAACCCAAGCATTA", m.alphabet) >>> len(test_seq) 26 \end{verbatim} \subsection{Searching for exact matches} The simplest way to find instances, is to look for exact matches of the true instances of the motif: %cont-doctest \begin{verbatim} >>> for pos, seq in m.instances.search(test_seq): ... print("%i %s" % (pos, seq)) ... 0 TACAC 10 TACAA 13 AACCC \end{verbatim} We can do the same with the reverse complement (to find instances on the complementary strand): %cont-doctest \begin{verbatim} >>> for pos, seq in r.instances.search(test_seq): ... print("%i %s" % (pos, seq)) ... 6 GCATT 20 GCATT \end{verbatim} \subsection{Searching for matches using the PSSM score} It's just as easy to look for positions, giving rise to high log-odds scores against our motif: %cont-doctest \begin{verbatim} >>> for position, score in pssm.search(test_seq, threshold=3.0): ... print("Position %d: score = %5.3f" % (position, score)) ... Position 0: score = 5.622 Position -20: score = 4.601 Position 10: score = 3.037 Position 13: score = 5.738 Position -6: score = 4.601 \end{verbatim} The negative positions refer to instances of the motif found on the reverse strand of the test sequence, and follow the Python convention on negative indices. Therefore, the instance of the motif at \verb|pos| is located at \verb|test_seq[pos:pos+len(m)]| both for positive and for negative values of \verb|pos|. You may notice the threshold parameter, here set arbitrarily to $3.0$. This is in $log_2$, so we are now looking only for words, which are eight times more likely to occur under the motif model than in the background. The default threshold is $0.0$, which selects everything that looks more like the motif than the background. You can also calculate the scores at all positions along the sequence: %Don't use a doc test for this as the spacing can differ \begin{verbatim} >>> pssm.calculate(test_seq) array([ 5.62230396, -5.6796999 , -3.43177247, 0.93827754, -6.84962511, -2.04066086, -10.84962463, -3.65614533, -0.03370807, -3.91102552, 3.03734159, -2.14918518, -0.6016975 , 5.7381525 , -0.50977498, -3.56422281, -8.73414803, -0.09919716, -0.6016975 , -2.39429784, -10.84962463, -3.65614533], dtype=float32) \end{verbatim} In general, this is the fastest way to calculate PSSM scores. The scores returned by \verb+pssm.calculate+ are for the forward strand only. To obtain the scores on the reverse strand, you can take the reverse complement of the PSSM: \begin{verbatim} >>> rpssm = pssm.reverse_complement() >>> rpssm.calculate(test_seq) array([ -9.43458748, -3.06172252, -7.18665981, -7.76216221, -2.04066086, -4.26466274, 4.60124254, -4.2480607 , -8.73414803, -2.26503372, -6.49598789, -5.64668512, -8.73414803, -10.84962463, -4.82356262, -4.82356262, -5.64668512, -8.73414803, -4.15613794, -5.6796999 , 4.60124254, -4.2480607 ], dtype=float32) \end{verbatim} \subsection{Selecting a score threshold} If you want to use a less arbitrary way of selecting thresholds, you can explore the distribution of PSSM scores. Since the space for a score distribution grows exponentially with motif length, we are using an approximation with a given precision to keep computation cost manageable: %cont-doctest \begin{verbatim} >>> distribution = pssm.distribution(background=background, precision=10**4) \end{verbatim} The \verb+distribution+ object can be used to determine a number of different thresholds. We can specify the requested false-positive rate (probability of ``finding'' a motif instance in background generated sequence): %cont-doctest \begin{verbatim} >>> threshold = distribution.threshold_fpr(0.01) >>> print("%5.3f" % threshold) 4.009 \end{verbatim} or the false-negative rate (probability of ``not finding'' an instance generated from the motif): %cont-doctest \begin{verbatim} >>> threshold = distribution.threshold_fnr(0.1) >>> print("%5.3f" % threshold) -0.510 \end{verbatim} or a threshold (approximately) satisfying some relation between the false-positive rate and the false-negative rate ($\frac{\textrm{fnr}}{\textrm{fpr}}\simeq t$): %cont-doctest \begin{verbatim} >>> threshold = distribution.threshold_balanced(1000) >>> print("%5.3f" % threshold) 6.241 \end{verbatim} or a threshold satisfying (roughly) the equality between the false-positive rate and the $-log$ of the information content (as used in patser software by Hertz and Stormo): %cont-doctest \begin{verbatim} >>> threshold = distribution.threshold_patser() >>> print("%5.3f" % threshold) 0.346 \end{verbatim} For example, in case of our motif, you can get the threshold giving you exactly the same results (for this sequence) as searching for instances with balanced threshold with rate of $1000$. %cont-doctest \begin{verbatim} >>> threshold = distribution.threshold_fpr(0.01) >>> print("%5.3f" % threshold) 4.009 >>> for position, score in pssm.search(test_seq, threshold=threshold): ... print("Position %d: score = %5.3f" % (position, score)) ... Position 0: score = 5.622 Position -20: score = 4.601 Position 13: score = 5.738 Position -6: score = 4.601 \end{verbatim} \section{Each motif object has an associated Position-Specific Scoring Matrix} To facilitate searching for potential TFBSs using PSSMs, both the position-weight matrix and the position-specific scoring matrix are associated with each motif. Using the Arnt motif as an example: %TODO - Start a new doctest here? %cont-doctest \begin{verbatim} >>> from Bio import motifs >>> handle = open("Arnt.sites") >>> motif = motifs.read(handle, 'sites') >>> print(motif.counts) 0 1 2 3 4 5 A: 4.00 19.00 0.00 0.00 0.00 0.00 C: 16.00 0.00 20.00 0.00 0.00 0.00 G: 0.00 1.00 0.00 20.00 0.00 20.00 T: 0.00 0.00 0.00 0.00 20.00 0.00 >>> print(motif.pwm) 0 1 2 3 4 5 A: 0.20 0.95 0.00 0.00 0.00 0.00 C: 0.80 0.00 1.00 0.00 0.00 0.00 G: 0.00 0.05 0.00 1.00 0.00 1.00 T: 0.00 0.00 0.00 0.00 1.00 0.00 \end{verbatim} %Can't use next bit in doctest, Windows Python 2.5 and 2.6 put -1.$ not -inf \begin{verbatim} >>> print(motif.pssm) 0 1 2 3 4 5 A: -0.32 1.93 -inf -inf -inf -inf C: 1.68 -inf 2.00 -inf -inf -inf G: -inf -2.32 -inf 2.00 -inf 2.00 T: -inf -inf -inf -inf 2.00 -inf \end{verbatim} The negative infinities appear here because the corresponding entry in the frequency matrix is 0, and we are using zero pseudocounts by default: %cont-doctest \begin{verbatim} >>> for letter in "ACGT": ... print("%s: %4.2f" % (letter, motif.pseudocounts[letter])) ... A: 0.00 C: 0.00 G: 0.00 T: 0.00 \end{verbatim} If you change the \verb+.pseudocounts+ attribute, the position-frequency matrix and the position-specific scoring matrix are recalculated automatically: %cont-doctest \begin{verbatim} >>> motif.pseudocounts = 3.0 >>> for letter in "ACGT": ... print("%s: %4.2f" % (letter, motif.pseudocounts[letter])) ... A: 3.00 C: 3.00 G: 3.00 T: 3.00 \end{verbatim} %Can't use this in doctest, Windows Python 2.5 and 2.6 give G/1 as 0.13 not 0.12 %TODO - Check why... \begin{verbatim} >>> print(motif.pwm) 0 1 2 3 4 5 A: 0.22 0.69 0.09 0.09 0.09 0.09 C: 0.59 0.09 0.72 0.09 0.09 0.09 G: 0.09 0.12 0.09 0.72 0.09 0.72 T: 0.09 0.09 0.09 0.09 0.72 0.09 \end{verbatim} %cont-doctest \begin{verbatim} >>> print(motif.pssm) 0 1 2 3 4 5 A: -0.19 1.46 -1.42 -1.42 -1.42 -1.42 C: 1.25 -1.42 1.52 -1.42 -1.42 -1.42 G: -1.42 -1.00 -1.42 1.52 -1.42 1.52 T: -1.42 -1.42 -1.42 -1.42 1.52 -1.42 \end{verbatim} You can also set the \verb+.pseudocounts+ to a dictionary over the four nucleotides if you want to use different pseudocounts for them. Setting \verb+motif.pseudocounts+ to \verb+None+ resets it to its default value of zero. The position-specific scoring matrix depends on the background distribution, which is uniform by default: %cont-doctest \begin{verbatim} >>> for letter in "ACGT": ... print("%s: %4.2f" % (letter, motif.background[letter])) ... A: 0.25 C: 0.25 G: 0.25 T: 0.25 \end{verbatim} Again, if you modify the background distribution, the position-specific scoring matrix is recalculated: %cont-doctest \begin{verbatim} >>> motif.background = {'A': 0.2, 'C': 0.3, 'G': 0.3, 'T': 0.2} >>> print(motif.pssm) 0 1 2 3 4 5 A: 0.13 1.78 -1.09 -1.09 -1.09 -1.09 C: 0.98 -1.68 1.26 -1.68 -1.68 -1.68 G: -1.68 -1.26 -1.68 1.26 -1.68 1.26 T: -1.09 -1.09 -1.09 -1.09 1.85 -1.09 \end{verbatim} Setting \verb+motif.background+ to \verb+None+ resets it to a uniform distribution: %cont-doctest \begin{verbatim} >>> motif.background = None >>> for letter in "ACGT": ... print("%s: %4.2f" % (letter, motif.background[letter])) ... A: 0.25 C: 0.25 G: 0.25 T: 0.25 \end{verbatim} If you set \verb+motif.background+ equal to a single value, it will be interpreted as the GC content: %cont-doctest \begin{verbatim} >>> motif.background = 0.8 >>> for letter in "ACGT": ... print("%s: %4.2f" % (letter, motif.background[letter])) ... A: 0.10 C: 0.40 G: 0.40 T: 0.10 \end{verbatim} Note that you can now calculate the mean of the PSSM scores over the background against which it was computed: %cont-doctest \begin{verbatim} >>> print("%f" % motif.pssm.mean(motif.background)) 4.703928 \end{verbatim} as well as its standard deviation: %cont-doctest \begin{verbatim} >>> print("%f" % motif.pssm.std(motif.background)) 3.290900 \end{verbatim} and its distribution: %cont-doctest \begin{verbatim} >>> distribution = motif.pssm.distribution(background=motif.background) >>> threshold = distribution.threshold_fpr(0.01) >>> print("%f" % threshold) 3.854375 \end{verbatim} Note that the position-weight matrix and the position-specific scoring matrix are recalculated each time you call \verb+motif.pwm+ or \verb+motif.pssm+, respectively. If speed is an issue and you want to use the PWM or PSSM repeatedly, you can save them as a variable, as in \begin{verbatim} >>> pssm = motif.pssm \end{verbatim} \section{Comparing motifs} \label{sec:comp} Once we have more than one motif, we might want to compare them. Before we start comparing motifs, I should point out that motif boundaries are usually quite arbitrary. This means we often need to compare motifs of different lengths, so comparison needs to involve some kind of alignment. This means we have to take into account two things: \begin{itemize} \item alignment of motifs \item some function to compare aligned motifs \end{itemize} To align the motifs, we use ungapped alignment of PSSMs and substitute zeros for any missing columns at the beginning and end of the matrices. This means that effectively we are using the background distribution for columns missing from the PSSM. The distance function then returns the minimal distance between motifs, as well as the corresponding offset in their alignment. To give an exmaple, let us first load another motif, which is similar to our test motif \verb|m|: %TODO - Start a new doctest here? %cont-doctest \begin{verbatim} >>> m_reb1 = motifs.read(open("REB1.pfm"), "pfm") >>> m_reb1.consensus Seq('GTTACCCGG', IUPACUnambiguousDNA()) >>> print(m_reb1.counts) 0 1 2 3 4 5 6 7 8 A: 30.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 15.00 C: 10.00 0.00 0.00 0.00 100.00 100.00 100.00 0.00 15.00 G: 50.00 0.00 0.00 0.00 0.00 0.00 0.00 60.00 55.00 T: 10.00 100.00 100.00 0.00 0.00 0.00 0.00 40.00 15.00 \end{verbatim} To make the motifs comparable, we choose the same values for the pseudocounts and the background distribution as our motif \verb|m|: %cont-doctest \begin{verbatim} >>> m_reb1.pseudocounts = {'A':0.6, 'C': 0.4, 'G': 0.4, 'T': 0.6} >>> m_reb1.background = {'A':0.3,'C':0.2,'G':0.2,'T':0.3} >>> pssm_reb1 = m_reb1.pssm >>> print(pssm_reb1) 0 1 2 3 4 5 6 7 8 A: 0.00 -5.67 -5.67 1.72 -5.67 -5.67 -5.67 -5.67 -0.97 C: -0.97 -5.67 -5.67 -5.67 2.30 2.30 2.30 -5.67 -0.41 G: 1.30 -5.67 -5.67 -5.67 -5.67 -5.67 -5.67 1.57 1.44 T: -1.53 1.72 1.72 -5.67 -5.67 -5.67 -5.67 0.41 -0.97 \end{verbatim} We'll compare these motifs using the Pearson correlation. Since we want it to resemble a distance measure, we actually take $1-r$, where $r$ is the Pearson correlation coefficient (PCC): %cont-doctest \begin{verbatim} >>> distance, offset = pssm.dist_pearson(pssm_reb1) >>> print("distance = %5.3g" % distance) distance = 0.239 >>> print(offset) -2 \end{verbatim} This means that the best PCC between motif \verb|m| and \verb|m_reb1| is obtained with the following alignment: \begin{verbatim} m: bbTACGCbb m_reb1: GTTACCCGG \end{verbatim} where \verb|b| stands for background distribution. The PCC itself is roughly $1-0.239=0.761$. \section{\emph{De novo} motif finding} \label{sec:find} Currently, Biopython has only limited support for \emph{de novo} motif finding. Namely, we support running and parsing of AlignAce and MEME. Since the number of motif finding tools is growing rapidly, contributions of new parsers are welcome. \subsection{MEME} \label{sec:meme} Let's assume, you have run MEME on sequences of your choice with your favorite parameters and saved the output in the file \verb|meme.out|. You can retrieve the motifs reported by MEME by running the following piece of code: %doctest ../Tests/Motif \begin{verbatim} >>> from Bio import motifs >>> motifsM = motifs.parse(open("meme.out"), "meme") \end{verbatim} \begin{verbatim} >>> motifsM [] \end{verbatim} Besides the most wanted list of motifs, the result object contains more useful information, accessible through properties with self-explanatory names: \begin{itemize} \item \verb|.alphabet| \item \verb|.datafile| \item \verb|.sequence_names| \item \verb|.version| \item \verb|.command| \end{itemize} The motifs returned by the MEME Parser can be treated exactly like regular Motif objects (with instances), they also provide some extra functionality, by adding additional information about the instances. %cont-doctest \begin{verbatim} >>> motifsM[0].consensus Seq('CTCAATCGTA', IUPACUnambiguousDNA()) >>> motifsM[0].instances[0].sequence_name 'SEQ10;' >>> motifsM[0].instances[0].start 3 >>> motifsM[0].instances[0].strand '+' \end{verbatim} \begin{verbatim} >>> motifsM[0].instances[0].pvalue 8.71e-07 \end{verbatim} \subsection{AlignAce} \label{sec:alignace} We can do very similar things with the AlignACE program. Assume, you have your output in the file \verb|alignace.out|. You can parse your output with the following code: %cont-doctest \begin{verbatim} >>> from Bio import motifs >>> motifsA = motifs.parse(open("alignace.out"), "alignace") \end{verbatim} Again, your motifs behave as they should: %cont-doctest \begin{verbatim} >>> motifsA[0].consensus Seq('TCTACGATTGAG', IUPACUnambiguousDNA()) \end{verbatim} In fact you can even see, that AlignAce found a very similar motif as MEME. It is just a longer version of a reverse complement of the MEME motif: %cont-doctest \begin{verbatim} >>> motifsM[0].reverse_complement().consensus Seq('TACGATTGAG', IUPACUnambiguousDNA()) \end{verbatim} If you have AlignAce installed on the same machine, you can also run it directly from Biopython. A short example of how this can be done is shown below (other parameters can be specified as keyword parameters): \begin{verbatim} >>> command="/opt/bin/AlignACE" >>> input_file="test.fa" >>> from Bio.motifs.applications import AlignAceCommandline >>> cmd = AlignAceCommandline(cmd=command, input=input_file, gcback=0.6, numcols=10) >>> stdout, stderr= cmd() \end{verbatim} Since AlignAce prints all of its output to standard output, you can get to your motifs by parsing the first part of the result: \begin{verbatim} >>> motifs = motifs.parse(stdout, "alignace") \end{verbatim} \section{Useful links } \label{sec:links} \begin{itemize} \item \href{http://en.wikipedia.org/wiki/Sequence_motif}{Sequence motif} in wikipedia \item \href{http://en.wikipedia.org/wiki/Position_weight_matrix}{PWM} in wikipedia \item \href{http://en.wikipedia.org/wiki/Consensus_sequence}{Consensus sequence} in wikipedia \item \href{http://bio.cs.washington.edu/assessment/}{Comparison of different motif finding programs} \end{itemize} \section{Obsolete Bio.Motif module} The rest of this chapter above describes the \verb|Bio.motifs| package included in Biopython 1.61 onwards, which is replacing the older \verb|Bio.Motif| package introduced with Biopython 1.50, which was in turn based on two older former Biopython modules, \verb|Bio.AlignAce| and \verb|Bio.MEME|. To allow for a smooth transition, the older \verb|Bio.Motif| package will be maintained in parallel with its replacement \verb|Bio.motifs| at least two more releases, and at least one year. \subsection{Motif objects} %TODO - turn these into doctests... Since we are interested in motif analysis, we need to take a look at \verb|Motif| objects in the first place. For that we need to import the Motif library: %doctest \begin{verbatim} >>> from Bio import Motif \end{verbatim} and we can start creating our first motif objects. Let's create a DNA motif: %cont-doctest \begin{verbatim} >>> from Bio.Alphabet import IUPAC >>> m = Motif.Motif(alphabet=IUPAC.unambiguous_dna) \end{verbatim} This is for now just an empty container, so let's add some sequences to our newly created motif: %cont-doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> m.add_instance(Seq("TATAA", m.alphabet)) >>> m.add_instance(Seq("TATTA", m.alphabet)) >>> m.add_instance(Seq("TATAA", m.alphabet)) >>> m.add_instance(Seq("TATAA", m.alphabet)) \end{verbatim} Now we have a full \verb|Motif| instance, so we can try to get some basic information about it. Let's start with length and consensus sequence: %cont-doctest \begin{verbatim} >>> len(m) 5 >>> m.consensus() Seq('TATAA', IUPACUnambiguousDNA()) \end{verbatim} In case of DNA motifs, we can also get a reverse complement of a motif: %>>> m.reverse_complement() % %cont-doctest \begin{verbatim} >>> m.reverse_complement().consensus() Seq('TTATA', IUPACUnambiguousDNA()) >>> for i in m.reverse_complement().instances: ... print(i) TTATA TAATA TTATA TTATA \end{verbatim} We can also calculate the information content of a motif with a simple call: %cont-doctest \begin{verbatim} >>> print("%0.2f" % m.ic()) 5.27 \end{verbatim} This gives us a number of bits of information provided by the motif, which tells us how much differs from background. The most common representation of a motif is a PWM (Position Weight Matrix). It summarizes the probabilities of finding any symbol (in this case nucleotide) in any position of a motif. It can be computed by calling the \verb|.pwm()| method: \begin{verbatim} >>> m.pwm() [{'A': 0.05, 'C': 0.05, 'T': 0.85, 'G': 0.05}, {'A': 0.85, 'C': 0.05, 'T': 0.05, 'G': 0.05}, {'A': 0.05, 'C': 0.05, 'T': 0.85, 'G': 0.05}, {'A': 0.65, 'C': 0.05, 'T': 0.25, 'G': 0.05}, {'A': 0.85, 'C': 0.05, 'T': 0.05, 'G': 0.05}] \end{verbatim} The probabilities in the motif's PWM are based on the counts in the instances, but we can see, that even though there were no Gs and no Cs in the instances, we still have non-zero probabilities assigned to them. These come from pseudo-counts which are, roughly speaking, a commonly used way to acknowledge the incompleteness of our knowledge and avoid technical problems with calculating logarithms of $0$. We can control the way that pseudo-counts are added with two properties of Motif objects \verb|.background| is the probability distribution over all symbols in the alphabet that we assume represents background, non-motif sequences (usually based on the GC content of the respective genome). It is by default set to a uniform distribution upon creation of a motif: \begin{verbatim} >>> m.background {'A': 0.25, 'C': 0.25, 'T': 0.25, 'G': 0.25} \end{verbatim} The other parameter is \verb|.beta|, which states the amount of pseudo-counts we should add to the PWM. By default it is set to $1.0$, %cont-doctest \begin{verbatim} >>> m.beta 1.0 \end{verbatim} so that the total input of pseudo-counts is equal to that of one instance. Using the background distribution and pwm with pseudo-counts added, it's easy to compute the log-odds ratios, telling us what are the log odds of a particular symbol to be coming from a motif against the background. We can use the \verb|.log_odds()| method: \begin{verbatim} >>> m.log_odds() [{'A': -2.3219280948873622, 'C': -2.3219280948873622, 'T': 1.7655347463629771, 'G': -2.3219280948873622}, {'A': 1.7655347463629771, 'C': -2.3219280948873622, 'T': -2.3219280948873622, 'G': -2.3219280948873622}, {'A': -2.3219280948873622, 'C': -2.3219280948873622, 'T': 1.7655347463629771, 'G': -2.3219280948873622}, {'A': 1.3785116232537298, 'C': -2.3219280948873622, 'T': 0.0, 'G': -2.3219280948873622}, {'A': 1.7655347463629771, 'C': -2.3219280948873622, 'T': -2.3219280948873622, 'G': -2.3219280948873622} ] \end{verbatim} Here we can see positive values for symbols more frequent in the motif than in the background and negative for symbols more frequent in the background. $0.0$ means that it's equally likely to see a symbol in background and in the motif (e.g. `T' in the second-last position). \subsubsection{Reading and writing} Creating motifs from instances by hand is a bit boring, so it's useful to have some I/O functions for reading and writing motifs. There are no really well established standards for storing motifs, but there's a couple of formats which are more used than others. The most important distinction is whether the motif representation is based on instances or on some version of PWM matrix. On of the most popular motif databases \href{http://jaspar.genereg.net}{JASPAR} stores motifs in both formats, so let's look at how we can import JASPAR motifs from instances: %doctest ../Tests/Motif \begin{verbatim} >>> from Bio import Motif >>> arnt = Motif.read(open("Arnt.sites"), "jaspar-sites") \end{verbatim} and from a count matrix: %cont-doctest \begin{verbatim} >>> srf = Motif.read(open("SRF.pfm"), "jaspar-pfm") \end{verbatim} The \verb|arnt| and \verb|srf| motifs can both do the same things for us, but they use different internal representations of the motif. We can tell that by inspecting the \verb|has_counts| and \verb|has_instances| properties: %cont-doctest \begin{verbatim} >>> arnt.has_instances True >>> srf.has_instances False >>> srf.has_counts True \end{verbatim} %TODO - sort order and formatting for doctest? \begin{verbatim} >>> srf.counts {'A': [2, 9, 0, 1, 32, 3, 46, 1, 43, 15, 2, 2], 'C': [1, 33, 45, 45, 1, 1, 0, 0, 0, 1, 0, 1], 'G': [39, 2, 1, 0, 0, 0, 0, 0, 0, 0, 44, 43], 'T': [4, 2, 0, 0, 13, 42, 0, 45, 3, 30, 0, 0]} \end{verbatim} There are conversion functions, which can help us convert between different representations: \begin{verbatim} >>> arnt.make_counts_from_instances() {'A': [8, 38, 0, 0, 0, 0], 'C': [32, 0, 40, 0, 0, 0], 'G': [0, 2, 0, 40, 0, 40], 'T': [0, 0, 0, 0, 40, 0]} >>> srf.make_instances_from_counts() [Seq('GGGAAAAAAAGG', IUPACUnambiguousDNA()), Seq('GGCCAAATAAGG', IUPACUnambiguousDNA()), Seq('GACCAAATAAGG', IUPACUnambiguousDNA()), .... \end{verbatim} The important thing to remember here is that the method \verb|make_instances_from_counts()| creates fake instances, because usually there are very many possible sets of instances which give rise to the same pwm, and if we have only the count matrix, we cannot reconstruct the original one. This does not make any difference if we are using the PWM as the representation of the motif, but one should be careful with exporting instances from count-based motifs. Speaking of exporting, let's look at export functions. We can export to fasta: \begin{verbatim} >>> print(m.format("fasta")) >instance0 TATAA >instance1 TATTA >instance2 TATAA >instance3 TATAA \end{verbatim} or to TRANSFAC-like matrix format (used by some motif processing software) \begin{verbatim} >>> print(m.format("transfac")) XX TY Motif ID BF undef P0 G A T C 01 0 0 4 0 02 0 4 0 0 03 0 0 4 0 04 0 3 1 0 05 0 4 0 0 XX \end{verbatim} Finally, if we have internet access, we can create a \href{http://weblogo.berkeley.edu}{weblogo}: \begin{verbatim} >>> arnt.weblogo("Arnt.png") \end{verbatim} We should get our logo saved as a png in the specified file. \subsection{Searching for instances} The most frequent use for a motif is to find its instances in some sequence. For the sake of this section, we will use an artificial sequence like this: \begin{verbatim} test_seq=Seq("TATGATGTAGTATAATATAATTATAA",m.alphabet) \end{verbatim} The simplest way to find instances, is to look for exact matches of the true instances of the motif: \begin{verbatim} >>> for pos, seq in m.search_instances(test_seq): ... print(pos, seq.tostring()) ... 10 TATAA 15 TATAA 21 TATAA \end{verbatim} We can do the same with the reverse complement (to find instances on the complementary strand): \begin{verbatim} >>> for pos, seq in m.reverse_complement().search_instances(test_seq): ... print(pos, seq.tostring()) ... 12 TAATA 20 TTATA \end{verbatim} It's just as easy to look for positions, giving rise to high log-odds scores against our motif: \begin{verbatim} >>> for pos, score in m.search_pwm(test_seq, threshold=5.0): ... print(pos, score) ... 10 8.44065060871 -12 7.06213898545 15 8.44065060871 -20 8.44065060871 21 8.44065060871 \end{verbatim} You may notice the threshold parameter, here set arbitrarily to $5.0$. This is in $log_2$, so we are now looking only for words, which are 32 times more likely to occur under the motif model than in the background. The default threshold is $0.0$, which selects everything that looks more like the motif than the background. If you want to use a less arbitrary way of selecting thresholds, you can explore the \verb|Motif.score_distribution| class implementing an distribution of scores for a given motif. Since the space for a score distribution grows exponentially with motif length, we are using an approximation with a given precision to keep computation cost manageable: \begin{verbatim} >>> sd = Motif.score_distribution(m, precision=10**4) \end{verbatim} The sd object can be used to determine a number of different thresholds. We can specify the requested false-positive rate (probability of ``finding'' a motif instance in background generated sequence): \begin{verbatim} >>> sd.threshold_fpr(0.01) 4.3535838726139886 \end{verbatim} or the false-negative rate (probability of ``not finding'' an instance generated from the motif): \begin{verbatim} >>> sd.threshold_fnr(0.1) 0.26651713652234044 \end{verbatim} or a threshold (approximately) satisfying some relation between fpr and fnr $\frac{fnr}{fpr}\simeq t$: \begin{verbatim} >>> sd.threshold_balanced(1000) 8.4406506087056368 \end{verbatim} or a threshold satisfying (roughly) the equality between the false-positive rate and the $-log$ of the information content (as used in patser software by Hertz and Stormo). For example, in case of our motif, you can get the threshold giving you exactly the same results (for this sequence) as searching for instances with balanced threshold with rate of $1000$. \begin{verbatim} >>> for pos, score in m.search_pwm(test_seq, threshold=sd.threshold_balanced(1000)): ... print(pos, score) ... 10 8.44065060871 15 8.44065060871 -20 8.44065060871 21 8.44065060871 \end{verbatim} \subsection{Comparing motifs} Once we have more than one motif, we might want to compare them. For that, we have currently three different methods of \verb|Bio.Motif| objects. Before we start comparing motifs, I should point out that motif boundaries are usually quite arbitrary. This means, that we often need to compare motifs of different lengths, so comparison needs to involve some kind of alignment. This means, that we have to take into account two things: \begin{itemize} \item alignment of motifs \item some function to compare aligned motifs \end{itemize} In \verb|Bio.Motif| we have 3 different functions for motif comparison, which are based on the same idea behind motif alignment, but use different functions to compare aligned motifs. Briefly speaking, we are using ungapped alignment of PWMs and substitute the missing columns at the beginning and end of the matrices with background distribution. All three comparison functions are written in such a way, that they can be interpreted as distance measures, however only one (\verb|dist_dpq|) satisfies the triangle inequality. All of them return the minimal distance and the corresponding offset between motifs. To show how these functions work, let us first load another motif, which is similar to our test motif \verb|m|: \begin{verbatim} >>> ubx=Motif.read(open("Ubx.pfm"), "jaspar-pfm") >>> ubx.consensus() Seq('TAAT', IUPACUnambiguousDNA()) \end{verbatim} The first function we'll use to compare these motifs is based on Pearson correlation. Since we want it to resemble a distance measure, we actually take $1-r$, where $r$ is the Pearson correlation coefficient (PCC): \begin{verbatim} >>> m.dist_pearson(ubx) (0.41740393308237722, 2) \end{verbatim} This means, that the best PCC between motif \verb|m| and \verb|Ubx| is obtained with the following alignment: \begin{verbatim} bbTAAT TATAAb \end{verbatim} where \verb|b| stands for background distribution. The PCC itself is roughly $1-0.42=0.58$. If we try the reverse complement of the Ubx motif: \begin{verbatim} >>> m.dist_pearson(ubx.reverse_complement()) (0.25784180151584823, 1) \end{verbatim} We can see that the PCC is better (almost $0.75$), and the alignment is also different: \begin{verbatim} bATTA TATAA \end{verbatim} There are two other functions: \verb|dist_dpq|, which is a true metric (satisfying traingle inequality) based on the Kullback-Leibler divergence \begin{verbatim} >>> m.dist_dpq(ubx.reverse_complement()) (0.49292358382899853, 1) \end{verbatim} and the \verb|dist_product| method, which is based on the product of probabilities which can be interpreted as the probability of independently generating the same instance by both motifs. \begin{verbatim} >>> m.dist_product(ubx.reverse_complement()) (0.16224587301064275, 1) \end{verbatim} \subsection{\emph{De novo} motif finding} Currently, Biopython has only limited support for \emph{de novo} motif finding. Namely, we support running and parsing of AlignAce and MEME. Since the number of motif finding tools is growing rapidly, contributions of new parsers are welcome. \subsubsection{MEME} Let's assume, you have run MEME on sequences of your choice with your favorite parameters and saved the output in the file \verb|meme.out|. You can retrieve the motifs reported by MEME by running the following piece of code: \begin{verbatim} >>> motifsM = list(Motif.parse(open("meme.out"), "MEME")) >>> motifsM [] \end{verbatim} Besides the most wanted list of motifs, the result object contains more useful information, accessible through properties with self-explanatory names: \begin{itemize} \item \verb|.alphabet| \item \verb|.datafile| \item \verb|.sequence_names| \item \verb|.version| \item \verb|.command| \end{itemize} The motifs returned by MEMEParser can be treated exactly like regular Motif objects (with instances), they also provide some extra functionality, by adding additional information about the instances. \begin{verbatim} >>> motifsM[0].consensus() Seq('CTCAATCGTA', IUPACUnambiguousDNA()) >>> motifsM[0].instances[0].pvalue 8.71e-07 >>> motifsM[0].instances[0].sequence_name 'SEQ10;' >>> motifsM[0].instances[0].start 3 >>> motifsM[0].instances[0].strand '+' \end{verbatim} \subsubsection{AlignAce} We can do very similar things with AlignACE program. Assume, you have your output in the file \verb|alignace.out|. You can parse your output with the following code: \begin{verbatim} >>> motifsA=list(Motif.parse(open("alignace.out"), "AlignAce")) \end{verbatim} Again, your motifs behave as they should: \begin{verbatim} >>> motifsA[0].consensus() Seq('TCTACGATTGAG', IUPACUnambiguousDNA()) \end{verbatim} In fact you can even see, that AlignAce found a very similar motif as MEME, it is just a longer version of a reverse complement of MEME motif: \begin{verbatim} >>> motifsM[0].reverse_complement().consensus() Seq('TACGATTGAG', IUPACUnambiguousDNA()) \end{verbatim} If you have AlignAce installed on the same machine, you can also run it directly from Biopython. Short example of how this can be done is shown below (other parameters can be specified as keyword parameters): \begin{verbatim} >>> command="/opt/bin/AlignACE" >>> input_file="test.fa" >>> from Bio.Motif.Applications import AlignAceCommandline >>> cmd = AlignAceCommandline(cmd=command, input=input_file, gcback=0.6, numcols=10) >>> stdout, stderr= cmd() \end{verbatim} Since AlignAce prints all its output to standard output, you can get to your motifs by parsing the first part of the result: \begin{verbatim} motifs=list(Motif.parse(stdout,"AlignAce")) \end{verbatim} \chapter{Cluster analysis} Cluster analysis is the grouping of items into clusters based on the similarity of the items to each other. In bioinformatics, clustering is widely used in gene expression data analysis to find groups of genes with similar gene expression profiles. This may identify functionally related genes, as well as suggest the function of presently unknown genes. The Biopython module \verb|Bio.Cluster| provides commonly used clustering algorithms and was designed with the application to gene expression data in mind. However, this module can also be used for cluster analysis of other types of data. \verb|Bio.Cluster| and the underlying C Clustering Library is described by De Hoon \textit{et al.} \cite{dehoon2004}. The following four clustering approaches are implemented in \verb|Bio.Cluster|: \begin{itemize} \item Hierarchical clustering (pairwise centroid-, single-, complete-, and average-linkage); \item $k$-means, $k$-medians, and $k$-medoids clustering; \item Self-Organizing Maps; \item Principal Component Analysis. \end{itemize} \subsection*{Data representation} The data to be clustered are represented by a $n \times m$ Numerical Python array \verb|data|. Within the context of gene expression data clustering, typically the rows correspond to different genes whereas the columns correspond to different experimental conditions. The clustering algorithms in \verb|Bio.Cluster| can be applied both to rows (genes) and to columns (experiments). \subsection*{Missing values} Often in microarray experiments, some of the data values are missing, which is indicated by an additional $n \times m$ Numerical Python integer array \verb|mask|. If \verb|mask[i,j]==0|, then \verb|data[i,j]| is missing and is ignored in the analysis. \subsection*{Random number generator} The $k$-means/medians/medoids clustering algorithms and Self-Organizing Maps (SOMs) include the use of a random number generator. The uniform random number generator in \verb|Bio.Cluster| is based on the algorithm by L'Ecuyer \cite{lecuyer1988}, while random numbers following the binomial distribution are generated using the BTPE algorithm by Kachitvichyanukul and Schmeiser \cite{kachitvichyanukul1988}. The random number generator is initialized automatically during its first call. As this random number generator uses a combination of two multiplicative linear congruential generators, two (integer) seeds are needed for initialization, for which we use the system-supplied random number generator \verb|rand| (in the C standard library). We initialize this generator by calling \verb|srand| with the epoch time in seconds, and use the first two random numbers generated by \verb|rand| as seeds for the uniform random number generator in \verb|Bio.Cluster|. \section{Distance functions} \label{sec:distancefunctions} In order to cluster items into groups based on their similarity, we should first define what exactly we mean by \emph{similar}. \verb|Bio.Cluster| provides eight distance functions, indicated by a single character, to measure similarity, or conversely, distance: \begin{itemize} \item \verb|'e'|: Euclidean distance; \item \verb|'b'|: City-block distance. \item \verb|'c'|: Pearson correlation coefficient; \item \verb|'a'|: Absolute value of the Pearson correlation coefficient; \item \verb|'u'|: Uncentered Pearson correlation (equivalent to the cosine of the angle between two data vectors); \item \verb|'x'|: Absolute uncentered Pearson correlation; \item \verb|'s'|: Spearman's rank correlation; \item \verb|'k'|: Kendall's $\tau$. \end{itemize} The first two are true distance functions that satisfy the triangle inequality: $$d\left(\underline{u},\underline{v}\right) \leq d\left(\underline{u},\underline{w}\right) + d\left(\underline{w},\underline{v}\right) \textrm{ for all } \underline{u}, \underline{v}, \underline{w},$$ and are therefore refered to as \emph{metrics}. In everyday language, this means that the shortest distance between two points is a straight line. The remaining six distance measures are related to the correlation coefficient, where the distance $d$ is defined in terms of the correlation $r$ by $d=1-r$. Note that these distance functions are \emph{semi-metrics} that do not satisfy the triangle inequality. For example, for $$\underline{u}=\left(1,0,-1\right);$$ $$\underline{v}=\left(1,1,0\right);$$ $$\underline{w}=\left(0,1,1\right);$$ we find a Pearson distance $d\left(\underline{u},\underline{w}\right) = 1.8660$, while $d\left(\underline{u},\underline{v}\right)+d\left(\underline{v},\underline{w}\right) = 1.6340$. \subsection*{Euclidean distance} In \verb|Bio.Cluster|, we define the Euclidean distance as $$d = {1 \over n} \sum_{i=1}^{n} \left(x_i-y_i\right)^{2}.$$ Only those terms are included in the summation for which both $x_i$ and $y_i$ are present, and the denominator $n$ is chosen accordingly. As the expression data $x_i$ and $y_i$ are subtracted directly from each other, we should make sure that the expression data are properly normalized when using the Euclidean distance. \subsection*{City-block distance} The city-block distance, alternatively known as the Manhattan distance, is related to the Euclidean distance. Whereas the Euclidean distance corresponds to the length of the shortest path between two points, the city-block distance is the sum of distances along each dimension. As gene expression data tend to have missing values, in \verb|Bio.Cluster| we define the city-block distance as the sum of distances divided by the number of dimensions: $$d = {1 \over n} \sum_{i=1}^n \left|x_i-y_i\right|.$$ This is equal to the distance you would have to walk between two points in a city, where you have to walk along city blocks. As for the Euclidean distance, the expression data are subtracted directly from each other, and we should therefore make sure that they are properly normalized. \subsection*{The Pearson correlation coefficient} The Pearson correlation coefficient is defined as $$r = \frac{1}{n} \sum_{i=1}^n \left( \frac{x_i -\bar{x}}{\sigma_x} \right) \left(\frac{y_i -\bar{y}}{\sigma_y} \right),$$ in which $\bar{x}, \bar{y}$ are the sample mean of $x$ and $y$ respectively, and $\sigma_x, \sigma_y$ are the sample standard deviation of $x$ and $y$. The Pearson correlation coefficient is a measure for how well a straight line can be fitted to a scatterplot of $x$ and $y$. If all the points in the scatterplot lie on a straight line, the Pearson correlation coefficient is either +1 or -1, depending on whether the slope of line is positive or negative. If the Pearson correlation coefficient is equal to zero, there is no correlation between $x$ and $y$. The \emph{Pearson distance} is then defined as $$d_{\textrm{P}} \equiv 1 - r.$$ As the Pearson correlation coefficient lies between -1 and 1, the Pearson distance lies between 0 and 2. \subsection*{Absolute Pearson correlation} By taking the absolute value of the Pearson correlation, we find a number between 0 and 1. If the absolute value is 1, all the points in the scatter plot lie on a straight line with either a positive or a negative slope. If the absolute value is equal to zero, there is no correlation between $x$ and $y$. The corresponding distance is defined as $$d_{\textrm A} \equiv 1 - \left|r\right|,$$ where $r$ is the Pearson correlation coefficient. As the absolute value of the Pearson correlation coefficient lies between 0 and 1, the corresponding distance lies between 0 and 1 as well. In the context of gene expression experiments, the absolute correlation is equal to 1 if the gene expression profiles of two genes are either exactly the same or exactly opposite. The absolute correlation coefficient should therefore be used with care. \subsection*{Uncentered correlation (cosine of the angle)} In some cases, it may be preferable to use the \emph{uncentered correlation} instead of the regular Pearson correlation coefficient. The uncentered correlation is defined as $$r_{\textrm U} = \frac{1}{n} \sum_{i=1}^{n} \left(\frac{x_i}{\sigma_x^{(0)}} \right) \left(\frac{y_i}{\sigma_y^{(0)}} \right),$$ where \begin{eqnarray} \sigma_x^{(0)} & = & \sqrt{{\frac{1}{n}} \sum_{i=1}^{n}x_i^2}; \nonumber \\ \sigma_y^{(0)} & = & \sqrt{{\frac{1}{n}} \sum_{i=1}^{n}y_i^2}. \nonumber \end{eqnarray} This is the same expression as for the regular Pearson correlation coefficient, except that the sample means $\bar{x}, \bar{y}$ are set equal to zero. The uncentered correlation may be appropriate if there is a zero reference state. For instance, in the case of gene expression data given in terms of log-ratios, a log-ratio equal to zero corresponds to the green and red signal being equal, which means that the experimental manipulation did not affect the gene expression. The distance corresponding to the uncentered correlation coefficient is defined as $$d_{\mbox{U}} \equiv 1 - r_{\mbox{U}},$$ where $r_{\mbox{U}}$ is the uncentered correlation. As the uncentered correlation coefficient lies between -1 and 1, the corresponding distance lies between 0 and 2. The uncentered correlation is equal to the cosine of the angle of the two data vectors in $n$-dimensional space, and is often referred to as such. \subsection*{Absolute uncentered correlation} As for the regular Pearson correlation, we can define a distance measure using the absolute value of the uncentered correlation: $$d_{\mbox{AU}} \equiv 1 - \left|r_{\mbox{U}}\right|,$$ where $r_{\mbox{U}}$ is the uncentered correlation coefficient. As the absolute value of the uncentered correlation coefficient lies between 0 and 1, the corresponding distance lies between 0 and 1 as well. Geometrically, the absolute value of the uncentered correlation is equal to the cosine between the supporting lines of the two data vectors (i.e., the angle without taking the direction of the vectors into consideration). \subsection*{Spearman rank correlation} The Spearman rank correlation is an example of a non-parametric similarity measure, and tends to be more robust against outliers than the Pearson correlation. To calculate the Spearman rank correlation, we replace each data value by their rank if we would order the data in each vector by their value. We then calculate the Pearson correlation between the two rank vectors instead of the data vectors. As in the case of the Pearson correlation, we can define a distance measure corresponding to the Spearman rank correlation as $$d_{\mbox{S}} \equiv 1 - r_{\mbox{S}},$$ where $r_{\mbox{S}}$ is the Spearman rank correlation. \subsection*{Kendall's $\tau$} Kendall's $\tau$ is another example of a non-parametric similarity measure. It is similar to the Spearman rank correlation, but instead of the ranks themselves only the relative ranks are used to calculate $\tau$ (see Snedecor \& Cochran \cite{snedecor1989}). We can define a distance measure corresponding to Kendall's $\tau$ as $$d_{\mbox{K}} \equiv 1 - \tau.$$ As Kendall's $\tau$ is always between -1 and 1, the corresponding distance will be between 0 and 2. \subsection*{Weighting} For most of the distance functions available in \verb|Bio.Cluster|, a weight vector can be applied. The weight vector contains weights for the items in the data vector. If the weight for item $i$ is $w_i$, then that item is treated as if it occurred $w_i$ times in the data. The weight do not have to be integers. For the Spearman rank correlation and Kendall's $\tau$, weights do not have a well-defined meaning and are therefore not implemented. \subsection*{Calculating the distance matrix} \label{subsec:distancematrix} The distance matrix is a square matrix with all pairwise distances between the items in \verb|data|, and can be calculated by the function \verb|distancematrix| in the \verb|Bio.Cluster| module: \begin{verbatim} >>> from Bio.Cluster import distancematrix >>> matrix = distancematrix(data) \end{verbatim} where the following arguments are defined: \begin{itemize} \item \verb|data| (required)\\ Array containing the data for the items. \item \verb|mask| (default: \verb|None|) \\ Array of integers showing which data are missing. If \verb|mask[i,j]==0|, then \verb|data[i,j]| is missing. If \verb|mask==None|, then all data are present. \item \verb|weight| (default: \verb|None|) \\ The weights to be used when calculating distances. If \verb|weight==None|, then equal weights are assumed. \item \verb|transpose| (default: \verb|0|) \\ Determines if the distances between the rows of \verb|data| are to be calculated (\verb|transpose==0|), or between the columns of \verb|data| (\verb|transpose==1|). \item \verb|dist| (default: \verb|'e'|, Euclidean distance) \\ Defines the distance function to be used (see \ref{sec:distancefunctions}). \end{itemize} To save memory, the distance matrix is returned as a list of 1D arrays. The number of columns in each row is equal to the row number. Hence, the first row has zero elements. An example of the return value is \begin{verbatim} [array([]), array([1.]), array([7., 3.]), array([4., 2., 6.])] \end{verbatim} This corresponds to the distance matrix $$ \left( \begin{array}{cccc} 0 & 1 & 7 & 4 \\ 1 & 0 & 3 & 2 \\ 7 & 3 & 0 & 6 \\ 4 & 2 & 6 & 0 \end{array} \right). $$ \section{Calculating cluster properties} \subsection*{Calculating the cluster centroids} \label{subsec:clustercentroids} The centroid of a cluster can be defined either as the mean or as the median of each dimension over all cluster items. The function \verb|clustercentroids| in \verb|Bio.Cluster| can be used to calculate either: \begin{verbatim} >>> from Bio.Cluster import clustercentroids >>> cdata, cmask = clustercentroids(data) \end{verbatim} where the following arguments are defined: \begin{itemize} \item \verb|data| (required) \\ Array containing the data for the items. \item \verb|mask| (default: \verb|None|) \\ Array of integers showing which data are missing. If \verb|mask[i,j]==0|, then \verb|data[i,j]| is missing. If \verb|mask==None|, then all data are present. \item \verb|clusterid| (default: \verb|None|) \\ Vector of integers showing to which cluster each item belongs. If \verb|clusterid| is \verb|None|, then all items are assumed to belong to the same cluster. \item \verb|method| (default: \verb|'a'|) \\ Specifies whether the arithmetic mean (\verb|method=='a'|) or the median (\verb|method=='m'|) is used to calculate the cluster center. \item \verb|transpose| (default: \verb|0|) \\ Determines if the centroids of the rows of \verb|data| are to be calculated (\verb|transpose==0|), or the centroids of the columns of \verb|data| (\verb|transpose==1|). \end{itemize} This function returns the tuple \verb|(cdata, cmask)|. The centroid data are stored in the 2D Numerical Python array \verb|cdata|, with missing data indicated by the 2D Numerical Python integer array \verb|cmask|. The dimensions of these arrays are $\left(\textrm{number of clusters}, \textrm{number of columns}\right)$ if \verb|transpose| is \verb|0|, or $\left(\textrm{number of rows}, \textrm{number of clusters}\right)$ if \verb|transpose| is \verb|1|. Each row (if \verb|transpose| is \verb|0|) or column (if \verb|transpose| is \verb|1|) contains the averaged data corresponding to the centroid of each cluster. \subsection*{Calculating the distance between clusters} Given a distance function between \emph{items}, we can define the distance between two \emph{clusters} in several ways. The distance between the arithmetic means of the two clusters is used in pairwise centroid-linkage clustering and in $k$-means clustering. In $k$-medoids clustering, the distance between the medians of the two clusters is used instead. The shortest pairwise distance between items of the two clusters is used in pairwise single-linkage clustering, while the longest pairwise distance is used in pairwise maximum-linkage clustering. In pairwise average-linkage clustering, the distance between two clusters is defined as the average over the pairwise distances. To calculate the distance between two clusters, use \begin{verbatim} >>> from Bio.Cluster import clusterdistance >>> distance = clusterdistance(data) \end{verbatim} where the following arguments are defined: \begin{itemize} \item \verb|data| (required)\\ Array containing the data for the items. \item \verb|mask| (default: \verb|None|) \\ Array of integers showing which data are missing. If \verb|mask[i,j]==0|, then \verb|data[i,j]| is missing. If \verb|mask==None|, then all data are present. \item \verb|weight| (default: \verb|None|) \\ The weights to be used when calculating distances. If \verb|weight==None|, then equal weights are assumed. \item \verb|index1| (default: \verb|0|) \\ A list containing the indices of the items belonging to the first cluster. A cluster containing only one item $i$ can be represented either as a list \verb|[i]|, or as an integer \verb|i|. \item \verb|index2| (default: \verb|0|) \\ A list containing the indices of the items belonging to the second cluster. A cluster containing only one items $i$ can be represented either as a list \verb|[i]|, or as an integer \verb|i|. \item \verb|method| (default: \verb|'a'|) \\ Specifies how the distance between clusters is defined: \begin{itemize} \item \verb|'a'|: Distance between the two cluster centroids (arithmetic mean); \item \verb|'m'|: Distance between the two cluster centroids (median); \item \verb|'s'|: Shortest pairwise distance between items in the two clusters; \item \verb|'x'|: Longest pairwise distance between items in the two clusters; \item \verb|'v'|: Average over the pairwise distances between items in the two clusters. \end{itemize} \item \verb|dist| (default: \verb|'e'|, Euclidean distance) \\ Defines the distance function to be used (see \ref{sec:distancefunctions}). \item \verb|transpose| (default: \verb|0|) \\ If \verb|transpose==0|, calculate the distance between the rows of \verb|data|. If \verb|transpose==1|, calculate the distance between the columns of \verb|data|. \end{itemize} \section{Partitioning algorithms} Partitioning algorithms divide items into $k$ clusters such that the sum of distances over the items to their cluster centers is minimal. The number of clusters $k$ is specified by the user. Three partitioning algorithms are available in \verb|Bio.Cluster|: \begin{itemize} \item $k$-means clustering \item $k$-medians clustering \item $k$-medoids clustering \end{itemize} These algorithms differ in how the cluster center is defined. In $k$-means clustering, the cluster center is defined as the mean data vector averaged over all items in the cluster. Instead of the mean, in $k$-medians clustering the median is calculated for each dimension in the data vector. Finally, in $k$-medoids clustering the cluster center is defined as the item which has the smallest sum of distances to the other items in the cluster. This clustering algorithm is suitable for cases in which the distance matrix is known but the original data matrix is not available, for example when clustering proteins based on their structural similarity. The expectation-maximization (EM) algorithm is used to find this partitioning into $k$ groups. In the initialization of the EM algorithm, we randomly assign items to clusters. To ensure that no empty clusters are produced, we use the binomial distribution to randomly choose the number of items in each cluster to be one or more. We then randomly permute the cluster assignments to items such that each item has an equal probability to be in any cluster. Each cluster is thus guaranteed to contain at least one item. We then iterate: \begin{itemize} \item Calculate the centroid of each cluster, defined as either the mean, the median, or the medoid of the cluster; \item Calculate the distances of each item to the cluster centers; \item For each item, determine which cluster centroid is closest; \item Reassign each item to its closest cluster, or stop the iteration if no further item reassignments take place. \end{itemize} To avoid clusters becoming empty during the iteration, in $k$-means and $k$-medians clustering the algorithm keeps track of the number of items in each cluster, and prohibits the last remaining item in a cluster from being reassigned to a different cluster. For $k$-medoids clustering, such a check is not needed, as the item that functions as the cluster centroid has a zero distance to itself, and will therefore never be closer to a different cluster. As the initial assignment of items to clusters is done randomly, usually a different clustering solution is found each time the EM algorithm is executed. To find the optimal clustering solution, the $k$-means algorithm is repeated many times, each time starting from a different initial random clustering. The sum of distances of the items to their cluster center is saved for each run, and the solution with the smallest value of this sum will be returned as the overall clustering solution. How often the EM algorithm should be run depends on the number of items being clustered. As a rule of thumb, we can consider how often the optimal solution was found; this number is returned by the partitioning algorithms as implemented in this library. If the optimal solution was found many times, it is unlikely that better solutions exist than the one that was found. However, if the optimal solution was found only once, there may well be other solutions with a smaller within-cluster sum of distances. If the number of items is large (more than several hundreds), it may be difficult to find the globally optimal solution. The EM algorithm terminates when no further reassignments take place. We noticed that for some sets of initial cluster assignments, the EM algorithm fails to converge due to the same clustering solution reappearing periodically after a small number of iteration steps. We therefore check for the occurrence of such periodic solutions during the iteration. After a given number of iteration steps, the current clustering result is saved as a reference. By comparing the clustering result after each subsequent iteration step to the reference state, we can determine if a previously encountered clustering result is found. In such a case, the iteration is halted. If after a given number of iterations the reference state has not yet been encountered, the current clustering solution is saved to be used as the new reference state. Initially, ten iteration steps are executed before resaving the reference state. This number of iteration steps is doubled each time, to ensure that periodic behavior with longer periods can also be detected. \subsection*{$k$-means and $k$-medians} The $k$-means and $k$-medians algorithms are implemented as the function \verb|kcluster| in \verb|Bio.Cluster|: \begin{verbatim} >>> from Bio.Cluster import kcluster >>> clusterid, error, nfound = kcluster(data) \end{verbatim} where the following arguments are defined: \begin{itemize} \item \verb|data| (required)\\ Array containing the data for the items. \item \verb|nclusters| (default: \verb|2|) \\ The number of clusters $k$. \item \verb|mask| (default: \verb|None|) \\ Array of integers showing which data are missing. If \verb|mask[i,j]==0|, then \verb|data[i,j]| is missing. If \verb|mask==None|, then all data are present. \item \verb|weight| (default: \verb|None|) \\ The weights to be used when calculating distances. If \verb|weight==None|, then equal weights are assumed. \item \verb|transpose| (default: \verb|0|) \\ Determines if rows (\verb|transpose| is \verb|0|) or columns (\verb|transpose| is \verb|1|) are to be clustered. \item \verb|npass| (default: \verb|1|) \\ The number of times the $k$-means/-medians clustering algorithm is performed, each time with a different (random) initial condition. If \verb|initialid| is given, the value of \verb|npass| is ignored and the clustering algorithm is run only once, as it behaves deterministically in that case. \item \verb|method| (default: \verb|a|) \\ describes how the center of a cluster is found: \begin{itemize} \item \verb|method=='a'|: arithmetic mean ($k$-means clustering); \item \verb|method=='m'|: median ($k$-medians clustering). \end{itemize} For other values of \verb|method|, the arithmetic mean is used. \item \verb|dist| (default: \verb|'e'|, Euclidean distance) \\ Defines the distance function to be used (see \ref{sec:distancefunctions}). Whereas all eight distance measures are accepted by \verb|kcluster|, from a theoretical viewpoint it is best to use the Euclidean distance for the $k$-means algorithm, and the city-block distance for $k$-medians. \item \verb|initialid| (default: \verb|None|) \\ Specifies the initial clustering to be used for the EM algorithm. If \verb|initialid==None|, then a different random initial clustering is used for each of the \verb|npass| runs of the EM algorithm. If \verb|initialid| is not \verb|None|, then it should be equal to a 1D array containing the cluster number (between \verb|0| and \verb|nclusters-1|) for each item. Each cluster should contain at least one item. With the initial clustering specified, the EM algorithm is deterministic. \end{itemize} This function returns a tuple \verb|(clusterid, error, nfound)|, where \verb|clusterid| is an integer array containing the number of the cluster to which each row or cluster was assigned, \verb|error| is the within-cluster sum of distances for the optimal clustering solution, and \verb|nfound| is the number of times this optimal solution was found. \subsection*{$k$-medoids clustering} The \verb+kmedoids+ routine performs $k$-medoids clustering on a given set of items, using the distance matrix and the number of clusters passed by the user: \begin{verbatim} >>> from Bio.Cluster import kmedoids >>> clusterid, error, nfound = kmedoids(distance) \end{verbatim} where the following arguments are defined: , nclusters=2, npass=1, initialid=None)| \begin{itemize} \item{\verb|distance|} (required) \\ The matrix containing the distances between the items; this matrix can be specified in three ways: \begin{itemize} \item as a 2D Numerical Python array (in which only the left-lower part of the array will be accessed): \begin{verbatim} distance = array([[0.0, 1.1, 2.3], [1.1, 0.0, 4.5], [2.3, 4.5, 0.0]]) \end{verbatim} \item as a 1D Numerical Python array containing consecutively the distances in the left-lower part of the distance matrix: \begin{verbatim} distance = array([1.1, 2.3, 4.5]) \end{verbatim} \item as a list containing the rows of the left-lower part of the distance matrix: \begin{verbatim} distance = [array([]|, array([1.1]), array([2.3, 4.5]) ] \end{verbatim} \end{itemize} These three expressions correspond to the same distance matrix. \item \verb|nclusters| (default: \verb|2|) \\ The number of clusters $k$. \item \verb|npass| (default: \verb|1|) \\ The number of times the $k$-medoids clustering algorithm is performed, each time with a different (random) initial condition. If \verb|initialid| is given, the value of \verb|npass| is ignored, as the clustering algorithm behaves deterministically in that case. \item \verb|initialid| (default: \verb|None|) \\ Specifies the initial clustering to be used for the EM algorithm. If \verb|initialid==None|, then a different random initial clustering is used for each of the \verb|npass| runs of the EM algorithm. If \verb|initialid| is not \verb|None|, then it should be equal to a 1D array containing the cluster number (between \verb|0| and \verb|nclusters-1|) for each item. Each cluster should contain at least one item. With the initial clustering specified, the EM algorithm is deterministic. \end{itemize} This function returns a tuple \verb|(clusterid, error, nfound)|, where \verb|clusterid| is an array containing the number of the cluster to which each item was assigned, \verb|error| is the within-cluster sum of distances for the optimal $k$-medoids clustering solution, and \verb|nfound| is the number of times the optimal solution was found. Note that the cluster number in \verb|clusterid| is defined as the item number of the item representing the cluster centroid. \section{Hierarchical clustering} Hierarchical clustering methods are inherently different from the $k$-means clustering method. In hierarchical clustering, the similarity in the expression profile between genes or experimental conditions are represented in the form of a tree structure. This tree structure can be shown graphically by programs such as Treeview and Java Treeview, which has contributed to the popularity of hierarchical clustering in the analysis of gene expression data. The first step in hierarchical clustering is to calculate the distance matrix, specifying all the distances between the items to be clustered. Next, we create a node by joining the two closest items. Subsequent nodes are created by pairwise joining of items or nodes based on the distance between them, until all items belong to the same node. A tree structure can then be created by retracing which items and nodes were merged. Unlike the EM algorithm, which is used in $k$-means clustering, the complete process of hierarchical clustering is deterministic. Several flavors of hierarchical clustering exist, which differ in how the distance between subnodes is defined in terms of their members. In \verb|Bio.Cluster|, pairwise single, maximum, average, and centroid linkage are available. \begin{itemize} \item In pairwise single-linkage clustering, the distance between two nodes is defined as the shortest distance among the pairwise distances between the members of the two nodes. \item In pairwise maximum-linkage clustering, alternatively known as pairwise complete-linkage clustering, the distance between two nodes is defined as the longest distance among the pairwise distances between the members of the two nodes. \item In pairwise average-linkage clustering, the distance between two nodes is defined as the average over all pairwise distances between the items of the two nodes. \item In pairwise centroid-linkage clustering, the distance between two nodes is defined as the distance between their centroids. The centroids are calculated by taking the mean over all the items in a cluster. As the distance from each newly formed node to existing nodes and items need to be calculated at each step, the computing time of pairwise centroid-linkage clustering may be significantly longer than for the other hierarchical clustering methods. Another peculiarity is that (for a distance measure based on the Pearson correlation), the distances do not necessarily increase when going up in the clustering tree, and may even decrease. This is caused by an inconsistency between the centroid calculation and the distance calculation when using the Pearson correlation: Whereas the Pearson correlation effectively normalizes the data for the distance calculation, no such normalization occurs for the centroid calculation. \end{itemize} For pairwise single-, complete-, and average-linkage clustering, the distance between two nodes can be found directly from the distances between the individual items. Therefore, the clustering algorithm does not need access to the original gene expression data, once the distance matrix is known. For pairwise centroid-linkage clustering, however, the centroids of newly formed subnodes can only be calculated from the original data and not from the distance matrix. The implementation of pairwise single-linkage hierarchical clustering is based on the SLINK algorithm (R. Sibson, 1973), which is much faster and more memory-efficient than a straightforward implementation of pairwise single-linkage clustering. The clustering result produced by this algorithm is identical to the clustering solution found by the conventional single-linkage algorithm. The single-linkage hierarchical clustering algorithm implemented in this library can be used to cluster large gene expression data sets, for which conventional hierarchical clustering algorithms fail due to excessive memory requirements and running time. \subsection*{Representing a hierarchical clustering solution} The result of hierarchical clustering consists of a tree of nodes, in which each node joins two items or subnodes. Usually, we are not only interested in which items or subnodes are joined at each node, but also in their similarity (or distance) as they are joined. To store one node in the hierarchical clustering tree, we make use of the class \verb|Node|, which defined in \verb|Bio.Cluster|. An instance of \verb|Node| has three attributes: \begin{itemize} \item \verb|left| \item \verb|right| \item \verb|distance| \end{itemize} Here, \verb|left| and \verb|right| are integers referring to the two items or subnodes that are joined at this node, and \verb|distance| is the distance between them. The items being clustered are numbered from 0 to $\left(\textrm{number of items} - 1\right)$, while clusters are numbered from -1 to $-\left(\textrm{number of items}-1\right)$. Note that the number of nodes is one less than the number of items. To create a new \verb|Node| object, we need to specify \verb|left| and \verb|right|; \verb|distance| is optional. \begin{verbatim} >>> from Bio.Cluster import Node >>> Node(2, 3) (2, 3): 0 >>> Node(2, 3, 0.91) (2, 3): 0.91 \end{verbatim} The attributes \verb|left|, \verb|right|, and \verb|distance| of an existing \verb|Node| object can be modified directly: \begin{verbatim} >>> node = Node(4, 5) >>> node.left = 6 >>> node.right = 2 >>> node.distance = 0.73 >>> node (6, 2): 0.73 \end{verbatim} An error is raised if \verb|left| and \verb|right| are not integers, or if \verb|distance| cannot be converted to a floating-point value. The Python class \verb|Tree| represents a full hierarchical clustering solution. A \verb|Tree| object can be created from a list of \verb|Node| objects: \begin{verbatim} >>> from Bio.Cluster import Node, Tree >>> nodes = [Node(1, 2, 0.2), Node(0, 3, 0.5), Node(-2, 4, 0.6), Node(-1, -3, 0.9)] >>> tree = Tree(nodes) >>> print(tree) (1, 2): 0.2 (0, 3): 0.5 (-2, 4): 0.6 (-1, -3): 0.9 \end{verbatim} The \verb|Tree| initializer checks if the list of nodes is a valid hierarchical clustering result: \begin{verbatim} >>> nodes = [Node(1, 2, 0.2), Node(0, 2, 0.5)] >>> Tree(nodes) Traceback (most recent call last): File "", line 1, in ? ValueError: Inconsistent tree \end{verbatim} Individual nodes in a \verb|Tree| object can be accessed using square brackets: \begin{verbatim} >>> nodes = [Node(1, 2, 0.2), Node(0, -1, 0.5)] >>> tree = Tree(nodes) >>> tree[0] (1, 2): 0.2 >>> tree[1] (0, -1): 0.5 >>> tree[-1] (0, -1): 0.5 \end{verbatim} As a \verb|Tree| object is read-only, we cannot change individual nodes in a \verb|Tree| object. However, we can convert the tree to a list of nodes, modify this list, and create a new tree from this list: \begin{verbatim} >>> tree = Tree([Node(1, 2, 0.1), Node(0, -1, 0.5), Node(-2, 3, 0.9)]) >>> print(tree) (1, 2): 0.1 (0, -1): 0.5 (-2, 3): 0.9 >>> nodes = tree[:] >>> nodes[0] = Node(0, 1, 0.2) >>> nodes[1].left = 2 >>> tree = Tree(nodes) >>> print(tree) (0, 1): 0.2 (2, -1): 0.5 (-2, 3): 0.9 \end{verbatim} This guarantees that any \verb|Tree| object is always well-formed. To display a hierarchical clustering solution with visualization programs such as Java Treeview, it is better to scale all node distances such that they are between zero and one. This can be accomplished by calling the \verb|scale| method on an existing \verb|Tree| object: \begin{verbatim} >>> tree.scale() \end{verbatim} This method takes no arguments, and returns \verb|None|. After hierarchical clustering, the items can be grouped into $k$ clusters based on the tree structure stored in the \verb|Tree| object by cutting the tree: \begin{verbatim} >>> clusterid = tree.cut(nclusters=1) \end{verbatim} where \verb|nclusters| (defaulting to \verb|1|) is the desired number of clusters $k$. This method ignores the top $k-1$ linking events in the tree structure, resulting in $k$ separated clusters of items. The number of clusters $k$ should be positive, and less than or equal to the number of items. This method returns an array \verb|clusterid| containing the number of the cluster to which each item is assigned. \subsection*{Performing hierarchical clustering} To perform hierarchical clustering, use the \verb|treecluster| function in \verb|Bio.Cluster|. \begin{verbatim} >>> from Bio.Cluster import treecluster >>> tree = treecluster(data) \end{verbatim} where the following arguments are defined: \begin{itemize} \item \verb|data| \\ Array containing the data for the items. \item \verb|mask| (default: \verb|None|) \\ Array of integers showing which data are missing. If \verb|mask[i,j]==0|, then \verb|data[i,j]| is missing. If \verb|mask==None|, then all data are present. \item \verb|weight| (default: \verb|None|) \\ The weights to be used when calculating distances. If \verb|weight==None|, then equal weights are assumed. \item \verb|transpose| (default: \verb|0|) \\ Determines if rows (\verb|transpose==0|) or columns (\verb|transpose==1|) are to be clustered. \item \verb|method| (default: \verb|'m'|) \\ defines the linkage method to be used: \begin{itemize} \item \verb|method=='s'|: pairwise single-linkage clustering \item \verb|method=='m'|: pairwise maximum- (or complete-) linkage clustering \item \verb|method=='c'|: pairwise centroid-linkage clustering \item \verb|method=='a'|: pairwise average-linkage clustering \end{itemize} \item \verb|dist| (default: \verb|'e'|, Euclidean distance) \\ Defines the distance function to be used (see \ref{sec:distancefunctions}). \end{itemize} To apply hierarchical clustering on a precalculated distance matrix, specify the \verb|distancematrix| argument when calling \verb|treecluster| function instead of the \verb|data| argument: \begin{verbatim} >>> from Bio.Cluster import treecluster >>> tree = treecluster(distancematrix=distance) \end{verbatim} In this case, the following arguments are defined: \begin{itemize} \item \verb|distancematrix| \\ The distance matrix, which can be specified in three ways: \begin{itemize} \item as a 2D Numerical Python array (in which only the left-lower part of the array will be accessed): \begin{verbatim} distance = array([[0.0, 1.1, 2.3], [1.1, 0.0, 4.5], [2.3, 4.5, 0.0]]) \end{verbatim} \item as a 1D Numerical Python array containing consecutively the distances in the left-lower part of the distance matrix: \begin{verbatim} distance = array([1.1, 2.3, 4.5]) \end{verbatim} \item as a list containing the rows of the left-lower part of the distance matrix: \begin{verbatim} distance = [array([]), array([1.1]), array([2.3, 4.5]) \end{verbatim} \end{itemize} These three expressions correspond to the same distance matrix. As \verb|treecluster| may shuffle the values in the distance matrix as part of the clustering algorithm, be sure to save this array in a different variable before calling \verb|treecluster| if you need it later. \item \verb|method| \\ The linkage method to be used: \begin{itemize} \item \verb|method=='s'|: pairwise single-linkage clustering \item \verb|method=='m'|: pairwise maximum- (or complete-) linkage clustering \item \verb|method=='a'|: pairwise average-linkage clustering \end{itemize} While pairwise single-, maximum-, and average-linkage clustering can be calculated from the distance matrix alone, pairwise centroid-linkage cannot. \end{itemize} When calling \verb|treecluster|, either \verb|data| or \verb|distancematrix| should be \verb|None|. This function returns a \verb|Tree| object. This object contains $\left(\textrm{number of items} - 1\right)$ nodes, where the number of items is the number of rows if rows were clustered, or the number of columns if columns were clustered. Each node describes a pairwise linking event, where the node attributes \verb|left| and \verb|right| each contain the number of one item or subnode, and \verb|distance| the distance between them. Items are numbered from 0 to $\left(\textrm{number of items} - 1\right)$, while clusters are numbered -1 to $-\left(\textrm{number of items}-1\right)$. \section{Self-Organizing Maps} Self-Organizing Maps (SOMs) were invented by Kohonen to describe neural networks (see for instance Kohonen, 1997 \cite{kohonen1997}). Tamayo (1999) first applied Self-Organizing Maps to gene expression data \cite{tamayo1999}. SOMs organize items into clusters that are situated in some topology. Usually a rectangular topology is chosen. The clusters generated by SOMs are such that neighboring clusters in the topology are more similar to each other than clusters far from each other in the topology. The first step to calculate a SOM is to randomly assign a data vector to each cluster in the topology. If rows are being clustered, then the number of elements in each data vector is equal to the number of columns. An SOM is then generated by taking rows one at a time, and finding which cluster in the topology has the closest data vector. The data vector of that cluster, as well as those of the neighboring clusters, are adjusted using the data vector of the row under consideration. The adjustment is given by $$\Delta \underline{x}_{\textrm{cell}} = \tau \cdot \left(\underline{x}_{\textrm{row}} - \underline{x}_{\textrm{cell}} \right).$$ The parameter $\tau$ is a parameter that decreases at each iteration step. We have used a simple linear function of the iteration step: $$\tau = \tau_{\textrm{init}} \cdot \left(1 - {i \over n}\right),$$ $\tau_{\textrm{init}}$ is the initial value of $\tau$ as specified by the user, $i$ is the number of the current iteration step, and $n$ is the total number of iteration steps to be performed. While changes are made rapidly in the beginning of the iteration, at the end of iteration only small changes are made. All clusters within a radius $R$ are adjusted to the gene under consideration. This radius decreases as the calculation progresses as $$R = R_{\textrm{max}} \cdot \left(1 - {i \over n}\right),$$ in which the maximum radius is defined as $$R_{\textrm{max}} = \sqrt{N_x^2 + N_y^2},$$ where $\left(N_x, N_y\right)$ are the dimensions of the rectangle defining the topology. The function \verb|somcluster| implements the complete algorithm to calculate a Self-Organizing Map on a rectangular grid. First it initializes the random number generator. The node data are then initialized using the random number generator. The order in which genes or microarrays are used to modify the SOM is also randomized. The total number of iterations in the SOM algorithm is specified by the user. To run \verb|somcluster|, use \begin{verbatim} >>> from Bio.Cluster import somcluster >>> clusterid, celldata = somcluster(data) \end{verbatim} where the following arguments are defined: \begin{itemize} \item \verb|data| (required) \\ Array containing the data for the items. \item \verb|mask| (default: \verb|None|) \\ Array of integers showing which data are missing. If \verb|mask[i,j]==0|, then \verb|data[i,j]| is missing. If \verb|mask==None|, then all data are present. \item \verb|weight| (default: \verb|None|) \\ contains the weights to be used when calculating distances. If \verb|weight==None|, then equal weights are assumed. \item \verb|transpose| (default: \verb|0|) \\ Determines if rows (\verb|transpose| is \verb|0|) or columns (\verb|transpose| is \verb|1|) are to be clustered. \item \verb|nxgrid, nygrid| (default: \verb|2, 1|) \\ The number of cells horizontally and vertically in the rectangular grid on which the Self-Organizing Map is calculated. \item \verb|inittau| (default: \verb|0.02|) \\ The initial value for the parameter $\tau$ that is used in the SOM algorithm. The default value for \verb|inittau| is 0.02, which was used in Michael Eisen's Cluster/TreeView program. \item \verb|niter| (default: \verb|1|) \\ The number of iterations to be performed. \item \verb|dist| (default: \verb|'e'|, Euclidean distance) \\ Defines the distance function to be used (see \ref{sec:distancefunctions}). \end{itemize} This function returns the tuple \verb|(clusterid, celldata)|: \begin{itemize} \item \verb|clusterid|: \\ An array with two columns, where the number of rows is equal to the number of items that were clustered. Each row contains the $x$ and $y$ coordinates of the cell in the rectangular SOM grid to which the item was assigned. \item \verb|celldata|: \\ An array with dimensions $\left(\verb|nxgrid|, \verb|nygrid|, \textrm{number of columns}\right)$ if rows are being clustered, or $\left(\verb|nxgrid|, \verb|nygrid|, \textrm{number of rows}\right)$ if columns are being clustered. Each element \verb|[ix][iy]| of this array is a 1D vector containing the gene expression data for the centroid of the cluster in the grid cell with coordinates \verb|[ix][iy]|. \end{itemize} \section{Principal Component Analysis} Principal Component Analysis (PCA) is a widely used technique for analyzing multivariate data. A practical example of applying Principal Component Analysis to gene expression data is presented by Yeung and Ruzzo (2001) \cite{yeung2001}. In essence, PCA is a coordinate transformation in which each row in the data matrix is written as a linear sum over basis vectors called principal components, which are ordered and chosen such that each maximally explains the remaining variance in the data vectors. For example, an $n \times 3$ data matrix can be represented as an ellipsoidal cloud of $n$ points in three dimensional space. The first principal component is the longest axis of the ellipsoid, the second principal component the second longest axis of the ellipsoid, and the third principal component is the shortest axis. Each row in the data matrix can be reconstructed as a suitable linear combination of the principal components. However, in order to reduce the dimensionality of the data, usually only the most important principal components are retained. The remaining variance present in the data is then regarded as unexplained variance. The principal components can be found by calculating the eigenvectors of the covariance matrix of the data. The corresponding eigenvalues determine how much of the variance present in the data is explained by each principal component. Before applying principal component analysis, typically the mean is subtracted from each column in the data matrix. In the example above, this effectively centers the ellipsoidal cloud around its centroid in 3D space, with the principal components describing the variation of points in the ellipsoidal cloud with respect to their centroid. The function \verb|pca| below first uses the singular value decomposition to calculate the eigenvalues and eigenvectors of the data matrix. The singular value decomposition is implemented as a translation in C of the Algol procedure \verb|svd| \cite{golub1971}, which uses Householder bidiagonalization and a variant of the QR algorithm. The principal components, the coordinates of each data vector along the principal components, and the eigenvalues corresponding to the principal components are then evaluated and returned in decreasing order of the magnitude of the eigenvalue. If data centering is desired, the mean should be subtracted from each column in the data matrix before calling the \verb|pca| routine. To apply Principal Component Analysis to a rectangular matrix \verb|data|, use \begin{verbatim} >>> from Bio.Cluster import pca >>> columnmean, coordinates, components, eigenvalues = pca(data) \end{verbatim} This function returns a tuple \verb|columnmean, coordinates, components, eigenvalues|: \begin{itemize} \item \verb|columnmean| \\ Array containing the mean over each column in \verb|data|. \item \verb|coordinates| \\ The coordinates of each row in \verb|data| with respect to the principal components. \item \verb|components| \\ The principal components. \item \verb|eigenvalues| \\ The eigenvalues corresponding to each of the principal components. \end{itemize} The original matrix \verb|data| can be recreated by calculating \verb|columnmean + dot(coordinates, components)|. \section{Handling Cluster/TreeView-type files} Cluster/TreeView are GUI-based codes for clustering gene expression data. They were originally written by \href{http://rana.lbl.gov}{Michael Eisen} while at Stanford University. \verb|Bio.Cluster| contains functions for reading and writing data files that correspond to the format specified for Cluster/TreeView. In particular, by saving a clustering result in that format, TreeView can be used to visualize the clustering results. We recommend using Alok Saldanha's \url{http://jtreeview.sourceforge.net/}{Java TreeView program}, which can display hierarchical as well as $k$-means clustering results. An object of the class \verb|Record| contains all information stored in a Cluster/TreeView-type data file. To store the information contained in the data file in a \verb|Record| object, we first open the file and then read it: \begin{verbatim} >>> from Bio import Cluster >>> handle = open("mydatafile.txt") >>> record = Cluster.read(handle) >>> handle.close() \end{verbatim} This two-step process gives you some flexibility in the source of the data. For example, you can use \begin{verbatim} >>> import gzip # Python standard library >>> handle = gzip.open("mydatafile.txt.gz") \end{verbatim} to open a gzipped file, or \begin{verbatim} >>> import urllib # Python standard library >>> handle = urllib.urlopen("http://somewhere.org/mydatafile.txt") \end{verbatim} to open a file stored on the Internet before calling \verb|read|. The \verb|read| command reads the tab-delimited text file \verb|mydatafile.txt| containing gene expression data in the format specified for Michael Eisen's Cluster/TreeView program. For a description of this file format, see the manual to Cluster/TreeView. It is available at \href{http://rana.lbl.gov/manuals/ClusterTreeView.pdf}{Michael Eisen's lab website} and at \href{http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/cluster3.pdf}{our website}. A \verb|Record| object has the following attributes: \begin{itemize} \item \verb|data| \\ The data array containing the gene expression data. Genes are stored row-wise, while microarrays are stored column-wise. \item \verb|mask| \\ This array shows which elements in the \verb|data| array, if any, are missing. If \verb|mask[i,j]==0|, then \verb|data[i,j]| is missing. If no data were found to be missing, \verb|mask| is set to \verb|None|. \item \verb|geneid| \\ This is a list containing a unique description for each gene (i.e., ORF numbers). \item \verb|genename| \\ This is a list containing a description for each gene (i.e., gene name). If not present in the data file, \verb|genename| is set to \verb|None|. \item \verb|gweight| \\ The weights that are to be used to calculate the distance in expression profile between genes. If not present in the data file, \verb|gweight| is set to \verb|None|. \item \verb|gorder| \\ The preferred order in which genes should be stored in an output file. If not present in the data file, \verb|gorder| is set to \verb|None|. \item \verb|expid| \\ This is a list containing a description of each microarray, e.g. experimental condition. \item \verb|eweight| \\ The weights that are to be used to calculate the distance in expression profile between microarrays. If not present in the data file, \verb|eweight| is set to \verb|None|. \item \verb|eorder| \\ The preferred order in which microarrays should be stored in an output file. If not present in the data file, \verb|eorder| is set to \verb|None|. \item \verb|uniqid| \\ The string that was used instead of UNIQID in the data file. \end{itemize} After loading a \verb|Record| object, each of these attributes can be accessed and modified directly. For example, the data can be log-transformed by taking the logarithm of \verb|record.data|. \subsection*{Calculating the distance matrix} To calculate the distance matrix between the items stored in the record, use \begin{verbatim} >>> matrix = record.distancematrix() \end{verbatim} where the following arguments are defined: \begin{itemize} \item \verb|transpose| (default: \verb|0|) \\ Determines if the distances between the rows of \verb|data| are to be calculated (\verb|transpose==0|), or between the columns of \verb|data| (\verb|transpose==1|). \item \verb|dist| (default: \verb|'e'|, Euclidean distance) \\ Defines the distance function to be used (see \ref{sec:distancefunctions}). \end{itemize} This function returns the distance matrix as a list of rows, where the number of columns of each row is equal to the row number (see section \ref{subsec:distancematrix}). \subsection*{Calculating the cluster centroids} To calculate the centroids of clusters of items stored in the record, use \begin{verbatim} >>> cdata, cmask = record.clustercentroids() \end{verbatim} \begin{itemize} \item \verb|clusterid| (default: \verb|None|) \\ Vector of integers showing to which cluster each item belongs. If \verb|clusterid| is not given, then all items are assumed to belong to the same cluster. \item \verb|method| (default: \verb|'a'|) \\ Specifies whether the arithmetic mean (\verb|method=='a'|) or the median (\verb|method=='m'|) is used to calculate the cluster center. \item \verb|transpose| (default: \verb|0|) \\ Determines if the centroids of the rows of \verb|data| are to be calculated (\verb|transpose==0|), or the centroids of the columns of \verb|data| (\verb|transpose==1|). \end{itemize} This function returns the tuple \verb|cdata, cmask|; see section \ref{subsec:clustercentroids} for a description. \subsection*{Calculating the distance between clusters} To calculate the distance between clusters of items stored in the record, use \begin{verbatim} >>> distance = record.clusterdistance() \end{verbatim} where the following arguments are defined: \begin{itemize} \item \verb|index1| (default: \verb|0|) \\ A list containing the indices of the items belonging to the first cluster. A cluster containing only one item $i$ can be represented either as a list \verb|[i]|, or as an integer \verb|i|. \item \verb|index2| (default: \verb|0|) \\ A list containing the indices of the items belonging to the second cluster. A cluster containing only one item $i$ can be represented either as a list \verb|[i]|, or as an integer \verb|i|. \item \verb|method| (default: \verb|'a'|) \\ Specifies how the distance between clusters is defined: \begin{itemize} \item \verb|'a'|: Distance between the two cluster centroids (arithmetic mean); \item \verb|'m'|: Distance between the two cluster centroids (median); \item \verb|'s'|: Shortest pairwise distance between items in the two clusters; \item \verb|'x'|: Longest pairwise distance between items in the two clusters; \item \verb|'v'|: Average over the pairwise distances between items in the two clusters. \end{itemize} \item \verb|dist| (default: \verb|'e'|, Euclidean distance) \\ Defines the distance function to be used (see \ref{sec:distancefunctions}). \item \verb|transpose| (default: \verb|0|) \\ If \verb|transpose==0|, calculate the distance between the rows of \verb|data|. If \verb|transpose==1|, calculate the distance between the columns of \verb|data|. \end{itemize} \subsection*{Performing hierarchical clustering} To perform hierarchical clustering on the items stored in the record, use \begin{verbatim} >>> tree = record.treecluster() \end{verbatim} where the following arguments are defined: \begin{itemize} \item \verb|transpose| (default: \verb|0|) \\ Determines if rows (\verb|transpose==0|) or columns (\verb|transpose==1|) are to be clustered. \item \verb|method| (default: \verb|'m'|) \\ defines the linkage method to be used: \begin{itemize} \item \verb|method=='s'|: pairwise single-linkage clustering \item \verb|method=='m'|: pairwise maximum- (or complete-) linkage clustering \item \verb|method=='c'|: pairwise centroid-linkage clustering \item \verb|method=='a'|: pairwise average-linkage clustering \end{itemize} \item \verb|dist| (default: \verb|'e'|, Euclidean distance) \\ Defines the distance function to be used (see \ref{sec:distancefunctions}). \item \verb|transpose| \\ Determines if genes or microarrays are being clustered. If \verb|transpose==0|, genes (rows) are being clustered. If \verb|transpose==1|, microarrays (columns) are clustered. \end{itemize} This function returns a \verb|Tree| object. This object contains $\left(\textrm{number of items} - 1\right)$ nodes, where the number of items is the number of rows if rows were clustered, or the number of columns if columns were clustered. Each node describes a pairwise linking event, where the node attributes \verb|left| and \verb|right| each contain the number of one item or subnode, and \verb|distance| the distance between them. Items are numbered from 0 to $\left(\textrm{number of items} - 1\right)$, while clusters are numbered -1 to $-\left(\textrm{number of items}-1\right)$. \subsection*{Performing $k$-means or $k$-medians clustering} To perform $k$-means or $k$-medians clustering on the items stored in the record, use \begin{verbatim} >>> clusterid, error, nfound = record.kcluster() \end{verbatim} where the following arguments are defined: \begin{itemize} \item \verb|nclusters| (default: \verb|2|) \\ The number of clusters $k$. \item \verb|transpose| (default: \verb|0|) \\ Determines if rows (\verb|transpose| is \verb|0|) or columns (\verb|transpose| is \verb|1|) are to be clustered. \item \verb|npass| (default: \verb|1|) \\ The number of times the $k$-means/-medians clustering algorithm is performed, each time with a different (random) initial condition. If \verb|initialid| is given, the value of \verb|npass| is ignored and the clustering algorithm is run only once, as it behaves deterministically in that case. \item \verb|method| (default: \verb|a|) \\ describes how the center of a cluster is found: \begin{itemize} \item \verb|method=='a'|: arithmetic mean ($k$-means clustering); \item \verb|method=='m'|: median ($k$-medians clustering). \end{itemize} For other values of \verb|method|, the arithmetic mean is used. \item \verb|dist| (default: \verb|'e'|, Euclidean distance) \\ Defines the distance function to be used (see \ref{sec:distancefunctions}). \end{itemize} This function returns a tuple \verb|(clusterid, error, nfound)|, where \verb|clusterid| is an integer array containing the number of the cluster to which each row or cluster was assigned, \verb|error| is the within-cluster sum of distances for the optimal clustering solution, and \verb|nfound| is the number of times this optimal solution was found. \subsection*{Calculating a Self-Organizing Map} To calculate a Self-Organizing Map of the items stored in the record, use \begin{verbatim} >>> clusterid, celldata = record.somcluster() \end{verbatim} where the following arguments are defined: \begin{itemize} \item \verb|transpose| (default: \verb|0|) \\ Determines if rows (\verb|transpose| is \verb|0|) or columns (\verb|transpose| is \verb|1|) are to be clustered. \item \verb|nxgrid, nygrid| (default: \verb|2, 1|) \\ The number of cells horizontally and vertically in the rectangular grid on which the Self-Organizing Map is calculated. \item \verb|inittau| (default: \verb|0.02|) \\ The initial value for the parameter $\tau$ that is used in the SOM algorithm. The default value for \verb|inittau| is 0.02, which was used in Michael Eisen's Cluster/TreeView program. \item \verb|niter| (default: \verb|1|) \\ The number of iterations to be performed. \item \verb|dist| (default: \verb|'e'|, Euclidean distance) \\ Defines the distance function to be used (see \ref{sec:distancefunctions}). \end{itemize} This function returns the tuple \verb|(clusterid, celldata)|: \begin{itemize} \item \verb|clusterid|: \\ An array with two columns, where the number of rows is equal to the number of items that were clustered. Each row contains the $x$ and $y$ coordinates of the cell in the rectangular SOM grid to which the item was assigned. \item \verb|celldata|: \\ An array with dimensions $\left(\verb|nxgrid|, \verb|nygrid|, \textrm{number of columns}\right)$ if rows are being clustered, or $\left(\verb|nxgrid|, \verb|nygrid|, \textrm{number of rows}\right)$ if columns are being clustered. Each element \verb|[ix][iy]| of this array is a 1D vector containing the gene expression data for the centroid of the cluster in the grid cell with coordinates \verb|[ix][iy]|. \end{itemize} \subsection*{Saving the clustering result} To save the clustering result, use \begin{verbatim} >>> record.save(jobname, geneclusters, expclusters) \end{verbatim} where the following arguments are defined: \begin{itemize} \item \verb|jobname| \\ The string \verb|jobname| is used as the base name for names of the files that are to be saved. \item \verb|geneclusters| \\ This argument describes the gene (row-wise) clustering result. In case of $k$-means clustering, this is a 1D array containing the number of the cluster each gene belongs to. It can be calculated using \verb|kcluster|. In case of hierarchical clustering, \verb|geneclusters| is a \verb|Tree| object. \item \verb|expclusters| \\ This argument describes the (column-wise) clustering result for the experimental conditions. In case of $k$-means clustering, this is a 1D array containing the number of the cluster each experimental condition belongs to. It can be calculated using \verb|kcluster|. In case of hierarchical clustering, \verb|expclusters| is a \verb|Tree| object. \end{itemize} This method writes the text file \verb|jobname.cdt|, \verb|jobname.gtr|, \verb|jobname.atr|, \verb|jobname*.kgg|, and/or \verb|jobname*.kag| for subsequent reading by the Java TreeView program. If \verb|geneclusters| and \verb|expclusters| are both \verb|None|, this method only writes the text file \verb|jobname.cdt|; this file can subsequently be read into a new \verb|Record| object. \section{Example calculation} This is an example of a hierarchical clustering calculation, using single linkage clustering for genes and maximum linkage clustering for experimental conditions. As the Euclidean distance is being used for gene clustering, it is necessary to scale the node distances \verb|genetree| such that they are all between zero and one. This is needed for the Java TreeView code to display the tree diagram correctly. To cluster the experimental conditions, the uncentered correlation is being used. No scaling is needed in this case, as the distances in \verb|exptree| are already between zero and two. The example data \verb|cyano.txt| can be found in the \verb|data| subdirectory. \begin{verbatim} >>> from Bio import Cluster >>> handle = open("cyano.txt") >>> record = Cluster.read(handle) >>> handle.close() >>> genetree = record.treecluster(method='s') >>> genetree.scale() >>> exptree = record.treecluster(dist='u', transpose=1) >>> record.save("cyano_result", genetree, exptree) \end{verbatim} This will create the files \verb|cyano_result.cdt|, \verb|cyano_result.gtr|, and \verb|cyano_result.atr|. Similarly, we can save a $k$-means clustering solution: \begin{verbatim} >>> from Bio import Cluster >>> handle = open("cyano.txt") >>> record = Cluster.read(handle) >>> handle.close() >>> (geneclusters, error, ifound) = record.kcluster(nclusters=5, npass=1000) >>> (expclusters, error, ifound) = record.kcluster(nclusters=2, npass=100, transpose=1) >>> record.save("cyano_result", geneclusters, expclusters) \end{verbatim} This will create the files \verb|cyano_result_K_G2_A2.cdt|, \verb|cyano_result_K_G2.kgg|, and \verb|cyano_result_K_A2.kag|. \section{Auxiliary functions} \verb|median(data)| returns the median of the 1D array \verb|data|. \verb|mean(data)| returns the mean of the 1D array \verb|data|. \verb|version()| returns the version number of the underlying C Clustering Library as a string. \chapter{Supervised learning methods} Note the supervised learning methods described in this chapter all require Numerical Python (numpy) to be installed. \section{The Logistic Regression Model} \label{sec:LogisticRegression} \subsection{Background and Purpose} Logistic regression is a supervised learning approach that attempts to distinguish $K$ classes from each other using a weighted sum of some predictor variables $x_i$. The logistic regression model is used to calculate the weights $\beta_i$ of the predictor variables. In Biopython, the logistic regression model is currently implemented for two classes only ($K = 2$); the number of predictor variables has no predefined limit. As an example, let's try to predict the operon structure in bacteria. An operon is a set of adjacent genes on the same strand of DNA that are transcribed into a single mRNA molecule. Translation of the single mRNA molecule then yields the individual proteins. For {\it Bacillus subtilis}, whose data we will be using, the average number of genes in an operon is about 2.4. As a first step in understanding gene regulation in bacteria, we need to know the operon structure. For about 10\% of the genes in {\it Bacillus subtilis}, the operon structure is known from experiments. A supervised learning method can be used to predict the operon structure for the remaining 90\% of the genes. For such a supervised learning approach, we need to choose some predictor variables $x_i$ that can be measured easily and are somehow related to the operon structure. One predictor variable might be the distance in base pairs between genes. Adjacent genes belonging to the same operon tend to be separated by a relatively short distance, whereas adjacent genes in different operons tend to have a larger space between them to allow for promoter and terminator sequences. Another predictor variable is based on gene expression measurements. By definition, genes belonging to the same operon have equal gene expression profiles, while genes in different operons are expected to have different expression profiles. In practice, the measured expression profiles of genes in the same operon are not quite identical due to the presence of measurement errors. To assess the similarity in the gene expression profiles, we assume that the measurement errors follow a normal distribution and calculate the corresponding log-likelihood score. We now have two predictor variables that we can use to predict if two adjacent genes on the same strand of DNA belong to the same operon: \begin{itemize} \item $x_1$: the number of base pairs between them; \item $x_2$: their similarity in expression profile. \end{itemize} In a logistic regression model, we use a weighted sum of these two predictors to calculate a joint score $S$: \begin{equation} S = \beta_0 + \beta_1 x_1 + \beta_2 x_2. \end{equation} The logistic regression model gives us appropriate values for the parameters $\beta_0$, $\beta_1$, $\beta_2$ using two sets of example genes: \begin{itemize} \item OP: Adjacent genes, on the same strand of DNA, known to belong to the same operon; \item NOP: Adjacent genes, on the same strand of DNA, known to belong to different operons. \end{itemize} In the logistic regression model, the probability of belonging to a class depends on the score via the logistic function. For the two classes OP and NOP, we can write this as \begin{eqnarray} \Pr(\mathrm{OP}|x_1, x_2) & = & \frac{\exp(\beta_0 + \beta_1 x_1 + \beta_2 x_2)}{1+\exp(\beta_0 + \beta_1 x_1 + \beta_2 x_2)} \label{eq:OP} \\ \Pr(\mathrm{NOP}|x_1, x_2) & = & \frac{1}{1+\exp(\beta_0 + \beta_1 x_1 + \beta_2 x_2)} \label{eq:NOP} \end{eqnarray} Using a set of gene pairs for which it is known whether they belong to the same operon (class OP) or to different operons (class NOP), we can calculate the weights $\beta_0$, $\beta_1$, $\beta_2$ by maximizing the log-likelihood corresponding to the probability functions (\ref{eq:OP}) and (\ref{eq:NOP}). \subsection{Training the logistic regression model} \label{subsec:LogisticRegressionTraining} \begin{table}[h] \begin{center} \caption{Adjacent gene pairs known to belong to the same operon (class OP) or to different operons (class NOP). Intergene distances are negative if the two genes overlap.} \begin{tabular}{|c|c|c|c|} \hline Gene pair & Intergene distance ($x_1$) & Gene expression score ($x_2$) & Class\\ \hline {\it cotJA} --- {\it cotJB} & -53 & -200.78 & OP\\ {\it yesK} --- {\it yesL} & 117 & -267.14 & OP\\ {\it lplA} --- {\it lplB} & 57 & -163.47 & OP\\ {\it lplB} --- {\it lplC} & 16 & -190.30 & OP\\ {\it lplC} --- {\it lplD} & 11 & -220.94 & OP\\ {\it lplD} --- {\it yetF} & 85 & -193.94 & OP\\ {\it yfmT} --- {\it yfmS} & 16 & -182.71 & OP\\ {\it yfmF} --- {\it yfmE} & 15 & -180.41 & OP\\ {\it citS} --- {\it citT} & -26 & -181.73 & OP\\ {\it citM} --- {\it yflN} & 58 & -259.87 & OP\\ {\it yfiI} --- {\it yfiJ} & 126 & -414.53 & NOP\\ {\it lipB} --- {\it yfiQ} & 191 & -249.57 & NOP\\ {\it yfiU} --- {\it yfiV} & 113 & -265.28 & NOP\\ {\it yfhH} --- {\it yfhI} & 145 & -312.99 & NOP\\ {\it cotY} --- {\it cotX} & 154 & -213.83 & NOP\\ {\it yjoB} --- {\it rapA} & 147 & -380.85 & NOP\\ {\it ptsI} --- {\it splA} & 93 & -291.13 & NOP \\ \hline \end{tabular} \label{table:training} \end{center} \end{table} Table \ref{table:training} lists some of the {\it Bacillus subtilis} gene pairs for which the operon structure is known. Let's calculate the logistic regression model from these data: %NOTE - can't use this as a doctest in case NumPy is missing (Jython). %Also the ... are missing but that means the user can copy/paste the example. \begin{verbatim} >>> from Bio import LogisticRegression >>> xs = [[-53, -200.78], [117, -267.14], [57, -163.47], [16, -190.30], [11, -220.94], [85, -193.94], [16, -182.71], [15, -180.41], [-26, -181.73], [58, -259.87], [126, -414.53], [191, -249.57], [113, -265.28], [145, -312.99], [154, -213.83], [147, -380.85], [93, -291.13]] >>> ys = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0] >>> model = LogisticRegression.train(xs, ys) \end{verbatim} Here, \verb+xs+ and \verb+ys+ are the training data: \verb+xs+ contains the predictor variables for each gene pair, and \verb+ys+ specifies if the gene pair belongs to the same operon (\verb+1+, class OP) or different operons (\verb+0+, class NOP). The resulting logistic regression model is stored in \verb+model+, which contains the weights $\beta_0$, $\beta_1$, and $\beta_2$: \begin{verbatim} >>> model.beta [8.9830290157144681, -0.035968960444850887, 0.02181395662983519] \end{verbatim} Note that $\beta_1$ is negative, as gene pairs with a shorter intergene distance have a higher probability of belonging to the same operon (class OP). On the other hand, $\beta_2$ is positive, as gene pairs belonging to the same operon typically have a higher similarity score of their gene expression profiles. The parameter $\beta_0$ is positive due to the higher prevalence of operon gene pairs than non-operon gene pairs in the training data. The function \verb+train+ has two optional arguments: \verb+update_fn+ and \verb+typecode+. The \verb+update_fn+ can be used to specify a callback function, taking as arguments the iteration number and the log-likelihood. With the callback function, we can for example track the progress of the model calculation (which uses a Newton-Raphson iteration to maximize the log-likelihood function of the logistic regression model): \begin{verbatim} >>> def show_progress(iteration, loglikelihood): print("Iteration:", iteration, "Log-likelihood function:", loglikelihood) >>> >>> model = LogisticRegression.train(xs, ys, update_fn=show_progress) Iteration: 0 Log-likelihood function: -11.7835020695 Iteration: 1 Log-likelihood function: -7.15886767672 Iteration: 2 Log-likelihood function: -5.76877209868 Iteration: 3 Log-likelihood function: -5.11362294338 Iteration: 4 Log-likelihood function: -4.74870642433 Iteration: 5 Log-likelihood function: -4.50026077146 Iteration: 6 Log-likelihood function: -4.31127773737 Iteration: 7 Log-likelihood function: -4.16015043396 Iteration: 8 Log-likelihood function: -4.03561719785 Iteration: 9 Log-likelihood function: -3.93073282192 Iteration: 10 Log-likelihood function: -3.84087660929 Iteration: 11 Log-likelihood function: -3.76282560605 Iteration: 12 Log-likelihood function: -3.69425027154 Iteration: 13 Log-likelihood function: -3.6334178602 Iteration: 14 Log-likelihood function: -3.57900855837 Iteration: 15 Log-likelihood function: -3.52999671386 Iteration: 16 Log-likelihood function: -3.48557145163 Iteration: 17 Log-likelihood function: -3.44508206139 Iteration: 18 Log-likelihood function: -3.40799948447 Iteration: 19 Log-likelihood function: -3.3738885624 Iteration: 20 Log-likelihood function: -3.3423876581 Iteration: 21 Log-likelihood function: -3.31319343769 Iteration: 22 Log-likelihood function: -3.2860493346 Iteration: 23 Log-likelihood function: -3.2607366863 Iteration: 24 Log-likelihood function: -3.23706784091 Iteration: 25 Log-likelihood function: -3.21488073614 Iteration: 26 Log-likelihood function: -3.19403459259 Iteration: 27 Log-likelihood function: -3.17440646052 Iteration: 28 Log-likelihood function: -3.15588842703 Iteration: 29 Log-likelihood function: -3.13838533947 Iteration: 30 Log-likelihood function: -3.12181293595 Iteration: 31 Log-likelihood function: -3.10609629966 Iteration: 32 Log-likelihood function: -3.09116857282 Iteration: 33 Log-likelihood function: -3.07696988017 Iteration: 34 Log-likelihood function: -3.06344642288 Iteration: 35 Log-likelihood function: -3.05054971191 Iteration: 36 Log-likelihood function: -3.03823591619 Iteration: 37 Log-likelihood function: -3.02646530573 Iteration: 38 Log-likelihood function: -3.01520177394 Iteration: 39 Log-likelihood function: -3.00441242601 Iteration: 40 Log-likelihood function: -2.99406722296 Iteration: 41 Log-likelihood function: -2.98413867259 \end{verbatim} The iteration stops once the increase in the log-likelihood function is less than 0.01. If no convergence is reached after 500 iterations, the \verb+train+ function returns with an \verb+AssertionError+. The optional keyword \verb+typecode+ can almost always be ignored. This keyword allows the user to choose the type of Numeric matrix to use. In particular, to avoid memory problems for very large problems, it may be necessary to use single-precision floats (Float8, Float16, etc.) rather than double, which is used by default. \subsection{Using the logistic regression model for classification} Classification is performed by calling the \verb+classify+ function. Given a logistic regression model and the values for $x_1$ and $x_2$ (e.g. for a gene pair of unknown operon structure), the \verb+classify+ function returns \verb+1+ or \verb+0+, corresponding to class OP and class NOP, respectively. For example, let's consider the gene pairs {\it yxcE}, {\it yxcD} and {\it yxiB}, {\it yxiA}: \begin{table}[h] \begin{center} \caption{Adjacent gene pairs of unknown operon status.} \begin{tabular}{|c|c|c|} \hline Gene pair & Intergene distance $x_1$ & Gene expression score $x_2$ \\ \hline {\it yxcE} --- {\it yxcD} & 6 & -173.143442352 \\ {\it yxiB} --- {\it yxiA} & 309 & -271.005880394 \\ \hline \end{tabular} \end{center} \end{table} The logistic regression model classifies {\it yxcE}, {\it yxcD} as belonging to the same operon (class OP), while {\it yxiB}, {\it yxiA} are predicted to belong to different operons: \begin{verbatim} >>> print("yxcE, yxcD:", LogisticRegression.classify(model, [6, -173.143442352])) yxcE, yxcD: 1 >>> print("yxiB, yxiA:", LogisticRegression.classify(model, [309, -271.005880394])) yxiB, yxiA: 0 \end{verbatim} (which, by the way, agrees with the biological literature). To find out how confident we can be in these predictions, we can call the \verb+calculate+ function to obtain the probabilities (equations (\ref{eq:OP}) and \ref{eq:NOP}) for class OP and NOP. For {\it yxcE}, {\it yxcD} we find \begin{verbatim} >>> q, p = LogisticRegression.calculate(model, [6, -173.143442352]) >>> print("class OP: probability =", p, "class NOP: probability =", q) class OP: probability = 0.993242163503 class NOP: probability = 0.00675783649744 \end{verbatim} and for {\it yxiB}, {\it yxiA} \begin{verbatim} >>> q, p = LogisticRegression.calculate(model, [309, -271.005880394]) >>> print("class OP: probability =", p, "class NOP: probability =", q) class OP: probability = 0.000321211251817 class NOP: probability = 0.999678788748 \end{verbatim} To get some idea of the prediction accuracy of the logistic regression model, we can apply it to the training data: \begin{verbatim} >>> for i in range(len(ys)): print("True:", ys[i], "Predicted:", LogisticRegression.classify(model, xs[i])) True: 1 Predicted: 1 True: 1 Predicted: 0 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 \end{verbatim} showing that the prediction is correct for all but one of the gene pairs. A more reliable estimate of the prediction accuracy can be found from a leave-one-out analysis, in which the model is recalculated from the training data after removing the gene to be predicted: \begin{verbatim} >>> for i in range(len(ys)): model = LogisticRegression.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:]) print("True:", ys[i], "Predicted:", LogisticRegression.classify(model, xs[i])) True: 1 Predicted: 1 True: 1 Predicted: 0 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 1 True: 0 Predicted: 0 True: 0 Predicted: 0 \end{verbatim} The leave-one-out analysis shows that the prediction of the logistic regression model is incorrect for only two of the gene pairs, which corresponds to a prediction accuracy of 88\%. \subsection{Logistic Regression, Linear Discriminant Analysis, and Support Vector Machines} The logistic regression model is similar to linear discriminant analysis. In linear discriminant analysis, the class probabilities also follow equations (\ref{eq:OP}) and (\ref{eq:NOP}). However, instead of estimating the coefficients $\beta$ directly, we first fit a normal distribution to the predictor variables $x$. The coefficients $\beta$ are then calculated from the means and covariances of the normal distribution. If the distribution of $x$ is indeed normal, then we expect linear discriminant analysis to perform better than the logistic regression model. The logistic regression model, on the other hand, is more robust to deviations from normality. Another similar approach is a support vector machine with a linear kernel. Such an SVM also uses a linear combination of the predictors, but estimates the coefficients $\beta$ from the predictor variables $x$ near the boundary region between the classes. If the logistic regression model (equations (\ref{eq:OP}) and (\ref{eq:NOP})) is a good description for $x$ away from the boundary region, we expect the logistic regression model to perform better than an SVM with a linear kernel, as it relies on more data. If not, an SVM with a linear kernel may perform better. Trevor Hastie, Robert Tibshirani, and Jerome Friedman: {\it The Elements of Statistical Learning. Data Mining, Inference, and Prediction}. Springer Series in Statistics, 2001. Chapter 4.4. \section{$k$-Nearest Neighbors} \subsection{Background and purpose} The $k$-nearest neighbors method is a supervised learning approach that does not need to fit a model to the data. Instead, data points are classified based on the categories of the $k$ nearest neighbors in the training data set. In Biopython, the $k$-nearest neighbors method is available in \verb+Bio.kNN+. To illustrate the use of the $k$-nearest neighbor method in Biopython, we will use the same operon data set as in section \ref{sec:LogisticRegression}. \subsection{Initializing a $k$-nearest neighbors model} Using the data in Table \ref{table:training}, we create and initialize a $k$-nearest neighbors model as follows: \begin{verbatim} >>> from Bio import kNN >>> k = 3 >>> model = kNN.train(xs, ys, k) \end{verbatim} where \verb+xs+ and \verb+ys+ are the same as in Section \ref{subsec:LogisticRegressionTraining}. Here, \verb+k+ is the number of neighbors $k$ that will be considered for the classification. For classification into two classes, choosing an odd number for $k$ lets you avoid tied votes. The function name \verb+train+ is a bit of a misnomer, since no model training is done: this function simply stores \verb+xs+, \verb+ys+, and \verb+k+ in \verb+model+. \subsection{Using a $k$-nearest neighbors model for classification} To classify new data using the $k$-nearest neighbors model, we use the \verb+classify+ function. This function takes a data point $(x_1,x_2)$ and finds the $k$-nearest neighbors in the training data set \verb+xs+. The data point $(x_1, x_2)$ is then classified based on which category (\verb+ys+) occurs most among the $k$ neighbors. For the example of the gene pairs {\it yxcE}, {\it yxcD} and {\it yxiB}, {\it yxiA}, we find: \begin{verbatim} >>> x = [6, -173.143442352] >>> print("yxcE, yxcD:", kNN.classify(model, x)) yxcE, yxcD: 1 >>> x = [309, -271.005880394] >>> print("yxiB, yxiA:", kNN.classify(model, x)) yxiB, yxiA: 0 \end{verbatim} In agreement with the logistic regression model, {\it yxcE}, {\it yxcD} are classified as belonging to the same operon (class OP), while {\it yxiB}, {\it yxiA} are predicted to belong to different operons. The \verb+classify+ function lets us specify both a distance function and a weight function as optional arguments. The distance function affects which $k$ neighbors are chosen as the nearest neighbors, as these are defined as the neighbors with the smallest distance to the query point $(x, y)$. By default, the Euclidean distance is used. Instead, we could for example use the city-block (Manhattan) distance: \begin{verbatim} >>> def cityblock(x1, x2): ... assert len(x1)==2 ... assert len(x2)==2 ... distance = abs(x1[0]-x2[0]) + abs(x1[1]-x2[1]) ... return distance ... >>> x = [6, -173.143442352] >>> print("yxcE, yxcD:", kNN.classify(model, x, distance_fn = cityblock)) yxcE, yxcD: 1 \end{verbatim} The weight function can be used for weighted voting. For example, we may want to give closer neighbors a higher weight than neighbors that are further away: \begin{verbatim} >>> def weight(x1, x2): ... assert len(x1)==2 ... assert len(x2)==2 ... return exp(-abs(x1[0]-x2[0]) - abs(x1[1]-x2[1])) ... >>> x = [6, -173.143442352] >>> print("yxcE, yxcD:", kNN.classify(model, x, weight_fn = weight)) yxcE, yxcD: 1 \end{verbatim} By default, all neighbors are given an equal weight. To find out how confident we can be in these predictions, we can call the \verb+calculate+ function, which will calculate the total weight assigned to the classes OP and NOP. For the default weighting scheme, this reduces to the number of neighbors in each category. For {\it yxcE}, {\it yxcD}, we find \begin{verbatim} >>> x = [6, -173.143442352] >>> weight = kNN.calculate(model, x) >>> print("class OP: weight =", weight[0], "class NOP: weight =", weight[1]) class OP: weight = 0.0 class NOP: weight = 3.0 \end{verbatim} which means that all three neighbors of \verb+x1+, \verb+x2+ are in the NOP class. As another example, for {\it yesK}, {\it yesL} we find \begin{verbatim} >>> x = [117, -267.14] >>> weight = kNN.calculate(model, x) >>> print("class OP: weight =", weight[0], "class NOP: weight =", weight[1]) class OP: weight = 2.0 class NOP: weight = 1.0 \end{verbatim} which means that two neighbors are operon pairs and one neighbor is a non-operon pair. To get some idea of the prediction accuracy of the $k$-nearest neighbors approach, we can apply it to the training data: \begin{verbatim} >>> for i in range(len(ys)): print("True:", ys[i], "Predicted:", kNN.classify(model, xs[i])) True: 1 Predicted: 1 True: 1 Predicted: 0 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 \end{verbatim} showing that the prediction is correct for all but two of the gene pairs. A more reliable estimate of the prediction accuracy can be found from a leave-one-out analysis, in which the model is recalculated from the training data after removing the gene to be predicted: \begin{verbatim} >>> for i in range(len(ys)): model = kNN.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:]) print("True:", ys[i], "Predicted:", kNN.classify(model, xs[i])) True: 1 Predicted: 1 True: 1 Predicted: 0 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 1 True: 1 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 1 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 0 True: 0 Predicted: 1 \end{verbatim} The leave-one-out analysis shows that $k$-nearest neighbors model is correct for 13 out of 17 gene pairs, which corresponds to a prediction accuracy of 76\%. \section{Na\"ive Bayes} This section will describe the \verb|Bio.NaiveBayes| module. \section{Maximum Entropy} This section will describe the \verb|Bio.MaximumEntropy| module. \section{Markov Models} This section will describe the \verb|Bio.MarkovModel| and/or \verb|Bio.HMM.MarkovModel| modules. \chapter{Graphics including GenomeDiagram} \label{chapter:graphics} The \verb|Bio.Graphics| module depends on the third party Python library \href{http://www.reportlab.org}{ReportLab}. Although focused on producing PDF files, ReportLab can also create encapsulated postscript (EPS) and (SVG) files. In addition to these vector based images, provided certain further dependencies such as the \href{http://www.pythonware.com/products/pil/}{Python Imaging Library (PIL)} are installed, ReportLab can also output bitmap images (including JPEG, PNG, GIF, BMP and PICT formats). \section{GenomeDiagram} \label{sec:genomediagram} \subsection{Introduction} The \verb|Bio.Graphics.GenomeDiagram| module was added to Biopython 1.50, having previously been available as a separate Python module dependent on Biopython. GenomeDiagram is described in the Bioinformatics journal publication by Pritchard et al. (2006) \cite{pritchard2006}, which includes some examples images. There is a PDF copy of the old manual here, \url{http://biopython.org/DIST/docs/GenomeDiagram/userguide.pdf} which has some more examples. %TODO - Leighton's old website is AWOL, put this link back later if possible. %http://bioinf.scri.ac.uk/lp/programs.php#genomediagram As the name might suggest, GenomeDiagram was designed for drawing whole genomes, in particular prokaryotic genomes, either as linear diagrams (optionally broken up into fragments to fit better) or as circular wheel diagrams. Have a look at Figure 2 in Toth \textit{et al.} (2006) \cite{toth2006} for a good example. It proved also well suited to drawing quite detailed figures for smaller genomes such as phage, plasmids or mitochrondia, for example see Figures 1 and 2 in Van der Auwera \textit{et al.} (2009) \cite{vanderauwera2009} (shown with additional manual editing). This module is easiest to use if you have your genome loaded as a \verb|SeqRecord| object containing lots of \verb|SeqFeature| objects - for example as loaded from a GenBank file (see Chapters~\ref{chapter:SeqRecord} and~\ref{chapter:Bio.SeqIO}). \subsection{Diagrams, tracks, feature-sets and features} GenomeDiagram uses a nested set of objects. At the top level, you have a diagram object representing a sequence (or sequence region) along the horizontal axis (or circle). A diagram can contain one or more tracks, shown stacked vertically (or radially on circular diagrams). These will typically all have the same length and represent the same sequence region. You might use one track to show the gene locations, another to show regulatory regions, and a third track to show the GC percentage. %Talk about cross-links here? Maybe better later... The most commonly used type of track will contain features, bundled together in feature-sets. You might choose to use one feature-set for all your CDS features, and another for tRNA features. This isn't required - they can all go in the same feature-set, but it makes it easier to update the properties of just selected features (e.g. make all the tRNA features red). There are two main ways to build up a complete diagram. Firstly, the top down approach where you create a diagram object, and then using its methods add track(s), and use the track methods to add feature-set(s), and use their methods to add the features. Secondly, you can create the individual objects separately (in whatever order suits your code), and then combine them. \subsection{A top down example} \label{sec:gd_top_down} We're going to draw a whole genome from a \verb|SeqRecord| object read in from a GenBank file (see Chapter~\ref{chapter:Bio.SeqIO}). This example uses the pPCP1 plasmid from \textit{Yersinia pestis biovar Microtus}, the file is included with the Biopython unit tests under the GenBank folder, or online \href{http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.gb} {\texttt{NC\_005816.gb}} from our website. \begin{verbatim} from reportlab.lib import colors from reportlab.lib.units import cm from Bio.Graphics import GenomeDiagram from Bio import SeqIO record = SeqIO.read("NC_005816.gb", "genbank") \end{verbatim} We're using a top down approach, so after loading in our sequence we next create an empty diagram, then add an (empty) track, and to that add an (empty) feature set: \begin{verbatim} gd_diagram = GenomeDiagram.Diagram("Yersinia pestis biovar Microtus plasmid pPCP1") gd_track_for_features = gd_diagram.new_track(1, name="Annotated Features") gd_feature_set = gd_track_for_features.new_set() \end{verbatim} Now the fun part - we take each gene \verb|SeqFeature| object in our \verb|SeqRecord|, and use it to generate a feature on the diagram. We're going to color them blue, alternating between a dark blue and a light blue. \begin{verbatim} for feature in record.features: if feature.type != "gene": #Exclude this feature continue if len(gd_feature_set) % 2 == 0: color = colors.blue else: color = colors.lightblue gd_feature_set.add_feature(feature, color=color, label=True) \end{verbatim} Now we come to actually making the output file. This happens in two steps, first we call the \verb|draw| method, which creates all the shapes using ReportLab objects. Then we call the \verb|write| method which renders these to the requested file format. Note you can output in multiple file formats: \begin{verbatim} gd_diagram.draw(format="linear", orientation="landscape", pagesize='A4', fragments=4, start=0, end=len(record)) gd_diagram.write("plasmid_linear.pdf", "PDF") gd_diagram.write("plasmid_linear.eps", "EPS") gd_diagram.write("plasmid_linear.svg", "SVG") \end{verbatim} Also, provided you have the dependencies installed, you can also do bitmaps, for example: \begin{verbatim} gd_diagram.write("plasmid_linear.png", "PNG") \end{verbatim} \begin{htmlonly} %The blank line below is important to start a new paragraph \imgsrc[width=550, height=400]{images/plasmid_linear.png} \end{htmlonly} \begin{latexonly} The expected output is shown in Figure~\ref{fig:plasmid_linear}. \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/plasmid_linear.png} \caption{Simple linear diagram for \textit{Yersinia pestis biovar Microtus} plasmid pPCP1.} \label{fig:plasmid_linear} \end{figure} \end{latexonly} Notice that the \verb|fragments| argument which we set to four controls how many pieces the genome gets broken up into. If you want to do a circular figure, then try this: \begin{verbatim} gd_diagram.draw(format="circular", circular=True, pagesize=(20*cm,20*cm), start=0, end=len(record), circle_core=0.7) gd_diagram.write("plasmid_circular.pdf", "PDF") \end{verbatim} \begin{htmlonly} %The blank line below is important to start a new paragraph \imgsrc[width=400, height=400]{images/plasmid_circular.png} \end{htmlonly} \begin{latexonly} The expected output is shown in Figure~\ref{fig:plasmid_circular}. \begin{figure}[htbp] \centering \includegraphics[width=8cm,height=8cm]{images/plasmid_circular.png} \caption{Simple circular diagram for \textit{Yersinia pestis biovar Microtus} plasmid pPCP1.} \label{fig:plasmid_circular} \end{figure} \end{latexonly} These figures are not very exciting, but we've only just got started. \subsection{A bottom up example} Now let's produce exactly the same figures, but using the bottom up approach. This means we create the different objects directly (and this can be done in almost any order) and then combine them. \begin{verbatim} from reportlab.lib import colors from reportlab.lib.units import cm from Bio.Graphics import GenomeDiagram from Bio import SeqIO record = SeqIO.read("NC_005816.gb", "genbank") #Create the feature set and its feature objects, gd_feature_set = GenomeDiagram.FeatureSet() for feature in record.features: if feature.type != "gene": #Exclude this feature continue if len(gd_feature_set) % 2 == 0: color = colors.blue else: color = colors.lightblue gd_feature_set.add_feature(feature, color=color, label=True) #(this for loop is the same as in the previous example) #Create a track, and a diagram gd_track_for_features = GenomeDiagram.Track(name="Annotated Features") gd_diagram = GenomeDiagram.Diagram("Yersinia pestis biovar Microtus plasmid pPCP1") #Now have to glue the bits together... gd_track_for_features.add_set(gd_feature_set) gd_diagram.add_track(gd_track_for_features, 1) \end{verbatim} You can now call the \verb|draw| and \verb|write| methods as before to produce a linear or circular diagram, using the code at the end of the top-down example above. The figures should be identical. \subsection{Features without a SeqFeature} \label{sec:gd_features_without_seqfeatures} In the above example we used a \verb|SeqRecord|'s \verb|SeqFeature| objects to build our diagram (see also Section~\ref{sec:seq_features}). Sometimes you won't have \verb|SeqFeature| objects, but just the coordinates for a feature you want to draw. You have to create minimal \verb|SeqFeature| object, but this is easy: \begin{verbatim} from Bio.SeqFeature import SeqFeature, FeatureLocation my_seq_feature = SeqFeature(FeatureLocation(50,100),strand=+1) \end{verbatim} For strand, use \texttt{+1} for the forward strand, \texttt{-1} for the reverse strand, and \texttt{None} for both. Here is a short self contained example: \begin{verbatim} from Bio.SeqFeature import SeqFeature, FeatureLocation from Bio.Graphics import GenomeDiagram from reportlab.lib.units import cm gdd = GenomeDiagram.Diagram('Test Diagram') gdt_features = gdd.new_track(1, greytrack=False) gds_features = gdt_features.new_set() #Add three features to show the strand options, feature = SeqFeature(FeatureLocation(25, 125), strand=+1) gds_features.add_feature(feature, name="Forward", label=True) feature = SeqFeature(FeatureLocation(150, 250), strand=None) gds_features.add_feature(feature, name="Strandless", label=True) feature = SeqFeature(FeatureLocation(275, 375), strand=-1) gds_features.add_feature(feature, name="Reverse", label=True) gdd.draw(format='linear', pagesize=(15*cm,4*cm), fragments=1, start=0, end=400) gdd.write("GD_labels_default.pdf", "pdf") \end{verbatim} \begin{htmlonly} The top part of the image in the next subsection shows the output \end{htmlonly} \begin{latexonly} The output is shown at the top of Figure~\ref{fig:gd_sigil_labels} \end{latexonly} (in the default feature color, pale green). Notice that we have used the \texttt{name} argument here to specify the caption text for these features. This is discussed in more detail next. \subsection{Feature captions} \label{sec:gd_feature_captions} Recall we used the following (where \texttt{feature} was a \verb|SeqFeature| object) to add a feature to the diagram: \begin{verbatim} gd_feature_set.add_feature(feature, color=color, label=True) \end{verbatim} In the example above the \verb|SeqFeature| annotation was used to pick a sensible caption for the features. By default the following possible entries under the \verb|SeqFeature| object's qualifiers dictionary are used: \texttt{gene}, \texttt{label}, \texttt{name}, \texttt{locus\_tag}, and \texttt{product}. More simply, you can specify a name directly: \begin{verbatim} gd_feature_set.add_feature(feature, color=color, label=True, name="My Gene") \end{verbatim} In addition to the caption text for each feature's label, you can also choose the font, position (this defaults to the start of the sigil, you can also choose the middle or at the end) and orientation (for linear diagrams only, where this defaults to rotated by $45$ degrees): \begin{verbatim} #Large font, parallel with the track gd_feature_set.add_feature(feature, label=True, color="green", label_size=25, label_angle=0) #Very small font, perpendicular to the track (towards it) gd_feature_set.add_feature(feature, label=True, color="purple", label_position="end", label_size=4, label_angle=90) #Small font, perpendicular to the track (away from it) gd_feature_set.add_feature(feature, label=True, color="blue", label_position="middle", label_size=6, label_angle=-90) \end{verbatim} \noindent Combining each of these three fragments with the complete example in the previous section should give something like \begin{htmlonly} this: %The blank lines above and below are important to trigger paragraph breaks \imgsrc[width=600, height=700]{images/GD_sigil_labels.png} \label{fig:gd_sigil_labels} \end{htmlonly} \begin{latexonly} the tracks in Figure~\ref{fig:gd_sigil_labels}. \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/GD_sigil_labels.png} \caption{Simple GenomeDiagram showing label options. The top plot in pale green shows the default label settings (see Section~\ref{sec:gd_features_without_seqfeatures}) while the rest show variations in the label size, position and orientation (see Section~\ref{sec:gd_feature_captions}). } \label{fig:gd_sigil_labels} \end{figure} \end{latexonly} We've not shown it here, but you can also set \texttt{label\_color} to control the label's color (used in Section~\ref{sec:gd_nice_example}). You'll notice the default font is quite small - this makes sense because you will usually be drawing many (small) features on a page, not just a few large ones as shown here. \subsection{Feature sigils} \label{sec:gd_sigils} The examples above have all just used the default sigil for the feature, a plain box, which was all that was available in the last publicly released standalone version of GenomeDiagram. Arrow sigils were included when GenomeDiagram was added to Biopython 1.50: \begin{verbatim} #Default uses a BOX sigil gd_feature_set.add_feature(feature) #You can make this explicit: gd_feature_set.add_feature(feature, sigil="BOX") #Or opt for an arrow: gd_feature_set.add_feature(feature, sigil="ARROW") \end{verbatim} \noindent Biopython 1.61 added three more sigils, \begin{verbatim} #Box with corners cut off (making it an octagon) gd_feature_set.add_feature(feature, sigil="OCTO") #Box with jagged edges (useful for showing breaks in contains) gd_feature_set.add_feature(feature, sigil="JAGGY") #Arrow which spans the axis with strand used only for direction gd_feature_set.add_feature(feature, sigil="BIGARROW") \end{verbatim} These are shown \begin{htmlonly} below. \end{htmlonly} \begin{latexonly}in Figure~\ref{fig:gd_sigils}. \end{latexonly} Most sigils fit into a bounding box (as given by the default BOX sigil), either above or below the axis for the forward or reverse strand, or straddling it (double the height) for strand-less features. The BIGARROW sigil is different, always straddling the axis with the direction taken from the feature's stand. \begin{htmlonly} \imgsrc[width=425, height=600]{images/GD_sigils.png} \end{htmlonly} \begin{latexonly} \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/GD_sigils.png} \caption{Simple GenomeDiagram showing different sigils (see Section~\ref{sec:gd_sigils})} \label{fig:gd_sigils} \end{figure} \end{latexonly} \subsection{Arrow sigils} \label{sec:gd_arrow_sigils} We introduced the arrow sigils in the previous section. There are two additional options to adjust the shapes of the arrows, firstly the thickness of the arrow shaft, given as a proportion of the height of the bounding box: \begin{verbatim} #Full height shafts, giving pointed boxes: gd_feature_set.add_feature(feature, sigil="ARROW", color="brown", arrowshaft_height=1.0) #Or, thin shafts: gd_feature_set.add_feature(feature, sigil="ARROW", color="teal", arrowshaft_height=0.2) #Or, very thin shafts: gd_feature_set.add_feature(feature, sigil="ARROW", color="darkgreen", arrowshaft_height=0.1) \end{verbatim} \begin{htmlonly} \noindent The results are shown below: \imgsrc[width=600, height=700]{images/GD_sigil_arrow_shafts.png} \end{htmlonly} \begin{latexonly} \noindent The results are shown in Figure~\ref{fig:gd_sigil_arrow_shafts}. \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/GD_sigil_arrow_shafts.png} \caption{Simple GenomeDiagram showing arrow shaft options (see Section~\ref{sec:gd_arrow_sigils})} \label{fig:gd_sigil_arrow_shafts} \end{figure} \end{latexonly} Secondly, the length of the arrow head - given as a proportion of the height of the bounding box (defaulting to $0.5$, or $50\%$): \begin{verbatim} #Short arrow heads: gd_feature_set.add_feature(feature, sigil="ARROW", color="blue", arrowhead_length=0.25) #Or, longer arrow heads: gd_feature_set.add_feature(feature, sigil="ARROW", color="orange", arrowhead_length=1) #Or, very very long arrow heads (i.e. all head, no shaft, so triangles): gd_feature_set.add_feature(feature, sigil="ARROW", color="red", arrowhead_length=10000) \end{verbatim} \begin{htmlonly} \noindent The results are shown below: \imgsrc[width=600, height=700]{images/GD_sigil_arrow_heads.png} \end{htmlonly} \begin{latexonly} \noindent The results are shown in Figure~\ref{fig:gd_sigil_arrow_heads}. \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/GD_sigil_arrow_heads.png} \caption{Simple GenomeDiagram showing arrow head options (see Section~\ref{sec:gd_arrow_sigils})} \label{fig:gd_sigil_arrow_heads} \end{figure} \end{latexonly} Biopython 1.61 adds a new \verb|BIGARROW| sigil which always stradles the axis, pointing left for the reverse strand or right otherwise: \begin{verbatim} #A large arrow straddling the axis: gd_feature_set.add_feature(feature, sigil="BIGARROW") \end{verbatim} \noindent All the shaft and arrow head options shown above for the \verb|ARROW| sigil can be used for the \verb|BIGARROW| sigil too. \subsection{A nice example} \label{sec:gd_nice_example} Now let's return to the pPCP1 plasmid from \textit{Yersinia pestis biovar Microtus}, and the top down approach used in Section~\ref{sec:gd_top_down}, but take advantage of the sigil options we've now discussed. This time we'll use arrows for the genes, and overlay them with strand-less features (as plain boxes) showing the position of some restriction digest sites. %NOTE - This *just* fits on one page in the PDF output :) \begin{verbatim} from reportlab.lib import colors from reportlab.lib.units import cm from Bio.Graphics import GenomeDiagram from Bio import SeqIO from Bio.SeqFeature import SeqFeature, FeatureLocation record = SeqIO.read("NC_005816.gb", "genbank") gd_diagram = GenomeDiagram.Diagram(record.id) gd_track_for_features = gd_diagram.new_track(1, name="Annotated Features") gd_feature_set = gd_track_for_features.new_set() for feature in record.features: if feature.type != "gene": #Exclude this feature continue if len(gd_feature_set) % 2 == 0: color = colors.blue else: color = colors.lightblue gd_feature_set.add_feature(feature, sigil="ARROW", color=color, label=True, label_size = 14, label_angle=0) #I want to include some strandless features, so for an example #will use EcoRI recognition sites etc. for site, name, color in [("GAATTC","EcoRI",colors.green), ("CCCGGG","SmaI",colors.orange), ("AAGCTT","HindIII",colors.red), ("GGATCC","BamHI",colors.purple)]: index = 0 while True: index = record.seq.find(site, start=index) if index == -1 : break feature = SeqFeature(FeatureLocation(index, index+len(site))) gd_feature_set.add_feature(feature, color=color, name=name, label=True, label_size = 10, label_color=color) index += len(site) gd_diagram.draw(format="linear", pagesize='A4', fragments=4, start=0, end=len(record)) gd_diagram.write("plasmid_linear_nice.pdf", "PDF") gd_diagram.write("plasmid_linear_nice.eps", "EPS") gd_diagram.write("plasmid_linear_nice.svg", "SVG") gd_diagram.draw(format="circular", circular=True, pagesize=(20*cm,20*cm), start=0, end=len(record), circle_core = 0.5) gd_diagram.write("plasmid_circular_nice.pdf", "PDF") gd_diagram.write("plasmid_circular_nice.eps", "EPS") gd_diagram.write("plasmid_circular_nice.svg", "SVG") \end{verbatim} \begin{htmlonly} \noindent And the output: \imgsrc[width=550, height=400]{images/plasmid_linear_nice.png} \imgsrc[width=591, height=591]{images/plasmid_circular_nice.png} \end{htmlonly} \begin{latexonly} \noindent The expected output is shown in Figures~\ref{fig:plasmid_linear_nice} and~\ref{fig:plasmid_circular_nice}. \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/plasmid_linear_nice.png} \caption{Linear diagram for \textit{Yersinia pestis biovar Microtus} plasmid pPCP1 showing selected restriction digest sites (see Section~\ref{sec:gd_nice_example}).} \label{fig:plasmid_linear_nice} \end{figure} \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/plasmid_circular_nice.png} \caption{Circular diagram for \textit{Yersinia pestis biovar Microtus} plasmid pPCP1 showing selected restriction digest sites (see Section~\ref{sec:gd_nice_example}).} \label{fig:plasmid_circular_nice} \end{figure} \end{latexonly} \subsection{Multiple tracks} \label{sec:gd_multiple_tracks} All the examples so far have used a single track, but you can have more than one track -- for example show the genes on one, and repeat regions on another. In this example we're going to show three phage genomes side by side to scale, inspired by Figure 6 in Proux {\textit et al.} (2002) \cite{proux2002}. We'll need the GenBank files for the following three phage: \begin{itemize} \item \verb|NC_002703| -- Lactococcus phage Tuc2009, complete genome ($38347$ bp) \item \verb|AF323668| -- Bacteriophage bIL285, complete genome ($35538$ bp) \item \verb|NC_003212| -- \textit{Listeria innocua} Clip11262, complete genome, of which we are focussing only on integrated prophage 5 (similar length). \end{itemize} You can download these using Entrez if you like, see Section~\ref{sec:efetch} for more details. For the third record we've worked out where the phage is integrated into the genome, and slice the record to extract it (with the features preserved, see Section~\ref{sec:SeqRecord-slicing}), and must also reverse complement to match the orientation of the first two phage (again preserving the features, see Section~\ref{sec:SeqRecord-reverse-complement}): \begin{verbatim} from Bio import SeqIO A_rec = SeqIO.read("NC_002703.gbk", "gb") B_rec = SeqIO.read("AF323668.gbk", "gb") C_rec = SeqIO.read("NC_003212.gbk", "gb")[2587879:2625807].reverse_complement(name=True) \end{verbatim} The figure we are imitating used different colors for different gene functions. One way to do this is to edit the GenBank file to record color preferences for each feature - something \href{http://www.sanger.ac.uk/resources/software/artemis/} {Sanger's Artemis editor} does, and which GenomeDiagram should understand. Here however, we'll just hard code three lists of colors. Note that the annotation in the GenBank files doesn't exactly match that shown in Proux \textit{et al.}, they have drawn some unannotated genes. \begin{verbatim} from reportlab.lib.colors import red, grey, orange, green, brown, blue, lightblue, purple A_colors = [red]*5 + [grey]*7 + [orange]*2 + [grey]*2 + [orange] + [grey]*11 + [green]*4 \ + [grey] + [green]*2 + [grey, green] + [brown]*5 + [blue]*4 + [lightblue]*5 \ + [grey, lightblue] + [purple]*2 + [grey] B_colors = [red]*6 + [grey]*8 + [orange]*2 + [grey] + [orange] + [grey]*21 + [green]*5 \ + [grey] + [brown]*4 + [blue]*3 + [lightblue]*3 + [grey]*5 + [purple]*2 C_colors = [grey]*30 + [green]*5 + [brown]*4 + [blue]*2 + [grey, blue] + [lightblue]*2 \ + [grey]*5 \end{verbatim} Now to draw them -- this time we add three tracks to the diagram, and also notice they are given different start/end values to reflect their different lengths (this requires Biopython 1.59 or later). \begin{verbatim} from Bio.Graphics import GenomeDiagram name = "Proux Fig 6" gd_diagram = GenomeDiagram.Diagram(name) max_len = 0 for record, gene_colors in zip([A_rec, B_rec, C_rec], [A_colors, B_colors, C_colors]): max_len = max(max_len, len(record)) gd_track_for_features = gd_diagram.new_track(1, name=record.name, greytrack=True, start=0, end=len(record)) gd_feature_set = gd_track_for_features.new_set() i = 0 for feature in record.features: if feature.type != "gene": #Exclude this feature continue gd_feature_set.add_feature(feature, sigil="ARROW", color=gene_colors[i], label=True, name = str(i+1), label_position="start", label_size = 6, label_angle=0) i+=1 gd_diagram.draw(format="linear", pagesize='A4', fragments=1, start=0, end=max_len) gd_diagram.write(name + ".pdf", "PDF") gd_diagram.write(name + ".eps", "EPS") gd_diagram.write(name + ".svg", "SVG") \end{verbatim} \begin{htmlonly} \noindent The result: \imgsrc[width=565, height=400]{images/three_track_simple.png} \end{htmlonly} \begin{latexonly} \noindent The expected output is shown in Figure~\ref{fig:three_track_simple}. \begin{figure}[htbp] \centering \includegraphics[width=\textwidth]{images/three_track_simple.png} \caption{Linear diagram with three tracks for Lactococcus phage Tuc2009 (NC\_002703), bacteriophage bIL285 (AF323668), and prophage 5 from \textit{Listeria innocua} Clip11262 (NC\_003212) (see Section~\ref{sec:gd_multiple_tracks}).} \label{fig:three_track_simple} \end{figure} \end{latexonly} I did wonder why in the original manuscript there were no red or orange genes marked in the bottom phage. Another important point is here the phage are shown with different lengths - this is because they are all drawn to the same scale (they \emph{are} different lengths). The key difference from the published figure is they have color-coded links between similar proteins -- which is what we will do in the next section. \subsection{Cross-Links between tracks} \label{sec:gd_cross_links} Biopython 1.59 added the ability to draw cross links between tracks - both simple linear diagrams as we will show here, but also linear diagrams split into fragments and circular diagrams. Continuing the example from the previous section inspired by Figure 6 from Proux \textit{et al.} 2002 \cite{proux2002}, we would need a list of cross links between pairs of genes, along with a score or color to use. Realistically you might extract this from a BLAST file computationally, but here I have manually typed them in. My naming convention continues to refer to the three phage as A, B and C. Here are the links we want to show between A and B, given as a list of tuples (percentage similarity score, gene in A, gene in B). \begin{verbatim} #Tuc2009 (NC_002703) vs bIL285 (AF323668) A_vs_B = [ (99, "Tuc2009_01", "int"), (33, "Tuc2009_03", "orf4"), (94, "Tuc2009_05", "orf6"), (100,"Tuc2009_06", "orf7"), (97, "Tuc2009_07", "orf8"), (98, "Tuc2009_08", "orf9"), (98, "Tuc2009_09", "orf10"), (100,"Tuc2009_10", "orf12"), (100,"Tuc2009_11", "orf13"), (94, "Tuc2009_12", "orf14"), (87, "Tuc2009_13", "orf15"), (94, "Tuc2009_14", "orf16"), (94, "Tuc2009_15", "orf17"), (88, "Tuc2009_17", "rusA"), (91, "Tuc2009_18", "orf20"), (93, "Tuc2009_19", "orf22"), (71, "Tuc2009_20", "orf23"), (51, "Tuc2009_22", "orf27"), (97, "Tuc2009_23", "orf28"), (88, "Tuc2009_24", "orf29"), (26, "Tuc2009_26", "orf38"), (19, "Tuc2009_46", "orf52"), (77, "Tuc2009_48", "orf54"), (91, "Tuc2009_49", "orf55"), (95, "Tuc2009_52", "orf60"), ] \end{verbatim} Likewise for B and C: \begin{verbatim} #bIL285 (AF323668) vs Listeria innocua prophage 5 (in NC_003212) B_vs_C = [ (42, "orf39", "lin2581"), (31, "orf40", "lin2580"), (49, "orf41", "lin2579"), #terL (54, "orf42", "lin2578"), #portal (55, "orf43", "lin2577"), #protease (33, "orf44", "lin2576"), #mhp (51, "orf46", "lin2575"), (33, "orf47", "lin2574"), (40, "orf48", "lin2573"), (25, "orf49", "lin2572"), (50, "orf50", "lin2571"), (48, "orf51", "lin2570"), (24, "orf52", "lin2568"), (30, "orf53", "lin2567"), (28, "orf54", "lin2566"), ] \end{verbatim} For the first and last phage these identifiers are locus tags, for the middle phage there are no locus tags so I've used gene names instead. The following little helper function lets us lookup a feature using either a locus tag or gene name: \begin{verbatim} def get_feature(features, id, tags=["locus_tag", "gene"]): """Search list of SeqFeature objects for an identifier under the given tags.""" for f in features: for key in tags: #tag may not be present in this feature for x in f.qualifiers.get(key, []): if x == id: return f raise KeyError(id) \end{verbatim} We can now turn those list of identifier pairs into SeqFeature pairs, and thus find their location co-ordinates. We can now add all that code and the following snippet to the previous example (just before the \verb|gd_diagram.draw(...)| line -- see the finished example script \href{http://biopython.org/SRC/biopython/Doc/examples/Proux_et_al_2002_Figure_6.py}{Proux\_et\_al\_2002\_Figure\_6.py} included in the \texttt{Doc/examples} folder of the Biopython source code) to add cross links to the figure: \begin{verbatim} from Bio.Graphics.GenomeDiagram import CrossLink from reportlab.lib import colors #Note it might have been clearer to assign the track numbers explicitly... for rec_X, tn_X, rec_Y, tn_Y, X_vs_Y in [(A_rec, 3, B_rec, 2, A_vs_B), (B_rec, 2, C_rec, 1, B_vs_C)]: track_X = gd_diagram.tracks[tn_X] track_Y = gd_diagram.tracks[tn_Y] for score, id_X, id_Y in X_vs_Y: feature_X = get_feature(rec_X.features, id_X) feature_Y = get_feature(rec_Y.features, id_Y) color = colors.linearlyInterpolatedColor(colors.white, colors.firebrick, 0, 100, score) link_xy = CrossLink((track_X, feature_X.location.start, feature_X.location.end), (track_Y, feature_Y.location.start, feature_Y.location.end), color, colors.lightgrey) gd_diagram.cross_track_links.append(link_xy) \end{verbatim} There are several important pieces to this code. First the \verb|GenomeDiagram| object has a \verb|cross_track_links| attribute which is just a list of \verb|CrossLink| objects. Each \verb|CrossLink| object takes two sets of track-specific co-ordinates (here given as tuples, you can alternatively use a \verb|GenomeDiagram.Feature| object instead). You can optionally supply a colour, border color, and say if this link should be drawn flipped (useful for showing inversions). You can also see how we turn the BLAST percentage identity score into a colour, interpolating between white ($0\%$) and a dark red ($100\%$). In this example we don't have any problems with overlapping cross-links. One way to tackle that is to use transparency in ReportLab, by using colors with their alpha channel set. However, this kind of shaded color scheme combined with overlap transparency would be difficult to interpret. %Again, HTML and PDF versions for the figure \begin{htmlonly} \noindent The result: \imgsrc[width=565, height=400]{images/three_track_cl.png} \end{htmlonly} \begin{latexonly} \noindent The expected output is shown in Figure~\ref{fig:three_track_cl}. \begin{figure}[htbp] \centering \includegraphics[width=\textwidth]{images/three_track_cl.png} \caption{Linear diagram with three tracks for Lactococcus phage Tuc2009 (NC\_002703), bacteriophage bIL285 (AF323668), and prophage 5 from \textit{Listeria innocua} Clip11262 (NC\_003212) plus basic cross-links shaded by percentage identity (see Section~\ref{sec:gd_cross_links}).} \label{fig:three_track_cl} \end{figure} \end{latexonly} There is still a lot more that can be done within Biopython to help improve this figure. First of all, the cross links in this case are between proteins which are drawn in a strand specific manor. It can help to add a background region (a feature using the `BOX' sigil) on the feature track to extend the cross link. Also, we could reduce the vertical height of the feature tracks to allocate more to the links instead -- one way to do that is to allocate space for empty tracks. Furthermore, in cases like this where there are no large gene overlaps, we can use the axis-straddling \verb|BIGARROW| sigil, which allows us to further reduce the vertical space needed for the track. These improvements are demonstrated in the example script \href{http://biopython.org/SRC/biopython/Doc/examples/Proux_et_al_2002_Figure_6.py}{Proux\_et\_al\_2002\_Figure\_6.py} included in the \texttt{Doc/examples} folder of the Biopython source code. %TODO - Add a link get the file directly (for Windows users etc). \begin{htmlonly} \noindent The result: \imgsrc[width=565, height=400]{images/three_track_cl2a.png} \end{htmlonly} \begin{latexonly} \noindent The expected output is shown in Figure~\ref{fig:three_track_cl2}. \begin{figure}[htbp] \centering \includegraphics[width=\textwidth]{images/three_track_cl2a.png} \caption{Linear diagram with three tracks for Lactococcus phage Tuc2009 (NC\_002703), bacteriophage bIL285 (AF323668), and prophage 5 from \textit{Listeria innocua} Clip11262 (NC\_003212) plus cross-links shaded by percentage identity (see Section~\ref{sec:gd_cross_links}).} \label{fig:three_track_cl2} \end{figure} \end{latexonly} Beyond that, finishing touches you might want to do manually in a vector image editor include fine tuning the placement of gene labels, and adding other custom annotation such as highlighting particular regions. Although not really necessary in this example since none of the cross-links overlap, using a transparent color in ReportLab is a very useful technique for superimposing multiple links. However, in this case a shaded color scheme should be avoided. \subsection{Further options} You can control the tick marks to show the scale -- after all every graph should show its units, and the number of the grey-track labels. Also, we have only used the \verb|FeatureSet| so far. GenomeDiagram also has a \verb|GraphSet| which can be used for show line graphs, bar charts and heat plots (e.g. to show plots of GC\% on a track parallel to the features). These options are not covered here yet, so for now we refer you to the \href{http://biopython.org/DIST/docs/GenomeDiagram/userguide.pdf} %\href{http://bioinf.scri.ac.uk/lp/downloads/programs/genomediagram/userguide.pdf} {User Guide (PDF)} included with the standalone version of GenomeDiagram (but please read the next section first), and the docstrings. \subsection{Converting old code} If you have old code written using the standalone version of GenomeDiagram, and you want to switch it over to using the new version included with Biopython then you will have to make a few changes - most importantly to your import statements. Also, the older version of GenomeDiagram used only the UK spellings of color and center (colour and centre). You will need to change to the American spellings, although for several years the Biopython version of GenomeDiagram supported both. For example, if you used to have: \begin{verbatim} from GenomeDiagram import GDFeatureSet, GDDiagram gdd = GDDiagram("An example") ... \end{verbatim} you could just switch the import statements like this: \begin{verbatim} from Bio.Graphics.GenomeDiagram import FeatureSet as GDFeatureSet, Diagram as GDDiagram gdd = GDDiagram("An example") ... \end{verbatim} and hopefully that should be enough. In the long term you might want to switch to the new names, but you would have to change more of your code: \begin{verbatim} from Bio.Graphics.GenomeDiagram import FeatureSet, Diagram gdd = Diagram("An example") ... \end{verbatim} or: \begin{verbatim} from Bio.Graphics import GenomeDiagram gdd = GenomeDiagram.Diagram("An example") ... \end{verbatim} If you run into difficulties, please ask on the Biopython mailing list for advice. One catch is that we have not included the old module \verb|GenomeDiagram.GDUtilities| yet. This included a number of GC\% related functions, which will probably be merged under \verb|Bio.SeqUtils| later on. %TODO - Deal with GenomeDiagram.GDUtilities \section{Chromosomes} The \verb|Bio.Graphics.BasicChromosome| module allows drawing of chromosomes. There is an example in Jupe \textit{et al.} (2012) \cite{jupe2012} (open access) using colors to highlight different gene families. \subsection{Simple Chromosomes} Here is a very simple example - for which we'll use \textit{Arabidopsis thaliana}. \begin{latexonly} \begin{figure}[p] \centering \includegraphics[scale=0.45]{images/simple_chrom.pdf} \caption{Simple chromosome diagram for \textit{Arabidopsis thaliana}.} \label{fig:simplechromosome} \end{figure} \begin{figure}[p] \centering \includegraphics[scale=0.45]{images/tRNA_chrom.pdf} \caption{Chromosome diagram for \textit{Arabidopsis thaliana} showing tRNA genes.} \label{fig:trnachromosome} \end{figure} \end{latexonly} You can skip this bit, but first I downloaded the five sequenced chromosomes from the NCBI's FTP site \url{ftp://ftp.ncbi.nlm.nih.gov/genomes/Arabidopsis_thaliana} and then parsed them with \verb|Bio.SeqIO| to find out their lengths. You could use the GenBank files for this, but it is faster to use the FASTA files for the whole chromosomes: \begin{verbatim} from Bio import SeqIO entries = [("Chr I", "CHR_I/NC_003070.fna"), ("Chr II", "CHR_II/NC_003071.fna"), ("Chr III", "CHR_III/NC_003074.fna"), ("Chr IV", "CHR_IV/NC_003075.fna"), ("Chr V", "CHR_V/NC_003076.fna")] for (name, filename) in entries: record = SeqIO.read(filename,"fasta") print(name, len(record)) \end{verbatim} \noindent This gave the lengths of the five chromosomes, which we'll now use in the following short demonstration of the \verb|BasicChromosome| module: \begin{verbatim} from reportlab.lib.units import cm from Bio.Graphics import BasicChromosome entries = [("Chr I", 30432563), ("Chr II", 19705359), ("Chr III", 23470805), ("Chr IV", 18585042), ("Chr V", 26992728)] max_len = 30432563 #Could compute this telomere_length = 1000000 #For illustration chr_diagram = BasicChromosome.Organism() chr_diagram.page_size = (29.7*cm, 21*cm) #A4 landscape for name, length in entries: cur_chromosome = BasicChromosome.Chromosome(name) #Set the scale to the MAXIMUM length plus the two telomeres in bp, #want the same scale used on all five chromosomes so they can be #compared to each other cur_chromosome.scale_num = max_len + 2 * telomere_length #Add an opening telomere start = BasicChromosome.TelomereSegment() start.scale = telomere_length cur_chromosome.add(start) #Add a body - using bp as the scale length here. body = BasicChromosome.ChromosomeSegment() body.scale = length cur_chromosome.add(body) #Add a closing telomere end = BasicChromosome.TelomereSegment(inverted=True) end.scale = telomere_length cur_chromosome.add(end) #This chromosome is done chr_diagram.add(cur_chromosome) chr_diagram.draw("simple_chrom.pdf", "Arabidopsis thaliana") \end{verbatim} This should create a very simple PDF file, shown \begin{htmlonly} here: %The blank lines above and below are important to trigger paragraph breaks \imgsrc[width=650, height=460]{images/simple_chrom.png} \end{htmlonly} \begin{latexonly} in Figure~\ref{fig:simplechromosome}. \end{latexonly} This example is deliberately short and sweet. The next example shows the location of features of interest. \subsection{Annotated Chromosomes} Continuing from the previous example, let's also show the tRNA genes. We'll get their locations by parsing the GenBank files for the five \textit{Arabidopsis thaliana} chromosomes. You'll need to download these files from the NCBI FTP site \url{ftp://ftp.ncbi.nlm.nih.gov/genomes/Arabidopsis_thaliana}, and preserve the subdirectory names or edit the paths below: \begin{verbatim} from reportlab.lib.units import cm from Bio import SeqIO from Bio.Graphics import BasicChromosome entries = [("Chr I", "CHR_I/NC_003070.gbk"), ("Chr II", "CHR_II/NC_003071.gbk"), ("Chr III", "CHR_III/NC_003074.gbk"), ("Chr IV", "CHR_IV/NC_003075.gbk"), ("Chr V", "CHR_V/NC_003076.gbk")] max_len = 30432563 #Could compute this telomere_length = 1000000 #For illustration chr_diagram = BasicChromosome.Organism() chr_diagram.page_size = (29.7*cm, 21*cm) #A4 landscape for index, (name, filename) in enumerate(entries): record = SeqIO.read(filename,"genbank") length = len(record) features = [f for f in record.features if f.type=="tRNA"] #Record an Artemis style integer color in the feature's qualifiers, #1 = Black, 2 = Red, 3 = Green, 4 = blue, 5 =cyan, 6 = purple for f in features: f.qualifiers["color"] = [index+2] cur_chromosome = BasicChromosome.Chromosome(name) #Set the scale to the MAXIMUM length plus the two telomeres in bp, #want the same scale used on all five chromosomes so they can be #compared to each other cur_chromosome.scale_num = max_len + 2 * telomere_length #Add an opening telomere start = BasicChromosome.TelomereSegment() start.scale = telomere_length cur_chromosome.add(start) #Add a body - again using bp as the scale length here. body = BasicChromosome.AnnotatedChromosomeSegment(length, features) body.scale = length cur_chromosome.add(body) #Add a closing telomere end = BasicChromosome.TelomereSegment(inverted=True) end.scale = telomere_length cur_chromosome.add(end) #This chromosome is done chr_diagram.add(cur_chromosome) chr_diagram.draw("tRNA_chrom.pdf", "Arabidopsis thaliana") \end{verbatim} It might warn you about the labels being too close together - have a look at the forward strand (right hand side) of Chr I, but it should create a colorful PDF file, shown \begin{htmlonly} here: %The blank lines above and below are important to trigger paragraph breaks \imgsrc[width=650, height=460]{images/tRNA_chrom.png} \end{htmlonly} \begin{latexonly} in Figure~\ref{fig:simplechromosome}. \end{latexonly} \chapter{Cookbook -- Cool things to do with it} \label{chapter:cookbook} Biopython now has two collections of ``cookbook'' examples -- this chapter (which has been included in this tutorial for many years and has gradually grown), and \url{http://biopython.org/wiki/Category:Cookbook} which is a user contributed collection on our wiki. We're trying to encourage Biopython users to contribute their own examples to the wiki. In addition to helping the community, one direct benefit of sharing an example like this is that you could also get some feedback on the code from other Biopython users and developers - which could help you improve all your Python code. In the long term, we may end up moving all of the examples in this chapter to the wiki, or elsewhere within the tutorial. \section{Working with sequence files} \label{seq:cookbook-sequences} This section shows some more examples of sequence input/output, using the \verb|Bio.SeqIO| module described in Chapter~\ref{chapter:Bio.SeqIO}. \subsection{Filtering a sequence file} Often you'll have a large file with many sequences in it (e.g. FASTA file or genes, or a FASTQ or SFF file of reads), a separate shorter list of the IDs for a subset of sequences of interest, and want to make a new sequence file for this subset. Let's say the list of IDs is in a simple text file, as the first word on each line. This could be a tabular file where the first column is the ID. Try something like this: %not a doctest to avoid temp files being left behind, also no >>> %makes it easier to copy and paste the example to a script file. \begin{verbatim} from Bio import SeqIO input_file = "big_file.sff" id_file = "short_list.txt" output_file = "short_list.sff" wanted = set(line.rstrip("\n").split(None,1)[0] for line in open(id_file)) print("Found %i unique identifiers in %s" % (len(wanted), id_file)) records = (r for r in SeqIO.parse(input_file, "sff") if r.id in wanted) count = SeqIO.write(records, output_file, "sff") print("Saved %i records from %s to %s" % (count, input_file, output_file)) if count < len(wanted): print("Warning %i IDs not found in %s" % (len(wanted)-count, input_file)) \end{verbatim} Note that we use a Python \verb|set| rather than a \verb|list|, this makes testing membership faster. \subsection{Producing randomised genomes} Let's suppose you are looking at genome sequence, hunting for some sequence feature -- maybe extreme local GC\% bias, or possible restriction digest sites. Once you've got your Python code working on the real genome it may be sensible to try running the same search on randomised versions of the same genome for statistical analysis (after all, any ``features'' you've found could just be there just by chance). For this discussion, we'll use the GenBank file for the pPCP1 plasmid from \textit{Yersinia pestis biovar Microtus}. The file is included with the Biopython unit tests under the GenBank folder, or you can get it from our website, \href{http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.gb} {\texttt{NC\_005816.gb}}. This file contains one and only one record, so we can read it in as a \verb|SeqRecord| using the \verb|Bio.SeqIO.read()| function: %doctest ../Tests/GenBank \begin{verbatim} >>> from Bio import SeqIO >>> original_rec = SeqIO.read("NC_005816.gb", "genbank") \end{verbatim} So, how can we generate a shuffled versions of the original sequence? I would use the built in Python \verb|random| module for this, in particular the function \verb|random.shuffle| -- but this works on a Python list. Our sequence is a \verb|Seq| object, so in order to shuffle it we need to turn it into a list: %cont-doctest \begin{verbatim} >>> import random >>> nuc_list = list(original_rec.seq) >>> random.shuffle(nuc_list) #acts in situ! \end{verbatim} Now, in order to use \verb|Bio.SeqIO| to output the shuffled sequence, we need to construct a new \verb|SeqRecord| with a new \verb|Seq| object using this shuffled list. In order to do this, we need to turn the list of nucleotides (single letter strings) into a long string -- the standard Python way to do this is with the string object's join method. %cont-doctest \begin{verbatim} >>> from Bio.Seq import Seq >>> from Bio.SeqRecord import SeqRecord >>> shuffled_rec = SeqRecord(Seq("".join(nuc_list), original_rec.seq.alphabet), ... id="Shuffled", description="Based on %s" % original_rec.id) \end{verbatim} Let's put all these pieces together to make a complete Python script which generates a single FASTA file containing 30 randomly shuffled versions of the original sequence. This first version just uses a big for loop and writes out the records one by one (using the \verb|SeqRecord|'s format method described in Section~\ref{sec:Bio.SeqIO-and-StringIO}): \begin{verbatim} import random from Bio.Seq import Seq from Bio.SeqRecord import SeqRecord from Bio import SeqIO original_rec = SeqIO.read("NC_005816.gb","genbank") handle = open("shuffled.fasta", "w") for i in range(30): nuc_list = list(original_rec.seq) random.shuffle(nuc_list) shuffled_rec = SeqRecord(Seq("".join(nuc_list), original_rec.seq.alphabet), \ id="Shuffled%i" % (i+1), \ description="Based on %s" % original_rec.id) handle.write(shuffled_rec.format("fasta")) handle.close() \end{verbatim} Personally I prefer the following version using a function to shuffle the record and a generator expression instead of the for loop: \begin{verbatim} import random from Bio.Seq import Seq from Bio.SeqRecord import SeqRecord from Bio import SeqIO def make_shuffle_record(record, new_id): nuc_list = list(record.seq) random.shuffle(nuc_list) return SeqRecord(Seq("".join(nuc_list), record.seq.alphabet), \ id=new_id, description="Based on %s" % original_rec.id) original_rec = SeqIO.read("NC_005816.gb","genbank") shuffled_recs = (make_shuffle_record(original_rec, "Shuffled%i" % (i+1)) \ for i in range(30)) handle = open("shuffled.fasta", "w") SeqIO.write(shuffled_recs, handle, "fasta") handle.close() \end{verbatim} \subsection{Translating a FASTA file of CDS entries} \label{sec:SeqIO-translate} Suppose you've got an input file of CDS entries for some organism, and you want to generate a new FASTA file containing their protein sequences. i.e. Take each nucleotide sequence from the original file, and translate it. Back in Section~\ref{sec:translation} we saw how to use the \verb|Seq| object's \verb|translate method|, and the optional \verb|cds| argument which enables correct translation of alternative start codons. We can combine this with \verb|Bio.SeqIO| as shown in the reverse complement example in Section~\ref{sec:SeqIO-reverse-complement}. The key point is that for each nucleotide \verb|SeqRecord|, we need to create a protein \verb|SeqRecord| - and take care of naming it. You can write you own function to do this, choosing suitable protein identifiers for your sequences, and the appropriate genetic code. In this example we just use the default table and add a prefix to the identifier: \begin{verbatim} from Bio.SeqRecord import SeqRecord def make_protein_record(nuc_record): """Returns a new SeqRecord with the translated sequence (default table).""" return SeqRecord(seq = nuc_record.seq.translate(cds=True), \ id = "trans_" + nuc_record.id, \ description = "translation of CDS, using default table") \end{verbatim} We can then use this function to turn the input nucleotide records into protein records ready for output. An elegant way and memory efficient way to do this is with a generator expression: \begin{verbatim} from Bio import SeqIO proteins = (make_protein_record(nuc_rec) for nuc_rec in \ SeqIO.parse("coding_sequences.fasta", "fasta")) SeqIO.write(proteins, "translations.fasta", "fasta") \end{verbatim} This should work on any FASTA file of complete coding sequences. If you are working on partial coding sequences, you may prefer to use \verb|nuc_record.seq.translate(to_stop=True)| in the example above, as this wouldn't check for a valid start codon etc. \subsection{Making the sequences in a FASTA file upper case} Often you'll get data from collaborators as FASTA files, and sometimes the sequences can be in a mixture of upper and lower case. In some cases this is deliberate (e.g. lower case for poor quality regions), but usually it is not important. You may want to edit the file to make everything consistent (e.g. all upper case), and you can do this easily using the \verb|upper()| method of the \verb|SeqRecord| object (added in Biopython 1.55): \begin{verbatim} from Bio import SeqIO records = (rec.upper() for rec in SeqIO.parse("mixed.fas", "fasta")) count = SeqIO.write(records, "upper.fas", "fasta") print("Converted %i records to upper case" % count) \end{verbatim} How does this work? The first line is just importing the \verb|Bio.SeqIO| module. The second line is the interesting bit -- this is a Python generator expression which gives an upper case version of each record parsed from the input file (\texttt{mixed.fas}). In the third line we give this generator expression to the \verb|Bio.SeqIO.write()| function and it saves the new upper cases records to our output file (\texttt{upper.fas}). The reason we use a generator expression (rather than a list or list comprehension) is this means only one record is kept in memory at a time. This can be really important if you are dealing with large files with millions of entries. \subsection{Sorting a sequence file} \label{sec:SeqIO-sort} Suppose you wanted to sort a sequence file by length (e.g. a set of contigs from an assembly), and you are working with a file format like FASTA or FASTQ which \verb|Bio.SeqIO| can read, write (and index). If the file is small enough, you can load it all into memory at once as a list of \verb|SeqRecord| objects, sort the list, and save it: \begin{verbatim} from Bio import SeqIO records = list(SeqIO.parse("ls_orchid.fasta","fasta")) records.sort(cmp=lambda x,y: cmp(len(x),len(y))) SeqIO.write(records, "sorted_orchids.fasta", "fasta") \end{verbatim} The only clever bit is specifying a comparison function for how to sort the records (here we sort them by length). If you wanted the longest records first, you could flip the comparison or use the reverse argument: \begin{verbatim} from Bio import SeqIO records = list(SeqIO.parse("ls_orchid.fasta","fasta")) records.sort(cmp=lambda x,y: cmp(len(y),len(x))) SeqIO.write(records, "sorted_orchids.fasta", "fasta") \end{verbatim} Now that's pretty straight forward - but what happens if you have a very large file and you can't load it all into memory like this? For example, you might have some next-generation sequencing reads to sort by length. This can be solved using the \verb|Bio.SeqIO.index()| function. \begin{verbatim} from Bio import SeqIO #Get the lengths and ids, and sort on length len_and_ids = sorted((len(rec), rec.id) for rec in \ SeqIO.parse("ls_orchid.fasta","fasta")) ids = reversed([id for (length, id) in len_and_ids]) del len_and_ids #free this memory record_index = SeqIO.index("ls_orchid.fasta", "fasta") records = (record_index[id] for id in ids) SeqIO.write(records, "sorted.fasta", "fasta") \end{verbatim} First we scan through the file once using \verb|Bio.SeqIO.parse()|, recording the record identifiers and their lengths in a list of tuples. We then sort this list to get them in length order, and discard the lengths. Using this sorted list of identifiers \verb|Bio.SeqIO.index()| allows us to retrieve the records one by one, and we pass them to \verb|Bio.SeqIO.write()| for output. These examples all use \verb|Bio.SeqIO| to parse the records into \verb|SeqRecord| objects which are output using \verb|Bio.SeqIO.write()|. What if you want to sort a file format which \verb|Bio.SeqIO.write()| doesn't support, like the plain text SwissProt format? Here is an alternative solution using the \verb|get_raw()| method added to \verb|Bio.SeqIO.index()| in Biopython 1.54 (see Section~\ref{sec:seqio-index-getraw}). \begin{verbatim} from Bio import SeqIO #Get the lengths and ids, and sort on length len_and_ids = sorted((len(rec), rec.id) for rec in \ SeqIO.parse("ls_orchid.fasta","fasta")) ids = reversed([id for (length, id) in len_and_ids]) del len_and_ids #free this memory record_index = SeqIO.index("ls_orchid.fasta", "fasta") handle = open("sorted.fasta", "w") for id in ids: handle.write(record_index.get_raw(id)) handle.close() \end{verbatim} As a bonus, because it doesn't parse the data into \verb|SeqRecord| objects a second time it should be faster. \subsection{Simple quality filtering for FASTQ files} \label{sec:FASTQ-filtering-example} The FASTQ file format was introduced at Sanger and is now widely used for holding nucleotide sequencing reads together with their quality scores. FASTQ files (and the related QUAL files) are an excellent example of per-letter-annotation, because for each nucleotide in the sequence there is an associated quality score. Any per-letter-annotation is held in a \verb|SeqRecord| in the \verb|letter_annotations| dictionary as a list, tuple or string (with the same number of elements as the sequence length). One common task is taking a large set of sequencing reads and filtering them (or cropping them) based on their quality scores. The following example is very simplistic, but should illustrate the basics of working with quality data in a \verb|SeqRecord| object. All we are going to do here is read in a file of FASTQ data, and filter it to pick out only those records whose PHRED quality scores are all above some threshold (here 20). For this example we'll use some real data downloaded from the ENA sequence read archive, \url{ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz} (2MB) which unzips to a 19MB file \texttt{SRR020192.fastq}. This is some Roche 454 GS FLX single end data from virus infected California sea lions (see \url{http://www.ebi.ac.uk/ena/data/view/SRS004476} for details). First, let's count the reads: \begin{verbatim} from Bio import SeqIO count = 0 for rec in SeqIO.parse("SRR020192.fastq", "fastq"): count += 1 print("%i reads" % count) \end{verbatim} \noindent Now let's do a simple filtering for a minimum PHRED quality of 20: \begin{verbatim} from Bio import SeqIO good_reads = (rec for rec in \ SeqIO.parse("SRR020192.fastq", "fastq") \ if min(rec.letter_annotations["phred_quality"]) >= 20) count = SeqIO.write(good_reads, "good_quality.fastq", "fastq") print("Saved %i reads" % count) \end{verbatim} \noindent This pulled out only $14580$ reads out of the $41892$ present. A more sensible thing to do would be to quality trim the reads, but this is intended as an example only. FASTQ files can contain millions of entries, so it is best to avoid loading them all into memory at once. This example uses a generator expression, which means only one \verb|SeqRecord| is created at a time - avoiding any memory limitations. \subsection{Trimming off primer sequences} \label{sec:FASTQ-slicing-off-primer} For this example we're going to pretend that \texttt{GATGACGGTGT} is a 5' primer sequence we want to look for in some FASTQ formatted read data. As in the example above, we'll use the \texttt{SRR020192.fastq} file downloaded from the ENA (\url{ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz}). The same approach would work with any other supported file format (e.g. FASTA files). This code uses \verb|Bio.SeqIO| with a generator expression (to avoid loading all the sequences into memory at once), and the \verb|Seq| object's \verb|startswith| method to see if the read starts with the primer sequence: \begin{verbatim} from Bio import SeqIO primer_reads = (rec for rec in \ SeqIO.parse("SRR020192.fastq", "fastq") \ if rec.seq.startswith("GATGACGGTGT")) count = SeqIO.write(primer_reads, "with_primer.fastq", "fastq") print("Saved %i reads" % count) \end{verbatim} \noindent That should find $13819$ reads from \texttt{SRR014849.fastq} and save them to a new FASTQ file, \texttt{with\_primer.fastq}. Now suppose that instead you wanted to make a FASTQ file containing these reads but with the primer sequence removed? That's just a small change as we can slice the \verb|SeqRecord| (see Section~\ref{sec:SeqRecord-slicing}) to remove the first eleven letters (the length of our primer): \begin{verbatim} from Bio import SeqIO trimmed_primer_reads = (rec[11:] for rec in \ SeqIO.parse("SRR020192.fastq", "fastq") \ if rec.seq.startswith("GATGACGGTGT")) count = SeqIO.write(trimmed_primer_reads, "with_primer_trimmed.fastq", "fastq") print("Saved %i reads" % count) \end{verbatim} \noindent Again, that should pull out the $13819$ reads from \texttt{SRR020192.fastq}, but this time strip off the first ten characters, and save them to another new FASTQ file, \texttt{with\_primer\_trimmed.fastq}. Finally, suppose you want to create a new FASTQ file where these reads have their primer removed, but all the other reads are kept as they were? If we want to still use a generator expression, it is probably clearest to define our own trim function: \begin{verbatim} from Bio import SeqIO def trim_primer(record, primer): if record.seq.startswith(primer): return record[len(primer):] else: return record trimmed_reads = (trim_primer(record, "GATGACGGTGT") for record in \ SeqIO.parse("SRR020192.fastq", "fastq")) count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq") print("Saved %i reads" % count) \end{verbatim} This takes longer, as this time the output file contains all $41892$ reads. Again, we're used a generator expression to avoid any memory problems. You could alternatively use a generator function rather than a generator expression. \begin{verbatim} from Bio import SeqIO def trim_primers(records, primer): """Removes perfect primer sequences at start of reads. This is a generator function, the records argument should be a list or iterator returning SeqRecord objects. """ len_primer = len(primer) #cache this for later for record in records: if record.seq.startswith(primer): yield record[len_primer:] else: yield record original_reads = SeqIO.parse("SRR020192.fastq", "fastq") trimmed_reads = trim_primers(original_reads, "GATGACGGTGT") count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq") print("Saved %i reads" % count) \end{verbatim} This form is more flexible if you want to do something more complicated where only some of the records are retained -- as shown in the next example. \subsection{Trimming off adaptor sequences} \label{sec:FASTQ-slicing-off-adaptor} This is essentially a simple extension to the previous example. We are going to going to pretend \texttt{GATGACGGTGT} is an adaptor sequence in some FASTQ formatted read data, again the \texttt{SRR020192.fastq} file from the NCBI (\url{ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz}). This time however, we will look for the sequence \emph{anywhere} in the reads, not just at the very beginning: \begin{verbatim} from Bio import SeqIO def trim_adaptors(records, adaptor): """Trims perfect adaptor sequences. This is a generator function, the records argument should be a list or iterator returning SeqRecord objects. """ len_adaptor = len(adaptor) #cache this for later for record in records: index = record.seq.find(adaptor) if index == -1: #adaptor not found, so won't trim yield record else: #trim off the adaptor yield record[index+len_adaptor:] original_reads = SeqIO.parse("SRR020192.fastq", "fastq") trimmed_reads = trim_adaptors(original_reads, "GATGACGGTGT") count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq") print("Saved %i reads" % count) \end{verbatim} Because we are using a FASTQ input file in this example, the \verb|SeqRecord| objects have per-letter-annotation for the quality scores. By slicing the \verb|SeqRecord| object the appropriate scores are used on the trimmed records, so we can output them as a FASTQ file too. Compared to the output of the previous example where we only looked for a primer/adaptor at the start of each read, you may find some of the trimmed reads are quite short after trimming (e.g. if the adaptor was found in the middle rather than near the start). So, let's add a minimum length requirement as well: \begin{verbatim} from Bio import SeqIO def trim_adaptors(records, adaptor, min_len): """Trims perfect adaptor sequences, checks read length. This is a generator function, the records argument should be a list or iterator returning SeqRecord objects. """ len_adaptor = len(adaptor) #cache this for later for record in records: len_record = len(record) #cache this for later if len(record) < min_len: #Too short to keep continue index = record.seq.find(adaptor) if index == -1: #adaptor not found, so won't trim yield record elif len_record - index - len_adaptor >= min_len: #after trimming this will still be long enough yield record[index+len_adaptor:] original_reads = SeqIO.parse("SRR020192.fastq", "fastq") trimmed_reads = trim_adaptors(original_reads, "GATGACGGTGT", 100) count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq") print("Saved %i reads" % count) \end{verbatim} By changing the format names, you could apply this to FASTA files instead. This code also could be extended to do a fuzzy match instead of an exact match (maybe using a pairwise alignment, or taking into account the read quality scores), but that will be much slower. \subsection{Converting FASTQ files} \label{sec:SeqIO-fastq-conversion} Back in Section~\ref{sec:SeqIO-conversion} we showed how to use \verb|Bio.SeqIO| to convert between two file formats. Here we'll go into a little more detail regarding FASTQ files which are used in second generation DNA sequencing. Please refer to Cock \textit{et al.} (2009) \cite{cock2010} for a longer description. FASTQ files store both the DNA sequence (as a string) and the associated read qualities. PHRED scores (used in most FASTQ files, and also in QUAL files, ACE files and SFF files) have become a \textit{de facto} standard for representing the probability of a sequencing error (here denoted by $P_e$) at a given base using a simple base ten log transformation: \begin{equation} Q_{\textrm{PHRED}} = - 10 \times \textrm{log}_{10} ( P_e ) \end{equation} This means a wrong read ($P_e = 1$) gets a PHRED quality of $0$, while a very good read like $P_e = 0.00001$ gets a PHRED quality of $50$. While for raw sequencing data qualities higher than this are rare, with post processing such as read mapping or assembly, qualities of up to about $90$ are possible (indeed, the MAQ tool allows for PHRED scores in the range 0 to 93 inclusive). The FASTQ format has the potential to become a \textit{de facto} standard for storing the letters and quality scores for a sequencing read in a single plain text file. The only fly in the ointment is that there are at least three versions of the FASTQ format which are incompatible and difficult to distinguish... \begin{enumerate} \item The original Sanger FASTQ format uses PHRED qualities encoded with an ASCII offset of 33. The NCBI are using this format in their Short Read Archive. We call this the \texttt{fastq} (or \texttt{fastq-sanger}) format in \verb|Bio.SeqIO|. \item Solexa (later bought by Illumina) introduced their own version using Solexa qualities encoded with an ASCII offset of 64. We call this the \texttt{fastq-solexa} format. \item Illumina pipeline 1.3 onwards produces FASTQ files with PHRED qualities (which is more consistent), but encoded with an ASCII offset of 64. We call this the \texttt{fastq-illumina} format. \end{enumerate} The Solexa quality scores are defined using a different log transformation: \begin{equation} Q_{\textrm{Solexa}} = - 10 \times \textrm{log}_{10} \left( \frac{P_e}{1-P_e} \right) \end{equation} Given Solexa/Illumina have now moved to using PHRED scores in version 1.3 of their pipeline, the Solexa quality scores will gradually fall out of use. If you equate the error estimates ($P_e$) these two equations allow conversion between the two scoring systems - and Biopython includes functions to do this in the \verb|Bio.SeqIO.QualityIO| module, which are called if you use \verb|Bio.SeqIO| to convert an old Solexa/Illumina file into a standard Sanger FASTQ file: \begin{verbatim} from Bio import SeqIO SeqIO.convert("solexa.fastq", "fastq-solexa", "standard.fastq", "fastq") \end{verbatim} If you want to convert a new Illumina 1.3+ FASTQ file, all that gets changed is the ASCII offset because although encoded differently the scores are all PHRED qualities: \begin{verbatim} from Bio import SeqIO SeqIO.convert("illumina.fastq", "fastq-illumina", "standard.fastq", "fastq") \end{verbatim} Note that using \verb|Bio.SeqIO.convert()| like this is \emph{much} faster than combining \verb|Bio.SeqIO.parse()| and \verb|Bio.SeqIO.write()| because optimised code is used for converting between FASTQ variants (and also for FASTQ to FASTA conversion). For good quality reads, PHRED and Solexa scores are approximately equal, which means since both the \texttt{fasta-solexa} and \texttt{fastq-illumina} formats use an ASCII offset of 64 the files are almost the same. This was a deliberate design choice by Illumina, meaning applications expecting the old \texttt{fasta-solexa} style files will probably be OK using the newer \texttt{fastq-illumina} files (on good data). Of course, both variants are very different from the original FASTQ standard as used by Sanger, the NCBI, and elsewhere (format name \texttt{fastq} or \texttt{fastq-sanger}). For more details, see the built in help (also \href{http://www.biopython.org/DIST/docs/api/Bio.SeqIO.QualityIO-module.html}{online}): \begin{verbatim} >>> from Bio.SeqIO import QualityIO >>> help(QualityIO) ... \end{verbatim} \subsection{Converting FASTA and QUAL files into FASTQ files} \label{sec:SeqIO-fasta-qual-conversion} FASTQ files hold \emph{both} sequences and their quality strings. FASTA files hold \emph{just} sequences, while QUAL files hold \emph{just} the qualities. Therefore a single FASTQ file can be converted to or from \emph{paired} FASTA and QUAL files. Going from FASTQ to FASTA is easy: \begin{verbatim} from Bio import SeqIO SeqIO.convert("example.fastq", "fastq", "example.fasta", "fasta") \end{verbatim} Going from FASTQ to QUAL is also easy: \begin{verbatim} from Bio import SeqIO SeqIO.convert("example.fastq", "fastq", "example.qual", "qual") \end{verbatim} However, the reverse is a little more tricky. You can use \verb|Bio.SeqIO.parse()| to iterate over the records in a \emph{single} file, but in this case we have two input files. There are several strategies possible, but assuming that the two files are really paired the most memory efficient way is to loop over both together. The code is a little fiddly, so we provide a function called \verb|PairedFastaQualIterator| in the \verb|Bio.SeqIO.QualityIO| module to do this. This takes two handles (the FASTA file and the QUAL file) and returns a \verb|SeqRecord| iterator: \begin{verbatim} from Bio.SeqIO.QualityIO import PairedFastaQualIterator for record in PairedFastaQualIterator(open("example.fasta"), open("example.qual")): print(record) \end{verbatim} This function will check that the FASTA and QUAL files are consistent (e.g. the records are in the same order, and have the same sequence length). You can combine this with the \verb|Bio.SeqIO.write()| function to convert a pair of FASTA and QUAL files into a single FASTQ files: \begin{verbatim} from Bio import SeqIO from Bio.SeqIO.QualityIO import PairedFastaQualIterator handle = open("temp.fastq", "w") #w=write records = PairedFastaQualIterator(open("example.fasta"), open("example.qual")) count = SeqIO.write(records, handle, "fastq") handle.close() print("Converted %i records" % count) \end{verbatim} \subsection{Indexing a FASTQ file} \label{sec:fastq-indexing} FASTQ files are often very large, with millions of reads in them. Due to the sheer amount of data, you can't load all the records into memory at once. This is why the examples above (filtering and trimming) iterate over the file looking at just one \verb|SeqRecord| at a time. However, sometimes you can't use a big loop or an iterator - you may need random access to the reads. Here the \verb|Bio.SeqIO.index()| function may prove very helpful, as it allows you to access any read in the FASTQ file by its name (see Section~\ref{sec:SeqIO-index}). Again we'll use the \texttt{SRR020192.fastq} file from the ENA (\url{ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz}), although this is actually quite a small FASTQ file with less than $50,000$ reads: \begin{verbatim} >>> from Bio import SeqIO >>> fq_dict = SeqIO.index("SRR020192.fastq", "fastq") >>> len(fq_dict) 41892 >>> fq_dict.keys()[:4] ['SRR020192.38240', 'SRR020192.23181', 'SRR020192.40568', 'SRR020192.23186'] >>> fq_dict["SRR020192.23186"].seq Seq('GTCCCAGTATTCGGATTTGTCTGCCAAAACAATGAAATTGACACAGTTTACAAC...CCG', SingleLetterAlphabet()) \end{verbatim} When testing this on a FASTQ file with seven million reads, indexing took about a minute, but record access was almost instant. The example in Section~\ref{sec:SeqIO-sort} show how you can use the \verb|Bio.SeqIO.index()| function to sort a large FASTA file -- this could also be used on FASTQ files. \subsection{Converting SFF files} \label{sec:SeqIO-sff-conversion} If you work with 454 (Roche) sequence data, you will probably have access to the raw data as a Standard Flowgram Format (SFF) file. This contains the sequence reads (called bases) with quality scores and the original flow information. A common task is to convert from SFF to a pair of FASTA and QUAL files, or to a single FASTQ file. These operations are trivial using the \verb|Bio.SeqIO.convert()| function (see Section~\ref{sec:SeqIO-conversion}): \begin{verbatim} >>> from Bio import SeqIO >>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.fasta", "fasta") 10 >>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.qual", "qual") 10 >>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.fastq", "fastq") 10 \end{verbatim} \noindent Remember the convert function returns the number of records, in this example just ten. This will give you the \emph{untrimmed} reads, where the leading and trailing poor quality sequence or adaptor will be in lower case. If you want the \emph{trimmed} reads (using the clipping information recorded within the SFF file) use this: \begin{verbatim} >>> from Bio import SeqIO >>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.fasta", "fasta") 10 >>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.qual", "qual") 10 >>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.fastq", "fastq") 10 \end{verbatim} If you run Linux, you could ask Roche for a copy of their ``off instrument'' tools (often referred to as the Newbler tools). This offers an alternative way to do SFF to FASTA or QUAL conversion at the command line (but currently FASTQ output is not supported), e.g. \begin{verbatim} $ sffinfo -seq -notrim E3MFGYR02_random_10_reads.sff > reads.fasta $ sffinfo -qual -notrim E3MFGYR02_random_10_reads.sff > reads.qual $ sffinfo -seq -trim E3MFGYR02_random_10_reads.sff > trimmed.fasta $ sffinfo -qual -trim E3MFGYR02_random_10_reads.sff > trimmed.qual \end{verbatim} \noindent The way Biopython uses mixed case sequence strings to represent the trimming points deliberately mimics what the Roche tools do. For more information on the Biopython SFF support, consult the built in help: \begin{verbatim} >>> from Bio.SeqIO import SffIO >>> help(SffIO) ... \end{verbatim} \subsection{Identifying open reading frames} A very simplistic first step at identifying possible genes is to look for open reading frames (ORFs). By this we mean look in all six frames for long regions without stop codons -- an ORF is just a region of nucleotides with no in frame stop codons. Of course, to find a gene you would also need to worry about locating a start codon, possible promoters -- and in Eukaryotes there are introns to worry about too. However, this approach is still useful in viruses and Prokaryotes. To show how you might approach this with Biopython, we'll need a sequence to search, and as an example we'll again use the bacterial plasmid -- although this time we'll start with a plain FASTA file with no pre-marked genes: \href{http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.fna} {\texttt{NC\_005816.fna}}. This is a bacterial sequence, so we'll want to use NCBI codon table 11 (see Section~\ref{sec:translation} about translation). %doctest ../Tests/GenBank \begin{verbatim} >>> from Bio import SeqIO >>> record = SeqIO.read("NC_005816.fna", "fasta") >>> table = 11 >>> min_pro_len = 100 \end{verbatim} Here is a neat trick using the \verb|Seq| object's \verb|split| method to get a list of all the possible ORF translations in the six reading frames: %cont-doctest \begin{verbatim} >>> for strand, nuc in [(+1, record.seq), (-1, record.seq.reverse_complement())]: ... for frame in range(3): ... length = 3 * ((len(record)-frame) // 3) #Multiple of three ... for pro in nuc[frame:frame+length].translate(table).split("*"): ... if len(pro) >= min_pro_len: ... print("%s...%s - length %i, strand %i, frame %i" \ ... % (pro[:30], pro[-3:], len(pro), strand, frame)) GCLMKKSSIVATIITILSGSANAASSQLIP...YRF - length 315, strand 1, frame 0 KSGELRQTPPASSTLHLRLILQRSGVMMEL...NPE - length 285, strand 1, frame 1 GLNCSFFSICNWKFIDYINRLFQIIYLCKN...YYH - length 176, strand 1, frame 1 VKKILYIKALFLCTVIKLRRFIFSVNNMKF...DLP - length 165, strand 1, frame 1 NQIQGVICSPDSGEFMVTFETVMEIKILHK...GVA - length 355, strand 1, frame 2 RRKEHVSKKRRPQKRPRRRRFFHRLRPPDE...PTR - length 128, strand 1, frame 2 TGKQNSCQMSAIWQLRQNTATKTRQNRARI...AIK - length 100, strand 1, frame 2 QGSGYAFPHASILSGIAMSHFYFLVLHAVK...CSD - length 114, strand -1, frame 0 IYSTSEHTGEQVMRTLDEVIASRSPESQTR...FHV - length 111, strand -1, frame 0 WGKLQVIGLSMWMVLFSQRFDDWLNEQEDA...ESK - length 125, strand -1, frame 1 RGIFMSDTMVVNGSGGVPAFLFSGSTLSSY...LLK - length 361, strand -1, frame 1 WDVKTVTGVLHHPFHLTFSLCPEGATQSGR...VKR - length 111, strand -1, frame 1 LSHTVTDFTDQMAQVGLCQCVNVFLDEVTG...KAA - length 107, strand -1, frame 2 RALTGLSAPGIRSQTSCDRLRELRYVPVSL...PLQ - length 119, strand -1, frame 2 \end{verbatim} Note that here we are counting the frames from the 5' end (start) of \emph{each} strand. It is sometimes easier to always count from the 5' end (start) of the \emph{forward} strand. You could easily edit the above loop based code to build up a list of the candidate proteins, or convert this to a list comprehension. Now, one thing this code doesn't do is keep track of where the proteins are. You could tackle this in several ways. For example, the following code tracks the locations in terms of the protein counting, and converts back to the parent sequence by multiplying by three, then adjusting for the frame and strand: \begin{verbatim} from Bio import SeqIO record = SeqIO.read("NC_005816.gb","genbank") table = 11 min_pro_len = 100 def find_orfs_with_trans(seq, trans_table, min_protein_length): answer = [] seq_len = len(seq) for strand, nuc in [(+1, seq), (-1, seq.reverse_complement())]: for frame in range(3): trans = str(nuc[frame:].translate(trans_table)) trans_len = len(trans) aa_start = 0 aa_end = 0 while aa_start < trans_len: aa_end = trans.find("*", aa_start) if aa_end == -1: aa_end = trans_len if aa_end-aa_start >= min_protein_length: if strand == 1: start = frame+aa_start*3 end = min(seq_len,frame+aa_end*3+3) else: start = seq_len-frame-aa_end*3-3 end = seq_len-frame-aa_start*3 answer.append((start, end, strand, trans[aa_start:aa_end])) aa_start = aa_end+1 answer.sort() return answer orf_list = find_orfs_with_trans(record.seq, table, min_pro_len) for start, end, strand, pro in orf_list: print("%s...%s - length %i, strand %i, %i:%i" \ % (pro[:30], pro[-3:], len(pro), strand, start, end)) \end{verbatim} \noindent And the output: \begin{verbatim} NQIQGVICSPDSGEFMVTFETVMEIKILHK...GVA - length 355, strand 1, 41:1109 WDVKTVTGVLHHPFHLTFSLCPEGATQSGR...VKR - length 111, strand -1, 491:827 KSGELRQTPPASSTLHLRLILQRSGVMMEL...NPE - length 285, strand 1, 1030:1888 RALTGLSAPGIRSQTSCDRLRELRYVPVSL...PLQ - length 119, strand -1, 2830:3190 RRKEHVSKKRRPQKRPRRRRFFHRLRPPDE...PTR - length 128, strand 1, 3470:3857 GLNCSFFSICNWKFIDYINRLFQIIYLCKN...YYH - length 176, strand 1, 4249:4780 RGIFMSDTMVVNGSGGVPAFLFSGSTLSSY...LLK - length 361, strand -1, 4814:5900 VKKILYIKALFLCTVIKLRRFIFSVNNMKF...DLP - length 165, strand 1, 5923:6421 LSHTVTDFTDQMAQVGLCQCVNVFLDEVTG...KAA - length 107, strand -1, 5974:6298 GCLMKKSSIVATIITILSGSANAASSQLIP...YRF - length 315, strand 1, 6654:7602 IYSTSEHTGEQVMRTLDEVIASRSPESQTR...FHV - length 111, strand -1, 7788:8124 WGKLQVIGLSMWMVLFSQRFDDWLNEQEDA...ESK - length 125, strand -1, 8087:8465 TGKQNSCQMSAIWQLRQNTATKTRQNRARI...AIK - length 100, strand 1, 8741:9044 QGSGYAFPHASILSGIAMSHFYFLVLHAVK...CSD - length 114, strand -1, 9264:9609 \end{verbatim} If you comment out the sort statement, then the protein sequences will be shown in the same order as before, so you can check this is doing the same thing. Here we have sorted them by location to make it easier to compare to the actual annotation in the GenBank file (as visualised in Section~\ref{sec:gd_nice_example}). If however all you want to find are the locations of the open reading frames, then it is a waste of time to translate every possible codon, including doing the reverse complement to search the reverse strand too. All you need to do is search for the possible stop codons (and their reverse complements). Using regular expressions is an obvious approach here (see the Python module \verb|re|). These are an extremely powerful (but rather complex) way of describing search strings, which are supported in lots of programming languages and also command line tools like \texttt{grep} as well). You can find whole books about this topic! \section{Sequence parsing plus simple plots} \label{seq:sequence-parsing-plus-pylab} This section shows some more examples of sequence parsing, using the \verb|Bio.SeqIO| module described in Chapter~\ref{chapter:Bio.SeqIO}, plus the Python library matplotlib's \verb|pylab| plotting interface (see \href{http://matplotlib.sourceforge.net/}{the matplotlib website for a tutorial}). Note that to follow these examples you will need matplotlib installed - but without it you can still try the data parsing bits. \subsection{Histogram of sequence lengths} There are lots of times when you might want to visualise the distribution of sequence lengths in a dataset -- for example the range of contig sizes in a genome assembly project. In this example we'll reuse our orchid FASTA file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta} which has only 94 sequences. First of all, we will use \verb|Bio.SeqIO| to parse the FASTA file and compile a list of all the sequence lengths. You could do this with a for loop, but I find a list comprehension more pleasing: \begin{verbatim} >>> from Bio import SeqIO >>> sizes = [len(rec) for rec in SeqIO.parse("ls_orchid.fasta", "fasta")] >>> len(sizes), min(sizes), max(sizes) (94, 572, 789) >>> sizes [740, 753, 748, 744, 733, 718, 730, 704, 740, 709, 700, 726, ..., 592] \end{verbatim} Now that we have the lengths of all the genes (as a list of integers), we can use the matplotlib histogram function to display it. \begin{verbatim} from Bio import SeqIO sizes = [len(rec) for rec in SeqIO.parse("ls_orchid.fasta", "fasta")] import pylab pylab.hist(sizes, bins=20) pylab.title("%i orchid sequences\nLengths %i to %i" \ % (len(sizes),min(sizes),max(sizes))) pylab.xlabel("Sequence length (bp)") pylab.ylabel("Count") pylab.show() \end{verbatim} % % Have a HTML version and a PDF version to display nicely... % \begin{htmlonly} \noindent That should pop up a new window containing the following graph: \imgsrc[width=600, height=450]{images/hist_plot.png} \end{htmlonly} % % Now the PDF equivalent where we cannot always expect the figure % to be positioned right next to the text, so will use a reference. % \begin{latexonly} \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/hist_plot.png} \caption{Histogram of orchid sequence lengths.} \label{fig:seq-len-hist} \end{figure} \noindent That should pop up a new window containing the graph shown in Figure~\ref{fig:seq-len-hist}. \end{latexonly} % % The text now continues... % Notice that most of these orchid sequences are about $740$ bp long, and there could be two distinct classes of sequence here with a subset of shorter sequences. \emph{Tip:} Rather than using \verb|pylab.show()| to show the plot in a window, you can also use \verb|pylab.savefig(...)| to save the figure to a file (e.g. as a PNG or PDF). \subsection{Plot of sequence GC\%} Another easily calculated quantity of a nucleotide sequence is the GC\%. You might want to look at the GC\% of all the genes in a bacterial genome for example, and investigate any outliers which could have been recently acquired by horizontal gene transfer. Again, for this example we'll reuse our orchid FASTA file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta}. First of all, we will use \verb|Bio.SeqIO| to parse the FASTA file and compile a list of all the GC percentages. Again, you could do this with a for loop, but I prefer this: \begin{verbatim} from Bio import SeqIO from Bio.SeqUtils import GC gc_values = sorted(GC(rec.seq) for rec in SeqIO.parse("ls_orchid.fasta", "fasta")) \end{verbatim} Having read in each sequence and calculated the GC\%, we then sorted them into ascending order. Now we'll take this list of floating point values and plot them with matplotlib: \begin{verbatim} import pylab pylab.plot(gc_values) pylab.title("%i orchid sequences\nGC%% %0.1f to %0.1f" \ % (len(gc_values),min(gc_values),max(gc_values))) pylab.xlabel("Genes") pylab.ylabel("GC%") pylab.show() \end{verbatim} % % Have an HTML version and a PDF version to display nicely... % \begin{htmlonly} \noindent As in the previous example, that should pop up a new window containing a graph: \imgsrc[width=600, height=450]{images/gc_plot.png} \end{htmlonly} % % Now the PDF equivalent where we cannot always expect the figure % to be positioned right next to the text, so we'll use a reference. % \begin{latexonly} \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/gc_plot.png} \caption{Histogram of orchid sequence lengths.} \label{fig:seq-gc-plot} \end{figure} \noindent As in the previous example, that should pop up a new window with the graph shown in Figure~\ref{fig:seq-gc-plot}. \end{latexonly} % % The text now continues... % If you tried this on the full set of genes from one organism, you'd probably get a much smoother plot than this. \subsection{Nucleotide dot plots} A dot plot is a way of visually comparing two nucleotide sequences for similarity to each other. A sliding window is used to compare short sub-sequences to each other, often with a mis-match threshold. Here for simplicity we'll only look for perfect matches (shown in black \begin{latexonly} in Figure~\ref{fig:nuc-dot-plot}). \end{latexonly} \begin{htmlonly} in the plot below). \end{htmlonly} % % Now the PDF equivalent where we cannot always expect the figure % to be positioned right next to the text, so we'll use a reference. % \begin{latexonly} \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/dot_plot.png} \caption{Nucleotide dot plot of two orchid sequence lengths (using pylab's imshow function).} \label{fig:nuc-dot-plot} \end{figure} \end{latexonly} To start off, we'll need two sequences. For the sake of argument, we'll just take the first two from our orchid FASTA file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta}: \begin{verbatim} from Bio import SeqIO handle = open("ls_orchid.fasta") record_iterator = SeqIO.parse(handle, "fasta") rec_one = next(record_iterator) rec_two = next(record_iterator) handle.close() \end{verbatim} We're going to show two approaches. Firstly, a simple naive implementation which compares all the window sized sub-sequences to each other to compiles a similarity matrix. You could construct a matrix or array object, but here we just use a list of lists of booleans created with a nested list comprehension: \begin{verbatim} window = 7 seq_one = str(rec_one.seq).upper() seq_two = str(rec_two.seq).upper() data = [[(seq_one[i:i+window] <> seq_two[j:j+window]) \ for j in range(len(seq_one)-window)] \ for i in range(len(seq_two)-window)] \end{verbatim} Note that we have \emph{not} checked for reverse complement matches here. Now we'll use the matplotlib's \verb|pylab.imshow()| function to display this data, first requesting the gray color scheme so this is done in black and white: \begin{verbatim} import pylab pylab.gray() pylab.imshow(data) pylab.xlabel("%s (length %i bp)" % (rec_one.id, len(rec_one))) pylab.ylabel("%s (length %i bp)" % (rec_two.id, len(rec_two))) pylab.title("Dot plot using window size %i\n(allowing no mis-matches)" % window) pylab.show() \end{verbatim} %pylab.savefig("dot_plot.png", dpi=75) %pylab.savefig("dot_plot.pdf") % % Have a HTML version and a PDF version to display nicely... % \begin{htmlonly} \noindent That should pop up a new window containing a graph like this: \imgsrc[width=600, height=450]{images/dot_plot.png} \end{htmlonly} \begin{latexonly} \noindent That should pop up a new window showing the graph in Figure~\ref{fig:nuc-dot-plot}. \end{latexonly} % % The text now continues... % As you might have expected, these two sequences are very similar with a partial line of window sized matches along the diagonal. There are no off diagonal matches which would be indicative of inversions or other interesting events. The above code works fine on small examples, but there are two problems applying this to larger sequences, which we will address below. First off all, this brute force approach to the all against all comparisons is very slow. Instead, we'll compile dictionaries mapping the window sized sub-sequences to their locations, and then take the set intersection to find those sub-sequences found in both sequences. This uses more memory, but is \emph{much} faster. Secondly, the \verb|pylab.imshow()| function is limited in the size of matrix it can display. As an alternative, we'll use the \verb|pylab.scatter()| function. We start by creating dictionaries mapping the window-sized sub-sequences to locations: \begin{verbatim} window = 7 dict_one = {} dict_two = {} for (seq, section_dict) in [(str(rec_one.seq).upper(), dict_one), (str(rec_two.seq).upper(), dict_two)]: for i in range(len(seq)-window): section = seq[i:i+window] try: section_dict[section].append(i) except KeyError: section_dict[section] = [i] #Now find any sub-sequences found in both sequences #(Python 2.3 would require slightly different code here) matches = set(dict_one).intersection(dict_two) print("%i unique matches" % len(matches)) \end{verbatim} \noindent In order to use the \verb|pylab.scatter()| we need separate lists for the $x$ and $y$ co-ordinates: \begin{verbatim} #Create lists of x and y co-ordinates for scatter plot x = [] y = [] for section in matches: for i in dict_one[section]: for j in dict_two[section]: x.append(i) y.append(j) \end{verbatim} \noindent We are now ready to draw the revised dot plot as a scatter plot: \begin{verbatim} import pylab pylab.cla() #clear any prior graph pylab.gray() pylab.scatter(x,y) pylab.xlim(0, len(rec_one)-window) pylab.ylim(0, len(rec_two)-window) pylab.xlabel("%s (length %i bp)" % (rec_one.id, len(rec_one))) pylab.ylabel("%s (length %i bp)" % (rec_two.id, len(rec_two))) pylab.title("Dot plot using window size %i\n(allowing no mis-matches)" % window) pylab.show() \end{verbatim} %pylab.savefig("dot_plot.png", dpi=75) %pylab.savefig("dot_plot.pdf") % % Have a HTML version and a PDF version to display nicely... % \begin{htmlonly} \noindent That should pop up a new window containing a graph like this: \imgsrc[width=600, height=450]{images/dot_plot_scatter.png} \end{htmlonly} \begin{latexonly} \noindent That should pop up a new window showing the graph in Figure~\ref{fig:nuc-dot-plot-scatter}. \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/dot_plot_scatter.png} \caption{Nucleotide dot plot of two orchid sequence lengths (using pylab's scatter function).} \label{fig:nuc-dot-plot-scatter} \end{figure}\end{latexonly} Personally I find this second plot much easier to read! Again note that we have \emph{not} checked for reverse complement matches here -- you could extend this example to do this, and perhaps plot the forward matches in one color and the reverse matches in another. \subsection{Plotting the quality scores of sequencing read data} If you are working with second generation sequencing data, you may want to try plotting the quality data. Here is an example using two FASTQ files containing paired end reads, \texttt{SRR001666\_1.fastq} for the forward reads, and \texttt{SRR001666\_2.fastq} for the reverse reads. These were downloaded from the ENA sequence read archive FTP site (\url{ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_1.fastq.gz} and \url{ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_2.fastq.gz}), and are from \textit{E. coli} -- see \url{http://www.ebi.ac.uk/ena/data/view/SRR001666} for details. %Originally from ftp://ftp.ncbi.nlm.nih.gov/sra/static/SRX000/SRX000430/ In the following code the \verb|pylab.subplot(...)| function is used in order to show the forward and reverse qualities on two subplots, side by side. There is also a little bit of code to only plot the first fifty reads. \begin{verbatim} import pylab from Bio import SeqIO for subfigure in [1,2]: filename = "SRR001666_%i.fastq" % subfigure pylab.subplot(1, 2, subfigure) for i,record in enumerate(SeqIO.parse(filename, "fastq")): if i >= 50 : break #trick! pylab.plot(record.letter_annotations["phred_quality"]) pylab.ylim(0,45) pylab.ylabel("PHRED quality score") pylab.xlabel("Position") pylab.savefig("SRR001666.png") print("Done") \end{verbatim} You should note that we are using the \verb|Bio.SeqIO| format name \texttt{fastq} here because the NCBI has saved these reads using the standard Sanger FASTQ format with PHRED scores. However, as you might guess from the read lengths, this data was from an Illumina Genome Analyzer and was probably originally in one of the two Solexa/Illumina FASTQ variant file formats instead. This example uses the \verb|pylab.savefig(...)| function instead of \verb|pylab.show(...)|, but as mentioned before both are useful. \begin{latexonly} \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{images/SRR001666.png} \caption{Quality plot for some paired end reads.} \label{fig:paired-end-qual-plot} \end{figure} The result is shown in Figure~\ref{fig:paired-end-qual-plot}. \end{latexonly} \begin{htmlonly} Here is the result: %Blank lines here are important! \imgsrc[width=600, height=600]{images/SRR001666.png} \end{htmlonly} \section{Dealing with alignments} This section can been seen as a follow on to Chapter~\ref{chapter:Bio.AlignIO}. \subsection{Calculating summary information} \label{sec:summary_info} Once you have an alignment, you are very likely going to want to find out information about it. Instead of trying to have all of the functions that can generate information about an alignment in the alignment object itself, we've tried to separate out the functionality into separate classes, which act on the alignment. Getting ready to calculate summary information about an object is quick to do. Let's say we've got an alignment object called \verb|alignment|, for example read in using \verb|Bio.AlignIO.read(...)| as described in Chapter~\ref{chapter:Bio.AlignIO}. All we need to do to get an object that will calculate summary information is: \begin{verbatim} from Bio.Align import AlignInfo summary_align = AlignInfo.SummaryInfo(alignment) \end{verbatim} The \verb|summary_align| object is very useful, and will do the following neat things for you: \begin{enumerate} \item Calculate a quick consensus sequence -- see section~\ref{sec:consensus} \item Get a position specific score matrix for the alignment -- see section~\ref{sec:pssm} \item Calculate the information content for the alignment -- see section~\ref{sec:getting_info_content} \item Generate information on substitutions in the alignment -- section~\ref{sec:sub_matrix} details using this to generate a substitution matrix. \end{enumerate} \subsection{Calculating a quick consensus sequence} \label{sec:consensus} The \verb|SummaryInfo| object, described in section~\ref{sec:summary_info}, provides functionality to calculate a quick consensus of an alignment. Assuming we've got a \verb|SummaryInfo| object called \verb|summary_align| we can calculate a consensus by doing: \begin{verbatim} consensus = summary_align.dumb_consensus() \end{verbatim} As the name suggests, this is a really simple consensus calculator, and will just add up all of the residues at each point in the consensus, and if the most common value is higher than some threshold value will add the common residue to the consensus. If it doesn't reach the threshold, it adds an ambiguity character to the consensus. The returned consensus object is Seq object whose alphabet is inferred from the alphabets of the sequences making up the consensus. So doing a \verb|print consensus| would give: \begin{verbatim} consensus Seq('TATACATNAAAGNAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAAT ...', IUPACAmbiguousDNA()) \end{verbatim} You can adjust how \verb|dumb_consensus| works by passing optional parameters: \begin{description} \item[the threshold] This is the threshold specifying how common a particular residue has to be at a position before it is added. The default is $0.7$ (meaning $70\%$). \item[the ambiguous character] This is the ambiguity character to use. The default is 'N'. \item[the consensus alphabet] This is the alphabet to use for the consensus sequence. If an alphabet is not specified than we will try to guess the alphabet based on the alphabets of the sequences in the alignment. \end{description} \subsection{Position Specific Score Matrices} \label{sec:pssm} Position specific score matrices (PSSMs) summarize the alignment information in a different way than a consensus, and may be useful for different tasks. Basically, a PSSM is a count matrix. For each column in the alignment, the number of each alphabet letters is counted and totaled. The totals are displayed relative to some representative sequence along the left axis. This sequence may be the consesus sequence, but can also be any sequence in the alignment. For instance for the alignment, \begin{verbatim} GTATC AT--C CTGTC \end{verbatim} \noindent the PSSM is: \begin{verbatim} G A T C G 1 1 0 1 T 0 0 3 0 A 1 1 0 0 T 0 0 2 0 C 0 0 0 3 \end{verbatim} Let's assume we've got an alignment object called \verb|c_align|. To get a PSSM with the consensus sequence along the side we first get a summary object and calculate the consensus sequence: \begin{verbatim} summary_align = AlignInfo.SummaryInfo(c_align) consensus = summary_align.dumb_consensus() \end{verbatim} Now, we want to make the PSSM, but ignore any \verb|N| ambiguity residues when calculating this: \begin{verbatim} my_pssm = summary_align.pos_specific_score_matrix(consensus, chars_to_ignore = ['N']) \end{verbatim} Two notes should be made about this: \begin{enumerate} \item To maintain strictness with the alphabets, you can only include characters along the top of the PSSM that are in the alphabet of the alignment object. Gaps are not included along the top axis of the PSSM. \item The sequence passed to be displayed along the left side of the axis does not need to be the consensus. For instance, if you wanted to display the second sequence in the alignment along this axis, you would need to do: \begin{verbatim} second_seq = alignment.get_seq_by_num(1) my_pssm = summary_align.pos_specific_score_matrix(second_seq chars_to_ignore = ['N']) \end{verbatim} \end{enumerate} The command above returns a \verb|PSSM| object. To print out the PSSM as shown above, we simply need to do a \verb|print(my_pssm)|, which gives: \begin{verbatim} A C G T T 0.0 0.0 0.0 7.0 A 7.0 0.0 0.0 0.0 T 0.0 0.0 0.0 7.0 A 7.0 0.0 0.0 0.0 C 0.0 7.0 0.0 0.0 A 7.0 0.0 0.0 0.0 T 0.0 0.0 0.0 7.0 T 1.0 0.0 0.0 6.0 ... \end{verbatim} You can access any element of the PSSM by subscripting like \verb|your_pssm[sequence_number][residue_count_name]|. For instance, to get the counts for the 'A' residue in the second element of the above PSSM you would do: \begin{verbatim} >>> print(my_pssm[1]["A"]) 7.0 \end{verbatim} The structure of the PSSM class hopefully makes it easy both to access elements and to pretty print the matrix. \subsection{Information Content} \label{sec:getting_info_content} A potentially useful measure of evolutionary conservation is the information content of a sequence. A useful introduction to information theory targeted towards molecular biologists can be found at \url{http://www.lecb.ncifcrf.gov/~toms/paper/primer/}. For our purposes, we will be looking at the information content of a consesus sequence, or a portion of a consensus sequence. We calculate information content at a particular column in a multiple sequence alignment using the following formula: \begin{displaymath} IC_{j} = \sum_{i=1}^{N_{a}} P_{ij} \mathrm{log}\left(\frac{P_{ij}}{Q_{i}}\right) \end{displaymath} \noindent where: \begin{itemize} \item $IC_{j}$ -- The information content for the $j$-th column in an alignment. \item $N_{a}$ -- The number of letters in the alphabet. \item $P_{ij}$ -- The frequency of a particular letter $i$ in the $j$-th column (i.~e.~if G occurred 3 out of 6 times in an aligment column, this would be 0.5) \item $Q_{i}$ -- The expected frequency of a letter $i$. This is an optional argument, usage of which is left at the user's discretion. By default, it is automatically assigned to $0.05 = 1/20$ for a protein alphabet, and $0.25 = 1/4$ for a nucleic acid alphabet. This is for geting the information content without any assumption of prior distributions. When assuming priors, or when using a non-standard alphabet, you should supply the values for $Q_{i}$. \end{itemize} Well, now that we have an idea what information content is being calculated in Biopython, let's look at how to get it for a particular region of the alignment. First, we need to use our alignment to get an alignment summary object, which we'll assume is called \verb|summary_align| (see section~\ref{sec:summary_info}) for instructions on how to get this. Once we've got this object, calculating the information content for a region is as easy as: \begin{verbatim} info_content = summary_align.information_content(5, 30, chars_to_ignore = ['N']) \end{verbatim} Wow, that was much easier then the formula above made it look! The variable \verb|info_content| now contains a float value specifying the information content over the specified region (from 5 to 30 of the alignment). We specifically ignore the ambiguity residue 'N' when calculating the information content, since this value is not included in our alphabet (so we shouldn't be interested in looking at it!). As mentioned above, we can also calculate relative information content by supplying the expected frequencies: \begin{verbatim} expect_freq = { 'A' : .3, 'G' : .2, 'T' : .3, 'C' : .2} \end{verbatim} The expected should not be passed as a raw dictionary, but instead by passed as a \verb|SubsMat.FreqTable| object (see section~\ref{sec:freq_table} for more information about FreqTables). The FreqTable object provides a standard for associating the dictionary with an Alphabet, similar to how the Biopython Seq class works. To create a FreqTable object, from the frequency dictionary you just need to do: \begin{verbatim} from Bio.Alphabet import IUPAC from Bio.SubsMat import FreqTable e_freq_table = FreqTable.FreqTable(expect_freq, FreqTable.FREQ, IUPAC.unambiguous_dna) \end{verbatim} Now that we've got that, calculating the relative information content for our region of the alignment is as simple as: \begin{verbatim} info_content = summary_align.information_content(5, 30, e_freq_table = e_freq_table, chars_to_ignore = ['N']) \end{verbatim} Now, \verb|info_content| will contain the relative information content over the region in relation to the expected frequencies. The value return is calculated using base 2 as the logarithm base in the formula above. You can modify this by passing the parameter \verb|log_base| as the base you want: \begin{verbatim} info_content = summary_align.information_content(5, 30, log_base = 10, chars_to_ignore = ['N']) \end{verbatim} Well, now you are ready to calculate information content. If you want to try applying this to some real life problems, it would probably be best to dig into the literature on information content to get an idea of how it is used. Hopefully your digging won't reveal any mistakes made in coding this function! \section{Substitution Matrices} \label{sec:sub_matrix} Substitution matrices are an extremely important part of everyday bioinformatics work. They provide the scoring terms for classifying how likely two different residues are to substitute for each other. This is essential in doing sequence comparisons. The book ``Biological Sequence Analysis'' by Durbin et al. provides a really nice introduction to Substitution Matrices and their uses. Some famous substitution matrices are the PAM and BLOSUM series of matrices. Biopython provides a ton of common substitution matrices, and also provides functionality for creating your own substitution matrices. \subsection{Using common substitution matrices} \subsection{Creating your own substitution matrix from an alignment} \label{sec:subs_mat_ex} A very cool thing that you can do easily with the substitution matrix classes is to create your own substitution matrix from an alignment. In practice, this is normally done with protein alignments. In this example, we'll first get a Biopython alignment object and then get a summary object to calculate info about the alignment. The file containing \href{examples/protein.aln}{protein.aln} (also available online \href{http://biopython.org/DIST/docs/tutorial/examples/protein.aln}{here}) contains the Clustalw alignment output. %doctest examples \begin{verbatim} >>> from Bio import AlignIO >>> from Bio import Alphabet >>> from Bio.Alphabet import IUPAC >>> from Bio.Align import AlignInfo >>> filename = "protein.aln" >>> alpha = Alphabet.Gapped(IUPAC.protein) >>> c_align = AlignIO.read(filename, "clustal", alphabet=alpha) >>> summary_align = AlignInfo.SummaryInfo(c_align) \end{verbatim} Sections~\ref{sec:align_clustal} and~\ref{sec:summary_info} contain more information on doing this. Now that we've got our \verb|summary_align| object, we want to use it to find out the number of times different residues substitute for each other. To make the example more readable, we'll focus on only amino acids with polar charged side chains. Luckily, this can be done easily when generating a replacement dictionary, by passing in all of the characters that should be ignored. Thus we'll create a dictionary of replacements for only charged polar amino acids using: %cont-doctest \begin{verbatim} >>> replace_info = summary_align.replacement_dictionary(["G", "A", "V", "L", "I", ... "M", "P", "F", "W", "S", ... "T", "N", "Q", "Y", "C"]) \end{verbatim} This information about amino acid replacements is represented as a python dictionary which will look something like (the order can vary): \begin{verbatim} {('R', 'R'): 2079.0, ('R', 'H'): 17.0, ('R', 'K'): 103.0, ('R', 'E'): 2.0, ('R', 'D'): 2.0, ('H', 'R'): 0, ('D', 'H'): 15.0, ('K', 'K'): 3218.0, ('K', 'H'): 24.0, ('H', 'K'): 8.0, ('E', 'H'): 15.0, ('H', 'H'): 1235.0, ('H', 'E'): 18.0, ('H', 'D'): 0, ('K', 'D'): 0, ('K', 'E'): 9.0, ('D', 'R'): 48.0, ('E', 'R'): 2.0, ('D', 'K'): 1.0, ('E', 'K'): 45.0, ('K', 'R'): 130.0, ('E', 'D'): 241.0, ('E', 'E'): 3305.0, ('D', 'E'): 270.0, ('D', 'D'): 2360.0} \end{verbatim} This information gives us our accepted number of replacements, or how often we expect different things to substitute for each other. It turns out, amazingly enough, that this is all of the information we need to go ahead and create a substitution matrix. First, we use the replacement dictionary information to create an Accepted Replacement Matrix (ARM): %cont-doctest \begin{verbatim} >>> from Bio import SubsMat >>> my_arm = SubsMat.SeqMat(replace_info) \end{verbatim} With this accepted replacement matrix, we can go right ahead and create our log odds matrix (i.~e.~a standard type Substitution Matrix): %cont-doctest \begin{verbatim} >>> my_lom = SubsMat.make_log_odds_matrix(my_arm) \end{verbatim} The log odds matrix you create is customizable with the following optional arguments: \begin{itemize} \item \verb|exp_freq_table| -- You can pass a table of expected frequencies for each alphabet. If supplied, this will be used instead of the passed accepted replacement matrix when calculate expected replacments. \item \verb|logbase| - The base of the logarithm taken to create the log odd matrix. Defaults to base 10. \item \verb|factor| - The factor to multiply each matrix entry by. This defaults to 10, which normally makes the matrix numbers easy to work with. \item \verb|round_digit| - The digit to round to in the matrix. This defaults to 0 (i.~e.~no digits). \end{itemize} Once you've got your log odds matrix, you can display it prettily using the function \verb|print_mat|. Doing this on our created matrix gives: %cont-doctest \begin{verbatim} >>> my_lom.print_mat() D 2 E -1 1 H -5 -4 3 K -10 -5 -4 1 R -4 -8 -4 -2 2 D E H K R \end{verbatim} Very nice. Now we've got our very own substitution matrix to play with! \section{BioSQL -- storing sequences in a relational database} \label{sec:BioSQL} \href{http://www.biosql.org/}{BioSQL} is a joint effort between the \href{http://open-bio.org/}{OBF} projects (BioPerl, BioJava etc) to support a shared database schema for storing sequence data. In theory, you could load a GenBank file into the database with BioPerl, then using Biopython extract this from the database as a record object with features - and get more or less the same thing as if you had loaded the GenBank file directly as a SeqRecord using \verb|Bio.SeqIO| (Chapter~\ref{chapter:Bio.SeqIO}). Biopython's BioSQL module is currently documented at \url{http://biopython.org/wiki/BioSQL} which is part of our wiki pages. \chapter{The Biopython testing framework} \label{sec:regr_test} Biopython has a regression testing framework (the file \verb|run_tests.py|) based on \href{http://docs.python.org/library/unittest.html}{unittest}, the standard unit testing framework for Python. Providing comprehensive tests for modules is one of the most important aspects of making sure that the Biopython code is as bug-free as possible before going out. It also tends to be one of the most undervalued aspects of contributing. This chapter is designed to make running the Biopython tests and writing good test code as easy as possible. Ideally, every module that goes into Biopython should have a test (and should also have documentation!). All our developers, and anyone installing Biopython from source, are strongly encouraged to run the unit tests. \section{Running the tests} When you download the Biopython source code, or check it out from our source code repository, you should find a subdirectory call \verb|Tests|. This contains the key script \verb|run_tests.py|, lots of individual scripts named \verb|test_XXX.py|, a subdirectory called \verb|output| and lots of other subdirectories which contain input files for the test suite. As part of building and installing Biopython you will typically run the full test suite at the command line from the Biopython source top level directory using the following: \begin{verbatim} python setup.py test \end{verbatim} This is actually equivalent to going to the \verb|Tests| subdirectory and running: \begin{verbatim} python run_tests.py \end{verbatim} You'll often want to run just some of the tests, and this is done like this: \begin{verbatim} python run_tests.py test_SeqIO.py test_AlignIO.py \end{verbatim} When giving the list of tests, the \verb|.py| extension is optional, so you can also just type: \begin{verbatim} python run_tests.py test_SeqIO test_AlignIO \end{verbatim} To run the docstring tests (see section \ref{section:doctest}), you can use \begin{verbatim} python run_tests.py doctest \end{verbatim} By default, \verb|run_tests.py| runs all tests, including the docstring tests. If an individual test is failing, you can also try running it directly, which may give you more information. Importantly, note that the individual unit tests come in two types: \begin{itemize} \item Simple print-and-compare scripts. These unit tests are essentially short example Python programs, which print out various output text. For a test file named \verb|test_XXX.py| there will be a matching text file called \verb|test_XXX| under the \verb|output| subdirectory which contains the expected output. All that the test framework does to is run the script, and check the output agrees. \item Standard \verb|unittest|- based tests. These will \verb|import unittest| and then define \verb|unittest.TestCase| classes, each with one or more sub-tests as methods starting with \verb|test_| which check some specific aspect of the code. These tests should not print any output directly. \end{itemize} Currently, about half of the Biopython tests are \verb|unittest|-style tests, and half are print-and-compare tests. Running a simple print-and-compare test directly will usually give lots of output on screen, but does not check the output matches the expected output. If the test is failing with an exception error, it should be very easy to locate where exactly the script is failing. For an example of a print-and-compare test, try: \begin{verbatim} python test_SeqIO.py \end{verbatim} The \verb|unittest|-based tests instead show you exactly which sub-section(s) of the test are failing. For example, \begin{verbatim} python test_Cluster.py \end{verbatim} \section{Writing tests} Let's say you want to write some tests for a module called \verb|Biospam|. This can be a module you wrote, or an existing module that doesn't have any tests yet. In the examples below, we assume that \verb|Biospam| is a module that does simple math. Each Biopython test can have three important files and directories involved with it: \begin{enumerate} \item \verb|test_Biospam.py| -- The actual test code for your module. \item \verb|Biospam| [optional]-- A directory where any necessary input files will be located. Any output files that will be generated should also be written here (and preferably cleaned up after the tests are done) to prevent clogging up the main Tests directory. \item \verb|output/Biospam| -- [for print-and-compare tests only] This file contains the expected output from running \verb|test_Biospam.py|. This file is not needed for \verb|unittest|-style tests, since there the validation is done in the test script \verb|test_Biospam.py| itself. \end{enumerate} It's up to you to decide whether you want to write a print-and-compare test script or a \verb|unittest|-style test script. The important thing is that you cannot mix these two styles in a single test script. Particularly, don't use \verb|unittest| features in a print-and-compare test. Any script with a \verb|test_| prefix in the \verb|Tests| directory will be found and run by \verb|run_tests.py|. Below, we show an example test script \verb|test_Biospam.py| both for a print-and-compare test and for a \verb|unittest|-based test. If you put this script in the Biopython \verb|Tests| directory, then \verb|run_tests.py| will find it and execute the tests contained in it: \begin{verbatim} $ python run_tests.py test_Ace ... ok test_AlignIO ... ok test_BioSQL ... ok test_BioSQL_SeqIO ... ok test_Biospam ... ok test_CAPS ... ok test_Clustalw ... ok \end{verbatim} \ldots \begin{verbatim} ---------------------------------------------------------------------- Ran 107 tests in 86.127 seconds \end{verbatim} \subsection{Writing a print-and-compare test} A print-and-compare style test should be much simpler for beginners or novices to write - essentially it is just an example script using your new module. Here is what you should do to make a print-and-compare test for the \verb|Biospam| module. \begin{enumerate} \item Write a script called \verb|test_Biospam.py| \begin{itemize} \item This script should live in the Tests directory \item The script should test all of the important functionality of the module (the more you test the better your test is, of course!). \item Try to avoid anything which might be platform specific, such as printing floating point numbers without using an explicit formatting string to avoid having too many decimal places (different platforms can give very slightly different values). \end{itemize} \item If the script requires files to do the testing, these should go in the directory Tests/Biospam (if you just need something generic, like a FASTA sequence file, or a GenBank record, try and use an existing sample input file instead). \item Write out the test output and verify the output to be correct. There are two ways to do this: \begin{enumerate} \item The long way: \begin{itemize} \item Run the script and write its output to a file. On UNIX (including Linux and Mac OS X) machines, you would do something like: \verb|python test_Biospam.py > test_Biospam| which would write the output to the file \verb|test_Biospam|. \item Manually look at the file \verb|test_Biospam| to make sure the output is correct. When you are sure it is all right and there are no bugs, you need to quickly edit the \verb|test_Biospam| file so that the first line is: `\verb|test_Biospam|' (no quotes). \item copy the \verb|test_Biospam| file to the directory Tests/output \end{itemize} \item The quick way: \begin{itemize} \item Run \verb|python run_tests.py -g test_Biospam.py|. The regression testing framework is nifty enough that it'll put the output in the right place in just the way it likes it. \item Go to the output (which should be in \verb|Tests/output/test_Biospam|) and double check the output to make sure it is all correct. \end{itemize} \end{enumerate} \item Now change to the Tests directory and run the regression tests with \verb|python run_tests.py|. This will run all of the tests, and you should see your test run (and pass!). \item That's it! Now you've got a nice test for your module ready to check in, or submit to Biopython. Congratulations! \end{enumerate} As an example, the \verb|test_Biospam.py| test script to test the \verb|addition| and \verb|multiplication| functions in the \verb|Biospam| module could look as follows: \begin{verbatim} from __future__ import print_function from Bio import Biospam print("2 + 3 =", Biospam.addition(2, 3)) print("9 - 1 =", Biospam.addition(9, -1)) print("2 * 3 =", Biospam.multiplication(2, 3)) print("9 * (- 1) =", Biospam.multiplication(9, -1)) \end{verbatim} We generate the corresponding output with \verb|python run_tests.py -g test_Biospam.py|, and check the output file \verb|output/test_Biospam|: \begin{verbatim} test_Biospam 2 + 3 = 5 9 - 1 = 8 2 * 3 = 6 9 * (- 1) = -9 \end{verbatim} Often, the difficulty with larger print-and-compare tests is to keep track which line in the output corresponds to which command in the test script. For this purpose, it is important to print out some markers to help you match lines in the input script with the generated output. \subsection{Writing a unittest-based test} We want all the modules in Biopython to have unit tests, and a simple print-and-compare test is better than no test at all. However, although there is a steeper learning curve, using the \verb|unittest| framework gives a more structured result, and if there is a test failure this can clearly pinpoint which part of the test is going wrong. The sub-tests can also be run individually which is helpful for testing or debugging. The \verb|unittest|-framework has been included with Python since version 2.1, and is documented in the Python Library Reference (which I know you are keeping under your pillow, as recommended). There is also \href{http://docs.python.org/library/unittest.html}{online documentaion for unittest}. If you are familiar with the \verb|unittest| system (or something similar like the nose test framework), you shouldn't have any trouble. You may find looking at the existing example within Biopython helpful too. Here's a minimal \verb|unittest|-style test script for \verb|Biospam|, which you can copy and paste to get started: \begin{verbatim} import unittest from Bio import Biospam class BiospamTestAddition(unittest.TestCase): def test_addition1(self): result = Biospam.addition(2, 3) self.assertEqual(result, 5) def test_addition2(self): result = Biospam.addition(9, -1) self.assertEqual(result, 8) class BiospamTestDivision(unittest.TestCase): def test_division1(self): result = Biospam.division(3.0, 2.0) self.assertAlmostEqual(result, 1.5) def test_division2(self): result = Biospam.division(10.0, -2.0) self.assertAlmostEqual(result, -5.0) if __name__ == "__main__": runner = unittest.TextTestRunner(verbosity = 2) unittest.main(testRunner=runner) \end{verbatim} In the division tests, we use \verb|assertAlmostEqual| instead of \verb|assertEqual| to avoid tests failing due to roundoff errors; see the \verb|unittest| chapter in the Python documentation for details and for other functionality available in \verb|unittest| (\href{http://docs.python.org/library/unittest.html}{online reference}). These are the key points of \verb|unittest|-based tests: \begin{itemize} \item Test cases are stored in classes that derive from \verb|unittest.TestCase| and cover one basic aspect of your code \item You can use methods \verb|setUp| and \verb|tearDown| for any repeated code which should be run before and after each test method. For example, the \verb|setUp| method might be used to create an instance of the object you are testing, or open a file handle. The \verb|tearDown| should do any ``tidying up'', for example closing the file handle. \item The tests are prefixed with \verb|test_| and each test should cover one specific part of what you are trying to test. You can have as many tests as you want in a class. \item At the end of the test script, you can use \begin{verbatim} if __name__ == "__main__": runner = unittest.TextTestRunner(verbosity = 2) unittest.main(testRunner=runner) \end{verbatim} to execute the tests when the script is run by itself (rather than imported from \verb|run_tests.py|). If you run this script, then you'll see something like the following: \begin{verbatim} $ python test_BiospamMyModule.py test_addition1 (__main__.TestAddition) ... ok test_addition2 (__main__.TestAddition) ... ok test_division1 (__main__.TestDivision) ... ok test_division2 (__main__.TestDivision) ... ok ---------------------------------------------------------------------- Ran 4 tests in 0.059s OK \end{verbatim} \item To indicate more clearly what each test is doing, you can add docstrings to each test. These are shown when running the tests, which can be useful information if a test is failing. \begin{verbatim} import unittest from Bio import Biospam class BiospamTestAddition(unittest.TestCase): def test_addition1(self): """An addition test""" result = Biospam.addition(2, 3) self.assertEqual(result, 5) def test_addition2(self): """A second addition test""" result = Biospam.addition(9, -1) self.assertEqual(result, 8) class BiospamTestDivision(unittest.TestCase): def test_division1(self): """Now let's check division""" result = Biospam.division(3.0, 2.0) self.assertAlmostEqual(result, 1.5) def test_division2(self): """A second division test""" result = Biospam.division(10.0, -2.0) self.assertAlmostEqual(result, -5.0) if __name__ == "__main__": runner = unittest.TextTestRunner(verbosity = 2) unittest.main(testRunner=runner) \end{verbatim} Running the script will now show you: \begin{verbatim} $ python test_BiospamMyModule.py An addition test ... ok A second addition test ... ok Now let's check division ... ok A second division test ... ok ---------------------------------------------------------------------- Ran 4 tests in 0.001s OK \end{verbatim} \end{itemize} If your module contains docstring tests (see section \ref{section:doctest}), you may want to include those in the tests to be run. You can do so as follows by modifying the code under \verb|if __name__ == "__main__":| to look like this: \begin{verbatim} if __name__ == "__main__": unittest_suite = unittest.TestLoader().loadTestsFromName("test_Biospam") doctest_suite = doctest.DocTestSuite(Biospam) suite = unittest.TestSuite((unittest_suite, doctest_suite)) runner = unittest.TextTestRunner(sys.stdout, verbosity = 2) runner.run(suite) \end{verbatim} This is only relevant if you want to run the docstring tests when you execute \verb|python test_Biospam.py|; with \verb|python run_tests.py|, the docstring tests are run automatically (assuming they are included in the list of docstring tests in \verb|run_tests.py|, see the section below). \section{Writing doctests} \label{section:doctest} Python modules, classes and functions support built in documentation using docstrings. The \href{http://docs.python.org/library/doctest.html}{doctest framework} (included with Python) allows the developer to embed working examples in the docstrings, and have these examples automatically tested. Currently only a small part of Biopython includes doctests. The \verb|run_tests.py| script takes care of running the doctests. For this purpose, at the top of the \verb|run_tests.py| script is a manually compiled list of modules to test, which allows us to skip modules with optional external dependencies which may not be installed (e.g. the Reportlab and NumPy libraries). So, if you've added some doctests to the docstrings in a Biopython module, in order to have them included in the Biopython test suite, you must update \verb|run_tests.py| to include your module. Currently, the relevant part of \verb|run_tests.py| looks as follows: \begin{verbatim} # This is the list of modules containing docstring tests. # If you develop docstring tests for other modules, please add # those modules here. DOCTEST_MODULES = ["Bio.Seq", "Bio.SeqRecord", "Bio.SeqIO", "...", ] #Silently ignore any doctests for modules requiring numpy! try: import numpy DOCTEST_MODULES.extend(["Bio.Statistics.lowess"]) except ImportError: pass \end{verbatim} Note that we regard doctests primarily as documentation, so you should stick to typical usage. Generally complicated examples dealing with error conditions and the like would be best left to a dedicated unit test. Note that if you want to write doctests involving file parsing, defining the file location complicates matters. Ideally use relative paths assuming the code will be run from the \verb|Tests| directory, see the \verb|Bio.SeqIO| doctests for an example of this. To run the docstring tests only, use \begin{verbatim} $ python run_tests.py doctest \end{verbatim} \chapter{Advanced} \label{chapter:advanced} \section{Parser Design} Many of the older Biopython parsers were built around an event-oriented design that includes Scanner and Consumer objects. Scanners take input from a data source and analyze it line by line, sending off an event whenever it recognizes some information in the data. For example, if the data includes information about an organism name, the scanner may generate an \verb|organism_name| event whenever it encounters a line containing the name. Consumers are objects that receive the events generated by Scanners. Following the previous example, the consumer receives the \verb|organism_name| event, and the processes it in whatever manner necessary in the current application. This is a very flexible framework, which is advantageous if you want to be able to parse a file format into more than one representation. For example, the \verb|Bio.GenBank| module uses this to construct either \verb|SeqRecord| objects or file-format-specific record objects. More recently, many of the parsers added for \verb|Bio.SeqIO| and \verb|Bio.AlignIO| take a much simpler approach, but only generate a single object representation (\verb|SeqRecord| and \verb|MultipleSeqAlignment| objects respectively). In some cases the \verb|Bio.SeqIO| parsers actually wrap another Biopython parser - for example, the \verb|Bio.SwissProt| parser produces SwissProt format specific record objects, which get converted into \verb|SeqRecord| objects. \section{Substitution Matrices} \subsection{SubsMat} This module provides a class and a few routines for generating substitution matrices, similar to BLOSUM or PAM matrices, but based on user-provided data. Additionally, you may select a matrix from MatrixInfo.py, a collection of established substitution matrices. The \verb+SeqMat+ class derives from a dictionary: \begin{verbatim} class SeqMat(dict) \end{verbatim} The dictionary is of the form \verb|{(i1,j1):n1, (i1,j2):n2,...,(ik,jk):nk}| where i, j are alphabet letters, and n is a value. \begin{enumerate} \item Attributes \begin{enumerate} \item \verb|self.alphabet|: a class as defined in Bio.Alphabet \item \verb|self.ab_list|: a list of the alphabet's letters, sorted. Needed mainly for internal purposes \end{enumerate} \item Methods \begin{enumerate} \item \begin{verbatim} __init__(self,data=None,alphabet=None, mat_name='', build_later=0): \end{verbatim} \begin{enumerate} \item \verb|data|: can be either a dictionary, or another SeqMat instance. \item \verb|alphabet|: a Bio.Alphabet instance. If not provided, construct an alphabet from data. \item \verb|mat_name|: matrix name, such as "BLOSUM62" or "PAM250" \item \verb|build_later|: default false. If true, user may supply only alphabet and empty dictionary, if intending to build the matrix later. this skips the sanity check of alphabet size vs. matrix size. \end{enumerate} \item \begin{verbatim} entropy(self,obs_freq_mat) \end{verbatim} \begin{enumerate} \item \verb|obs_freq_mat|: an observed frequency matrix. Returns the matrix's entropy, based on the frequency in \verb|obs_freq_mat|. The matrix instance should be LO or SUBS. \end{enumerate} \item \begin{verbatim} sum(self) \end{verbatim} Calculates the sum of values for each letter in the matrix's alphabet, and returns it as a dictionary of the form \verb|{i1: s1, i2: s2,...,in:sn}|, where: \begin{itemize} \item i: an alphabet letter; \item s: sum of all values in a half-matrix for that letter; \item n: number of letters in alphabet. \end{itemize} \item \begin{verbatim} print_mat(self,f,format="%4d",bottomformat="%4s",alphabet=None) \end{verbatim} prints the matrix to file handle f. \verb|format| is the format field for the matrix values; \verb|bottomformat| is the format field for the bottom row, containing matrix letters. Example output for a 3-letter alphabet matrix: \begin{verbatim} A 23 B 12 34 C 7 22 27 A B C \end{verbatim} The \verb|alphabet| optional argument is a string of all characters in the alphabet. If supplied, the order of letters along the axes is taken from the string, rather than by alphabetical order. \end{enumerate} \item Usage The following section is laid out in the order by which most people wish to generate a log-odds matrix. Of course, interim matrices can be generated and investigated. Most people just want a log-odds matrix, that's all. \begin{enumerate} \item Generating an Accepted Replacement Matrix Initially, you should generate an accepted replacement matrix (ARM) from your data. The values in ARM are the counted number of replacements according to your data. The data could be a set of pairs or multiple alignments. So for instance if Alanine was replaced by Cysteine 10 times, and Cysteine by Alanine 12 times, the corresponding ARM entries would be: \begin{verbatim} ('A','C'): 10, ('C','A'): 12 \end{verbatim} as order doesn't matter, user can already provide only one entry: \begin{verbatim} ('A','C'): 22 \end{verbatim} A SeqMat instance may be initialized with either a full (first method of counting: 10, 12) or half (the latter method, 22) matrices. A full protein alphabet matrix would be of the size 20x20 = 400. A half matrix of that alphabet would be 20x20/2 + 20/2 = 210. That is because same-letter entries don't change. (The matrix diagonal). Given an alphabet size of N: \begin{enumerate} \item Full matrix size:N*N \item Half matrix size: N(N+1)/2 \end{enumerate} The SeqMat constructor automatically generates a half-matrix, if a full matrix is passed. If a half matrix is passed, letters in the key should be provided in alphabetical order: ('A','C') and not ('C',A'). At this point, if all you wish to do is generate a log-odds matrix, please go to the section titled Example of Use. The following text describes the nitty-gritty of internal functions, to be used by people who wish to investigate their nucleotide/amino-acid frequency data more thoroughly. \item Generating the observed frequency matrix (OFM) Use: \begin{verbatim} OFM = SubsMat._build_obs_freq_mat(ARM) \end{verbatim} The OFM is generated from the ARM, only instead of replacement counts, it contains replacement frequencies. \item Generating an expected frequency matrix (EFM) Use: \begin{verbatim} EFM = SubsMat._build_exp_freq_mat(OFM,exp_freq_table) \end{verbatim} \begin{enumerate} \item \verb|exp_freq_table|: should be a FreqTable instance. See section~\ref{sec:freq_table} for detailed information on FreqTable. Briefly, the expected frequency table has the frequencies of appearance for each member of the alphabet. It is implemented as a dictionary with the alphabet letters as keys, and each letter's frequency as a value. Values sum to 1. \end{enumerate} The expected frequency table can (and generally should) be generated from the observed frequency matrix. So in most cases you will generate \verb|exp_freq_table| using: \begin{verbatim} >>> exp_freq_table = SubsMat._exp_freq_table_from_obs_freq(OFM) >>> EFM = SubsMat._build_exp_freq_mat(OFM, exp_freq_table) \end{verbatim} But you can supply your own \verb|exp_freq_table|, if you wish \item Generating a substitution frequency matrix (SFM) Use: \begin{verbatim} SFM = SubsMat._build_subs_mat(OFM,EFM) \end{verbatim} Accepts an OFM, EFM. Provides the division product of the corresponding values. \item Generating a log-odds matrix (LOM) Use: \begin{verbatim} LOM=SubsMat._build_log_odds_mat(SFM[,logbase=10,factor=10.0,round_digit=1]) \end{verbatim} \begin{enumerate} \item Accepts an SFM. \item \verb|logbase|: base of the logarithm used to generate the log-odds values. \item \verb|factor|: factor used to multiply the log-odds values. Each entry is generated by log(LOM[key])*factor And rounded to the \verb|round_digit| place after the decimal point, if required. \end{enumerate} \end{enumerate} \item Example of use As most people would want to generate a log-odds matrix, with minimum hassle, SubsMat provides one function which does it all: \begin{verbatim} make_log_odds_matrix(acc_rep_mat,exp_freq_table=None,logbase=10, factor=10.0,round_digit=0): \end{verbatim} \begin{enumerate} \item \verb|acc_rep_mat|: user provided accepted replacements matrix \item \verb|exp_freq_table|: expected frequencies table. Used if provided, if not, generated from the \verb|acc_rep_mat|. \item \verb|logbase|: base of logarithm for the log-odds matrix. Default base 10. \item \verb|round_digit|: number after decimal digit to which result should be rounded. Default zero. \end{enumerate} \end{enumerate} \subsection{FreqTable} \label{sec:freq_table} \begin{verbatim} FreqTable.FreqTable(UserDict.UserDict) \end{verbatim} \begin{enumerate} \item Attributes: \begin{enumerate} \item \verb|alphabet|: A Bio.Alphabet instance. \item \verb|data|: frequency dictionary \item \verb|count|: count dictionary (in case counts are provided). \end{enumerate} \item Functions: \begin{enumerate} \item \verb|read_count(f)|: read a count file from stream f. Then convert to frequencies \item \verb|read_freq(f)|: read a frequency data file from stream f. Of course, we then don't have the counts, but it is usually the letter frquencies which are interesting. \end{enumerate} \item Example of use: The expected count of the residues in the database is sitting in a file, whitespace delimited, in the following format (example given for a 3-letter alphabet): \begin{verbatim} A 35 B 65 C 100 \end{verbatim} And will be read using the \verb|FreqTable.read_count(file_handle)| function. An equivalent frequency file: \begin{verbatim} A 0.175 B 0.325 C 0.5 \end{verbatim} Conversely, the residue frequencies or counts can be passed as a dictionary. Example of a count dictionary (3-letter alphabet): \begin{verbatim} {'A': 35, 'B': 65, 'C': 100} \end{verbatim} Which means that an expected data count would give a 0.5 frequency for 'C', a 0.325 probability of 'B' and a 0.175 probability of 'A' out of 200 total, sum of A, B and C) A frequency dictionary for the same data would be: \begin{verbatim} {'A': 0.175, 'B': 0.325, 'C': 0.5} \end{verbatim} Summing up to 1. When passing a dictionary as an argument, you should indicate whether it is a count or a frequency dictionary. Therefore the FreqTable class constructor requires two arguments: the dictionary itself, and FreqTable.COUNT or FreqTable.FREQ indicating counts or frequencies, respectively. Read expected counts. readCount will already generate the frequencies Any one of the following may be done to geerate the frequency table (ftab): \begin{verbatim} >>> from SubsMat import * >>> ftab = FreqTable.FreqTable(my_frequency_dictionary, FreqTable.FREQ) >>> ftab = FreqTable.FreqTable(my_count_dictionary, FreqTable.COUNT) >>> ftab = FreqTable.read_count(open('myCountFile')) >>> ftab = FreqTable.read_frequency(open('myFrequencyFile')) \end{verbatim} \end{enumerate} \chapter{Where to go from here -- contributing to Biopython} \section{Bug Reports + Feature Requests} Getting feedback on the Biopython modules is very important to us. Open-source projects like this benefit greatly from feedback, bug-reports (and patches!) from a wide variety of contributors. The main forums for discussing feature requests and potential bugs are the \href{http://biopython.org/wiki/Mailing_lists}{Biopython mailing lists}: \begin{itemize} \item \mailto{biopython@biopython.org} -- An unmoderated list for discussion of anything to do with Biopython. \item \mailto{biopython-dev@biopython.org} -- A more development oriented list that is mainly used by developers (but anyone is free to contribute!). \end{itemize} Additionally, if you think you've found a new bug, you can submit it to our issue tracker at \url{https://github.com/biopython/biopython/issues} (this has replaced the older tracker hosted at \url{http://redmine.open-bio.org/projects/biopython}). This way, it won't get buried in anyone's Inbox and forgotten about. \section{Mailing lists and helping newcomers} We encourage all our uses to sign up to the main Biopython mailing list. Once you've got the hang of an area of Biopython, we'd encourage you to help answer questions from beginners. After all, you were a beginner once. \section{Contributing Documentation} We're happy to take feedback or contributions - either via a bug-report or on the Mailing List. While reading this tutorial, perhaps you noticed some topics you were interested in which were missing, or not clearly explained. There is also Biopython's built in documentation (the docstrings, these are also \href{http://biopython.org/DIST/docs/api}{online}), where again, you may be able to help fill in any blanks. \section{Contributing cookbook examples} As explained in Chapter~\ref{chapter:cookbook}, Biopython now has a wiki collection of user contributed ``cookbook'' examples, \url{http://biopython.org/wiki/Category:Cookbook} -- maybe you can add to this? \section{Maintaining a distribution for a platform} \label{sec:maintain_dist} We currently provide source code archives (suitable for any OS, if you have the right build tools installed), and Windows Installers which are just click and run. This covers all the major operating systems. Most major Linux distributions have volunteers who take these source code releases, and compile them into packages for Linux users to easily install (taking care of dependencies etc). This is really great and we are of course very grateful. If you would like to contribute to this work, please find out more about how your Linux distribution handles this. Below are some tips for certain platforms to maybe get people started with helping out: \begin{description} \item[Windows] -- Windows products typically have a nice graphical installer that installs all of the essential components in the right place. We use Distutils to create a installer of this type fairly easily. You must first make sure you have a C compiler on your Windows computer, and that you can compile and install things (this is the hard bit - see the Biopython installation instructions for info on how to do this). Once you are setup with a C compiler, making the installer just requires doing: \begin{verbatim} python setup.py bdist_wininst \end{verbatim} Now you've got a Windows installer. Congrats! At the moment we have no trouble shipping installers built on 32 bit windows. If anyone would like to look into supporting 64 bit Windows that would be great. \item[RPMs] -- RPMs are pretty popular package systems on some Linux platforms. There is lots of documentation on RPMs available at \url{http://www.rpm.org} to help you get started with them. To create an RPM for your platform is really easy. You just need to be able to build the package from source (having a C compiler that works is thus essential) -- see the Biopython installation instructions for more info on this. To make the RPM, you just need to do: \begin{verbatim} python setup.py bdist_rpm \end{verbatim} This will create an RPM for your specific platform and a source RPM in the directory \verb|dist|. This RPM should be good and ready to go, so this is all you need to do! Nice and easy. \item[Macintosh] -- Since Apple moved to Mac OS X, things have become much easier on the Mac. We generally treat it as just another Unix variant, and installing Biopython from source is just as easy as on Linux. The easiest way to get all the GCC compilers etc installed is to install Apple's X-Code. We might be able to provide click and run installers for Mac OS X, but to date there hasn't been any demand. \end{description} Once you've got a package, please test it on your system to make sure it installs everything in a good way and seems to work properly. Once you feel good about it, send it off to one of the Biopython developers (write to our main mailing list at biopython@biopython.org if you're not sure who to send it to) and you've done it. Thanks! \section{Contributing Unit Tests} Even if you don't have any new functionality to add to Biopython, but you want to write some code, please consider extending our unit test coverage. We've devoted all of Chapter~\ref{sec:regr_test} to this topic. \section{Contributing Code} There are no barriers to joining Biopython code development other than an interest in creating biology-related code in Python. The best place to express an interest is on the Biopython mailing lists -- just let us know you are interested in coding and what kind of stuff you want to work on. Normally, we try to have some discussion on modules before coding them, since that helps generate good ideas -- then just feel free to jump right in and start coding! The main Biopython release tries to be fairly uniform and interworkable, to make it easier for users. You can read about some of (fairly informal) coding style guidelines we try to use in Biopython in the contributing documentation at \url{http://biopython.org/wiki/Contributing}. We also try to add code to the distribution along with tests (see Chapter~\ref{sec:regr_test} for more info on the regression testing framework) and documentation, so that everything can stay as workable and well documented as possible (including docstrings). This is, of course, the most ideal situation, under many situations you'll be able to find other people on the list who will be willing to help add documentation or more tests for your code once you make it available. So, to end this paragraph like the last, feel free to start working! Please note that to make a code contribution you must have the legal right to contribute it and license it under the Biopython license. If you wrote it all yourself, and it is not based on any other code, this shouldn't be a problem. However, there are issues if you want to contribute a derivative work - for example something based on GPL or LPGL licenced code would not be compatible with our license. If you have any queries on this, please discuss the issue on the biopython-dev mailing list. Another point of concern for any additions to Biopython regards any build time or run time dependencies. Generally speaking, writing code to interact with a standalone tool (like BLAST, EMBOSS or ClustalW) doesn't present a big problem. However, any dependency on another library - even a Python library (especially one needed in order to compile and install Biopython like NumPy) would need further discussion. Additionally, if you have code that you don't think fits in the distribution, but that you want to make available, we maintain Script Central (\url{http://biopython.org/wiki/Scriptcentral}) which has pointers to freely available code in Python for bioinformatics. Hopefully this documentation has got you excited enough about Biopython to try it out (and most importantly, contribute!). Thanks for reading all the way through! \chapter{Appendix: Useful stuff about Python} \label{sec:appendix} If you haven't spent a lot of time programming in Python, many questions and problems that come up in using Biopython are often related to Python itself. This section tries to present some ideas and code that come up often (at least for us!) while using the Biopython libraries. If you have any suggestions for useful pointers that could go here, please contribute! \section{What the heck is a handle?} \label{sec:appendix-handles} Handles are mentioned quite frequently throughout this documentation, and are also fairly confusing (at least to me!). Basically, you can think of a handle as being a ``wrapper'' around text information. Handles provide (at least) two benefits over plain text information: \begin{enumerate} \item They provide a standard way to deal with information stored in different ways. The text information can be in a file, or in a string stored in memory, or the output from a command line program, or at some remote website, but the handle provides a common way of dealing with information in all of these formats. \item They allow text information to be read incrementally, instead of all at once. This is really important when you are dealing with huge text files which would use up all of your memory if you had to load them all. \end{enumerate} Handles can deal with text information that is being read (e.~g.~reading from a file) or written (e.~g.~writing information to a file). In the case of a ``read'' handle, commonly used functions are \verb|read()|, which reads the entire text information from the handle, and \verb|readline()|, which reads information one line at a time. For ``write'' handles, the function \verb|write()| is regularly used. The most common usage for handles is reading information from a file, which is done using the built-in Python function \verb|open|. Here, we open a handle to the file \href{examples/m\_cold.fasta}{m\_cold.fasta} (also available online \href{http://biopython.org/DIST/docs/tutorial/examples/m\_cold.fasta}{here}): \begin{verbatim} >>> handle = open("m_cold.fasta", "r") >>> handle.readline() ">gi|8332116|gb|BE037100.1|BE037100 MP14H09 MP Mesembryanthemum ...\n" \end{verbatim} Handles are regularly used in Biopython for passing information to parsers. For example, since Biopython 1.54 the main functions in \verb|Bio.SeqIO| and \verb|Bio.AlignIO| have allowed you to use a filename instead of a handle: \begin{verbatim} from Bio import SeqIO for record in SeqIO.parse("m_cold.fasta", "fasta"): print(record.id, len(record)) \end{verbatim} On older versions of Biopython you had to use a handle, e.g. \begin{verbatim} from Bio import SeqIO handle = open("m_cold.fasta", "r") for record in SeqIO.parse(handle, "fasta"): print(record.id, len(record)) handle.close() \end{verbatim} This pattern is still useful - for example suppose you have a gzip compressed FASTA file you want to parse: \begin{verbatim} import gzip from Bio import SeqIO handle = gzip.open("m_cold.fasta.gz") for record in SeqIO.parse(handle, "fasta"): print(record.id, len(record)) handle.close() \end{verbatim} See Section~\ref{sec:SeqIO_compressed} for more examples like this, including reading bzip2 compressed files. \subsection{Creating a handle from a string} One useful thing is to be able to turn information contained in a string into a handle. The following example shows how to do this using \verb|cStringIO| from the Python standard library: %doctest \begin{verbatim} >>> my_info = 'A string\n with multiple lines.' >>> print(my_info) A string with multiple lines. >>> from StringIO import StringIO >>> my_info_handle = StringIO(my_info) >>> first_line = my_info_handle.readline() >>> print(first_line) A string >>> second_line = my_info_handle.readline() >>> print(second_line) with multiple lines. \end{verbatim} \begin{thebibliography}{99} \bibitem{cock2009} Peter J. A. Cock, Tiago Antao, Jeffrey T. Chang, Brad A. Chapman, Cymon J. Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski, Michiel J. L. de Hoon: ``Biopython: freely available Python tools for computational molecular biology and bioinformatics''. {\it Bioinformatics} {\bf 25} (11), 1422--1423 (2009). \href{http://dx.doi.org/10.1093/bioinformatics/btp163}{doi:10.1093/bioinformatics/btp163}, \bibitem{pritchard2006} Leighton Pritchard, Jennifer A. White, Paul R.J. Birch, Ian K. Toth: ``GenomeDiagram: a python package for the visualization of large-scale genomic data''. {\it Bioinformatics} {\bf 22} (5): 616--617 (2006). \href{http://dx.doi.org/10.1093/bioinformatics/btk021}{doi:10.1093/bioinformatics/btk021}, \bibitem{toth2006} Ian K. Toth, Leighton Pritchard, Paul R. J. Birch: ``Comparative genomics reveals what makes an enterobacterial plant pathogen''. {\it Annual Review of Phytopathology} {\bf 44}: 305--336 (2006). \href{http://dx.doi.org/10.1146/annurev.phyto.44.070505.143444}{doi:10.1146/annurev.phyto.44.070505.143444}, \bibitem{vanderauwera2009} G\'eraldine A. van der Auwera, Jaroslaw E. Kr\'ol, Haruo Suzuki, Brian Foster, Rob van Houdt, Celeste J. Brown, Max Mergeay, Eva M. Top: ``Plasmids captured in C. metallidurans CH34: defining the PromA family of broad-host-range plasmids''. \textit{Antonie van Leeuwenhoek} {\bf 96} (2): 193--204 (2009). \href{http://dx.doi.org/10.1007/s10482-009-9316-9}{doi:10.1007/s10482-009-9316-9} \bibitem{proux2002} Caroline Proux, Douwe van Sinderen, Juan Suarez, Pilar Garcia, Victor Ladero, Gerald F. Fitzgerald, Frank Desiere, Harald Br\"ussow: ``The dilemma of phage taxonomy illustrated by comparative genomics of Sfi21-Like Siphoviridae in lactic acid bacteria''. \textit{Journal of Bacteriology} {\bf 184} (21): 6026--6036 (2002). \href{http://dx.doi.org/10.1128/JB.184.21.6026-6036.2002}{http://dx.doi.org/10.1128/JB.184.21.6026-6036.2002} \bibitem{jupe2012} Florian Jupe, Leighton Pritchard, Graham J. Etherington, Katrin MacKenzie, Peter JA Cock, Frank Wright, Sanjeev Kumar Sharma1, Dan Bolser, Glenn J Bryan, Jonathan DG Jones, Ingo Hein: ``Identification and localisation of the NB-LRR gene family within the potato genome''. \textit{BMC Genomics} {\bf 13}: 75 (2012). \href{http://dx.doi.org/10.1186/1471-2164-13-75}{http://dx.doi.org/10.1186/1471-2164-13-75} \bibitem{cock2010} Peter J. A. Cock, Christopher J. Fields, Naohisa Goto, Michael L. Heuer, Peter M. Rice: ``The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants''. \textit{Nucleic Acids Research} {\bf 38} (6): 1767--1771 (2010). \href{http://dx.doi.org/10.1093/nar/gkp1137}{doi:10.1093/nar/gkp1137} \bibitem{brown1999} Patrick O. Brown, David Botstein: ``Exploring the new world of the genome with DNA microarrays''. \textit{Nature Genetics} {\bf 21} (Supplement 1), 33--37 (1999). \href{http://dx.doi.org/10.1038/4462}{doi:10.1038/4462} \bibitem{talevich2012} Eric Talevich, Brandon M. Invergo, Peter J.A. Cock, Brad A. Chapman: ``Bio.Phylo: A unified toolkit for processing, analyzing and visualizing phylogenetic trees in Biopython''. \textit{BMC Bioinformatics} {\bf 13}: 209 (2012). \href{http://dx.doi.org/10.1186/1471-2105-13-209}{doi:10.1186/1471-2105-13-209} \bibitem{cornish1985} Athel Cornish-Bowden: ``Nomenclature for incompletely specified bases in nucleic acid sequences: Recommendations 1984.'' \textit{Nucleic Acids Research} {\bf 13} (9): 3021--3030 (1985). \href{http://dx.doi.org/10.1093/nar/13.9.3021}{doi:10.1093/nar/13.9.3021} \bibitem{cavener1987} Douglas R. Cavener: ``Comparison of the consensus sequence flanking translational start sites in Drosophila and vertebrates.'' \textit{Nucleic Acids Research} {\bf 15} (4): 1353--1361 (1987). \href{http://dx.doi.org/10.1093/nar/15.4.1353}{doi:10.1093/nar/15.4.1353} \bibitem{bailey1994} Timothy L. Bailey and Charles Elkan: ``Fitting a mixture model by expectation maximization to discover motifs in biopolymers'', \textit{Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology} 28--36. AAAI Press, Menlo Park, California (1994). \bibitem{chapman2000} Brad Chapman and Jeff Chang: ``Biopython: Python tools for computational biology''. \textit{ACM SIGBIO Newsletter} {\bf 20} (2): 15--19 (August 2000). \bibitem{dehoon2004} Michiel J. L. de Hoon, Seiya Imoto, John Nolan, Satoru Miyano: ``Open source clustering software''. \textit{Bioinformatics} {\bf 20} (9): 1453--1454 (2004). \href{http://dx.doi.org/10.1093/bioinformatics/bth078}{doi:10.1093/bioinformatics/bth078} \bibitem{eisen1998} Michiel B. Eisen, Paul T. Spellman, Patrick O. Brown, David Botstein: ``Cluster analysis and display of genome-wide expression patterns''. \textit{Proceedings of the National Academy of Science USA} {\bf 95} (25): 14863--14868 (1998). \href{http://dx.doi.org/10.1073/pnas.96.19.10943-c}{doi:10.1073/pnas.96.19.10943-c} \bibitem{golub1971} Gene H. Golub, Christian Reinsch: ``Singular value decomposition and least squares solutions''. In \textit{Handbook for Automatic Computation}, {\bf 2}, (Linear Algebra) (J. H. Wilkinson and C. Reinsch, eds), 134--151. New York: Springer-Verlag (1971). \bibitem{golub1989} Gene H. Golub, Charles F. Van Loan: \textit{Matrix computations}, 2nd edition (1989). \bibitem{hamelryck2003a} Thomas Hamelryck and Bernard Manderick: 11PDB parser and structure class implemented in Python''. \textit{Bioinformatics}, \textbf{19} (17): 2308--2310 (2003) \href{http://dx.doi.org/10.1093/bioinformatics/btg299}{doi: 10.1093/bioinformatics/btg299}. \bibitem{hamelryck2003b} Thomas Hamelryck: ``Efficient identification of side-chain patterns using a multidimensional index tree''. \textit{Proteins} {\bf 51} (1): 96--108 (2003). \href{http://dx.doi.org/10.1002/prot.10338}{doi:10.1002/prot.10338} \bibitem{hamelryck2005} Thomas Hamelryck: ``An amino acid has two sides; A new 2D measure provides a different view of solvent exposure''. \textit{Proteins} {\bf 59} (1): 29--48 (2005). \href{http://dx.doi.org/10.1002/prot.20379}{doi:10.1002/prot.20379}. \bibitem{hartigan1975} John A. Hartiga. \textit{Clustering algorithms}. New York: Wiley (1975). \bibitem{jain1988} Anil L. Jain, Richard C. Dubes: \textit{Algorithms for clustering data}. Englewood Cliffs, N.J.: Prentice Hall (1988). \bibitem{kachitvichyanukul1988} Voratas Kachitvichyanukul, Bruce W. Schmeiser: Binomial Random Variate Generation. \textit{Communications of the ACM} {\bf 31} (2): 216--222 (1988). \href{http://dx.doi.org/10.1145/42372.42381}{doi:10.1145/42372.42381} \bibitem{kohonen1997} Teuvo Kohonen: ``Self-organizing maps'', 2nd Edition. Berlin; New York: Springer-Verlag (1997). \bibitem{lecuyer1988} Pierre L'Ecuyer: ``Efficient and Portable Combined Random Number Generators.'' \textit{Communications of the ACM} {\bf 31} (6): 742--749,774 (1988). \href{http://dx.doi.org/10.1145/62959.62969}{doi:10.1145/62959.62969} \bibitem{majumdar2005} Indraneel Majumdar, S. Sri Krishna, Nick V. Grishin: ``PALSSE: A program to delineate linear secondary structural elements from protein structures.'' \textit{BMC Bioinformatics}, {\bf 6}: 202 (2005). \href{http://dx.doi.org/10.1186/1471-2105-6-202}{doi:10.1186/1471-2105-6-202}. \bibitem{matys2003} V. Matys, E. Fricke, R. Geffers, E. G\"ossling, M. Haubrock, R. Hehl, K. Hornischer, D. Karas, A.E. Kel, O.V. Kel-Margoulis, D.U. Kloos, S. Land, B. Lewicki-Potapov, H. Michael, R. M\"unch, I. Reuter, S. Rotert, H. Saxel, M. Scheer, S. Thiele, E. Wingender E: ``TRANSFAC: transcriptional regulation, from patterns to profiles.'' Nucleic Acids Research {\bf 31} (1): 374--378 (2003). \href{http://dx.doi.org/10.1093/nar/gkg108}{doi:10.1093/nar/gkg108} \bibitem{sibson1973} Robin Sibson: ``SLINK: An optimally efficient algorithm for the single-link cluster method''. \textit{The Computer Journal} {\bf 16} (1): 30--34 (1973). \href{http://dx.doi.org/10.1093/comjnl/16.1.30}{doi:10.1093/comjnl/16.1.30} \bibitem{snedecor1989} George W. Snedecor, William G. Cochran: \textit{Statistical methods}. Ames, Iowa: Iowa State University Press (1989). \bibitem{tamayo1999} Pablo Tamayo, Donna Slonim, Jill Mesirov, Qing Zhu, Sutisak Kitareewan, Ethan Dmitrovsky, Eric S. Lander, Todd R. Golub: ``Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation''. \textit{Proceedings of the National Academy of Science USA} {\bf 96} (6): 2907--2912 (1999). \href{http://dx.doi.org/10.1073/pnas.96.6.2907}{doi:10.1073/pnas.96.6.2907} \bibitem{tryon1970} Robert C. Tryon, Daniel E. Bailey: \textit{Cluster analysis}. New York: McGraw-Hill (1970). \bibitem{tukey1977} John W. Tukey: ``Exploratory data analysis''. Reading, Mass.: Addison-Wesley Pub. Co. (1977). \bibitem{yeung2001} Ka Yee Yeung, Walter L. Ruzzo: ``Principal Component Analysis for clustering gene expression data''. \textit{Bioinformatics} {\bf 17} (9): 763--774 (2001). \href{http://dx.doi.org/10.1093/bioinformatics/17.9.763}{doi:10.1093/bioinformatics/17.9.763} \bibitem{saldanha2004} Alok Saldanha: ``Java Treeview---extensible visualization of microarray data''. \textit{Bioinformatics} {\bf 20} (17): 3246--3248 (2004). \href{http://dx.doi.org/10.1093/bioinformatics/bth349}{http://dx.doi.org/10.1093/bioinformatics/bth349} \end{thebibliography} \end{document} biopython-1.63/Doc/cookbook/0000755000175000017500000000000012250104773015370 5ustar tratra00000000000000biopython-1.63/Doc/cookbook/motif/0000755000175000017500000000000012250104773016506 5ustar tratra00000000000000biopython-1.63/Doc/cookbook/motif/Makefile0000644000175000017500000000112012250104543020133 0ustar tratra00000000000000# create pdf, txt and html documentation FILENAME=motif all: ${FILENAME}.html ${FILENAME}.pdf ${FILENAME}.txt clean: rm -f ${FILENAME}.aux rm -f ${FILENAME}.log rm -f ${FILENAME}.out rm -f ${FILENAME}.toc rm -f ${FILENAME}.haux rm -f ${FILENAME}.htoc distclean: clean rm -f ${FILENAME}.pdf rm -f ${FILENAME}.html rm -f ${FILENAME}.txt ${FILENAME}.pdf: ${FILENAME}.tex pdflatex ${FILENAME}.tex pdflatex ${FILENAME}.tex pdflatex ${FILENAME}.tex ${FILENAME}.html: ${FILENAME}.tex hevea -fix ${FILENAME}.tex ${FILENAME}.txt: ${FILENAME}.tex hevea -fix -text ${FILENAME}.tex biopython-1.63/Doc/cookbook/motif/motif.tex0000644000175000017500000004755112250104543020355 0ustar tratra00000000000000% Documentation describing writing Tests for Biopython modules \documentclass{article} \usepackage{url} \usepackage{fullpage} \usepackage{hevea} \usepackage{graphicx} % Make links between references \usepackage{hyperref} \newif\ifpdf \ifx\pdfoutput\undefined \pdffalse \else \pdfoutput=1 \pdftrue \fi \ifpdf \hypersetup{colorlinks=true, hyperindex=true, citecolor=red, urlcolor=blue} \fi \begin{document} \title{Sequence motif analysis using Biopython} \author{Bartek Wilczynski (bartek@mimuw.edu.pl)} \maketitle \tableofcontents \section{Short introduction} \label{sec:intro} This short tutorial describes some of the functionality of the \verb|Bio.Motif| package included in Biopython distribution. It is intended for people who are involved in analysis of sequence motif, so I'll assume that you are familiar with basic notions of motif analysis. In case something is unclear, please look into Section \ref{sec:links} for some relevant links. It should be also noted, that \verb|Bio.Motif| is based on two Biopython modules, \verb|Bio.AlignAce| and \verb|Bio.MEME| and is meant to replace them. It provides (almost) all of the functionality of these modules and unifies the basic motif object implementation. Speaking of other libraries, if you are reading this you might be interested in the TAMO \cite{tamo} (\url{http://fraenkel.mit.edu/TAMO/}), which is another python library designed to deal with sequence motifs. It supports more \emph{de-novo} motif finders, but it is not a part of biopython (so requires a bit of work) and has some restrictions on commercial use. \section{Motif objects} \label{sec:object} Since we are interested in motif analysis, we need to take a look at \verb|Motif| objects in the first place. For that we need to import the Motif library: \begin{verbatim} from Bio import Motif \end{verbatim} and we can start creating our first motif objects. Let's create a DNA motif: \begin{verbatim} from Bio.Alphabet import IUPAC m=Motif.Motif(alphabet=IUPAC.unambiguous_dna) \end{verbatim} This is for now just an empty container, so let's add some sequences to our newly created motif: \begin{verbatim} from Bio.Seq import Seq m.add_instance(Seq("TATAA",m.alphabet)) m.add_instance(Seq("TATTA",m.alphabet)) m.add_instance(Seq("TATAA",m.alphabet)) m.add_instance(Seq("TATAA",m.alphabet)) \end{verbatim} Now we have a full \verb|Motif| instance, so we can try to get some basic information about it. Let's start with length and consensus sequence: \begin{verbatim} >>> m.length 5 >>> m.consensus Seq('TATAA', IUPACUnambiguousDNA()) \end{verbatim} In case of DNA motifs, we can also get a reverse complement of a motif: \begin{verbatim} >>> m.reverse_complement() >>> m.reverse_complement().consensus Seq('TTATA', IUPACUnambiguousDNA()) >>> m.reverse_complement().instances [Seq('TTATA', IUPACUnambiguousDNA()), Seq('TAATA', IUPACUnambiguousDNA()), Seq('TTATA', IUPACUnambiguousDNA()), Seq('TTATA', IUPACUnambiguousDNA())] \end{verbatim} We can also calculate the information content of a motif with a simple call: \begin{verbatim} >>> m.ic() 5.2735010263278932 \end{verbatim} This gives us a number of bits of information provided by the motif, which tells us how much we are different from background. The most common representation of a motif is a PWM (Position Weight Matrix). It summarizes the probabilities of finding any symbol (in this case nucleotide) in any position of a motif. It can be computed by calling the \verb|.pwm()| method: \begin{verbatim} >>> m.pwm() [{'A': 0.05, 'C': 0.05, 'T': 0.85, 'G': 0.05}, {'A': 0.85, 'C': 0.05, 'T': 0.05, 'G': 0.05}, {'A': 0.05, 'C': 0.05, 'T': 0.85, 'G': 0.05}, {'A': 0.65, 'C': 0.05, 'T': 0.25, 'G': 0.05}, {'A': 0.85, 'C': 0.05, 'T': 0.05, 'G': 0.05}] \end{verbatim} The probabilities in the motif's PWM are based on the counts in the instances, but we can see, that even though there were no Gs and no Cs in the instances, we still have non-zero probabilities assigned to them. These come from pseudo-counts which are, roughly speaking, a commonly used way to acknowledge the incompleteness of our knowledge and avoid technical problems with calculating logarithms of $0$. We can control the way that pseudo-counts are added with two properties of Motif objects \verb|.background| is the probability distribution over all symbols in the alphabet that we assume is found in background (usually based on the GC content of the respective genome). It is by default set to a uniform distribution upon creation of a motif: \begin{verbatim} >>> m.background {'A': 0.25, 'C': 0.25, 'T': 0.25, 'G': 0.25} \end{verbatim} The other parameter is \verb|.beta|, which states the amount of pseudo-counts we should add to the PWM. By default it is set to $1.0$, \begin{verbatim} >>> m.beta 1.0 \end{verbatim} so that the total input of pseudo-counts is equal to that of one instance. Using the background distribution and pwm with pseudo-counts added, it's easy to compute the log-odds ratios, telling us what are the log odds of a particular symbol to be coming from a motif against the background. We can use the \verb|.log_odds()| method: \begin{verbatim} >>> m.log_odds() [{'A': -2.3219280948873622, 'C': -2.3219280948873622, 'T': 1.7655347463629771, 'G': -2.3219280948873622}, {'A': 1.7655347463629771, 'C': -2.3219280948873622, 'T': -2.3219280948873622, 'G': -2.3219280948873622}, {'A': -2.3219280948873622, 'C': -2.3219280948873622, 'T': 1.7655347463629771, 'G': -2.3219280948873622}, {'A': 1.3785116232537298, 'C': -2.3219280948873622, 'T': 0.0, 'G': -2.3219280948873622}, {'A': 1.7655347463629771, 'C': -2.3219280948873622, 'T': -2.3219280948873622, 'G': -2.3219280948873622} ] \end{verbatim} Here we can see positive values for symbols more frequent in the motif than in the background and negative for symbols more frequent in the background. $0.0$ means that it's equally likely to see a symbol in background and in the motif (e.g. 'T' in the second-last position). \subsection{Reading and writing} \label{sec:io} Creating motifs from instances by hand is useful but boring, so it's useful to have some I/O functions for reading and writing motifs. There are no really well established standards for storing motifs, but there's a couple of formats which are more used than others. The most important distinction is whether the motif representation is based on instances or on some version of PWM matrix. On of the most popular motif databases JASPAR (\url{http://jaspar.genereg.net}) stores motifs in both formats, so let's look at how we can import JASPAR motifs from instances: \begin{verbatim} arnt=Motif.read(open("Arnt.sites"),"jaspar-sites") \end{verbatim} and from a count matrix: \begin{verbatim} srf=Motif.read(open("SRF.pfm"),"jaspar-pfm") \end{verbatim} The \verb|arnt| and \verb|srf| motifs can both do the same things for us, but they use different internal representations of the motif. We can tell that by inspecting the \verb|has_counts| and \verb|has_instances| properties: \begin{verbatim} >>> arnt.has_instances True >>> srf.has_instances False >>> srf.has_counts True >>> srf.counts {'A': [2, 9, 0, 1, 32, 3, 46, 1, 43, 15, 2, 2], 'C': [1, 33, 45, 45, 1, 1, 0, 0, 0, 1, 0, 1], 'G': [39, 2, 1, 0, 0, 0, 0, 0, 0, 0, 44, 43], 'T': [4, 2, 0, 0, 13, 42, 0, 45, 3, 30, 0, 0]} \end{verbatim} There are conversion functions, which can help us convert between different representations: \begin{verbatim} >>> arnt.make_counts_from_instances() {'A': [8, 38, 0, 0, 0, 0], 'C': [32, 0, 40, 0, 0, 0], 'G': [0, 2, 0, 40, 0, 40], 'T': [0, 0, 0, 0, 40, 0]} >>> srf.make_instances_from_counts() [Seq('GGGAAAAAAAGG', IUPACUnambiguousDNA()), Seq('GGCCAAATAAGG', IUPACUnambiguousDNA()), Seq('GACCAAATAAGG', IUPACUnambiguousDNA()), .... \end{verbatim} The important thing to remember here is that the method \verb|make_instances_from_counts()| creates fake instances, because usually there are very many possible sets of instances which give rise to the same pwm, and if we have only the count matrix, we cannot reconstruct the original one. This does not make any difference if we are using the PWM as the representation of the motif, but one should be careful with exporting instances from count-based motifs. Speaking of exporting, let's look at export functions. We can export to fasta: \begin{verbatim} >>> print m.format("fasta") > instance 0 TATAA > instance 1 TATTA > instance 2 TATAA > instance 3 TATAA \end{verbatim} or to TRANSFAC-like matrix format (used by some motif processing software) \begin{verbatim} >>> print m.format("transfac") XX TY Motif ID BF undef P0 G A T C 01 0 0 4 0 02 0 4 0 0 03 0 0 4 0 04 0 3 1 0 05 0 4 0 0 XX \end{verbatim} Finally, if we have internet access, we can create a weblogo using a nice service at \url{http://weblogo.berkeley.edu} by Crooks et al. \cite{crooks2004}: \begin{verbatim} >>> arnt.weblogo("Arnt.png") \end{verbatim} We should get our logo saved as a png in the specified file. \subsection{Searching for instances} \label{sec:search} The most frequent use for a motif is to find its instances in some sequence. For the sake of this section, we will use an artificial sequence like this: \begin{verbatim} test_seq=Seq("TATGATGTAGTATAATATAATTATAA",m.alphabet) \end{verbatim} The simplest way to find instances, is to look for exact matches of the true instances of the motif: \begin{verbatim} >>> for pos,seq in m.search_instances(test_seq): ... print pos,seq.tostring() ... 10 TATAA 15 TATAA 21 TATAA \end{verbatim} We can do the same with the reverse complement (to find instances on the complementary strand): \begin{verbatim} >>> for pos,seq in m.reverse_complement().search_instances(test_seq): ... print pos,seq.tostring() ... 12 TAATA 20 TTATA \end{verbatim} It's just as easy to look for positions, giving rise to high log-odds scores against our motif: \begin{verbatim} >>> for position, score in m.search(test_seq,threshold=5.0): ... print position, score ... 10 8.44065060871 -12 7.06213898545 15 8.44065060871 -20 8.44065060871 21 8.44065060871 \end{verbatim} You may notice the threshold parameter, here set arbitrarily to $5.0$. This is in $log_2$, so we are now looking only for words, which are 32 times more likely to occur under the motif model than in the background. The default threshold is $0.0$, which selects everything that looks more like the motif, than the background. If you want to use a less arbitrary way of selecting thresholds, you can explore the \verb|Motif.score_distribution| class implementing an distribution of scores for a given motif. Since the space for a score distribution grows exponentially with motif.length, we are using an approximation with a given precision to keep computation cost manageable: \begin{verbatim} >>> sd = Motif.score_distribution(m,precision=10**4) \end{verbatim} The \verb+sd+ object can be used to determine a number of different thresholds. We can specify the requested false-positive rate (probability of ``finding'' a motif instance in background generated sequence): \begin{verbatim} >>> sd.threshold_fpr(0.01) 4.3535838726139886 \end{verbatim} or the false-negative rate (probability of ``not finding'' an instance generated from the motif): \begin{verbatim} >>> sd.threshold_fnr(0.1) 0.26651713652234044 \end{verbatim} or a threshold (approximately) satisfying some relation between fpr and fnr ($\frac{fnr}{fpr}$, as proposed by Rahmann \cite{Rahmann2003}): \begin{verbatim} >>> sd.threshold_balanced(1000) 8.4406506087056368 \end{verbatim} or a threshold satisfying (roughly) the equality between the false-positive rate and the $-log$ of the information content (as used in patser software by Hertz and Stormo). For example, in case of our motif, you can get the threshold giving you exactly the same results (for this sequence) as searching for instances with balanced threshold with rate of $1000$. \begin{verbatim} >>> threshold=sd.threshold_balanced(1000) >>> for position, score in m.search(test_seq, threshold=threshold): ... print position, score ... 10 8.44065060871 15 8.44065060871 -20 8.44065060871 21 8.44065060871 \end{verbatim} \subsection{Comparing motifs} \label{sec:comp} Once we have more than one motif, we might want to compare them. For that, we have currently three different methods of \verb|Bio.Motif| objects. Before we start comparing motifs, I should point out that motif boundaries are usually quite arbitrary. This means, that we often need to compare motifs of different lengths, so comparison needs to involve some kind of alignment. This means, that we have to take into account two things: \begin{itemize} \item alignment of motifs \item some function to compare aligned motifs \end{itemize} In \verb|Bio.Motif| we have 3 different functions for motif comparison, which are based on the same idea behind motif alignment, but use different functions to compare aligned motifs. Briefly speaking, we are using ungapped alignment of PWMs and substitute the missing columns at the beginning and end of the matrices with background distribution. All three comparison functions are written in such a way, that they can be interpreted as distance measures, however only one (\verb|dist_dpq|) satisfies the triangle inequality. All of them return the minimal distance and the corresponding offset between motifs. To show how these functions work, let us first load another motif, which is similar to our test motif \verb|m|: \begin{verbatim} >>> ubx=Motif.read(open("Ubx.pfm"),"jaspar-pfm") >>> ubx.consensus Seq('TAAT', IUPACUnambiguousDNA()) \end{verbatim} The first function we'll use to compare these motifs is based on Pearson correlation. Since we want it to resemble a distance measure. we actually take $1-r$, where r is the Pearson correlation coefficient (PCC): \begin{verbatim} >>> m.dist_pearson(ubx) (0.41740393308237722, 2) \end{verbatim} This means, that the best PCC between motif \verb|m| and \verb|Ubx| is obtained with the following alignment: \begin{verbatim} bbTAAT TATAAb \end{verbatim} where \verb|b| stands for background distribution. The PCC itself is roughly $1-0.42=0.58$. If we try the reverse complement of the Ubx motif: \begin{verbatim} >>> m.dist_pearson(ubx.reverse_complement()) (0.25784180151584823, 1) \end{verbatim} We can see that the PCC is better (almost $0.75$), and the alignment is also different: \begin{verbatim} bATTA TATAA \end{verbatim} There are two other functions \verb|dist_dpq|, which is a true metric based on the Kullback-Leibler divergence and proposed by Endres and Sendelin \cite{Endres2003} \begin{verbatim} >>> m.dist_dpq(ubx.reverse_complement()) (0.49292358382899853, 1) \end{verbatim} In case you need yet another way of comparing motifs, you can use the \verb|dist_product| method, which is based on the product of probabilities which can be interpreted as the probability of independently generating the same instance by both motifs. \begin{verbatim} >>> m.dist_product(ubx.reverse_complement()) (0.16224587301064275, 1) \end{verbatim} \section{\emph{De novo} motif finding} \label{sec:find} Currently, biopython has only limited support for \emph{de novo} motif finding. Namely, we support running and parsing of AlignAce \cite{Hughes2000} and MEME\cite{Bailey1994}. Since the number of motif finding tools is growing rapidly, contributions of new parsers are welcome. \subsection{MEME} \label{sec:meme} Let's assume, you have run MEME on sequences of your choice with your favorite parameters and saved the output in the file \verb|meme.out|. You can retrieve the motifs reported by MEME by running the following piece of code: \begin{verbatim} >>> motifsM = list(Motif.parse(open("meme.out"),"MEME")) >>> motifsM [] \end{verbatim} Besides the most wanted list of motifs, the result object contains more useful information, accessible through properties with self-explanatory names: \begin{itemize} \item \verb|.alphabet| \item \verb|.datafile| \item \verb|.sequence_names| \item \verb|.version| \item \verb|.command| \end{itemize} The motifs returned by MEMEParser can be treated exactly like regular Motif objects (with instances), they also provide some extra functionality, by adding additional information about the instances. \begin{verbatim} >>> motifsM[0].consensus Seq('CTCAATCGTA', IUPACUnambiguousDNA()) >>> motifsM[0].instances[0].pvalue 8.71e-07 >>> motifsM[0].instances[0].sequence_name 'SEQ10;' >>> motifsM[0].instances[0].start 3 >>> motifsM[0].instances[0].strand '+' \end{verbatim} \subsection{AlignAce} \label{sec:alignace} We can do very similar things with AlignACE program. Assume, you have your output in the file \verb|alignace.out|. You can parse your output with the following code: \begin{verbatim} >>> motifsA=list(Motif.parse(open("alignace.out"),"AlignAce")) \end{verbatim} Again, your motifs behave as they should: \begin{verbatim} >>> motifsA[0].consensus Seq('TCTACGATTGAG', IUPACUnambiguousDNA()) \end{verbatim} In fact you can even see, that AlignAce found a very similar motif as MEME, it is just a longer version of a reverse complement of MEME motif: \begin{verbatim} >>> motifsM[0].reverse_complement().consensus Seq('TACGATTGAG', IUPACUnambiguousDNA()) \end{verbatim} If you have AlignAce installed on the same machine, you can also run it directly from Biopython. Short example of how this can be done is shown below (other parameters can be specified as keyword parameters): \begin{verbatim} >>> command="/opt/bin/AlignACE" >>> input_file="test.fa" >>> result=Motif.AlignAce(input_file,cmd=command,gcback=0.6,numcols=10) >>> result (, , ) \end{verbatim} Since AlignAce prints all its output to standard output, you can get to your motifs by parsing the second member of the result: \begin{verbatim} motifs=list(Motif.parse(result[1],"AlignAce")) \end{verbatim} \section{Useful links } \label{sec:links} Wikipedia definitions: \begin{itemize} \item \url{http://en.wikipedia.org/wiki/Sequence_motif} \item \url{http://en.wikipedia.org/wiki/Position_weight_matrix} \item \url{http://en.wikipedia.org/wiki/Consensus_sequence} \end{itemize} Motif finding and comparison methods: \begin{itemize} \item AlignAce and CompareAce\url{http://www.psc.edu/general/software/packages/alignace/} \item MEME and MAST \url{http://meme.sdsc.edu/meme/} \item BioProspector \url{http://bioprospector.stanford.edu/} \item MDScan \url{http://ai.stanford.edu/~xsliu/MDscan/} \item Weeder \item STAMP \url{http://www.benoslab.pitt.edu/stamp/} \item WebMotifs \url{http://fraenkel.mit.edu/webmotifs/} \item Comparison of different programs \url{http://bio.cs.washington.edu/assessment/} \end{itemize} \begin{thebibliography}{10} \bibitem{tamo} Gordon DB, Nekludova L, McCallum S, Fraenkel E: \textbf{TAMO: a flexible, object-oriented framework for analyzing transcriptional regulation using DNA-sequence motifs.} \emph{Bioinformatics} 2005, \textbf{21}(14):3164--5. \bibitem{crooks2004} Crooks GE, Hon G, Chandonia JM, steven E~Brenner: \textbf{WebLogo: A Sequence Logo Generator}. \emph{Genome Research} 2004, \textbf{14}:1188--1190. \bibitem{Rahmann2003} Rahmann S, Mueller T, Vingron M: \textbf{On the power of profiles for transcription factor binding site detection.} \emph{Stat Appl Genet Mol Biol} 2003, \textbf{2}:Article7. \bibitem{Endres2003} Endres D, Schindelin J: \textbf{A new metric for probability distributions}. \emph{IEEE transactions on Information Theory} 2003, \textbf{49}(7):1858--1860. \bibitem{Bailey1994} Bailey TL, Elkan C: \textbf{{Fitting a mixture model by expectation maximization to discover motifs in biopolymers}}. \emph{Proc Int Conf Intell Syst Mol Biol} 1994, \textbf{2}:28--36. \bibitem{Hughes2000} Hughes JD, Estep PW, Tavazoie S, Church GM: \textbf{{Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae}}. \emph{J Mol Biol} 2000, \textbf{296}(5):1205--1214. \end{thebibliography} \end{document} biopython-1.63/Doc/cookbook/Restriction/0000755000175000017500000000000012250104773017675 5ustar tratra00000000000000biopython-1.63/Doc/cookbook/Restriction/Restriction.html0000644000175000017500000023124212250104543023067 0ustar tratra00000000000000 Restriction.html

  Working with restriction enzymes


Table of content


1  The restriction enzymes classes


The restriction enzyme package is situated in Bio/Restriction. This package will allow you to work with restriction enzymes and realise restriction analysis on your sequence. Restriction make use of the facilities offered by Rebase and contains classes for more than 600 restriction enzymes. This chapter will lead you through a quick overview of the facilities offered by the Restriction package of biopython. The chapter is constructed as an interactive python session and the best way to read it is with a python shell open alongside you.

1.1  Importing the enzymes

To import the enzymes, open a python shell and type :
>>> from Bio import Restriction
>>> dir()
['Restriction', '__builtins__', '__doc__', '__name__']
>>> Restriction.EcoRI
EcoRI
>>> Restriction.EcoRI.site
'GAATTC'
>>>
You will certainly notice that the package is quite slow to load. This is normal as each enzyme possess its own class and there is a lot of them. This will not affect the speed of python after the initial import.

I don't know for you but I find it quite cumbersome to have to prefix each operation with Restriction., so here is another way to import the package.
>>> from Bio.Restriction import *
>>> EcoRI
EcoRI
>>> EcoRI.site
'GAATTC'
>>>
However, this method has one big disadvantage :
It is almost impossible to use the command 'dir()' anymore as there is so much enzymes the results is hardly readable. A workaround is provided at the end of this tutorial. I let you decide which method you prefer. But in this tutorial I will use the second. If you prefer the first method you will need to prefix each call to a restriction enzyme with 'Restriction.' in the remaining of the tutorial.

1.2  Naming convention

To access an Enzyme simply enter it's name. You must respect the usual naming convention with the upper case letters and Latin numbering (in upper case as well):
>>> EcoRI 
EcoRI
>>> ecori

Traceback (most recent call last):
  File "<pyshell#25>", line 1, in -toplevel-
    ecori
NameError: name 'ecori' is not defined
>>> EcoR1

Traceback (most recent call last):
  File "<pyshell#26>", line 1, in -toplevel-
    EcoR1
NameError: name 'EcoR1' is not defined
>>> KpnI
KpnI
>>>
ecori or EcoR1 are not enzymes, EcoRI and KpnI are.

1.3  Searching for restriction sites

So what can we do with these restriction enzymes. To see that we will need a DNA sequence. Restriction enzymes support both Bio.Seq.MutableSeq and Bio.Seq.Seq objects. You can use any DNA alphabet which complies with the IUPAC alphabet. 
>>> from Bio.Seq import Seq
>>> from Bio.Alphabet.IUPAC import IUPACAmbiguousDNA
>>> amb = IUPACAmbiguousDNA()
>>> my_seq = Seq('AAAAAAAAAAAAAA', amb)
>>> my_seq
Seq('AAAAAAAAAAAAAA', IUPACAmbiguousDNA())
Searching a sequence for the presence of restriction site for your preferred enzyme is as simple as :
>>> EcoRI.search(my_seq)
[]
The results is a list. Here the list is empty since there is obviously no site EcoRI site in my_seq.  Let's try to get a sequence with a EcoRI site.
>>> ecoseq = my_seq + Seq(EcoRI.site, amb) + my_seq
>>> ecoseq
Seq('AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())
>>> EcoRI.search(ecoseq)
[16]
We therefore have a site at position 16 of the sequence ecoseq. The position returned by the method search is the first base of the downstream segment produced by a restriction (i.e. the first base after the position where the enzyme will cut). The Restriction package follows biological convention (the first base of a sequence is base 1). No need to make difficult conversions between your recorded biological data and the results produced by the enzymes in this package.

1.4  Retrieving the sequences produced by a digestion

Seq objects as all python sequence, have different conventions and the first base of a sequence is base 0. Therefore to get the sequences produced by an EcoRI digestion of ecoseq, one should do the following :
>>> ecoseq[:15], ecoseq[15:] 
(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA()))
I hear you thinking "this is a cumbersome and error prone method to get these sequences".  To simplify your life, enzymes provide another method to get theses sequences without hassle : catalyse. This method will return a tuple containing all the fragments produced by a complete digestion of the sequence. Using it is as simple as before :
>>> EcoRI.catalyse(ecoseq)
(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA()))

1.5  Analysing circular sequences

Now, if you have entered the previous command in your shell you may have noticed that both 'search' and 'catalyse' can take a second argument 'linear' which default to True. Using this will allow you to simulate circular sequences such as plasmids. Setting linear to False inform the enzyme to make the search over a circular sequence and to search for potential sites spanning over the boundaries of the sequence.
>>> EcoRI.search(ecoseq, linear=False)
[16]
>>> EcoRI.catalyse(ecoseq, linear=False)
(Seq('AATTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAG', IUPACAmbiguousDNA()),)
>>> ecoseq  # for memory
Seq('AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())
OK, this is quite a difference, we only get one fragment, which correspond to the linearised sequence. The beginning sequence has been shifted to take this fact into account. Moreover we can see another difference :
>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
>>> EcoRI.search(new_seq)
[]
>>> EcoRI.search(new_seq, linear=False)
[33]
As you can see using 'linear=False', make a site appears in the sequence new_seq. This site does not exist in a linear sequence as the EcoRI site is split into two halves at the start and the end of the sequence. In a circular sequence however, the site is effectively present when the beginning and end of the sequence are joined.

1.6 Comparing enzymes with each others

Restriction Enzymes define 4 comparative operators ==, !=, >> and %. All these operator compares two enzymes together and either return True or False.

== :

It will return True if the two sides of the operator are the same. same is defined as : same name, same site, same overhang (i.e. the only thing which is equal to EcoRI is EcoRI).

!= :

It will return True if the two sides of the operator are different. Two enzymes are not different if the result produced by one enzyme will always be the same as the result produced by the other (i.e. True isoschizomers will not being the same enzymes, are not different since they are interchangeable).

>> :

True if the enzymes recognise the same site, but cut it in a different way (i.e. the enzymes are neoschizomers).

% :

Test the compatibility of the ending produced by the enzymes. (will be True if the fragments produced with one of the enzyme can directly be ligated to fragments produced by the other).

Let's use Acc65I and its isoschizomers as example :
>>> Acc65I.isoschizomers()
[Asp718I, KpnI]
>>> Acc65I.elucidate()
'G^GTAC_C'
>>> Asp718I.elucidate()
'G^GTAC_C'
>>> KpnI.elucidate()
'G_GTAC^C'
>>> # Asp718I and Acc65I are True isoschizomers, 
>>> # they recognise the same site and cut it the 
>>> # same way.
>>> # KpnI is a neoschizomers of the 2  others.
>>> # here is the results of the 4 operator
>>> # for each pair of enzymes
>>> 
>>> ############# x == y  (x is y)
>>> Acc65I == Acc65I  	# same enzyme => True
True
>>> Acc65I == KpnI    	# all other cases => False
False
>>> Acc65I == Asp718I
False
>>> Acc65I == EcoRI
False
>>> ############ x != y  (x and y are not true isoschizomers)
>>> Acc65I != Acc65I	# same enzyme => False
False
>>> Acc65I != Asp718I	# different enzymes, but cut same manner => False
False
>>> Acc65I != KpnI      # all other cases => True
True
>>> Acc65I != EcoRI
True
>>> ###########  x >> y (x is neoschizomer of y)
>>> Acc65I >> Acc65I	# same enzyme => False
False
>>> Acc65I >> Asp718I   # same site, same cut => False
False
>>> Acc65I >> EcoRI     # different site => False
False
>>> Acc65I >> KpnI      # same site, different cut => True
True 
>>> ########### x % y   (fragments produced by x and fragments produced by y
>>> #			 can be directly ligated to each other)
>>> Acc65I % Asp718I
True
>>> Acc65I % Acc65I
True
>>> Acc65I % KpnI   # KpnI -> '3 overhang, Acc65I-> 5' overhang => False 
False
>>>
>>> SunI.elucidate()
'C^GTAC_G'
>>> SunI == Acc65I
False
>>> SunI != Acc65I
True
>>> SunI >> Acc65I
False
>>> SunI % Acc65I  # different site, same overhang (5' GTAC) => True
True
>>> SmaI % EcoRV   # 2 Blunt enzymes, all blunt enzymes are compatible => True
True

1.7 Other facilities provided by the enzyme classes

The restriction enzymes class provided quite a number of others methods. We will not go through all of them, but only have a quick look to the most useful ones.

Not all enzymes possess the same properties when it comes to the way they digest a DNA. If you want to know more about the way a particular enzyme cut you can use the three following methods. They are fairly straightforward to understand and refer to the ends that the enzyme produces blunt, 5' overhanging (also called 3' recessed) sticky end and 3' overhanging (or 5' recessed) sticky end.
>>> EcoRI.is_blunt()
False
>>> EcoRI.is_5overhang()
True
>>> EcoRI.is_3overhang()
False
A more detailled view of the restriction site can be produced using the elucidate() method. the "^" refers to the position of the cut in the sense strand of the sequence. "_" to the cut on the antisense or complementary strand.  "^_" means blunt. 
>>> EcoRI.elucidate()
'G^AATT_C'
>>> KpnI.elucidate()
'G_GTAC^C'
>>> EcoRV.elucidate()
'GAT^_ATC'
The method frequency will give you the statiscal frequency() of the enzyme site. 
>>> EcoRI.frequency()
4096
>>> XhoII.elucidate()
'R^GATC_Y'
>>> XhoII.frequency()
1024
To get the length of a the recognition sequence of an enzyme use the built-in function len() :
>>> len(EcoRI)
6
>>> BstXI.elucidate()
'CCAN_NNNN^NTGG'
>>> len(BstXI)
12
>>> FokI.site
'GGATG'
>>> FokI.elucidate()  	# FokI cut well outside its recognition site
'GGATGNNNNNNNNN^NNNN_N'
>>> len(FokI)	   	# its length is the length of the recognition site	
5
Also interesting are the methods dealing with isoschizomers. For memory, two enzymes are isoschizomers if they share a same recognition site.
A further division is made between isoschizomers (same name, recognise the same sequence and cut the same way) and neoschizomers which cut at different positions. equischizomer is an arbitrary choice to design "isoschizomers_that_are_not_neoschizomers" as this last one was a bit long.
Another set of method one_enzyme.is_*schizomers(one_other_enzyme), allow to test 2 enzymes against each other.
>>> Acc65I.isoschizomers()
[Asp718I, KpnI]
>>> Acc65I.neoschizomers()
[KpnI]
>>> Acc65I.equischizomers()
[Asp718I]
>>> KpnI.elucidate()
'G_GTAC^C'
>>> Acc65I.elucidate()
'G^GTAC_C'
>>> KpnI.is_neoschizomer(Acc65I)
True
>>> KpnI.is_neoschizomer(KpnI)
False
>>> KpnI.is_isoschizomer(Acc65I)
True
>>> KpnI.is_isoschizomer(KpnI)
True
>>> KpnI.is_equischizomer(Acc65I)
False
>>> KpnI.is_equischizomer(KpnI)
True
suppliers() will get you the list of all the suppliers of the enzyme. all_suppliers() will give you all the suppliers in the database.

2  The RestrictionBatch class : a class to deal with several enzymes

If you want to make a restriction map of a sequence, using individual enzymes can become tedious and will endures a big overhead due to the repetitive conversion of the sequence to a FormattedSeq. Restriction provides a class to make easier the use of large number of enzymes in one go : RestrictionBatch.
RestrictionBatch will help you to manipulate lots of enzymes with a single command. Moreover all the enzymes in the restrictionBatch will share the same converted sequence, reducing the overhead to

2.1 Creating a RestrictionBatch

You can initiate a restriction batch by passing it a list of enzymes or enzymes name as argument. 
>>> rb = RestrictionBatch([EcoRI])
>>> rb
RestrictionBatch(['EcoRI'])
>>> rb2 = RestrictionBatch(['EcoRI'])
RestrictionBatch(['EcoRI'])
>>> rb == rb2
True
Adding a new enzyme to a restriction batch is easy :
>>> rb.add(KpnI)
>>> rb
RestrictionBatch(['EcoRI', 'KpnI'])
>>> rb += EcoRV
>>> rb
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])])
Another way to create a RestrictionBatch is by simply adding restriction enzymes together, this is particularly useful for small batches :
>>> rb3 = EcoRI + KpnI + EcoRV
>>> rb3
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])

2.2 Restricting a RestrictionBatch to a particular supplier

The Restriction package is based upon the Rebase database. This database gives a list of suppliers for each enzyme. It would be a shame not to make use of this facility. You can Produce a RestrictionBatch containing only enzymes from one or a few supplier(s). Here is how to do it :
>>> rb_supp = RestrictionBatch(first=[], suppliers=['A','C','E','G','F','I','H','K','J','M','O','N','Q','P','S','R','U','V','X'])
>>> # This will create a RestrictionBatch with the all enzymes which possess a supplier.
The argument 'suppliers' take a list of one or several single letter codes corresponding to the supplier(s). The codes are the same as defined in Rebase. As it would be a pain to have to remember each supplier code, RestrictionBatch provides a method which show the pair code <=> supplier :
>>> RestrictionBatch.show_codes()	# as of july 2004 Rebase release.
A = Amersham Pharmacia Biotech
C = Minotech Biotechnology
E = Stratagene
G = Qbiogene
F = Fermentas AB
I = SibEnzyme Ltd.
H = American Allied Biochemical, Inc.
K = Takara Shuzo Co. Ltd.
J = Nippon Gene Co., Ltd.
M = Roche Applied Science
O = Toyobo Biochemicals
N = New England Biolabs
Q = CHIMERx
P = Megabase Research Products
S = Sigma Chemical Corporation
R = Promega Corporation
U = Bangalore Genei
V = MRC-Holland
X = EURx Ltd.
>>> # You can now choose a code and built your RestrictionBatch
This way of producing a RestrictionBatch can drastically reduce the amount of useless output from a restriction analysis, limiting the search to enzymes that you can get hold of and limiting the risks of nervous breakdown. Nothing is more frustrating than to get the perfect enzyme for a sub-cloning only to find it's not commercially available.

2.3 Adding enzymes to a RestrictionBatch

Adding an enzyme to a batch if the enzyme is already present will not raise an exception, but will have no effects. Sometimes you want to get an enzyme from a RestrictionBatch or add it to the batch if it is not present.
You will use the get method setting the second argument to True.
>>> rb3
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
>>> rb3.add(EcoRI)
>>> rb3
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
>>> rb3.get(EcoRI)
EcoRI
>>> rb3.get(SmaI)

Traceback (most recent call last):
  File "<pyshell#4>", line 1, in -toplevel-
    rb3.get(SmaI)
  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 1800, in get
    raise ValueError, 'enzyme %s is not in RestrictionBatch'%e.__name__
ValueError: enzyme SmaI is not in RestrictionBatch
>>> rb3.get(SmaI, True)
SmaI
>>> rb3
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI', 'SmaI'])

2.4 Removing enzymes from a RestrictionBatch

Removing enzymes from a Batch is done using the remove() method. If the enzyme is not present in the batch this will raise a KeyError. If the value you want to remove is not an enzyme this will raise a ValueError.
>>> rb3.remove(EcoRI)
>>> rb3
RestrictionBatch(['EcoRV', 'KpnI', 'SmaI'])
>>> rb3.remove(EcoRI)

Traceback (most recent call last):
  File "<pyshell#14>", line 1, in -toplevel-
    rb3.remove('EcoRI')
  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 1839, in remove
    return Set.remove(self, self.format(other))
  File "/usr/lib/python2.3/sets.py", line 534, in remove
    del self._data[element]
KeyError: EcoRI
>>> rb3 += EcoRI
>>> rb3 
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI', 'SmaI'])
>>> rb3.remove('EcoRI')
>>> rb3
RestrictionBatch(['EcoRV', 'KpnI', 'SmaI'])
>>> rb3.remove('spam')

Traceback (most recent call last):
  File "<pyshell#18>", line 1, in -toplevel-
    rb3.remove('spam')
  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 1839, in remove
    return Set.remove(self, self.format(other))
  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 1871, in format
    raise ValueError, '%s is not a RestrictionType'%y.__class__
ValueError: <type 'str'> is not a RestrictionType

2.5 Manipulating RestrictionBatch

You can not however add batch together, as they are python sets. You must use the operator | instead. You can find the intersection between 2 batches using & (see the python documentation about sets for more information (or import sets; help(sets)).
>>> rb3 = EcoRI+KpnI+EcoRV
>>> rb3
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
>>> rb4 = SmaI + PstI
>>> rb4
RestrictionBatch(['PstI', 'SmaI'])
>>> rb3 + rb4

Traceback (most recent call last):
  File "<pyshell#23>", line 1, in -toplevel-
    rb3 + rb4
  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 1829, in __add__
    new.add(other)
  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 1848, in add
    return Set.add(self, self.format(other))
  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 1871, in format
    raise ValueError, '%s is not a RestrictionType'%y.__class__
ValueError: <class 'Bio.Restriction.Restriction.RestrictionBatch'> is not a RestrictionType
>>> rb3 | rb4
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI', 'PstI', 'SmaI'])
>>> rb3 & rb4
RestrictionBatch([])
>>> rb4 += EcoRI
>>> rb4
RestrictionBatch(['EcoRI', 'PstI', 'SmaI'])
>>> rb3 & rb4
RestrictionBatch(['EcoRI'])

2.6 Analysing sequences with a RestrictionBatch

To analyse a sequence for potential site, you can use the search method of the batch, the same way you did for restriction enzymes. The results is no longer a list however, but a dictionary. The keys of the dictionary are the names of the enzymes and the value a list of position site. RestrictionBatch does not implement a catalyse method, as it would not have a real meaning when used with large batch.
>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
>>> rb.search(new_seq)
{'KpnI': [], 'EcoRV': [], 'EcoRI': []}
>>> rb.search(new_seq, linear=False)
{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}

2.7 Other RestrictionBatch methods

Amongst the other methods provided by RestrictionBatch elements() which return a list of all the element names alphabetically sorted is certainly the most useful.
>>> rb = EcoRI + KpnI + EcoRV
>>> rb.elements()
['EcoRI', 'EcoRV', 'KpnI']
If you don't care about the alphabetical order use the method as_string(), to get the same thing a bit faster. The list is not sorted. The order is random as python sets are dictionary.
>>> rb = EcoRI + KpnI + EcoRV 
>>> rb.as_string()
['EcoRI', 'KpnI', 'EcoRV']
Other RestrictionBatch methods are generally used for particular purposes and will not be discussed here. See the source if you are interested.

3  AllEnzymes and CommOnly : two preconfigured RestrictionBatches

While it is sometime practical to produce a RestrictionBatch of your own you will certainly more frequently use the two batches provided with the Restriction packages : AllEnzymes and CommOnly. These two batches contain respectively all the enzymes in the database and only the enzymes which have a commercial supplier. They are rather big, but that's what make them useful. With these batch you can produce a full description of a sequence with a single command. You can use these two batch as any other batch.
>>> len(AllEnzymes)
671
>>> len(CommOnly)
589
>>> AllEnzymes.search(new_seq) ...
There is not a lot to say about them apart the fact that they are present. They are really normal batches, and you can use them as any other batch.

4  The Analysis class : even simpler restriction analysis

RestrictionBatch can give you a dictionary with the sites for all the enzymes in a RestrictionBatch. However, it is sometime nice to get something a bit easier to read than a python dictionary. Complex restriction analysis are not easy with RestrictionBatch. Some refinements in the way to search a sequence for restriction sites will help. Analysis provides a serie of command to customise the results obtained from a pair RestrictionBatch/sequence and some facilities to make the output sligthly more human readable.

4.1 Setting up an Analysis

To build a Restriction Analysis you will need a RestrictionBatch and a sequence and to tell it if the sequence is linear or circular. The first argument Analysis take is the RestrictionBatch, the second is the sequence. If the third argument is not provided Analysis will assume the sequence is linear.
>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
>>> rb = RestrictionBatch([EcoRI, KpnI, EcoRV])
>>> Ana = Analysis(rb, new_seq, linear=False)
>>> Ana
Analysis(RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI']),Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA()),False)

4.2 Full restriction analysis

Once you have created your new Analysis, you can use it to get a restriction analysis of your sequence. The way to make a full restriction analysis of the sequence is :
>>> Ana.full()
{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}
This is much the same as the output of a RestrictionBatch.search method. You will get a more easy to read output with "print_that" used without argument :
>>> 
>>> # let's create a something a bit more complex to analyse.
>>>
>>> rb = RestrictionBatch([], ['A'])	# we will explain the meaning of the
>>>					# double list argument later. 
>>>
>>> multi_site = Seq('AAA' + EcoRI.site +'G' + KpnI.site + EcoRV.site + 'CT' +\  
SmaI.site + 'GT' + FokI.site + 'GAAAGGGC' + EcoRI.site + 'ACGT', IUPACAmbiguousDNA())
>>>
>>> Analong = Analysis(rb, multi_site) 
>>> Analong.full()
{'EaeI': [], 'CpoI': [], 'AccII': [], 'Bpu1102I': [], 'HindIII': [], 'BalI': [],
 'NaeI': [], 'BssHII': [], 'HapII': [27], 'BamHI': [], 'XhoI': [], 'EcoO109I': [
], 'XbaI': [], 'SacII': [], 'AvaII': [], 'BbeI': [], 'FokI': [47], 'Eco81I': [],
 'BanII': [], 'KpnI': [16], 'NcoI': [], 'FseI': [], 'ApaLI': [], 'NheI': [], 'Ap
aI': [], 'AatII': [], 'DraI': [], 'EcoRV': [20], 'BstXI': [], 'HaeIII': [], 'Mlu
I': [], 'Aor51HI': [], 'EcoRI': [5, 47], 'AvaI': [26], 'PvuI': [], 'EcoT22I': []
, 'PstI': [], 'MvaI': [], 'NotI': [], 'HinfI': [], 'ScaI': [], 'NdeI': [], 'AccI
': [], 'MboII': [], 'SnaBI': [], 'SspI': [], 'HhaI': [], 'BglI': [], 'Cfr13I': [
], 'SpeI': [], 'AflII': [], 'HpaI': [], 'Van91I': [], 'SfiI': [], 'SphI': [], 'B
sp1286I': [], 'SmaI': [28], 'NruI': [], 'FbaI': [], 'AluI': [], 'BglII': [], 'Hi
ncII': [], 'StuI': [], 'Sse8387I': [], 'ClaI': [], 'Sau3AI': [], 'MspI': [27], '
PshAI': [], 'AfaI': [14], 'MboI': [], 'PmaCI': [], 'SacI': [], 'PvuII': [], 'Eco
T14I': [], 'SalI': [], 'BlnI': [], 'TaqI': []}
>>>
>>> # The results are here but it is difficult to read. let's try print_that
>>>
>>> Analong.print_that()

AfaI       :  14.
AvaI       :  26.
EcoRI      :  5, 47.
EcoRV      :  20.
FokI       :  47.
HapII      :  27.
KpnI       :  16.
MspI       :  27.
SmaI       :  28.

   Enzymes which do not cut the sequence.

AatII     AccI      AccII     AflII     AluI      Aor51HI   ApaI      ApaLI     
AvaII     BalI      BamHI     BanII     BbeI      BglI      BglII     BlnI      
Bpu1102I  Bsp1286I  BssHII    BstXI     Cfr13I    ClaI      CpoI      DraI      
EaeI      Eco81I    EcoO109I  EcoT14I   EcoT22I   FbaI      FseI      HaeIII    
HhaI      HincII    HindIII   HinfI     HpaI      MboI      MboII     MluI      
MvaI      NaeI      NcoI      NdeI      NheI      NotI      NruI      PmaCI     
PshAI     PstI      PvuI      PvuII     SacI      SacII     SalI      Sau3AI    
ScaI      SfiI      SnaBI     SpeI      SphI      Sse8387I  SspI      StuI      
TaqI      Van91I    XbaI      XhoI
Much clearer, is'nt ? The output is optimised for a shell 80 columns wide. If the output seems odd, check that  the width of your shell is at least 80 columns.

4.3 Changing the title

You can provide a title to the analysis and modify the sentence 'Enzymes which do not cut the sequence', by setting the two optional arguments of print_that  "title" and "s1". No formating will be done on these strings so if you have to include the newline ('\n') as you see fit :
>>> Analong.print_that(None, title='sequence = multi_site\n\n')

sequence = multi_site

AfaI       :  14.
AvaI       :  26.
EcoRI      :  5, 47.
EcoRV      :  20.
FokI       :  47.
HapII      :  27.
KpnI       :  16.
MspI       :  27.
SmaI       :  28.

   Enzymes which do not cut the sequence.

AatII     AccI      AccII     AflII     AluI      Aor51HI   ApaI      ApaLI     
AvaII     BalI      BamHI     BanII     BbeI      BglI      BglII     BlnI      
Bpu1102I  Bsp1286I  BssHII    BstXI     Cfr13I    ClaI      CpoI      DraI      
EaeI      Eco81I    EcoO109I  EcoT14I   EcoT22I   FbaI      FseI      HaeIII    
HhaI      HincII    HindIII   HinfI     HpaI      MboI      MboII     MluI      
MvaI      NaeI      NcoI      NdeI      NheI      NotI      NruI      PmaCI     
PshAI     PstI      PvuI      PvuII     SacI      SacII     SalI      Sau3AI    
ScaI      SfiI      SnaBI     SpeI      SphI      Sse8387I  SspI      StuI      
TaqI      Van91I    XbaI      XhoI  
>>> Analong.print_that(None, 
		       title = 'sequence = multi_site\n\n',
		        s1 = '\n no site :\n\n')

sequence = multi_site

AfaI       :  14.
AvaI       :  26.
EcoRI      :  5, 47.
EcoRV      :  20.
FokI       :  47.
HapII      :  27.
KpnI       :  16.
MspI       :  27.
SmaI       :  28.

 no site :

AatII     AccI      AccII     AflII     AluI      Aor51HI   ApaI      ApaLI     
AvaII     BalI      BamHI     BanII     BbeI      BglI      BglII     BlnI      
Bpu1102I  Bsp1286I  BssHII    BstXI     Cfr13I    ClaI      CpoI      DraI      
EaeI      Eco81I    EcoO109I  EcoT14I   EcoT22I   FbaI      FseI      HaeIII    
HhaI      HincII    HindIII   HinfI     HpaI      MboI      MboII     MluI      
MvaI      NaeI      NcoI      NdeI      NheI      NotI      NruI      PmaCI     
PshAI     PstI      PvuI      PvuII     SacI      SacII     SalI      Sau3AI    
ScaI      SfiI      SnaBI     SpeI      SphI      Sse8387I  SspI      StuI      
TaqI      Van91I    XbaI      XhoI

4.4 Customising the output

You can modify some aspects of the output interactively. There is three main type of output, two listing types (alphabetically sorted and sorted by number of site) and map-like type. To change the output, use the method print_as() of Analysis. The change of output is permanent for the instance of Analysis (that is until the next time you use  print_as()). The argument of print_as() are strings : 'map', 'number' or 'alpha'. As you have seen previously the default behaviour is an alphabetical list ('alpha').
>>> Analong.print_as('map')    
>>> Analong.print_that()

    5 EcoRI
    |                                                       
    |        14 AfaI
    |        |                                              
    |        | 16 KpnI
    |        | |                                            
    |        | |   20 EcoRV
    |        | |   |                                        
    |        | |   |     26 AvaI
    |        | |   |     |                                  
    |        | |   |     |27 HapII MspI
    |        | |   |     ||                                 
    |        | |   |     ||28 SmaI
    |        | |   |     |||                                
    |        | |   |     |||                  47 EcoRI FokI
    |        | |   |     |||                  |             
AAAGAATTCGGGTACCGATATCCTCCCGGGGTGGATGGAAAGGGCGAATTCACGT
|||||||||||||||||||||||||||||||||||||||||||||||||||||||
TTTCTTAAGCCCATGGCTATAGGAGGGCCCCACCTACCTTTCCCGCTTAAGTGCA
1                                                    55


   Enzymes which do not cut the sequence.

AatII     AccI      AccII     AflII     AluI      Aor51HI   ApaI      ApaLI     
AvaII     BalI      BamHI     BanII     BbeI      BglI      BglII     BlnI      
Bpu1102I  Bsp1286I  BssHII    BstXI     Cfr13I    ClaI      CpoI      DraI      
EaeI      Eco81I    EcoO109I  EcoT14I   EcoT22I   FbaI      FseI      HaeIII    
HhaI      HincII    HindIII   HinfI     HpaI      MboI      MboII     MluI      
MvaI      NaeI      NcoI      NdeI      NheI      NotI      NruI      PmaCI     
PshAI     PstI      PvuI      PvuII     SacI      SacII     SalI      Sau3AI    
ScaI      SfiI      SnaBI     SpeI      SphI      Sse8387I  SspI      StuI      
TaqI      Van91I    XbaI      XhoI 

>>> Analong.print_as('number')
>>> Analong.print_that()



enzymes which cut 1 times :

AfaI       :  15.
AvaI       :  27.
EcoRV      :  21.
FokI       :  48.
HapII      :  28.
KpnI       :  17.
MspI       :  28.
SmaI       :  29.


enzymes which cut 2 times :

EcoRI      :  6, 48.

   Enzymes which do not cut the sequence.

AatII     AccI      AccII     AflII     AluI      Aor51HI   ApaI      ApaLI     
AvaII     BalI      BamHI     BanII     BbeI      BglI      BglII     BlnI      
Bpu1102I  Bsp1286I  BssHII    BstXI     Cfr13I    ClaI      CpoI      DraI      
EaeI      Eco81I    EcoO109I  EcoT14I   EcoT22I   FbaI      FseI      HaeIII    
HhaI      HincII    HindIII   HinfI     HpaI      MboI      MboII     MluI      
MvaI      NaeI      NcoI      NdeI      NheI      NotI      NruI      PmaCI     
PshAI     PstI      PvuI      PvuII     SacI      SacII     SalI      Sau3AI    
ScaI      SfiI      SnaBI     SpeI      SphI      Sse8387I  SspI      StuI      
TaqI      Van91I    XbaI      XhoI      

>>>
To come back to the previous behaviour :
>>> Analong.print_as('alpha')
>>> Analong.print_that()

AfaI       :  14.
AvaI       :  26.
EcoRI      :  5, 47.
EcoRV      :  20.
etc ...

4.5 Fancier restriction analysis

I will not go into the detail for each single method, here are all the functions that are available. Most are perfectly self explanatory and the others are fairly well documented (use help('Analysis.command_name')). The methods are :
	full(self,linear=True) 
	blunt(self,dct = None)             
	overhang5(self, dct=None) 
	overhang3(self, dct=None)  
	defined(self,dct=None)  
	with_sites(self, dct=None)  
	without_site(self, dct=None)  
	with_N_sites(self, N, dct=None)  
	with_number_list(self, list, dct=None)  
	with_name(self, names, dct=None)    
	with_site_size(self, site_size, dct=None)    
	only_between(self, start, end, dct=None)    
	between(self,start, end, dct=None)    
	show_only_between(self, start, end, dct=None)    
	only_outside(self, start, end, dct =None)    
	outside(self, start, end, dct=None)    
	do_not_cut(self, start, end, dct =None)
Using these methods is simple :
>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
>>> rb = RestrictionBatch([EcoRI, KpnI, EcoRV])
>>> Ana = Analysis(rb, new_seq, linear=False)
>>> Ana
Analysis(RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI']),Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA()),False)
>>> Ana.blunt()  		# output only the result for enzymes which cut blunt
{'EcoRV': []}
>>> Ana.full()			# all the enzymes in the RestrictionBatch
{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}
>>> Ana.with_sites()		# output only the result for enzymes which have a site in the sequence
{'EcoRI': [33]}
>>> Ana.without_site()		# output only the enzymes which have no site in the sequence
{'KpnI': [], 'EcoRV': []}
>>> Ana.only_between(1, 20)	# the enzymes which cut between the base pairs 1 and 20
{}
>>> Ana.only_between(20, 34)    # etc...
{'EcoRI': [33]}
>>> Ana.only_outside(20, 34)
{}
>>> Ana.with_name([EcoRI])
{'EcoRI': [33]}
>>>
To get a nice output, you still use print_that but this time with the command you want executed as argument.
>>> Ana.print_that(Ana.blunt())


   Enzymes which do not cut the sequence.

EcoRV     

>>> pt = Ana.print_that
>>> pt(Ana.with_sites())

EcoRI      :  33.

>>> pt(Ana.without_site())


   Enzymes which do not cut the sequence.

EcoRV     KpnI      

>>> # etc ...

4.6 More complex analysis

All of these methods (except full() which, well ... do a full restriction analysis) can be supplied with an additional dictionary.
If no dictionary is supplied a full restriction analysis is used as starting point. Otherwise the dictionary provided by the argument dct is used. The dictionary must be formatted as the result of RestrictionBatch.search. Therefore of the form {'enzyme_name' : [position1, position2],...}, where position1 and position2 are integer. All methods list previously output such dictionaries and can be used as starting point.

Using this method you can build really complex query by chaining several method one after the other. For example if you want all the enzymes which are 5' overhang and cut the sequence only once, you have two ways to go :

The hard way consist to build a RestrictionBatch containing only 5' overhang enzymes and use this batch to create a new analysis instance and then use the method with_N_sites() as follow :
>>> rbov5 = RestrictionBatch([x for x in rb if x.is_5overhang()])
>>> Anaov5 = Analysis(rbov5, new_seq, linear=False)
>>> Anaov5.with_N_sites(1)
{'EcoRI' : [33]}
The easy solution is to chain several Analysis methods. This is possible since each method return a dictionary as results and is able to take a dictionary as input:
>>> Ana.with_N_sites(1, Ana.overhang5())
{'EcoRI': [33]}
The dictionary is always the last argument whatever the command you use.

The way to prefer certainly depends of the conditions you will use your Analysis instance. If you are likely to frequently reuse the same batch with different sequences, using a dedicated RestrictionBatch might be faster as the batch is likely to be smaller. Chaining methods is generally quicker when working with an interactive shell.  In a script, the extended syntax may be easier to understand in a few months.

5  Advanced features : the FormattedSeq class

Restriction enzymes require a much more strict formatting of the DNA sequences than Bio.Seq object provides. For example, the restriction enzymes expect to find an ungapped (no space) upper-case sequence, while Bio.Seq object allow sequences to be in lower-case separated by spaces. Therefore when  a restriction enzyme analyse a Bio.Seq object (be it a Seq or a MutableSeq), the object undergoes a conversion. The class FormattedSeq ensure the smooth conversion from a  Bio.Seq object to something which can be safely be used by the enzyme.

While this conversion is done automatically by the enzymes if you provide them with a Seq  or a MutableSeq, there is time where it will be more efficient to realise the conversion before hand. Each time a Seq object is passed to an enzyme for analysis you pay a overhead due to the conversion. When analysing the same sequence over and over, it will be faster to convert the sequence, store the conversion and then use only the converted sequence.

5.1 Creating a FormattedSeq

Creating a FormattedSeq from a Bio.Seq object is simple. The first argument of FormattedSeq is the sequence you wish to convert. You can specify a shape with the second argument linear, if you don't the FormattedSeq will be linear : 
>>> from Bio.Restriction import *
>>> from Bio.Seq import Seq
>>> seq = Seq('TTCAAAAAAAAAAGAATTCAAAAGAA')
>>> linear_fseq = FormattedSeq(seq, linear=True) 
>>> default_fseq = FormattedSeq(seq)
>>> circular_fseq = FormattedSeq(seq, linear=False)
>>> linear_fseq
FormattedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)
>>> linear_fseq.is_linear()
True
>>> default_fseq.is_linear()
True
>>> circular_fseq.is_linear()
False
>>> circular_fseq
FormattedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)

5.2 Unlike Bio.Seq, FormattedSeq retains information about their shape

FormattedSeq retains information about the shape of the sequence. Therefore unlike with Seq and MutableSeq you don't need to specify the shape of the sequence when using search() or catalyse():
>>> EcoRI.search(linear_fseq)
[15]
>>> EcoRI.search(circular_fseq)  # no need to specify the shape
[15, 25]
In fact, the shape of a FormattedSeq is not altered by the second argument of the commands search() and catalyse() :
>>> # In fact the shape is blocked.
>>> # The 3 following commands give the same results
>>> # which correspond to a circular sequence
>>> EcoRI.search(circular_fseq) 
[15, 25]
>>> EcoRI.search(circular_fseq, linear=True)
[15, 25]
>>> EcoRI.search(circular_fseq, linear=False)
[15, 25]
>>>

5.3 Changing the shape of a FormattedSeq

You can however change the shape of the FormattedSeq. The command to use are :
FormattedSeq.to_circular() => new FormattedSeq, shape will be circular. 
FormattedSeq.to_linear()   => new FormattedSeq, shape will be linear
FormattedSeq.circularise() => change the shape of FormattedShape to circular
FormattedSeq.linearise()   => change the shape of FormattedShape to linear

>>> circular_fseq
FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)
>>> circular_fseq.is_linear()
False
>>> circular_fseq == linear_fseq
False
>>> newseq = circular_fseq.to_linear()
>>> circular_fseq
FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)
>>> newseq
FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)
>>> circular_fseq.linearise()
>>> circular_fseq
FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)
>>> circular_fseq.is_linear()
True
>>> circular_fseq == linear_fseq
True
>>> EcoRI.search(circular_fseq) # which is now linear
[15]

5.4 Using / and // operators with FormattedSeq

Not having to specify the shape of the sequence to analyse gives you the opportunity to use the shorthand '/' and '//' with restriction enzymes :
>>> EcoRI/linear_fseq  # <=> EcoRI.search(linear_fseq)
[15]
>>> linear_fseq/EcoRI  # <=> EcoRI.search(linear_fseq)
[15]
>>> EcoRI//linear_fseq # <=> linear_fseq//EcoRI <=> EcoRI.catalyse(linear_fseq)
(Seq('TTCAAAAAAAAAAG', Alphabet()), Seq('AATTCAAAAGAA', Alphabet()))
Another way to avoid the overhead due to a repetitive conversion from a Seq object to a FormattedSeq is to use a RestrictionBatch.

To conclude, the performance gain achieved when using a FormattedSeq instead of a Seq is not huge. The analysis of a 10 kb sequence by all the enzymes in AllEnzymes (for x in AllEnzymes : x.search(seq), 671 enzymes) is 7 % faster when using a FormattedSeq than a Seq. Using a RestrictionBatch (AllEnzymes.search(seq)) is about as fast as using a FormattedSeq the first time the search is run. This however is dramatically reduced in subsequent runs with the same sequence (RestrictionBatch keep in memory the result of their last run while the sequence is not changed).

6  More advanced features

This chapter addresses some more advanced features of the packages, most users can safely ignore it.

6.1 Updating the enzymes from Rebase : rebase_update.py

Most people will certainly not need to update the enzymes. The restriction enzyme package will be updated in with each new release of biopython. But if you wish to get an update in between biopython-releases here is how to do it. Each month, Rebase release a new compilation of data about restriction enzymes. While the enzymes do not change so frequently, you may wish to update the restriction enzymes classes. The first thing to do is to get the last rebase file. You can find the release of Rebase at http://rebase.neb.com/rebase.files.html. The file you are interested in are in the EMBOSS format. You can download the files directly from the rebase ftp server using your browser. The file are situated at ftp://ftp.neb.com/pub/rebase.
You will have to download 3 files :
                emboss_e.###
                emboss_r.###
                emboss_s.###
The ### is a 3 digit number corresponding to the year and month of the release. The first digit is the year, the two last are the month : so July 2004 will be : 407; October 2005 : 510, etc... Download the three file corresponding to the current month and place them in a folder.

Another way to do the same thing is to use the rebase_update.py. script provided in the package. The script is in biopython/Bio/Restriction/Scripts. It will connect directly to the rebase ftp server and download the last batch of emboss files. From a DOS or Unix shell do the following :
$ cd path_to_/Scripts
$ ls
ranacompiler.py  rebase_update.py
$ ./rebase_update.py -m your_e_mail@my_address.com -p http://www.somewhere.com:8000

Please wait, trying to connect to Rebase


copying ftp://ftp.neb.com/pub/rebase/emboss_e.407
to /cvsroot/biopython/Bio/Restriction/Scripts/emboss_e.407
copying ftp://ftp.neb.com/pub/rebase/emboss_s.407
to /cvsroot/biopython/Bio/Restriction/Scripts/emboss_s.407
copying ftp://ftp.neb.com/pub/rebase/emboss_r.407
to /cvsroot/biopython/Bio/Restriction/Scripts/emboss_r.407

Some explanation are needed : -m stands for e-mail, in order to connect to the ftp server you need to provide a your e-mail address. So replace your_e_mail@your_address.com with your e-mail address. -p is the switch to indicate to the script you are using a proxy. If you use a ftp proxy enter its address and the connection port after the ':'.

6.2 Compiling a new dictionary : the script ranacompiler.py

Once you have got a the last serie of emboss files you can compile a new module containing the data necessary to create restriction enzyme. You will need to get out of the python shell and open either a DOS shell on windows, or your prefered Unix shell for the others.

Note : if the emboss files are not present in the current directory or if they are not up to date, ranacompiler.py will invoke the script rebase_update. You will need to use the same options as before (ie -m and -p). See the previous paragraph on rebase_update.py for more details.

For simplicity let's assume we have put the emboss files in the same folder as the files which contains the script ranacompiler.py.

$ cd path_to_/Scripts
$ ls
emboss_e.407  emboss_r.407   emboss_s.407 ranacompiler.py  rebase_update.py

We will use the script ranacompiler.py. You may have the change the mode of the file to make it executable :
$ chmod '+x' ranacompiler.py
Now execute the script :
$ python ranacompiler.py  # or ./ranacompiler.py
You get normally the following message :
$ ./ranacompiler.py

 Using the files : emboss_e.407, emboss_r.407, emboss_s.407

WARNING : HaeIV cut twice with different overhang length each time.
	Unable to deal with this behaviour.
        This enzyme will not be included in the database. Sorry.
        Checking : Anyway, HaeIV is not commercially available.


WARNING : TaqII has two different sites.

WARNING : It seems that AspCNI is both commercially available
        and its characteristics are unknown.
        This seems counter-intuitive.
        There is certainly an error either in ranacompiler or
        in this REBASE release.
        The supplier is : New England Biolabs.

The new database contains 671 enzymes.

Writing the dictionary containing the new Restriction classes.  OK.

Writing the dictionary containing the suppliers datas.          OK.

Writing the dictionary containing the Restriction types.        OK.


 ******************************************************************************

                Compilation of the new dictionary : OK.
                Installation : No.

 You will find the newly created 'Restriction_Dictionary.py' file
 in the folder :

        /cvsroot/biopython/Bio/Restriction/Scripts

 Make a copy of 'Restriction_Dictionary.py' and place it with
 the other Restriction libraries.

 note :
 This folder should be :

        /usr/lib/python2.3/site-packages/Bio/Restriction


 ******************************************************************************
The first line indicate which emboss files have been used for the present compilation. You can safely ignore the warnings as long as the  compilation of the new dictionary : OK. is present in the last part of the output. They are here for debugging purpose. The number of enzymes in the new module is indicated as well as a list of the dictionary which have been compiled. The last part indicate that the module has been succesfully created but not installed. To finish the update you must copy the file /cvsroot/biopython/Bio/Restriction/Scripts/Restriction_Dictionary.py into the folder /usr/lib/python2.3/site-packages/Bio/Restriction/ as indicated by the script. Looking into the present folder, you will see to new files : the newly created dictionary Restriction_Dictionary.py and Restriction_Dictionary.old . This last file containing the old dictionary to which you can revert in case anything the new file is corrupted (this should not happen since the script is happy enough the new dictionary is good, but if there is a problem it is always nice to know you can revert to the previous setting without having to reinstall the whole thing.
$ ls
emboss_e.407  ranacompiler.py*            Restriction_Dictionary.py
emboss_r.407  rebase_update.py*
emboss_s.407  Restriction_Dictionary.old
$
$ # complete the installation by copying the new dictionary to the Bio/Restriction/ folder.
$ # You may have to become root to do this :
$
$ su -c "cp Restriction_Dictionary.py /usr/lib/python2.3/site-packages/Bio/Restriction/"
password :
Enter your password and that's it. If you whish, the script may install the folder for you as well, but you will have to run it as root if your normal user has no write access to your python installation (and it should'nt). Use the command ranacompiler.py -i or ranacompiler.py --install.
$ su -c "./ranacompiler.py -i"
password :

 Using the files : emboss_e.407, emboss_r.407, emboss_s.407

WARNING : HaeIV cut twice with different overhang length each time.
        Unable to deal with this behaviour.
        This enzyme will not be included in the database. Sorry.
        Checking : Anyway, HaeIV is not commercially available.


WARNING : TaqII has two different sites.

WARNING : It seems that AspCNI is both commercially available
        and its characteristics are unknown.
        This seems counter-intuitive.
        There is certainly an error either in ranacompiler or
        in this REBASE release.
        The supplier is : New England Biolabs.

The new database contains 671 enzymes.

Writing the dictionary containing the new Restriction classes.  OK.

Writing the dictionary containing the suppliers datas.          OK.

Writing the dictionary containing the Restriction types.        OK.


 ******************************************************************************


                Installing Restriction_Dictionary.py

        The new file seems ok. Proceeding with the installation.

         Everything ok. If you need it a version of the old
         dictionary have been saved in the Updates folder under
         the name Restriction_Dictionary.old.

 ******************************************************************************
Much of the same really, but this time the module has directly been installed with your other python modules, you don't need to do anything more. If anything goes wrong (you have no write access to the destination folder for example) the script will let you know it did not perform the installation. It will however still save the new module in the current directory :
$ ./ranacompiler.py -i

 Using the files : emboss_e.407, emboss_r.407, emboss_s.407

WARNING : HaeIV cut twice with different overhang length each time.
        Unable to deal with this behaviour.
        This enzyme will not be included in the database. Sorry.
        Checking : Anyway, HaeIV is not commercially available.


WARNING : TaqII has two different sites.

WARNING : It seems that AspCNI is both commercially available
        and its characteristics are unknown.
        This seems counter-intuitive.
        There is certainly an error either in ranacompiler or
        in this REBASE release.
        The supplier is : New England Biolabs.

The new database contains 671 enzymes.

Writing the dictionary containing the new Restriction classes.  OK.

Writing the dictionary containing the suppliers datas.          OK.

Writing the dictionary containing the Restriction types.        OK.


 ******************************************************************************


                Installing Restriction_Dictionary.py

        The new file seems ok. Proceeding with the installation.

 ******************************************************************************


         WARNING : Impossible to install the new dictionary.
         Are you sure you have write permission to the folder :

         /usr/lib/python2.3/site-packages/Bio/Restriction ?



 ******************************************************************************

                Compilation of the new dictionary : OK.
                Installation : No.

 You will find the newly created 'Restriction_Dictionary.py' file
 in the folder :

        /home/bssfs/cvsroot/biopython/Bio/Restriction/Scripts

 Make a copy of 'Restriction_Dictionary.py' and place it with
 the other Restriction libraries.

 note :
 This folder should be :

        /usr/lib/python2.3/site-packages/Bio/Restriction


 ******************************************************************************
As you can see the script is not very bright and will redo the compilation each time it is invoked, no matter if a previous version of the module is already present.

6.3 Subclassing the class Analysis

As seen previously, you can modify some aspects of the Analysis output interactively. However if you want to write your own Analysis class, you may wish to provide others output facilities than is given in this package. Depending on what you want to do you may get away with simply changing the make_format method of your derived class or you will need to provide new methods. Rather than get into a long explanation, here is the implementation of a rather useless Analysis class :
>>> class UselessAnalysis(Analysis) :

    def __init__(self, rb=RestrictionBatch(), seq=Seq(''), lin=True) :
	"""UselessAnalysis -> A class that waste your time"""
	#
	#    Unless you want to do something more fancy all
	#    you need to do here is instantiate Analysis.
	#    Don't forget the self in __init__
	#
        Analysis.__init__(self, rb, seq, lin)

    def make_format(self, cut=[], t='', nc=[], s='') :
	"""not funny"""
	#
	#    Generally, you don't need to do anything else here
	#    This will tell to your new class to default to the 
	#    _make_joke format.
	#
        return self._make_joke(cut, t, nc, s)

    def print_as(self, what='joke') :
	"""Never know somebody might want to change the behaviour of
	this class."""
	#
	#    add your new option to print_as
	#
    	if what == 'joke' :
	    self.make_format = self._make_joke
            return
	else :
	    #
	    #	The other options will be treated as before
	    # 
	    return Analysis.print_as(self, what)

    def _make_joke(self, cut=[], title='', nc=[], s1='') :
	"""UA._make_joke(cut, t, nc, s) -> new analysis output"""
	#
	#    starting your new method with '_make_'
	#    will give a hint to what it is suppose to do 
	#   
	#    We will not process the non-cutting enzymes 
	#    Their names are in nc
	#    s1 is the string printed before them	
	#
	if not title :
		title = '\nYou have guessed right the following enzymes :\n\n'         
	for name, sites in cut :
	    #
	    #    cut contains :
	    #    - the name of the enzymes which cut the sequence (name) 
	    #    - a list of the site positions (sites)
	    #
	    guess = raw_input("next enzyme is %s, Guess how many sites ?\n>>> "%name)
            try :
                guess = int(guess)
            except :
                guess = None
            if guess == len(sites) :
                print 'You did guess right. Good. Next.'
		result = '%i site' % guess
		if guess > 1 :
			result += 's' 	
					
		#
		#    now we format the line. See the PrintFormat module
		#    for some examples 
		#	PrintFormat.__section_list and _make_map are good start.
		#	
                title=''.join((title, str(name).ljust(self.NameWidth),
				' :  ', result, '.\n'))
	print '\nNo more enzyme.'
        return  title 
	#
	#    I you want to print the non cutting enzymes use 
	#    the following return instead of the previous one :
	#
	#return  title + t + self._make_nocut_only(nc,s1)

>>> # You initiate and use it as before
>>> rb = RestrictionBatch([], ['A'])	
>>> multi_site = Seq('AAA' + EcoRI.site +'G' + KpnI.site + EcoRV.site + 'CT' +\  
SmaI.site + 'GT' + FokI.site + 'GAAAGGGC' + EcoRI.site + 'ACGT', IUPACAmbiguousDNA())
>>>
>>> b = UselessAnalysis(rb, multi_site)
>>> b.print_that() # Well, I let you discover if you haven't already guessed

Using this example, as a template you should now be able to subclass Analysis as you wish. You will found more implementation details in the module Bio.Restriction.PrintFormat which contains the class providing all the _make_* methods.

7  Limitation and caveat

You must be aware that Restriction is a fairly young package. Particularly, the class Analysis is a quick and dirty implementation based on the facilities furnished by the package. Please check your results and report any fault.

On a more general basis, Restriction have some other limitations :

7.1 All DNA are non methylated

No facility to work with methylated DNA has been implemented yet. As far as the enzyme classes are concerned all DNA is non methylated DNA. Implementation of methylation sensibility will certainly occur in the future. But for now, if your sequence is methylated, you will have to check if the site is methylated using other means.

7.2 No support for star activity

As before no support has been yet implemented to find site mis-recognised by enzymes under high salt concentration conditions, the so-called star activity. This will be implemented as soon as I can get a good source of information for that.

7.3 Safe to use with degenerated DNA

It is safe to use degenerated DNA as input for the query. You will not be flooded with meaningless results. But this come at a price : GAANTC will not be recognised as a potential EcoRI site for example, in fact it will not be recognised at all. Degenerated sequences will not be analysed. If your sequence is not fully sequenced, you will certainly miss restriction sites :
>>> a = Seq('nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAATTCrrrrrrrrrrr', IUPACAmbiguousDNA())
>>> EcoRI.search(a)
[36]
>>> b = Seq('nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAAnTCrrrrrrrrrrr', IUPACAmbiguousDNA())
>>> EcoRI.search(b)
[]

7.4 Non standard bases in DNA are not allowed

While you can use degenerated DNA, using non standard base alphabet will make the enzymes choke, even if Bio.Seq.Seq accepts them. However, space-like characters (' ', '\n', '\t', ...) and digit will be removed but will not stop the enzyme analysing the sequence. You can use them but the fragments produced by catalyse will have lost any formatting. Catalyse try to keep the original case of the sequence (i.e lower case sequences will generate lower case fragments, upper case sequences upper case fragments), but mixed case will return upper case fragments :
>>> c = Seq('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxGAANTCrrrrrrrrrrr', IUPACAmbiguousDNA())
>>> EcoRI.search(c)

Traceback (most recent call last):
  File "<pyshell#110>", line 1, in -toplevel-
    EcoRI.search(b)
  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 396, in search
    cls.dna = FormatedSeq(dna, linear)
  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 137, in __init__
    self.format()
  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 153, in format
    raise AlphabetError, " '%s' is not in the IUPAC alphabet" % s
AlphabetError: 'X' is not in the IUPAC alphabet
>>> d = Seq('1 nnnnn nnnnn nnnnn nnnnn nnnnn \n\
26 nnnnn nnnnG AATTC rrrrr rrrrr \n\
51 r', IUPACAmbiguousDNA())
>>> d
Seq('1 nnnnn nnnnn nnnnn nnnnn nnnnn \n26 nnnnn nnnnG AATTC rrrrr rrrrr \n51 r', IUPACAmbiguousDNA())
>>> EcoRI.search(d)
[36]
>>> EcoRI.catalyse(d)
(Seq('AATTCRRRRRRRRRRR', IUPACAmbiguousDNA()), Seq('NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNG', IUPACAmbiguousDNA()))
>>> e = Seq('nnnnGAATTCrr', IUPACAmbiguousDNA())
>>> f = Seq('NNNNGAATTCRR', IUPACAmbiguousDNA())
>>> g = Seq('nnnngaattcrr', IUPACAmbiguousDNA())
>>> EcoRI.catalyse(e)
(Seq('NNNNG', IUPACAmbiguousDNA()), Seq('AATTCRR', IUPACAmbiguousDNA()))
>>> EcoRI.catalyse(f)
(Seq('NNNNG', IUPACAmbiguousDNA()), Seq('AATTCRR', IUPACAmbiguousDNA()))
>>> EcoRI.catalyse(g)
(Seq('nnnng', IUPACAmbiguousDNA()), Seq('aattcrr', IUPACAmbiguousDNA()))
Not allowing other letters than IUPAC might seems drastic but this is really to limit errors. It is not totally fool proof but it does help.

7.5 Sites found at the edge of linear DNA might not be accessible in a real digestion

While sites clearly outsides a sequence will not be reported, nothing has been done to try to determine if a restriction site at the end of a linear sequence is valid :
>>> d = Seq('GAATTCAAAAAAAAAAAAAAAAAAAAAAAAAAGGATG', IUPACAmbiguousDNA())
>>> FokI.site			# site present
'GGATG'
>>> FokI.elucidate()  		# but cut outside the sequence 
'GGATGNNNNNNNNN^NNNN_N'
>>> FokI.search(d)		# therefore no site found
[]
>>> EcoRI.search(d)
[2]
EcoRI finds a site at position 2 even if it is highly unlikely that EcoRI accepts to cut this site in a tube. It is generally considered that at about 5 nucleotides must separate the site from the edge of the sequence to be reasonably sure the enzyme will work correctly. This "security margin" is variable from one enzyme to the other. In doubt consult the documentation for the enzyme.

7.6 Restriction reports cutting sites not enzyme recognition sites

Some enzymes will cut twice each time they encounter a restriction site. The enzymes in this package report both cut not the site. Other software may only reports restriction sites. Therefore the output given for some enzymes might seems to be the double when compared with the results of these software. It is not a bug.
>>> AloI.cut_twice()
True
>>> AloI.fst5              # first cut
-7
>>> AloI.scd5		   # second cut	
25
>>> AloI.site
'GAACNNNNNNTCC'
>>> b = Seq('AAAAAAAAAAA'+ AloI.site + 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA')
>>> b
Seq('AAAAAAAAAAAGAACNNNNNNTCCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA', Alphabet())
>>> AloI.search(b)	# one site, two cuts -> two positions
[5, 37]



8  Annexe : modifying dir() to use with from Bio.Restriction import *

Having all the enzymes imported directly in the shell is useful when working in an interactive shell (even if it is not recommended by the purists). Here is a little hack to get some sanity back when using dir() in those conditions :
>>> # we will change the builtin dir() function to get ride of the enzyme names.
>>> import sys
>>> def dir(object=None) :
	"""dir([object]) -> list of string.

	over-ride the built-in function to get some clarity."""
	if object :
		# we only want to modify dir(),
		# so here we return the result of the builtin function.
		return __builtins__.dir(object)
	else :
		# now the part we want to modify.
		# All the enzymes are in a RestrictionBatch (we will talk about
		# that later, for the moment simply believe me).
		# So if we remove from the results of dir() everything which is
		# in AllEnzymes we will get a much shorter list when we do dir()
		#
		# the current level is __main__ ie dir() is equivalent to
		# ask what's in __main__ at the moment.
		# we can't access __main__ directly.
		# so we will use sys.modules['__main__'] to reach it.
		# the following list comprehension remove from the result of
		# dir() everything which is also present in AllEnzymes.
		#
		return [x for x in __builtins__.dir(sys.modules['__main__'])
			if not x in AllEnzymes]

	
>>> # now let's see if it works.
>>> dir()
['AllEnzymes', 'Analysis', 'CommOnly', 'NonComm', 'PrintFormat', 'RanaConfig',
 'Restriction', 'RestrictionBatch', 'Restriction_Dictionary', '__builtins__',
 '__doc__', '__name__', 'dir', 'sys']
>>> # ok that's much better.
>>> # The enzymes are still there
>>> EcoRI.site
'GAATTC'


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Y]ʄ]yZYmQ０> q#<0$? =dZU"*ڌ$ * -'*sۖCv/Y{,zAco*ń\ 1p6%?!4aTEG0nEgd\ؕ"d$\xpXB =ȸzEwXPEuh5G`=04q{EU]BX,iDsTCń!йA!nxɍ @RYEUXw[c[ l]"L*" ĹN_ ŭS:DD^^Em :mOPc1Câ.-xmC_5z/V/.Gt뚡y5aofoi3qdzbG4E4tY'cl/hY]W 1"M+HGa䮮!y[ħ~g8⋯ EA䂚UO-|X7PBw7wALK`^8f(Ǝr82:e(э!86=w)NJ5psBJj1go:ZqV?tj/:Kp endstream endobj 1879 0 obj << /Length 1998 /Filter /FlateDecode >> stream xXK6ϯf-#mF[ dLA!2ִ,9zd@~|XEٲXldX*ʞflwo;Lzg>BgJ[n8K,WD󅔎oسj8vE]h8!ٞ-)"Gwn|:ecQpZfZwx.he7̵Ҫ-Q2k'^+ZnQ:sm+1_xc--GC]5ՍoBF)EdcU |'.Ù{"M&PGBg}cB/"SDWUfj}t4b<#kK6zK7{> *[|Z_K팫칀tщ8PNN,IZ-I0Y-U$0Na\%i>alSG- (7e#>nchڙI+j)%&U;G)m`Ր:ˬKK]f;dAdyXY6 HLDj~m{BP`pYIo&CH̑ & ?O#Q a3_: x"" sh?'D3z3z^^sF)#іQ "ڿ3DIqmU?CZ\JaE!%,zWf>u$yֱ!`b^7<}h6GpsuQAXiiyK^uP\C.Z qr8vq)Bņʕ!tf+yp ~OG@ Z [T񮨏k&ѝ@!늼%ٺSj!xO#v#.ʟmѨ+h +{j]|Y*hMfBX01TwqPP>o;s<,jTmm/3<ðHDŕ i[VuLɖTG瓦kq/\:X)'Ti;}A7 o5NdE>9QC}rm )J vgŽq0N!y#E)hht)H\޾} (#p@ 8uc62m}Qݵ럒勒m2;(Ս-Wg牾b}MtaBUVFUUY4TJK_天BEӀ]=vk [%qq߰-sS%aҁ7<rc=RYgHҐ|mgͅ!}A? xd冄m08@mj1i@ Um[Fi-ՙp> stream xXK6r+zY u7-v")ZmuelwCnCzp8Aưrqn`,Fc[h|r4s];.Һ_heU?Xdi߲^մBbëU=ϧ|6.igt:z |ϟV٦)AfT\hEߚeg)A{l Zs]B$UZˣ.|1EZںP.öthW݃[1mszndJŖ 2ZDApʼnrO?y2;f}!+=ߡĚdEzDs]ɡȱB';^o؍Tƞ#KS"YA,w)ck4}|5<$nXo2Oy<ϬxΙำwlhJQ61S2?8uIDG ុK 3^{-;&ԯ"Pì5gS2Fpe5BmQm "gG)_ΕlÞh|yGKq(M]Qp%›؀kf(t)؎c}i&?D n G:KPn-E/:YH-I弖zm&ve,0I866E` R<' $U!i~Lk؆ @?iU2#F\շ3@qzgQw' A ?Չrm*ʼd ˉq? $A9{V1?_P;~f~ꉶ&r?+Ӳ@)i;0T^<H)wY_THXAEjB/QDUw;%V $ !% ܿ4HI0 ?txCqZp@'Dφm<)?`( FuSlWJB^6z*yU6LO,k:<^;<`Oq}-BlRoZA' hû,m&}H h` =,_- j^(IyGbYT "&g$iHʗY!~rOÀ#ɿa 2w "' dKl$9) ZT#J4+6Rf@ՕDmė2 C b2G-۫o!bpز/2R/)78 E endstream endobj 1895 0 obj << /Length 1986 /Filter /FlateDecode >> stream xkSF;+z iC TLV#K$Cޞd @6|}>=h[7˷T1^8F5U3^>)^؆DydqԪ`Y{mKg,xq/옕Uq!st^OjG%:%"5]\=vc‡L]Ħ ΟokvHaeH*p4f3=U%-e sˤuUiSCY 9q[Ћr DtiKMǴ0 Y(ĵ C P3M$Y.c۬Qcɓ|Bʗ/_<*YAaT°ER~e؈lfȈF6 ~H;Rp<4%>׬P+q /T{va@ʳi"ք稨%UH%ԕcB= Px}1(ø#`U6;K1<e1+by y yk)E&y!76Jh.ky?,URy^# iYWxfS~2 ĉ{)hRsIrr-"G7*(t1c׼#)0=4 |a0p_#oF0`_ ]B Ń 8)ч@*@!~8&F4#Ĝ b> P"'PG)rAA?,Ѐ)r-h @AyAD_"ħ‰>!705kzIAخ ktѪ?<:|ӫ˫E 8pqx dG*H<~)>){Η|Hܭzc7ב&uNiu:suԽCu5qZ$]dnl(cSvܴFY..kqeg,EI]"Μ}%R. P?Ъ,Md(oSƘ$n &҈E Z?|K/@+C*~㛌TVB|ӁGRv0hbڣE$ ~ry͗p7iwGaӟ@|o~u%55Ӂe .up`:5k^ɇ Djjoo!?ee2_V0mujn (42 endstream endobj 1899 0 obj << /Length 1477 /Filter /FlateDecode >> stream xXmo6_!dEY *ӪbkEVJTl;-^ [>D䉼{'&2l#9d Y8Ƙ;ٖ΍Ҹ4] ٓ)Bo.&giݕ))z2u˜^y7yv<4"+ 'A9#s>LEw,fۉb)I_td Ӧ'{,в0C(P% }}A&snd4Drn۷Y5EKwHĻ}q;3-9DPd[<D3 t55GHfhф=r]•ȣ/sׄ9m]|:91."PVHegYS6N%k+,w9w"d&0ɴ _\-)8țu@|ӑ 4m% R"!ve ہ/! ~[uv]XuYYuV,I>.-SViɀaY>Kߏ)245c '! @dyU/5mloMTwHQv-je<+tCu͔></6OB`/SnϿ%NYa T#s"*3K)>Xpex[ȐrYޝ,O ^]^{6j0(frr@Jo&/oI~ݦD "^EZwgbkP}|?z7(Q* 3#+Cy$> stream xZmo_/ b[ 'p5дm #8SeޭNio~Yr8>ژ2Ɯu^draIVEA*1IéK+?҂$yR2|bl.c"o1N$:esȲ"+zEi"f#(\IQIx+EsPSΕى(>\HɗA5@i/)vE7ʊ9JJe q8jx>#Lw 9<8&\p{gYLBW/USeDC*yFe@4^E"hEY[0.h Ť1*_ǡb"p3bSn+'MOc0%!M#Fr OblM2 eG+R>5Ʋ ̩9[KT6dZRCAVZr_WDSdqvWZUO&::?^7wOI=mp͛o׶\T/R&&clXv8skk rOᡪT髩ogٲkHPcۼYُh-'ﴜaFgX17RVR=9<,3TOf:__>ߞ-B5ws=E=Ϫ? N.[٬7Egsq3Y~G \VEj9_DMN>Uf<8t:=e DfVM{3p: pHAB:ՁuY&ͷ&kODx2lm6ӝ)[Fl`:*haMjE8Ig/|4k{U0o%0ꥪHlhv!a$p dY0u1x&˳]_ y 1Jb>X^<}Wzi8Ϙ,B`/D|X/VR4Tj@؊Y`A`ّ&.J)|jpmۆo+B*p F7-jZ8 6Emqs17z2IsT0z^/iGtٜqqB&2RDpLQwlB,% @̞ٗL_4 yTŰ:,lSt[xVO3*XVm#x endstream endobj 1905 0 obj << /Length 2195 /Filter /FlateDecode >> stream xڵXm6ȗq1+z=Q{iH@kkjmi#3RdimÚÇ3 gw3{\",7[|1lϖ{k9_µC?˥/屖#-m O<d Y xB< !BD FޔZ퓉퓉퓑퓱-VOG ؐh?6$z;}?r xB< !B4!?!DK $ dW&V2vq3b@L1FAē ,_b D4B9qK$ ӌ!S;AbȆJ@nj 7TJ.LCxB<!y!M@Q%V_TJ N@$ Q_N߼:wX8DBmH.:`GXlkh6Y v[ԇUy-pÚ\5kZeTϾ֝ߒ J6աݲ)I.{+*>z(sYTh4js3f/G)?56c5$Or]_CuA;iې:ӪytKzqry|{pCZdgkDDS:@,)ڤqd}Z)'Kwgj7'߳USR IgJܳV.\+I}tDet&[#C+p1<*]4Viz:[LCc*IPZt`p 6_8n›WԄ(wP1wYZSFYd^O[ Xɾ4$Xg:I댆Hk*=Y:g]cӷnL8 IMZhs 2MNBsgKe  dVAVn7)͡z4I'D ,]7:^>Ơ8!Ȧ)͵mKV^B*/޸䭋!wiEIVèh{z b>s ;͚ӺeX S%੖QȢ9^طcRZujtrvdn vZ؀5SǠNJ?`>䵺+Mm/Bw7^٤iQ7?!˝s^q!YQ(H+bL5?m}n}WZ[^}Bv3>s Lx y8[gkd .@k;{w'#~T@Ӄ.W RH|p=Ptm zTX /jc"lKk@XE,*Dzݩ+q 石kЍ p 7% Sqaà *q W l`xKȑ9svvLxQ!M h(S> d{e&,[]i*(o&3oZO%/!?)ByޱޕO=N_`DA"@$^#DhU YuW%HeR}ƃ)9Rs|Tؙ$ U-f<6?AAн ij&%ݕuC-MR}AfH5ML~ߛEЬ*Xx+4-: w컦.%+lʑt_yP (c8pDgfalz#A%1 TAEm0:ā*߯D੯#!snrG-x2;` 7lP_E.Ѭ9գ&xqj}n}>?ujZ> stream xڭXێH}xG4_`f! Ҳ rNKnoUWcg!SʶG=]غ}>x2%, p4_%?^4Fj<<Ϻۦzr~&/ڠzl?$՟={Z<֡ӧ팫l_22 rzGQHkmQ՚Fe))Z'-pz0Ile] j]&* AԢ[oǎzY2ύUb5zEkvb)EY\܉9>Ov`/=P檵;䟏7%:<6QZqdR\~hb ݉,ޮ*"ӲR3UB"Z"Òt{M5VqR, {2*6)#$}@Gړ~hkjnDCeKaj޵SJkߨ;Vtߏ:AL;  X'(n'4Z̲wĤ0m q ŦKU')aF'!pXt ~2Ȏ&KU˿K^ԢCtj5qm1z9jR ^ `O7 xo#fa-ko*H8-܂C Th-PbT4ps>3z Uu.F&=dL8DXs5NПz6(joaM iʦ*RaZԼjk1tmPMd0 ^S|(pډgn2TcgGGÙ:]EFвyYkT| _ {Ԕh'C` ^UUVOH%x傾U*ž Um4`Xi]MzgP1o+t۩&DW bqQT\uF?a:>f~QB꘭%k^3*XWvċſMw endstream endobj 1916 0 obj << /Length 1739 /Filter /FlateDecode >> stream xڽko6{~/րp؀eڢP$V#D%-[uC'b`f_<%,`0D=orn8g\-E5k=JFNhm / Z-{ mQ6IYڈ25f4HaSZv<*Hʅ~}5>ʼn sO[4sK|d;jTrSϠ2cY;_m߾M|6Llfl2zlrdLV7Mj ?^g8>Ϛ鏳Ub%#ໍ܋-薉V@Ae`t tS~#!|*tTD)nd2Ob/nM 1s\ޢ=#}Rn阿,;?9RuYE+R@}Xn2պ+Q}kG{aKYC xq7zTԤ-y9s ,cei\xݧ7B牱:E|IbyrY&Ն@gQZo4v8P몯VU^.h-ǭt"{.99w:jߠyƺ܈U˼l``Gkgf2qAf;3Ʈ+z)2ڋ,WdqW]i `NTO?4CR9AmzC+%7SCIV"Ʋ,6}FPl"ቐ$yqHKфY*lb1 ٕA7eq*YS;6[N=8?W hiRԒN{I g\=N\ l _ڬYbXFJaTRb.~:ۃj1ş2hr:GwK pze<+C3G@H9E'^āgO-9Qό=3pW ҴHjs"o! IjxD29["|/8*4UMuSb6Z@d4TXWxkx5 *F01=BރrB- PV`jpn`UrGIP5kFeaMMe%€Hv VjaTف8U׽ԋaZ8%nAxH*Z CP%=g ip][M.V-!8ϋ"F׏tnx .EIAOwph +ѭQa*E)KZf@Lyv@ :Ѓ&imb!*=m1^] siݭ6ɦ4Vb|E=|L+M2#sbmS5i˳5=fICgX:Q >Ќ>Up|(XhQzy4%;5+]\[=kdkBHg-oStkRLbmEԃW΅yYSr _gt*8C.|٥*"~SD[t9M:1N'#sV&'S u^hl;"STLբ'=s锽<~ʞ n9N11vXk endstream endobj 1921 0 obj << /Length 1752 /Filter /FlateDecode >> stream xko6{~/XE=BR{-3=\INGٖ#;-Hgr` 3Kӳ篂Ah ׃3C2-gQr3;7Q%!(J[]Xh!+Lfr>p738YZ3'<Էlx UZ#87"A즽1/{kd%p6-=3-wȬ)ʻW\ 13tczOιq$PURѴu1Q2|$r5}!T~[+;Ȥپthb 6ŋ jQ˾ZuN_ls-gI?GJT"idjng h&ed.#{ߣM%:ҹ} 1ȴ|%:Jm=齧ixAB WZԿ gxeüUH:e[HeJ)i"晆0bNлuYP~zjM02뚜هGbчpW3-[.Ioo۵ֺ>KoTI[%D=97u~bCOU7@-)sIdTe] BEL?t*LjZ~k-%D%!qN/v&p|c+k֠Z8n̄jϙakZ~";f+"IZn댢=8OZ3sr:j\'NЉOL cP. T<˪ ÿu跛[cj=i7}/82_WeNP IuYv[ݐ s\=n珓mx~E>Йœi> stream xڕYKs6WrTY郫lVRڃErfq1֪p(l_4@hx;/]歊=8Vwb ->?Pvkm"?u/Z +:LE"CZ@#dž?˼xmkR!C]K)(v+ |l_/6/O fhfM. ESj34JDq&ZkbKIvFյn{KRMC2ƮPL!ۉ*J ^v k O!mOAd=$toi")20iE|D熖~)TÞypV Vhu۷@6F(\،tk=2Bs6BHF0ZW,NS}UlkYo L >y ~v[\툝V6lڪВhr 6ึfp^_5+,!߸R?nuҘ7Mvk;(}u$`3[;H1mhh+JQ0@(~9TllO1Ɍ8Úl>EX 8c*7ݐQPԝY AGخv ϪXGCfsEK};k-_U o]1@9©8WJ /cxiIݿMxga؜p/>40uR$X ZKD >k%r}EzَV y[|Ț2jþmDn7gۙo` %mRU7,n`k> Z=A|sUPIHEx_W 8 V=ةO hއ9+1\DU%AevUlg[=5g5zN lfʁtUaЇuq]Vi޼j45)t6 }gOQa6!gZ6%CKv/tsDXy˷Ɋ_~FOwlDxKba 9udjϰS > stream xXݏ4_Jn8_N:N Z6%lߙjOv{w>j  nK:!d+wQ4UlaU׌kq 8/of<8l9U eX( rAU}!pUc1h`B! xy-L = F-4yW:pѺ_)Q̸Hd=2BK"56A sHw%P{(r6TdPڐȞ۠68Ϣrb}h)K)P0XIS Y fh0 } S̍(ՑzղM$1;\ouYj=Rϟ%y*#GλrqTv-0#c>5҄;pygKlQI&I'Y,b\{ܾոu:L\tU7m~ѫa:CH/Ջa`pm.7K=u} |-\L[\f) *XU7N@#R<3V'p+'SVE3H6E-apߕyCDƸӞtt3ܙq\g94&Ծzm@"ƗVj*Fm_K[sAQ *z\&#N{ Bv:w wza"֟#ܖ`%x"Ol$͌󂞦pk9])}j<#Đ2-Lࡃ6 䵑6ͫn%)ZH~2b2 Y[ͩk{dzp O܇2|/lPN}'$ёhn-`O/Ē[`C*K6@&w{P.04*B$?.sˆ_-+"kA? 1ďhvAt|;ᶴ)Zm# r#.*f2_2N?(<q>{3&UE!KUuXq'XGVQm]Oݍ WgH@\?,iJ@L2לg.U56<&TJ6wXoϫqkGiTĎ}ke &KY^C[2I/`ճߜRu4&ΌcCRwȒXp-~i?mc/g瓍z K..}1ٟh endstream endobj 1947 0 obj << /Length 2279 /Filter /FlateDecode >> stream xڽXo8Bȗ@̥-.rݭ"E(GSc{pHYKp8|p8wnqӾ[>u2~,VN, ȝ ߓ4pV7iyjHf2WB-4,V*ZҨ;Utr~68SѧNWÝY.P(A'jD~n'(#Dk%yXO5+ai;ScY(@0Aш⚪NꊦQ7% Gmוh}'˥,IEh@]f]uENU٣'}=WѶ2+ڊΉ5lp*{RP@Fr"Z\5ˮiP4dJbke擧 5PyCz}Fqmb$:F:o2W*U%?z񁳪;Opbxs/,,:e=rC_z vka bAFQߘyp4@: ΈGGeԱ `a] +@\-x%(A$e7^YjVgQ?ղF.ej{\*1>$yCB4w*7N aV7Wn^y(J0Ma+}+:GA)NBiW439R/fR u Z-f ~? g XQWe#})a耇B/p`&g#Dcl+~Tyx) $O wrw ڛD7'3*ř#Yj>lyB**\\>#\ h.Ё#) nK-<zsjNp۾V T3Rk0҃}E\}VyG9'5}{XҤ.|#<ۿ'R"~Lh-^Nt4"3Ϗ0bOzH*쁬̓\:yř7^MC./.x@}AqW+Z]{AsL({#KYuGh@K%&YN/dG^ⷖRdE_SaE*mIkQ8Mgyw]g`nVX@o"gPCϜXSï.=tNtGqa.dh%DC<_g7K`$C NHl & o "*sH_{CKv^% l0Rbk} GWd XwZK-xgxp,h7/Xˎ endstream endobj 1954 0 obj << /Length 2323 /Filter /FlateDecode >> stream xڽko8{~_"VGpm6nk^%E\QN gȉp"9狓^Ni:^Ne΢r>3=FyOm8R4R@,'0s "<"u cNDP*ZjW7eד b32GkI jS_z4@rV5k<;)~xLa^$$}E"xM༆sؖDkGU4[ x\Lh8a1|f^Dsg 򊬰)$(ʧd؋` Z{4cwa$|mښVIl #籌+d,@yq#B 2|` ,,gXal$ZI]"vԮWmLK~+Y+a`;=a=m)Jd]tvZZ~oYmxe> E`G T [qcgp3PNyGƄѕڞEvkچaʮd!?hh<kvVw"F60<i*j;(bmV.K9W-vrci"!G?CkAjo\p ZxJ'WՒ&=a3qIvkW3BŌ`it"w%TW39B5*7ii4C-?͌x8DR|H -XV ZE0&K) JaicDG+P)hFoǞ5AG}'ʞ&0~Ό°_L)e2CXTɫwĀܙ< H3Uc48$v&hc' d @*9sX\ C iHdk/<8GL0@bZ56Q ̴[QIgE aTEs XVL0DAѱU4:[ ՜}\Qc SMYS)c[&jw7Camߘ @+*n㇒]ں2%Տ@m fRN$(!)[N^-N)NSh@ 4ȝr{TF;u"/rߩ <8-N /H C*m|?Ƀ[/CJ|/¨d8μ,;TI&LKziBs,,P6`GYv"Zgl'6JRl&VAs1uS=tOVT!]B亚NҝLE\8cxot*UWB1|A0433c/p' ,}Qs$O@N$uhǫj4z_y h?7Pf;pOrv07)H&M&XsEYlYEC4 1^}z8]Ql:ʋQ013dљ^*+5/8$9*_Nl3\E\YFO=w7M|zF$X@u>Ɂ`pp%!#j ˪jkƃnӧh[?{;3(–  evz4g_~x[KC!AA񏯤#>e0>wA=8 C ݹ~ ~~"tc?‰0:aH=lP)Hvȶ&\Q Rh1%!'^5pJݪjoF`C$^Pml7ezI)NE9\-LA K, oS?C+~ʚC`h!4o@8)Ü2d߷؀\_Z m;O$aQ=w%_00$8$(i U endstream endobj 1962 0 obj << /Length 2092 /Filter /FlateDecode >> stream xڵXmo8_ae fEk?p&nC[,hy%W7 ,Y{kP<3mGfqKG)K#/-6e͛7iN)WEfZ~|[ 4 n1nq73g|XaV3ZcNK=Ga69ј, K}RgرsY^KeyV^ԏDJڽQ+ȟ(D 3|\N l;(rxdjP55*YҤ}QԧC96!_bCfYv or/lBVVQ\׌m mgHWe dYj||$sMNDQp}/f3c?Ѱ.D9.JJX8mQ@x| fTSI!!DğUָ~rUO.˩[,1ӃmN4M"3 pR|{"s@,$EBҦRIHrl%-T*PL$b]-'vV6N~ ]I&Ij wh"ހ :[w0dԜ9'}I(asgƓjV&Jls=TVAt* F*tAثƢ܊\CUU Ve7 ;RIb_@,MuV ǧ@- '/ʃSoGIF/\Q5Xgt_ZeƵf4+4!h]".NOMOXA3~'[[zVݽ*ڞU|vNG@(X ~ij*1`;ִ2Q ø|"F;- ']ܤwNg9 Һ"c8:9&2BƪmNvڸ"'HۧwnWs[RC(|gmhl!8%V+̴ >wC>' +ZiLC˧c]ժ~TUjoWlwMfʠ3joΩd4|S+d >`Q\$uF j\ǪW-6.aH]g$# 5Q&1ydL0 |IcJؚ YܳvD , ~ڳIcW?^|+5沒 (#4QWp>է)Rv'p_OC^eXodagl9B  t `V0M _2e ޠL0S%RkD݁Rn .zi3?z(D9Qx`!tiJwq)ʧ,}+]*hmEܰC; U9֑ݪ)j@B V(Wȃ))K~az7| endstream endobj 1966 0 obj << /Length 3141 /Filter /FlateDecode >> stream xڵY[~_!e)93, "7OqPsmyz?s^$yE͕gf;G~NW7W/tvusJ*UQtuS~ ~mtx\oqn~?~&}hPgD7o ltл/Pytݶ} {fAz=U1HɆn;2BѪ_q]~MqhB05peHCd"3$IC3E#CJwE@bPZvHZxCM_c H^j} O]a|z0-<&ՇYqzE_IS`P=U}*SeՄQIK:&9J/?H׷'WddՒ_5T⚎<V$p$E}F0i| wĂnݺRlQgpC'Лu3xw_6POzwMs@*5 nY_yA&B.٫9KJm. ?إD15Rn9J'+EDAuf$Z{ljCHP5ÞoP)rE})@=2EjTZժJrXׂıƌTf(:, ;a&aA{c>%1qe 0e,uKuf*`cCĥb> Z)W5 ?,X4ISp1F8qy!LdžuI%ԣ <nK^\&=*z`_'"gU(@H胈61ڪPң HoipJn]t2[CeݖQ%s  _g.lbWS>B= [C`cK[,nZt-|gbZz!H?5]ʼnG߁FY=M3 M3Y.E,SwV!,"9"TtnOg&2XgN#%E''Q5@$v0M5(EhWO;L/yNS1*qr`8f$V/NgaVXE)oJh΋0~)F@'j Y \~ZOcfW,m3M:~V#}_@gD7yMAAğџ<Ù#mBWm񐧸rWIB";5ch,:d> stream xڕZY~ׯ< ,!)MISDZ :>}  g .v)xe_QzqȢ, .7?{ʏȏW0LHܯq8 m/|6\܏]Uz۲Jݭ_Em{WönkvZZ*5[0T%v\pFivܸ3^N{iܫpN֩[چǵT^#nIg z]3xnrۆ;oL[{nQ+CN7i`:]2 yCLZ,#`n̒:0]8Rb-ޮ@ڎMeSKlPG^`v{ⓇnZ&4 Vv03xl>ilŲ;~'M,0 (<8z7pry6teWf{vV 9_a#Н2 BQ]#G{dd%[uXc= o@}bȽl?2ҙc=Xv(cY ^{^׷\;D4jmGPX&r#yJՂHPna7f.8UŸ4z1T7dd墈=ݱq%YrV* Pe[UX8@bA\Vs ¨ :hc53)Iq~.k|B#/:*'hu8Ȅ^2(D"-W#B[p.kUJ-"X\ߎ +Na1qK$B}9qfrFNUpeep B OF VvX g*J0Dzn "bN ?jSuqLR$y駪fG'Ռ/ /hچGY#R@",ձY8-Z'ITp`ufLb0…ͿCB~:V8<@,QGYJrD&$gͮ1qb2!ٙ'BgNHQz:|ZJ,YWzKlp-x*$8z€zaَ[ DM K3lSIqhe; ?G+$ Pdbij{L2*-+cWRS $ <)\J%'JfZ`1~.i% 壣bͱxjOVN54ba*  fȍgo) c;N + Xg7 `G܃R~_tYj|[#TF$Qhjlg$X }?l7TIH}ge=>Sg \}59\sɠ=}a|{rh7lUaP|ӫH`G:ަtXBxNb5YC\ug40{V,|)>(mv5X@bߟ$Zd ]5"kЏyߵ'\Q챼A[/.ߚFbkvN˶<̔!sf[7rH5#=SIj@0ğ?Hzad۵]+pۢEb[kW!}%6_.'!6ڏ L? Y\Zo4:dt׺7q~K)ash\-T62 H=߽( 4SiB#%-=l{`YH ft XlvqEf9z4s~߯,)HE5&Ӈ>v~]ٷՄC,8/4Œx蘸`~/xLY&u2BP`QiBa<7X" u/E $TYO@RBz9ʧyS!w'!s3"9w6I_;OC]kbʳܒ̔c6X|O ,K2jǩ,HJ[phL%h?̰b-)!K0 <Ƈϯph_r> stream xڥWێ6}W8_$@Sd-MKm6cYtE*;!eK4ۼh8 g($8MTJ̢lt(?|{xF}i!k_i?$ 2MIׅ;} fgwۈZg +8ZB+/ ;ir_BT-^7&~$IaЂxG'[eLGg%Z9+-w$܎'AAЌޗV~웽 J`#x jIXzzŭupfeUeŪmgS+R s0^4ȝ9O]u#}dBlڭ "U֌CʺVL;̓=.N"Nj1+]'! -Ouل)!-kW$Yb`; `SQ *-bqxaJzB3#ӛ5iH4<8wjҶD$-FXi8o6=>ljUmq`E51;2>X@2dCpgsA?:<0uO3 U5TXN2 :SЕ^{ oX5è,7y+uΒE};Y`/?Ogy] Բ3F*+1B~es8 sjTLŮA5gk(3 jCܻW*Z5M\eytJ 8N)a5WR;P@ΆuM#*J%ۺ>uP4=B$*_J6_A8r iS3gJ -Li{zxtp(_zz4;Pmq;TN22di6/sll'(yґ=[MS,;DmϴTeD,a2ůyk% $M~f“=Mbi:$c44>b y VjcIbzCEpL䬄%8]&I3GP;RGB##M_z=-Ab%JޞԆ@*19BAE &@aW\cСm ]02"+.|#Q tlc؀P8L84UӦ5Y(*Yo Ê#6w=[6gM-a ٬KBnPmLqn 6YY#?4Y4rC#hFo5oAm ;3}Wtpu H0+˞z}Ł0E%8\EQX\z+^?kG endstream endobj 1983 0 obj << /Length 1776 /Filter /FlateDecode >> stream xڵ]oH=EbkHDhԻ+/X)؜mٙ5@K3޸Y \^Ggon$hQ^GIW&;۷^δ(i.5mRr"V]v?~ =)EJy~5*_91Veן=UwshA"f_؋"fLNRO/`#1-b],wu\䗞Ŀ:ΛCo]z*RJz{e*V2{d&q DLeLL,jP\5ABi>A@lX,4B\qO m'}9IyJvih:ϊEL4U^nb\|K7EMqb+hi@|. ۂUIoyJ16gs _pj\fҤnEa]qZ%^x#&~}s7!׃hvr^0 C=xI`4;U]xj£~v@0@^lظ ˦KijZ8r5TgvYg;3Gl] 7ߋBQ;y'b?v&p $yVp;YPTn M?B/0^M4RZEY;sӻig@ٳk&Nx)ccDb@1eDUX; (lכ9c\5P@ u3e!0]p3.dbymL"!dlΆ8lxM5a}1_FvӡZxhMx t2r%vMg91>sT G⽉^fx5ݹ? `8}?2h@?_D@Qp~P5\Uڻ B;783\cޜ3'GȡI5Għ -- "3Mxe| 33|MǜÈhR((Lvzn()ҒœƘ1+<禕U0!b}wֺ|Ilgn6_in8m~^m29¬eÈ xMi2h7P$6`|qtAaMN4!T3M#ԗ} `c2Dme,uUm'p[0;> stream xڥYYs~ϯ2Ph/*JxLrH,P"$%͏gSy|gAmzzoLgMgؚefoyf3tMb{߷5{04 Z"E9sch ao ڊb0]q;`&/DUbŝ*S/5@'ekP),J{]i_ߧIU? 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( ^vBtyv gxZ 4eBdZT$F&lԶr_up֏_Fަ݀# i  8&YtvF >ǜk>CJ# Ar1g(R!;Q@rf\ v3}j j#8*[LR9rM&~;m0P)FڲT^ˏH#'bj1)?oj]9 >i笛*Va\iMt4 eb=m6o M2N!;BJWڰg- '>|" 5(;7f?w! %uy|&c9mǦ\߇ZJ+:9T_rg#GaSbWnj0I-i8VEI^b e]eEuI_pbED`KeYg`=B0#Lj#\2#3$l`KP݃4s 5cMŤ:Ԧ\s,-Jbi nYQhRۗ&nDXVd}cW$c }pFL>w/QŨVb!n9_t$^qŀ6 a24iSJyrI#LE=3"R3r@c[s(QH:Ҍ3x~)B,ϣG';z+ġUh7h";cǑ{[5nenVS9k.-^br*-B9ZIt=tJO;lo9m-f4=HjӍ\炥lƛ n^iGܘ=T7~ꢢ g!2`DPNq6Whޯzc3%5b5 %[x KL6 eR86v;m+n_-f$yݬt;K:M{xHk(6Dn/L65Uh.vEWOm> ͲxRS5{܉W; {J˙0Z}AlGDɛRr^Awt$AHZ2q\no|]w']wf> stream xYo8޿}\Qouq5bwo5pEȌ͍,ԇ7 iQw{+c5Eѫuf]}<댜'/YnH .+;U1 <ޞN{Zˈe3 ЪתkEzՈ 7GD aү}qBVʶYG, h+=vȣY,!?J}BZҩ֮ ...fs h30LSqG'1UN+x,+mO^VG\9xm9mWF4,yi(̢lhؚ0]bssĨUE4I擎۞k#զ`=[ʯXPj8$sקٹI7R n5 ^Iw<Eک)GKGHޫɣ-6Dp=eh)F[AxLHpڤ /<[l.AS:4UsvkgF:>r|:@|ك?l|&dd F]4@Ꝫ)IR.=KjDhgm4sgbFԵ}d{wPqz[-l5 (1y1[X [ %‡d{9THfHQЋԠ o;DS&2P,J/wzYa aLaX)_qmTexFTXeܔ#عĜ%C e FCкS[t|Ŀxn+ l,cz.c,X3IjI|dw#FMj aӹi4!Jr܁P%fkʵ:!Nj>j{J~Qù˱72{}~\A2'HXPB5do4_k:AeO$tqWI YwP WCJz@00˾d^xDݐ\@(W8oGH􉢌QaBp0Wk endstream endobj 2407 0 obj << /Length 2626 /Filter /FlateDecode >> stream xYm۸_,kF/n^IqIsF^6m3+K(eopFl+rH gf({؃g6?_M=n8q(D-l/L;j##7Gc󭢮YF>Nt8w~9 8bHqh V(J+Ü4VEIZӪ2jeJŊZiWʥv\.J3BeY) cODa2;H{==.[-Jy%SQdmZZ~e@i~4ъWd4\:ͲG[]]IMCD.=t$o4E5`Sy/0eҨ*R~C|M+B&+?H%%9^M'Wj:1ڄ]Oq '!`~2?L&˪`]4rn4t+/i&Cm 6R/JC\n5 &Kҗwϝ;YiBTwsofg;I ߰{Sl 5ujQ7' \{N- >o'# 1 ۂ~%GDAfX0EE- 6;@˔$`$@qL5nLh ,Kibi *P JtQV@p;JP{ yR8W%Py&EH$<(ރ5K&`BEo< rA6v¹R/G} na`hL\*S䆦> stream xڵYYoH~ϯ$H0ML#im0E*$eSTl`g Y]]WUmk]Y?|\=b3`uu Uh[冫txz;qSk,oy$o[UƶyU}X?%뇿*GHS?F=3ƱL'ee\?2jqBCKZn?- Bj3]D͡s\ͪ635;4ޮ#רd -NdTJTɔ\hbm$vX^VrfŽ궪w[O )㏜ݒj)Jh*g&K${-iE>졵E"]{[OlvFM,O|mx?xARlJ;x-9H`?m@r?<3'nM[rKxn05Oe`s_M\65=.hSY5I, #Z.6xP.oH_AB%K+[ejn "mo Cmouv$l .5t ꎻJQ|_h>g0i/x .?2+S;d>yo>F$‹8mP?֑Gj6S2'wJA+dN CN3qQ $]l vVNSQ>3@:0+#^5[?96|0,G ZmCK%;%AN!`&^REFI9GXdo|%I30Zu׋ ?!Aіw A.B>7Py)Z!3ow\6 #7[jUy4,th;} \ZqIOs7ax*_: <l˰kI':\/y=ЈbfGl^= Mߞg@BV7KrMcC5K^GMؒ QR JlVC u VL‡Bd|rW*DN)QAVM(Wwy{@ ᢯1aK $Ў7v,*{D(GnC 9ն5ݹ+%J9S^Y•lF%P}r}λDGU)dG0 }PTZS5՜ ܏"ĸ>4"ua;\VNΨgA3உ2Q!ײ{'yU!J;Fv(Y4`Hs#<׭T>cH.kxbo~LONDfDs5w01Gx@Vl6d(QYu CaZN["6Av iA H$jnP( YUC ?kҩ! r3œ*6Щ˕rF,QT`c\B>_!ɺkS:VmlqJ[ $:l̘A ˹nA 'ƒGflN?ON0[!HQ~х&`NO89ĦkSx;e ;YXOvt[P0׋ }̋Y1^n qhv0}v1Q뎦 \A:@1&v{tdmmJuaSEx.{&H_} tv[* ʼ^)]x<}.ڎicR."l׌@Q~:t-~' ı9(XĎb s&1:4r҈뗸$-"v>9e[!{?mIT'yEwKlo^biz-ڭ# O#jX>fyg6|v1n|<=l=,yw\wxSaPJ^z2Pa.&u ;ARIS+#| .lg_Wbq endstream endobj 2416 0 obj << /Length 2620 /Filter /FlateDecode >> stream xڭYmo6_}|Is鵎JF53Vڕ(`E3o\Y=_]>\eLd׋D-P j>x2RQʻ*.6o$oD.o'iWewKeO?\.PdQDB&#u(ؘXëpʫtLkf͉~^ xT `픜Quۼ|yhi<({ du>JGZ4LBF?.eٖ ^?.blj׿=0^oeg~-JMpSq@Q(plTF{P!KߝX* 9mlċsXy׵ǝwnEtJO#pfYOàm6ƫ~$7~- ְ]!4QHIN&&+x^xgX HD@"]v\4ѵNE"PfGܦ&W OtG3lb*&s`?2KokkWHMфH4/[eXMDԀ lL{m I RAKlrI}'|KcN7#^e&-\{nap`Ϧ[M*F)vHyoHC=T_wL;ڣwkΡW{{O>a&4$Ix$Ѥ"A޴`:Gl5v Hcsm n~;zI(Pq@︗a" .'F'x"-Ve$`ei,2An8j0z {α*\ ,)ZaD̈-R-s&-So"BHr tU#Vo[~Oqv.RF2Y|Mg5%ORaƯ]Gɸh6m :(S.!o rC`e+: :CY&tb*Ba@b43_[ z4{zH[mGߏzhʛ]u|{BID?1T%5y|3;M,(]-ՙM?1:ejj+`}=f`wOE.%q:,mۭ, ⮇2VL$k1y{7Is"~~QN+]aA߱"WUh&qeLX-uoVh^OhJ~0P 1*iʈ C=?߅/ endstream endobj 2420 0 obj << /Length 2742 /Filter /FlateDecode >> stream xڭYs pF8Zdwǵ98DEDxв2㻯'G{~ћ\O#O_$s8'$ &^L[?;m( zOb"χ?b2}7"y!q _WZR^6宪""71Ɍ?|~yy9__/ \?A6? lMZ䬧C'7N=l3572އ]mQendμ͂k U渪(ucF>ż^qa%͔fKԸP9Tĉ6( h.M:[[Y6=8OlO>*lWɟrg|QpRݽGmwۖ't  Dh,zy_Ye]VF{/,rv:I^׵^=`;@MՈZ-t-6i^s- }05 i>B۳ 8nS7x+R ͥ b:lͪfrG. `{'<i4dbKsKԁ`" Q:%nR iZ%MKHPŎUڦ@WCP|4;nkv"4Ab@v޲ B}:Y]2l<>pgEL}Bq±Adr΋5g}bۮ17DqVw[t.U(d]lGp : +hV7XUMJ`Me#0CScɪC8Ϟ.FNZG\6y%#"DK,uEZ7!BB7[D@$RE'$6 aѰ#8*߁S[}BDr?SȚ@O d Ar} q~qgTp#|-ZFO3bB5vm?S0ǧޛx!v]MXq7;$m<.ȗy6\M+C⮒ؑC8 F]:.$Ihm dV,-atjHءA"16Dm'n0ptM;-K?յFo9 1̴»ӅA%w\&G.VE5e`eȪa%䞉%8luAֲXfuڶjehqC)@!N0NI v'@c|S|yݲ+ҦF%yg)tEQ(ɲ0!K!fKr~%}8^22G)UT% Q%% A @8H"j L,8GG#II#ؿ ~a B_8P ,Vm$n2ZJ^Ϋr^B 2É)mca"XrFr6<-o 'nj|l: Y30 vdU?M#7o_9Nx8-6;".g޼pF,vQA 4g>6n7pH2FU񅯆aרP=^ԣ\[ei*MR:Et- K2nd2gS^u,(κ)K3 ? ? f,=%l;\G.CaM^YԈ!\a֋|/4%(O?$rN2Eҗ`h`h`x H߼\]yǢ[R4$+'O0GP%nFK`؅e8Cv6f6 QG4'fHZCپa#EGF{Zs=u6MBt.WS{.3KP^*#΀%("w ,X k4X'B.>[Ŋ!j/Ye gk%@D(= Q5+PEI &a@FKhddEyI)g@x`˅rU'39q&c *Ll>px,^Y$:ȴCȢЋKKht%nK;͵axۅ 3oCVߎ> <ߍ1!HT}5x5 endstream endobj 2425 0 obj << /Length 2498 /Filter /FlateDecode >> stream xY[۸~ϯhM i6b`QE[r$9){.%l ")<<|ۙ:5Ȇ*2ļ2WJqܫ/K c2N-{(#Pp dsy~&wM9؂ws#2FJf¦Yҏ'i#0U}&05f07 N&'!K2=n3kdZblocްF;wj۩S+,3Kw*Z4XL%g G-E:+ 9Uyߡ*bPºk`ہED!Z~:pB4S+?NMatm}qD/URh,zb1/5Ȳ]SvnB!$oޒz ~xOAn︽mM(5("3]{460 a~- 56z3\ݪN;]j h+R 9O\ڤAlEjBBb' XEFA& t $ziT~ol%ƌȴ `##5Cܑf]YW-w lh657$Cea6 A>ɒ'G@mϜHȇ,+=kd;^3{rܔ(B%GyplpIkj*=zSV"lf+~鸻(ZNZ^Tyu2t(nGyIek48" Amx*Oh13H1x o (XchbZ>1iR?v]SpK Í#7m}S)JHS&H3knBB,-{T "$bb@2nܫj ќkܗ $1@nC +1H|~._]$,~oY=A[-6m[uŠ0*6 wBͲ/nCLa4|>C0 ȼ EPmFmZ֣[!XWR};%43<9~} [E)&o~x)nXA{cBfqydV![f<*fx]>6ߥ%L`B'RoDKӘ?1ufJ ҄t7xT}҈kw񜿸9L )}8!xqrDO d;kn2d3տr׾nbZ85U7C(pDq"5 \%p,N>i?Q5D^,Yw!a>#(Wu)N? ሷSuB`4YQz =v|i)N'T&9 pܧ7)08t\ǭq0xs8tsł 'ҳyjM/8.+ y8MB "wL. 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