pax_global_header00006660000000000000000000000064143325101120014502gustar00rootroot0000000000000052 comment=9a5cfdd81292a0fc3c8ae133d8003bc2ffefddbb shasta-0.11.1/000077500000000000000000000000001433251011200130455ustar00rootroot00000000000000shasta-0.11.1/.github/000077500000000000000000000000001433251011200144055ustar00rootroot00000000000000shasta-0.11.1/.github/workflows/000077500000000000000000000000001433251011200164425ustar00rootroot00000000000000shasta-0.11.1/.github/workflows/Build.yml000066400000000000000000000047101433251011200202260ustar00rootroot00000000000000name: build on: push: branches: - '*' jobs: ubuntu-22-04: runs-on: ubuntu-22.04 steps: - uses: actions/checkout@master - name: Install-prerequisites run: sudo ./scripts/InstallPrerequisites-Ubuntu.sh - name: Build-ubuntu-22.04 run: | lsb_release -a tar -cvf shasta-docs.tar --transform='s/docs/shastaDocs/' docs mkdir shasta-build cd shasta-build # cmake .. -DBUILD_ID="Shasta unreleased test build newer than release 0.11.0 at commit "$GITHUB_SHA cmake .. -DBUILD_ID="Shasta Release 0.11.1" make -j 2 all make install/strip mv shasta-install shasta-Ubuntu-22.04 tar -cvf shasta-Ubuntu-22.04.tar shasta-Ubuntu-22.04 - name: Test-ubuntu-22.04 run: | sudo apt install gzip gunzip tests/TinyTest.fasta.gz shasta-build/shasta-Ubuntu-22.04/bin/shasta --config Nanopore-Sep2020 --input tests/TinyTest.fasta ls -l ShastaRun/Assembly.fasta - uses: actions/upload-artifact@master with: name: shasta-docs.tar path: shasta-docs.tar - uses: actions/upload-artifact@master with: name: shasta-Linux path: shasta-build/shasta-Ubuntu-22.04/bin/shasta - uses: actions/upload-artifact@master with: name: shasta-Ubuntu-22.04.tar path: shasta-build/shasta-Ubuntu-22.04.tar ubuntu-22-04-minimal: runs-on: ubuntu-22.04 steps: - uses: actions/checkout@master - name: Install-prerequisites (minimal) run: sudo ./scripts/InstallPrerequisites-Ubuntu.sh --minimal - name: Build-ubuntu-22.04 (minimal) run: | lsb_release -a mkdir shasta-build cd shasta-build # cmake .. -DBUILD_DYNAMIC_EXECUTABLE=OFF -DBUILD_DYNAMIC_LIBRARY=OFF -DBUILD_ID="Shasta unreleased test build newer than release 0.11.0 at commit "$GITHUB_SHA cmake .. -DBUILD_DYNAMIC_EXECUTABLE=OFF -DBUILD_DYNAMIC_LIBRARY=OFF -DBUILD_ID="Shasta Release 0.11.1 minimal build" make -j 2 all make install/strip mv shasta-install shasta-Ubuntu-22.04 tar -cvf shasta-Ubuntu-22.04.tar shasta-Ubuntu-22.04 - name: Test-ubuntu-22.04 run: | sudo apt install gzip gunzip tests/TinyTest.fasta.gz shasta-build/shasta-Ubuntu-22.04/bin/shasta --config Nanopore-Sep2020 --input tests/TinyTest.fasta --Reads.noCache ls -l ShastaRun/Assembly.fasta shasta-0.11.1/.gitignore000066400000000000000000000001741433251011200150370ustar00rootroot00000000000000/scripts/__pycache__/ docs/PythonApi/_build/doctrees docs/doxygen/html docs/doxygen/latex .idea cmake-build-debug/ build/ shasta-0.11.1/.nojekyll000066400000000000000000000002471433251011200147000ustar00rootroot00000000000000The presence of this file tells GitHub not to use Jekyll when building the GitHub Pages site. This is necessary for files beginning with un underscore to be included. shasta-0.11.1/CMakeLists.txt000066400000000000000000000071151433251011200156110ustar00rootroot00000000000000cmake_minimum_required(VERSION 3.16) project(shasta) # This is the top level cmake file for a Shasta build. # Before starting a build, # run shasta/scripts/InstallPrerequisites-Ubuntu.sh # Use "cmake -D" to modify the options defined below # in each "option" line. For example, to turn off # building the dynamic executable use # cmake -DBUILD_DYNAMIC_EXECUTABLE=OFF # Figure out if we are on Intel (as opposed to ARM). set(X86_64 OFF) if(${CMAKE_HOST_SYSTEM_PROCESSOR} MATCHES "x86_64") set(X86_64 ON) endif() # Summarize the environment. message(STATUS "Processor architecture is " ${CMAKE_HOST_SYSTEM_PROCESSOR}) message(STATUS "CMAKE_SYSTEM_NAME is " ${CMAKE_SYSTEM_NAME}) # Decide what we want to build. option(BUILD_STATIC_LIBRARY "Build the static library." ON) option(BUILD_STATIC_EXECUTABLE "Build the static executable." ON) option(BUILD_DYNAMIC_LIBRARY "Build the shared library." ON) option(BUILD_DYNAMIC_EXECUTABLE "Build the dynamic executable." OFF) # To build the static executable, we also need the static library. if(BUILD_STATIC_EXECUTABLE AND NOT BUILD_STATIC_LIBRARY) set(BUILD_STATIC_LIBRARY ON) message(STATUS "Turned on BUILD_STATIC_LIBRARY because it is needed for BUILD_STATIC_EXECUTABLE.") endif(BUILD_STATIC_EXECUTABLE AND NOT BUILD_STATIC_LIBRARY) # To build the dynamic executable, we also need the dynamic library. if(BUILD_DYNAMIC_EXECUTABLE AND NOT BUILD_DYNAMIC_LIBRARY) set(BUILD_DYNAMIC_LIBRARY ON) message(STATUS "Turned on BUILD_DYNAMIC_LIBRARY because it is needed for BUILD_DYNAMIC_EXECUTABLE.") endif(BUILD_DYNAMIC_EXECUTABLE AND NOT BUILD_DYNAMIC_LIBRARY) # Write out what we will actually build. message(STATUS "BUILD_STATIC_LIBRARY is " ${BUILD_STATIC_LIBRARY}) message(STATUS "BUILD_STATIC_EXECUTABLE is " ${BUILD_STATIC_EXECUTABLE}) message(STATUS "BUILD_DYNAMIC_LIBRARY is " ${BUILD_DYNAMIC_LIBRARY}) message(STATUS "BUILD_DYNAMIC_EXECUTABLE is " ${BUILD_DYNAMIC_EXECUTABLE}) # Option to build with -march=native. option(BUILD_NATIVE "Build with -march=native." OFF) message(STATUS "BUILD_NATIVE is " ${BUILD_NATIVE}) # Option to request a debug build. option(BUILD_DEBUG "Make a debuggable build." OFF) message(STATUS "BUILD_DEBUG is " ${BUILD_DEBUG}) # The BUILD_ID can be specified to identify the build # This is normally used only when building a new GitHub release, # in which case we use the following option when running Cmake: # -DBUILD_ID="Shasta Release X.Y.Z" # BUILD_ID should not contain a comma, otherwise the C preprocessor does not handle it correctly. if(NOT DEFINED BUILD_ID) set(BUILD_ID "Shasta development build. This is not a released version.") endif(NOT DEFINED BUILD_ID) message(STATUS "BUILD_ID is: " ${BUILD_ID}) # Add the subdirectories we need. if(BUILD_STATIC_LIBRARY) add_subdirectory(staticLibrary) endif(BUILD_STATIC_LIBRARY) if(BUILD_STATIC_EXECUTABLE) add_subdirectory(staticExecutable) endif(BUILD_STATIC_EXECUTABLE) if(BUILD_DYNAMIC_LIBRARY) add_subdirectory(dynamicLibrary) endif(BUILD_DYNAMIC_LIBRARY) if(BUILD_DYNAMIC_EXECUTABLE) add_subdirectory(dynamicExecutable) endif(BUILD_DYNAMIC_EXECUTABLE) # Install to the shasta-install directory. set(CMAKE_INSTALL_PREFIX .) # Install the scripts. file(GLOB SCRIPTS scripts/*.py scripts/*.sh) install(PROGRAMS ${SCRIPTS} DESTINATION shasta-install/bin) # Install the configuration files. install(DIRECTORY conf DESTINATION shasta-install USE_SOURCE_PERMISSIONS) # Install the docs directory. install(DIRECTORY docs DESTINATION shasta-install) # The targets built in each subdirectory are # installed by the cmake file of each subdirectory. shasta-0.11.1/CONTRIBUTING.md000066400000000000000000000031421433251011200152760ustar00rootroot00000000000000# Contributing to shasta Contributions of code, ideas, computational experiments, or documentation are welcome. To contribute, please use the standard GitHub Pull Request process and take into consideration the coding suggestions below. #### Contributing C++ code * Add at least a minimal amount of comments to explain or clarify what the code does. * Use blank lines generously to separate code blocks. * Set your editor to convert tabs to 4 spaces. * To minimize name pollution of the global namespace, all code should be in the `shasta` namespace. Do not use `using` directives at global scope. * If you need to add a large amount of code to an existing file, consider creating a new file instead. * Keep in mind that the code is currently compiled using the C++20 standard. Use features in that standard liberally. However, you cannot use C++ features that are being considered for the C++23 standard. * Build and test your code using the current long term support Ubuntu release. * For functionality not provided by the standard libraries, you can also use the Boost libraries. * If possible, avoid adding dependencies on other packages. #### Contributing ideas, computational experiments, comments, or criticism * Please use the GitHub [Issues](https://github.com/paoloshasta/shasta/issues) section of the GitHub repository as appropriate. #### Contributing documentation * Put your contribution in the `docs` directory, so it becomes automatically visible in GitHub Pages. * Feel free to create new `.html` files, if appropriate. * Attempt to harmonize your new materials with the existing documentation, if possible. shasta-0.11.1/LICENSE000066400000000000000000000236311433251011200140570ustar00rootroot00000000000000LICENSE NOTICES The paoloshasta/shasta repository was forked from the chanzuckerberg/shasta repository on 10/4/2022. They both use an MIT license, but with different copyright owners - see below for details. Contents: * Licensing notice for GitHub repository paoloshasta/shasta. * Licensing notice for GitHub repository chanzuckerberg/shasta. * Licensing notice for GitHub repository aappleby/smhasher * Licensing notice for GitHub repository wjakob/dset * Licensing notice for GitHub repository seqan/seqan. * Licensing notice for GitHub repository rvaser/spoa * Licensing notice for Lapack and Blas See here for more details on how the Shasta software uses each of these packages: https://chanzuckerberg.github.io/shasta/Acknowledgments.html ------------------------------------------------------------------------------- LICENSING NOTICE FOR GITHUB REPOSITORY paoloshasta/shasta This notice applies to all code changes made on 10/4/2022 or later. These code changes were committed to the paoloshasta/shasta repository. MIT License Copyright (c) 2022- Paolo Carnevali Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ------------------------------------------------------------------------------- LICENSING NOTICE FOR GITHUB REPOSITORY chanzuckerberg/shasta This notice applies to all code changes made before 10/4/2022. These code changes were originally committed to the chanzuckerberg/shasta repository. MIT License Copyright (c) 2018-2022 Chan Zuckerberg Initiative Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ------------------------------------------------------------------------------- LICENSING NOTICE FOR GITHUB REPOSITORY aappleby/smhasher Files src/MurmurHash2.hpp and src/MurmurHash2.cpp are from GitHub repository aappleby/smhasher, with a minor name change (".hpp" instead of "*.cpp" for the first file). This repository does not contain a licensing notice. However ths source code contains the following licensing statement: MurmurHash2 was written by Austin Appleby, and is placed in the public domain. The author hereby disclaims copyright to this source code. ------------------------------------------------------------------------------- LICENSING NOTICE FOR GITHUB REPOSITORY wjakob/dset Files src/dset64.hpp and src/dset64-gccAtomic.hpp are modified versions of file dset.h from GitHub repository wjakob/dset by Wenzel Jakob. That GitHub repository comes with the following license notice: Copyright (c) 2015 Wenzel Jakob This software is provided 'as-is', without any express or implied warranty. In no event will the authors be held liable for any damages arising from the use of this software. Permission is granted to anyone to use this software for any purpose, including commercial applications, and to alter it and redistribute it freely, subject to the following restrictions: 1. The origin of this software must not be misrepresented; you must not claim that you wrote the original software. If you use this software in a product, an acknowledgment in the product documentation would be appreciated but is not required. 2. Altered source versions must be plainly marked as such, and must not be misrepresented as being the original software. 3. This notice may not be removed or altered from any source distribution. ------------------------------------------------------------------------------- LICENSING NOTICE FOR GITHUB REPOSITORY seqan/seqan. Copyright (c) 2006-2018, Knut Reinert, FU Berlin All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Knut Reinert or the FU Berlin nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL KNUT REINERT OR THE FU BERLIN BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ------------------------------------------------------------------------------- LICENSING NOTICE FOR GITHUB REPOSITORY rvaser/spoa The MIT License (MIT) Copyright (c) 2016 Robert Vaser Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ------------------------------------------------------------------------------- LICENSING NOTICE FOR LAPACK AND BLAS Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: . - Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. . - Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer listed in this license in the documentation and/or other materials provided with the distribution. . - Neither the name of the copyright holders nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. . The copyright holders provide no reassurances that the source code provided does not infringe any patent, copyright, or any other intellectual property rights of third parties. The copyright holders disclaim any liability to any recipient for claims brought against recipient by any third party for infringement of that parties intellectual property rights. . THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. shasta-0.11.1/README.md000066400000000000000000000047771433251011200143430ustar00rootroot00000000000000# Shasta long read assembler De novo assembler for long reads, optimized for Oxford Nanopore (ONT) reads. ___ **Shasta development continues in this fork.** New releases will appear in the [Releases page](https://github.com/paoloshasta/shasta/releases) of this repository. Previous releases (up to 0.10.0) are available from the [Release page](https://github.com/chanzuckerberg/shasta/releases) of pre-fork repository `chanzuckerberg/shasta`. ___ **The complete user documentation is available [here](https://paoloshasta.github.io/shasta/).** **For quick start information see [here](https://paoloshasta.github.io/shasta/QuickStart.html).** The main paper describing Shasta and its methods and results is [Shafin et al., Nature Biotechnology 2020](https://www.nature.com/articles/s41587-020-0503-6). Reads from this paper are available [here](https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html). The assembly results are [here](https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html?prefix=publications/SHASTA2019/assemblies/). **Requests for help:** please file GitHub issues to report problems, request help, or ask questions. **Please keep each issue on a single topic when possible.** ___ Main features of the Shasta long read assembler: * Optimized to rapidly produce accurate assembled sequence using DNA reads generated by [Oxford Nanopore](https://nanoporetech.com) flow cells as input. * High performance (a few hours for a human assembly using a single machine of appropriate size). * Haploid or phased diploid assembly. Computational methods used by the Shasta assembler include: * Using a [run-length](https://en.wikipedia.org/wiki/Run-length_encoding) representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads. * Most phases of the computation use a representation of the read sequence based on *markers*, a fixed subset of short k-mers (k ≈ 10). See [this documentation page](https://paoloshasta.github.io/shasta/ComputationalMethods.html) for more information on computational methods. #### Acknowledgments The Shasta software uses various external software packages. See [here](https://paoloshasta.github.io/shasta/Acknowledgments.html) for more information. ___ **The complete user documentation is available [here](https://paoloshasta.github.io/shasta/).** **For quick start information see [here](https://paoloshasta.github.io/shasta/QuickStart.html).** ___ shasta-0.11.1/conf/000077500000000000000000000000001433251011200137725ustar00rootroot00000000000000shasta-0.11.1/conf/HiFi-Oct2021.conf000066400000000000000000000021331433251011200165070ustar00rootroot00000000000000# This was tested on the "HG002 Data Freeze (v1.0) # Recommended downsampled data mix", a 34x HiFi dataset # for HG002. See here for more information: # https://github.com/human-pangenomics/HG002_Data_Freeze_v1.0#hg002-data-freeze-v10-recommended-downsampled-data-mix # It assembled 3078 Mb of sequence with an N50 of 35 Mb. # The quality of assembled sequence was estimated # at Q = 46 dB via k-mer based methods. It is limited # by the fact that this creates a haploid assembly # in which one of the alleles of each heterozygous locus # is removed. [Reads] minReadLength = 8000 noCache = True [Kmers] k = 14 [MinHash] hashFraction = 0.05 minHashIterationCount = 100 minFrequency = 3 minBucketSize = 10 maxBucketSize = 60 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 minAlignedFraction = 0.97 minAlignedMarkerCount = 200 maxSkip = 6 maxDrift = 4 maxTrim = 2 [ReadGraph] maxAlignmentCount = 30 maxChimericReadDistance = 2 [MarkerGraph] minCoverage = 6 simplifyMaxLength = 10,100,1000,10000,100000 crossEdgeCoverageThreshold = 3 [Assembly] consensusCaller = Modal detangleMethod = 2 shasta-0.11.1/conf/Nanopore-Dec2019.conf000066400000000000000000000032101433251011200174230ustar00rootroot00000000000000# This file contains Shasta options that, as of December 2019, # are known to work with Oxford Nanopore reads under the following # circumstances: # - Human genome assembly. # - Coverage between 40x and 80x. If you have more coverage than that, # you can use option "--Reads.minReadLength" to adjust coverage as desired. # - Reads from Guppy 3.0.5 base caller. Also known to work with # reads from other Guppy releases 3.0.x and 3.1.x. # To use this configuration file, specify Shasta option "--config PathToThisFile". # If you specify any conflicting values on the command line, # the values specified on the command line take precedence. # In most cases, for best performance on a large assembly # you will usually also want to use the following options, which # cannot be specified in a configuration file: # --memoryMode filesystem # --memoryBacking 2M # Using these options require root access. # For detailed information on all available options see here: # https://paoloshasta.github.io/shasta/CommandLineOptions.html # For information on running a small assembly for which # performance is not essential see here: # https://paoloshasta.github.io/shasta/QuickStart.html # For more information on running an assembly see here: # https://paoloshasta.github.io/shasta/Running.html # For information on optimizing assembly performance see here: # https://paoloshasta.github.io/shasta/Performance.html [Reads] # If you have extra coverage, use this option to adjust coverage. minReadLength = 10000 [MinHash] minBucketSize = 5 maxBucketSize = 30 minFrequency = 5 [Align] minAlignedFraction = 0.4 [Assembly] consensusCaller = Bayesian:guppy-3.0.5-a shasta-0.11.1/conf/Nanopore-Human-SingleFlowcell-May2022.conf000066400000000000000000000020611433251011200234300ustar00rootroot00000000000000# Shasta assembly configuration specialized for human assemblies # at low coverage using a single flowcell of nanopore reads. # It is meant to be used under the following conditions: # # - Human genome. # - Nanopore reads, single flowcell (100-120 Gb of coverage or around 35x). # - Guppy 5.0.7 base caller or newer, with "super" accuracy. # - Read N50 around 30 Kb. # - Haploid assembly. [Reads] minReadLength = 10000 noCache = True [Kmers] k = 14 [MinHash] minBucketSize = 5 maxBucketSize = 30 minHashIterationCount = 100 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 maxSkip = 30 maxDrift = 15 maxTrim = 30 minAlignedMarkerCount = 200 minAlignedFraction = 0.6 [ReadGraph] creationMethod = 0 maxAlignmentCount = 12 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 crossEdgeCoverageThreshold = 3 # Adaptive estimation of coverage threshold to generate marker graph vertices. minCoverage = 0 [Assembly] consensusCaller = Bayesian:guppy-5.0.7-b detangleMethod = 2 shasta-0.11.1/conf/Nanopore-Human-SingleFlowcell-Phased-May2022.conf000066400000000000000000000021401433251011200246300ustar00rootroot00000000000000# Shasta assembly configuration specialized for phased diploid # human assemblies at low coverage using # a single flowcell of nanopore reads. # Because of low coverage, it assembles relatively smalll diploid bubbles. # It is meant to be used under the following conditions: # # - Human genome. # - Nanopore reads, single flowcell (100-120 Gb of coverage or around 35x). # - Guppy 5.0.7 base caller or newer, with "super" accuracy. # - Read N50 around 30 Kb. # - Phased diploid assembly. [Reads] noCache = True [MinHash] minBucketSize = 5 maxBucketSize = 30 minHashIterationCount = 100 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 maxSkip = 30 maxDrift = 15 maxTrim = 30 minAlignedMarkerCount = 200 minAlignedFraction = 0.6 [ReadGraph] strandSeparationMethod = 2 maxAlignmentCount = 12 [MarkerGraph] minCoverage = 6 minCoveragePerStrand = 1 minEdgeCoverage = 6 minEdgeCoveragePerStrand = 1 [Assembly] mode = 2 consensusCaller = Bayesian:guppy-5.0.7-b pruneLength = 100 mode2.bubbleRemoval.minConcordantReadCount = 2 shasta-0.11.1/conf/Nanopore-Jun2020.conf000066400000000000000000000042111433251011200174560ustar00rootroot00000000000000# DO NOT USE THIS FILE IF YOU HAVE READS CREATED BY A # GUPPY VERSION OLDER THAN 3.6.0. # This file contains Shasta options that, as of June 2020, # are known to work with Oxford Nanopore reads under the following # circumstances: # - Human genome assembly. # - Coverage between 40x and 80x. If you have more coverage than that, # you can use option "--Reads.minReadLength" to adjust coverage as desired. # - Reads from Guppy 3.6.0 base caller. If you have reads # from an older version of Guppy, use configuration file # Nanopore-Dec2019.conf instead. # To use this configuration file, specify Shasta option # "--config AbsolutePathToThisFile". # If you specify any conflicting values on the command line, # the values specified on the command line take precedence. # In most cases, for best performance on a large assembly # you will usually also want to use the following options, which # cannot be specified in a configuration file: # --memoryMode filesystem # --memoryBacking 2M # Using these options requires root access. # For detailed information on all available options see here: # https://paoloshasta.github.io/shasta/CommandLineOptions.html # For information on running a small assembly for which # performance is not essential see here: # https://paoloshasta.github.io/shasta/QuickStart.html # For more information on running an assembly see here: # https://paoloshasta.github.io/shasta/Running.html # For information on optimizing assembly performance see here: # https://paoloshasta.github.io/shasta/Performance.html [Reads] # If you have extra coverage, use this option to adjust coverage. minReadLength = 10000 noCache = True [Kmers] # Due to the higher accuracy of Guppy 3.6.0 we use longer # markers than usual. k = 14 [MinHash] minBucketSize = 5 maxBucketSize = 30 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 minAlignedFraction = 0.55 minAlignedMarkerCount = 400 sameChannelReadAlignment.suppressDeltaThreshold = 30 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 refineThreshold = 6 crossEdgeCoverageThreshold = 3 [Assembly] consensusCaller = Bayesian:guppy-3.6.0-a detangleMethod = 1 shasta-0.11.1/conf/Nanopore-May2022.conf000066400000000000000000000026421433251011200174600ustar00rootroot00000000000000# This is known to work at least under the following conditions: # - Oxford Nanopore Ultra-Long reads. # - Human genomes. # - Guppy 5 base caller or newer with "super" accuracy. # - Coverage 40x to 80x. If you have more coverage, # adjust --Reads.minReadLength or --Reads.desiredCoverage # to bring coverage down to this range. # Under the above conditions, this should give an assembly with # N50 in the tens of Mb. [Reads] minReadLength = 10000 noCache = True [Kmers] k = 14 [MinHash] minBucketSize = 5 maxBucketSize = 30 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # The following Align parameters are set to very permissive values to allow the majority of alignments # to be assessed during the initial stage of automatic alignment parameter selection # (ReadGraph.creationMethod 2). maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] # This uses the observed distribution of alignment statistics to choose thresholds for # maxSkip, maxDrift, maxTrim, minAlignedMarkerCount, and minAlignedFraction creationMethod = 2 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 crossEdgeCoverageThreshold = 3 # Adaptive estimation of coverage threshold to generate marker graph vertices. minCoverage = 0 [Assembly] consensusCaller = Bayesian:guppy-5.0.7-b detangleMethod = 2 shasta-0.11.1/conf/Nanopore-Oct2021.conf000066400000000000000000000022321433251011200174510ustar00rootroot00000000000000# This is known to work at least under the following conditions: # - Oxford Nanopore reads. # - Guppy 5 base caller. # - Human genome. # - Coverage 30x to 80x. [Reads] minReadLength = 10000 noCache = True [Kmers] k = 14 [MinHash] minBucketSize = 5 maxBucketSize = 30 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # The following Align parameters are set to very permissive values to allow the majority of alignments # to be assessed during the initial stage of automatic alignment parameter selection # (ReadGraph.creationMethod 2). maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] # This uses the observed distribution of alignment statistics to choose thresholds for # maxSkip, maxDrift, maxTrim, minAlignedMarkerCount, and minAlignedFraction creationMethod = 2 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 crossEdgeCoverageThreshold = 3 # Adaptive estimation of coverage threshold to generate marker graph vertices. minCoverage = 0 [Assembly] consensusCaller = Bayesian:guppy-5.0.7-a detangleMethod = 2 shasta-0.11.1/conf/Nanopore-OldGuppy-Sep2020.conf000066400000000000000000000054451433251011200212240ustar00rootroot00000000000000# This file contains Shasta options which attempt to partially automate # parameter selection. It is based on an earlier config, which, as of Jun 2020, # was known to work with Oxford Nanopore reads under the following circumstances: # - Human genome assembly. # - Coverage between 40x and 80x. If you have more coverage than that, # you can use option "--Reads.minReadLength" to adjust coverage as desired. # - Reads from Guppy 3.0.5 base caller. Also known to work with # reads from other Guppy releases 3.0.x and 3.1.x. # The automation provided by this config is particularly applicable to # low coverage or non-human samples. It also matches or exceeds continuity # in human samples, relative to the appropriately chosen config file. # Updating to guppy 3.6.0 or higher will greatly improve assembly # quality and is therefore strongly recommended. # To use this configuration file, specify Shasta option # "--config AbsolutePathToThisFile". # If you specify any conflicting values on the command line, # the values specified on the command line take precedence. # In most cases, for best performance on a large assembly # you will usually also want to use the following options, which # cannot be specified in a configuration file: # --memoryMode filesystem # --memoryBacking 2M # Using these options requires root access. # For detailed information on all available options see here: # https://paoloshasta.github.io/shasta/CommandLineOptions.html # For information on running a small assembly for which # performance is not essential see here: # https://paoloshasta.github.io/shasta/QuickStart.html # For more information on running an assembly see here: # https://paoloshasta.github.io/shasta/Running.html # For information on optimizing assembly performance see here: # https://paoloshasta.github.io/shasta/Performance.html [Reads] # If you have extra coverage, use this option to adjust coverage. minReadLength = 10000 noCache = True [MinHash] minBucketSize = 5 maxBucketSize = 30 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # The following Align parameters are set to very permissive values to allow the majority of alignments # to be assessed during the initial stage of automatic alignment parameter selection maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] # This method uses the observed distribution of alignment stats to choose a cutoff for # maxSkip, maxDrift, maxTrim, minAlignedMarkerCount, and minAlignedFraction creationMethod = 2 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 crossEdgeCoverageThreshold = 3 # Automatically determine this using PeakFinder minCoverage = 0 [Assembly] consensusCaller = Bayesian:guppy-3.0.5-a detangleMethod = 2 shasta-0.11.1/conf/Nanopore-Phased-Aug2021.conf000066400000000000000000000021321433251011200206410ustar00rootroot00000000000000# THIS IS ONLY COMPATIBLE WITH shasta 0.8.0. # IT CANNOT BE USED WITH MORE RECENT RELEASES. # EXPERIMENTAL configuration file that uses assembly mode 2 # to create a phased diploid assembly. # Suitable for Guppy 5.0.7 reads at coverage 40x to 80x. # Same as Nanopore-Sep2020.conf, except for the following: # - Added options to use assembly mode 2. # - Removed options that are not relevant for assembly mode 2. # - Bayesian consensus caller for Guppy 5.0.7. [Reads] minReadLength = 10000 noCache = True [Kmers] k = 14 [MinHash] minBucketSize = 5 maxBucketSize = 30 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # Permissive alignment criteria as required for read graph creation method 2. maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] creationMethod = 2 strandSeparationMethod = 2 [MarkerGraph] minCoverage = 6 minCoveragePerStrand = 1 minEdgeCoverage = 6 minEdgeCoveragePerStrand = 1 [Assembly] mode = 2 consensusCaller = Bayesian:guppy-5.0.7-a shasta-0.11.1/conf/Nanopore-Phased-Jan2022.conf000066400000000000000000000032641433251011200206450ustar00rootroot00000000000000 # Configuration file that uses assembly mode 2 # to create a phased diploid assembly using # Nanopore reads. # This is known to work at least under the following conditions: # - Oxford Nanopore reads. # - Guppy 5 base caller with "super" accuracy. # - Human genome. # - Coverage 40x to 80x. If you have more coverage, # adjust --Reads.minReadLength or --Reads.desiredCoverage # to bring coverage down to this range. [Reads] # Don't use the RLE representation of the reads. representation = 0 # Read length. Adjust as necessary, # or use desiredCoverage option to get coverage # around 40x to 80x. minReadLength = 10000 noCache = True [Kmers] # Because we are not using the RLE representation, # use shorter k-mers. k = 8 # Compensate for the non-RLE representation # to get about the same number of markers # we would get with the RLE representation and probability = 0.1. probability = 0.07 [MinHash] minBucketSize = 5 maxBucketSize = 30 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # Permissive alignment criteria as required for read graph creation method 2. maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] # Automatic adjustment of alignment criteria. creationMethod = 2 # Strict strand separation is required for Mode 2 (phased) assembly. strandSeparationMethod = 2 maxAlignmentCount = 6 [MarkerGraph] minCoverage = 6 minCoveragePerStrand = 1 minEdgeCoverage = 6 minEdgeCoveragePerStrand = 1 [Assembly] mode = 2 consensusCaller = Bayesian:guppy-5.0.7-a pruneLength = 100 mode2.bubbleRemoval.minConcordantReadCount = 2 shasta-0.11.1/conf/Nanopore-Phased-May2022.conf000066400000000000000000000024431433251011200206610ustar00rootroot00000000000000 # This is known to work at least under the following conditions: # - Phased assembly. # - Oxford Nanopore Ultra-Long reads. # - Human genomes. # - Guppy 5 base caller or newer with "super" accuracy. # - Coverage 40x to 80x. If you have more coverage, # adjust --Reads.minReadLength or --Reads.desiredCoverage # to bring coverage down to this range. [Reads] # Read length. Adjust as necessary, # or use desiredCoverage option to get coverage # around 40x to 80x. minReadLength = 10000 noCache = True [MinHash] minBucketSize = 5 maxBucketSize = 30 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # Permissive alignment criteria as required for read graph creation method 2. maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] # Automatic adjustment of alignment criteria. creationMethod = 2 # Strict strand separation is required for Mode 2 (phased) assembly. strandSeparationMethod = 2 maxAlignmentCount = 6 [MarkerGraph] minCoverage = 6 minCoveragePerStrand = 1 minEdgeCoverage = 6 minEdgeCoveragePerStrand = 1 [Assembly] mode = 2 consensusCaller = Bayesian:guppy-5.0.7-b pruneLength = 100 mode2.bubbleRemoval.minConcordantReadCount = 2 shasta-0.11.1/conf/Nanopore-Phased-R10-Fast-Nov2022.conf000066400000000000000000000040351433251011200221270ustar00rootroot00000000000000# This Shasta assembly configuration for phased diploid assembly # was tested under the following conditions: # - ONT R10 chemistry, fast mode, chimera rate around 2%. # - Guppy 6 basecaller with "super" accuracy. # - Human genome at low coverage (HG002, one flowcell, coverage around 35x). # - Phased diploid assembly. # Under these conditions, a test run for HG002 produced an assembly # consisting of about 2.7 Gb in bubble chains and 0.3 Gb outside bubble chains. # Of the 2.7 Gb in bubble chains, about 2.0 Gb were assembled diploid and phased. # The N50 for bubble chains was about 5 Mb, and the N50 for phased bubbles was about 0.5 Mb. # This is longer than most genes, which means that for many genes # the assembly contains both haplotypes, entirely phased. # When mapping each branch of a phased bubble against the correct reference haplotype # for HG002, base level quality was around Q = 47 dB for mismatches, # and around Q = 40 dB for indels. # The fraction assembled diploid and phased can be improved with the use of # Ultra-Long reads (e. g. 2.7 Gb were assembled diploid and phased with R9, # Ultra-Long reads at high coverage). A separate assembly configuration # for phased diploid assembly using R10 Ultra-Long reads will # be provided when possible. # This configuration might also be usable under different conditions, # but that was not tested. [Reads] representation = 0 minReadLength = 10000 noCache = True [Kmers] k = 14 [MinHash] minHashIterationCount = 100 minBucketSize = 10 maxBucketSize = 40 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 minAlignedMarkerCount = 1000 minAlignedFraction = 0.85 maxSkip = 20 maxDrift = 10 maxTrim = 20 [ReadGraph] creationMethod = 0 maxAlignmentCount = 15 strandSeparationMethod = 2 [MarkerGraph] minCoverage = 6 minCoveragePerStrand = 1 minEdgeCoverage = 6 minEdgeCoveragePerStrand = 1 [Assembly] mode = 2 consensusCaller = Modal pruneLength = 100 mode2.bubbleRemoval.minConcordantReadCount = 2 shasta-0.11.1/conf/Nanopore-Phased-R10-Slow-Nov2022.conf000066400000000000000000000036221433251011200221570ustar00rootroot00000000000000# This Shasta assembly configuration for phased diploid assembly # was tested under the following conditions: # - ONT R10 chemistry, slow mode, chimera rate around 2%. # - Guppy 6 basecaller with "super" accuracy. # - Human genome at medium coverage (HG002, two flowcells, coverage around 45x). # - Phased diploid assembly. # Under these conditions, a test run for HG002 produced an assembly # consisting of about 2.7 Gb in bubble chains and 0.3 Gb outside bubble chains. # Of the 2.7 Gb in bubble chains, about 2.2 Gb were assembled diploid and phased. # The N50 for bubble chains and the N50 for phased bubbles was about 10 Mb. # When mapping each branch of a phased bubble against the correct reference haplotype # for HG002, base level quality was around Q = 50 dB for mismatches, # and around Q = 40 dB for indels. # The fraction assembled diploid and phased can be improved with the use of # Ultra-Long reads (e. g. 2.7 Gb were assembled diploid and phased with R9, # Ultra-Long reads at high coverage). A separate assembly configuration # for phased diploid assembly using R10 Ultra-Long reads will # be provided when possible. # This configuration might also be usable under different conditions, # but that was not tested. [Reads] representation = 0 minReadLength = 10000 noCache = True [Kmers] k = 14 [MinHash] minHashIterationCount = 100 minBucketSize = 20 maxBucketSize = 60 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 minAlignedMarkerCount = 1200 minAlignedFraction = 0.9 maxSkip = 12 maxDrift = 8 maxTrim = 10 [ReadGraph] creationMethod = 0 strandSeparationMethod = 2 maxAlignmentCount = 15 [MarkerGraph] minCoverage = 6 minCoveragePerStrand = 1 minEdgeCoverage = 6 minEdgeCoveragePerStrand = 1 [Assembly] mode = 2 consensusCaller = Modal pruneLength = 100 mode2.bubbleRemoval.minConcordantReadCount = 2 shasta-0.11.1/conf/Nanopore-Plants-Apr2021.conf000066400000000000000000000025431433251011200207120ustar00rootroot00000000000000# This was used in April 2021 for an assembly of Lolium Perenne # under the following conditions: # - Oxford Nanopore reads, basecalled with Guppy 4.0.14. # - Coverage: 30x. # - Reads N50: 62 Kb. # The assembly was run: # - Using Shasta at commit 266d4c8e65ff408db2dfd1381bb14648be83dad3. # - On AWS Graviton2 (ARM) r6g.16xlarge instance type, 512 GB, 64 vCPUs. # - Using memory options --memoryMode filesystem --memoryBacking 2M. # Assembly results: # - Assembled 2.185 Mb of sequence. Estimated genome size # is 2.46 Gb from k-mer analysis, 2.72 Gb from flow cytometry. # - Assembly N50: 5.5 Mb. # - Elapsed time for assembly: 95 minutes. # Many thanks to Dario Copetti (Molecular Plant Breeding, ETH Zurich, Switzerland) # for providing access to the reads in advance of publication. # Also see Shasta issue #200 for some discussion # https://github.com/paoloshasta/shasta/issues/200 [Reads] noCache = True [MinHash] minBucketSize = 5 maxBucketSize = 30 minHashIterationCount = 50 minFrequency = 5 [Align] downsamplingFactor = 0.05 sameChannelReadAlignment.suppressDeltaThreshold = 30 maxSkip = 60 maxDrift = 20 maxTrim = 60 minAlignedMarkerCount = 200 minAlignedFraction = 0.3 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 crossEdgeCoverageThreshold = 3 minCoverage = 0 [Assembly] consensusCaller = Bayesian:guppy-3.6.0-a detangleMethod = 2 shasta-0.11.1/conf/Nanopore-R10-Fast-Nov2022.conf000066400000000000000000000023371433251011200207300ustar00rootroot00000000000000# This Shasta assembly configuration was tested under the following conditions: # - ONT R10 chemistry, fast mode, chimera rate around 2%. # - Guppy 6 basecaller with "super" accuracy. # - Human genome at low coverage (one flowcell, coverage around 35x). # - Haploid assembly. # Under these conditions it produced a haploid assembly with N50 around 20 Mb # and with base level accuracy limited mostly by heterozygosity # of the human genome. # This configuration might also be usable for other genomes and at higher coverage, # but that was not tested. [Reads] representation = 0 minReadLength = 10000 noCache = True [Kmers] k = 14 [MinHash] minHashIterationCount = 100 minBucketSize = 10 maxBucketSize = 40 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 minAlignedMarkerCount = 1000 minAlignedFraction = 0.85 maxSkip = 20 maxDrift = 10 maxTrim = 20 [ReadGraph] creationMethod = 0 maxAlignmentCount = 15 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 crossEdgeCoverageThreshold = 3 # Adaptive estimation of coverage threshold to generate marker graph vertices. minCoverage = 0 [Assembly] consensusCaller = Modal detangleMethod = 2 shasta-0.11.1/conf/Nanopore-R10-Slow-Nov2022.conf000066400000000000000000000023541433251011200207560ustar00rootroot00000000000000# This Shasta assembly configuration was tested under the following conditions: # - ONT R10 chemistry, slow mode, chimera rate around 2%. # - Guppy 6 basecaller with "super" accuracy. # - Human genome at medium coverage (two flowcells, coverage around 45x). # - Haploid assembly. # Under these conditions it produced a haploid assembly with N50 around 30 Mb # and with base level accuracy limited mostly by heterozygosity # of the human genome. # This configuration might also be usable for other genomes # and at higher or lower coverage, but that was not tested. [Reads] representation = 0 minReadLength = 10000 noCache = True [Kmers] k = 14 [MinHash] minHashIterationCount = 100 minBucketSize = 20 maxBucketSize = 60 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 minAlignedMarkerCount = 1200 minAlignedFraction = 0.9 maxSkip = 12 maxDrift = 8 maxTrim = 10 [ReadGraph] creationMethod = 0 maxAlignmentCount = 15 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 crossEdgeCoverageThreshold = 3 # Adaptive estimation of coverage threshold to generate marker graph vertices. minCoverage = 0 [Assembly] consensusCaller = Modal detangleMethod = 2 shasta-0.11.1/conf/Nanopore-Sep2020.conf000066400000000000000000000061641433251011200174620ustar00rootroot00000000000000# DO NOT USE THIS FILE IF YOU HAVE READS CREATED BY A # GUPPY VERSION OLDER THAN 3.6.0. # This file contains Shasta options which attempt to partially automate # parameter selection. It is based on an earlier config, which, as of Jun 2020, # was known to work with Oxford Nanopore reads under the following circumstances: # - Human genome assembly. # - Coverage between 40x and 80x. If you have more coverage than that, # you can use option "--Reads.minReadLength" to adjust coverage as desired. # - Reads from Guppy 3.6.0 base caller. If you have reads # from an older version of Guppy, use configuration file # Nanopore-Dec2019.conf instead. # The automation provided by this config is particularly applicable to # low coverage or non-human samples. It also matches or exceeds continuity # in human samples, relative to the appropriately chosen 3.6.0 or 3.6.0-UL conf. # Automation can also be activated with parameters designed for earlier basecallers, # if needed, but updating to guppy 3.6.0 or higher will greatly improve assembly # quality and is therefore strongly recommended. # To use this configuration file, specify Shasta option # "--config AbsolutePathToThisFile". # If you specify any conflicting values on the command line, # the values specified on the command line take precedence. # In most cases, for best performance on a large assembly # you will usually also want to use the following options, which # cannot be specified in a configuration file: # --memoryMode filesystem # --memoryBacking 2M # Using these options requires root access. # For detailed information on all available options see here: # https://paoloshasta.github.io/shasta/CommandLineOptions.html # For information on running a small assembly for which # performance is not essential see here: # https://paoloshasta.github.io/shasta/QuickStart.html # For more information on running an assembly see here: # https://paoloshasta.github.io/shasta/Running.html # For information on optimizing assembly performance see here: # https://paoloshasta.github.io/shasta/Performance.html [Reads] # If you have extra coverage, use this option to adjust coverage. minReadLength = 10000 noCache = True [Kmers] # Due to the higher accuracy of Guppy 3.6.0 we use longer # markers than usual. k = 14 [MinHash] minBucketSize = 5 maxBucketSize = 30 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # The following Align parameters are set to very permissive values to allow the majority of alignments # to be assessed during the initial stage of automatic alignment parameter selection maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] # This method uses the observed distribution of alignment stats to choose a cutoff for # maxSkip, maxDrift, maxTrim, minAlignedMarkerCount, and minAlignedFraction creationMethod = 2 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 crossEdgeCoverageThreshold = 3 # Automatically determine this using PeakFinder minCoverage = 0 [Assembly] consensusCaller = Bayesian:guppy-3.6.0-a detangleMethod = 2 shasta-0.11.1/conf/Nanopore-UL-Dec2019.conf000066400000000000000000000032671433251011200177550ustar00rootroot00000000000000# This file contains Shasta options that, as of December 2019, # are known to work with Ultra-Long (UL) Oxford Nanopore reads # under the following circumstances: # - Human genome assembly. # - Coverage between 40x and 80x. If you have more coverage than that, # you can use option "--Reads.minReadLength" to adjust coverage as desired. # - Reads from Guppy 3.0.5 base caller. Also known to work with # reads from other Guppy releases 3.0.x and 3.1.x. # To use this configuration file, specify Shasta option "--config PathToThisFile". # If you specify any conflicting values on the command line, # the values specified on the command line take precedence. # In most cases, for best performance on a large assembly # you will usually also want to use the following options, which # cannot be specified in a configuration file: # --memoryMode filesystem # --memoryBacking 2M # Using these options require root access. # For detailed information on all available options see here: # https://paoloshasta.github.io/shasta/CommandLineOptions.html # For information on running a small assembly for which # performance is not essential see here: # https://paoloshasta.github.io/shasta/QuickStart.html # For more information on running an assembly see here: # https://paoloshasta.github.io/shasta/Running.html # For information on optimizing assembly performance see here: # https://paoloshasta.github.io/shasta/Performance.html [Reads] # If you have extra coverage, use this option to adjust coverage. minReadLength = 50000 [MinHash] minBucketSize = 5 maxBucketSize = 40 minFrequency = 10 [Align] maxSkip = 60 maxDrift = 60 minAlignedMarkerCount = 400 [Assembly] consensusCaller = Bayesian:guppy-3.0.5-a shasta-0.11.1/conf/Nanopore-UL-Jan2022.conf000066400000000000000000000030571433251011200177610ustar00rootroot00000000000000# This is known to work at least under the following conditions: # - Oxford Nanopore Ultra-Long (UL) reads with read N50 50 Kb or more. # - Guppy 5 base caller with "super" accuracy. # - Human genome. # - Coverage 60x to 80x. If you have more coverage, # adjust --Reads.minReadLength or --Reads.desiredCoverage # to bring coverage down to this range. # For a human genome you can set --Reads.desiredCoverage 200G. # Under the above conditions, this should give an assembly with # N50 in the tens of Mb. [Reads] minReadLength = 50000 noCache = True [Kmers] k = 14 [MinHash] minBucketSize = 10 maxBucketSize = 50 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # The following Align parameters are set to very permissive values to allow the majority of alignments # to be assessed during the initial stage of automatic alignment parameter selection # (ReadGraph.creationMethod 2). maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] # This uses the observed distribution of alignment statistics to choose thresholds for # maxSkip, maxDrift, maxTrim, minAlignedMarkerCount, and minAlignedFraction creationMethod = 2 maxAlignmentCount = 12 strandSeparationMethod = 2 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 crossEdgeCoverageThreshold = 3 # Adaptive estimation of coverage threshold to generate marker graph vertices. minCoverage = 0 [Assembly] consensusCaller = Bayesian:guppy-5.0.7-a detangleMethod = 2 shasta-0.11.1/conf/Nanopore-UL-Jun2020.conf000066400000000000000000000043021433251011200177750ustar00rootroot00000000000000# DO NOT USE THIS FILE IF YOU HAVE READS CREATED BY A # GUPPY VERSION OLDER THAN 3.6.0. # This file contains Shasta options that, as of June 2020, # are known to work with Ultra-Long (UL) Oxford Nanopore reads # under the following circumstances: # - Human genome assembly. # - Coverage between 40x and 80x. If you have more coverage than that, # you can use option "--Reads.minReadLength" to adjust coverage as desired. # - Reads from Guppy 3.6.0 base caller. If you have reads # from an older version of Guppy, use configuration file # Nanopore-UL-Dec2019.conf instead. # To use this configuration file, specify Shasta option # "--config AbsolutePathToThisFile". # If you specify any conflicting values on the command line, # the values specified on the command line take precedence. # In most cases, for best performance on a large assembly # you will usually also want to use the following options, which # cannot be specified in a configuration file: # --memoryMode filesystem # --memoryBacking 2M # Using these options requires root access. # For detailed information on all available options see here: # https://paoloshasta.github.io/shasta/CommandLineOptions.html # For information on running a small assembly for which # performance is not essential see here: # https://paoloshasta.github.io/shasta/QuickStart.html # For more information on running an assembly see here: # https://paoloshasta.github.io/shasta/Running.html # For information on optimizing assembly performance see here: # https://paoloshasta.github.io/shasta/Performance.html [Reads] # If you have extra coverage, use this option to adjust coverage. minReadLength = 50000 noCache = True [Kmers] # Due to the higher accuracy of Guppy 3.6.0 we use longer # markers than usual. k = 14 [MinHash] minBucketSize = 10 maxBucketSize = 30 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 minAlignedFraction = 0.55 minAlignedMarkerCount = 600 sameChannelReadAlignment.suppressDeltaThreshold = 30 [ReadGraph] maxAlignmentCount = 12 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 refineThreshold = 6 crossEdgeCoverageThreshold = 3 [Assembly] consensusCaller = Bayesian:guppy-3.6.0-a detangleMethod = 1 shasta-0.11.1/conf/Nanopore-UL-May2022.conf000066400000000000000000000030701433251011200177720ustar00rootroot00000000000000# This is known to work at least under the following conditions: # - Oxford Nanopore Ultra-Long (UL) reads with read N50 50 Kb or more. # - Guppy 5 base caller or newer with "super" accuracy. # - Human genome. # - Coverage 60x to 80x. If you have more coverage, # adjust --Reads.minReadLength or --Reads.desiredCoverage # to bring coverage down to this range. # For a human genome you can set --Reads.desiredCoverage 200G. # Under the above conditions, this should give an assembly with # N50 in the tens of Mb. [Reads] minReadLength = 50000 noCache = True [Kmers] k = 14 [MinHash] minBucketSize = 10 maxBucketSize = 50 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # The following Align parameters are set to very permissive values to allow the majority of alignments # to be assessed during the initial stage of automatic alignment parameter selection # (ReadGraph.creationMethod 2). maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] # This uses the observed distribution of alignment statistics to choose thresholds for # maxSkip, maxDrift, maxTrim, minAlignedMarkerCount, and minAlignedFraction creationMethod = 2 maxAlignmentCount = 12 strandSeparationMethod = 2 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 crossEdgeCoverageThreshold = 3 # Adaptive estimation of coverage threshold to generate marker graph vertices. minCoverage = 0 [Assembly] consensusCaller = Bayesian:guppy-5.0.7-b detangleMethod = 2 shasta-0.11.1/conf/Nanopore-UL-Oct2021.conf000066400000000000000000000022551433251011200177740ustar00rootroot00000000000000# This is known to work at least under the following conditions: # - Oxford Nanopore Ultra-Long (UL) reads. # - Guppy 5 base caller. # - Human genome. # - Coverage 30x to 80x. [Reads] minReadLength = 50000 noCache = True [Kmers] k = 14 [MinHash] minBucketSize = 10 maxBucketSize = 30 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # The following Align parameters are set to very permissive values to allow the majority of alignments # to be assessed during the initial stage of automatic alignment parameter selection # (ReadGraph.creationMethod 2). maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] # This uses the observed distribution of alignment statistics to choose thresholds for # maxSkip, maxDrift, maxTrim, minAlignedMarkerCount, and minAlignedFraction creationMethod = 2 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 crossEdgeCoverageThreshold = 3 # Adaptive estimation of coverage threshold to generate marker graph vertices. minCoverage = 0 [Assembly] consensusCaller = Bayesian:guppy-5.0.7-a detangleMethod = 2 shasta-0.11.1/conf/Nanopore-UL-Phased-Jan2022.conf000066400000000000000000000035101433251011200211550ustar00rootroot00000000000000 # Configuration file that uses assembly mode 2 # to create a phased diploid assembly using # Nanopore Ultra-Long (UL) reads. # This is known to work at least under the following conditions: # - Oxford Nanopore Ultra-Long (UL) reads with read N50 50 Kb or more. # - Guppy 5 base caller with "super" accuracy. # - Human genome. # - Coverage 60x to 80x. If you have more coverage, # adjust --Reads.minReadLength or --Reads.desiredCoverage # to bring coverage down to this range. # For a human genome you can set --Reads.desiredCoverage 200G. [Reads] # Don't use the RLE representation of the reads. representation = 0 # Read length cutoff for UL reads, adjust as necessary, # or use desiredCoverage option to get coverage # around 40x to 80x. minReadLength = 50000 noCache = True [Kmers] # Because we are not using the RLE representation, # use shorter k-mers. k = 8 # Compensate for the non-RLE representation # to get about the same number of markers # we would get with the RLE representation and probability = 0.1. probability = 0.07 [MinHash] minBucketSize = 10 maxBucketSize = 50 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # Permissive alignment criteria as required for read graph creation method 2. maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] # Automatic adjustment of alignment criteria. creationMethod = 2 # Strict strand separation is required for Mode 2 (phased) assembly. strandSeparationMethod = 2 maxAlignmentCount = 12 [MarkerGraph] minCoverage = 6 minCoveragePerStrand = 1 minEdgeCoverage = 6 minEdgeCoveragePerStrand = 1 [Assembly] mode = 2 consensusCaller = Bayesian:guppy-5.0.7-a pruneLength = 100 mode2.bubbleRemoval.minConcordantReadCount = 2 shasta-0.11.1/conf/Nanopore-UL-Phased-May2022.conf000066400000000000000000000027721433251011200212040ustar00rootroot00000000000000 # Configuration file that uses assembly mode 2 # to create a phased diploid assembly using # Nanopore Ultra-Long (UL) reads. # This is known to work at least under the following conditions: # - Oxford Nanopore Ultra-Long (UL) reads with read N50 50 Kb or more. # - Guppy 5 base caller with "super" accuracy. # - Human genome. # - Coverage 60x to 80x. If you have more coverage, # adjust --Reads.minReadLength or --Reads.desiredCoverage # to bring coverage down to this range. # For a human genome you can set --Reads.desiredCoverage 200G. [Reads] # Read length cutoff for UL reads, adjust as necessary, # or use desiredCoverage option to get coverage # around 40x to 80x. minReadLength = 50000 noCache = True [MinHash] minBucketSize = 10 maxBucketSize = 50 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # Permissive alignment criteria as required for read graph creation method 2. maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] # Automatic adjustment of alignment criteria. creationMethod = 2 # Strict strand separation is required for Mode 2 (phased) assembly. strandSeparationMethod = 2 maxAlignmentCount = 12 [MarkerGraph] minCoverage = 6 minCoveragePerStrand = 1 minEdgeCoverage = 6 minEdgeCoveragePerStrand = 1 [Assembly] mode = 2 consensusCaller = Bayesian:guppy-5.0.7-b pruneLength = 100 mode2.bubbleRemoval.minConcordantReadCount = 2 shasta-0.11.1/conf/Nanopore-UL-Phased-Nov2022.conf000066400000000000000000000032751433251011200212170ustar00rootroot00000000000000 # Configuration file that uses assembly mode 2 # to create a phased diploid assembly using # Nanopore Ultra-Long (UL) reads. # This is known to work at least under the following conditions: # - Oxford Nanopore Ultra-Long (UL) reads with read N50 50 Kb or more. # - Guppy 5 or 6 base caller with "super" accuracy. # - Human genome. # - Coverage 60x to 80x. If you have more coverage, # adjust --Reads.minReadLength or --Reads.desiredCoverage # to bring coverage down to this range. # For a human genome you can set --Reads.desiredCoverage 200G. # Compared to the Nanopore-UL-Phased-May2022 configuration, # this provides better phasing accuracy without significantly decreasing # the size of phased bubbles. [Reads] # Read length cutoff for UL reads, adjust as necessary, # or use desiredCoverage option to get coverage # around 40x to 80x. minReadLength = 50000 noCache = True [MinHash] minBucketSize = 10 maxBucketSize = 50 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # Permissive alignment criteria as required for read graph creation method 2. maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] # Automatic adjustment of alignment criteria. creationMethod = 2 # Strict strand separation is required for Mode 2 (phased) assembly. strandSeparationMethod = 2 maxAlignmentCount = 12 [MarkerGraph] minCoverage = 6 minCoveragePerStrand = 1 minEdgeCoverage = 6 minEdgeCoveragePerStrand = 1 [Assembly] mode = 2 consensusCaller = Bayesian:guppy-5.0.7-b pruneLength = 100 mode2.bubbleRemoval.minConcordantReadCount = 2 mode2.phasing.minLogP = 50 shasta-0.11.1/conf/Nanopore-UL-Phased-Oct2021.conf000066400000000000000000000015671433251011200212030ustar00rootroot00000000000000# THIS IS ONLY COMPATIBLE WITH shasta 0.8.0. # IT CANNOT BE USED WITH MORE RECENT RELEASES. # EXPERIMENTAL configuration file that uses assembly mode 2 # to create a phased diploid assembly. # Tested with Guppy 5.0.7 Ultra-Long (ULT) reads at coverage around 60x. [Reads] minReadLength = 35000 noCache = True [Kmers] k = 14 [MinHash] minBucketSize = 10 maxBucketSize = 60 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 minAlignedMarkerCount = 400 minAlignedFraction = 0.6 maxDrift = 20 maxSkip = 50 maxTrim = 50 sameChannelReadAlignment.suppressDeltaThreshold = 30 [ReadGraph] creationMethod = 0 maxAlignmentCount = 12 strandSeparationMethod = 2 [MarkerGraph] minCoverage = 8 minCoveragePerStrand = 1 minEdgeCoverage = 8 minEdgeCoveragePerStrand = 1 [Assembly] mode = 2 consensusCaller = Bayesian:guppy-5.0.7-a phasing.minReadCount = 2 shasta-0.11.1/conf/Nanopore-UL-Sep2020.conf000066400000000000000000000056321433251011200177770ustar00rootroot00000000000000# DO NOT USE THIS FILE IF YOU HAVE READS CREATED BY A # GUPPY VERSION OLDER THAN 3.6.0. # This file contains Shasta options which attempt to partially automate # parameter selection. It is based on an earlier config, which, as of Jun 2020, # was known to work with Oxford Nanopore reads under the following circumstances: # - Human genome assembly. # - Coverage between 40x and 80x. If you have more coverage than that, # you can use option "--Reads.minReadLength" to adjust coverage as desired. # - Reads from Guppy 3.6.0 base caller. If you have reads # from an older version of Guppy, use configuration file # Nanopore-UL-Dec2019.conf instead. # The automation provided by this config is particularly applicable to # low coverage or non-human samples. It also matches or exceeds continuity # in human samples, relative to the appropriately chosen 3.6.0 or 3.6.0-UL conf. # Automation can be activated with parameters designed for earlier basecallers, # if needed, but updating to guppy 3.6.0 or higher will greatly improve assembly # quality and is therefore strongly recommended. # In most cases, for best performance on a large assembly # you will usually also want to use the following options, which # cannot be specified in a configuration file: # --memoryMode filesystem # --memoryBacking 2M # Using these options requires root access. # For detailed information on all available options see here: # https://paoloshasta.github.io/shasta/CommandLineOptions.html # For information on running a small assembly for which # performance is not essential see here: # https://paoloshasta.github.io/shasta/QuickStart.html # For more information on running an assembly see here: # https://paoloshasta.github.io/shasta/Running.html # For information on optimizing assembly performance see here: # https://paoloshasta.github.io/shasta/Performance.html [Reads] # If you have extra coverage, use this option to adjust coverage. minReadLength = 50000 noCache = True [Kmers] # Due to the higher accuracy of Guppy 3.6.0 we use longer # markers than usual. k = 14 [MinHash] minBucketSize = 10 maxBucketSize = 30 minFrequency = 5 [Align] alignMethod = 3 downsamplingFactor = 0.05 matchScore = 6 sameChannelReadAlignment.suppressDeltaThreshold = 30 # The following Align parameters are set to very permissive values to allow the majority of alignments # to be assessed during the initial stage of automatic alignment parameter selection maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 [ReadGraph] # This method uses the observed distribution of alignment stats to choose a cutoff for # maxSkip, maxDrift, maxTrim, minAlignedMarkerCount, and minAlignedFraction creationMethod = 2 [MarkerGraph] simplifyMaxLength = 10,100,1000,10000,100000 crossEdgeCoverageThreshold = 3 # Automatically determine this using PeakFinder minCoverage = 0 [Assembly] consensusCaller = Bayesian:guppy-3.6.0-a detangleMethod = 2 shasta-0.11.1/conf/Nanopore-UL-iterative-Sep2020.conf000066400000000000000000000016771433251011200217760ustar00rootroot00000000000000# This configuration file is EXPERIMENTAL and should only # be used under the following conditions: # - Nanopore reads created by Guppy 3.6.0 or newer. # - Ultra-Long (UL) reads with typical N50 80Kb or more. # - High coverage 80X. # Iterative assembly results in some separation of haplotypes # and does better at resolving segmental duplications. [Reads] minReadLength = 30000 noCache = True [Kmers] k = 10 [MinHash] minBucketSize = 10 maxBucketSize = 40 minFrequency = 5 [Align] alignMethod = 3 matchScore = 6 gapScore = -3 downsamplingFactor = 0.05 maxSkip = 100 maxDrift = 100 maxTrim = 100 minAlignedMarkerCount = 10 minAlignedFraction = 0.1 sameChannelReadAlignment.suppressDeltaThreshold = 30 [ReadGraph] maxAlignmentCount = 12 creationMethod = 2 [MarkerGraph] minCoveragePerStrand = 3 simplifyMaxLength = 10,100 crossEdgeCoverageThreshold = 3 [Assembly] detangleMethod = 2 consensusCaller = Bayesian:guppy-3.6.0-a iterative = True shasta-0.11.1/conf/PacBio-CCS-Dec2019.conf000066400000000000000000000027501433251011200174150ustar00rootroot00000000000000# This file contains Shasta options that, as of December 2019, # are known to work with PacBio CCS reads under the following # circumstances: # - Human genome assembly. # - Coverage between 40x and 80x. If you have more coverage than that, # you can use option "--Reads.minReadLength" to adjust coverage as desired. # To use this configuration file, specify Shasta option "--config PathToThisFile". # If you specify any conflicting values on the command line, # the values specified on the command line take precedence. # In most cases, for best performance on a large assembly # you will usually also want to use the following options, which # cannot be specified in a configuration file: # --memoryMode filesystem # --memoryBacking 2M # Using these options require root access. # For detailed information on all available options see here: # https://paoloshasta.github.io/shasta/CommandLineOptions.html # For information on running a small assembly for which # performance is not essential see here: # https://paoloshasta.github.io/shasta/QuickStart.html # For more information on running an assembly see here: # https://paoloshasta.github.io/shasta/Running.html # For information on optimizing assembly performance see here: # https://paoloshasta.github.io/shasta/Performance.html [Kmers] k = 15 probability = 0.02 [MinHash] m = 12 minBucketSize = 20 maxBucketSize = 100 minHashIterationCount = 25 minFrequency = 10 [ReadGraph] maxAlignmentCount = 20 [Assembly] consensusCaller = Modal shasta-0.11.1/conf/PacBio-CLR-Dec2019.conf000066400000000000000000000026431433251011200174260ustar00rootroot00000000000000# This file contains Shasta options that, as of December 2019, # are known to work with PacBio CLR reads under the following # circumstances: # - Human genome assembly. # - Coverage between 40x and 80x. If you have more coverage than that, # you can use option "--Reads.minReadLength" to adjust coverage as desired. # To use this configuration file, specify Shasta option "--config PathToThisFile". # If you specify any conflicting values on the command line, # the values specified on the command line take precedence. # In most cases, for best performance on a large assembly # you will usually also want to use the following options, which # cannot be specified in a configuration file: # --memoryMode filesystem # --memoryBacking 2M # Using these options require root access. # For detailed information on all available options see here: # https://paoloshasta.github.io/shasta/CommandLineOptions.html # For information on running a small assembly for which # performance is not essential see here: # https://paoloshasta.github.io/shasta/QuickStart.html # For more information on running an assembly see here: # https://paoloshasta.github.io/shasta/Running.html # For information on optimizing assembly performance see here: # https://paoloshasta.github.io/shasta/Performance.html [Reads] # Use this option to adjust coverage. minReadLength = 10000 [ReadGraph] maxAlignmentCount = 20 [Assembly] consensusCaller = Modal shasta-0.11.1/conf/README.md000066400000000000000000000037711433251011200152610ustar00rootroot00000000000000# shasta/conf directory Most Shasta users don't need to use the contents of this directory. Instead, use the following Shasta commands: * `shasta --command listConfigurations` to get a list of available built-in configuration names. * `shasta --command listConfiguration --config configurationName` to list the details of a specific configuration. Its output will include comments describing the intended usage of the specified configuration. See [here](https://chanzuckerberg.github.io/shasta/Configurations.html) for more information about Shasta assembly configurations. See below for more detailed information about the contents of this directory. Most of this information is not necessary or useful to most Shasta users. ## Shasta configuration files The Shasta `--config` option accepts either a built-in configuration name (one of the names listed by `shasta --command listConfigurations`) or a Shasta configuration file. In most cases, a built-in configuration name is used. A Shasta configuration file can be created by a user for a specific application or can selected from this directory. This directory includes a collection of Shasta configuration file, including ones that are now obsolete. To get a list of currently relevant assembly configurations, use `shasta --command listConfigurations`. Each of the built-in configurations also has a corresponding configuration file in this directory. There is no reason for any user ever to use one of them. ## Bayesian models for repeat counts Files named `SimpleBayesianConsensusCaller-X.csv`, where `X` is a number, describe Bayesian models used by Shasta to make probabilistic calls on the repeat count at each base position, using as input the observed repeat counts. The first two lines of each file contain a succinct description of the Bayesian model contained in that file. In normal circumstances, these files are not used. Instead, the user specifies one of the built-in Bayesian models - for example `--Assembly.consensusCaller Bayesian:guppy-5.0.7-b`. shasta-0.11.1/conf/RemoveConflicts.conf000066400000000000000000000015731433251011200177510ustar00rootroot00000000000000 # Tentative configuration file for conflict removal using the conflict graph. # Experimental. Do not use. [Reads] # This is the same as in Nanopore-Dec2019.conf. minReadLength = 10000 [Kmers] # Don't use as markers k-mers that are overabundant even in a single read. generationMethod = 2 enrichmentThreshold = 100 [MinHash] # MinHash parameters are the same as in Nanopore-Dec2019.conf. minBucketSize = 5 maxBucketSize = 30 minFrequency = 5 [Align] # This is the same as in Nanopore-Dec2019.conf. minAlignedFraction = 0.4 # Don't allow alignments in which one read is completely contained # in another read. suppressContainments = True # Use SeqAn banded alignments. alignMethod = 3 matchScore = 6 [ReadGraph] # Use the directed read graph. creationMethod = 1 # Turn on conflict removal. removeConflicts = True [Assembly] consensusCaller = Bayesian:guppy-3.0.5-a shasta-0.11.1/conf/SimpleBayesianConsensusCaller-10.csv000066400000000000000000006133431433251011200226700ustar00rootroot00000000000000>Name guppy5.0.7_hg002_chr1-2 with pseudocounts 1, Jun 1 2021 >AT prior 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shasta-0.11.1/docs/000077500000000000000000000000001433251011200137755ustar00rootroot00000000000000shasta-0.11.1/docs/Acknowledgments.html000066400000000000000000000060351433251011200200140ustar00rootroot00000000000000
Table of contents

Acknowledgments

The Shasta software uses the external software packages listed below. This list does not include standard components of the Ubuntu distribution of the Linux operating system.

Licensing information for each of these packages is contained in the Shasta LICENSE file.

MurmurHash2

MurmurHash2 from GitHub repository aappleby/smhasher, by Austin Appleby, is a hash function with excellent performance and randomness properties. It is used as a hash function in the Shasta software.

Dset

Files unused/dset64.hpp and src/dset64-gccAtomic.hpp are modified versions of file dset.h from GitHub repository wjakob/dset by Wenzel Jakob. They are lock-free implementation in C++ of the algorithm described in Wait-free Parallel Algorithms for the Union-Find Problem by Richard J. Anderson and Heather Woll, STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing.

The code was modified to support the union-find algorithm for 64-bit data using 128-bit primitives for atomic memory access. Kind assistance by Wenzel Jakob in making these changes is acknowledged.

SeqAn

The SeqAn software is used as an option for multiple sequence alignment of sequences encountered in edges of the Shasta marker graph. It is not directly included in the Shasta repository. Instead, it is downloaded from GitHub and installed on the user's machine when the user invokes scripts/InstallPrerequisites-Ubuntu.sh. It is a header only library.

Spoa

The Spoa software by Robert Vaser is used as an option for multiple sequence alignment of sequences encountered in edges of the Shasta marker graph. It is not directly included in the Shasta repository. Instead, it is downloaded from GitHub, built, and installed on the user's machine when the user invokes scripts/InstallPrerequisites-Ubuntu.sh..

Table of contents
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Table of contents

Building the code from source

When is it necessary to build from source?

In most cases, you can directly run a Shasta static executable from a release build downloaded from one of the release pages, chanzuckerberg/shasta/releases for releases up to 0.10.0 or paoloshasta/shasta/releases for newer relesases. These static executables are portable to almost any version of Linux currently in use. See the quick start page for more information. You need to build from source in one of the following circumstances:

  • To build a version of Shasta for which no release exists. For example, if you want to get a recently added feature not available in the most recent release build. For this specific use case, it might be easiest to instead download a Shasta test build from GitHub, as described below.
  • To test, develop, or debug Shasta code.
  • To port the Shasta build to a different platform.

How to build from source

Build platforms

We currently support building from source on the following platforms:

  • Ubuntu 22.04 for Intel/AMD processors. The Shasta static executable created by this build has no dependencies (and therefore requires no installation) and runs on most current 64-bit Linux platforms that use kernel version 3.2.0 or newer.
  • Ubuntu 22.04 for ARM processors (for example, AWS Graviton/Graviton2/Graviton3 processors running Ubuntu, Raspberry Pi).

Shasta no longer builds on Ubuntu 20.04 or on any version of macOS.

Build commands

  1. Check out the Shasta repository (you need to have git installed on your machine) 
    git clone https://github.com/paoloshasta/shasta.git
  2. Install prerequisite packages 
    sudo shasta/scripts/InstallPrerequisites-Ubuntu.sh
  3. Build 
    mkdir shasta-build
cd shasta-build
cmake ../shasta
make all -j
make install

    Build contents

    The build process described above creates in the shasta-build directory a shasta-install directory that contains the following:

    • A bin directory containing the Shasta static executable, named shasta, Shasta shared library shasta.so, and several scripts. The static executable is highly portable to other Linux versions.
    • A conf directory containing sample config files.
    • A docs directory containing this and other documentation.

    Building smaller binaries

    If you don't plan to use your build for debugging, you can use command make install/strip instead of make install as the last step of the build process. This will strip debug symbols from the Shasta executable and shared library, which significantly reduces the size of the build directory.

    An alternative to building from source: downloading a test build

    Shasta uses GitHub Actions to perform a test build every time a push to the repository occurs. The main goal of these builds is to test that new code continues to build on all supported platforms, but the builds are accessible to all users for download for 90 days after their creation. The test builds are generally not as reliable as release builds and can sometimes be non-functional. With this in mind, you can use the following steps to download one of these test builds:

    • Make sure to be logged in to GitHub. Any user will work, but GitHub will not allow downloading a test build anonymously.
    • Go to the GitHub Actions page of the Shasta repository.
    • Choose a build that has green checkmark and click on it. The checkmark indicates that a push occurred at that commit, followed by a successful test build.
    • Scroll down to the Artifacts section and select what you want to download. Your browser will then download the file you selected as zipped. You can use the unzip command, or equivalent, to unzip it. After unzipping it, if it is an executable. Before running it, you will have to set its execute permission bit via chmod ugo+x filename.
    Table of contents
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Choosing the options controlling memory modes

The dialog below will help you choose the values to use for the two options controlling memory modes:

  • --memoryMode
  • --memoryBacking
These options are described in more detail here.

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shasta-0.11.1/docs/CommandLineOptions.html000066400000000000000000001250611433251011200204320ustar00rootroot00000000000000
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Command line options

This page summarizes command line options for the Shasta executable. We will make an effort to keep this page consistent with the code, but discrepancies may temporarily appear as we add new features. In that case, the list of command line options obtained by invoking the Shasta executable with --help is more authoritative than the contents of this page.

Saving some typing

Many option names are long and descriptive. To make it easier to type them Shasta provides the ability to create a bash completion script. If you are using the bash shell (the default shell in most modern Linux distributions), you can use the commands below to save yourself some typing. This assumes that the shasta executable is named just like this - shasta - and is accessible via your PATH environment variable (if it is not, you have to invoke shasta using an absolute or relative path): 

shasta --command createBashCompletionScript
source shastaCompletion.sh
The first command creates a script named shastaCompletion.sh. The second command runs that script in the current shell (this is important - running the script without the source command will not work). Now, while typing a shasta option or keyword, you can press TAB, and the option name will be completed as much as can be done without ambiguity. You can also press TAB a second time to get suggestions for what could be allowed next.

The command that generates the script can be run only once, but you will need to rerun the source command above every time you start a new shell. Alternatively, you can put the source command in your .bashrc file or in one of the standard locations used to store bash completion scripts.

Options allowed only on the command line

OptionDefault
value		Description
--help or -h Use this option to obtain a summary of allowed command line options.
--version or -v Identify the Shasta version.
--config Specifies the name of a pre-built configuration or a configuration file. This option has no default and is mandatory when running an assembly.
--input Specifies the names of the input files for the assembly. This option is mandatory. At least one input file must be specified. To specify multiple input files, enter them separated by space after --input.
--assemblyDirectoryShastaRun Specifies the name of the directory where assembly output is stored. If --command is assemble (the default), this directory must not exist and is automatically created. For most other commands, this directory must exist. See here for more information on the output files created by Shasta.
--commandassemble Specifies the Shasta command to be run.
--memoryMode
(not supported on MacOS)		anonymous
  • Can be anonymous or filesystem.
  • For best performance use --memoryMode filesystem --memoryBacking 2M. However, using these options require root access via sudo. Depending on sudo setup, this may result in prompting for a password.
  • Not supported on MacOS. On MacOS, Shasta operates as if --memoryMode filesystem --memoryBacking disk was specified.
--memoryBacking
(not supported on MacOS)		4K
  • Can be disk, 4K, or 2M.
  • For best performance use --memoryMode filesystem --memoryBacking 2M. However, using these options requires root access via sudo. Depending on sudo setup, this may result in prompting for a password.
  • Not supported on MacOS. On MacOS, Shasta operates as if --memoryMode filesystem --memoryBacking disk was specified.
--threads0 Specifies the number of threads to be used, or 0 to request one thread per virtual processor.
--suppressStdoutLogfalse This is a Boolean switch. Normally, Shasta duplicates output to stdout by sending a copy to stdout.log in the assembly directory. If this option is used, this behavior is suppressed, and stdout.log is not created.
--exploreAccessuser Specifies access control for --command explore.
--port17100 The TCP port to be used for --command explore. If the specified port is not available, Shasta will try again after incrementing the port number a few times.
--alignmentsPafFile"" The name of a PAF file containing alignments of reads to a reference. Only used for --command explore, for display of the alignment candidate graph. Experimental.

Options allowed on the command line and in a configuration file

See here for the format required to enter these options in a configuration file.
OptionDefault
value		Description
--Reads.representation1 Read representation: 0 = raw sequence, 1 = Run-Length Encoded (RLE) sequence. Experimental. Do not use.
--Reads.minReadLength10000 Read length cutoff. Reads shorter than this number of bases are discarded on input and not used in the assembly.
--Reads.desiredCoverage0 Specifies a desired value for total coverage, in bases. If not zero, the read length cutoff specified via --Reads.minReadLength is increased to reduce coverage to the specified value. This value can be specified as a number of bases, or using power of 10 multipliers immediately following the numeric value. The powers of ten can be abbreviated as G, Gb, or Gbp for 109 bases, M, Mb, or Mbp for 106 bases, K, Kb, or Kbp for 103 bases. For example, a desired coverage of 120×109 bases can be requested specifying 120G, 120Gb, 120Gbp, or 120000000000. If coverage available using only reads longer than Reads.minReadLength is less than the value specified for --Reads.desiredCoverage, the assembly terminates with an error message.
--Reads.noCacheFalse This is a Boolean switch. If set, requests skipping the Linux cache when loading reads. Implemented for Linux only (uses the O_DIRECT flag). Can help performance, but only use it if you know you will not need to access the input files again soon.
--Reads.palindromicReads.skipFlaggingFalse Skip flagging palindromic reads. Oxford Nanopore reads should be flagged for better results.
--Reads.palindromicReads.maxSkip100 Same as Align.maxSkip but applied only to self reverse-complement alignments for the purpose of detecting palindromes.
--Reads.palindromicReads.maxMarkerFrequency10 Same as Align.maxMarkerFrequency but applied only to self reverse-complement alignments for the purpose of detecting palindromes.
--Reads.palindromicReads.alignedFractionThreshold0.1 Same as Align.maxMarkerFrequency but applied only to self reverse-complement alignments for the purpose of detecting palindromes.
--Reads.palindromicReads.nearDiagonalFractionThreshold0.1 Count the fraction of markers within deltaThreshold (distance in markers) from the diagonal and only flag reads above this fraction as palindromic.
--Reads.palindromicReads.deltaThreshold100 Used in combination with nearDiagonalFraction. This value defines how far from the diagonal markers must be to be counted towards the nearDiagonalFraction. Unit is in markers.
--Kmers.generationMethod0 Used to select how k-mers to be used as markers are generated. Can be one of the following:
  • 0: Random selection.
  • 1: Random selection, excluding k-mers that are globally overenriched, as defined by their global frequency in input reads, and by the value specified as --Kmers.enrichmentThreshold.
  • 2: Random selection, excluding k-mers that are overenriched even in a single read, as defined by the value specified as --Kmers.enrichmentThreshold.
  • 3: Read from file. Use --Kmers.file to specify the file.
  • 4: Random selection, excluding k-mers that appear in two copies close to each other, even in a single read. The two k-mer copies are considered close if they occur at a distance from each other less than --Kmers.distanceThreshold RLE bases.
--Kmers.k10 Length of marker k-mers (in run-length representation).
--Kmers.probability0.1 Probability that a k-mer is selected to be used as a marker.
--Kmers.enrichmentThreshold100. This controls the enrichment threshold above which k-mers are considered overenriched. Enrichment is ratio of k-mer frequency in reads to random. Only used if --Kmers.generationMethod is 1 or 2.
--Kmers.distanceThreshold1000 Distance threshold, in RLE bases, for --Kmers.generationMethod 4. Only used if --Kmers.generationMethod is 4.
--Kmers.file The absolute path of a file containing the k-mers to be used as markers, one per line. Only used if --Kmers.generationMethod is 3.
--MinHash.version0 The version of the MinHash/LowHash algorithm to be used. Can be 0 (default) or 1 (experimental).
--MinHash.m4 The number of consecutive markers that define a MinHash/LowHash feature.
--MinHash.hashFraction0.01 Defines how low a hash has to be to be used with the LowHash algorithm.
--MinHash.minHashIterationCount10 The number of MinHash/LowHash iterations, or 0 to let --MinHash.alignmentCandidatesPerRead control the number of iterations.
--MinHash.alignmentCandidatesPerRead20 If --MinHash.minHashIterationCount is 0, MinHash iteration is stopped when the average number of alignment candidates that each read is involved in reaches this value. If --MinHash.minHashIterationCount is not 0, this is not used.
--MinHash.minBucketSize0 The minimum size for a bucket to be used by the MinHash/LowHash algoritm.
--MinHash.maxBucketSize10 The maximum size for a bucket to be used by the MinHash/LowHash algoritm.
--MinHash.minFrequency2 The minimum number of times a pair of reads must be found by the MinHash/LowHash algorithm in order to be considered a candidate alignment.
--MinHash.allPairsFalse This is a Boolean switch that causes the MinHash process to be skipped. Instead, all possible read pairs are marked as alignment candidates, on both relative orientations. This should only be used for very small test assemblies as it can become prohibitively slow for large assemblies.
--Align.alignMethod3 The alignment method to be used to compute marker alignments between reads:
  • 0 = Old Shasta alignment method. Use this to reproduce Shasta behavior before release 0.5.0.
  • 1 = SeqAn. This gives the best alignment results but it is slow and should only be used for testing.
  • 3 = Banded SeqAn.
  • 4 = New Shasta alignment method (experimental).
--Align.maxSkip30 The maximum number of markers that an alignment is allowed to skip.
--Align.maxSkip30 The maximum amount of marker drift that an alignment is allowed to tolerate between successive markers.
--Align.maxTrim30 The maximum number of skipped markers tolerated in an alignment at the beginning and end of a read.
--Align.maxMarkerFrequency10 Marker frequency threshold when computing alignments. Markers that occur more than this number of times in either of the two reads to be aligned are ignored.
--Align.minAlignedMarkerCount100 The minimum number of aligned markers for an alignment to be used.
--Align.minAlignedFraction0 The minimum fraction of aligned markers for an alignment to be used.
--Align.matchScore6 Match score for marker alignments (only for alignment methods 1 and 3).
--Align.mismatchScore-1 Mismatch score for marker alignments (only for alignment methods 1 and 3).
--Align.gapScore-1 Gap score for marker alignments (only for alignment methods 1 and 3).
--Align.downsamplingFactor0.1 Downsampling factor for downsampled marker alignments (only for alignment method 3).
--Align.bandExtend10 Amount to extend the alignment band, in markers (only used for alignment method 3).
--Align.maxBand1000 Maximum band width, in markers, for banded marker alignments (only used for alignment method 3).
--Align.sameChannelReadAlignment.suppressDeltaThreshold0 If not zero, alignments between reads from the same nanopore channel and close in time are suppressed. The read meta data fields from the FASTA or FASTQ header are checked. If their difference, in absolute value, is less than the value of this option, the alignment is suppressed. This can help avoid assembly artifact. This check is only done if the two reads have identical metadata fields runid, sampleid, and ch. If any of these metadata fields are missing, this check is suppressed and this option has no effect.
--Align.suppressContainmentsFalse This is a Boolean switch. If set, containment alignments are suppressed. Containment alignments are alignments in which one read is entirely contained in another read, except possibly for up to maxTrim markers at the beginning and end.
--Align.align4.deltaX200 Only used for alignment method 4 (experimental).
--Align.align4.deltaY10 Only used for alignment method 4 (experimental).
--Align.align4.minEntryCountPerCell10 Only used for alignment method 4 (experimental).
--Align.align4.maxDistanceFromBoundary100 Only used for alignment method 4 (experimental).
--ReadGraph.creationMethod0 The method used to create the read graph (0 or 2).
--ReadGraph.maxAlignmentCount6 The maximum alignments to be kept in the read graph for each read.
--ReadGraph.maxChimericReadDistance2 Used for chimeric read detection.
--ReadGraph.strandSeparationMethod1 Strand separation method. Spwecify one of the following values:
  • 0 = No strand separation.
  • 1 = Limited strand separation (default).
  • 2 = Strict strand separation.
--ReadGraph.crossStrandMaxDistanceFalse Maximum distance (edges) for strand separation method 1.
--ReadGraph.removeConflictsFalse This is a Boolean switch. Remove conflicts from the read graph. Experimental - do not use.
--ReadGraph.markerCountPercentile 0.015 Percentile for adaptive selection of --ReadGraph. markerCount (only used when --ReadGraph.creationMethod is 2).
--ReadGraph.alignedFractionPercentile 0.12 Percentile for adaptive selection of --ReadGraph.alignedFraction (only used when --ReadGraph.creationMethod is 2).
--ReadGraph.maxSkipPercentile 0.12 Percentile for adaptive selection of --ReadGraph.maxSkip (only used when --ReadGraph.creationMethod is 2).
--ReadGraph.maxDriftPercentile 0.12 Percentile for adaptive selection of --ReadGraph.maxDrift (only used when --ReadGraph.creationMethod is 2).
--ReadGraph.maxTrimPercentile 0.015 Percentile for adaptive selection of --ReadGraph.maxTrim (only used when --ReadGraph.creationMethod is 2).
--ReadGraph.flagInconsistentAlignmentsFalse This is a Boolean switch. Flag inconsistent alignments. Experimental.
--ReadGraph.flagInconsistentAlignments.triangleErrorThreshold200 Triangle error threshold, in markers, for flagging inconsistent alignments. Only used if --ReadGraph.flagInconsistentAlignments is set. Experimental.
--ReadGraph.flagInconsistentAlignments.leastSquareErrorThreshold200 Least square error threshold, in markers, for flagging inconsistent alignments. Only used if --ReadGraph.flagInconsistentAlignments is set. Experimental.
--ReadGraph.flagInconsistentAlignments.leastSquareMaxDistance1 Least square max distance for flagging inconsistent alignments. Only used if --ReadGraph.flagInconsistentAlignments is set. Experimental.
--MarkerGraph.minCoverage10 The minimum coverage for a marker graph vertex. Vertices with lower coverage are not generated. Specifying 0 causes a suitable value of this parameter to be selected automatically.
--MarkerGraph.maxCoverage100 The maximum coverage for a marker graph vertex. Vertices with higher coverage are not generated.
--MarkerGraph.minCoveragePerStrand0 The minimum coverage per strand for a marker graph vertex. Vertices with lower coverage on either strand are not generated.
--MarkerGraph.minEdgeCoverage6 Minimum edge coverage (number of supporting oriented reads) for a marker graph edge to be created. Only used with --Assembly.mode 2.
--MarkerGraph.minEdgeCoveragePerStrand2 Minimum edge coverage (number of supporting oriented reads) on each strand for a marker graph edge to be created. Experimental. Only used with --Assembly.mode 2.
--MarkerGraph.allowDuplicateMarkersFalse This is a Boolean switch. It specifies whether to allow more than one marker on the same oriented read on a single marker graph vertex. Experimental.
--MarkerGraph.cleanupDuplicateMarkersFalse This is a Boolean switch. It specifies whether to clean up marker graph vertices with more than one marker on the same oriented read. Experimental.
--MarkerGraph.duplicateMarkersPattern1Threshold0.5 Used when cleaning up marker graph vertices with more than one marker on the same oriented read. Experimental.
--MarkerGraph.lowCoverageThreshold0 Used during approximate transitive reduction. Edges with coverage less than or equal to this value are unconditionally removed from the marker graph, even at the cost of breaking reachability. This never happens with the default value 0.
--MarkerGraph.highCoverageThreshold256 Used during approximate transitive reduction. Edges with coverage greater than or equal to this value are unconditionally kept in the marker graph, even if they could be removed without breaking reachability. This never happens with the default value 256, because marker graph edge coverage is stored in one byte and saturates at 255.
--MarkerGraph.maxDistance30 Used during approximate transitive reduction of the marker graph. It controls the length of each Breadth First Search (BFS) used to determine reachability. This length is expressed in marker graph edges.
--MarkerGraph.edgeMarkerSkipThreshold100 Used during approximate transitive reduction of the marker graph. Edges with coverage 1 are unconditionally removed from the marker graph if the only supporting read has a marker skip of more than this number of markers on that edge. Large marker skips are indicative of artifacts or errors.
--MarkerGraph.pruneIterationCount6 The number of marker graph prune iterations. This equals the maximum length of dead branches that are removed.
--MarkerGraph.simplifyMaxLength10,100,1000 Used for bubble removal.
--MarkerGraph.crossEdgeCoverageThreshold0. Experimental. Cross edge coverage threshold. If this is not zero, assembly graph cross-edges with average edge coverage less than this value are removed, together with the corresponding marker graph edges. A cross edge is defined as an edge v0->v1 with out-degree(v0)>1, in-degree(v1)>1.
--MarkerGraph.reverseTransitiveReductionFalse This is a Boolean switch. If set, approximate reverse transitive reduction of the marker graph in the reverse direction is also performed.
--MarkerGraph.peakFinder.minAreaFraction0.08 Used in the automatic selection of --MarkerGraph.minCoverage when --MarkerGraph.minCoverage is set to 0.
--MarkerGraph.peakFinder.areaStartIndex2 Used in the automatic selection of --MarkerGraph.minCoverage when --MarkerGraph.minCoverage is set to 0.
--MarkerGraph.secondaryEdges.maxSkip1000000 Maximum number of markers skipped by a secondary edge (mode 2 assembly only).
--MarkerGraph.secondaryEdges.split.errorRateThreshold0.25 Error rate threshold used for splitting secondary edges (mode 2 assembly only).
--MarkerGraph.secondaryEdges.split.minCoverage4 Minimum coverage for secondary edges generated during splitting (mode 2 assembly only).
--Assembly.mode 0 Assembly mode (0 = haploid assembly, 2 = phased diploid assembly).
--Assembly.markerGraphEdgeLengthThresholdForConsensus 0 Controls assembly of long marker graph edges.
--Assembly.consensusCaller Modal Used to select the consensus caller for repeat counts.
--Assembly.storeCoverageDataFalse This is a Boolean switch used to request storing of coverage data (only useful in conjunction with --memoryMode filesystem).
--Assembly.writeReadsByAssembledSegmentFalse This is a Boolean switch used to request writing a csv file containing all the reads that were used to assemble each segment).
--Assembly.pruneLength0 Prune length (in markers) for pruning of the assembly graph. Assembly graph leaves shorter than this number of markers are iteratively pruned. Set to zero to suppress pruning of the assembly graph. Assembly graph pruning takes place separately and in addition to marker graph pruning.
--Assembly.detangleMethod0 Method used to detangle the assembly graph. Valid values:
  • 0 = no detangling.
  • 1 = strict detangling.
  • 2 = less strict detangling, controlled by Assembly.detangle.* options.
--Assembly.detangle.diagonalReadCountMin1 Experimental. Minimum number of reads on detangle matrix diagonal elements required for detangling. Only used with --Assembly.detangleMethod 2.
--Assembly.detangle.offDiagonalReadCountMax2 Experimental. Maximum number of reads on detangle matrix off-diagonal elements allowed for detangling. Only used with --Assembly.detangleMethod 2.
--Assembly.detangle.offDiagonalRatio0.3 Experimental. Maximum ratio of total off-diagonal elements over diagonal element allowed for detangling. Only used with --Assembly.detangleMethod 2.
--Assembly.iterativeFalse This is a Boolean switch used to request iterative assembly (experimental).
--Assembly.iterative.iterationCount3 Number of iterations for iterative assembly (experimental).
--Assembly.iterative.pseudoPathAlignMatchScore1 Pseudopath alignment match score for iterative assembly (experimental).
--Assembly.iterative.pseudoPathAlignMismatchScore-1 Pseudopath alignment mismatch score for iterative assembly (experimental).
--Assembly.iterative.pseudoPathAlignGapScore-1 Pseudopath alignment gap score for iterative assembly (experimental).
--Assembly.iterative.mismatchSquareFactor3. Mismatch square factor for iterative assembly (experimental).
--Assembly.iterative.minScore0. Minimum pseudo-alignment score for iterative assembly (experimental).
--Assembly.iterative.maxAlignmentCount6 Maximum number of read graph neighbors for iterative assembly (experimental).
--Assembly.iterative.bridgeRemovalIterationCount3 Number of read graph bridge removal iterations for iterative assembly (experimental).
--Assembly.iterative.bridgeRemovalMaxDistance2 Maximum distance for read graph bridge removal for iterative assembly (experimental).
--Assembly.mode2.strongBranchThreshold 2 Minimum number of supporting reads required for a strong branch. Mode 2 assembly only.
--Assembly.mode2.epsilon 0.1 Epsilon for the Bayesian model used for phasing and bubble removal. This is the probability that a read appears on the wrong branch of a diploid bubble. Mode 2 assembly only.
--Assembly.mode2.bubbleRemoval.minConcordantReadCount 3 Minimum number of concordant reads for bubble removal. Mode 2 assembly only.
--Assembly.mode2.bubbleRemoval.maxDiscordantReadCount 6 Maximum number of discordant reads for bubble removal. Mode 2 assembly only.
--Assembly.mode2.bubbleRemoval.minLogP 30 Minimum log(P) (in decibels) for bubble removal. Mode 2 assembly only.
--Assembly.mode2.bubbleRemoval.componentSizeThreshold 10 Component size threshold for bubble removal. Mode 2 assembly only.
--Assembly.mode2.phasing.minConcordantReadCount 2 Minimum number of concordant reads for phasing. Mode 2 assembly only.
--Assembly.mode2.phasing.maxDiscordantReadCount 1 Maximum number of discordant reads for phasing. Mode 2 assembly only.
--Assembly.mode2.phasing.minLogP10 Minimum log(P) (in decibels) for phasing. Mode 2 assembly only.
--Assembly.mode2.superbubble.maxSize 50 Maximum size (number of edges) of a superbubble to be processed. Mode 2 assembly only.
--Assembly.mode2.superbubble.maxChunkSize 20 Maximum size (number of edges) of a superbubble chunk to be processed. Mode 2 assembly only.
--Assembly.mode2.superbubble.edgeLengthThreshold 6 Edge length threshold in markers for superbubble removal. Mode 2 assembly only.
--Assembly.mode2.suppressGfaOutput False This is a Boolean switch. If set, all GFA output is suppressed. Mode 2 assembly only.
--Assembly.mode2.suppressFastaOutput False This is a Boolean switch. If set, all FASTA output is suppressed. Mode 2 assembly only.
--Assembly.mode2.suppressDetailedOutput False This is a Boolean switch. If set, output of the detailed representation of the assembly is suppressed. Mode 2 assembly only.
--Assembly.mode2.suppressPhasedOutput False This is a Boolean switch. If set, output of the phased representation of the assembly is suppressed. Mode 2 assembly only.
--Assembly.mode2.suppressHaploidOutput False This is a Boolean switch. If set, output of the haploid representation of the assembly is suppressed. Mode 2 assembly only.

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Shasta commands

Shasta is most commonly used to run an assembly. However, it offers additional utility functions via command line option --command. Following --command, you specify a command name which can be one of the following:

  • assemble
  • cleanupBinaryData
  • createBashCompletionScript
  • explore
  • listCommands
  • listConfiguration
  • listConfigurations
  • saveBinaryData
You can also use the following to get an up to date list of valid Shasta commands:
shasta --command listCommands
The default command is assemble, so you don't need to use the --command option to run an assembly.

The rest of this page provides a brief description of the functionality offered by each of these commands.

Command assemble

This is the default command and is used to run an assembly. See here for more information.

Command cleanupBinaryData

This command is used to unmount the filesystem in memory used to hold Shasta binary data, mounted at the Data directory under the assembly directory. It frees the memory (in 4 Kb or 2 MB pages depending on the setting of --memoryBacking) used to store data in the filesystem.

It also removes the Data directory. If a DataOnDisk directory exists, it creates a symbolic link named Data. This makes it possible to use the Shasta http server functionality and Python API even after the binary data are no longer in memory, as long as they were saved on disk using --command saveBinaryData.

This command may require root privilege via sudo, depending on the setting of --memoryBacking. See here for more information.

Command createBashCompletionScript

Running this command creates a Bash completion script that completes Shasta commands to facilitate typing. The script is named shastaCompletion.sh and can be invoked from a Bash shell as follows: source shastaCompletion.sh

See here for more information.

Command explore

This command starts Shasta in a mode that behaves as an http server. You can use a browser to connect to it and visualize detailed information about your assembly, and this requires the binary data for the assembly to be available. See here for more details.

Command listCommands

This command prints an updated list of all valid commands known to the Shasta executable being invoked.

Command listConfiguration

A configuration is a built-in set of Shasta command line options that can be selected by name. To print details of a specific configuration, use: shasta --command listConfiguration --config Name For example, shasta --command listConfiguration --config Nanopore-Sep2020 To see a list of available configuration names, use shasta --command listConfigurations

You can also create a configuration with command line options that you select and store it in a file. See here for more information.

Command listConfigurations

A configuration is a built-in set of Shasta command line options that can be selected by name. This command prints a list of the available configurations. See here for more information.

Command saveBinaryData

This command is used to save Shasta binary data. Shasta stores binary data in directory Data in the assembly directory. This command makes a copy on disk in Data. You will usually want to run --command cleanupBinaryData after this command completes.

This command may require root privilege via sudo, depending on the setting of --memoryBacking. See here for more information.

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Compatibility across releases

Binary data compatibility

Depending on how you use Shasta, the code may generate for each run a Data directory containing binary data containing the data structures used for assembly. These binary data can be used for processing in two ways:

Binary data created by a version of the Shasta assembler will generally be incompatible with a different version. In other words, you will only be able to use the http server or the Python API using the same Shasta version that was used for the run that you want to postprocess.

API compatibility

The Python API is subject to changes across Shasta versions. Therefore, a Python script that was developed for a specific Shasta version is generally not going to continue to work with future versions.

Command line options for the Shasta executable and scripts will generally change across releases. There is no guarantee that options that were valid in a release will remain valid in a future release. It is also possible that the meaning of some options will change from release to release, although a reasonable effort will be made to limit this.

Release numbering

Shasta releases are named using Semantic Versioning conventions. Because of the lack of compatibility described above, it is likely that the Shasta major release number will stay at 0 for the foreseeable future - that is, Shasta releases will be numbered 0.Y.Z.

Table of contents
shasta-0.11.1/docs/ComputationalMethods.html000066400000000000000000003440571433251011200210430ustar00rootroot00000000000000
Table of contents

Computational methods

Long reads

The Shasta assembler can be used to assemble DNA sequence from long reads. It is optimized especially for Oxford Nanopore reads, but can also be used to assemble other types of long reads such as those generated by Pacific Biosciences sequencing platforms.

Oxford Nanopore reads are rapidly evolving in characteristics, and therefore cannot be characterized precisely. Here is a summary of read metrics for the best available reads as of September 2020:

  • Typical length around 50 Kb, with just a few percent of coverage in reads below 10 Kb and 10% or more of coverage in reads longer than 100 Kb. There are also Ultra-Long (UL) protocols that provide longer reads, with typical lengths around 100 Kb.
  • Sequence identity around 97%, or equivalently an error rate around 3%. This relatively high error rate has been improving significantly in time.
  • The dominant error mode consists of errors in the length of homopolymer runs of all lengths. Errors in short homopolymer runs (lengths 1-5) are particularly deleterious due to their high frequency.

Computational challenges

Due to their length, Oxford Nanopore reads have unique value for de novo assembly. However, a successful approach needs to deal with the high error rate.

Traditional approaches to de novo assembly typically rely on selecting a k-mer length that satisfies the following:

  • K-mers of the selected length are reasonably unique in the target genome. For human assemblies, this typically means k ⪆ 30.
  • Most k-mers of the selected length are error-free in the target reads. This means that k must be significantly less than the inverse of the error rate.

For many sequencing technolgies, the above conditions are satisfied for k around 30. But for our target reads, with an error rate around 3%, most 30-mers contain errors, and therefore assembly algorithms based on using such k-mers become unfeasible.

A possible approach consists of adding a preliminary error correction step in which reads are aligned to each other and corrected based on consensus and then presenting to the assembler the corrected reads, which hopefully have a much lower error rate. But such approaches tend to be slow, and in addition, any errors made in the error correction step are permanent and cannot be fixed during the assembly process.

Read representation

The computational techniques used in the Shasta assembler rely on representing the sequence of input reads in a way that reduces the effect of errors on the de novo assembly process:

  • The sequence of input reads is represented using run-length encoding.
  • In many assembly steps, the sequence of input reads is described by using occurrences of a pre-determined subset of short k-mers (k ≈ 10 in run-encoding) called markers.
The next two sections expand on these two methods of representing the input reads.

Run-length encoding

With run-length encoding, the sequence of each input read is represented as a sequence of bases, each with a repeat count that says how many times each of the bases is repeated. For example, the following read 

CGATTTAAGTTA
is represented as follows using run-length encoding: 
CGATAGTA
11132121

Using run-length encoding makes the assembly process less sensitive to errors in the length of homopolymer runs, which are the most common type of errors in Oxford Nanopore reads. For example, consider these two reads: 

CGATTTAAGTTA
CGATTAAGGGTTA
Using their raw representation above, these reads can be aligned like this: 
CGATTTAAG--TTA
CGATT-AAGGGTTA
Aligning the second read to the first required a deletion and two insertions. But in run-length encoding, the two reads become: 
CGATAGTA
11132121
CGATAGTA
11122321
The sequence portions are now identical and can be aligned trivially and exactly, without any insertions or deletions: 
CGATAGTA
CGATAGTA
The differences between the two reads only appear in the repeat counts: 
11132121
11122321
   * *

The Shasta assembler uses one byte to represent repeat counts, and as a result, it only represents repeat counts between 1 and 255. If a read contains more than 255 consecutive bases, it is discarded on input. Such reads are extremely rare, and the occurrence of such a large number of repeated bases is probably a symptom that something is wrong with the read anyway.

Some properties of base sequences in run-length encoding
  • In the sequence portion of the run-length encoding, consecutive bases are always distinct. If they were not, the second one would be removed from the run-length encoded sequence, while increasing the repeat count for the first one.
  • With ordinary base sequences, the number of distinct k-mers of length k is 4k. But with run-length base sequences, the number of distinct k-mers of length k is 4×3k-1. This is a consequence of the previous bullet.
  • The run-length sequence is generally shorter than the raw sequence, and cannot be longer. For a long random sequence, the number of bases in the run-length representation is 3/4 of the number of bases in the raw representation.

Markers

Even with run-length encoding, errors in input reads are still frequent. To further reduce sensitivity to errors, and also to speed up some of the computational steps in the assembly process, the Shasta assembler also uses a read representation based on markers. Markers are occurrences in reads of a pre-determined subset of short k-mers. By default, Shasta uses for this purpose k-mers with k=10 in run-length encoding, corresponding to an average of approximately 13 bases in raw read representation.

Just for illustration, consider a description using markers of length 3 in run-length encoding. There is a total 4×32 = 36 distinct such markers. We arbitrarily choose the following fixed subset of the 36, and we assign an id to each of the kmers in the subset:
TGC0
GCA1
GAC2
CGC3

Consider now the following portion of a read in run-length representation (here, the repeat counts are irrelevant and so they are omitted): 

CGACACGTATGCGCACGCTGCGCTCTGCAGC
 GAC     TGC   CGC       TGC
           CGC    TGC     GCA
            GCA     CGC
Occurrences of the k-mers defined in the table above are shown and define the markers in this read. Note that markers can overlap. Using the marker ids defined in the table above, we can summarize the sequence of this read portion as follows: 
2 0 3 1 3 0 3 0 1
This is the marker representation of the read portion above. It just includes the sequence of markers occurring in the read, not their positions.

In Shasta documentation and code, the zero-based sequence number of a marker in the marker representation of an oriented read is called the marker ordinal or simply ordinal. The first marker in an oriented read has ordinal 0, the second one has ordinal 1, and so on.

Note that the marker representation loses information, as it is not possible to reconstruct the complete initial sequence from the marker representation. This also means that the marker representation is insensitive to errors in the sequence portions that don't belong to any markers.

The Shasta assembler uses a random choice of the k-mers to be used as markers. The length of the markers k is controlled by assembly parameter --Kmers.k with a default value of 10. Each k-mer is randomly chosen to be used as a marker with probability determined by assembly parameter --Kmers.probability with a default value of 0.1. There are 4 possibilities for the first base in an RLE k-mer, but only 3 for the remaining k-1 bases since RLE k-mers do not contain consecutive bases. Thus, 4×3k-1 is equal to the total number of RLE k-mers. With these default values, the total number of distinct markers is approximately 0.1×4×39≈7900.

Options are also provided to read from a file the RLE k-mers to be used as markers. This permits experimentation with marker k-mers chosen in ways other than random. To date, no marker selection algorithm has proven more successful than random selection but it is very possible that assembly quality can be improved by non-random selection of markers.

The only constraint used in selecting k-mers to be used as markers is that if a k-mer is a marker, its reverse complement should also be a marker. This makes it easy to construct the marker representation of the reverse complement of a read from the marker representation of the original read. It also ensures strand symmetry in some of the computational steps.

Below is the run-length representation of a portion of a read and its markers, as displayed by the Shasta http server.

Marker alignments

The marker representation is a sequence

The marker representation of a read is a sequence in an alphabet consisting of the marker ids. This sequence is much shorter than the original sequence of the read but uses a much larger alphabet. For example, with default Shasta assembly parameters, the marker representation is 10 times shorter than the run-length encoded read sequence, or about 14 times shorter than the raw read sequence. Its alphabet has around 8000 symbols, many more than the 4 symbols that the original read sequence uses.

Because the marker representation of a read is a sequence, we can compute an alignment of two reads directly in marker representation. Computing an alignment in this way has two important advantages:

  • The shorter sequences and larger alphabet make the alignment much faster to compute.
  • The alignment is insensitive to read errors in the portions that are not covered by any marker.
These advantages are illustrated below using alignment matrices.

Alignment matrix

Consider two sequences on any alphabet, sequence x with nx symbols xi (i=0,...nx-1) and sequence y with ny symbols yj (j=0,...ny-1). The alignment matrix of the two sequences, , Aij, is a nx×ny matrix with elements

Aij = δxiyj

or, in words, Aij is 1 if xi=yj and 0 otherwise. (The Shasta assembler never explicitly constructs alignment matrices except for display when requested interactively. Alignment matrices are used here just for illustration).

In portions where the sequences x and y are perfectly aligned, the alignment matrix consists of a matrix diagonal set to 1. Most of the remaining elements will be 0, but many can be 1 just because the same symbol appears at two unrelated locations in the two sequences.

Alignment matrix in raw base representation

The picture below shows a portion of a typical alignment matrix of two reads in their representation as a raw base sequence (not the run-length encoded representation) and a computed optimal alignment. This is for illustration only, as the Shasta assembler never constructs such a matrix, except when requested interactively.

Here, elements of the alignment matrix are colored as follows:

  • Red dots are alignment matrix elements that are 1 but that are not part of the computed optimal alignment.
  • Green dots are alignment matrix elements that are 1 and that are part of the computed optimal alignment.
  • Yellow matrix elements are alignment matrix elements that are 0 but that were computed to be part of the optimal alignment as mismatching alignment positions.
  • Black or grey matrix elements are matrix elements that are 0 and that were not computed to be part of the optimal alignment. Grey is used instead of black every 10 bases to facilitate counting bases in the figure.

On average, about 25% of the matrix elements are 1, simply because the alphabet has 4 symbols. Because of the large fraction of 1 elements and because of the high error rate, it would be hard to visually locate the optimal alignment in this picture, if it was not highlighted using colors. This is emphasized by the following figure, which represents the same matrix, but with all 1 elements now colored red and all 0 elements black.

Note that the alignment matrix contains frequent square or rectangular blocks of 1 elements. They correspond to homopolymer run. A square block indicates that the two reads agree on the length of that homopolymer run, and a non-square rectangular block indicates that the two reads disagree. If we were using run-length encoding for this picture, these blocks would all collapse to a single matrix element.

Alignment matrix in marker representation

For comparison, the picture below shows a portion of the alignment matrix of two reads, in marker representation, as displayed by the Shasta http server. In this alignment matrix, the marker ordinal on the first read is on the horizontal axis (increasing towards the right) and the marker ordinal for the second read (increasing towards the bottom) is on the vertical axis. Here, matrix elements that are 1 are displayed in green or red. The ones in green are the ones that are part of the optimal alignment computed by the Shasta assembler - see below for more information. The grey lines are drawn 10 markers apart from each other and their only purpose is to facilitate reading the picture - the corresponding matrix elements are 0. Shasta marker alignments only include matching markers and gaps. Mismatching markers are never included in the alignment.

Because of the much larger alphabet, matrix elements that are 1 but are not part of the optimal alignment are infrequent. In addition, each alignment matrix element here corresponds on average to a 13×13 block in the alignment matrix in raw base sequence shown above. The portion of alignment matrix in marker space shown here covers about 120 markers or about 1500 bases in the original representation of the read, compared to only about 100 bases in the alignment matrix in raw representation shown above. For these reasons, the marker representation is orders of magnitude more efficient than the raw base representation when computing read alignments.

Alignment metrics

Shasta uses several metrics to characterize alignments in marker space. As shown in the picture above, a typical alignment consists of stretches of consecutive aligned markers intermixed with gaps in the alignment. Call xi (i=0,...,N-1) the aligned marker ordinals on the first oriented read and yj (j=0,...,N-1) the aligned marker ordinals on the second oriented read, where N is the number of aligned markers. That is, xi and yi are the coordinates of the green points in the alignment matrix as depicted above. Also, call Nx the number of markers in the first oriented read and Ny the number of markers in the second oriented read. Nx and Ny are the horizontal and vertical sizes, respectively, of the alignment matrix.

With this notation, we define the following metrics for a marker alignment. For each metric, there is a command line option that controls the minimum or maximum value of the metric for an alignment to be considered valid and used in the assembly. These options can be used to control the quality of marker alignments to be used during assembly.

  • N is the number of aligned markers. An alignment is only used if N is at least equal to the value of --Align.minAlignedMarkerCount. This option can be used to discard alignments that are too short to be considered reliable.
  • For each oriented read in the alignment, the alignedFraction is defined as the ratio of the number of aligned markers over the length of the range of ordinals covered by the alignment, that is N / (xN-1 - x0 + 1) for the first oriented read and N / (yN-1 - y0 + 1) for the second oriented read. The lesser of the alignedFraction of the two oriented reads is called the alignedFraction of the alignment. It is a measure of the accuracy of the alignment. An alignment is only used if alignedFraction is at least equal to the value of --Align.minAlignedFraction.
  • Even when an alignment gap occurs, successive aligned markers tend to be approximately on the same diagonal of the alignment matrix. This reflects the fact that, even when sequencing errors occur, the number of bases read is likely to still be approximately correct. The maximum diagonal shift between successive aligned markers is called drift and is defined as the maximum value of the absolute value of (xi+1 - yi+1) - (xi - yi) computed over i=1,...,N-1. Note that, in this case too, alignment gaps at the beginning and end of the alignment are not considered. An alignment is only used if drift is at most equal to the value of --Align.maxDrift. This option can be used to discard alignments containing gaps with large diagonal shift.
  • An alignment should always begin near the beginning of at least one of the two oriented reads, but we need to allow for an initial incorrect portion at the beginning of each read. The minimum over the two oriented reads of the number of markers skipped at the beginning of the alignment, min(x0, y0) is called the leftTrim. Similarly, the minimum over the two oriented reads of the number of markers skipped at the end of the alignment, min(Nx - 1 - xN-1, Ny - 1 - yN-1) is called the rightTrim. The lesser of leftTrim and rightTrim is called trim An alignment is only used if trim. is at most equal to the value of --Align.maxTrim.

Computing optimal alignments in marker representation

To compute the optimal alignment highlighted in green in the above figure, one of the following methods is used, under control of command line option --Align.alignMethod:

  • --Align.alignMethod 0 selects the legacy Shasta algorithm. Do not use for production assemblies.
  • --Align.alignMethod 1 computes marker alignments using SeqAn. Do not use for production assemblies.
  • --Align.alignMethod 3 (the default choice) uses a faster two-step algorithm that also uses SeqAn and achieves better performance using a combination of downsampling and banded alignments.

The computation of marker alignments for all alignment candidates found by the LowHash algorithm is one of the most expensive phases of an assembly, and typically takes around half of total elapsed time. For this reason, it is recommended that only the default --Align.alignMethod 3 be used for production assemblies.

--Align.alignMethod 0

This selects a simple alignment algorithm on the marker representations of the two reads to be aligned. Do not use this method for production assemblies, as the default alignment method selected by --Align.alignMethod 3 provides better performance and accuracy. This algorithm effectively constructs an optimal path in the alignment matrix, but uses some heuristics to speed up the computation:

  • The maximum number of markers that an alignment can skip on either read is limited to a maximum, under control of assembly parameter --Align.maxSkip (default value 30 markers, corresponding to around 400 bases when all other Shasta parameters are at their default). This reflects the fact that Oxford Nanopore reads can often have long stretches in error. In the alignment matrix shown above, there is a skip of about 20 markers (2 light grey squares) following the first 10 aligned markers (green dots) on the top left.
  • The maximum number of markers that an alignment can skip at the beginning or end of a read is limited to a maximum, under control of assembly parameter --Align.maxTrim (default value 30 markers, corresponding to around 400 bases when all other Shasta parameters are at their default). This reflects the fact that Oxford Nanopore reads often have an initial or final portion that is not usable.
  • To avoid alignment artifacts, marker k-mers that are too frequent in either of the two reads being aligned are not used in the alignment computation. For this purpose, the Shasta assembler uses a criterion based on an absolute number of occurrences of marker k-mers in the two reads, although a relative criterion (occurrences per Kb) may be more appropriate. The current absolute frequency threshold is under control of assembly parameter --Align.maxMarkerFrequency (default 10 occurrences).

--Align.alignMethod 1

This causes marker alignments to be computed using SeqAn overlap alignments. This guarantees an optimal alignment, but is computationally very expensive, with cost proportional to the size of the alignment matrix, therefore O(N2) in the number of markers in the two reads being aligned. Do not use this method for production assemblies, as the default alignment method selected by --Align.alignMethod 3 provides much better performance with minimal degradation in accuracy.

--Align.alignMethod 3

This is the default alignment method used by Shasta and provides a good combination of performance and accuracy. It uses the SeqAn in a two-step process:

  • In a first step, SeqAn is used to compute an overlap alignment between two downsampled versions of the marker sequences of the reads to be aligned. This is O(N2) in the number of markers in the two reads being aligned, but still reasonably fast because of downsampling.
  • In a second step, SeqAn is used to compute a banded alignment of the marker representation of the two reads. The position and width of the band is obtained from the downsampled alignment computed in the first step.

Finding overlapping reads

Even though computing read alignments in marker representation is fast, it still is not feasible to compute alignments among all possible pairs of reads. For a human-size genome with ≈106-107 reads, the number of pairs to consider would be ≈1012-1014, and even at 10-3 seconds per alignment the compute time would be ≈109-1011 seconds, or ≈107-109 seconds elapsed time (≈102-104 days) when using 128 virtual processors.

Therefore some means of narrowing down substantially the number of pairs to be considered is essential. The Shasta assembler uses for this purpose a slightly modified MinHash scheme based on the marker representation of reads.

For a general description of the MinHash algorithm see the Wikipedia article or this excellent book chapter. In summary, the MinHash algorithm takes as input a set of items each characterized by a set of features. Its goal is to find pairs of the input items that have a high Jaccard similarity index - that is, pairs of items that have many features in common. The algorithm proceeds by iterations. At each iteration, a new hash table is created and a hash function that operates on the feature set is selected. For each item, the hash function of each of its features is evaluated, and the minimum hash function value found is used to select the hash table bucket that each item is stored in. It can be proven that the probability of two items ending up in the same bucket equals the Jaccard similarity index of the two items - that is, items in the same bucket are more likely to be highly similar than items in different buckets. The algorithm then adds to the pairs of potentially similar items all pairs of items that are in the same bucket.

When all iterations are complete, the probability that a pair of items was found at least once is an increasing function of the Jaccard similarity of the two items. In other words, the pairs found are enriched for pairs that have high similarity. One can now consider all the pairs found (hopefully a much smaller set than all possible pairs) and compute the Jaccard similarity index for each, then keep only the pairs for which the index is sufficiently high. The algorithm does not guarantee that all pairs with high similarity will be found - only that the probability of finding all pairs is an increasing function of their similarities.

The algorithm is used by Shasta with items being oriented reads (a read in either original or reverse complemented orientation) and features being consecutive occurrences of m markers in the marker representation of the oriented read. For example, consider an oriented read with the following marker representation: 

18,45,71,3,15,6,21
If m is selected equal to 4 (the Shasta default, controlled by assembly parameter --MinHash.m), the oriented read is assigned the following features: 
(18,45,71,3)
(45,71,3,15)
(71,3,15,6)
(3,15,6,21)

From the picture above of an alignment matrix in marker representation, we see that streaks of 4 or more common consecutive markers are relatively common. We have to keep in mind that, with Shasta default parameters, 4 consecutive markers span an average 40 bases in run-length encoding or about 52 bases in the original raw base representation. At a typical error rate of around 10%, such a portion of a read would contain on average 5 errors. Yet, the marker representation in run-length space is sufficiently robust that these common "features" are relatively common despite the high error rate. This indicates that we can expect the MinHash algorithm to be effective in finding pairs of overlapping reads.

However, the MinHash algorithm has a feature that is undesirable for our purposes: namely, that the algorithm is good at finding read pairs with high Jaccard similarity index. For two sets X and Y, the Jaccard similarity index is defined as the ratio 

J = |X∩Y| / |X∪Y|
Because the read length distribution of Oxford Nanopore reads is wide, it is common to have pairs of reads with very different lengths. Consider now two reads with lengths nx and ny, with nx<ny, that overlap exactly over the entire length nx. The Jaccard similarity is in this case given by nx/ny < 1. This means that, if one of the reads in a pair is much shorter than the other one, their Jaccard similarity will be low even in the best case of exact overlap. As a result, the unmodified MinHash algorithm will not do a good job at finding overlapping pairs of reads with very different lengths.

For this reason, the Shasta assembler uses a small modification to the MinHash algorithm: instead of just using the minimum hash for each oriented read for each iteration, it keeps all hashes below a given threshold. Each oriented read can be stored in multiple buckets, one for each low hash encountered. This has the effect of eliminating the bias against pairs in which one read is much shorter than the other. The modified algorithm is referred to as LowHash in the Shasta source code. It is effectively equivalent to an indexing approach in which we index all features with low hash.

The LowHash algorithm is controlled by the following assembly parameters:

  • --MinHash.m (default 4): the number of consecutive markers that define a feature.
  • --MinHash.hashFraction (default 0.01): The fraction of hash values that count as "low".
  • --MinHash.minHashIterationCount (default 10): The number of iterations.
  • --MinHash.maxBucketSize (default 10): The maximum number of items for a bucket to be considered. Buckets with more than this number of items are ignored. The goal of this parameter is to mitigate the effect of common repeats, which can result in buckets containing large numbers of unrelated oriented reads.
  • --MinHash.minBucketSize (default 0): The minimum number of items for a bucket to be considered. Buckets with less than this number of items are ignored because MinHash features in these buckets are likely to be in error.
  • --MinHash.minFrequency (default 2): the number of times a pair of oriented reads has to be found to be considered and stored as a possible pair of overlapping reads.

Initial assembly steps

Initial steps of a Shasta assembly proceed as follows. If the assembly is setup for best performance (--memoryMode filesystem --memoryBacking 2M if using the Shasta executable), all data structures are stored in memory, and no disk activity takes place except for initial loading of the input reads, storing of assembly results, and storing a small number of small files with useful summary information.

  • Input reads are read from Fasta files and converted to run-length representation. Read shorter than the read length cutoff --Reads.minReadLength bases (default 10000) are discarded. Reads that contain bases with repeat counts greater than 255 are also discarded. This is a consequence of the fact that repeat counts are stored using one byte, and therefore there would be no way to store such reads. Reads with such long repeat counts are extremely rare, however, and when they occur they are of suspicious quality.
  • In addition, if a non-zero value is specified for --Reads.desiredCoverage, the read length cutoff is further increased until coverage is just above the specified number of bases.
  • K-mers to be used as markers are randomly selected.
  • Occurrences of those marker k-mers in all oriented reads are found.
  • Reads are aligned to their reverse complement to determine if they are palindromic. Palindromes are flagged and ignored during assembly.
  • The LowHash algorithm finds candidate pairs of overlapping oriented reads.
  • A marker alignment is computed for each candidate pair of oriented reads. If the alignment metrics are sufficiently good, the alignment is stored for use in the assembly.

Read graph

Using the methods covered so far, an assembly has created a list of pairs of oriented reads, each pair having a plausible marker alignment. How to use this type of information for assembly is a classical problem with a standard solution (Myers, 2005), the string graph. However, the prescriptions in the Myers paper cannot be directly used here, mostly due to the marker representation.

Shasta proceeds by creating a Read Graph, an undirected graph in which each vertex corresponds to an oriented read. A subset of the available alignments is selected using one of two methods described below, under control of command line option --ReadGraph.creationMethod. For each selected alignment, an undirected edge is created in the read graph between the vertices corresponding to the oriented reads in the selected alignment.

If we used all of the available alignments to create read graph edges, the resulting read graph would suffers from high connectivity in repeat regions, due to spurious alignments between oriented reads originating in different but similar regions of the genome. Therefore it is important to select alignments to be used in a way that minimizes these spurious alignments while keeping true ones as much as possible.

Note that each read contributes two vertices to the read graph, one in its original orientation, and one in reverse complemented orientation. Therefore the read graph contains two strands, each strand at full coverage. This makes it easy to investigate and potentially detect erroneous strand jumps that would be much less obvious if using alternative approaches with one vertex per read.

An example of a portion of the read graph, as displayed by the Shasta http server, is shown here.

Even though the graph is undirected, edges that correspond to overlap alignments are drawn with an arrow that points from the leftmost oriented read to the rightmost one. Edges that correspond to containment alignments are drawn in red and without an arrow. Vertices are drawn with area proportional to the length of the corresponding reads.

The linear structure of the read graph successfully reflects the linear arrangement of the input reads and their origin on the genome being assembled.

However, deviations from the linear structure can occur in the presence of long repeats, typically for high similarity segment duplications:

Obviously incorrect connections can destroy the linear structure of assembled sequence. This is dealt with later in the assembly process.

Read graph creation method 0

When --ReadGraph.creationMethod 0 (the default) is selected, the Shasta assembler uses a very simple method to select alignments: it only keeps a k-Nearest-Neighbor subset of the alignments. That is, for each vertex (oriented read) it only keeps the best k alignments, as measured by the number of aligned markers. The number of edges k kept for each vertex is controlled by assembly option --ReadGraph.maxAlignmentCount, with a default value of 6. Note that, despite the k-Nearest-Neighbor subset, it remains possible for a vertex to have a degree more than k.

Read graph creation method 2

When --ReadGraph.creationMethod 2 is selected, a more sophisticated approach, developed by Ryan Lorig-Roach at U. C. Santa Cruz, is used. Shasta first inspects all stored alignments to compute statistical distributions (histograms) for the five alignment metrics in the table below. Alignments for which one of these metrics is worse than a set percentile are discarded. The threshold percentiles are controlled by the command line options shown in the table.
Alignment metric Percentile option Default value
Number of aligned markers --ReadGraph.markerCountPercentile 0.015
Aligned marker fraction --ReadGraph.alignedFractionPercentile 0.12
Maximum number of markers skipped --ReadGraph.maxSkipPercentile 0.12
Maximum diagonal drift --ReadGraph.maxDriftPercentile 0.12
Number of markers trimmed --ReadGraph.maxTrimPercentile 0.015

The process than continues with the same k-Nearest-Neighbor procedure used with --ReadGraph.creationMethod 0, but only considering alignments that were not ruled out by above percentile criteria.

Because of its adaptiveness to alignment characteristics, this method is more robust and less sensitive to the choice of alignment criteria and results in more accurate and more contiguous assemblies. However, when using --ReadGraph.creationMethod 2 it is important to use very liberal choices for the options that control alignment quality because bad alignments will be discarded anyway using the percentile criteria. An example of assembly options with --ReadGraph.creationMethod 2 is provided in Shasta configuration files Nanopore-Sep2020.conf and Nanopore-UL.Sep2020.conf. These files apply to Oxford Nanopore reads created with base caller Guppy version 3.6.0 or newer (standard and ultra-long version, respectively). They are available in the conf directory in the source code tree or in a Shasta build.

Marker graph

Consider a read whose marker representation is: 

a b c d e
We can represent this read as a directed graph that describes the sequence in which its markers appear:

This is not very useful but illustrates the simplest form of a marker graph as used in the Shasta assembler. The marker graph is a directed graph in which each vertex represents a marker and each edge represents the transition between consecutive markers. We can associate sequence with each vertex and edge of the marker graph:

  • Each vertex is associated with the sequence of the corresponding marker.
  • If the markers of the source and target vertex of an edge do not overlap, the edge is associated with the sequence intervening between the two markers.
  • If the markers of the source and target vertex of an edge do overlap, the edge is associated with the overlapping portion of the marker sequences.

Consider now a second read with the following marker representation, which differs from the previous one just by replacing marker c with x: 

a b x d e

The marker graph for the two reads is:

In the optimal alignment of the two reads, markers a, b, d, e are aligned. We can redraw the marker graph grouping together vertices that correspond to aligned markers:

Finally, we can merge aligned vertices to obtain a marker graph describing the two aligned reads:

Here, by construction, each vertex still has a unique sequence associated with it - the common sequence of the markers that were merged (however the corresponding repeat counts can be different for each contributing read). An edge, on the other hand, can have different sequences associated with it, one corresponding to each of the contributing reads. In this example, edges a->b and d->e have two contributing reads, which can each have distinct sequence between the two markers.

We call coverage of a vertex or edge the number of reads "contributing" to it. In this example, vertices a, b, d, e have coverage 2, and vertices c, x have coverage 1. Edges a->b and d->e have coverage 2, and the remaining edges have coverage 1.

The construction of the marker graph was illustrated above for two reads, but the Shasta assembler constructs a global marker graph which takes into account all oriented reads:

  • The process starts with a distinct vertex for each marker of each oriented read. Note that at this stage the marker graph is large (≈ 2×1010 vertices for a human assembly using default assembly parameters).
  • For each marker alignment corresponding to an edge of the read graph, we merge vertices corresponding to aligned markers.
  • Of the resulting merged vertices, we remove those whose coverage is too low or too high, indicating that the contributing reads or some of the alignments involved are probably in error. This is controlled by assembly parameters MarkerGraph.minCoverage (default 10) and MarkerGraph.maxCoverage (default 100), which specify the minimum and maximum coverage for a vertex to be kept.
  • Edges are created. An edge v0->v1 is created if there is at least a read contributing to both v0 and v1 and for which all markers intervening between v0 and v1 belong to vertices that were removed.
Note that this does not mean that all vertices with the same marker sequence are merged - two vertices are only merged if they have the same marker sequence, and if there are at least two reads for which the corresponding markers are aligned.

Given the large number of initial vertices involved, this computation is not trivial. To allow efficient computation in parallel on many threads, a lock-free implementation of the disjoint data set data structure, as first described by Anderson and Woll (1991), Anderson and Woll (1994), is used for merging vertices. Some code changes were necessary to permit large numbers of vertices, as the initial implementation by Wenzel Jakob only allowed for 32-bit vertex ids.

A portion of the marker graph, as displayed by the Shasta http server, is shown here:

Each vertex, shown in green, contains a list of the oriented reads that contribute to it. Each oriented read is labeled with the read id (a number) followed by "-0" for original orientation and "-1" for reverse complemented orientation. Each vertex is also labeled with the marker sequence (run-length encoded). Edges are drawn with an arrow whose thickness is proportional to edge coverage and labeled with the sequence contributed by each read. In edge labels, a sequence consisting of a number indicates the number of overlapping bases between adjacent markers.

On a larger scale and with less detail, a typical portion of the marker graph looks like this:

Here, each vertex is drawn with a size proportional to its coverage. Edge arrows are again displayed with thickness proportional to edge coverage. The marker graph has a linear structure with a dominant path and side branches due to errors.

Assembly graph

The Shasta assembly process also uses a compact representation of the marker graph, called the assembly graph, in which each linear sequence of edges is replaced by a single edge. For example, this marker graph

can be represented as an assembly graph as follows. Colors were chosen to indicate the correspondence to marker graph edges:

The length of an edge of the assembly graph is defined as the number of marker graph edges that it corresponds to. For each edge of the assembly graph, average coverage is also computed, by averaging the coverage of the marker graph edges it corresponds to.

Using the marker graph to assemble sequence

The marker graph is a partial description of the multiple sequence alignment between reads and can be used to assemble a consensus sequence. One simple way to do that is to only keep the "dominant" path in the graph, and then move on that path from vertex to edge to vertex, assembling run-length encoded sequence as follows:

  1. On a vertex, all reads have the same sequence, by construction: the marker sequence associated with the vertex. There is trivial consensus among all the reads contributing to a vertex, and the marker sequence can be used directly as the contribution of the vertex to assembled sequence.
  2. For edges, there are two possible situations plus a hybrid case:
    • 2.1. If the adjacent markers overlap, in most cases, all contributing reads have the same number of overlapping bases between the two markers, and we are again in a situation of trivial consensus, where all reads contribute the same sequence, which also agrees with the sequence of adjacent vertices. In cases where not all reads are in agreement on the number of overlapping bases, only reads with the most frequent number of overlapping bases are taken into account.
    • 2.2. If the adjacent markers don't overlap, then each read can have a different sequence between the two markers. In this situation, we compute a multiple sequence alignment of the sequences and a consensus using the Spoa library. The multiple sequence alignment is computed constrained at both ends, because all reads contributing to the edge have, by construction, identical markers at both sides.
    • 2.3. A hybrid situation occasionally arises, in which some reads have the two markers overlapping, and some do not. In this case, we count reads of the two kinds and discard the reads of the minority kind, then revert to one of the two cases 2.1 or 2.2 above.

This is the process used for sequence assembly by the current Shasta implementation. It requires a process to select and define dominant paths, which is described in the next section. It is algorithmically simple, but its main shortcoming is that it does not use for assembly reads that contribute to the abundant side branches. This means that coverage is lost, and therefore the sequence of assembled accuracy is not as good as it could be if all available coverage was used. Means to eliminate this shortcoming and use information from the side branches of the marker graph could be a subject of future work on the Shasta assembler.

Assembling repeat counts

The process described above works with run-length encoded sequence and therefore assembles run-length encoded sequence. The final step to create raw assembled sequence is to compute the most likely repeat count for each sequence position in run-length encoding. In Shasta 0.1.0 this was done by choosing as the most likely repeat count the one that appears the most frequently in the reads that contributed to each assembled position.

The latest version of the Shasta assembler supports three different options for that, under control of command line option --Assembly.consensusCaller:

  • --Assembly.consensusCaller Modal (the default) requests the same algorithm used in Shasta 0.1.0, that is, the most frequent repeat count seen at each base position is used in the assembly.
  • --Assembly.consensusCaller Median requests an algorithm that stores in the assembly the median repeat count seen at each base position.
  • --Assembly.consensusCaller Bayesian:name requests a Bayesian algorithm to determine the most likely repeat count. name can be one of the following:
    • One of the following built-in Bayesian models:
      • guppy-2.3.1-a to use a Bayesian model optimized for the Guppy 2.3.1 base caller.
      • guppy-3.0.5-a to use a Bayesian model optimized for the Guppy 3.0.5 base caller.
      • guppy-3.4.4-a to use a Bayesian model optimized for the Guppy 3.4.4 base caller.
      • guppy-3.6.0-a to use a Bayesian model optimized for the Guppy 3.6.5 base caller.
      • r10-guppy-3.4.8-a to use a Bayesian model optimized for r10 reads and the Guppy 3.4.8 base caller.
      • bonito-0.3.1-a to use a Bayesian model optimized for the Bonito 0.3.1 base caller.
      • guppy-5.0.7-b to use a Bayesian model optimized for the Guppy 5.0.7 base caller. There is also an older and less accurate model guppy-5.0.7-a.
    • The name of a configuration file describing the Bayesian model to be used, which can be specified as a relative or absolute path. Earlier versions of Shasta required an absolute path, but this is no longer the case. Sample configuration files are available in shasta/conf or shasta-install/conf. They are named SimpleBayesianConsensusCaller-*.csv.
The default is --Assembly.Modal. Some testing showed a significant decrease of false positive indels in assembled sequence compared to Shasta 0.1.0 behavior, corresponding to --Assembly.consensusCaller Modal. This testing was done for reads called using Guppy 2.3.5. However, even using --Assembly.consensusCaller Bayesian:guppy-2.3.1-a resulted in significant improvement over --Assembly.consensusCaller Modal, indicating that the Bayesian model is somewhat resilient to discrepancies between the reads used to construct the Bayesian model and the reads being assembled.

Testing also showed that --Assembly.consensusCaller Median is generally inferior to --Assembly.consensusCaller Modal.

Software to create a new Bayesian model for a new data type is available at https://github.com/rlorigro/runlength_analysis_cpp. This creates a configuration file containing the definition of the newly created Bayesian model, in a csv format that can be used directly in Shasta via --Assembly.consensusCaller Bayesian:fileName.

Selecting assembly paths

The sequence assembly procedure described in the previous section can be used to assemble sequence for any path in the marker graph. This section describes the selection of paths for assembly in the current Shasta implementation. This is done by a series of steps that "remove" edges (but not vertices) from the marker graph until the marker graph consists mainly of linear sections which can be used as the assembly paths. For speed, edges are not actually removed but just marked as removed using a set of flag bits allocated for this purpose in each edge. However, the description below will use the loose term remove to indicate that an edge was flagged as removed.

This process consists of the following three steps, described in more detail in the following sections:

Approximate transitive reduction of the marker graph

The goal of this step is to eliminate the side branches in the marker graph, which are the result of errors. Although the number of side branches is substantially reduced thanks to the use of run-length encoding, side branches are still abundant. This step uses an approximate transitive reduction of the marker graph which only considers reachability up to a maximum distance, controlled by assembly parameter MarkerGraph.maxDistance (default 30 marker graph edges). Using a maximum distance makes sure that the process remains computationally affordable, and also has the advantage of not removing long-range edges in the marker graph, which could be significant.

In detail, the process works as follows. In this description, the edge being considered for removal is the edge v0→v1 with source vertex v0 and target vertex v1. The first two steps are not really part of the transitive reduction but are performed by the same code for convenience.

  • All edges with coverage less than or equal to MarkerGraph.lowCoverageThreshold are unconditionally removed. The default value for this assembly parameter is 0, so this step does nothing when using default parameters.
  • All edges with coverage 1 and for which the only supporting read has a large marker skip are unconditionally removed. The marker skip of an edge, for a given read, is defined as the distance (in markers) between the v0 marker for that read and the v1 marker for the same read. Most marker skips are small, and a large skip is indicative of an artifact. Keeping those edges could result in assembly errors. The marker skip threshold is controlled by assembly parameter MarkerGraph.edgeMarkerSkipThreshold (default 100 markers).
  • Edges with coverage greater than MarkerGraph.lowCoverageThreshold (default 0) and less than MarkerGraph.highCoverageThreshold (default 256), and that were not previously removed, are processed in order of increasing coverage. Note that with the default values of these parameters all edges are processed because edge coverage is stored using one byte and therefore can never be more than 255 (it is saturated at 255). For each edge v0→v1, a Breadth-First Search (BFS) in the marker graph is performed starting at source vertex v0 and with a limit of MarkerGraph.maxDistance (default 30) edges distance from vertex v0. The BFS is constrained to not use edge v0→v1. If the BFS reaches v1, indicating that an alternative path from v0 to v1 exists, edge v0→v1 is removed. Note that the BFS does not use edges that have already been removed, and so the process is guaranteed not to affect reachability. Processing edges in order of increasing coverage makes sure that low coverage edges are the most likely to be removed.
The transitive reduction step is intrinsically sequential and so it is currently performed in sequential code for simplicity. It could be in principle be parallelized, but that would require sophisticated locking of marker graph edges to make sure independent threads don't step on each other, possibly reducing reachability. However, even with sequential code, this step is not computationally expensive, taking typically only a small fraction of total assembly time.

When the transitive reduction step is complete, the marker graph consists mostly of linear sections composed of vertices with in-degree and out-degree one, with occasional side branches and bubbles or superbubbles, which are handled in the next two phases described below.

Pruning of short side branches (leaves)

At this stage, a few iterations of pruning are done by simply removing, at each iteration, edge v0→v1 if v0 has in-degree 0 (that is, is a backward-pointing leaf) or v1 has out-degree 0 (that is, is a forward-pointing leaf). The net effect is that all side branches of length (number of edges) at most equal to the number of iterations are removed. This leaves the leaf vertex isolated, which causes no problems. The number of iterations is controlled by assembly parameter MarkerGraph.pruneIterationCount (default 6).

Removal of bubbles and superbubbles

The marker graph now consists of mostly linear section with occasional bubbles or superbubbles. Most of the bubbles and superbubbles are caused by errors, but some of those are due to heterozygous loci in the genome being assembled. Bubbles and superbubbles of the latter type could be used for separating haplotypes (phasing) - a possibility that will be addressed in future Shasta releases. However, the goal of the current Shasta implementation is to create a haploid assembly at all scales but the very long ones. Accordingly, bubbles and superbubbles at short scales are treated as errors, and the goal of the bubble/superbubble removal step is to keep the most significant path in each bubble or superbubble.

The figures below show typical examples of a bubble and superbubble in the marker graph.

The bubble/superbubble removal process is iterative. Early iterations work on short scales, and late iterations fork on longer scales. Each iteration uses a length threshold that controls the maximum number of marker graph edges for features to be considered for removal. The value of the threshold for each iteration is specified using assembly parameter MarkerGraph.simplifyMaxLength, which consists of a comma-separated string of integer numbers, each specifying the threshold for one iteration in the process. The default value is 10,100,1000, which means that three iterations of this process are performed. The first iteration uses a threshold of 10 marker graph edges, and the second and third iterations use length thresholds of 100 and 1000 marker graph edges, respectively. The last and largest of the threshold values used determines the size of the smallest bubble or superbubble that will survive the process. The default 1000 markers is equivalent to roughly 13 Kb. To suppress more bubble/superbubbles, increase the threshold for the last iteration. To see more bubbles/superbubbles, decrease the length threshold for the last iteration, or remove the last iteration entirely.

The goal of the increased threshold values is to work on small features at first, and on larger features in the later iterations. The best choice of MarkerGraph.simplifyMaxLength is application dependent. The default value is a reasonable compromise useful if one desires a mostly haploid assembly with just some large heterozygous features.

Each iteration consists of two steps. The first removes bubbles and the second removes superbubbles. Only bubbles/superbubbles consisting of features shorter than the threshold for the current iteration are considered:

  1. Bubble removal
    • An assembly graph corresponding to the current marker graph is created.
    • Bubbles are located in which the length of all branches (number of marker graph edges) is no more than the length threshold at the current iteration. In the assembly graph, a bubble appears as a set of parallel edges (edges with the same source and target).
    • In each bubble, only the assembly graph edge with the highest average coverage is kept. Marker graph edges corresponding to all other assembly graph edges in the bubble are flagged as removed.
  2. Superbubble removal:
    • An assembly graph corresponding to the current marker graph is created.
    • Connected components of the assembly graph are computed, but only considering edges below the current length threshold. This way, each connected component corresponds to a "cluster" of "short" assembly graph edges.
    • For each cluster, entries into the cluster are located. These are vertices that have in-edges from a vertex outside the cluster. Similarly, exists are located (vertices that have out-edges outside the cluster).
    • For each entry/exit pair, the shortest path is computed. However, in this case the "length" of an assembly graph edge is defined as the inverse of its average coverage - that is, the inverse of average coverage for all the contributing marker graph edges.
    • Edges on each shortest path are marked as edges to be kept.
    • All other edges internal to the cluster are removed.

When all iterations of bubble/superbubble removal are complete, the assembler creates a final version of the assembly graph. Each edge of the assembly graph corresponds to a path in the marker graph, for which sequence can be assembled using the method described above. Note, however, that the marker graph and the assembly graph have been constructed to contain both strands. Special care is taken during al transformation steps to make sure that the marker graph (and therefore the assembly graph) remain symmetric with respect to strand swaps. Therefore, the majority of assembly graph edges come in reverse complemented pairs, of which we assemble only one. It is however possible but rare for an assembly graph to be its own reverse complement.

Detangling

In many real-life situations, the assembly graph contains features like this one, called a tangle:

A tangle consists of an edge v0→v1 (depicted here in green) for which the following is true:

out-degree(v0) = 1 (No outgoing edges of v0 other than the green edge)
in-degree(v1) = 1 (No incoming edges of v1 other than the green edge)
in-degree(v0) > 1 (v0 has more than one incoming edge - if that was not the case, the one incoming edge could be trivially merged with the green edge).
out-degree(v1) > 1 (v1 has more than one outgoing edge - if that was not the case, the one outgoing edge could be trivially merged with the green edge).

In the most common case, in-degree(v0) = out-degree(v1) = 2, and this what the above picture and the following text assume, for clarity.

Because DNA structure is linear, this pattern expressed that two portions of sequence have similar portions (the green edge) which the assembly is not able to separate. However, in some cases, it is possible to separate or "detangle" these two copies of similar sequence.

If the green edge is short enough, there may be reads long enough to span it in its entirety. For those reads, we will be able to tell which of the edges on the left and right of the tangle they reach. Now, suppose we only see reads going from the blue edge on the left to the blue edge on the right, and reads going from the red edge on the left to the red edge on the right - but no reads crossing between blue and red edges. Then we can infer that one of the copies follows the blue edges on both sides, while the other copy follows the red edge. This allows us to detangle the two copies as follows:

Note that the sequence in the green edge was duplicated. This makes sense because there are two copies of that sequence. This detangling scheme was first proposed, in a different context, by Pevzner et al. (2001). It can be applied with no conceptual changes to the Shasta assembly graph. Command line option --Assembly.detangleMethod is used to control detangling.

Iterative assembly

Iterative assembly is a Shasta experimental feature that provides the ability to separate haplotypes or similar copies of long repeats. In preliminary tests on a human genome, it has been shown to provide some amount of haplotype separation, and therefore partially phased diploid assembly, as well as improved ability to resolve segmental duplications. In current tests, it requires Ultra-Long (UL) Nanopore reads created by base caller Guppy 3.6.0 or newer at high coverage 80X. The assembly options that were used in this process are captured in the configuration file Nanopore-UL-iterative-Sep2020.conf.

The Shasta iterative assembly code is at an experimental stage and therefore still subject to further improvements and developments. In its implementation as of September 2020, it operates as follows. In this description, copy refers to a haplotype or a copy of a segmental duplication or other long repeats.

  • The assembly process runs as usual, without the final phases of bubble/superbubble removal and final sequence assembly.
  • Shasta then computes and stores the sequence of assembly graph edges encountered by each oriented read, called the pseudo-path of the oriented read. These pseudo-paths contain detailed information on how each read traverses each bubble/superbubble in the assembly graph. Therefore, two oriented reads that originate from the same copy (in the sense defined above) are likely to have largely concordant pseudo-paths.
  • For each stored marker alignment between two oriented reads, Shasta now computes an alignment of the pseudo-paths of the two reads. If the alignment of the two pseudo-paths is not sufficiently good, indicating that the two oriented reads originate from distinct copies, that marker alignment is flagged as not to be used.
  • A new version of the read graph is created normally, but excluding from consideration the marker alignments that were flagged as not to be used in the previous step. As a result, in the new read graph edges are created preferentially between oriented reads originating from the same copy. Edges between oriented reads originating from distinct copies are less likely to be created. Therefore the resulting read graph has achieved some amount of separation between the copies.
  • The process is repeated a few times. At each iteration, the new read graph achieves cleaner separation between copies.
  • When the last iteration completes, the assembly process continues normally, with optional bubble/superbubble removal and detangling, followed by sequence assembly.

Phased diploid assembly

Note: a preliminary implementation of phased (mode 2) assembly was included in Shasta 0.8.0. For documentation of that preliminary implementation, get the tar file for Shasta 0.8.0. The documentation below describes the current, improved implementation of phased assembly.

Shasta provides a second assembly workflow specially tuned for phased diploid assembly. It still uses the same basic computational methods (MinHash/LowHash algorithm, read graph, marker graph), but adds a phasing process for diploid genomes that results in a good amount of haplotype separation in many cases.

Being specialized for the separation of haplotypes in a diploid assembly, this process is not effective at separating copies of segmental duplications, and for the same reason is not effective for genomes with higher ploidy. However, it can be used for genomes with mixed ploidy 1 and 2 such as the human genome. It will typically assemble two haplotypes at most locations in the genomes except segmental duplication, if:

  • Coverage is high enough.
  • The reads are long enough.
  • Heterozygosity is not too low.

Because mode 2 assembly is less capable than mode 0 assembly (standard Shasta haploid assembly) in segmental duplications, mode 0 assembly can be more effective if assembly contiguity (N50) is the main goal, and separating haplotypes is not important.

Assembly mode 2 does not work by assigning haplotypes to reads. Rather, it assigns haplotypes to branches of heterozygous bubbles in the marker graph.

See the following sections for a description of the computational process used for mode 2 assembly and the output it creates.

Marker graph creation in mode 2 assembly

Mode 0 and mode 2 assembly generate marker graph vertices in the same way: at the end of the disjoint sets process, each disjoint set containing at least --MarkerGraph.minCoverage markers is allowed to generate a vertex if:

  • It also contains at least --MarkerGraph.minCoveragePerStrand markers on each strand. This is to avoid vertices with extreme strand bias, which are likely to correspond to systematic errors.
  • It does not contain more than one marker belonging to the same read and with the same orientation. This avoids some undesirable cyclic features in the marker graph.
However, the generation of marker graph edges is different: in mode 0 all possible edges are initially generated, without regard to coverage. That is, an edge A→B between vertices A and B is generated if there is at least one (oriented) read that visits vertex B immediately after vertex A, without visiting any other vertices in between. Then, edges are gradually removed by a process, including transitive reduction and bubble/superbubble removal, whose main goal is to keep contiguity unaltered. This results in highly contiguous haploid assemblies (large N50).

In mode 2 assembly, the process of generating edges is in a way reversed: rather than being very permissive in the initial creation of edges, edges are initially created subject to strict coverage criteria similar to those used for vertices, under control of --MarkerGraph.minEdgeCoverage and --MarkerGraph.minEdgeCoveragePerStrand. This ensures that only edges with good read support are generated. As an additional requirement, all oriented reads that participate in an edge are required to have exactly the same sequence on the edge (or the same RLE sequence, if working with reads in RLE representation). If this is not the case, the edge is split.

This way, a bubble is generated (see below) if any two oriented reads have different sequences (or RLE sequences) in any vertex or edge.

This creates the following problem when working using reads in RLE representation. In that case, this process does not generate bubbles for heterozygous loci in which the two branches have the same RLE sequence. For example, consider a SNP A->G in which the two alleles are 

AAAG
AAGG
For both alleles, the RLE sequence is AG, so no bubble is generated, and that SNP could be missed. Nevertheless, with current ONT reads the benefits of RLE still outweigh this issue, and the current assembly configurations for phased assembly do use RLE.

However, the strict criteria for edge creation result in frequent breaks of contiguity at places where coverage is locally low due to errors. To avoid fragmented assemblies, this is addressed by adding after the facts a minimal number of secondary edges at the locations where breaks occur. For these secondary edges, the above coverage criteria are not used, but the logic to create secondary edges attempts to maximize coverage of the secondary edges that are created.

Because of the strict coverage criteria used, this results in a mostly linear marker graph. The transitive reduction used for mode 2 assembly is not used. And the bubble and superbubble removal process work differently in mode 2 assembly, as described below.

Heterozygous bubbles

The marker graph created as described above is mostly linear but has occasional bubbles caused by heterozygous loci. A bubble is a set of parallel edges in the marker graph. The number of parallel edges is the ploidy of the bubble.

Mode 2 assembly works under a diploidy assumption (ploidy is 2 everywhere). If any bubbles with ploidy greater than 2 are found, all branches except for the two strongest ones (most supporting reads) are kept.

The two branches of each diploid heterozygous bubble are ordered arbitrarily and labeled 0 and 1. We keep track of which reads appear in the marker graph edges of the two branches. In the next sections, the numbers of reads that appear on the two branches are called n0 and n1.

Diploid bubbles in the marker graph don't necessarily reflect the difference between haplotypes at a heterozygous locus and can be caused by errors. In fact, with current Nanopore reads most heterozygous bubbles are caused by errors. Therefore, heterozygous bubbles are handled in two steps in mode 2 assembly:

  • In the first step, bubble removal, bubbles that are likely to be due to errors are removed by only keeping the strongest branch (that is, the one supported by the most reads).
  • In the second step, phasing, a haplotype is assigned to each branch of each bubble that was not removed in the first step.

The first step is essential. Attempting to phase bubbles without first removing the ones caused by errors would render that phasing process noisy and unreliable.

In the phasing step, it is impossible to assign haplotypes globally. Rather, in the phasing step, heterozygous bubbles are partitioned into phasing components, and all bubbles in a phasing component are assigned a haplotype, which is valid only relative to other bubbles in the same component. Each phasing component corresponds to one "large bubble" in the phased representation of the assembly (see below). Occasionally, it can also correspond to more than one large bubble.

Superbubbles

Structures with connectivity more complex than simple bubbles (superbubbles) are generally present in the marker graph. Handling of these structures is currently limited to superbubbles with one entrance and one exit. The superbubble is replaced with a diploid bubble by choosing the two most likely paths in the superbubble.

Simple Bayesian model for a pair of diploid bubbles

The bubble removal and the phasing steps use a simple Bayesian model describing two diploid bubbles.

Given two diploid bubbles, bubble A and bubble B, we can use their read composition to tell how the haplotypes of the first bubble are related to the haplotypes of the second bubble. We can create a 2 by 2 phasing matrix that counts the number of common reads between each side of the two bubbles. That is, nij is the number of common reads between branch i of bubble A and branch j of bubble B. In an ideal error-free scenario, one of two following situations would occur:

  • nij is diagonal, that is, n01 = n10 = 0. All common reads that visit branch 0 of bubble A also visit branch 0 of bubble B, and all common reads that visit branch 1 of bubble A also visit branch 1 of bubble B. In this case, we can say that the two bubbles are in-phase, that is, branch 0 of bubble A is on the same haplotype as branch 0 of bubble B, and branch 1 of bubble A is on the same haplotype as branch 1 of bubble B,
  • nij has a zero diagonal, that is, n00 = n11 = 0. All common reads that visit branch 0 of bubble A also visit branch 1 of bubble B, and all common reads that visit branch 1 of bubble A also visit branch 0 of bubble B. In this case, we can say that the two bubbles are out-of-phase, that is, branch 0 of bubble A is on the same haplotype as branch 1 of bubble B, and branch 1 of bubble A is on the same haplotype as branch 0 of bubble B.

However, due to errors that can be deviations from this ideal behavior. In addition, if even one of the two bubbles is caused by errors the distribution of the reads between the branches of the two bubbles can be entirely random. To describe this quantitatively, we use a simple Bayesian model for the pair of diploid bubbles.

Here are some additional quantities we need below.

  • The number of reads on branch 0 of bubble A that also appear on either branch of bubble B on is nA0 = n00 + n01.
  • The number of reads on branch 1 of bubble A that also appear on either branch of bubble B on is nA1 = n10 + n11.
  • The number of reads on branch 0 of bubble B that also appear on either branch of bubble A on is nB0 = n00 + n10.
  • The number of reads on branch 1 of bubble B that also appear on either branch of bubble B on is nB1 = n01 + n11.
  • The number of reads on the diagonal of nij is defined as ndiagonal = n00 + n11. This is the number of reads that suggest that the two bubbles are in-phase.
  • The number of reads off the diagonal of nij is defined as noff-diagonal = n01 + n10. This is the number of reads that suggest that the two bubbles are out of phase.
  • The number of reads that suggest the "best" of the in-phase and out of phase hypotheses (based on simple read counts) is defined as nconcordant = max(ndiagonal, noff-diagonal).
  • The number of reads that suggest the "worst" of the in-phase and out of phase hypotheses (based on simple read counts) is defined as ndiscordant = min(ndiagonal, noff-diagonal).
  • The total number of reads that appear on both bubbles is n = nA0 + nA1 = nB0 + nB1 = n00 + n01 + n10 + n11 = ndiagonal + noff-diagonal = nconcordant + ndiscordant

With the above values given and considered fixed, we now consider three possible hypotheses for the two bubbles:

  • Random hypothesis: one or both of the two bubbles are caused by errors, and therefore reads visits the two bubbles in an entirely uncorrelated fashion.
  • Ideal in-phase hypothesis: the two bubbles are in-phase, that is, branches 0 of the two bubbles are on the same haplotype, and similarly for branches 1. Under this ideal version of the in-phase hypothesis we also rule out all errors, and as a result, the phasing matrix is exactly diagonal.
  • Ideal out-of-phase hypothesis: the two bubbles are out-of-phase, that is, branch 0 of bubble A is on the same haplotype as branch 1 of bubble B, and similarly for the other two haplotypes. Under this ideal version of the out-of-phase hypothesis we also rule out all errors, and as a result, the diagonal of the phasing matrix is exactly zero.
Random hypothesis

Under the random hypothesis, reads visit the two branches of the two bubbles in an entirely uncorrelated fashion. The probability that one of the n reads visits branch i of bubble A and branch j of bubble B is:

P(ij   |   random) = (nAi   /   n) (nBj   /   n) = nAi nBj / n 2

It can be easily verified that the sum of all four values for P(ij|random) equals 1, as it should.

Ideal in-phase hypothesis

Under the ideal in-phase hypothesis, reads visit the two bubbles just like in the random hypothesis, except that the off-diagonal elements are zero. Therefore P(ij   |   in-phase) can be obtained from P(ij   |   random) by setting the off-diagonal entries to zero and renormalizing the diagonal entries so they add up to 1. The result is:

P(ij   |   ideal in-phase) = δij nAi nBj / (nA0nB0 + nA1nB1)

Ideal out-of-phase hypothesis

The corresponding expression for the ideal out-of-phase hypothesis can be obtained in a similar way. The result is:

P(ij   |   ideal out-of-phase) = (1-δij) nAi nBj / (nA0nB1 + nA1nB0)

Non-ideal hypotheses

In reality, we have to consider non-ideal in-phase and out-of-phase hypotheses in which reads have probability ε>0 of visiting branches in the two bubbles that is inconsistent with the corresponding ideal hypothesis (a simple but perhaps oversimplistic assumption). Under these non-ideal hypotheses, the probability that one of the n reads visits branch i of bubble A and branch j of bubble B is

P(ij   |   in-phase) = (1 - ε) P(ij   |   ideal in-phase) + ε P(ij   |   ideal out-of-phase) =

P(ij   |   out-of-phase) = (1 - ε) P(ij   |   ideal out-of-phase) + ε P(ij   |   ideal in-phase) =

Bayesian model

With the above expressions available, we are in a position to construct a simple Bayesian model in which we evaluate posterior probability ratios of the above hypotheses conditional to the observed distribution of reads visiting the two branches of each bubble.

We use "neutral" assumptions on prior probabilities:

Pprior(in-phase) = Pprior(out-of-phase) = Pprior(random)

Posterior probability ratios are then given by:

log[Pposterior(in-phase) / Pposterior(random)] = ∑ij nij log[P(ij   |   in-phase) / P(ij   |   random)]

Here, the sums over i and j run over values 0 and 1, that is, over the entire 2 by 2 phasing matrix. In the expressions on the right, nij are entries of the phasing matrix, and the remaining expressions have been evaluated in the above sections. Because we assumed ε>0, all terms in the above expressions are non-singular.

Extension of the Bayesian model to two sets of diploid bubbles

Consider two disjoint sets of diploid bubbles, S0 and S1. Assume the bubbles in each of the two sets have already been phased relative to each other, that is, we know how the haplotypes of each bubble in each of the set correspond to haplotypes of other bubbles in the same set. The Bayesian model described in the previous section can be extended to apply to the phasing of S0 relative to S1. This can be useful, as it is possible that there is insufficient evidence to phase individual bubbles of S0 relative to bubbles of S1, while sufficient combined evidence still is present to support phasing the two sets relative to each other.

Such an extended Bayesian model permits a hierarchical approach to phasing, described in more details below, in which sets of bubbles are iteratively phased and combined into larger phased sets.

The phasing matrix for the extended Bayesian model can be constructed similarly for the case of a single pair of bubbles. The phasing matrix counts distinct reads, so if a read appears in multiple bubbles of the two sets, it is counted only once.

Phasing graph

Phasing information for a set of diploid bubbles can be described using a phasing graph, an undirected graph in which each vertex represents a bubble or a set of bubbles already phased relative to each other. The phasing graph is used in two steps, described in more detail below.

  • The goal of the first step, bubble removal, is to remove bubbles that are likely to be the result of errors. In this step, each vertex of the phasing graph corresponds to a single bubble rather than a set of bubbles.
  • The goal of the second step, phasing, is to assign a haplotype to each branch of each bubble. This is done hierarchically by iteratively phasing groups of bubbles relative to each other - see below for more details.

The phasing graph is used in both steps, with some differences.

An edge between two vertices can be created if the two corresponding bubbles or sets of bubbles have enough common reads to permit phasing. The precise criteria for edge creation are different for bubble removal and phasing, but in both cases each edge stores the nij phasing matrix for the bubbles corresponding to the vertices it joins. But in both cases, we enforce a minimum allowed value for nconcordant and a maximum allowed value for ndiscordant. This reflects the fact that, for an edge to carry sufficient information, we would like nconcordant to be high and ndiscordant to be small. These thresholds are controlled by the following command line options:

In addition, an edge is only generated if it carries sufficient useful information. For the simpler case of phasing, when bubbles in error have been removed, we can ignore the random hypothesis. We use the Bayesian model to compute

log(Pphasing) = | log [ Pposterior(in-phase) / Pposterior(out-of-phase)] |

Because of the absolute value, this gives the logarithmic spacing between the in-phase and out-of-phase hypothesis. A large value means that the edge strongly supports either the in-phase or out-of-phase hypothesis. The edge is only generated if log(Pphasing) is greater than the value specified by command line option --Assembly.mode2.phasing.minLogP. This value specifies the minimum required value of log(Pphasing), expressed in decibels (dB).

The generated edge is then assigned a relative phase equal to 0 (in-phase) if

Pposterior(in-phase) > Pposterior(out-of-phase)

and 1 (out-of-phase) otherwise.

For bubble removal, things are a bit more complicated because now we also have to allow for the random hypothesis. For an edge to be generated we want it to decisively support either the in-phase or out-of-phase against both the random hypothesis and the opposite phasing hypothesis. Therefore in this case log(Pbubble-removal) for an edge is defined as follows:

If log [ Pposterior(in-phase) / Pposterior(out-of-phase) ] > 0 :

log(Pbubble-removal) = min { log [ Pposterior(in-phase) / Pposterior(out-of-phase) ] , log [ Pposterior(in-phase) / Pposterior(random) ] }

Otherwise:

log(Pbubble-removal) = min { log [ Pposterior(out-of-phase) / Pposterior(in-phase) ] , log [ Pposterior(out-of-phase) / Pposterior(random) ] }

In both cases, log(Pbubble-removal) is the logarithmic spacing between the best of the two phasing hypotheses against the next best hypothesis - either the opposite phasing hypothesis or the random hypothesis.

The edge is only generated if log(Pbubble-removal) is greater than the value specified by command line option --Assembly.mode2.bubbleRemoval.minLogP. This value specifies the minimum required value of log(Pbubble-removal), expressed in decibels (dB).

Therefore the presence of an edge indicates good support for one of the phasing hypotheses, which in turn means that neither of the two bubbles are due to errors.

Bubble removal

Bubble removal is an iterative process. At each iteration we remove bubbles that don't phase well with adjacent bubbles. We create a phasing graph as described above and allowing the random hypothesis in the Bayesian model. This is done under control of the following command line options:

Isolated vertices in the phasing graph correspond to bubbles that don't phase well with any adjacent bubbles. However bubbles that correspond to vertices that are "almost" isolated (that is, belong to a small connected component of the phasing graph) are also suspicious. Therefore, the process for flagging bad bubbles at each iteration works as follows:
  • The phasing graph is created, with one vertex per bubble.
  • Connected components are computed.
  • All bubbles that correspond to vertices in small connected components are flagged as bad and removed from the assembly graph by only keeping the strongest branch. A connected component is considered small if it contains less than the number of bubbles specified via command line option --Assembly.mode2.bubbleRemoval.componentSizeThreshold.
  • Any assembly graph simplifications made possible by the removal of some bubbles are made. This includes merging of adjacent linear segments and superbubble removal.
  • The iteration continues by recreating the phasing graph from scratch, and stops when no more bubbles are flagged as bad.

Note this process can erroneusly flag as bad bubbles that are in low heterozygosity regions of the genome. This can cause low heterozygosity regions to be assembled haploid. Phasing of bubbles

Mode 2 assembly works by phasing the bubbles, not the reads. That is, the phasing process does not assign a haplotype to reads. Rather, it assigns a haplotype to bubbles.

To achieve this, the phasing graph is now created again, but now the log(Pphasing) for each edge is computed without allowing the random hypothesis, as described above. We can do this because at this stage all bubbles in error should have been removed. Creation of the phasing graph is now under control of the following command line options:

With the majority of bubbles in error having been removed, the phasing graph is generally well behaved and contains a small number of inconsistencies. We assign to each edge a weight equal to log(Pphasing) and compute an optimal spanning tree that maximizes the sum of these weights. With this definition, the spanning tree "prefers" edges for which log(Pphasing) is large. Once the spanning tree is computed, we assign haplotypes arbitrarily on a randomly chosen bubble, and then use the spanning tree to assign haplotypes to the remaining bubbles. This can be done using a Bread First Search (BFS) on the spanning tree and assigning haplotypes to vertices based on the log(Pphasing) values of the tree edges.

The spanning tree process has to be done separately for each connected component of the phasing graph, because there are no edges the allow the BFS to jump across connected components. This expresses the fact that we cannot phase across regions in the absence of a sufficient number of common reads.

The bubbles of each connected component are then combined into a single vertex of the phasing graph, keeping track of the relative phasing of the bubbles as determined by the spanning tree. This hierarchical phasing process is then repeated until all vertices are isolated. Each vertex then corresponds to a "phased component" in the final phased assembly.

At the end of this process, each bubble that was not marked as bad has a componentId that defines the final phasing graph vertex each bubble was part of, and haplotypes 0 and 1 are assigned to its two branches.

Bubble chains

A bubble chain is a linear sequence of diploid bubbles and intervening segments assembled haploid. A portion of a phased bubble chain, as displayed in Bandage, is shown below.

On each diploid bubble, the two branches are colored red or blue depending on the haplotype assigned to each branch during phasing. The intervening haploid segments are shown in grey. In most cases, there is a haploid segment in between each pair of adjacent bubbles, but sometimes two bubbles can immediately follow each other.

Bubble chains are useful for organizing assembly output. As explained above, the phasing process assigns each bubble to a connected component and each branch to a haplotype. With this information, we can find in the assembly graph paths that represent each of the two haplotypes. For example, in the above picture, we can create a path that follows the haploid segments plus the blue branches of diploid segments:

Paths created in this way provide haplotype separation on a much longer scale than individual bubbles. Haplotype separation can continue on both sides as long as we stay in the same connected component.

A portion of a bubble chain in which all bubbles belong to the same connected component of the bubble graph is called a phased region. The intervening segments in between adjacent phased regions on the same bubble chain are called unphased regions. The two paths for a phased region constructed as defined above provide a representation of the two haplotypes over the entire phased region. This permits an assembly representation in which each phased region is shown as a large bubble, each of the two branches corresponding to one of the two paths for the phased region.

Mode 2 assembly output

After a mode 2 assembly run, the resulting assembly is output in three different representations containing different levels of detail:

  • A Detailed representation that includes all of the bubbles and their assignments to bubble graph components and haplotypes. This representation also includes paths representing haplotypes.
  • A Phased representation in which each phased region is represented as a large bubble, each branch corresponding to a path in the Detailed representation of the assembly.
  • A Haploid representation in which each bubble chain is replaced by a single linear segment. This is done by discarding, in each phased region, the branch with the lowest average coverage. This representation can be useful to understand and describe the large-scale structure of the assembly.

All three representations also include the unstructured and unphased assembled segments that are not part of a bubble chain.

For each of the three representations, four files are written out:

  • A complete GFA file.
  • A GFA file without sequence.
  • A FASTA file.
  • A csv file containing various columns of information for each segment in the GFA and FASTA files.
All GFA files are in GFA 1 format. So in total, there are 12 output files, summarized in the table below:

Detailed Phased Haploid
Complete GFA file Assembly-Detailed.gfa Assembly-Phased.gfa Assembly-Haploid.gfa
GFA file without sequence Assembly-Detailed-NoSequence.gfa Assembly-Phased-NoSequence.gfa Assembly-Haploid-NoSequence.gfa
FASTA file Assembly-Detailed.fasta Assembly-Phased.fasta Assembly-Haploid.fasta
FASTA file Assembly-Detailed.csv Assembly-Phased.csv Assembly-Haploid.csv

GFA files without sequence are much smaller than the complete GFA files. So they can be useful to quickly inspect the assembly in Bandage or to do other processing that does not require sequence.

FASTA files contain sequence in FASTA format for all segments in each of the GFA files.

The csv files can be loaded in Bandage and used to display information about the segments in each file. They also define custom colors which facilitate the display of the GFA files in Bandage.

Not all of the 12 files are necessary for all applications, and so the following command line options can be used to selectively suppress output:

  • --Assembly.mode2.suppressGfaOutput turns off all GFA output.
  • --Assembly.mode2.suppressFastaOutput turns off all FASTA output.
  • --Assembly.mode2.suppressDetailedOutput turns off output of the Detailed representation of the assembly.
  • --Assembly.mode2.suppressPhasedOutput turns off output of the Phased representation of the assembly.
  • --Assembly.mode2.suppressHaploidOutput turns off output of the Haploid representation of the assembly.

Naming schemes for assembled segments and paths

Assembled segments in the detailed representation of the assembly are named as follows:

  • Segments that are not part of a bubble are named using a numeric identifier. For example, 12497.
  • Segments that are part of a bubble are named using a numeric identifier, followed by .0 and .1. For example, 17495.0 and 17495.1. The portion preceding the period is the same for the two branches of a bubble.

Paths in the detailed representation of the assembly are named like the corresponding segments in the phased representation of the assembly and follow the segment naming schemes:

  • The two branches of a phased region are named PR.bubbleChain.position.component.haplotype (example: PR.17.41.268.1) where PR stands for "Phased Region", and the four fields are numeric identifiers with the following meaning:
    • bubbleChain identifies the bubble chain that the segment belongs to.
    • position is the position of the segment in the bubble chain. This begins at 0 and increments by one at each phased or unphased region encountered in the bubble chain.
    • component identifies the connected component of the bubble graph that this phased region belongs to.
    • haplotype (0 or 1) for each of the two branches (haplotypes) of the phasing region. Usually a component corresponds to a single phased region, but there are exceptions to that. If two phased regions have the same component, their haplotypes are phased relative to each other (that is, branch 0 of the first phased region occurs in the same haplotype as branch 0 of the second phased region).
  • Unphased region are named UR.bubbleChain.position (example: PR.17.42) where UR stands for "Unphased Region", and the two fields are numeric identifiers with the same meaning as for phased regions above.
To clarify the above description, the figure below shows names for a portion of bubble chain 0. Note how the position field gets incremented along the bubble chain.

Bubble chains in the haploid representation of the assembly are named BC.bubbleChain where bubbleChain identifies the bubble chain. For example, BC.18.

In the phased and haploid representations of the assembly, segments that are not part of a bubble chain have the same name as the corresponding segment in the detailed representation of the assembly, that is, a numeric identifier possibly followed by .0 or .1.

Using mode 2 assembly

Phased diploid assembly is turned on via --Assembly.mode 2 or, more conveniently, using command line option --config to specify one of the following configurations:

  • Nanopore-Phased-Jan2022.conf. for standard nanopore reads.
  • Nanopore-UL-Phased-Jan2022.conf. for Ultra-Long (UL) nanopore reads.

You can use `shasta --command listConfiguration --config to get a ful description of each these two configurations, including comments the describe in more details the conditions under which eacxh of the two configurations were tested.

Please file an issue in the Shasta GitHub repository for discussion of mode 2 assembly parameters.

Assembly mode 2 requires the following setting (both included in the configurations mentioned above):

  • --Kmers.k must be even.
  • --ReadGraph.strandSeparationMethod 2 (strict strand separation in the read graph).

Assembly mode 2: typical results for diploid assembly

Limited testing of phased diploid (mode 2) assembly was done using current (Guppy 5) nanopore reads for a human genome. Longer reads give better phasing results. When using Ultra-Long (UL, N50>50Kb) reads base-called with Guppy 5 and "super" accuracy at coverage 60-80x, typical results for human genomes are as follows:

  • 80 to 90% of the genome sequence is assembled diploid (that is, in phased regions).
  • Typical length of the large diploid bubbles in the phased representation is 2 to 10 Mb.
  • Each of the branches in one of the large bubbles maps very well to one haplotype of a reference genome for the assembly being sequenced, much less well to the opposite haplotype.

Comparison of assembly mode 2 and iterative assembly

Iterative assembly similarly addresses the separation of copies of similar sequence due to ploidy, segmental duplication, or other repeats. It is more general in that it does not make any assumption on the number of copies, while assembly mode 2 has a built-in assumption of two copies.

However, iterative assembly uses a different approach: it uses the assembly graph to separate reads and then iteratively reassembles while enforcing separation of the read pairs found in the previous iteration to belong to different copies. In summary, one could say that iterative assembly "phases the reads", while mode 2 assembly "phases the bubbles".

This approach used an iterative assembly, while effective in some cases, proved to be much less robust and causes abundant assembly artifacts and breaks. A future generalization of mode 2 assembly without the assumption of two copies may be able to provide robust separation in the presence of arbitrary numbers of copies.

High performance computing techniques

The Shasta assembler is designed to run on a single machine with an amount of memory sufficient to hold all of its data structures (1-2 TB for a human assembly, depending on coverage). All data structures are memory mapped and can be set up to remain available after assembly completes. Note that using such a large memory machine does not substantially increase the cost per CPU cycle. For example, on Amazon AWS the cost per virtual processor hour for large memory instances is no more than twice the cost for laptop-sized instances.

There are various advantages to running assemblies in this way:

  • Running on a single machine simplifies the logistics of running an assembly, versus for example running on a cluster of smaller machines with shared storage.
  • No disk input/output takes place during assembly, except for loading the reads in memory and writing out assembly results plus a few small files containing summary information. This eliminates performance bottlenecks commonly caused by disk I/O.
  • Having all data structures in memory makes it easier and more efficient to exploit parallelism, even at very low granularity.
  • Algorithm development is easier, as all data are immediately accessible without the need to read files from disk. For example, it is possible to easily rerun a specific portion of an assembly for experimentation and debugging without any wait time for data structures to be read from disk.
  • When the assembler data structures are set up to remain in memory after the assembler completes, it is possible to use the Python API or the Shasta http server to inspect and analyze an assembly and its data structures (for example, display a portion of the read graph, marker graph, or assembly graph).
  • For optimal performance, assembler data structures can be mapped to Linux 2 MB pages (huge pages). This makes it faster for the operating system to allocate and manage the memory and improves TLB efficiency. Using huge pages mapped on the hugetlbfs filesystem (Shasta executable options --memoryMode filesystem --memoryBacking 2M) can result in a significant speed up (20-30%) for large assemblies. However, it requires root privilege via sudo.

To optimize performance in this setting, the Shasta assembler uses various techniques:

  • In most parallel steps, the division of work among threads is not set up in advance but decided dynamically (Dynamic load balancing). As a thread finishes a piece of work assigned to it, it grabs another chunk of work to do. The process of assigning work items to threads is lock-free (that is, it uses atomic memory primitives rather than mutexes or other synchronization methods provided by the operating system).
  • Most large memory allocations are done via mmap and can optionally be mapped to Linux 2 MB pages. The Shasta code includes a C++ class for conveniently handling these large memory-mapped regions as C++ containers with familiar semantics (class shasta::MemoryMapped::Vector).
  • In situations where a large number of small vectors are required, a two-pass process is used (class shasta::MemoryMapped::VectorOfVectors). In the first pass, one computes the length of each of the vectors. A single large area is then allocated to hold all of the vectors contiguously, together with another area to hold indexes pointing to the beginning of each of the short vectors. In a second pass, the vectors are then filled. Both passes can be performed in parallel and are entirely lock-free. This process eliminates memory allocation overhead that would be incurred if each of the vectors were to be allocated individually.

Thanks to these techniques, Shasta achieves close to 100% CPU utilization during its parallel phases, even when using large numbers of threads. However, a number of sequential phases remain, which typically result in average CPU utilization during a large assembly around 70%. Some of these sequential phases can be parallelized, which would result in increased average CPU utilization and improved assembly performance.

As of Shasta release 0.1.0, a typical human assembly at coverage 60x runs in about 6 hours on a x1.32xlarge AWS instance, which has 64 cores (128 virtual CPUs) and 1952 GB of memory.

See here for information on maximizing assembly performance.

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shasta-0.11.1/docs/Configurations.html000066400000000000000000000177221433251011200176660ustar00rootroot00000000000000
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Assembly configurations

Shasta provides a number of command line options that can be used to set computational parameters and thresholds for assemblies. All of these options have default values, but the default values are not necessarily optimal for any particular combination of a number of factors:

  • The technology used to generate the reads. Technologies currently available to generate the long reads supported by Shasta are Oxford Nanopore (ONT) and Pacific BioSciences (HiFi and others).
  • The amount of coverage available (average number of reads overlapping each genome region).
  • The characteristics of the genome being sequenced, including heterozygosity, ploidy, and repeats content.
To adjust to these and other factors, options adjustments are generally necessary to achieve good quality assemblies. To facilitate the process of generating useful assembly options for a particular situation, Shasta uses assembly configurations. An assembly configuration is a predefined set of assembly options that can be stored in a configuration file in a format defined below. A number of sample configuration files applicable to specific situations are provided in shasta/conf. The applicability of each of the files is described in comments embedded in each file.

Shasta command line option --config is used to specify the configuration to be used, as described below in details. This option is mandatory when running an assembly. If any option is specified both in a configuration and explictly on the command line, the value on the command line takes precedence. This allows you to use a configuration as a useful set of defaults, while still overriding some of its options as desired.

In addition to configuration files, Shasta also provides a set of built-in configurations that are compiled in the Shasta executable. These built-in configurations can be used without the need for a configuration file. Each built-in configuration has a corresponding configuration file with the same name in shasta/conf, with an extension .conf. For example, configuration Nanopore-Oct2021 can be specified in one of two ways: 

shasta --config Nanopore-May2022
or 
shasta --config .../shasta/conf/Nanopore-May2022.conf
When using the second form, the file must be available, and the ... should be replaced depending on the location of the shasta directory.

To obtain a list of available built-in configurations, use Shasta command listConfigurations as follows: 

shasta --command listConfigurations
At the time of writing (May 2022), this outputs the following list of built-in configurations: 
Nanopore-Dec2019
Nanopore-UL-Dec2019
Nanopore-Sep2020
Nanopore-UL-Sep2020
Nanopore-UL-iterative-Sep2020
Nanopore-OldGuppy-Sep2020
Nanopore-Plants-Apr2021
Nanopore-Oct2021
Nanopore-UL-Oct2021
HiFi-Oct2021
Nanopore-UL-Jan2022
Nanopore-Phased-Jan2022
Nanopore-UL-Phased-Jan2022
Nanopore-May2022
Nanopore-Phased-May2022
Nanopore-UL-May2022
Nanopore-UL-Phased-May2022
Nanopore-Human-SingleFlowcell-May2022
Nanopore-Human-SingleFlowcell-Phased-May2022
Nanopore-UL-Phased-Nov2022
Nanopore-R10-Fast-Nov2022
Nanopore-R10-Slow-Nov2022
Nanopore-Phased-R10-Fast-Nov2022
Nanopore-Phased-R10-Slow-Nov2022

The following table summarizes configurations recommended at the time of writing (November 2022, Shasta 0.11.0) under the following conditions:

  • Human assemblies
  • Oxford Nanopore reads.
  • Guppy 5 or 6 basecaller with "super" accuracy.

ONT chemistryRead lengthCoverageHaploid assemblyPhased assembly
R9Standard40x to 80x Nanopore-May2022 Nanopore-Phased-May2022
R9Ultra-Long (UL) 40x to 80x Nanopore-UL-May2022 Nanopore-UL-Phased-Nov2022
R9StandardHuman genome with a single flowcell (about 30x) Nanopore-Human-SingleFlowcell-May2022 Nanopore-Human-SingleFlowcell-Phased-May2022
R10, fast modeStandardHuman genome with a single flowcell (about 30x) Nanopore-R10-Fast-Nov2022 Nanopore-Phased-R10-Fast-Nov2022
R10, slow modeStandardHuman genome with two flowcells (about 45x) Nanopore-R10-Slow-Nov2022 Nanopore-Phased-R10-Slow-Nov2022

To get details of a specific built-in configuration use Shasta command listConfiguration as follows, specifiying the built-in configuration of interest after --config: 

shasta --command listConfiguration --config Nanopore-May2022

This output includes comments that describe the applicability of the selected configuration. Details of the configuration are written out in the configuration file format defined below. This allows you to create your own configuration file using a built-in configuration as a starting point.

Shasta command line option --config must be used to specified the desired configuration to be used for an assembly. The option must specify either a build-in configuration or a path to a configuration file.

Configuration file

Some options are only allowed on the command line, but most of them can also optionally be specified using a configuration file. Values specified on the command line take precedence over values specified in the configuration file. This makes it easy to override specific values in a configuration file.

Options that can be specified both on the command line and in a configuration file are of the form --SectionName.optionName. The format of the configuration file is as follows: 

[SectionA]
option1 = valueA1
option2 = valueA2
[SectionB]
option1 = valueB1
option2 = valueB2
The above is equivalent to using the following command line options: 
--SectionA.option1 valueA1 
--SectionA.option2 valueA2 
--SectionB.option1 valueB1 
--SectionB.option2 valueB2

For example, the value for option MarkerGraph.minCoverage can be specified in the [MarkerGraph] section of the configuration file as follows: 

[MarkerGraph]
minCoverage = 0

In the configuration file, blank lines and lines begining with # are ignored and can be used to add coments and to improve readability of the configuration file.

Boolean switches

Some command line options are boolean switches, that is, control options that can be turned on or off rather then be given a value.

To turn on one of these switches on the command line, just add it to the command line without any value, for example --Assembly.storeCoverageData. To turn it off, just omit it from the command line (the default value is turned off).

To turn on one of these switches in a configuration file, you can either enter it without value 

storeCoverageData =
or assign to it one of the following values: 1, true, True, yes, Yes. To turn off one of these switches in a configuration file, assign to it one of the following values: 0, false, False,no, No.

Boolean switches are indicated as such in the Description column in he tables below.

Table of contents
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Table of contents

Exploring assembly results

Shasta has functionality for exploring details of many data structures used during assembly. It can be useful to understand assembler behavior for testing, debugging, parameter optimization, development of new algorithms, or for gaining insight not available by visually examining the assembled sequence files. When this functionality is activated, Shasta behaves as an http server, and the user communicates with it via a standard Internet browser.

Follow the direction below to activate this functionality.

Starting the Shasta http server on Linux

The Shasta http server uses Graphviz software to display graphs. To install it, use one of the following commands depending on the Linux system you are using:

  • On Debian, Ubuntu, and derived distributions, use: 
    sudo apt install graphviz
  • On CentOS, Red Hat, Fedora, and related distributions, use: 
    sudo yum install graphviz
  • On a Linux distribution where neither of the above commands works, please file an issue on the Shasta GitHub repository mentioning the distribution you are using. We will make an effort to update the documentation to cover that distribution.

To start the Shasta http server on Linux, follow these steps:

  1. Run an assembly with option --memoryMode filesystem. When this option is used, binary data used by the assembly are stored in memory mapped files that remain accessible after assembly is complete.
    • If you don't have root access via sudo on the machine you are using, also use option --memoryBacking disk. This is slower but guarantees that the results remain permanently available after an assembly completes, unless you use --command cleanupBinaryData.
    • If you do have root access via sudo on the machine you are using and you want to maximize assembly performance, also use option --memoryBacking 2M, which results in binary data being stored in memory on the Linux hugetlbfs filesystem (2 MB "huge" pages). These data only remain available until the next reboot or until you clean them up via --command cleanupBinaryData, but you can save them persistently on disk using --command saveBinaryData.
  2. Run the assembler again, this time specifying option --command explore, plus the same --assemblyDirectory option used for the assembly run (default is ShastaRun).

See here for more information on the --memoryMode and --memoryBacking command line options.

Starting the Shasta http server on macOS

The Shasta http server uses Graphviz software to display graphs. It also needs command gtimeout which is part of the coreutils package. To install them, use this command: 

brew install graphviz coreutils

To start the Shasta http server on macOS, follow these steps:

  1. Run an assembly as usual. The macOS version of Shasta always stores binary data on disk. This is slower but guarantees that the binary data remain permanently available after an assembly completes, unless you use --command cleanupBinaryData.
  2. Run the assembler again, this time specifying option --command explore, plus the same --assemblyDirectory option used for the assembly run (default is ShastaRun).

Using a browser to explore assembly results

Following the above directions will start a Shasta process in a mode in which it behaves as an http server. It will also start your default browser and point it to the Shasta http server process. If you want to start additional browser sessions, just point your browser to the URL shown when the Shasta http server starts, usually http://localhost:17100.

The browser session will initially show an assembly summary page. At the top you will see a navigation menu that allows you to explore details of many assembler data structures. For example, this allows you to look at local subgraphs of the read graph, marker graph, and assembly graph (see here for more information). It also provides details of sequence assembly and of the input reads used. Finally, there are several useful links between the various data structures. For example, you can easily navigate from the assembly graph to the marker graph and vice versa, and from the read graph to the reads.

When you are done using the browser, remember to stop the server using Ctrl^C in the command window in which you started the server. You can restart the server later as many times as you like, as long as the binary data remain available.

Access control

By default, the server only responds to requests from the same user and computer running the server. However, you can use the command line option --exploreAccess to relax this restriction:

  • --exploreAccess user (default) to only allow access on the local machine and to the user that started the server. THIS DEFAULT OPTION IS THE ONLY ONE THAT GUARANTEES THAT NOBODY ELSE WILL BE ABLE TO ACCESS YOUR ASSEMBLY. YOU SHOULD USE THIS OPTION IF YOUR ASSEMBLY DATA IS SUBJECT TO CONFIDENTIALITY RESTRICTIONS OR IS NOT CLEARED OR CONSENTED FOR PUBLIC RELEASE.
  • --exploreAccess local to allow access to ALL USERS on the local machine. YOU SHOULD NOT USE THIS OPTION IF YOUR ASSEMBLY DATA IS SUBJECT TO CONFIDENTIALITY RESTRICTIONS OR IS NOT CLEARED OR CONSENTED FOR PUBLIC RELEASE.
  • --exploreAccess unrestricted for completely unrestricted access from any user, even from different computers on your local area network, and potentially even from the entire Internet, if you are not protected by a firewall. Some firewall may, however, be necessary for this to work. YOU SHOULD NOT USE THIS OPTION IF YOUR ASSEMBLY DATA IS SUBJECT TO CONFIDENTIALITY RESTRICTIONS OR IS NOT CLEARED OR CONSENTED FOR PUBLIC RELEASE.

Things you can do with the HTTP server

  • Look at a specific read, its run-length encoding and location of markers
  • Find all alignments for a given read
  • Align any two reads
  • Align a read with all other reads
  • Visualize individual alignments as a grid
  • Visualize alignment graphs
and several other things.

Screenshots

Below are some sample screenshots obtained using --command explore.

Assembly Summary

Investigating individual reads

You can see the run-length representation of the read along with its markers.

Investigating Read Graph

Investigating local Marker Graph

Investigate Assembly Graph

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shasta-0.11.1/docs/Introduction.html000066400000000000000000000042311433251011200173440ustar00rootroot00000000000000
Table of contents

Shasta long read assembler

This is the documentation for the Shasta long read assembler. If you are seeing this documentation on github.io, it applies to the latest version of Shasta on GitHub (not necessarily the same as the latest release). Documentation for any version of Shasta is available in the source code in the shasta/docs directory and in any build under the shasta-build/shasta-install/docs directory.

Abstract

The goal of Shasta is to rapidly produce accurate assembled sequence using as input DNA reads generated by Oxford Nanopore flow cells.

Computational methods used by the Shasta assembler include:

  • Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.
  • Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

An current implementation of Shasta is complete and functional, but significant improvements in several areas are possible. As implemented, it can run an assembly of a human genome at coverage around 60x in about 5 hours using a single, large machine (AWS instance type x1.32xlarge, with 128 virtual processors and 1952 GB of memory). The compute cost of such an assembly is around $20 at AWS spot market or reserved prices.

See Shafin et al, Nature Biotechnology 2020 for an error analysis of the Shasta assembler and more.

Table of contents
shasta-0.11.1/docs/MakeRelease.html000066400000000000000000000067131433251011200170500ustar00rootroot00000000000000
Table of contents

Creating a new release

  • Make sure there are no outstanding commits or pull requests that should be in the new release.
  • Do some testing.
  • Run CheckConfigurations.py (from the shasta-install/bin directory) to check that all built-in configurations are functional.
  • Pick a release number X.Y.Z where X, Y, and Z are numbers (no additional characters). To stay consistent with Semantic Versioning, for now stay with X=0. Increment Z by one if only bug fixes were made, and otherwise increment Y by one and reset Z to zero. In the directions below, replace X.Y.Z with the release number you picked.
  • In shasta/.github/workflows/Build.yml, change BUILD_ID to Shasta Release X.Y.Z in all cmake commands (2 instances at the time of last update of this documentation).
  • Commit and push this change to trigger a new build on GitHub Actions. Wait for that build to complete.
  • Download the 3 artifacts. Unzip them and rename them to the following:
    • shasta-Linux-X.Y.Z
    • shasta-Ubuntu-20.04-X.Y.Z.tar
    • shasta-docs-X.Y.Z.tar
  • Make sure shasta-Linux-X.Y.Z is executable.
  • Build Shasta for 64-bit ARM (aarch64). Github Actions currently does not support ARM. So the easiest way to do this is on a Graviton2 ec2 instance in AWS as follows:
    • git clone https://github.com/paoloshasta/shasta.git sudo ./shasta/scripts/InstallPrerequisites-Ubuntu.sh mkdir -p shastaBuild cd shastaBuild cmake ../shasta -DBUILD_ID="Shasta Release X.Y.Z for 64 bit ARM" make install/strip -j
    • Download the aarch64 Shasta binary (using scp) and rename it to shasta-Linux-ARM-X.Y.Z, then make sure it is executable.
  • Create release notes in markdown format. This is a web page that can be used to review the release notes before posting on GitHub. Just paste the markdown to the left pane.
    • Include the minimum kernel version for each binary in the notes. You can find this by running the `file` command (`file /path/to/shasta-*-X.Y.Z`)
  • Create the release on GitHub and paste the release notes. The release name should be just/simply X.Y.Z (no additional characters). Specify the tag to be created, also X.Y.Z without any additional characters. The release will not be visible to users until it gets published two steps below.
  • Upload the unzipped and renamed artifacts (4) to the new release as assets.
  • Publish the release on GitHub.
  • After the release has been published it should not be modified or deleted.
  • In shasta/.github/workflows/Build.yml, change BUILD_ID to Shasta unreleased test build newer than release X.Y.Z in all cmake commands for future GitHub Actions builds (2 instances at the time of last update of this documentation).
Table of contents
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Table of contents

Maximizing assembly performance

Use the following tips to obtain the best performance from the Shasta assembler.

  • If you are using the Shasta executable (the simplest and recommended option), use the following options to allocate memory on 2MB huge pages mapped to the hugetlbfs filesystem. However, this requires root privilege to be obtained via sudo. Depending on your sudo set up, this may ask for your password, or fail. 
    --memoryMode filesystem --memoryBacking 2M
  • Don't use macOS or Windows. Use a 64-bit Linux system instead. The Shasta executable runs on most current 64-bit Linux distributions and requires no set up or installation.
Table of contents
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Table of contents

Prerequisite packages

To download, build and run the shasta software on a Ubuntu (16.04, 18.04, 20.04) or macOS (10.14, 10.15) machine, the following packages are required:
Package Ubuntu package name Needed
to build
from
source? 		Needed
to run
assembly? 		Used by
http server? 		Comments
Git git Only needed to get the source code from GitHub.
The g++ compiler g++ ✔ ✔ ✔ Runtime libraries only needed at run time.
Cmake cmake ✔
The Boost libraries libboost-dev ✔
Graphviz graphviz ✔ Used for graph layout.
Blast ncbi-blast+ ✔ Used by the http server for interactively computing alignments.
Python 3 python3 ✔ ✔ ✔
pip3 pip3 Only needed to install pybind11 (see below)
Pybind11 pybind11 (pip3 package) ✔ Used to provide Python bindings to C++ code.
The png library libpng-dev ✔ ✔ ✔ Used to display marker alignments in the http server. But it is a dependency of shasta.so, so it must be installed whether it is used or not.
SeqAn 2.4.0 ✔ See InstallPrerequisites-Ubuntu.sh & InstallPrerequisites-macOS.sh for installation from GitHub.
SPOA 3.4.0 ✔ See InstallPrerequisites-Ubuntu.sh & InstallPrerequisites-macOS.sh for installation from GitHub.

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shasta-0.11.1/docs/Python.html000066400000000000000000000022211433251011200161410ustar00rootroot00000000000000
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Python API

On Ubuntu 16.04 and Ubuntu 18.04, a Shasta build includes a shared library shasta-build/shasta-install/shasta.so which behaves as a Python module. Python code can import shasta to use the functionality provided by the module. To create documentation for the Python API, see directions in shasta/docs/PythonApi/README. For the Python interpreter to find the module you must either:

  • Make sure the Python script that contains import shasta is in the same directory that contains shasta.so (shasta-build/shasta-install/bin). All Python scripts provided with Shasta are copied to that location during the build process.
  • Or, set environment variable PYTHONPATH to the name of the directory where shasta.so is located.
Table of contents
shasta-0.11.1/docs/PythonApi/000077500000000000000000000000001433251011200157105ustar00rootroot00000000000000shasta-0.11.1/docs/PythonApi/Makefile000066400000000000000000000167501433251011200173610ustar00rootroot00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = _build # User-friendly check for sphinx-build ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) $(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) endif # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . # the i18n builder cannot share the environment and doctrees with the others I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . .PHONY: help help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " singlehtml to make a single large HTML file" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and a HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " applehelp to make an Apple Help Book" @echo " devhelp to make HTML files and a Devhelp project" @echo " epub to make an epub" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " latexpdf to make LaTeX files and run them through pdflatex" @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" @echo " text to make text files" @echo " man to make manual pages" @echo " texinfo to make Texinfo files" @echo " info to make Texinfo files and run them through makeinfo" @echo " gettext to make PO message catalogs" @echo " changes to make an overview of all changed/added/deprecated items" @echo " xml to make Docutils-native XML files" @echo " pseudoxml to make pseudoxml-XML files for display purposes" @echo " linkcheck to check all external links for integrity" @echo " doctest to run all doctests embedded in the documentation (if enabled)" @echo " coverage to run coverage check of the documentation (if enabled)" .PHONY: clean clean: rm -rf $(BUILDDIR)/* .PHONY: html html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." .PHONY: dirhtml dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." .PHONY: singlehtml singlehtml: $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml @echo @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." .PHONY: pickle pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle @echo @echo "Build finished; now you can process the pickle files." .PHONY: json json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json @echo @echo "Build finished; now you can process the JSON files." .PHONY: htmlhelp htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in $(BUILDDIR)/htmlhelp." .PHONY: qthelp qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in $(BUILDDIR)/qthelp, like this:" @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/Shasta.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/Shasta.qhc" .PHONY: applehelp applehelp: $(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp @echo @echo "Build finished. The help book is in $(BUILDDIR)/applehelp." @echo "N.B. You won't be able to view it unless you put it in" \ "~/Library/Documentation/Help or install it in your application" \ "bundle." .PHONY: devhelp devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/Shasta" @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/Shasta" @echo "# devhelp" .PHONY: epub epub: $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub @echo @echo "Build finished. The epub file is in $(BUILDDIR)/epub." .PHONY: latex latex: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." @echo "Run \`make' in that directory to run these through (pdf)latex" \ "(use \`make latexpdf' here to do that automatically)." .PHONY: latexpdf latexpdf: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through pdflatex..." $(MAKE) -C $(BUILDDIR)/latex all-pdf @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." .PHONY: latexpdfja latexpdfja: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through platex and dvipdfmx..." $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." .PHONY: text text: $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text @echo @echo "Build finished. The text files are in $(BUILDDIR)/text." .PHONY: man man: $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man @echo @echo "Build finished. The manual pages are in $(BUILDDIR)/man." .PHONY: texinfo texinfo: $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." @echo "Run \`make' in that directory to run these through makeinfo" \ "(use \`make info' here to do that automatically)." .PHONY: info info: $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo "Running Texinfo files through makeinfo..." make -C $(BUILDDIR)/texinfo info @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." .PHONY: gettext gettext: $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale @echo @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." .PHONY: changes changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." .PHONY: linkcheck linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." .PHONY: doctest doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." .PHONY: coverage coverage: $(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage @echo "Testing of coverage in the sources finished, look at the " \ "results in $(BUILDDIR)/coverage/python.txt." .PHONY: xml xml: $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml @echo @echo "Build finished. The XML files are in $(BUILDDIR)/xml." .PHONY: pseudoxml pseudoxml: $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml @echo @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." shasta-0.11.1/docs/PythonApi/README000066400000000000000000000005051433251011200165700ustar00rootroot00000000000000To create html documentation for the Python API: - Make sure sphinx is installed. Package namne on Ubuntu is python3-sphinx. - Set PYTHONPATH to point to a directory containing shasta.so. Sphinx uses the documentation strings stored there. - In this directory, do "make clean; make html". - The output goes to _build/html shasta-0.11.1/docs/PythonApi/conf.py000066400000000000000000000177731433251011200172260ustar00rootroot00000000000000#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # Shasta documentation build configuration file, created by # sphinx-quickstart on Thu Aug 16 14:14:37 2018. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = 'Shasta' # copyright = '2018, paoloczi' # author = 'paoloczi' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '' # The full version, including alpha/beta/rc tags. release = '' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'classic' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = {'sidebarwidth': 250} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (relative to this directory) to use as a favicon of # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr' #html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # Now only 'ja' uses this config value #html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. #html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = 'Shastadoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', # Latex figure (float) alignment #'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'Shasta.tex', 'Shasta Documentation', 'paoloczi', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # If true, show URL addresses after external links. #man_show_urls = False shasta-0.11.1/docs/PythonApi/index.rst000066400000000000000000000010121433251011200175430ustar00rootroot00000000000000.. Shasta documentation master file, created by sphinx-quickstart on Thu Aug 16 14:14:37 2018. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. Shasta Python API documentation =============================== Indices and tables ------------------ * :ref:`genindex` * :ref:`search` .. toctree:: :maxdepth: 2 .. automodule:: shasta Class Assembler --------------- .. autoclass:: Assembler :members: .. automethod:: __init__ shasta-0.11.1/docs/QuickStart.html000066400000000000000000000071461433251011200167650ustar00rootroot00000000000000
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Quick start

Note that the Shasta executable has no dependencies and requires no installation or set up. This means that you can use it immediately afterdownloading it and setting its execute permission. See below for more information.

Linux

You can use the following commands to download the executable from the latest release and run an assembly: 

# Download the executable for the latest release.
curl -O -L https://github.com/chanzuckerberg/shasta/releases/download/0.10.0/shasta-Linux-0.10.0

# Grant execute permissions.
chmod ugo+x shasta-Linux-0.10.0

# Run an assembly.
./shasta-Linux-0.10.0 --input input.fasta --config Nanopore-May2022

The above is valid for releases up to 0.10.0. Newer releases will appear in the paoloshasta/shasta repository instead, so the download command would be: 

curl -O -L https://github.com/paoloshasta/shasta/releases/download/x.y.z/shasta-Linux-x.y.z
(Replace x.y.z with the identifier for the release you want to use).

You can specify multiple input FASTA files, if necessary. On a typical laptop, this will run in minutes for a bacterial genome. For a human size assembly, AWS instance type x1.32xlarge is recommended. It is usually available at a cost around $4/hour on the AWS spot market and should complete the human size assembly in a few hours, at coverage around 60x.

Assembly output will be created in a new directory named ShastaRun. Output includes the assembly in FASTA and GFA 1.0 formats in files:

  • ShastaRun/Assembly.fasta
  • ShastaRun/Assembly.gfa
  • ShastaRun/Assembly-BothStrands.gfa
This will work on all current 64 bit Linux distributions that use Linux kernel 3.2.0 or newer.

Note that the procedure above does not require root privilege, unless some non-default options are used when invoking the executable. Those non-default options are, however, necessary to achieve maximum performance.

Windows


Quick test and demonstration

You can use the following commands to run a quick test and demonstration of the Shasta assembler: 

curl -O -L https://s3-us-west-2.amazonaws.com/lc2019/shasta/ecoli_test/r94_ec_rad2.181119.60x-10kb.fasta.gz
gunzip r94_ec_rad2.181119.60x-10kb.fasta.gz
/path/to/shasta_executable --input r94_ec_rad2.181119.60x-10kb.fasta --config Nanopore-May2022
The first two commands download and decompress an input fasta file containing Oxford Nanopore reads for E. coli. The last command runs a Shasta assembly, which should complete in a few minutes on a laptop with at least 8 GB of memory. Assembly output will appear in a new directory ShastaRun. See in particular ShastaRun/Assembly.fasta and ShastaRun/Assembly.gfa.

For more information

More detailed information on running an assembly is available here. A list of command line options can be found here.

Table of contents
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Table of contents

Reporting problems or asking questions

If you bump into problems using Shasta, or if you have questions or suggestions, feel free to submit an issue on GitHub. We will make an effort to respond to issues rapidly. However, we are a small team so please be patient.

Please use one issue per topic when possible:

  • If a new problem occurs, create a new issue rather than appending an old one. It is fine to append an existing issue for discussion of the same problem for which the issue was originally filed.
  • If two different problems arise on the same run, please file two separate issues.
Table of contents
shasta-0.11.1/docs/Running.html000066400000000000000000000513311433251011200163060ustar00rootroot00000000000000
Table of contents

Running an assembly

Refer the quick start guide to download an executable or build one from the source code.

Selecting assembly options

You can invoke the Shasta executable without options or with --help to get a description of the options. A list of available options can also be found here.

The only mandatory option is --input which must be used to specify the input FASTA or FASTQ files containing reads to be used for the assembly. If there is more than one file, the names should be specified separated by white space, entering --input only once, like this: 

--input a.fasta b.fasta c.fasta

The Shasta assembly process is controlled by many parameters whose values can be specified by using command line options or a configuration file. Configuration files for some common situations are provided in shasta/conf or shasta-install/conf (download a tar file from a Shasta release if you don't have access to these directories). Creating options for a new type of data or genome can require some work, experimentation, and some knowledge of Shasta computational methods. If you are unable to get a satisfactory assembly for your data, feel free to file an issue in the Shasta GitHub repository and we will help. You can also use an experimental script, GenerateConfig.py, that will help you create a starting configuration file. This script is available in shasta/scripts or shasta-install/bin (download a tar file from a Shasta release if you don't have access to these directories).

There is also another experimental script, GenerateFeedback.py, that can be used after an assembly is complete to assess some (limited) aspects of assembly quality. If you decide to file an issue on the Shasta GitHub repository, including the output of this script would be helpful.

Memory requirements

For best performance, the Shasta assembler uses a single large machine rather than a cluster of smaller machines, and operates in memory, with no access to data on disk except during initial input of the reads, the final output of the assembly, and for small output files containing summary assembly information. As a result, for optimal performance, Shasta memory requirements are higher than comparable tools that keep some or most of their data on disk.

Memory requirements for optimal performance are roughly proportional to genome size and coverage and are around 4 to 6 bytes per input base. This only counts input bases that are used in the assembly - that is, excluding reads that were discarded because they were too short or for other reasons. For a human-size genome (≈3 Gb) at coverage 60x, this works out to around 1 TB of memory.

Machines with 1 to 4 TB of memory have become available at reasonable prices in the last few years, and they are widely available on cloud computing platforms. For example, for human assemblies using Shasta, we have routinely been using AWS x1.32xlarge instances with 1952 TB of memory and 128 virtual processors (64 cores with hyperthreading). These machines are available at around $13/hour as on demand instances and they complete a typical human assembly at coverage 60x in about 4 hours, at a compute cost of around $50 per assembly, much lower than current sequencing costs to produce the input reads. Much lower prices (2x to 3x lower) are also available for reserved instances and on the AWS spot market, which means that a production facility could achieve a compute cost of around $20 per genome.

Running with less than optimal memory

Shasta also supports a mode of operation with data structures physically on disk or other storage systems, mapped to virtual memory. In this mode of operation, the operating system dynamically moves pages to disk from memory and back as needed, which can result in a huge performance penalty. For this reason, this mode of operation is not suggested, except in the following conditions:

  • For very small genomes.
  • If the amount of memory available is not much smaller than the amount required for optimal performance.
  • If your machine has SSD or, better, NVMe storage. HDD storage (hard disk) is not suitable because of its high latency for random access.

Under these conditions, this mode of operation can be selected using the following Shasta command line options:
--memoryMode filesystem --memoryBacking disk

See the next section for more information on these options. In addition to specifying these options, make sure the assembly directory (command line option --assemblyDirectory) is on a filesystem backed by the storage type (SSD or NVMe) you want to use. If you are using a local or departmental machine, likely that a filesystem is already mounted on that storage. If you are using an AWS instance, you can use the directions here and here to make your SSD or NVMe storage accessible.

To illustrate the effect of using less than optimal memory, the table below summarizes benchmark results for a human assembly at coverage 45x. This assembly requires about 650 GB to run in memory - see the first row in the table. The remaining rows show alternative scenarios on machines with less memory. These data show that Shasta still runs in reasonable amounts of time with less than optimal memory, as long as NVMe or SSD storage is available. Note the competitive assembly time and AWS cost of the ARM Graviton2 assembly. Some notes on the table:

  • The benchmarks were run on AWS EC2 - details are provided in the right section of the table.
  • All assemblies ran with the default number of threads, equal to the number of virtual processors.
  • Costs are shown as on demand AWS prices in the AWS us-west-2 region (Oregon).
  • For Intel machines, there is one core for each two virtual processors, while for ARM processors there is one core for every virtual processor.
  • A Shasta executable for ARM is currently available starting with Shasta Release 0.7.0.
Architecture Virtual
processors 		Memory
(GiB) 		Data location Elapsed
Time
(h) 		AWS benchmark information
Instance
type 		Data location Hourly
cost ($) 		Assembly
cost ($)
Intel Skylake-SP 96 768 Memory (2 MB pages) 2.5 r5.24xlarge Memory (2 MB pages) 6.05 15.12
Intel Skylake-SP 96 384 NVMe 4.5 m5d.24xlarge Local NVMe volume 5.42 24.51
Intel Skylake-SP 96 384 SSD 8.0 m5.24xlarge EBS gp2 volume 4.61 36.82
Intel Skylake-SP 96 384 Disk Too slow m5.24xlarge EBS st1 volume
Intel Skylake-SP 48 192 NVMe 6.6 m5d.12xlarge Local NVMe volume 2.71 18.00
ARM Graviton2 48 384 NVMe 4.1 r6gd.12xlarge Local NVMe volume 2.76 11.39

Memory modes

(This section does not apply to macOS).

For performance, the Shasta executable operates in memory, with no access to data on disk except during initial input of the reads, final output of the assembly, and for small output files containing summary assembly information. All large memory areas are allocated using mmap calls in one of several different modes of operation described below. The choice of the optimal mode of operation is dependent on many factors and decribed below. The default mode of operation works reasonably well in most cases and does not require root privilege. However, it does not deliver the best possible performance.

The memory modes are controlled by two command line options:

  • --memoryMode controls whether mmap allocates anonymous memory or memory mapped to a filesystem. It can take one of the following values:
    • anonymous (the default value)
    • filesystem
  • --memoryBacking specifies the physical backing of pages allocated via mmap and can take one of the following values:
    • disk: mmap uses standard 4 KB pages mapped to the existing filesystem on disk in the current directory.
    • 4K (the default value): mmap uses standard 4 KB pages (anonymous or on a tmpfs filesystem, depending on the setting of --memoryBacking).
    • 2M: mmap uses large 2 MB pages (anonymous or on a hugetlbfs filesystem, depending on the setting of --memoryBacking). The 2MB pages are often referred to as "huge pages".

There are a total six possible combinations of these two options, summarized in the table below.
--memoryMode
anonymous
(default) 		filesystem
--memoryBacking disk Not allowed Memory allocated by mmap uses 4 KB pages on a the filesystem on disk that the run output directory is in. This mode of operation can incur severe performance degradation and is therefore not generally suggested. After the run terminates, binary data are permanently available on disk and you can use the http server or the Python API to inspect assembly results.
4K
(default) 		The default option. Memory allocated by mmap uses anonymous 4 KB pages. After the run terminates, binary data are destroyed, which means you cannot use the http server or the Python API to inspect assembly results. Performance is less than optimal (typically 30% degradation on a large run). Memory allocated by mmap uses 4 KB pages on a tmpfs filesystem which is created and mounted on the Data directory under the run output directory. After the run terminates, binary data are available (until the next reboot) and you can use the http server or the Python API to inspect assembly results. Performance is less than optimal. When done using the binary data, you can free the memory using the following command: shasta --command cleanupBinaryData.
2M Memory allocated by mmap uses anonymous 2 MB pages. After the run terminates, binary data are destroyed, which means you cannot use the http server or the Python API to inspect assembly results. Performance is less than optimal (typically 30% degradation on a large run). Memory allocated by mmap uses 2 MB pages on a hugetlbfs filesystem which is created and mounted on the Data directory under the run output directory. After the run terminates, binary data are available (until the next reboot) and you can use the http server or the Python API to inspect assembly results. This mode of operation delivers optimal performance. When done using the binary data, you can free the memory using the following command: shasta --command cleanupBinaryData.

The table is color coded with the following meaning:
This combination is not allowed.
This combination requires root privilege to be acquired via sudo. Depending on sudo settings, this may fail or ask for a user password.
This combination is allowed and does not require root privilege.

In summary:

  • For large assemblies it is best to make sure to have root privilege and use --memoryMode filesystem --memoryBacking 2M. Remember to use shasta --command cleanupBinaryData to free up the memory when done using the binary data!
  • For small assemblies for which performance is not important use the default mode --memoryMode anonymous --memoryBacking 4K. However, if access to binary data is required after the assembly completes to inspect assembly results using the http server or the Python API, use --memoryMode filesystem --memoryBacking disk.

See here for interactive help to select these options.

Scripted approaches to running an assembly

The Shasta assembler provides a Python API that can be used for scripting. This makes it possible to write Python scripts to run assemblies that have more flexibility or functionality than allowed by the Shasta executable. For example, these scripts allow rerunning only a portion of an assembly, which can be useful for development of new assembly functionality.

These scripts use a Python import to import the Shasta shared library, shasta.so, which provides Python bindings to Shasta functionality. The scripts and the library are in shasta-install/bin, but they will normally not be needed during basic operation of the Shasta assembler. They are more likely to be needed for debugging, testing, or development.

The Python API also allows you to write your own assembly scripts. For such a script to work, and under the assumption that the script is not located at shasta-install/bin, it will be necessary to set environment variable PYTHONPATH to (actual path)/shasta-install/bin, so the Python interpreter can locate the Shasta shared library during import.

Input files

The Shasta assembler uses as input one or more files containing reads in one of the supported formats listed below. Shasta uses the file extension to deduce the file format, as described below.

  • FASTA: input files in this format must be named with a file extension .fasta, .fa, .FASTA, or .FA. Older versions of Shasta had strict restrictions on Fasta files, but these restrictions were eliminated. In particular, multiline reads are now supported. Reads containing no-called or invalid bases are also allowed but are discarded on input. The file can have either Unix-style (LF) or Windows-style (CR+LF) line ends.
  • FASTQ: input files in this format must be named with a file extension .fastq, .fq, .FASTQ, or .FQ. Shasta requires the stricter FASTQ format with exactly four lines per read. The third line must consist of just the "+" sign. The file must have Unix-style (LF) line ends. Windows-style (CR+LF) line ends are not supported.

Input from compressed files is not supported. if your input files are compressed, you have to decompress them first using the appropriate decompression utilities.

Any reads shorter than Reads.minReadLength bases (default 10000) present in the input files are discarded on input. Reads that contain bases with repeat counts greater than 255 are also discarded. This is a consequence of the fact that repeat counts are stored using one byte, and therefore there would be no way to store such reads. Reads with such long repeat counts are extremely rare, however, and when they occur they are of suspicious quality.

Output files

The Shasta executable creates output in a directory with a name specified by the --assemblyDirectory option (default ShastaRun). The directory is created automatically at the beginning of the run. The run stops if the directory already exists. This reduces the possibility of unwanted deletion of data.

Contents of the output directory after a successful run include the following:

  • Assembly.fasta: The assembly results in FASTA format. The id of each assembled segment is the same as the edge id of the corresponding edge in the assembly graph.
  • Assembly.gfa: The assembly results in GFA 1.0 format. This contains the same sequences in the FASTA file (as GFA segments), plus their connectivity information (as GFA links). A convenient tool to inspect and study these files is Bandage. Segment ids in the GFA file correspond to FASTA ids in the FASTA file and also to assembly graph edge ids.
  • Assembly-BothStrands.gfa: An alternative GFA output file for the assembly. This contains both strands of the assembly. This can be useful in some cases to clarify connectivity of assembled segments. See AssemblySummary.csv to find the id of the reverse complement of each assembled segment.
  • AssemblySummary.html: An html file summarizing many assembly metrics. It can be viewed using an Internet browser. It has the same content shown by the summary page of the Shasta http server (--command explore).
  • shasta.conf: a configuration file containing the values of all assembly parameters used. This file uses a format that can also be used as input for a subsequent Shasta run using option --config.
  • ReadLengthHistogram.csv: A spreadsheet file containing statistics of the read length distribution. This only includes reads that were used by the assembler. The assembler discards reads shorter than Reads.minReadLength bases (default 10000) and reads that contain bases repeated more than 255 times. The fifth field of the last line of this file contains the total number of input bases used by the assembler in this run.
  • Binned-ReadLengthHistogram.csv: Similar to ReadLengthHistogram.csv, but using 1 Kb bins of read lengths.
  • Data: A directory containing binary data that can later be used by the Shasta http server or with the Shasta Python API. This is only created if option --memoryMode filesystem was used for the run. Keep in mind that, unless you used option --memoryBacking disk, these data are in memory, not on disk, and will disappear at next reboot. If you want to save them permanently, you can use script shasta-install/bin/SaveRun.py to create a copy on disk of the binary data directory named DataOnDisk. (You can also make the copy yourself using the cp command). To free up the memory used without rebooting, you can invoke the Shasta executable again using the following options: 
    --command cleanupBinaryData --assemblyDirectory outputDirectoryName
    Here, outputDirectoryName should specify the same value used in the assembly run (that is, the name of the directory containing the Data directory). Equivalently, you can just umount the Data directory, then remove it.
Table of contents
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Table of contents

Supported platforms

Running an assembly

Basic Shasta functionality (running an assembly) is supported by the Shasta executable on the following platforms:

  • Most current 64-bit Linux distributions for the x86_64 architecture, including the following on which it was actually tested:
    • Ubuntu 16.04 LTS
    • Ubuntu 18.04 LTS
    • Ubuntu 20.04 LTS
  • macOS, using the macOS specific version of the Shasta executable.
  • Windows, using the Linux version of the Shasta executable and running under Windows Subsystem for Linux (WSL).

See here for more information.

Extended functionality

Extended Shasta functionality (http server, Python API) is only available on Ubuntu 16.04, Ubuntu 18.04 and Ubuntu 20.04 LTS. Porting to other Linux platforms is possible.

Table of contents
shasta-0.11.1/docs/doxygen/000077500000000000000000000000001433251011200154525ustar00rootroot00000000000000shasta-0.11.1/docs/doxygen/Doxyfile000066400000000000000000003171401433251011200171660ustar00rootroot00000000000000# Doxyfile 1.8.11 # This file describes the settings to be used by the documentation system # doxygen (www.doxygen.org) for a project. # # All text after a double hash (##) is considered a comment and is placed in # front of the TAG it is preceding. # # All text after a single hash (#) is considered a comment and will be ignored. # The format is: # TAG = value [value, ...] # For lists, items can also be appended using: # TAG += value [value, ...] # Values that contain spaces should be placed between quotes (\" \"). #--------------------------------------------------------------------------- # Project related configuration options #--------------------------------------------------------------------------- # This tag specifies the encoding used for all characters in the config file # that follow. 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PROJECT_NUMBER = # Using the PROJECT_BRIEF tag one can provide an optional one line description # for a project that appears at the top of each page and should give viewer a # quick idea about the purpose of the project. Keep the description short. PROJECT_BRIEF = # With the PROJECT_LOGO tag one can specify a logo or an icon that is included # in the documentation. The maximum height of the logo should not exceed 55 # pixels and the maximum width should not exceed 200 pixels. Doxygen will copy # the logo to the output directory. PROJECT_LOGO = # The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path # into which the generated documentation will be written. If a relative path is # entered, it will be relative to the location where doxygen was started. If # left blank the current directory will be used. OUTPUT_DIRECTORY = # If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub- # directories (in 2 levels) under the output directory of each output format and # will distribute the generated files over these directories. Enabling this # option can be useful when feeding doxygen a huge amount of source files, where # putting all generated files in the same directory would otherwise causes # performance problems for the file system. # The default value is: NO. CREATE_SUBDIRS = NO # If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII # characters to appear in the names of generated files. If set to NO, non-ASCII # characters will be escaped, for example _xE3_x81_x84 will be used for Unicode # U+3044. # The default value is: NO. ALLOW_UNICODE_NAMES = NO # The OUTPUT_LANGUAGE tag is used to specify the language in which all # documentation generated by doxygen is written. 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ABBREVIATE_BRIEF = # If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then # doxygen will generate a detailed section even if there is only a brief # description. # The default value is: NO. ALWAYS_DETAILED_SEC = NO # If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all # inherited members of a class in the documentation of that class as if those # members were ordinary class members. Constructors, destructors and assignment # operators of the base classes will not be shown. # The default value is: NO. INLINE_INHERITED_MEMB = NO # If the FULL_PATH_NAMES tag is set to YES, doxygen will prepend the full path # before files name in the file list and in the header files. If set to NO the # shortest path that makes the file name unique will be used # The default value is: YES. FULL_PATH_NAMES = NO # Stripping is only done if one of the specified strings matches the left-hand # part of the path. The tag can be used to show relative paths in the file list. # If left blank the directory from which doxygen is run is used as the path to # strip. # # Note that you can specify absolute paths here, but also relative paths, which # will be relative from the directory where doxygen is started. # This tag requires that the tag FULL_PATH_NAMES is set to YES. STRIP_FROM_PATH = # The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of the # path mentioned in the documentation of a class, which tells the reader which # header file to include in order to use a class. If left blank only the name of # the header file containing the class definition is used. Otherwise one should # specify the list of include paths that are normally passed to the compiler # using the -I flag. STRIP_FROM_INC_PATH = # If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but # less readable) file names. This can be useful is your file systems doesn't # support long names like on DOS, Mac, or CD-ROM. # The default value is: NO. SHORT_NAMES = NO # If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the # first line (until the first dot) of a Javadoc-style comment as the brief # description. If set to NO, the Javadoc-style will behave just like regular Qt- # style comments (thus requiring an explicit @brief command for a brief # description.) # The default value is: NO. JAVADOC_AUTOBRIEF = YES # If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first # line (until the first dot) of a Qt-style comment as the brief description. If # set to NO, the Qt-style will behave just like regular Qt-style comments (thus # requiring an explicit \brief command for a brief description.) # The default value is: NO. QT_AUTOBRIEF = NO # The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a # multi-line C++ special comment block (i.e. a block of //! or /// comments) as # a brief description. This used to be the default behavior. The new default is # to treat a multi-line C++ comment block as a detailed description. Set this # tag to YES if you prefer the old behavior instead. # # Note that setting this tag to YES also means that rational rose comments are # not recognized any more. # The default value is: NO. MULTILINE_CPP_IS_BRIEF = NO # If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the # documentation from any documented member that it re-implements. # The default value is: YES. INHERIT_DOCS = YES # If the SEPARATE_MEMBER_PAGES tag is set to YES then doxygen will produce a new # page for each member. If set to NO, the documentation of a member will be part # of the file/class/namespace that contains it. # The default value is: NO. SEPARATE_MEMBER_PAGES = NO # The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen # uses this value to replace tabs by spaces in code fragments. # Minimum value: 1, maximum value: 16, default value: 4. TAB_SIZE = 4 # This tag can be used to specify a number of aliases that act as commands in # the documentation. An alias has the form: # name=value # For example adding # "sideeffect=@par Side Effects:\n" # will allow you to put the command \sideeffect (or @sideeffect) in the # documentation, which will result in a user-defined paragraph with heading # "Side Effects:". You can put \n's in the value part of an alias to insert # newlines. ALIASES = # This tag can be used to specify a number of word-keyword mappings (TCL only). # A mapping has the form "name=value". For example adding "class=itcl::class" # will allow you to use the command class in the itcl::class meaning. TCL_SUBST = # Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources # only. Doxygen will then generate output that is more tailored for C. For # instance, some of the names that are used will be different. The list of all # members will be omitted, etc. # The default value is: NO. OPTIMIZE_OUTPUT_FOR_C = NO # Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or # Python sources only. Doxygen will then generate output that is more tailored # for that language. For instance, namespaces will be presented as packages, # qualified scopes will look different, etc. # The default value is: NO. OPTIMIZE_OUTPUT_JAVA = NO # Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran # sources. Doxygen will then generate output that is tailored for Fortran. # The default value is: NO. OPTIMIZE_FOR_FORTRAN = NO # Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL # sources. Doxygen will then generate output that is tailored for VHDL. # The default value is: NO. OPTIMIZE_OUTPUT_VHDL = NO # Doxygen selects the parser to use depending on the extension of the files it # parses. With this tag you can assign which parser to use for a given # extension. Doxygen has a built-in mapping, but you can override or extend it # using this tag. The format is ext=language, where ext is a file extension, and # language is one of the parsers supported by doxygen: IDL, Java, Javascript, # C#, C, C++, D, PHP, Objective-C, Python, Fortran (fixed format Fortran: # FortranFixed, free formatted Fortran: FortranFree, unknown formatted Fortran: # Fortran. In the later case the parser tries to guess whether the code is fixed # or free formatted code, this is the default for Fortran type files), VHDL. For # instance to make doxygen treat .inc files as Fortran files (default is PHP), # and .f files as C (default is Fortran), use: inc=Fortran f=C. # # Note: For files without extension you can use no_extension as a placeholder. # # Note that for custom extensions you also need to set FILE_PATTERNS otherwise # the files are not read by doxygen. EXTENSION_MAPPING = # If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments # according to the Markdown format, which allows for more readable # documentation. See http://daringfireball.net/projects/markdown/ for details. # The output of markdown processing is further processed by doxygen, so you can # mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in # case of backward compatibilities issues. # The default value is: YES. MARKDOWN_SUPPORT = YES # When enabled doxygen tries to link words that correspond to documented # classes, or namespaces to their corresponding documentation. Such a link can # be prevented in individual cases by putting a % sign in front of the word or # globally by setting AUTOLINK_SUPPORT to NO. # The default value is: YES. AUTOLINK_SUPPORT = YES # If you use STL classes (i.e. std::string, std::vector, etc.) but do not want # to include (a tag file for) the STL sources as input, then you should set this # tag to YES in order to let doxygen match functions declarations and # definitions whose arguments contain STL classes (e.g. func(std::string); # versus func(std::string) {}). This also make the inheritance and collaboration # diagrams that involve STL classes more complete and accurate. # The default value is: NO. BUILTIN_STL_SUPPORT = YES # If you use Microsoft's C++/CLI language, you should set this option to YES to # enable parsing support. # The default value is: NO. CPP_CLI_SUPPORT = NO # Set the SIP_SUPPORT tag to YES if your project consists of sip (see: # http://www.riverbankcomputing.co.uk/software/sip/intro) sources only. Doxygen # will parse them like normal C++ but will assume all classes use public instead # of private inheritance when no explicit protection keyword is present. # The default value is: NO. SIP_SUPPORT = NO # For Microsoft's IDL there are propget and propput attributes to indicate # getter and setter methods for a property. Setting this option to YES will make # doxygen to replace the get and set methods by a property in the documentation. # This will only work if the methods are indeed getting or setting a simple # type. If this is not the case, or you want to show the methods anyway, you # should set this option to NO. # The default value is: YES. IDL_PROPERTY_SUPPORT = YES # If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC # tag is set to YES then doxygen will reuse the documentation of the first # member in the group (if any) for the other members of the group. By default # all members of a group must be documented explicitly. # The default value is: NO. DISTRIBUTE_GROUP_DOC = NO # If one adds a struct or class to a group and this option is enabled, then also # any nested class or struct is added to the same group. By default this option # is disabled and one has to add nested compounds explicitly via \ingroup. # The default value is: NO. GROUP_NESTED_COMPOUNDS = NO # Set the SUBGROUPING tag to YES to allow class member groups of the same type # (for instance a group of public functions) to be put as a subgroup of that # type (e.g. under the Public Functions section). Set it to NO to prevent # subgrouping. Alternatively, this can be done per class using the # \nosubgrouping command. # The default value is: YES. SUBGROUPING = YES # When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions # are shown inside the group in which they are included (e.g. using \ingroup) # instead of on a separate page (for HTML and Man pages) or section (for LaTeX # and RTF). # # Note that this feature does not work in combination with # SEPARATE_MEMBER_PAGES. # The default value is: NO. INLINE_GROUPED_CLASSES = NO # When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions # with only public data fields or simple typedef fields will be shown inline in # the documentation of the scope in which they are defined (i.e. file, # namespace, or group documentation), provided this scope is documented. If set # to NO, structs, classes, and unions are shown on a separate page (for HTML and # Man pages) or section (for LaTeX and RTF). # The default value is: NO. INLINE_SIMPLE_STRUCTS = NO # When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or # enum is documented as struct, union, or enum with the name of the typedef. So # typedef struct TypeS {} TypeT, will appear in the documentation as a struct # with name TypeT. When disabled the typedef will appear as a member of a file, # namespace, or class. And the struct will be named TypeS. This can typically be # useful for C code in case the coding convention dictates that all compound # types are typedef'ed and only the typedef is referenced, never the tag name. # The default value is: NO. TYPEDEF_HIDES_STRUCT = NO # The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This # cache is used to resolve symbols given their name and scope. Since this can be # an expensive process and often the same symbol appears multiple times in the # code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small # doxygen will become slower. If the cache is too large, memory is wasted. The # cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range # is 0..9, the default is 0, corresponding to a cache size of 2^16=65536 # symbols. At the end of a run doxygen will report the cache usage and suggest # the optimal cache size from a speed point of view. # Minimum value: 0, maximum value: 9, default value: 0. LOOKUP_CACHE_SIZE = 0 #--------------------------------------------------------------------------- # Build related configuration options #--------------------------------------------------------------------------- # If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in # documentation are documented, even if no documentation was available. Private # class members and static file members will be hidden unless the # EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES. # Note: This will also disable the warnings about undocumented members that are # normally produced when WARNINGS is set to YES. # The default value is: NO. EXTRACT_ALL = YES # If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will # be included in the documentation. # The default value is: NO. EXTRACT_PRIVATE = YES # If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal # scope will be included in the documentation. # The default value is: NO. EXTRACT_PACKAGE = YES # If the EXTRACT_STATIC tag is set to YES, all static members of a file will be # included in the documentation. # The default value is: NO. EXTRACT_STATIC = YES # If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined # locally in source files will be included in the documentation. If set to NO, # only classes defined in header files are included. Does not have any effect # for Java sources. # The default value is: YES. EXTRACT_LOCAL_CLASSES = YES # This flag is only useful for Objective-C code. If set to YES, local methods, # which are defined in the implementation section but not in the interface are # included in the documentation. If set to NO, only methods in the interface are # included. # The default value is: NO. EXTRACT_LOCAL_METHODS = YES # If this flag is set to YES, the members of anonymous namespaces will be # extracted and appear in the documentation as a namespace called # 'anonymous_namespace{file}', where file will be replaced with the base name of # the file that contains the anonymous namespace. By default anonymous namespace # are hidden. # The default value is: NO. EXTRACT_ANON_NSPACES = YES # If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all # undocumented members inside documented classes or files. If set to NO these # members will be included in the various overviews, but no documentation # section is generated. This option has no effect if EXTRACT_ALL is enabled. # The default value is: NO. HIDE_UNDOC_MEMBERS = NO # If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all # undocumented classes that are normally visible in the class hierarchy. If set # to NO, these classes will be included in the various overviews. This option # has no effect if EXTRACT_ALL is enabled. # The default value is: NO. HIDE_UNDOC_CLASSES = NO # If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend # (class|struct|union) declarations. If set to NO, these declarations will be # included in the documentation. # The default value is: NO. HIDE_FRIEND_COMPOUNDS = NO # If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any # documentation blocks found inside the body of a function. If set to NO, these # blocks will be appended to the function's detailed documentation block. # The default value is: NO. HIDE_IN_BODY_DOCS = NO # The INTERNAL_DOCS tag determines if documentation that is typed after a # \internal command is included. If the tag is set to NO then the documentation # will be excluded. Set it to YES to include the internal documentation. # The default value is: NO. INTERNAL_DOCS = NO # If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file # names in lower-case letters. If set to YES, upper-case letters are also # allowed. This is useful if you have classes or files whose names only differ # in case and if your file system supports case sensitive file names. Windows # and Mac users are advised to set this option to NO. # The default value is: system dependent. CASE_SENSE_NAMES = YES # If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with # their full class and namespace scopes in the documentation. If set to YES, the # scope will be hidden. # The default value is: NO. HIDE_SCOPE_NAMES = NO # If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will # append additional text to a page's title, such as Class Reference. If set to # YES the compound reference will be hidden. # The default value is: NO. HIDE_COMPOUND_REFERENCE= NO # If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of # the files that are included by a file in the documentation of that file. # The default value is: YES. SHOW_INCLUDE_FILES = YES # If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each # grouped member an include statement to the documentation, telling the reader # which file to include in order to use the member. # The default value is: NO. SHOW_GROUPED_MEMB_INC = NO # If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include # files with double quotes in the documentation rather than with sharp brackets. # The default value is: NO. FORCE_LOCAL_INCLUDES = NO # If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the # documentation for inline members. # The default value is: YES. INLINE_INFO = YES # If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the # (detailed) documentation of file and class members alphabetically by member # name. If set to NO, the members will appear in declaration order. # The default value is: YES. SORT_MEMBER_DOCS = YES # If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief # descriptions of file, namespace and class members alphabetically by member # name. If set to NO, the members will appear in declaration order. Note that # this will also influence the order of the classes in the class list. # The default value is: NO. SORT_BRIEF_DOCS = NO # If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the # (brief and detailed) documentation of class members so that constructors and # destructors are listed first. If set to NO the constructors will appear in the # respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS. # Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief # member documentation. # Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting # detailed member documentation. # The default value is: NO. SORT_MEMBERS_CTORS_1ST = NO # If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy # of group names into alphabetical order. If set to NO the group names will # appear in their defined order. # The default value is: NO. SORT_GROUP_NAMES = NO # If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by # fully-qualified names, including namespaces. If set to NO, the class list will # be sorted only by class name, not including the namespace part. # Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES. # Note: This option applies only to the class list, not to the alphabetical # list. # The default value is: NO. SORT_BY_SCOPE_NAME = NO # If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper # type resolution of all parameters of a function it will reject a match between # the prototype and the implementation of a member function even if there is # only one candidate or it is obvious which candidate to choose by doing a # simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still # accept a match between prototype and implementation in such cases. # The default value is: NO. STRICT_PROTO_MATCHING = NO # The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo # list. This list is created by putting \todo commands in the documentation. # The default value is: YES. GENERATE_TODOLIST = YES # The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test # list. This list is created by putting \test commands in the documentation. # The default value is: YES. GENERATE_TESTLIST = YES # The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug # list. This list is created by putting \bug commands in the documentation. # The default value is: YES. GENERATE_BUGLIST = YES # The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO) # the deprecated list. This list is created by putting \deprecated commands in # the documentation. # The default value is: YES. GENERATE_DEPRECATEDLIST= YES # The ENABLED_SECTIONS tag can be used to enable conditional documentation # sections, marked by \if ... \endif and \cond # ... \endcond blocks. ENABLED_SECTIONS = # The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the # initial value of a variable or macro / define can have for it to appear in the # documentation. If the initializer consists of more lines than specified here # it will be hidden. Use a value of 0 to hide initializers completely. The # appearance of the value of individual variables and macros / defines can be # controlled using \showinitializer or \hideinitializer command in the # documentation regardless of this setting. # Minimum value: 0, maximum value: 10000, default value: 30. MAX_INITIALIZER_LINES = 30 # Set the SHOW_USED_FILES tag to NO to disable the list of files generated at # the bottom of the documentation of classes and structs. If set to YES, the # list will mention the files that were used to generate the documentation. # The default value is: YES. SHOW_USED_FILES = YES # Set the SHOW_FILES tag to NO to disable the generation of the Files page. This # will remove the Files entry from the Quick Index and from the Folder Tree View # (if specified). # The default value is: YES. SHOW_FILES = YES # Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces # page. This will remove the Namespaces entry from the Quick Index and from the # Folder Tree View (if specified). # The default value is: YES. SHOW_NAMESPACES = YES # The FILE_VERSION_FILTER tag can be used to specify a program or script that # doxygen should invoke to get the current version for each file (typically from # the version control system). Doxygen will invoke the program by executing (via # popen()) the command command input-file, where command is the value of the # FILE_VERSION_FILTER tag, and input-file is the name of an input file provided # by doxygen. Whatever the program writes to standard output is used as the file # version. For an example see the documentation. FILE_VERSION_FILTER = # The LAYOUT_FILE tag can be used to specify a layout file which will be parsed # by doxygen. The layout file controls the global structure of the generated # output files in an output format independent way. To create the layout file # that represents doxygen's defaults, run doxygen with the -l option. You can # optionally specify a file name after the option, if omitted DoxygenLayout.xml # will be used as the name of the layout file. # # Note that if you run doxygen from a directory containing a file called # DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE # tag is left empty. LAYOUT_FILE = # The CITE_BIB_FILES tag can be used to specify one or more bib files containing # the reference definitions. This must be a list of .bib files. The .bib # extension is automatically appended if omitted. This requires the bibtex tool # to be installed. See also http://en.wikipedia.org/wiki/BibTeX for more info. # For LaTeX the style of the bibliography can be controlled using # LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the # search path. See also \cite for info how to create references. CITE_BIB_FILES = #--------------------------------------------------------------------------- # Configuration options related to warning and progress messages #--------------------------------------------------------------------------- # The QUIET tag can be used to turn on/off the messages that are generated to # standard output by doxygen. If QUIET is set to YES this implies that the # messages are off. # The default value is: NO. QUIET = NO # The WARNINGS tag can be used to turn on/off the warning messages that are # generated to standard error (stderr) by doxygen. If WARNINGS is set to YES # this implies that the warnings are on. # # Tip: Turn warnings on while writing the documentation. # The default value is: YES. WARNINGS = YES # If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate # warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag # will automatically be disabled. # The default value is: YES. WARN_IF_UNDOCUMENTED = YES # If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for # potential errors in the documentation, such as not documenting some parameters # in a documented function, or documenting parameters that don't exist or using # markup commands wrongly. # The default value is: YES. WARN_IF_DOC_ERROR = YES # This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that # are documented, but have no documentation for their parameters or return # value. If set to NO, doxygen will only warn about wrong or incomplete # parameter documentation, but not about the absence of documentation. # The default value is: NO. WARN_NO_PARAMDOC = NO # If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when # a warning is encountered. # The default value is: NO. WARN_AS_ERROR = NO # The WARN_FORMAT tag determines the format of the warning messages that doxygen # can produce. The string should contain the $file, $line, and $text tags, which # will be replaced by the file and line number from which the warning originated # and the warning text. Optionally the format may contain $version, which will # be replaced by the version of the file (if it could be obtained via # FILE_VERSION_FILTER) # The default value is: $file:$line: $text. WARN_FORMAT = "$file:$line: $text" # The WARN_LOGFILE tag can be used to specify a file to which warning and error # messages should be written. If left blank the output is written to standard # error (stderr). WARN_LOGFILE = #--------------------------------------------------------------------------- # Configuration options related to the input files #--------------------------------------------------------------------------- # The INPUT tag is used to specify the files and/or directories that contain # documented source files. You may enter file names like myfile.cpp or # directories like /usr/src/myproject. Separate the files or directories with # spaces. See also FILE_PATTERNS and EXTENSION_MAPPING # Note: If this tag is empty the current directory is searched. INPUT = ../../src # This tag can be used to specify the character encoding of the source files # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses # libiconv (or the iconv built into libc) for the transcoding. See the libiconv # documentation (see: http://www.gnu.org/software/libiconv) for the list of # possible encodings. # The default value is: UTF-8. INPUT_ENCODING = UTF-8 # If the value of the INPUT tag contains directories, you can use the # FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and # *.h) to filter out the source-files in the directories. # # Note that for custom extensions or not directly supported extensions you also # need to set EXTENSION_MAPPING for the extension otherwise the files are not # read by doxygen. # # If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp, # *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h, # *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc, # *.m, *.markdown, *.md, *.mm, *.dox, *.py, *.pyw, *.f90, *.f, *.for, *.tcl, # *.vhd, *.vhdl, *.ucf, *.qsf, *.as and *.js. FILE_PATTERNS = # The RECURSIVE tag can be used to specify whether or not subdirectories should # be searched for input files as well. # The default value is: NO. RECURSIVE = NO # The EXCLUDE tag can be used to specify files and/or directories that should be # excluded from the INPUT source files. This way you can easily exclude a # subdirectory from a directory tree whose root is specified with the INPUT tag. # # Note that relative paths are relative to the directory from which doxygen is # run. EXCLUDE = # The EXCLUDE_SYMLINKS tag can be used to select whether or not files or # directories that are symbolic links (a Unix file system feature) are excluded # from the input. # The default value is: NO. EXCLUDE_SYMLINKS = NO # If the value of the INPUT tag contains directories, you can use the # EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude # certain files from those directories. # # Note that the wildcards are matched against the file with absolute path, so to # exclude all test directories for example use the pattern */test/* EXCLUDE_PATTERNS = # The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names # (namespaces, classes, functions, etc.) that should be excluded from the # output. The symbol name can be a fully qualified name, a word, or if the # wildcard * is used, a substring. Examples: ANamespace, AClass, # AClass::ANamespace, ANamespace::*Test # # Note that the wildcards are matched against the file with absolute path, so to # exclude all test directories use the pattern */test/* EXCLUDE_SYMBOLS = # The EXAMPLE_PATH tag can be used to specify one or more files or directories # that contain example code fragments that are included (see the \include # command). EXAMPLE_PATH = # If the value of the EXAMPLE_PATH tag contains directories, you can use the # EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and # *.h) to filter out the source-files in the directories. If left blank all # files are included. EXAMPLE_PATTERNS = # If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be # searched for input files to be used with the \include or \dontinclude commands # irrespective of the value of the RECURSIVE tag. # The default value is: NO. EXAMPLE_RECURSIVE = NO # The IMAGE_PATH tag can be used to specify one or more files or directories # that contain images that are to be included in the documentation (see the # \image command). IMAGE_PATH = # The INPUT_FILTER tag can be used to specify a program that doxygen should # invoke to filter for each input file. Doxygen will invoke the filter program # by executing (via popen()) the command: # # # # where is the value of the INPUT_FILTER tag, and is the # name of an input file. Doxygen will then use the output that the filter # program writes to standard output. If FILTER_PATTERNS is specified, this tag # will be ignored. # # Note that the filter must not add or remove lines; it is applied before the # code is scanned, but not when the output code is generated. If lines are added # or removed, the anchors will not be placed correctly. # # Note that for custom extensions or not directly supported extensions you also # need to set EXTENSION_MAPPING for the extension otherwise the files are not # properly processed by doxygen. INPUT_FILTER = # The FILTER_PATTERNS tag can be used to specify filters on a per file pattern # basis. Doxygen will compare the file name with each pattern and apply the # filter if there is a match. The filters are a list of the form: pattern=filter # (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how # filters are used. If the FILTER_PATTERNS tag is empty or if none of the # patterns match the file name, INPUT_FILTER is applied. # # Note that for custom extensions or not directly supported extensions you also # need to set EXTENSION_MAPPING for the extension otherwise the files are not # properly processed by doxygen. FILTER_PATTERNS = # If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using # INPUT_FILTER) will also be used to filter the input files that are used for # producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES). # The default value is: NO. FILTER_SOURCE_FILES = NO # The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file # pattern. A pattern will override the setting for FILTER_PATTERN (if any) and # it is also possible to disable source filtering for a specific pattern using # *.ext= (so without naming a filter). # This tag requires that the tag FILTER_SOURCE_FILES is set to YES. FILTER_SOURCE_PATTERNS = # If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that # is part of the input, its contents will be placed on the main page # (index.html). This can be useful if you have a project on for instance GitHub # and want to reuse the introduction page also for the doxygen output. USE_MDFILE_AS_MAINPAGE = #--------------------------------------------------------------------------- # Configuration options related to source browsing #--------------------------------------------------------------------------- # If the SOURCE_BROWSER tag is set to YES then a list of source files will be # generated. Documented entities will be cross-referenced with these sources. # # Note: To get rid of all source code in the generated output, make sure that # also VERBATIM_HEADERS is set to NO. # The default value is: NO. SOURCE_BROWSER = YES # Setting the INLINE_SOURCES tag to YES will include the body of functions, # classes and enums directly into the documentation. # The default value is: NO. INLINE_SOURCES = YES # Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any # special comment blocks from generated source code fragments. Normal C, C++ and # Fortran comments will always remain visible. # The default value is: YES. STRIP_CODE_COMMENTS = YES # If the REFERENCED_BY_RELATION tag is set to YES then for each documented # function all documented functions referencing it will be listed. # The default value is: NO. REFERENCED_BY_RELATION = YES # If the REFERENCES_RELATION tag is set to YES then for each documented function # all documented entities called/used by that function will be listed. # The default value is: NO. REFERENCES_RELATION = YES # If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set # to YES then the hyperlinks from functions in REFERENCES_RELATION and # REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will # link to the documentation. # The default value is: YES. REFERENCES_LINK_SOURCE = YES # If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the # source code will show a tooltip with additional information such as prototype, # brief description and links to the definition and documentation. Since this # will make the HTML file larger and loading of large files a bit slower, you # can opt to disable this feature. # The default value is: YES. # This tag requires that the tag SOURCE_BROWSER is set to YES. SOURCE_TOOLTIPS = YES # If the USE_HTAGS tag is set to YES then the references to source code will # point to the HTML generated by the htags(1) tool instead of doxygen built-in # source browser. The htags tool is part of GNU's global source tagging system # (see http://www.gnu.org/software/global/global.html). You will need version # 4.8.6 or higher. # # To use it do the following: # - Install the latest version of global # - Enable SOURCE_BROWSER and USE_HTAGS in the config file # - Make sure the INPUT points to the root of the source tree # - Run doxygen as normal # # Doxygen will invoke htags (and that will in turn invoke gtags), so these # tools must be available from the command line (i.e. in the search path). # # The result: instead of the source browser generated by doxygen, the links to # source code will now point to the output of htags. # The default value is: NO. # This tag requires that the tag SOURCE_BROWSER is set to YES. USE_HTAGS = NO # If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a # verbatim copy of the header file for each class for which an include is # specified. Set to NO to disable this. # See also: Section \class. # The default value is: YES. VERBATIM_HEADERS = YES # If the CLANG_ASSISTED_PARSING tag is set to YES then doxygen will use the # clang parser (see: http://clang.llvm.org/) for more accurate parsing at the # cost of reduced performance. This can be particularly helpful with template # rich C++ code for which doxygen's built-in parser lacks the necessary type # information. # Note: The availability of this option depends on whether or not doxygen was # generated with the -Duse-libclang=ON option for CMake. # The default value is: NO. CLANG_ASSISTED_PARSING = NO # If clang assisted parsing is enabled you can provide the compiler with command # line options that you would normally use when invoking the compiler. Note that # the include paths will already be set by doxygen for the files and directories # specified with INPUT and INCLUDE_PATH. # This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES. CLANG_OPTIONS = #--------------------------------------------------------------------------- # Configuration options related to the alphabetical class index #--------------------------------------------------------------------------- # If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all # compounds will be generated. Enable this if the project contains a lot of # classes, structs, unions or interfaces. # The default value is: YES. ALPHABETICAL_INDEX = YES # The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in # which the alphabetical index list will be split. # Minimum value: 1, maximum value: 20, default value: 5. # This tag requires that the tag ALPHABETICAL_INDEX is set to YES. COLS_IN_ALPHA_INDEX = 5 # In case all classes in a project start with a common prefix, all classes will # be put under the same header in the alphabetical index. The IGNORE_PREFIX tag # can be used to specify a prefix (or a list of prefixes) that should be ignored # while generating the index headers. # This tag requires that the tag ALPHABETICAL_INDEX is set to YES. IGNORE_PREFIX = #--------------------------------------------------------------------------- # Configuration options related to the HTML output #--------------------------------------------------------------------------- # If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output # The default value is: YES. GENERATE_HTML = YES # The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a # relative path is entered the value of OUTPUT_DIRECTORY will be put in front of # it. # The default directory is: html. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_OUTPUT = html # The HTML_FILE_EXTENSION tag can be used to specify the file extension for each # generated HTML page (for example: .htm, .php, .asp). # The default value is: .html. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_FILE_EXTENSION = .html # The HTML_HEADER tag can be used to specify a user-defined HTML header file for # each generated HTML page. If the tag is left blank doxygen will generate a # standard header. # # To get valid HTML the header file that includes any scripts and style sheets # that doxygen needs, which is dependent on the configuration options used (e.g. # the setting GENERATE_TREEVIEW). It is highly recommended to start with a # default header using # doxygen -w html new_header.html new_footer.html new_stylesheet.css # YourConfigFile # and then modify the file new_header.html. See also section "Doxygen usage" # for information on how to generate the default header that doxygen normally # uses. # Note: The header is subject to change so you typically have to regenerate the # default header when upgrading to a newer version of doxygen. For a description # of the possible markers and block names see the documentation. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_HEADER = # The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each # generated HTML page. If the tag is left blank doxygen will generate a standard # footer. See HTML_HEADER for more information on how to generate a default # footer and what special commands can be used inside the footer. See also # section "Doxygen usage" for information on how to generate the default footer # that doxygen normally uses. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_FOOTER = # The HTML_STYLESHEET tag can be used to specify a user-defined cascading style # sheet that is used by each HTML page. It can be used to fine-tune the look of # the HTML output. If left blank doxygen will generate a default style sheet. # See also section "Doxygen usage" for information on how to generate the style # sheet that doxygen normally uses. # Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as # it is more robust and this tag (HTML_STYLESHEET) will in the future become # obsolete. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_STYLESHEET = # The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined # cascading style sheets that are included after the standard style sheets # created by doxygen. Using this option one can overrule certain style aspects. # This is preferred over using HTML_STYLESHEET since it does not replace the # standard style sheet and is therefore more robust against future updates. # Doxygen will copy the style sheet files to the output directory. # Note: The order of the extra style sheet files is of importance (e.g. the last # style sheet in the list overrules the setting of the previous ones in the # list). For an example see the documentation. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_EXTRA_STYLESHEET = # The HTML_EXTRA_FILES tag can be used to specify one or more extra images or # other source files which should be copied to the HTML output directory. Note # that these files will be copied to the base HTML output directory. Use the # $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these # files. In the HTML_STYLESHEET file, use the file name only. Also note that the # files will be copied as-is; there are no commands or markers available. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_EXTRA_FILES = # The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen # will adjust the colors in the style sheet and background images according to # this color. Hue is specified as an angle on a colorwheel, see # http://en.wikipedia.org/wiki/Hue for more information. For instance the value # 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300 # purple, and 360 is red again. # Minimum value: 0, maximum value: 359, default value: 220. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_COLORSTYLE_HUE = 220 # The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors # in the HTML output. For a value of 0 the output will use grayscales only. A # value of 255 will produce the most vivid colors. # Minimum value: 0, maximum value: 255, default value: 100. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_COLORSTYLE_SAT = 100 # The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the # luminance component of the colors in the HTML output. Values below 100 # gradually make the output lighter, whereas values above 100 make the output # darker. The value divided by 100 is the actual gamma applied, so 80 represents # a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not # change the gamma. # Minimum value: 40, maximum value: 240, default value: 80. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_COLORSTYLE_GAMMA = 80 # If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML # page will contain the date and time when the page was generated. Setting this # to YES can help to show when doxygen was last run and thus if the # documentation is up to date. # The default value is: NO. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_TIMESTAMP = NO # If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML # documentation will contain sections that can be hidden and shown after the # page has loaded. # The default value is: NO. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_DYNAMIC_SECTIONS = NO # With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries # shown in the various tree structured indices initially; the user can expand # and collapse entries dynamically later on. Doxygen will expand the tree to # such a level that at most the specified number of entries are visible (unless # a fully collapsed tree already exceeds this amount). So setting the number of # entries 1 will produce a full collapsed tree by default. 0 is a special value # representing an infinite number of entries and will result in a full expanded # tree by default. # Minimum value: 0, maximum value: 9999, default value: 100. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_INDEX_NUM_ENTRIES = 100 # If the GENERATE_DOCSET tag is set to YES, additional index files will be # generated that can be used as input for Apple's Xcode 3 integrated development # environment (see: http://developer.apple.com/tools/xcode/), introduced with # OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a # Makefile in the HTML output directory. Running make will produce the docset in # that directory and running make install will install the docset in # ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at # startup. See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html # for more information. # The default value is: NO. # This tag requires that the tag GENERATE_HTML is set to YES. GENERATE_DOCSET = NO # This tag determines the name of the docset feed. A documentation feed provides # an umbrella under which multiple documentation sets from a single provider # (such as a company or product suite) can be grouped. # The default value is: Doxygen generated docs. # This tag requires that the tag GENERATE_DOCSET is set to YES. DOCSET_FEEDNAME = "Doxygen generated docs" # This tag specifies a string that should uniquely identify the documentation # set bundle. This should be a reverse domain-name style string, e.g. # com.mycompany.MyDocSet. Doxygen will append .docset to the name. # The default value is: org.doxygen.Project. # This tag requires that the tag GENERATE_DOCSET is set to YES. DOCSET_BUNDLE_ID = org.doxygen.Project # The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify # the documentation publisher. This should be a reverse domain-name style # string, e.g. com.mycompany.MyDocSet.documentation. # The default value is: org.doxygen.Publisher. # This tag requires that the tag GENERATE_DOCSET is set to YES. DOCSET_PUBLISHER_ID = org.doxygen.Publisher # The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher. # The default value is: Publisher. # This tag requires that the tag GENERATE_DOCSET is set to YES. DOCSET_PUBLISHER_NAME = Publisher # If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three # additional HTML index files: index.hhp, index.hhc, and index.hhk. The # index.hhp is a project file that can be read by Microsoft's HTML Help Workshop # (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on # Windows. # # The HTML Help Workshop contains a compiler that can convert all HTML output # generated by doxygen into a single compiled HTML file (.chm). Compiled HTML # files are now used as the Windows 98 help format, and will replace the old # Windows help format (.hlp) on all Windows platforms in the future. Compressed # HTML files also contain an index, a table of contents, and you can search for # words in the documentation. The HTML workshop also contains a viewer for # compressed HTML files. # The default value is: NO. # This tag requires that the tag GENERATE_HTML is set to YES. GENERATE_HTMLHELP = NO # The CHM_FILE tag can be used to specify the file name of the resulting .chm # file. You can add a path in front of the file if the result should not be # written to the html output directory. # This tag requires that the tag GENERATE_HTMLHELP is set to YES. CHM_FILE = # The HHC_LOCATION tag can be used to specify the location (absolute path # including file name) of the HTML help compiler (hhc.exe). If non-empty, # doxygen will try to run the HTML help compiler on the generated index.hhp. # The file has to be specified with full path. # This tag requires that the tag GENERATE_HTMLHELP is set to YES. HHC_LOCATION = # The GENERATE_CHI flag controls if a separate .chi index file is generated # (YES) or that it should be included in the master .chm file (NO). # The default value is: NO. # This tag requires that the tag GENERATE_HTMLHELP is set to YES. GENERATE_CHI = NO # The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc) # and project file content. # This tag requires that the tag GENERATE_HTMLHELP is set to YES. CHM_INDEX_ENCODING = # The BINARY_TOC flag controls whether a binary table of contents is generated # (YES) or a normal table of contents (NO) in the .chm file. Furthermore it # enables the Previous and Next buttons. # The default value is: NO. # This tag requires that the tag GENERATE_HTMLHELP is set to YES. BINARY_TOC = NO # The TOC_EXPAND flag can be set to YES to add extra items for group members to # the table of contents of the HTML help documentation and to the tree view. # The default value is: NO. # This tag requires that the tag GENERATE_HTMLHELP is set to YES. TOC_EXPAND = NO # If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and # QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that # can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help # (.qch) of the generated HTML documentation. # The default value is: NO. # This tag requires that the tag GENERATE_HTML is set to YES. GENERATE_QHP = NO # If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify # the file name of the resulting .qch file. The path specified is relative to # the HTML output folder. # This tag requires that the tag GENERATE_QHP is set to YES. QCH_FILE = # The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help # Project output. For more information please see Qt Help Project / Namespace # (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#namespace). # The default value is: org.doxygen.Project. # This tag requires that the tag GENERATE_QHP is set to YES. QHP_NAMESPACE = org.doxygen.Project # The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt # Help Project output. For more information please see Qt Help Project / Virtual # Folders (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#virtual- # folders). # The default value is: doc. # This tag requires that the tag GENERATE_QHP is set to YES. QHP_VIRTUAL_FOLDER = doc # If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom # filter to add. For more information please see Qt Help Project / Custom # Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom- # filters). # This tag requires that the tag GENERATE_QHP is set to YES. QHP_CUST_FILTER_NAME = # The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the # custom filter to add. For more information please see Qt Help Project / Custom # Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom- # filters). # This tag requires that the tag GENERATE_QHP is set to YES. QHP_CUST_FILTER_ATTRS = # The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this # project's filter section matches. Qt Help Project / Filter Attributes (see: # http://qt-project.org/doc/qt-4.8/qthelpproject.html#filter-attributes). # This tag requires that the tag GENERATE_QHP is set to YES. QHP_SECT_FILTER_ATTRS = # The QHG_LOCATION tag can be used to specify the location of Qt's # qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the # generated .qhp file. # This tag requires that the tag GENERATE_QHP is set to YES. QHG_LOCATION = # If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be # generated, together with the HTML files, they form an Eclipse help plugin. To # install this plugin and make it available under the help contents menu in # Eclipse, the contents of the directory containing the HTML and XML files needs # to be copied into the plugins directory of eclipse. The name of the directory # within the plugins directory should be the same as the ECLIPSE_DOC_ID value. # After copying Eclipse needs to be restarted before the help appears. # The default value is: NO. # This tag requires that the tag GENERATE_HTML is set to YES. GENERATE_ECLIPSEHELP = NO # A unique identifier for the Eclipse help plugin. When installing the plugin # the directory name containing the HTML and XML files should also have this # name. Each documentation set should have its own identifier. # The default value is: org.doxygen.Project. # This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES. ECLIPSE_DOC_ID = org.doxygen.Project # If you want full control over the layout of the generated HTML pages it might # be necessary to disable the index and replace it with your own. The # DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top # of each HTML page. A value of NO enables the index and the value YES disables # it. Since the tabs in the index contain the same information as the navigation # tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES. # The default value is: NO. # This tag requires that the tag GENERATE_HTML is set to YES. DISABLE_INDEX = NO # The GENERATE_TREEVIEW tag is used to specify whether a tree-like index # structure should be generated to display hierarchical information. If the tag # value is set to YES, a side panel will be generated containing a tree-like # index structure (just like the one that is generated for HTML Help). For this # to work a browser that supports JavaScript, DHTML, CSS and frames is required # (i.e. any modern browser). Windows users are probably better off using the # HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can # further fine-tune the look of the index. As an example, the default style # sheet generated by doxygen has an example that shows how to put an image at # the root of the tree instead of the PROJECT_NAME. Since the tree basically has # the same information as the tab index, you could consider setting # DISABLE_INDEX to YES when enabling this option. # The default value is: NO. # This tag requires that the tag GENERATE_HTML is set to YES. GENERATE_TREEVIEW = NO # The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that # doxygen will group on one line in the generated HTML documentation. # # Note that a value of 0 will completely suppress the enum values from appearing # in the overview section. # Minimum value: 0, maximum value: 20, default value: 4. # This tag requires that the tag GENERATE_HTML is set to YES. ENUM_VALUES_PER_LINE = 4 # If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used # to set the initial width (in pixels) of the frame in which the tree is shown. # Minimum value: 0, maximum value: 1500, default value: 250. # This tag requires that the tag GENERATE_HTML is set to YES. TREEVIEW_WIDTH = 250 # If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to # external symbols imported via tag files in a separate window. # The default value is: NO. # This tag requires that the tag GENERATE_HTML is set to YES. EXT_LINKS_IN_WINDOW = NO # Use this tag to change the font size of LaTeX formulas included as images in # the HTML documentation. When you change the font size after a successful # doxygen run you need to manually remove any form_*.png images from the HTML # output directory to force them to be regenerated. # Minimum value: 8, maximum value: 50, default value: 10. # This tag requires that the tag GENERATE_HTML is set to YES. FORMULA_FONTSIZE = 10 # Use the FORMULA_TRANPARENT tag to determine whether or not the images # generated for formulas are transparent PNGs. Transparent PNGs are not # supported properly for IE 6.0, but are supported on all modern browsers. # # Note that when changing this option you need to delete any form_*.png files in # the HTML output directory before the changes have effect. # The default value is: YES. # This tag requires that the tag GENERATE_HTML is set to YES. FORMULA_TRANSPARENT = YES # Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see # http://www.mathjax.org) which uses client side Javascript for the rendering # instead of using pre-rendered bitmaps. Use this if you do not have LaTeX # installed or if you want to formulas look prettier in the HTML output. When # enabled you may also need to install MathJax separately and configure the path # to it using the MATHJAX_RELPATH option. # The default value is: NO. # This tag requires that the tag GENERATE_HTML is set to YES. USE_MATHJAX = NO # When MathJax is enabled you can set the default output format to be used for # the MathJax output. See the MathJax site (see: # http://docs.mathjax.org/en/latest/output.html) for more details. # Possible values are: HTML-CSS (which is slower, but has the best # compatibility), NativeMML (i.e. MathML) and SVG. # The default value is: HTML-CSS. # This tag requires that the tag USE_MATHJAX is set to YES. MATHJAX_FORMAT = HTML-CSS # When MathJax is enabled you need to specify the location relative to the HTML # output directory using the MATHJAX_RELPATH option. The destination directory # should contain the MathJax.js script. For instance, if the mathjax directory # is located at the same level as the HTML output directory, then # MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax # Content Delivery Network so you can quickly see the result without installing # MathJax. However, it is strongly recommended to install a local copy of # MathJax from http://www.mathjax.org before deployment. # The default value is: http://cdn.mathjax.org/mathjax/latest. # This tag requires that the tag USE_MATHJAX is set to YES. MATHJAX_RELPATH = http://cdn.mathjax.org/mathjax/latest # The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax # extension names that should be enabled during MathJax rendering. For example # MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols # This tag requires that the tag USE_MATHJAX is set to YES. MATHJAX_EXTENSIONS = # The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces # of code that will be used on startup of the MathJax code. See the MathJax site # (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an # example see the documentation. # This tag requires that the tag USE_MATHJAX is set to YES. MATHJAX_CODEFILE = # When the SEARCHENGINE tag is enabled doxygen will generate a search box for # the HTML output. The underlying search engine uses javascript and DHTML and # should work on any modern browser. Note that when using HTML help # (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET) # there is already a search function so this one should typically be disabled. # For large projects the javascript based search engine can be slow, then # enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to # search using the keyboard; to jump to the search box use + S # (what the is depends on the OS and browser, but it is typically # , /