pax_global_header00006660000000000000000000000064142240470660014517gustar00rootroot0000000000000052 comment=1d08eeec1a24178de69488127faff09082dac0cd phyml-3.3.20220408/000077500000000000000000000000001422404706600134155ustar00rootroot00000000000000phyml-3.3.20220408/.gitignore000077500000000000000000000006141422404706600154110ustar00rootroot00000000000000# Object files *.o *.ko *.obj *.elf # Precompiled Headers *.gch *.pch # Libraries *.lib *.a *.la *.lo # Shared objects (inc. Windows DLLs) *.dll *.so *.so.* *.dylib # Executables *.exe *.out *.app *.i*86 *.x86_64 *.hex Makefile.in config.guess* config.sub* config.h.in aclocal.m4 compile configure depcomp install-sh missing autom4te.cache *~ doc/html doc/latex \#*# ltmain.sh m4/*.m4 *.origphyml-3.3.20220408/.travis.yml000066400000000000000000000011171422404706600155260ustar00rootroot00000000000000# sudo: required # cache: apt # language: c # os: # - linux # - osx # compiler: # - gcc # script: # # build and install software into $HOME/local # - ./autogen.sh # - ./configure # - make # - sudo make install # - ./configure --enable-phyrex # - make clean # - make # - ./configure --enable-phytime # - make clean # - make # # put the installed binary into the $PATH # - export PATH=$HOME/local/bin:$PATH # # install testiphy # # - cd # # - git clone https://gitlab.com/testiphy/testiphy.git # # run testiphy # # - cd testiphy # # - ./testiphy phymlphyml-3.3.20220408/COPYING000066400000000000000000001045151422404706600144560ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read . phyml-3.3.20220408/Makefile.am000077500000000000000000000001061422404706600154510ustar00rootroot00000000000000SUBDIRS = src EXTRA_DIST = doc bin examples DIST_SUBDIRS = $(SUBDIRS) phyml-3.3.20220408/README.md000066400000000000000000000053521422404706600147010ustar00rootroot00000000000000 [![Build Status](https://travis-ci.org/stephaneguindon/phyml.svg?branch=master)](https://travis-ci.org/stephaneguindon/phyml) [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/phyml/README.html) ### Overview PhyML is a software package that uses modern statistical approaches to analyse alignments of nucleotide or amino acid sequences in a phylogenetic framework. The main tool in this package builds phylogenies under the maximum likelihood criterion. It implements a large number of substitution models coupled to efficient options to search the space of phylogenetic tree topologies. PhyTime is another tool in the PhyML package that focuses on divergence date estimation in a Bayesian setting. The main strengths of PhyTime lies in its ability to accommodate for uncertrainty in the placement of fossil calibration and the use of realistic models of rate variation along the tree. Finally, PhyREX fits the spatial-Lambda-Fleming-Viot model to geo-referenced genetic data. This model is similar to the structured coalescent but assumes that individuals are distributed along a spatial continuum rather than discrete demes. PhyREX can be used to estimate population densities and rates of dispersal. Its output can be processed by treeannotator (from the BEAST package) as well as SPREAD. ### Citations - New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0 S Guindon, JF Dufayard, V Lefort, M Anisimova, W Hordijk, O Gascuel *Systematic Biology* 59 (3), 307-321, 2010. - Accounting for calibration uncertainty: Bayesian molecular dating as a “doubly intractable” problem S Guindon *Systematic Biology* 67 (4), 651–661, 2018. - Demographic inference under the coalescent in a spatial continuum S Guindon, H Guo, D Welch *Theoretical Population Biology* 111, 43-50, 2016. ### Installation To install any program that is part of the PhyML package, type the following command: ```bash sh ./autogen.sh; ``` If you are using a Mac computer or running a Unix-like operating system, you will need to install the packages autoconf automake and pkg-config. On a Mac, the following command should set you up (provided Homebrew is installed on your Mac...): brew install pkg-config autoconf automake; Next, to install any program that is part of the PhyML package, type the following commands: ```bash ./configure --enable-XXXX; make; ``` where XXXX is phyml or phyrex or phytime. To compile a Windows executable, install MinGW and run: ```bash ./configure --enable-win --enable-XXXX; make; ``` To install the MPI version of PhyML, type the following commands: ```bash autoreconf -i; ./configure --enable-phyml-mpi; make; ``` phyml-3.3.20220408/autogen.sh000077500000000000000000000000521422404706600154130ustar00rootroot00000000000000#!/bin/sh autoreconf --force --install -v phyml-3.3.20220408/configure.ac000077500000000000000000000150001422404706600157020ustar00rootroot00000000000000# -*- Autoconf -*- # Process this file with autoconf to produce a configure script. AC_INIT([PhyML],esyscmd([sh -c "echo "3.3." | tr -d '\n' ; echo "20220408" | tr -d '\n'"]),[guindon@lirmm.fr]) AM_INIT_AUTOMAKE([foreign]) AC_CONFIG_SRCDIR([src/main.c],[doc/phyml-manual.tex]) AC_CONFIG_HEADERS([config.h]) AC_DEFINE([UNIX],[1],[Unix tag on]) AC_DEFINE([DEBUG],[1],[Debug tag on]) AC_CANONICAL_HOST AC_PROG_CC([gcc]) # Checks for libraries. AC_CHECK_LIB([m], [log]) # Checks for header files. AC_HEADER_STDC AC_CHECK_HEADERS([float.h stdlib.h string.h unistd.h]) # Checks for typedefs, structures, and compiler characteristics. AC_C_CONST AC_C_INLINE AC_TYPE_SIZE_T # Check for presence of LaTeX and pdfLaTeX AC_CHECK_PROGS([PDFLATEX], pdflatex, [/bin/false]) AC_CHECK_PROGS([LATEX], latex, [/bin/false]) if test "x$PDFLATEX" = x/bin/false || test "x$LATEX" = x/bin/false; then enable_docs=no AC_MSG_WARN([Documentation will not be built!]) fi AM_CONDITIONAL([HAVE_PDFLATEX], test -n "$PDFLATEX") # Checks for library functions. AC_FUNC_MALLOC AC_FUNC_REALLOC AC_FUNC_SETVBUF_REVERSED AC_FUNC_VPRINTF AC_CHECK_FUNCS([floor pow rint sqrt strchr strstr]) dnl DEFAULT_VECTOR_FLAG= dnl DEFAULT_VECTOR_FLAG="-mavx -mfma" dnl DEFAULT_VECTOR_FLAG="-mavx2" dnl DEFAULT_VECTOR_FLAG=-msse4.2 DEFAULT_VECTOR_FLAG="-march=native" dnl DEFAULT_VECTOR_FLAG="-march=skylake -mprefer-vector-width=256" AC_ARG_ENABLE([native], [AS_HELP_STRING([--disable-native], [Remove AVX or SSE compilation options.])]) AS_IF([test "x$enable_native" = "xno"], [VECTOR_FLAG=], [VECTOR_FLAG="${DEFAULT_VECTOR_FLAG}"]) LDFLAGS="${ARGC_flag}" dnl Uncomment two rows below when compiling rwarp (need to have libtool installed) dnl LT_INIT dnl AC_PROG_LIBTOOL dnl DEFAULT_C_FLAG="-fno-inline-functions -fno-inline-functions-called-once -fno-optimize-sibling-calls -O2 -fno-omit-frame-pointer -g ${VECTOR_FLAG}" DEFAULT_C_FLAG="-std=c99 -O3 -fomit-frame-pointer -funroll-loops -Wall -Winline -finline ${VECTOR_FLAG}" dnl DEFAULT_C_FLAG="-O2 ${VECTOR_FLAG}" dnl DEFAULT_C_FLAG="${VECTOR_FLAG}" dnl DEFAULT_C_FLAG="-pthread -O3 -g -ffunction-sections -fdata-sections" AC_ARG_ENABLE([debug], [AS_HELP_STRING([--enable-debug], [Remove optimization options and add debug informations.])]) AS_IF([test "x$enable_debug" = "xyes"], [CFLAGS="-ansi ${VECTOR_FLAG} -pedantic -Wall -std=c99 -O0 -g -Wuninitialized -Wunused -Wunreachable-code"], [CFLAGS="${DEFAULT_C_FLAG}"]) AC_ARG_ENABLE([safemode], AS_HELP_STRING([--enable-safemode],[Enable safe mode.])) AS_IF([test "x$enable_safemode" = "xyes"], [CFLAGS="-Wall -DSAFEMODE -O3 ${VECTOR_FLAG}"], [AS_IF([test "x$enable_debug" = "xno"],[CFLAGS="${DEFAULT_C_FLAG}"])]) AC_ARG_ENABLE([gprof], [AS_HELP_STRING([--enable-gprof], [Remove optimization options and add profiling informations.])]) AS_IF([test "x$enable_gprof" = "xyes"], [CFLAGS="-g -pg -fno-inline -fprofile-arcs -ftest-coverage ${VECTOR_FLAG}"], [AS_IF([test "x$enable_debug" = "xno"],[CFLAGS="${DEFAULT_C_FLAG}"])]) AC_ARG_ENABLE([phyml-mpi], [AS_HELP_STRING([--enable-phyml-mpi], [Compile with mpicc instead of gcc.])]) AS_IF([test "x$enable_phyml_mpi" = "xyes"],[CC="mpicc"]) AS_IF([test "x$enable_phyml_mpi" = "xyes"],AC_DEFINE([MPI],[1],[MPI tag on])) AM_CONDITIONAL([WANT_MPI], [test "$enable_phyml_mpi" = yes]) AC_ARG_ENABLE([win], [AS_HELP_STRING([--enable-win], [Compile with mingw instead of gcc.])]) AS_IF([test "x$enable_win" = "xyes"],[CC="i686-w64-mingw32-gcc"]) AS_IF([test "x$enable_win" = "xyes"],AC_DEFINE([WIN32],[1],[WIN32 tag on])) AM_CONDITIONAL([WANT_WIN], [test "$enable_win" = yes]) AC_ARG_ENABLE([phytime],[AS_HELP_STRING([--enable-phytime],[Compile PhyTime])],[phytime=yes],[phytime=no]) AM_CONDITIONAL([WANT_PHYTIME], [test "$phytime" = yes]) if test "$phytime" = yes; then AC_DEFINE([PHYTIME],[1],[PHYTIME tag on]) fi AC_ARG_ENABLE([phyml],[AS_HELP_STRING([--enable-phyml],[Compile PhyML])],[phyml=yes],[phyml=no]) AM_CONDITIONAL([WANT_PHYML], [test "$phyml" = yes]) if test "$phyml" = yes; then AC_DEFINE([PHYML],[1],[PHYML tag on]) fi AC_ARG_ENABLE([rwrap],[AS_HELP_STRING([--enable-rwrap],[Compile Rwrap])],[rwrap=yes],[rwrap=no]) AM_CONDITIONAL([WANT_RWRAP], [test "$rwrap" = yes]) if test "$rwrap" = yes; then AC_DEFINE([RWRAP],[1],[RWRAP tag on]) fi AC_ARG_ENABLE([rf],[AS_HELP_STRING([--enable-rf],[Compile RF])],[rf=yes],[rf=no]) AM_CONDITIONAL([WANT_RF], [test "$rf" = yes]) if test "$rf" = yes; then AC_DEFINE([RF],[1],[RF tag on]) fi AC_ARG_ENABLE([test],[AS_HELP_STRING([--enable-test],[Compile test])],[test=yes],[test=no]) AM_CONDITIONAL([WANT_TEST], [test "$test" = yes]) if test "$test" = yes; then AC_DEFINE([TEST],[1],[TEST tag on]) fi AC_ARG_ENABLE([evolve],[AS_HELP_STRING([--enable-evolve],[Compile evolve])],[evolve=yes],[evolve=no]) AM_CONDITIONAL([WANT_EVOLVE], [test "$evolve" = yes]) if test "$evolve" = yes; then AC_DEFINE([EVOLVE],[1],[EVOLVE tag on]) fi AC_ARG_ENABLE([phyrex],[AS_HELP_STRING([--enable-phyrex],[Compile phyrex])],[phyrex=yes],[phyrex=no]) AM_CONDITIONAL([WANT_PHYREX], [test "$phyrex" = yes]) if test "$phyrex" = yes; then AC_DEFINE([PHYREX],[1],[PHYREX tag on]) fi AC_ARG_ENABLE([phyrexsim],[AS_HELP_STRING([--enable-phyrexsim],[Compile phyrexsim])],[phyrexsim=yes],[phyrexsim=no]) AM_CONDITIONAL([WANT_PHYREXSIM], [test "$phyrexsim" = yes]) if test "$phyrexsim" = yes; then AC_DEFINE([PHYREXSIM],[1],[PHYREXSIM tag on]) fi dnl AS_IF([test "x$enable_phyrex" = "xyes"],[PKG_CHECK_MODULES([GTK], [gtk+-3.0])]) dnl AS_IF([test "x$enable_phyrex" = "xyes"],[PKG_CHECK_MODULES([CAIRO], [cairo])]) dnl AS_IF([test "x$enable_phyrex" = "xyes"],[CFLAGS="${CFLAGS} `pkg-config --cflags --libs gtk+-3.0`"]) dnl AM_PATH_GTK_3_0(,,AC_MSG_ERROR(windoe-default needs GTK+-3)) AC_ARG_ENABLE([date],[AS_HELP_STRING([--enable-date],[Compile date])],[date=yes],[date=no]) AM_CONDITIONAL([WANT_DATE], [test "$date" = yes]) if test "$date" = yes; then AC_DEFINE([DATE],[1],[DATE tag on]) fi if test "$phytime" = no; then if test "$rwrap" = no; then if test "$test" = no; then if test "$rf" = no; then if test "$evolve" = no; then if test "$phyrex" = no; then if test "$phyrexsim" = no; then if test "$date" = no; then AC_DEFINE([PHYML],[1],[PHYML tag on]) fi fi fi fi fi fi fi fi AC_CONFIG_FILES([Makefile src/Makefile doc/Makefile]) AC_OUTPUT phyml-3.3.20220408/doc/000077500000000000000000000000001422404706600141625ustar00rootroot00000000000000phyml-3.3.20220408/doc/Makefile.am000077500000000000000000000013041422404706600162170ustar00rootroot00000000000000docdir = $(datadir)/doc docfiles = phyml-manual.tex ./fig/phyrexlog.pdf ./fig/phytimetrace.pdf if HAVE_PDFLATEX docfiles += phyml-manual.pdf endif MANNAME = phyml-manual MANTEXSRC = $(MANNAME).tex MANIDX = $(MANNAME).idx CLEANFILES = $(MANNAME).pdf $(MANNAME).log $(MANNAME).idx $(MANNAME).out \ $(MANNAME).toc $(MANNAME).aux $(MANNAME).ilg $(MANNAME).ind $(MANNAME).dvi latex: $(MANTEXSRC) latex $< pdflatex: $(MANTEXSRC) pdflatex $< makeindex: $(MANTEXSRC) makeindex $< phyml-manual.pdf: intro pdflatex makeindex # phyml-manual.pdf: intro pdflatex @echo "" @echo "Done." intro: @echo "" @echo "" @echo ".: Building documentation :." @echo "" @echo "" dist_doc_DATA = $(docfiles) phyml-3.3.20220408/doc/fig/000077500000000000000000000000001422404706600147275ustar00rootroot00000000000000phyml-3.3.20220408/doc/fig/phyrexlog.pdf000077500000000000000000016114261422404706600174610ustar00rootroot00000000000000%PDF-1.5 % 3 0 obj << /Length 4 0 R /Filter /FlateDecode >> stream x+ T(2P0034R016WR  DYX($r'(+W*s! endstream endobj 4 0 obj 71 endobj 2 0 obj << /ExtGState << /a0 << /CA 1 /ca 1 >> >> /XObject << /x5 5 0 R >> >> endobj 6 0 obj << /Type /Page /Parent 1 0 R /MediaBox [ 0 0 437.52 183.12 ] /Contents 3 0 R /Group << /Type /Group /S /Transparency /I true /CS /DeviceRGB >> /Resources 2 0 R >> endobj 5 0 obj << /Length 8 0 R /Filter /FlateDecode /Type /XObject /Subtype /Form /BBox [ 0 0 438 184 ] /Resources 7 0 R >> stream x+ T(2P00Q01635R50342T(JUW*J(ԁ@L= \.Db K|@ Цy endstream endobj 8 0 obj 78 endobj 7 0 obj << /ExtGState << /a0 << /CA 1 /ca 1 >> >> /XObject << /x9 9 0 R >> >> endobj 9 0 obj << /Length 10 0 R /Filter /FlateDecode /Type /XObject /Subtype /Image /Width 1823 /Height 763 /ColorSpace /DeviceRGB /Interpolate true /BitsPerComponent 8 >> stream xtY[S0 ֛5ޝ{N4eWWW]2 eAId˶,S`1SvV:3"22Rʪ[;d,+)%%%HRPX/#^(I(U(a(mJ!J'#PMȈ |ATDt &670&.1^(I(U**+ )y((((`JiiiQ$1%7 8 8\R\&@B CiC*)) Ԣ &%%%EEX痾EvD)'R+,"ҌM80stEDe4` H_(AU][KiFF)`/, FJ",19xߑHC£"X'YmllNL ,dI$y9e)bIQUa:AiA@wJJJrss;UTTZa^1nZqh+/]{<mOѻ"HAaofv.f/SKkj$H$8??"/?$;'Xc 64jcxOqSv;KC iaPZZ޵s {Ӱ$x۴4Rib=<yyTsuB|le222ģ$o .8$F@#Q1q> E숍mfO_59{eŸDŽYDgdE bB&+ 0ٿl$^[o[ 6WY%U`0u޻~uꋂ^rObYb F.\8wܴٳgUģ$^}B>8|?|ǍXf2f +**xWII'L޼ʵ/:=emu9yo\k4n~,U(٤gL?UnMLhTuaK3\0q#~e0RLMSMLg#sΕr/N;6Zts,TεfE ;jcɪonxpɩ;&񍍍<q[ņ\Oܸtݵضm4gxTZm`0{={ ?w;Jk5ߔn~ bjZ-%Ay I> y~e_a>'OT̳%%a!ﴫ eU3＝`=RZZ]A첝)[DGg :ncЮyWڳыg '$fMjm=KN2 >[p1yq>ڶ"NiwY$U];{!= 9~OX]~|3_HIH3[pgޞG 0'&[5\ #;mS/33Sߣ(英7Wn3zznxoliVcVwH~ƛZ\\ YwvvW+++)nV|Ʉ zc[7䌆31ʼntЩѧ5vΝ[]Q!t`T}V.N^_@>}㡭W]}( &\)))/^7orϟOQnhh( 5mjewޑKvN9u|\f[{8׮]\3uwwgddRXX@tƛZVVm-hufjPBpWTh[ɡѽ8… |dPsΉކ`˴9[:u^NӘAxep;7RD2~ر M-*m'4~`}\`󼙅((.3g7Wɹr 'NԈTOXXC'N̪ 9딦פ7jj'մQ:*;!β8gްaÚ5k ˫\3ĬJ*ڑkX*&]~~~n-[R[[ԻWLLLNN΄i0e;ȫbm̸T#psˡ/uϞ=c_Zݕ1nW?IְLzZ1cɓ͛7oӦMʡZb#樆sR{@ŨwpG2Ӫ+C/..^f Օ֮]KG/ՏN7nCLΪ~UzϧS^p!E`wfh6GT'\s:v|Eٳ3g@رc֬Y_ʡZKӦMr|hX؉sFF2GښoŵuTVT$վHIo`nWPJ`Buѫ6\533[bj,_ 󠠠4uU"66Feh|L윓10M{/垍CVJ:p5-S/(BGG=PG ܑA,p4Cm?}.@m+ sMY3 whz{{)o{j<0q#SU_6rhD dkܑ\믿e˖\rq~ժUK.]hŋ\g=gM6m۶Mi#}k.BE7!+FRhUP333ˉ&-ѨTyttܹs̙͛7ϛ7O# qVU+kRoCdA lTl$GYԩIY-5ò+|x;7\飆+UQ3ׯ_'''ocǎ]z5))RZUcccJ`O? 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either % version 1 of the License, or any later version. % =============================================================== % Name and version information of the main mbs file: % \ProvidesFile{merlin.mbs}[2002/10/21 4.05 (PWD, AO, DPC)] % For use with BibTeX version 0.99a or later %------------------------------------------------------------------- % This bibliography style file is intended for texts in ENGLISH % This is a numerical citation style, and as such is standard LaTeX. % It requires no extra package to interface to the main text. % The form of the \bibitem entries is % \bibitem{key}... % Usage of \cite is as follows: % \cite{key} ==>> [#] % \cite[chap. 2]{key} ==>> [#, chap. 2] % where # is a number determined by the ordering in the reference list. % The order in the reference list is that by which the works were originally % cited in the text, or that in the database. %--------------------------------------------------------------------- ENTRY { address archive author booktitle chapter edition editor eprint howpublished institution journal key month note number organization pages publisher school series title type url volume year } {} { label } INTEGERS { output.state before.all mid.sentence after.sentence after.block } FUNCTION {init.state.consts} { #0 'before.all := #1 'mid.sentence := #2 'after.sentence := #3 'after.block := } STRINGS { s t} FUNCTION {output.nonnull} { 's := output.state mid.sentence = { ", " * write$ } { output.state after.block = { add.period$ write$ newline$ "\newblock " write$ } { output.state before.all = 'write$ { add.period$ " " * write$ } if$ } if$ mid.sentence 'output.state := } if$ s } FUNCTION {output} { duplicate$ empty$ 'pop$ 'output.nonnull if$ } FUNCTION {output.check} { 't := duplicate$ empty$ { pop$ "empty " t * " in " * cite$ * warning$ } 'output.nonnull if$ } FUNCTION {fin.entry} { add.period$ write$ newline$ } FUNCTION {new.block} { output.state before.all = 'skip$ { after.block 'output.state := } if$ } FUNCTION {new.sentence} { output.state after.block = 'skip$ { output.state before.all = 'skip$ { after.sentence 'output.state := } if$ } if$ } FUNCTION {add.blank} { " " * before.all 'output.state := } FUNCTION {date.block} { new.block } FUNCTION {not} { { #0 } { #1 } if$ } FUNCTION {and} { 'skip$ { pop$ #0 } if$ } FUNCTION {or} { { pop$ #1 } 'skip$ if$ } FUNCTION {new.block.checka} { empty$ 'skip$ 'new.block if$ } FUNCTION {new.block.checkb} { empty$ swap$ empty$ and 'skip$ 'new.block if$ } FUNCTION {new.sentence.checka} { empty$ 'skip$ 'new.sentence if$ } FUNCTION {new.sentence.checkb} { empty$ swap$ empty$ and 'skip$ 'new.sentence if$ } FUNCTION {field.or.null} { duplicate$ empty$ { pop$ "" } 'skip$ if$ } FUNCTION {emphasize} { duplicate$ empty$ { pop$ "" } { "\emph{" swap$ * "}" * } if$ } FUNCTION {bolden} { duplicate$ empty$ { pop$ "" } { "\textbf{" swap$ * "}" * } if$ } FUNCTION {tie.or.space.prefix} { duplicate$ text.length$ #3 < { "~" } { " " } if$ swap$ } FUNCTION {capitalize} { "u" change.case$ "t" change.case$ } FUNCTION {space.word} { " " swap$ * " " * } % Here are the language-specific definitions for explicit words. % Each function has a name bbl.xxx where xxx is the English word. % The language selected here is ENGLISH FUNCTION {bbl.and} { "and"} FUNCTION {bbl.etal} { "et~al." } FUNCTION {bbl.editors} { "eds." } FUNCTION {bbl.editor} { "ed." } FUNCTION {bbl.edby} { "edited by" } FUNCTION {bbl.edition} { "edn." } FUNCTION {bbl.volume} { "vol." } FUNCTION {bbl.of} { "of" } FUNCTION {bbl.number} { "no." } FUNCTION {bbl.nr} { "no." } FUNCTION {bbl.in} { "in" } FUNCTION {bbl.pages} { "" } FUNCTION {bbl.page} { "" } FUNCTION {bbl.chapter} { "chap." } FUNCTION {bbl.techrep} { "Tech. Rep." } FUNCTION {bbl.mthesis} { "Master's thesis" } FUNCTION {bbl.phdthesis} { "Ph.D. thesis" } MACRO {jan} {"Jan."} MACRO {feb} {"Feb."} MACRO {mar} {"Mar."} MACRO {apr} {"Apr."} MACRO {may} {"May"} MACRO {jun} {"Jun."} MACRO {jul} {"Jul."} MACRO {aug} {"Aug."} MACRO {sep} {"Sep."} MACRO {oct} {"Oct."} MACRO {nov} {"Nov."} MACRO {dec} {"Dec."} %------------------------------------------------------------------- % Begin module: % \ProvidesFile{physjour.mbs}[2002/01/14 2.2 (PWD)] MACRO {aa}{"Astron. \& Astrophys."} MACRO {aasup}{"Astron. \& Astrophys. Suppl. Ser."} MACRO {aj} {"Astron. J."} MACRO {aph} {"Acta Phys."} MACRO {advp} {"Adv. Phys."} MACRO {ajp} {"Amer. J. Phys."} MACRO {ajm} {"Amer. J. Math."} MACRO {amsci} {"Amer. Sci."} MACRO {anofd} {"Ann. Fluid Dyn."} MACRO {am} {"Ann. Math."} MACRO {ap} {"Ann. Phys. (NY)"} MACRO {adp} {"Ann. Phys. (Leipzig)"} MACRO {ao} {"Appl. Opt."} MACRO {apl} {"Appl. Phys. Lett."} MACRO {app} {"Astroparticle Phys."} MACRO {apj} {"Astrophys. 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Phys."} MACRO {jne} {"J. Nucl. Energy"} MACRO {jnec} {"J. Nucl. Energy, C: Plasma Phys., Accelerators, Thermonucl. Res."} MACRO {jnm} {"J. Nucl. Mat."} MACRO {jpc} {"J. Phys. Chem."} MACRO {jpp} {"J. Plasma Phys."} MACRO {jpsj} {"J. Phys. Soc. Japan"} MACRO {jsi} {"J. Sci. Instrum."} MACRO {jvst} {"J. Vac. Sci. \& Tech."} MACRO {nat} {"Nature"} MACRO {nature} {"Nature"} MACRO {nedf} {"Nucl. Eng. \& Design/Fusion"} MACRO {nf} {"Nucl. Fusion"} MACRO {nim} {"Nucl. Inst. \& Meth."} MACRO {nimpr} {"Nucl. Inst. \& Meth. in Phys. Res."} MACRO {np} {"Nucl. Phys."} MACRO {npb} {"Nucl. Phys. B"} MACRO {nt/f} {"Nucl. Tech./Fusion"} MACRO {npbpc} {"Nucl. Phys. B (Proc. Suppl.)"} MACRO {inc} {"Nuovo Cimento"} MACRO {nc} {"Nuovo Cimento"} MACRO {pf} {"Phys. Fluids"} MACRO {pfa} {"Phys. Fluids A: Fluid Dyn."} MACRO {pfb} {"Phys. Fluids B: Plasma Phys."} MACRO {pl} {"Phys. Lett."} MACRO {pla} {"Phys. Lett. A"} MACRO {plb} {"Phys. Lett. B"} MACRO {prep} {"Phys. Rep."} MACRO {pnas} {"Proc. Nat. Acad. Sci. USA"} MACRO {pp} {"Phys. Plasmas"} MACRO {ppcf} {"Plasma Phys. \& Controlled Fusion"} MACRO {phitrsl} {"Philos. Trans. Roy. Soc. London"} MACRO {prl} {"Phys. Rev. Lett."} MACRO {pr} {"Phys. Rev."} MACRO {physrev} {"Phys. Rev."} MACRO {pra} {"Phys. Rev. A"} MACRO {prb} {"Phys. Rev. B"} MACRO {prc} {"Phys. Rev. C"} MACRO {prd} {"Phys. Rev. D"} MACRO {pre} {"Phys. Rev. E"} MACRO {ps} {"Phys. Scripta"} MACRO {procrsl} {"Proc. Roy. Soc. London"} MACRO {rmp} {"Rev. Mod. Phys."} MACRO {rsi} {"Rev. Sci. Inst."} MACRO {science} {"Science"} MACRO {sciam} {"Sci. Am."} MACRO {sam} {"Stud. Appl. Math."} MACRO {sjpp} {"Sov. J. Plasma Phys."} MACRO {spd} {"Sov. Phys.--Doklady"} MACRO {sptp} {"Sov. Phys.--Tech. Phys."} MACRO {spu} {"Sov. Phys.--Uspeki"} MACRO {st} {"Sky and Telesc."} % End module: physjour.mbs %------------------------------------------------------------------- % Begin module: % \ProvidesFile{geojour.mbs}[2002/07/10 2.0h (PWD)] MACRO {aisr} {"Adv. Space Res."} MACRO {ag} {"Ann. Geophys."} MACRO {anigeo} {"Ann. Geofis."} MACRO {angl} {"Ann. Glaciol."} MACRO {andmet} {"Ann. d. Meteor."} MACRO {andgeo} {"Ann. d. Geophys."} MACRO {andphy} {"Ann. Phys.-Paris"} MACRO {afmgb} {"Arch. Meteor. Geophys. Bioklimatol."} MACRO {atph} {"Atm\'osphera"} MACRO {aao} {"Atmos. Ocean"} MACRO {ass}{"Astrophys. Space Sci."} MACRO {atenv} {"Atmos. Environ."} MACRO {aujag} {"Aust. J. Agr. Res."} MACRO {aumet} {"Aust. Meteorol. Mag."} MACRO {blmet} {"Bound.-Lay. Meteorol."} MACRO {bams} {"Bull. Amer. Meteorol. Soc."} MACRO {cch} {"Clim. Change"} MACRO {cdyn} {"Clim. Dynam."} MACRO {cbul} {"Climatol. Bull."} MACRO {cap} {"Contrib. Atmos. Phys."} MACRO {dsr} {"Deep-Sea Res."} MACRO {dhz} {"Dtsch. Hydrogr. Z."} MACRO {dao} {"Dynam. Atmos. Oceans"} MACRO {eco} {"Ecology"} MACRO {empl}{"Earth, Moon and Planets"} MACRO {envres} {"Environ. Res."} MACRO {envst} {"Environ. Sci. Technol."} MACRO {ecms} {"Estuarine Coastal Mar. Sci."} MACRO {expa}{"Exper. Astron."} MACRO {geoint} {"Geofis. Int."} MACRO {geopub} {"Geofys. Publ."} MACRO {geogeo} {"Geol. Geofiz."} MACRO {gafd} {"Geophys. Astrophys. Fluid Dyn."} MACRO {gfd} {"Geophys. Fluid Dyn."} MACRO {geomag} {"Geophys. Mag."} MACRO {georl} {"Geophys. Res. Lett."} MACRO {grl} {"Geophys. Res. Lett."} MACRO {ga} {"Geophysica"} MACRO {gs} {"Geophysics"} MACRO {ieeetap} {"IEEE Trans. Antenn. Propag."} MACRO {ijawp} {"Int. J. Air Water Pollut."} MACRO {ijc} {"Int. J. Climatol."} MACRO {ijrs} {"Int. J. Remote Sens."} MACRO {jam} {"J. Appl. Meteorol."} MACRO {jaot} {"J. Atmos. Ocean. Technol."} MACRO {jatp} {"J. Atmos. Terr. Phys."} MACRO {jastp} {"J. Atmos. Solar-Terr. Phys."} MACRO {jce} {"J. Climate"} MACRO {jcam} {"J. Climate Appl. Meteor."} MACRO {jcm} {"J. Climate Meteor."} MACRO {jcy} {"J. Climatol."} MACRO {jgr} {"J. Geophys. Res."} MACRO {jga} {"J. Glaciol."} MACRO {jh} {"J. Hydrol."} MACRO {jmr} {"J. Mar. Res."} MACRO {jmrj} {"J. Meteor. Res. Japan"} MACRO {jm} {"J. Meteor."} MACRO {jpo} {"J. Phys. Oceanogr."} MACRO {jra} {"J. Rech. Atmos."} MACRO {jaes} {"J. Aeronaut. Sci."} MACRO {japca} {"J. Air Pollut. Control Assoc."} MACRO {jas} {"J. Atmos. Sci."} MACRO {jmts} {"J. Mar. Technol. Soc."} MACRO {jmsj} {"J. Meteorol. Soc. Japan"} MACRO {josj} {"J. Oceanogr. Soc. Japan"} MACRO {jwm} {"J. Wea. Mod."} MACRO {lao} {"Limnol. Oceanogr."} MACRO {mwl} {"Mar. Wea. Log"} MACRO {mau} {"Mausam"} MACRO {meteor} {"``Meteor'' Forschungsergeb."} MACRO {map} {"Meteorol. Atmos. Phys."} MACRO {metmag} {"Meteor. Mag."} MACRO {metmon} {"Meteor. Monogr."} MACRO {metrun} {"Meteor. Rundsch."} MACRO {metzeit} {"Meteor. Z."} MACRO {metgid} {"Meteor. Gidrol."} MACRO {mwr} {"Mon. Weather Rev."} MACRO {nwd} {"Natl. Weather Dig."} MACRO {nzjmfr} {"New Zeal. J. Mar. Freshwater Res."} MACRO {npg} {"Nonlin. Proc. Geophys."} MACRO {om} {"Oceanogr. Meteorol."} MACRO {ocac} {"Oceanol. Acta"} MACRO {oceanus} {"Oceanus"} MACRO {paleoc} {"Paleoceanography"} MACRO {pce} {"Phys. Chem. Earth"} MACRO {pmg} {"Pap. Meteor. Geophys."} MACRO {ppom} {"Pap. Phys. Oceanogr. Meteor."} MACRO {physzeit} {"Phys. Z."} MACRO {pps} {"Planet. Space Sci."} MACRO {pss} {"Planet. Space Sci."} MACRO {pag} {"Pure Appl. Geophys."} MACRO {qjrms} {"Quart. J. Roy. Meteorol. Soc."} MACRO {quatres} {"Quat. Res."} MACRO {rsci} {"Radio Sci."} MACRO {rse} {"Remote Sens. Environ."} MACRO {rgeo} {"Rev. Geophys."} MACRO {rgsp} {"Rev. Geophys. Space Phys."} MACRO {rdgeo} {"Rev. Geofis."} MACRO {revmeta} {"Rev. Meteorol."} MACRO {sgp}{"Surveys in Geophys."} MACRO {sp} {"Solar Phys."} MACRO {ssr} {"Space Sci. Rev."} MACRO {tellus} {"Tellus"} MACRO {tac} {"Theor. Appl. Climatol."} MACRO {tagu} {"Trans. Am. Geophys. Union (EOS)"} MACRO {wrr} {"Water Resour. Res."} MACRO {weather} {"Weather"} MACRO {wafc} {"Weather Forecast."} MACRO {ww} {"Weatherwise"} MACRO {wmob} {"WMO Bull."} MACRO {zeitmet} {"Z. Meteorol."} % End module: geojour.mbs %------------------------------------------------------------------- % Begin module: % \ProvidesFile{photjour.mbs}[1999/02/24 2.0b (PWD)] MACRO {appopt} {"Appl. Opt."} MACRO {bell} {"Bell Syst. Tech. J."} MACRO {ell} {"Electron. Lett."} MACRO {jasp} {"J. Appl. Spectr."} MACRO {jqe} {"IEEE J. Quantum Electron."} MACRO {jlwt} {"J. Lightwave Technol."} MACRO {jmo} {"J. Mod. Opt."} MACRO {josa} {"J. Opt. Soc. America"} MACRO {josaa} {"J. Opt. Soc. Amer.~A"} MACRO {josab} {"J. Opt. Soc. Amer.~B"} MACRO {jdp} {"J. Phys. (Paris)"} MACRO {oc} {"Opt. Commun."} MACRO {ol} {"Opt. Lett."} MACRO {phtl} {"IEEE Photon. Technol. Lett."} MACRO {pspie} {"Proc. Soc. Photo-Opt. Instrum. Eng."} MACRO {sse} {"Solid-State Electron."} MACRO {sjot} {"Sov. J. Opt. Technol."} MACRO {sjqe} {"Sov. J. Quantum Electron."} MACRO {sleb} {"Sov. Phys.--Leb. Inst. Rep."} MACRO {stph} {"Sov. Phys.--Techn. Phys."} MACRO {stphl} {"Sov. Techn. Phys. Lett."} MACRO {vr} {"Vision Res."} MACRO {zph} {"Z. f. Physik"} MACRO {zphb} {"Z. f. Physik~B"} MACRO {zphd} {"Z. f. Physik~D"} MACRO {CLEO} {"CLEO"} MACRO {ASSL} {"Adv. Sol.-State Lasers"} MACRO {OSA} {"OSA"} % End module: photjour.mbs %% Copyright 1994-2002 Patrick W Daly MACRO {acmcs} {"ACM Comput. Surv."} MACRO {acta} {"Acta Inf."} MACRO {cacm} {"Commun. ACM"} MACRO {ibmjrd} {"IBM J. Res. Dev."} MACRO {ibmsj} {"IBM Syst.~J."} MACRO {ieeese} {"IEEE Trans. Software Eng."} MACRO {ieeetc} {"IEEE Trans. Comput."} MACRO {ieeetcad} {"IEEE Trans. Comput. Aid. Des."} MACRO {ipl} {"Inf. Process. Lett."} MACRO {jacm} {"J.~ACM"} MACRO {jcss} {"J.~Comput. Syst. Sci."} MACRO {scp} {"Sci. Comput. Program."} MACRO {sicomp} {"SIAM J. Comput."} MACRO {tocs} {"ACM Trans. Comput. Syst."} MACRO {tods} {"ACM Trans. Database Syst."} MACRO {tog} {"ACM Trans. Graphic."} MACRO {toms} {"ACM Trans. Math. Software"} MACRO {toois} {"ACM Trans. Office Inf. Syst."} MACRO {toplas} {"ACM Trans. Progr. Lang. Syst."} MACRO {tcs} {"Theor. Comput. Sci."} FUNCTION {bibinfo.check} { swap$ duplicate$ missing$ { pop$ pop$ "" } { duplicate$ empty$ { swap$ pop$ } { swap$ "\bibinfo{" swap$ * "}{" * swap$ * "}" * } if$ } if$ } FUNCTION {bibinfo.warn} { swap$ duplicate$ missing$ { swap$ "missing " swap$ * " in " * cite$ * warning$ pop$ "" } { duplicate$ empty$ { swap$ "empty " swap$ * " in " * cite$ * warning$ } { swap$ "\bibinfo{" swap$ * "}{" * swap$ * "}" * } if$ } if$ } FUNCTION {format.eprint} { eprint duplicate$ empty$ 'skip$ { "\eprint" archive empty$ 'skip$ { "[" * archive * "]" * } if$ "{" * swap$ * "}" * } if$ } FUNCTION {format.url} { url empty$ { "" } { "\urlprefix\url{" url * "}" * } if$ } STRINGS { bibinfo} INTEGERS { nameptr namesleft numnames } FUNCTION {format.names} { 'bibinfo := duplicate$ empty$ 'skip$ { 's := "" 't := #1 'nameptr := s num.names$ 'numnames := numnames 'namesleft := { namesleft #0 > } { s nameptr "{vv~}{ll}{, f.}{, jj}" format.name$ bibinfo bibinfo.check 't := nameptr #1 > { nameptr #1 #1 + = numnames #5 > and { "others" 't := #1 'namesleft := } 'skip$ if$ namesleft #1 > { ", " * t * } { s nameptr "{ll}" format.name$ duplicate$ "others" = { 't := } { pop$ } if$ t "others" = { " " * bbl.etal emphasize * } { "\&" space.word * t * } if$ } if$ } 't if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ } if$ } FUNCTION {format.names.ed} { format.names } FUNCTION {format.authors} { author "author" format.names } FUNCTION {get.bbl.editor} { editor num.names$ #1 > 'bbl.editors 'bbl.editor if$ } FUNCTION {format.editors} { editor "editor" format.names duplicate$ empty$ 'skip$ { " " * get.bbl.editor "(" swap$ * ")" * * } if$ } FUNCTION {format.note} { note empty$ { "" } { note #1 #1 substring$ duplicate$ "{" = 'skip$ { output.state mid.sentence = { "l" } { "u" } if$ change.case$ } if$ note #2 global.max$ substring$ * "note" bibinfo.check } if$ } FUNCTION {format.title} { title duplicate$ empty$ 'skip$ { "t" change.case$ } if$ "title" bibinfo.check } FUNCTION {output.bibitem} { newline$ "\bibitem{" write$ cite$ write$ "}" write$ newline$ "" before.all 'output.state := } FUNCTION {n.dashify} { 't := "" { t empty$ not } { t #1 #1 substring$ "-" = { t #1 #2 substring$ "--" = not { "--" * t #2 global.max$ substring$ 't := } { { t #1 #1 substring$ "-" = } { "-" * t #2 global.max$ substring$ 't := } while$ } if$ } { t #1 #1 substring$ * t #2 global.max$ substring$ 't := } if$ } while$ } FUNCTION {word.in} { bbl.in capitalize " " * } FUNCTION {format.date} { "" duplicate$ empty$ year "year" bibinfo.check duplicate$ empty$ { swap$ 'skip$ { "there's a month but no year in " cite$ * warning$ } if$ * } { swap$ 'skip$ { swap$ " " * swap$ } if$ * } if$ duplicate$ empty$ 'skip$ { before.all 'output.state := " (" swap$ * ")" * } if$ } FUNCTION {format.btitle} { title "title" bibinfo.check duplicate$ empty$ 'skip$ { emphasize } if$ } FUNCTION {either.or.check} { empty$ 'pop$ { "can't use both " swap$ * " fields in " * cite$ * warning$ } if$ } FUNCTION {format.bvolume} { volume empty$ { "" } { bbl.volume volume tie.or.space.prefix "volume" bibinfo.check * * series "series" bibinfo.check duplicate$ empty$ 'pop$ { swap$ bbl.of space.word * swap$ emphasize * } if$ "volume and number" number either.or.check } if$ } FUNCTION {format.number.series} { volume empty$ { number empty$ { series field.or.null } { series empty$ { number "number" bibinfo.check } { output.state mid.sentence = { bbl.number } { bbl.number capitalize } if$ number tie.or.space.prefix "number" bibinfo.check * * bbl.in space.word * series "series" bibinfo.check * } if$ } if$ } { "" } if$ } FUNCTION {format.edition} { edition duplicate$ empty$ 'skip$ { output.state mid.sentence = { "l" } { "t" } if$ change.case$ "edition" bibinfo.check " " * bbl.edition * } if$ } INTEGERS { multiresult } FUNCTION {multi.page.check} { 't := #0 'multiresult := { multiresult not t empty$ not and } { t #1 #1 substring$ duplicate$ "-" = swap$ duplicate$ "," = swap$ "+" = or or { #1 'multiresult := } { t #2 global.max$ substring$ 't := } if$ } while$ multiresult } FUNCTION {format.pages} { pages duplicate$ empty$ 'skip$ { duplicate$ multi.page.check { n.dashify } { } if$ "pages" bibinfo.check } if$ } FUNCTION {format.journal.pages} { pages duplicate$ empty$ 'pop$ { swap$ duplicate$ empty$ { pop$ pop$ format.pages } { ", " * swap$ n.dashify "pages" bibinfo.check * } if$ } if$ } FUNCTION {format.vol.num.pages} { volume field.or.null duplicate$ empty$ 'skip$ { "volume" bibinfo.check } if$ bolden format.journal.pages } FUNCTION {format.chapter.pages} { chapter empty$ 'format.pages { type empty$ { bbl.chapter } { type "l" change.case$ "type" bibinfo.check } if$ chapter tie.or.space.prefix "chapter" bibinfo.check * * pages empty$ 'skip$ { ", " * format.pages * } if$ } if$ } FUNCTION {format.booktitle} { booktitle "booktitle" bibinfo.check emphasize } FUNCTION {format.in.ed.booktitle} { format.booktitle duplicate$ empty$ 'skip$ { editor "editor" format.names.ed duplicate$ empty$ 'pop$ { " " * get.bbl.editor "(" swap$ * ") " * * swap$ * } if$ word.in swap$ * } if$ } FUNCTION {empty.misc.check} { author empty$ title empty$ howpublished empty$ month empty$ year empty$ note empty$ and and and and and { "all relevant fields are empty in " cite$ * warning$ } 'skip$ if$ } FUNCTION {format.thesis.type} { type duplicate$ empty$ 'pop$ { swap$ pop$ "t" change.case$ "type" bibinfo.check } if$ } FUNCTION {format.tr.number} { number "number" bibinfo.check type duplicate$ empty$ { pop$ bbl.techrep } 'skip$ if$ "type" bibinfo.check swap$ duplicate$ empty$ { pop$ "t" change.case$ } { tie.or.space.prefix * * } if$ } FUNCTION {format.article.crossref} { key duplicate$ empty$ { pop$ journal duplicate$ empty$ { "need key or journal for " cite$ * " to crossref " * crossref * warning$ } { "journal" bibinfo.check emphasize word.in swap$ * } if$ } { word.in swap$ * " " *} if$ " \cite{" * crossref * "}" * } FUNCTION {format.crossref.editor} { editor #1 "{vv~}{ll}" format.name$ "editor" bibinfo.check editor num.names$ duplicate$ #2 > { pop$ "editor" bibinfo.check " " * bbl.etal emphasize * } { #2 < 'skip$ { editor #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = { "editor" bibinfo.check " " * bbl.etal emphasize * } { " \& " * editor #2 "{vv~}{ll}" format.name$ "editor" bibinfo.check * } if$ } if$ } if$ } FUNCTION {format.book.crossref} { volume duplicate$ empty$ { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ pop$ word.in } { bbl.volume capitalize swap$ tie.or.space.prefix "volume" bibinfo.check * * bbl.of space.word * } if$ editor empty$ editor field.or.null author field.or.null = or { key empty$ { series empty$ { "need editor, key, or series for " cite$ * " to crossref " * crossref * warning$ "" * } { series emphasize * } if$ } { key * } if$ } { format.crossref.editor * } if$ " \cite{" * crossref * "}" * } FUNCTION {format.incoll.inproc.crossref} { editor empty$ editor field.or.null author field.or.null = or { key empty$ { format.booktitle duplicate$ empty$ { "need editor, key, or booktitle for " cite$ * " to crossref " * crossref * warning$ } { word.in swap$ * } if$ } { word.in key * " " *} if$ } { word.in format.crossref.editor * " " *} if$ " \cite{" * crossref * "}" * } FUNCTION {format.org.or.pub} { 't := "" year empty$ { "empty year in " cite$ * warning$ } 'skip$ if$ address empty$ t empty$ and year empty$ and 'skip$ { add.blank "(" * t empty$ { address "address" bibinfo.check * } { t * address empty$ 'skip$ { ", " * address "address" bibinfo.check * } if$ } if$ year empty$ 'skip$ { t empty$ address empty$ and 'skip$ { ", " * } if$ year "year" bibinfo.check * } if$ ")" * } if$ } FUNCTION {format.publisher.address} { publisher "publisher" bibinfo.warn format.org.or.pub } FUNCTION {format.organization.address} { organization "organization" bibinfo.check format.org.or.pub } FUNCTION {article} { output.bibitem format.authors "author" output.check new.block format.title "title" output.check new.block crossref missing$ { journal "journal" bibinfo.check emphasize "journal" output.check add.blank format.vol.num.pages output format.date "year" output.check } { format.article.crossref output.nonnull format.pages output } if$ new.block format.url output new.block format.note output format.eprint output fin.entry } FUNCTION {book} { output.bibitem author empty$ { format.editors "author and editor" output.check add.blank } { format.authors output.nonnull crossref missing$ { "author and editor" editor either.or.check } 'skip$ if$ } if$ new.block format.btitle "title" output.check crossref missing$ { format.bvolume output new.block format.number.series output new.sentence format.publisher.address output } { new.block format.book.crossref output.nonnull format.date "year" output.check } if$ format.edition output new.block format.url output new.block format.note output format.eprint output fin.entry } FUNCTION {booklet} { output.bibitem format.authors output new.block format.title "title" output.check new.block howpublished "howpublished" bibinfo.check output address "address" bibinfo.check output format.date output new.block format.url output new.block format.note output format.eprint output fin.entry } FUNCTION {inbook} { output.bibitem author empty$ { format.editors "author and editor" output.check } { format.authors output.nonnull crossref missing$ { "author and editor" editor either.or.check } 'skip$ if$ } if$ new.block format.btitle "title" output.check crossref missing$ { format.bvolume output format.chapter.pages "chapter and pages" output.check new.block format.number.series output new.sentence format.publisher.address output } { format.chapter.pages "chapter and pages" output.check new.block format.book.crossref output.nonnull format.date "year" output.check } if$ format.edition output new.block format.url output new.block format.note output format.eprint output fin.entry } FUNCTION {incollection} { output.bibitem format.authors "author" output.check new.block format.title "title" output.check new.block crossref missing$ { format.in.ed.booktitle "booktitle" output.check format.bvolume output format.number.series output format.chapter.pages output new.sentence format.publisher.address output format.edition output } { format.incoll.inproc.crossref output.nonnull format.chapter.pages output } if$ new.block format.url output new.block format.note output format.eprint output fin.entry } FUNCTION {inproceedings} { output.bibitem format.authors "author" output.check new.block format.title "title" output.check new.block crossref missing$ { format.in.ed.booktitle "booktitle" output.check format.bvolume output format.number.series output format.pages output new.sentence publisher empty$ { format.organization.address output } { organization "organization" bibinfo.check output format.publisher.address output } if$ } { format.incoll.inproc.crossref output.nonnull format.pages output } if$ new.block format.url output new.block format.note output format.eprint output fin.entry } FUNCTION {conference} { inproceedings } FUNCTION {manual} { output.bibitem author empty$ { organization "organization" bibinfo.check duplicate$ empty$ 'pop$ { output address "address" bibinfo.check output } if$ } { format.authors output.nonnull } if$ new.block format.btitle "title" output.check author empty$ { organization empty$ { address new.block.checka address "address" bibinfo.check output } 'skip$ if$ } { organization address new.block.checkb organization "organization" bibinfo.check output address "address" bibinfo.check output } if$ format.edition output format.date output new.block format.url output new.block format.note output format.eprint output fin.entry } FUNCTION {mastersthesis} { output.bibitem format.authors "author" output.check new.block format.btitle "title" output.check new.block bbl.mthesis format.thesis.type output.nonnull school "school" bibinfo.warn output address "address" bibinfo.check output format.date "year" output.check new.block format.url output new.block format.note output format.eprint output fin.entry } FUNCTION {misc} { output.bibitem format.authors output title howpublished new.block.checkb format.title output howpublished new.block.checka howpublished "howpublished" bibinfo.check output format.date output new.block format.url output new.block format.note output format.eprint output fin.entry empty.misc.check } FUNCTION {phdthesis} { output.bibitem format.authors "author" output.check new.block format.btitle "title" output.check new.block bbl.phdthesis format.thesis.type output.nonnull school "school" bibinfo.warn output address "address" bibinfo.check output format.date "year" output.check new.block format.url output new.block format.note output format.eprint output fin.entry } FUNCTION {proceedings} { output.bibitem editor empty$ { organization "organization" bibinfo.check output } { format.editors output.nonnull } if$ new.block format.btitle "title" output.check format.bvolume output format.number.series output editor empty$ { publisher empty$ 'skip$ { new.sentence format.publisher.address output } if$ } { publisher empty$ { new.sentence format.organization.address output } { new.sentence organization "organization" bibinfo.check output format.publisher.address output } if$ } if$ new.block format.url output new.block format.note output format.eprint output fin.entry } FUNCTION {techreport} { output.bibitem format.authors "author" output.check new.block format.title "title" output.check new.block format.tr.number output.nonnull institution "institution" bibinfo.warn output address "address" bibinfo.check output format.date "year" output.check new.block format.url output new.block format.note output format.eprint output fin.entry } FUNCTION {unpublished} { output.bibitem format.authors "author" output.check new.block format.title "title" output.check format.date output new.block format.url output new.block format.note "note" output.check format.eprint output fin.entry } FUNCTION {default.type} { misc } READ STRINGS { longest.label } INTEGERS { number.label longest.label.width } FUNCTION {initialize.longest.label} { "" 'longest.label := #1 'number.label := #0 'longest.label.width := } FUNCTION {longest.label.pass} { number.label int.to.str$ 'label := number.label #1 + 'number.label := label width$ longest.label.width > { label 'longest.label := label width$ 'longest.label.width := } 'skip$ if$ } EXECUTE {initialize.longest.label} ITERATE {longest.label.pass} FUNCTION {begin.bib} { preamble$ empty$ 'skip$ { preamble$ write$ newline$ } if$ "\begin{thebibliography}{" longest.label * "}" * write$ newline$ "\expandafter\ifx\csname url\endcsname\relax" write$ newline$ " \def\url#1{\texttt{#1}}\fi" write$ newline$ "\expandafter\ifx\csname urlprefix\endcsname\relax\def\urlprefix{URL }\fi" write$ newline$ "\providecommand{\bibinfo}[2]{#2}" write$ newline$ "\providecommand{\eprint}[2][]{\url{#2}}" write$ newline$ } EXECUTE {begin.bib} EXECUTE {init.state.consts} ITERATE {call.type$} FUNCTION 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Yedl|^81dt"}*TS#l.1'i-gRHNҁ6VF90jU<|5UgͬV:2 h"1;ݟD$WΘkD;ˍR+'UxqW\Wډ)22p:@-*EGI=ք`1)ob3H ~$'03hd0#u' T"u|OWJ<}TH,>%x,bHpmԮQA&ٮd f٭mjw E)nOYwwaq7pBz_?3IgT+v=kO fC=}dv:R/]qRf~x'd4b5>1쐀\i:Ш^Gty/)F<ı?7$GNܳ԰\Ȇ(E:|*iԦ—c S7ð MPJx|"-rT֌6큫&wLUirpOm&H0:fqEds.up"=\B%cﬦYf!%l endstream endobj 520 0 obj << /Length 2353 /Filter /FlateDecode >> stream xڥYKs6WHUE4밇jlj׵T` XC)  6JoR=H2=l",mT*Qx\"~9vO ?b9(Rk((HdERγũ{}Z,mgug0>~u˜UL|s*OBw wn*Y-MeS5/C=`a辠QY:4ve<6/,whZf:sYLAe,c57]/IQƷk3*G+k2v h]JRdzx,gy|ܦHI;@+M$?_Ѝ{pi`mG8x"KIAE{7Җ93 ~3.vZ.f "^AkpQs?HN1B`J.avu=f*B4^ӡb ; Rt m*oelbRƟPs)S5ѥر$)PqoϰcG&|?Y~فWރkK{W|"1Ѐu{8\Ge;wlFXBJʀX(3*GL@{"eg <~q?@/m/$:~ Tf}KXV' ƽE5 ?!PIl/JˀMwl`_ՌV(7GX[gv$( fv@qsqjjW;. "b^ 5w[ Bn-=Nz@ Il.HZ#)a$")Xcc427=csX`ʰ K+\lNFW<T|GuNziLA<6ֆԈ fcW ׁi)ZFˀO~E~.q@ Q\GB9.v4, Yp"#k,.];qE05Gp iS,j_ǟ-+de&N\'_6 kIԊBaҖ gp~`b/5!kifvk^H !MTt0 BېȄR7B.BtZ-q,*<- ;|@ #w#5U|1{"&O]7c  8}AP -2_ۀ< pTp & .= k,!zºNأ#\(sF1 ʼD"F0 01<4pG)YC gHfs"o ECL>"JoV0|%zb~<] vt^Z Q(N+t6G9E( ¹Yە`uxR "MDNWL~_YN̺[N.Uha];j ڼܓwYI^+XXtW) #9vxM_okBK,ʁ'M⽇ ҿt(Ҕ5CsPz{>tKd"?t{jXgX}il5!>#̉žfTͿyaJP ߋʽ0irt́ z*^aR:x( G#N׊Yp}װiqYl *;An=^kѫmZ\k^rZ;WZBAPd-Ͼʹ erU\'BP`+`Gm%ɵko0k˹zK0Q.X֐rމq%)e4CQH\=!;{|9Y/G恾U_þ֢njI',46˅i؈DQ@nI7ܪh\UYB ڤ X^͠ endstream endobj 525 0 obj << /Length 2104 /Filter /FlateDecode >> stream xYݏ6߿Bo#~Sz%h;lipeZ,9|ga[dދMșoQgeU_\xdF)d]FKF.)d7]^7^L|NͶuKIf& _a$ׅ,Y+wgaId.>ۗ<"+7~ DMAagݪ&߰ " vNK#ƀq9|ڎz[I~%)HЖ8+d|L <V/6zu&q [,mnj`W<.4|BoӵØiOsuf>zza49HĤN854âЙg*a4ypIۦm+t6`hBy4W.La`!dll*ڦC.Zg9 wv,0c*xCQ)p,`[.'7Xx74  lPp'k빺J-Udɂ?n>5F>w}$t'WcZ˨mu5nq}XD=pYiF^>@:A%XM{]H* Gw7Wۈp9gR3*՛wl/A-+=U6{{0*aWiݹXH$es _d9#013[0¿mr(.dܢ=(qddFXg0& لQhjek_jLK0{X75xq#K mޑFy8r)1,#Vr4]r0~(sG!r_.ᨦ'>/$Ctsihn3R . vPx-Hn"V~anmEvKĈD C@d'*5l$RQ'|V슼SsN1#L/C5}^pn9?8 KcW< Wc$]rޫ~BKgDjt5Kf&$7ZލJj1kB~ Qә3C01hKG3w4&5?.ۅ$KRƟ h*89O&D]A|ˈ5 \16xZ. 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G)|4-ṧJo}냰Y)C߇VfR`%X-lYuJЇ9Ji!눳!C|_u+ߎI2G.4Tsa*J6B< 8 }##¸4YcCp]`Dq:!F[{Y1TT }׽62:(~U獱Zji뀦|]ywqxI +ǸTsx.]q b⏧}:4O c .Nu`U 5 q8$ߜUNԄ!ܼ04Y) ,if1eG3e&(YXJgUЙC^XV|کev-lrAMtR24Sҳ8c̸btrXtcؕ8҉d5&C.o bz DŽ+ґ\4!y,(ifMfr%qM#ړ9u2ညoB̷g@ ( ubwu_T)1k]OxTdseE?$|XaBQ/<)Spuc_yR{cځiNyV!Sc+į5N^A+ԡ,uQ™jϢV|0V| vB7YyX&n%ږUK=YI+Wo^)ސH/ Ա @I1Mz,$&ʾ*fK9iB+"?7#;q6PJUP]͆|Xeɪ=F1>Ʃ'?}m_1a=f4lr?(KM endstream endobj 887 0 obj << /Length 2693 /Filter /FlateDecode >> stream xڭ[ks۸_ʳ+O>v̴ivIN |(|N~|/"(~hĽ!&qN/~63X, ſ.r}̮3r=o!('r)X|,<]oÿ_~~?^ͱ@F݅x "BWxFQ_5C( No!љB{GWC#_p:m@<]G>}ds=^_7WspnuT\៹ZbFi%&c.by tmtַQDfWs[߉|V>C>6}O4$6e3,\`>+v#E$޵`qVЉ(GЅ@p  fwL6o"('j9'ʹh5KQ歀Ywmwf>(u[ԩM84 `t^\iWQ7蒲FQrY[Yc$2*I;4˥GG <顃?"4um[8d"ӖY2ʽ>!  1^Ӳy48V*bڥS@{c稈#ճrۺ|4S&3 (ہySP32GB([w4 nQe]|G~笕ˏC `Iӵ˽,(Q(/P*#_8FW}XUvi9!>93>;d@p5z?,/v7h"խYQ%4Qp2$"cR萄B 09hw-IHHwc1w!ZF{F݁j|59?kJ^sȃ(8E`̉Qg]u{5?Nw *t[?Nm4=@DkUZ(GE0 36͐0 Ae!ڟȼ]}E.ΏHh.MTW1oUCt #Q%2"%yc%fu(GqyQFvbK\[<fEK iUL6{FWUtQRCX|?0(/**rB?)GjeqD*fğՋflЎ2mG+}搊Yy&Z{&0t!g^'19*2 m/ZfkypmH*I'ןxH<dapȮ:J⛢m"e(w gbsKٙ6WI~'xB?@ SH&1RL:A5{X?8n4Ġu(LNkoI,vr7aՀ+ ?wA`?Mڄ\w.[ 69)ݑn7Vo4~°j *gӨd2@&s>DZbC9p±gd2@'ot:d &5VԙF8d%`IRWP<;f0ksCuF; CyQ6W{c_lۮic?Ȩk/ 9'ʤ` F;4G=G&[Y<>ty1Z G9oԜ3A7q&ZQ HY$k/*ScB]WiyP.»K/Ϩ@/] 5u^\E!=T>}+um9SE%ԗZtVVl X>As֨GumiGEJ0S} ^jՋM m4Ue]4*gSYA,תa~JMԥiİnSͽFU߄wRIoXfK ncwx{  d<^u$|jƨQʬ=\dzNlM_h]^Г endstream endobj 893 0 obj << /Length 1327 /Filter /FlateDecode >> stream xڽWMs6Whrf" ̩Nv&!SPQ7ł2)Kks!>o߾z> Wo?J>eRr5"dq,g*lY~,o|^wq^U}VkuQ2.u kݔf;_Dnck썾) |=* Z7u\[SEt`]# -)  D$m1@0n1X80̞N.r H[v9F|av2A;VJ[Ms@oSx;8 h@)[Вй@xt[ߴ]"4`U{6]zq"7lYn 0՞|YL(Bc:Ľm.hv4kS S|b,J1=H,M xV՗Q3&xr`hQ^<ۉM v_)L3_]9LѨ ȋzzJ#&񁣨OP; u'V Yn}j#Τcr{=3빑[%i1Ó0،ft8Ұ1p%.oiǶ$ :p;n^7)htC[ƽWGCo \ }ӕt P@_O7нAu)+G}Y*햭Ni,2;Xe2Lr kV>d> stream xڝɮ6_# <+` A9ٴE|}j, \$Hk/&|&7_MTq& ,MFAE]ř5A.)2w?t˓i'|@$ifWAQ~oKM"u,6l^Q^9a۸Puo4Uԑ`mwbnNc2n{d\ Wm<„8Nq9sjQJQN3N7ͅT3LݥI(QFQndr Qc1j0x 1@.P$ !<h^oMC1SXYyҽ .tLM_Ҳ/`sfaa9S ET/[e7TY (" <5Bg1i M]c[;mqLVFQGۚ`-ITEOhB* So78"˒MnaݦIy(@< Rqg606$i{-Wۖ7,/3EC@'`zRP#OkJ^DV KL l]=Z]1=Hq 7<' uT! 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In no event shall the authors or his employer be held responsible for any damage resulting from the use of this software, including but not limited to the frustration that you may experience in using the package. All parts of the source and documentation except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. } { \noindent \setlength{\baselineskip}{0.5\baselineskip} \section{Availability} \begin{itemize} \item Binaries: \href{http://www.atgc-montpellier.fr/phyml}{http://www.atgc-montpellier.fr/phyml} \item Sources: \href{http://stephaneguindon.github.io/phyml-downloads/}{http://stephaneguindon.github.io/phyml-downloads/} \item Discussion forum: \href{http://groups.google.com/group/phyml-forum}{http://groups.google.com/group/phyml-forum} \end{itemize} } { \noindent \setlength{\baselineskip}{0.7\baselineskip} \section{Authors} \begin{itemize} \item { St\'ephane Guindon} and { Olivier Gascuel} conceived the original PhyML algorithm. \item { St\'ephane Guindon} conceived the PhyTime method. \item { St\'ephane Guindon, David Welch and Louis Ranjard} conceived the PhyloGeo method. \item { St\'ephane Guindon, Wim Hordjik} and { Olivier Gascuel} conceived the SPR-based tree search algorithm. \item { Maria Anisimova} and { Olivier Gascuel} conceived the aLRT method for branch support. \item { St\'ephane Guindon, Franck Lethiec}, Jean-Francois Dufayard and Vincent Lefort implemented PhyML. \item St\'ephane Guindon implemented PhyloGeo, PhyREX and PhyTime. \item { Jean-Francois Dufayard} created the benchmark and implemented the tools that are used to check PhyML accuracy and performances. \item { Vincent Lefort, St\'ephane Guindon, Patrice Duroux} and { Olivier Gascuel} conceived and implemented PhyML web server. \item { Imran Fanaswala} interfaced PhyML with BEAGLE. \item St\'ephane Guindon wrote this document. \end{itemize} } \clearpage % \section{ML in phylogenetics: the basics.} \section{Overview} PhyML \cite{guindon03} is a software package which primary task that is to estimate maximum likelihood phylogenies from alignments of nucleotide or amino-acid sequences. It provides a wide range of options that were designed to facilitate standard phylogenetic analyses. The main strength of PhyML lies in the large number of substitution models coupled to various options to search the space of phylogenetic tree topologies, going from very fast and efficient methods to slower but generally more accurate approaches. It also implements two methods to evaluate branch supports in a sound statistical framework (the non-parametric bootstrap and the approximate likelihood ratio test). PhyML was designed to process moderate to large data sets. In theory, alignments with up to 4,000 sequences 2,000,000 character-long can analyzed. In practice however, the amount of memory required to process a data set is proportional of the product of the number of sequences by their length. Hence, a large number of sequences can only be processed provided that they are short. Also, PhyML can handle long sequences provided that they are not numerous. With most standard personal computers, the ``comfort zone'' for PhyML generally lies around 100-500 sequences less than 10,000 character long. For larger data sets, we recommend using other softwares such as RAxML \cite{raxml}\index{RAxML} or GARLI \cite{garli}\index{GARLI} or Treefinder (\href{http://www.treefinder.de}{http://www.treefinder.de}). \section{Bug report}\index{bug} While PhyML is, of course, bug-free (!) (please read the disclaimer carefuly...), if you ever come across an issue, please feel free to report it using the discuss group web site at the following address: \url{https://groups.google.com/forum/?fromgroups#!forum/phyml-forum}. Alternatively, you can send an email to \url{s.guindon@auckland.ac.nz}. Do not forget to mention the version of PhyML and program options you are using. \section{Installing PhyML} \subsection{Sources and compilation}\index{compilation} The sources of the program are available free of charge from \url{http://stephaneguindon.github.io/phyml-downloads/}. The compilation on UNIX-like systems is fairly standard. It is described in the `\x{INSTALL}' file that comes with the sources. In a command-line window, go to the directory that contains the sources and type: {\setlength{\baselineskip}{0.5\baselineskip} \begin{verbatim} ./configure; make clean; make V=0; \end{verbatim} } By default, PhyML will be compiled with optimization flags turned on. It is possible to generate a version of PhyML that can run through a debugging tool (such as \x{ddd}\label{ddd}) or a profiling tool (such as \x{gprof}\label{gprof}) using the following instructions: {\setlength{\baselineskip}{0.5\baselineskip} \begin{verbatim} ./configure --enable-debug; make clean; make V=0; \end{verbatim} } % {\em Note} -- when PhyML is going to be used mostly of exclusively in batch mode, it is preferable % to turn on the batch mode option in the Makefile. In order to do so, the file \x{Makefile.am} needs % to be modified: add \x{-DBATCH} to the line with \x{DEFS=-DUNIX -D\$(PROG) -DDEBUG}. \subsection{Installing PhyML on UNIX-like systems (including Mac OS)} Copy PhyML binary file in the directory you like. For the operating system to be able to locate the program, this directory must be specified in the global variable \x{PATH}. In order to achieve this, you will have to add \x{export PATH="/your\_path/:\${PATH}"} to the \x{.bashrc} or the \x{.bash\_profile} located in your home directory (\x{your\_path} is the path to the directory that contains PhyML binary). \subsection{Installing PhyML on Microsoft Windows}\label{sec:install-windows} Copy the files \x{phyml.exe} and \x{phyml.bat} in the same directory. To launch PhyML, click on the icon corresponding to \x{phyml.bat}. Clicking on the icon for \x{phyml.exe} works too but the dimensions of the window will not fit PhyML PHYLIP-like interface. \subsection{Installing the parallel version of PhyML}\label{sec:MPI}\index{MPI}\index{bootstrap!parallel} Bootstrap analysis can run on multiple processors. Each processor analyses one bootstraped dataset. Therefore, the computing time needed to perform $R$ bootstrap replicates is divided by the number of processors available. This feature of PhyML relies on the MPI (Message Passing Interface) library. To use it, your computer must have MPI installed on it. In case MPI is not installed, you can dowload it from \href{http://www.mcs.anl.gov/research/projects/mpich2/}{http://www.mcs.anl.gov/research/projects/mpich2/}. Once MPI is installed, it is necessary to launch the MPI daemon. This can be done by entering the following instruction: \x{mpd \&}. Note however that in most cases, the MPI daemon will already be running on your server so that you most likely do not need to worry about this. You can then just go in the \x{phyml/} directory (the directory that contains the \x{src/}, \x{examples/} and \x{doc/} folders) and enter the commands below: {\setlength{\baselineskip}{0.5\baselineskip} \begin{verbatim} ./configure --enable-phyml-mpi; make clean; make; \end{verbatim} } A binary file named \x{phyml-mpi} has now been created in the \x{src/} directory and is ready to use with MPI. A typical MPI command-line which uses 4 CPUs is given below: {\setlength{\baselineskip}{0.5\baselineskip} \begin{verbatim} mpirun -n 4 ./phyml-mpi -i myseq -b 100 \end{verbatim} } \noindent Please read section \ref{sec:parallel_bootstrap} of this document for more information. \subsection{Installing PhyML-BEAGLE}\label{sec:install-phyml-beagle} {\em Note: I haven't found the time nor the resources to make sure the code of PhyML stays compatible with the BEAGLE library. Please do not hesitate at all to contact me if you'd like to contribute here.} \\ \\ PhyML can use the BEAGLE\cite{ayres12} library for the likelihood computation. BEAGLE provides provides significant speed-up: the single core version of PhyML-BEAGLE can be up to 10 times faster than PhyML on a single core and up to 150 times on Graphical Processing Units. PhyML-BEAGLE will eventually have of the features of PhyML, even though at the moment the boostrap and the invariant site options are not available. Also, please note that in some cases, the final log-likelihood reported by PhyML and PhyML-BEALGE may not exactly match, though the differences observed are very minor (in the 10$^{-4}$ to 10$^{-4}$ range). In order to install PhyML-BEAGLE, you first need to download and install the BEAGLE library available from \url{https://code.google.com/p/beagle-lib/}. Then run the following commands: {\setlength{\baselineskip}{0.5\baselineskip} \begin{verbatim} ./configure --enable-beagle; make clean; make; \end{verbatim} } A binary file named \x{phyml-beagle} will be created in the \x{src/} directory. The interface to \x{phyml-beagle} (i.e., commandline option of PHYLIP-like interface) is exactly identical to that of PhyML. \section{Program usage.}\label{sec:phyml_new} PhyML has three distinct user-interfaces. The first corresponds to a PHYLIP-like text interface that makes the choice of the options self-explanatory. The command-line interface is well-suited for people that are familiar with PhyML options or for running PhyML in batch mode. The XML interface is more sophisticated. It allows the user to analyse partitionned data using flexible mixture models of evolution. \subsection{PHYLIP-like interface} The default is to use the PHYLIP-like text interface by simply typing `\x{phyml}' in a command-line window or by clicking on the PhyML icon (see Section \ref{sec:install-windows}). After entering the name of the input sequence file, a list of sub-menus helps the users set up the analysis. There are currently four distinct sub-menus: % \begin{figure} % \resizebox{15cm}{9cm}{\includegraphics{./fig/interface.eps}} % \caption{PHYLIP-like interface to PhyML.} % \label{fig:interface} % \end{figure} \begin{enumerate} \item {\em Input Data}: specify whether the input file contains amino-acid or nucleotide sequences. What the sequence format is (see Section \ref{sec:input_output}) and how many data sets should be analysed. \item {\em Substitution Model}: selection of the Markov model of substitution. \item {\em Tree Searching}: selection of the tree topology searching algorithm. \item {\em Branch Support}: selection of the method that is used to measure branch support. \end{enumerate} \noindent `\x{+}' and `\x{-}' keys are used to move forward and backward in the sub-menu list. Once the model parameters have been defined, typing `\x{Y}' (or `\x{y}') launches the calculations. The meaning of some options may not be obvious to users that are not familiar with phylogenetics. In such situation, we strongly recommend to use the default options. As long as the format of the input sequence file is correctly specified (sub-menu {\em Input data}), the safest option for non-expert users is to use the default settings. The different options provided within each sub-menu are described in what follows. \subsubsection{Input Data sub-menu} \begin{center}\framebox{\x{[D] ............................... Data type (DNA/AA)}} \end{center} Type of data in the input file. It can be either DNA or amino-acid sequences in PHYLIP format (see Section \ref{sec:input_output}). Type \x{D} to change settings. \vspace{0.7cm} \begin{center} \framebox{\x{[I] ...... Input sequences interleaved (or sequential)}} \end{center} PHYLIP format comes in two flavours: interleaved or sequential (see Section \ref{sec:input_output}). Type \x{I} to selected among the two formats. \vspace{0.7cm} \begin{center} \framebox{\x{[M] ....................... Analyze multiple data sets}} \end{center} If the input sequence file contains more than one data sets, PhyML can analyse each of them in a single run of the program. Type \x{M} to change settings. \vspace{0.7cm} \begin{center} \framebox{\x{[R] ............................................ Run ID}} \end{center} This option allows you to append a string that identifies the current PhyML run. Say for instance that you want to analyse the same data set with two models. You can then `tag' the first PhyML run with the name of the first model while the second run is tagged with the name of the second model.\index{run ID} \subsubsection{Substitution model sub-menu}\label{sec:submenus} \begin{center} \framebox{\x{[M] ................. Model of nucleotide substitution}} \end{center} \begin{center} \framebox{\x{[M] ................ Model of amino-acids substitution}} \end{center} PhyML implements a wide range of substitution models: JC69 \cite{jukes69}, K80 \cite{kimura80}, F81 \cite{felsenstein81a}, F84 \cite{phylip2}, HKY85 \cite{hasegawa85}, TN93 \cite{tamura93} GTR \cite{lanave84,tavare86} and custom for nucleotides; LG \cite{le08}, WAG \cite{whelan01b}, Dayhoff \cite{dayhoff78}, JTT \cite{jones92}, Blosum62 \cite{henikoff92}, mtREV \cite{adachi96}, rtREV \cite{dimmic02}, cpREV \cite{adachi00}, DCMut \cite{kosiol04}, VT \cite{muller00} and mtMAM \cite{cao98} and custom for amino acids. Cycle through the list of nucleotide or amino-acids substitution models by typing \x{M}. Both nucleotide and amino-acid lists include a `custom' model. The custom option provides the most flexible way to specify the nucleotide substitution model. The model is defined by a string made of six digits. The default string is `\x{000000}', which means that the six relative rates of nucleotide changes: $A \leftrightarrow C$, $A \leftrightarrow G$, $A \leftrightarrow T$, $C \leftrightarrow G$, $C \leftrightarrow T$ and $G \leftrightarrow T$, are equal. The string `\x{010010}' indicates that the rates $A \leftrightarrow G$ and $C \leftrightarrow T$ are equal and distinct from $A \leftrightarrow C = A \leftrightarrow T = C \leftrightarrow G = G \leftrightarrow T$. This model corresponds to HKY85 (default) or K80 if the nucleotide frequencies are all set to 0.25. `\x{010020}' and `\x{012345}' correspond to TN93 and GTR models respectively. The digit string therefore defines groups of relative substitution rates. The initial rate within each group is set to 1.0, which corresponds to F81 (JC69 if the base frequencies are equal). Users also have the opportunity to define their own initial rate values. These rates are then optimised afterwards (option `\x{O}') or fixed to their initial values. The custom option can be used to implement all substitution models that are special cases of GTR. Table \ref{tab:modelcode} on page \pageref{tab:modelcode} gives the correspondence between the `standard' name of the model (see \url{http://mbe.oxfordjournals.org/content/18/6/897/F2.large.jpg}) and the custom model code. The custom model also exists for protein sequences. It is useful when one wants to use an amino-acid substitution model that is not hard-coded in PhyML. The symmetric part of the rate matrix, as well as the equilibrium amino-acid frequencies, are given in a file which name is given as input of the program. The format of this file is described in the section \ref{sec:customaa}. \vspace{0.7cm} \begin{center} \framebox{\x{[F] ................. Optimise equilibrium frequencies}} \end{center} \begin{center} \framebox{\x{[E] ......... Equilibrium frequencies (empirical/user)}} \end{center} \begin{center} \framebox{\x{[F] . Amino acid frequencies (empirical/model defined)}} \end{center} For nucleotide sequences, optimising equilibrium frequencies means that the values of these parameters are estimated in the maximum likelihood framework. When the custom model option is selected, it is also possible to give the program a user-defined nucleotide frequency distribution at equilibrium (option \x{E}). For protein sequences, the stationary amino-acid frequencies are either those defined by the substitution model or those estimated by counting the number of different amino-acids observed in the data. Hence, the meaning of the \x{F} option depends on the type of the data to be processed. \vspace{0.7cm} \begin{center} \framebox{\x{[T] .................... Ts/tv ratio (fixed/estimated)}} \end{center}\index{$\kappa$}\index{ts/tv ratio} Fix or estimate the transition/transversion ratio in the maximum likelihood framework. This option is only available when DNA sequences are to be analysed under K80, HKY85 or TN93 models. The definition given to this parameter by PhyML is the same as PAML's\index{PAML} one. Therefore, the value of this parameter does {\it not} correspond to the ratio between the expected number of transitions and the expected number of transversions during a unit of time. This last definition is the one used in PHYLIP\index{PHYLIP}. PAML's manual gives more detail about the distinction between the two definitions (\url{http://abacus.gene.ucl.ac.uk/software/paml.html}). \vspace{0.7cm} \begin{center} \framebox{\x{[V] . Proportion of invariable sites (fixed/estimated)}} \end{center}\index{invariable sites}\index{proportion of invariants} The proportion of invariable sites, i.e., the expected frequency of sites that do not evolve, can be fixed or estimated. The default is to fix this proportion to 0.0. By doing so, we consider that each site in the sequence may accumulate substitutions at some point during its evolution, even if no differences across sequences are actually observed at that site. Users can also fix this parameter to any value in the $[0.0,1.0]$ range or estimate it from the data in the maximum-likelihood framework. \vspace{0.7cm} \index{gamma distribution (discrete)!mean vs. median} \index{gamma distribution (discrete)!number of categories} \index{gamma distribution (discrete)!shape parameter} \begin{center} \framebox{\x{[R] ....... One category of substitution rate (yes/no)}} \end{center} \begin{center} \framebox{\x{[C] ........... Number of substitution rate categories}} \end{center} \begin{center} \framebox{\x{[A] ... Gamma distribution parameter (fixed/estimated)}} \end{center} \begin{center} \framebox{\x{[G] .........`Middle' of each rate class (mean/median)}} \end{center} Rates of evolution often vary from site to site. This heterogeneity can be modelled using a discrete gamma distribution. Type \x{R} to switch this option on or off. The different categories of this discrete distribution correspond to different (relative) rates of evolution. The number of categories of this distribution is set to 4 by default. It is probably not wise to go below this number. Larger values are generally preferred. However, the computational burden involved is proportional to the number of categories (i.e., an analysis with 8 categories will generally take twice the time of the same analysis with only 4 categories). Note that the likelihood will not necessarily increase as the number of categories increases. Hence, the number of categories should be kept below a ``reasonable'' number, say 20. The default number of categories can be changed by typing \x{C}. The middle of each discretized substitution rate class can be determined using the mean or the median. PAML, MrBayes and RAxML use the mean. However, the median is generally associated with greater likelihoods than the mean. This conclusion is based on our analysis of several real-world data sets extracted from TreeBase. Despite this, the default option in PhyML is to use the mean in order to make PhyML likelihoods comparable to those of other phylogenetic software. One must bare in mind that {\color{red}{likelihoods calculated with the mean approximation are not directly comparable to the likelihoods calculated using the median approximation}}. The shape of the gamma distribution determines the range of rate variation across sites. Small values, typically in the $[0.1,1.0]$ range, correspond to large variability. Larger values correspond to moderate to low heterogeneity. The gamma shape parameter can be fixed by the user or estimated via maximum-likelihood. Type \x{A} to select one or the other option. \subsubsection{Tree searching sub-menu} \begin{center} \framebox{\x{[O] ........................... Optimise tree topology}} \end{center} By default the tree topology is optimised in order to maximise the likelihood. However, it is also possible to avoid any topological alteration. This option is useful when one wants to compute the likelihood of a tree given as input (see below). Type \x{O} to select among these two options. \vspace{0.7cm} \begin{center} \framebox{\x{[S] .................. Tree topology search operations}} \end{center} PhyML proposes three different methods to estimate tree topologies. The default approach is to use simultaneous NNI. This option corresponds to the original PhyML algorithm \cite{guindon03}. The second approach relies on subtree pruning and regrafting (SPR). It generally finds better tree topologies compared to NNI but is also significantly slower. The third approach, termed BEST, simply estimates the phylogeny using both methods and returns the best solution among the two. Type \x{S} to choose among these three choices. \vspace{0.7cm} \begin{center} \framebox{\x{[R] ......................... Use random starting tree}} \end{center} \begin{center} \framebox{\x{[N] .................. Number of random starting trees}} \end{center} When the SPR or the BEST options are selected, is is possible to use random trees rather than BioNJ or a user-defined tree, as starting tree. If this option is turned on (type \x{R} to change), five trees, corresponding to five random starts, will be estimated. The output tree file will contain the best tree found among those five. The number of random starts can be modified by typing \x{N}. Setting the number of random starting trees to $N$ means that the analysis will take (slightly more than) $N$ times the time required for a standard analysis where only one (BioNJ) starting tree is used. However, the analysis of real data sets shows that the best trees estimated using the random start option almost systematically have higher likelihoods than those inferred using a single starting tree. \vspace{0.7cm} \begin{center} \framebox{\x{[U] ........ Starting tree (BioNJ/parsimony/user tree)}} \end{center}\index{BioNJ} When the tree topology optimisation option is turned on, PhyML proceeds by refining an input tree. By default, this input tree is estimated using BioNJ \cite{gascuelNJ}. The alternative option is to use a parsimony tree. We found this option specially useful when analysing large data sets with NNI moves as it generally leads to greater likelihoods than those obtained when starting from a BioNJ trees. The user can also to input her/his own tree. This tree should be in Newick format (see Section \ref{sec:input_output}). This option is useful when one wants to evaluate the likelihood of a given tree with a fixed topology, using PhyML. Type \x{U} to choose among these two options. \subsubsection{Branch support sub-menu} \begin{center} \framebox{\x{[B] ................ Non parametric bootstrap analysis}} \end{center} The support of the data for each internal branch of the phylogeny can be estimated using non-parametric bootstrap. By default, this option is switched off. Typing \x{B} switches on the bootstrap analysis. The user is then prompted for a number of bootstrap replicates. The largest this number the more precise the bootstrap support estimates are. However, for each bootstrap replicate a phylogeny is estimated. Hence, the time needed to analyse $N$ bootstrap replicates corresponds to $N$-times the time spent on the analysis of the original data set. $N=100$ is generally considered as a reasonable number of replicates. \begin{center} \framebox{\x{[A] ................ Approximate likelihood ratio test}} \end{center} When the bootstrap option is switched off (see above), approximate likelihood branch supports are estimated. This approach is considerably faster than the bootstrap one. However, both methods intend to estimate different quantities and conducting a fair comparison between both criteria is not straightforward. The estimation of approximate likelihood branch support comes in multiple flavours. The default is set to aBayes, corresponding to the approximate Bayes method described in \cite{anisimova11}. The approximate likelihood ratio test (aLRT) \cite{anisimova06}, Shimodaira–Hasegawa aLRT (SH-aLRT) statistics are the other available options. \subsection{Command-line interface} An alternative to the PHYLIP-like interface is the command-line interface. Users that do not need to modify the default parameters can launch the program with the `\x{phyml -i seq\_file\_name}' command. The list of all command line arguments and how to use them is given in the `Help' section which is displayed when entering the `\x{phyml --help}' command. The available command-line options are described in what follows. \begin{itemize} \item \x{-i} (or \x{--input}) \x{seq\_file\_name}\index{command-line options!\x{--input}} \\ \x{seq\_file\_name} is the name of the nucleotide or amino-acid sequence file in PHYLIP format. \item \x{-d} (or \x{--datatype}) \x{data\_type}\index{command-line options!\x{--data\_type}}\\ \x{data\_type} is \x{nt} for nucleotide (default) and \x{aa} for amino-acid sequences. \item \x{-q} (or \x{--sequential})\index{sequence format!interleaved}\index{sequence format!sequential}\index{command-line options!\x{--sequential}} \\ Changes interleaved format (default) to sequential format. \item \x{-n} (or \x{--multiple}) \x{nb\_data\_sets}\index{multiple data sets}\index{command-line options!\x{--multiple}}\\ \x{nb\_data\_sets} is an integer giving the number of data sets to analyse. \item \x{-p} (or \x{--pars})\index{command-line options!\x{--pars}}\\ Use a minimum parsimony starting tree. This option is taken into account when the `-u' option is absent and when tree topology modifications are to be done. \item \x{-b} (or \x{--bootstrap}) \x{int}\index{bootstrap}\index{command-line options!\x{--bootstrap}} \begin{itemize} \item \x{int} $>$ 0: \x{int} is the number of bootstrap replicates. \item \x{int} = 0: neither approximate likelihood ratio test nor bootstrap values are computed. \item \x{int} = -1: approximate likelihood ratio test returning aLRT statistics. \item \x{int} = -2: approximate likelihood ratio test returning Chi2-based parametric branch supports. % \item \x{int} = -3: minimum of Chi2-based parametric and SH-like branch supports. \item \x{int} = -4: SH-like branch supports alone. \item \x{int} = -5: (default) approximate Bayes branch supports. \end{itemize} \item \x{-m} (or \x{--model}) \x{model\_name}\index{substitution models!DNA}\index{substitution models!amino acids}\index{command-line options!\x{--model}} \\ \x{model\_name} : substitution model name. \begin{itemize} \item {\it Nucleotide-based models}: \x{HKY85} (default) \x{| JC69 | K80 | F81 | F84 | TN93 | GTR | custom | r(ac),r(ag),r(at),r(cg),r(ct),r(gt)} \\ JC90, K80, F81. F84, TN93 and GTR denote the ``standard'' substitution models. The \x{custom} option can be used to define a new substitution model. A string of six digits identifies the model. For instance, 000000 corresponds to F81 (or JC69 provided the distribution of nucleotide frequencies is uniform). 012345 corresponds to GTR. This option can be used for encoding any model that is a nested within GTR. See Section \ref{sec:submenus} and Table \ref{tab:modelcode}. {\em NOTE:} the substitution parameters of the custom model will be optimised so as to maximise the likelihood. Also, it is possible to specify and fix (i.e., avoid optimisation) the values of the substitution rates in the GTR model by passing the corresponding five rates to the \x{-m} option. Commas are used to separate the five relative rates and no blank space should be introduced here. \item {\it Amino-acid based models}: \x{LG} (default) \x{| WAG | JTT | MtREV | Dayhoff | DCMut | RtREV | CpREV | VT | Blosum62 | MtMam | MtArt | HIVw | HIVb | custom} \\ The \x{custom} option is useful when one wants to use an amino-acid substitution model that is not available by default in PhyML. The symmetric part of the rate matrix, as well as the equilibrium amino-acid frequencies, are given in a file which name is asked for by the program. The format of this file is described in section \ref{sec:customaa}. \end{itemize} \begin{table}\index{custom models} \begin{center} \begin{tabular}{ll} \hline Name & Command-line option \\ \hline JC69 & \x{-m 000000 -f 0.25,0.25,0.25,0.25} \\ F81 & \x{-m 000000}\\ K80 & \x{-m 010010 -f 0.25,0.25,0.25,0.25} \\ HKY85 & \x{-m 010010}\\ TrNef & \x{-m 010020 -f 0.25,0.25,0.25,0.25} \\ TrN & \x{-m 010020}\\ K81 & \x{-m 123321 -f 0.25,0.25,0.25,0.25}\\ K81uf & \x{-m 123321}\\ TIMef & \x{-m 132241 -f 0.25,0.25,0.25,0.25}\\ TIM & \x{-m 132241}\\ TVMef & \x{-m 102304 -f 0.25,0.25,0.25,0.25}\\ TVM & \x{-m 102304}\\ SYM & \x{-m 123456 -f 0.25,0.25,0.25,0.25}\\ GTR & \x{-m 123456}\\ \hline \end{tabular} \caption{Nucleotide substitution model names (as defined in \cite{posada01}) and the corresponding custom model code used in PhyML.}\label{tab:modelcode} \end{center} \end{table} \item \x{--aa\_rate\_file file\_name}\index{command-line options!\x{--aa\_rate\_file}} \\ This option is compulsory when analysing amino-acid sequences under a `custom' model (see above). \x{file\_name} should provide a rate matrix and equilibrium amino acid in PAML format (see Section \ref{sec:customaa}). \item \x{-f e}, \x{m}, \x{o} or \x{fA,fC,fG,fT} \index{frequencies!nucleotide}\index{frequencies!amino-acid}\index{stationary frequencies}\index{command-line options!\x{-f}}\\ Nucleotide or amino-acid frequencies. \begin{itemize} \item \x{e} : the character frequencies are determined as follows : \begin{itemize} \item {\it Nucleotide sequences}: (Empirical) the equilibrium base frequencies are estimated by counting the occurence of the different bases in the alignment. \item {\it Amino-acid sequences}: (Empirical) the equilibrium amino-acid frequencies are estimated by counting the occurence of the different amino-acids in the alignment. \end{itemize} \item \x{m} : the character frequencies are determined as follows : \begin{itemize} \item {\it Nucleotide sequences}: (Model) the equilibrium base frequencies are optimized using maximum likelihood. \item {\it Amino-acid sequences}: (Model) the equilibrium amino-acid frequencies are estimated using the frequencies defined by the substitution model. \end{itemize} \item \x{o} : the character frequencies (nucleotides or amino-acids) are optimized using maximum likelihood. \item \x{fA,fC,fG,fT}: only valid for nucleotide-based models. \x{fA}, \x{fC}, \x{fG} and \x{fT} are floating numbers that correspond to the frequencies of A, C, G and T respectively. \end{itemize} \item \x{-t} (or \x{--ts/tv}) \x{ts/tv\_ratio} \index{$\kappa$}\index{ts/tv ratio}\index{command-line options!\x{--ts/tv}}\\ \x{ts/tv\_ratio}: transition/transversion ratio. DNA sequences only. Can be a fixed positive value (e.g., 4.0) or type \x{e} to get the maximum likelihood estimate. \item \x{-v} (or \x{--pinv}) \x{prop\_invar}\index{proportion of invariants}\index{invariable sites} \index{command-line options!\x{--pinv}}\\ \x{prop\_invar}: proportion of invariable sites. Can be a fixed value in the [0,1] range or type \x{e} to get the maximum likelihood estimate. \item \x{-c} (or \x{--nclasses}) \x{nb\_subst\_cat}\index{gamma distribution (discrete)!number of categories} \index{command-line options!\x{--nclasses}}\\ \x{nb\_subst\_cat}: number of relative substitution rate categories. Default: \x{nb\_subst\_cat=4}. Must be a positive integer. \item \x{-a} (or \x{--alpha}) \x{gamma} \index{gamma distribution (discrete)!shape parameter}\index{command-line options!\x{--alpha}} \\ \x{gamma}: value of the gamma shape parameter. Can be a fixed positive value or e to get the maximum likelihood estimate. The value of this parameter is estimated in the maximum likelihood framework by default. \item \x{--use\_median} \index{gamma distribution (discrete)!mean vs. median} \index{command-line options!\x{--use\_median}}\\ The middle of each substitution rate class in the discrete gamma distribution is taken as the median. The mean is used by default. \item \x{--free\_rates} or \x{--freerates} \index{command-line options!\x{--free\_rates}}\\ As an alternative to the discrete gamma model, it is possible to estimate the (relative) rate in each class of the (mixture) model and the corresponding frequencies directly from the data. This model, called FreeRate, has more parameters than the discrete gamma one but usually provides a significantly better fit to the data. See \cite{soubrier12} for more information about this model and an illustration of its use. \item \x{--il} \index{command-line options!\x{--il}}\\ \x{il} stands here for integrated (branch) length. This model, described in \cite{guindon13} in the context of molecular dating, provides an efficient way to implement the covarion model \index{covarion}. Under the integrated length (IL) model, the length of each edge is described by a distribution of values, instead of a single value corresponding to the expected number of substitutions per position along the sequence. Let $l_{a,s}$ and $l_{b,s}$ be the number of substitutions at site $s$ along edges $a$ and $b$, and $l_{a,t}$ and $l_{b,t}$, the number of substitutions at site $t$. Standard models have $l_{a,s}=l_{a,t}$ and $l_{b,s}=l_{b,t}$, or $l_{a,s}=\alpha l_{a,t}$ and $l_{b,s}=\alpha l_{b,t}$ if rates vary across sites. The IL model has instead $l_{a,s}= \alpha l_{a,t}$ and $l_{b,s}= \beta l_{b,t}$ with $\alpha \neq \beta$, i.e. substitution rates vary across sites and edges. The IL approach is somehow an analytical approximation to the covarion model that, unlike the covarion model, does not incur any computational overhead compared to the traditional models. A notable difference with the plain vanilla covarion model and the IL model however is that substitution rates are not autocorrelated along the phylogeny under the IL model. \item \x{--codpos} \x{1,2 or 3} \index{command-line options!\x{--codpos}}\\ When analysing an alignment of coding sequences, use this option to consider only the first, second or the third coding position for the estimation. \item \x{-s} (or \x{--search}) \x{move}\index{NNI}\index{SPR} \index{command-line options!\x{--search}}\\ Tree topology search operation option. Can be either \x{NNI} (default, fast) or \x{SPR} (usually slower than \x{NNI} but more accurate) or \x{BEST} (best of NNI and SPR search). \item \x{-u} (or \x{--inputtree}) \x{user\_tree\_file}\index{input tree}\index{user tree} \index{command-line options!\x{--inputtree}}\\ \x{user\_tree\_file}: starting tree filename. The tree must be in Newick format. \item \x{-o params}\index{optimisation!topology}\index{optimisation!substitution parameters} \index{command-line options!\x{-o}}\\ This option focuses on specific parameter optimisation. \begin{itemize} \item \x{params=tlr}: tree topology (\x{t}), branch length (\x{l}) and substitution rate parameters (\x{r}) are optimised. \item \x{params=tl}: tree topology and branch lengths are optimised. \item \x{params=lr}: branch lengths and substitution rate parameters are optimised. \item \x{params=l}: branch lengths are optimised. \item \x{params=r}: substitution rate parameters are optimised. \item \x{params=n}: no parameter is optimised. \end{itemize} \item \x{--rand\_start}\index{random tree} \index{command-line options!\x{--rand\_start}}\\ This option sets the initial tree to random. It is only valid if SPR searches are to be performed. \item \x{--n\_rand\_starts num} \index{command-line options!\x{--n\_rand\_starts}}\\ \x{num} is the number of initial random trees to be used. It is only valid if SPR searches are to be performed. \item \x{--r\_seed num}\index{random number} \index{command-line options!\x{--r\_seed}}\\ \x{num} is the seed used to initiate the random number generator. Must be an integer. \item \x{--print\_site\_lnl}\index{likelihood!print site likelihood} \index{command-line options!\x{--print\_site\_lk}}\\ Print the likelihood for each site in file *\_phyml\_lk.txt. For $\Gamma$ or $\Gamma$+I or FreeRate models, this option returns the posterior probability of each relative rate class at each site. Such information can then be used to identify fast- and slow-evolving regions of the alignment. \item \x{--print\_trace}\index{command-line options!\x{--print\_trace}}\\ Print each phylogeny explored during the tree search process in file *\_phyml\_trace.txt. This option can be useful for monitoring the progress of the analysis for very large data sets and have an approximate idea of what the final phylogeny will look like. \item \x{--json\_trace}\index{command-line options!\x{--json\_trace}}\\ Print each phylogeny explored during the tree search process in file *\_phyml\_json\_trace.txt in JSON format (see \url{http://www.json.org/}). This option can be useful for monitoring the progress of the analysis for very large data sets and have an approximate idea of what the final phylogeny will look like. \item \x{--run\_id ID\_string}\index{run ID} \index{command-line options!\x{--run\_id}}\\ Append the string ID\_string at the end of each PhyML output file. This option may be useful when running simulations involving PhyML. It can also be used to `tag' multiple analysis of the same data set with various program settings. \item \x{--no\_memory\_check} \index{command-line options!\x{--no\_memory\_check}}\\ By default, when processing a large data set, PhyML will pause and ask the user to confirm that she/he wants to continue with the execution of the analysis despite the large amount of memory required. The \x{--no\_memory\_check} skips this question. It is especially useful when running PhyML in batch mode. \item \x{--no\_colalias} \index{command-line options!\x{--no\_colalias}}\\ By default, PhyML preprocesses each alignment by putting together (or aliasing) the columns that are identical. Use this option to skip this step but be aware that the analysis might then take more time to complete. \item \x{--constrained\_lens} \index{command-line options!\x{--constrained\_lens}}\\ When an input tree with branch lengths is provided, this option will find the branch multiplier that maximises the likelihood (i.e., the relative branch lengths remain constant) \item \x{--constraint\_file} \x{file\_name} \index{command-line options!\x{--constraint\_file}}\\ \x{file\_name} lists the topological constraints under which the tree topology search is conducted. This option should be used in conjunction with \x{-u} \x{file\_name}. See Section \ref{sec:topoconstraints} for more information. \item \x{--quiet} \index{command-line options!\x{--quiet}}\\ Runs PhyML in quiet mode. The program will not pause if the memory required to run the analysis exceeds 256MB and will not output the progression of the log-likelihood scores on the standard output. \item \x{--ancestral} \index{command-line options!\x{--ancestral}}\\ PhyML calculates the marginal probabilities of each character state at each internal node and each site of the sequence alignment. It then uses the ``Minimum Posterior Expected Error'' criterion to infer ancestral sequences. Works for both nucleotide and amino-acid data. \item \x{--leave\_duplicates} \index{command-line options!\x{--leave\_duplicates}}\\ PhyML removes duplicate sequences by default. Use this option if you do not want PhyML to discard them (even though leaving these sequences in will slow down the analysis). \end{itemize} \subsection{XML interface} \begin{itemize} \item \x{--xml=xml\_file\_name}\index{command-line options!\x{--xml}} \\ \x{xml\_file\_name} is the name of the XML file containing the information required to run the analysis. More details about this type of file is given in the section \ref{sec:xmlio}. \end{itemize} \subsection{Parallel bootstrap}\label{sec:parallel_bootstrap}\index{MPI}\index{bootstrap!parallel} Bootstrapping is a highly parallelizable task. Indeed, bootstrap replicates are independent from one another. Each bootstrap replicate can then be analysed separately. Modern computers often have more than one CPU. Each CPU can therefore be used to process a bootstrap sample. Using this parallel strategy, performing $R$ bootstrap replicates on $C$ CPUs `costs' the same amount of computation time as processing $R \times C$ bootstrap replicates on a single CPU. In other words, for a given number of replicates, the computation time is divided by $R$ compared to the non-parallel approach. PhyML sources must be compiled with specific options to turn on the parallel option (see Section \ref{sec:MPI}). Once the binary file (\x{phyml}) has been generated, running a bootstrap analysis with, say 100 replicates on 2 CPUs, can be done by typing the following command-line: \begin{verbatim} mpd &; mpirun -np 2 ./phyml-mpi -i seqfile -b 100; \end{verbatim} The first command launches the mpi daemon while the second launches the analysis. Note that launching the daemon needs to be done only once. The output files are similar to the ones generated using the standard, non-parallel, analysis (see Section \ref{sec:input_output}). Note that running the program in batch mode, i.e.: \begin{verbatim} mpirun -np 2 ./phyml-mpi -i seqfile -b 100 & \end{verbatim} will probably NOT work. I do not know how to run a mpi process in batch mode yet. Suggestions welcome... Also, at the moment, the number of bootstrap replicates must be a multiple of the number of CPUs required in the mpirun command. \section{Inputs \& outputs for command-line and PHYLIP interface }\label{sec:input_output} PhyML reads data from standard text files, without the need for any particular file name extension. \subsection{Sequence formats} \begin{figure} \begin{small} \begin{Verbatim}[frame=single, label=PHYLIP interleaved, samepage=true, baselinestretch=0.5] 5 80 seq1 CCATCTCACGGTCGGTACGATACACCKGCTTTTGGCAGGAAATGGTCAATATTACAAGGT seq2 CCATCTCACGGTCAG---GATACACCKGCTTTTGGCGGGAAATGGTCAACATTAAAAGAT seq3 RCATCTCCCGCTCAG---GATACCCCKGCTGTTG????????????????ATTAAAAGGT seq4 RCATCTCATGGTCAA---GATACTCCTGCTTTTGGCGGGAAATGGTCAATCTTAAAAGGT seq5 RCATCTCACGGTCGGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAAT????????GT ATCKGCTTTTGGCAGGAAAT ATCKGCTTTTGGCGGGAAAT AGCKGCTGTTG????????? ATCTGCTTTTGGCGGGAAAT ATCTGCTTTTGGCGGGAAAT \end{Verbatim} \begin{Verbatim}[frame=single, label=PHYLIP sequential, samepage=true, baselinestretch=0.5] 5 40 seq1 CCATCTCANNNNNNNNACGATACACCKGCTTTTGGCAGG seq2 CCATCTCANNNNNNNNGGGATACACCKGCTTTTGGCGGG seq3 RCATCTCCCGCTCAGTGAGATACCCCKGCTGTTGXXXXX seq4 RCATCTCATGGTCAATG-AATACTCCTGCTTTTGXXXXX seq5 RCATCTCACGGTCGGTAAGATACACCTGCTTTTGxxxxx \end{Verbatim} \end{small} \label{fig:align_tree} \caption{\bf PHYLIP interleaved and sequential formats.} \end{figure} \begin{figure} \begin{small} \begin{Verbatim}[frame=single, label=Nexus nucleotides, samepage=true, baselinestretch=0.5] [ This is a comment ] #NEXUS BEGIN DATA; DIMENSIONS NTAX=10 NCHAR=20; FORMAT DATATYPE=DNA; MATRIX tax1 ?ATGATTTCCTTAGTAGCGG tax2 CAGGATTTCCTTAGTAGCGG tax3 ?AGGATTTCCTTAGTAGCGG tax4 ?????????????GTAGCGG tax5 CAGGATTTCCTTAGTAGCGG tax6 CAGGATTTCCTTAGTAGCGG tax7 ???GATTTCCTTAGTAGCGG tax8 ???????????????????? tax9 ???GGATTTCTTCGTAGCGG tax10 ???????????????AGCGG; END; \end{Verbatim} \end{small} \begin{small} \begin{Verbatim}[frame=single, label=Nexus digits, samepage=true, baselinestretch=0.5] [ This is a comment ] #NEXUS BEGIN DATA; DIMENSIONS NTAX=10 NCHAR=20; FORMAT DATATYPE=STANDARD SYMBOLS="0 1 2 3"; MATRIX tax1 ?0320333113302302122 tax2 10220333113302302122 tax3 ?0220333113302302122 tax4 ?????????????2302122 tax5 10220333113302302122 tax6 10220333113302302122 tax7 ???20333113302302122 tax8 ???????????????????? tax9 ???22033313312302122 tax10 ???????????????02122; END; \end{Verbatim} \end{small} \begin{small} \begin{Verbatim}[frame=single, label=Nexus digits, samepage=true, baselinestretch=0.5] [ This is a comment ] #NEXUS BEGIN DATA; DIMENSIONS NTAX=10 NCHAR=20; FORMAT DATATYPE=STANDARD SYMBOLS="00 01 02 03"; MATRIX tax1 ??00030200030303010103030002030002010202 tax2 0100020200030303010103030002030002010202 tax3 ??00020200030303010103030002030002010202 tax4 ??????????????????????????02030002010202 tax5 0100020200030303010103030002030002010202 tax6 0100020200030303010103030002030002010202 tax7 ??????0200030303010103030002030002010202 tax8 ???????????????????????????????????????? tax9 ??????0202000303030103030102030002010202 tax10 ??????????????????????????????0002010202; END; \end{Verbatim} \end{small} \caption{\bf NEXUS formats.}\label{fig:nexus} \end{figure} Alignments of DNA or protein sequences must be in PHYLIP\index{PHYLIP} or NEXUS \cite{maddison97}\index{NEXUS} sequential\index{sequential} or interleaved\index{interleaved} format (Figures \ref{fig:align_tree} and \ref{fig:nexus}). For PHYLIP formated sequence alignments, the first line of the input file contains the number of species and the number of characters, in free format, separated by blank characters. One slight difference with PHYLIP format deals with sequence name lengths. While PHYLIP format limits this length to ten characters, PhyML can read up to hundred character long sequence names. Blanks and the symbols ``(),:'' are not allowed within sequence names because the Newick tree format makes special use of these symbols. Another slight difference with PHYLIP format is that actual sequences must be separated from their names by at least one blank character. A PHYLIP input sequence file may also display more than a single data set. Each of these data sets must be in PHYLIP format and two successive alignments must be separated by an empty line. Processing multiple data sets requires to toggle the `\x{M}' option in the {\em Input Data} sub-menu or use the `\x{-n}' command line option and enter the number of data sets to analyse. The multiple data set option can be used to process re-sampled data that were generated using a non-parametric procedure such as cross-validation or jackknife (a bootstrap option is already included in PhyML). This option is also useful in multiple gene studies, even if fitting the same substitution model to all data sets may not be suitable. PhyML can also process alignments in NEXUS format. Although not all the options provided by this format are supported by PhyML, a few specific features are exploited. Of course, this format can handle nucleotide and protein sequence alignments in sequential or interleaved format. It is also possible to use custom alphabets, replacing the standard 4-state and 20-state alphabets for nucleotides and amino-acids respectively. Examples of a 4-state custom alphabet are given in Figure \ref{fig:nexus}. Each state must here correspond to one digit or more. The set of states must be a list of consecutive digits starting from 0. For instance, the list ``0, 1, 3, 4'' is not a valid alphabet. Each state in the symbol list must be separated from the next one by a space. Hence, alphabets with large number of states can be easily defined by using two-digit number (starting with 00 up to 19 for a 20 state alphabet). Most importantly, this feature gives the opportunity to analyse data sets made of presence/absence character states (use the \texttt{symbols=``0 1''} option for such data).\index{binary characters} Alignments made of custom-defined states will be processed using the Jukes and Cantor model. Other options of the program (e.g., number of rate classes, tree topology search algorithm) are freely configurable. Note that, at the moment, the maximum number of different states is set to 22 in order to save memory space. It is however possible to lift this threshold by modifiying the value of the variable \x{T\_MAX\_ALPHABET} in the file `\x{utilities.h}'. The program will then have to be re-compiled. \subsubsection{Gaps and ambiguous characters} Gaps correspond to the `\x{-}' symbol. They are systematically treated as unknown characters ``on the grounds that we don't know what would be there if something were there'' (J. Felsenstein, PHYLIP main documentation). The likelihood at these sites is summed over all the possible states (i.e., nucleotides or amino acids) that could actually be observed at these particular positions. Note however that columns of the alignment that display only gaps or unknown characters are simply discarded because they do not carry any phylogenetic information (they are equally well explained by any model). PhyML also handles ambiguous characters such as $R$ for $A$ or $G$ (purines) and $Y$ for $C$ or $T$ (pyrimidines). Tables \ref{tab:ambigu_nt} and \ref{tab:ambigu_aa} give the list of valid characters/symbols and the corresponding nucleotides or amino acids. \begin{table} \begin{center} \begin{tabular}{lr|lr} \hline Character & Nucleotide & Character & Nucleotide \\ \hline $A$ & Adenosine & $Y$ & $C$ or $T$ \\ $G$ & Guanosine & $K$ & $G$ or $T$ \\ $C$ & Cytidine & $B$ & $C$ or $G$ or $T$\\ $T$ & Thymidine & $D$ & $A$ or $G$ or $T$ \\ $U$ & Uridine (=$T$) & $H$ & $A$ or $C$ or $T$ \\ $M$ & $A$ or $C$ & $V$ & $A$ or $C$ or $G$ \\ $R$ & $A$ or $G$ & $-$ or $N$ or $X$ or $?$ & unknown \\ $W$ & $A$ or $T$ & & (=$A$ or $C$ or $G$ or $T$)\\ $S$ & $C$ or $G$ & & \\ \hline \end{tabular} \end{center} \caption{{\bf List of valid characters in DNA sequences and the corresponding nucleotides.}}\label{tab:ambigu_nt} \end{table} \begin{table} \begin{center} \begin{tabular}{lr|lr} \hline Character & Amino-Acid & Character & Amino-Acid \\ \hline $A$ & Alanine & $L$ & Leucine \\ $R$ & Arginine & $K$ & Lysine \\ $N$ or $B$& Asparagine & $M$ & Methionine \\ $D$ & Aspartic acid & $F$ & Phenylalanine \\ $C$ & Cysteine & $P$ & Proline \\ $Q$ or $Z$& Glutamine & $S$ & Serine \\ $E$ & Glutamic acid & $T$ & Threonine \\ $G$ & Glycine & $W$ & Tryptophan \\ $H$ & Histidine & $Y$ & Tyrosine \\ $I$ & Isoleucine & $V$ & Valine \\ $L$ & Leucine & $-$ or $X$ or $?$ & unknown \\ $K$ & Lysine & & (can be any amino acid) \\ \hline \end{tabular} \end{center} \caption{{\bf List of valid characters in protein sequences and the corresponding amino acids.}}\label{tab:ambigu_aa} \end{table} \subsubsection{Specifying outgroup sequences}\label{sec:outgroupspecify} PhyML can return rooted trees provided outgroup taxa are identified from the sequence file. In order to do so, sequence names that display a `*' character will be automatically considered as belonging to the outgroup. The topology of the rooted tree is exactly the same as the unrooted version of the same tree. In other words, PhyML first ignores the distinction between ingroup and outgroup sequences, builds a maximum likelihood unrooted tree and then tries to add the root. If the outgroup has more than one sequence, the position of the root might be ambiguous. In such situation, PhyML tries to identify the most relevant position of the root by considering which edge provides the best separation between ingroup and outgroup taxa (i.e., we are trying to make the outgroup ``as monophyletic as possible''). \subsection{Tree format} PhyML can read one or several phylogenetic trees from an input file. This option is accessible through the {\em Tree Searching} sub menu or the `\x{-u}' argument from the command line. Input trees are generally used as initial maximum likelihood estimates to be subsequently adjusted by the tree searching algorithm. Trees can be either rooted or unrooted and multifurcations are allowed. Taxa names must, of course, match the corresponding sequence names. \begin{figure}[h] \begin{small} \begin{minipage}{\textwidth} \begin{verbatim} ((seq1:0.03,seq2:0.01):0.04,(seq3:0.01,(seq4:0.2,seq5:0.05):0.2):0.01); ((seq3,seq2),seq1,(seq4,seq5)); \end{verbatim} \end{minipage} \end{small} \caption{{\bf Input trees}. The first tree (top) is rooted and has branch lengths. The second tree (bottom) is unrooted and does not have branch lengths.} \label{fig:trees}\index{Newick format} \end{figure} \subsection{Multiple alignments and trees}\index{multiple data sets} Single or multiple sequence data sets may be used in combination with single or multiple input trees. When the number of data sets is one ($n_D = 1$) and there is only one input tree ($n_T = 1$), then this tree is simply used as input for the single data set analysis. When $n_D = 1$ and $n_T > 1$, each input tree is used successively for the analysis of the single alignment. PhyML then outputs the tree with the highest likelihood. If $n_D > 1$ and $n_T = 1$, the same input tree is used for the analysis of each data set. The last combination is $n_D > 1$ and $n_T > 1$. In this situation, the $i$-th tree in the input tree file is used to analyse the $i$-th data set. Hence, $n_D$ and $n_T$ must be equal here. \subsection{Custom amino-acid rate model}\label{sec:customaa} The custom amino-acid model of substitutions can be used to implement a model that is not hard-coded in PhyML. This model must be time-reversible. Hence, the matrix of substitution rates is symmetrical. The format of the rate matrix with the associated stationary frequencies is identical to the one used in PAML\index{PAML}. An example is given below: \begin{center} {\tiny \begin{tabular}{p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}p{0.33cm}} % Ala & Arg & Asn & Asp & Cys & Gln & Glu & Gly & His & Ile & Leu & Lys & Met & Phe & Pro & Ser & Thr & Trp & Tyr & Val \\ &&&&&&&&&&&&&&&&&&& \\ 0.55 & &&&&&&&&&&&&&&&&&& \\ 0.51 & 0.64 & &&&&&&&&&&&&&&&& \\ 0.74 & 0.15 & 5.43 & &&&&&&&&&&&&&&&& \\ 1.03 & 0.53 & 0.27 & 0.03 & &&&&&&&&&&&&&&& \\ 0.91 & 3.04 & 1.54 & 0.62 & 0.10 & &&&&&&&&&&&&&& \\ 1.58 & 0.44 & 0.95 & 6.17 & 0.02 & 5.47 & &&&&&&&&&&&&& \\ 1.42 & 0.58 & 1.13 & 0.87 & 0.31 & 0.33 & 0.57 & &&&&&&&&&&&& \\ 0.32 & 2.14 & 3.96 & 0.93 & 0.25 & 4.29 & 0.57 & 0.25 & &&&&&&&&&&& \\ 0.19 & 0.19 & 0.55 & 0.04 & 0.17 & 0.11 & 0.13 & 0.03 & 0.14 & &&&&&&&&&& \\ 0.40 & 0.50 & 0.13 & 0.08 & 0.38 & 0.87 & 0.15 & 0.06 & 0.50 & 3.17 & &&&&&&&&& \\ 0.91 & 5.35 & 3.01 & 0.48 & 0.07 & 3.89 & 2.58 & 0.37 & 0.89 & 0.32 & 0.26 & &&&&&&&& \\ 0.89 & 0.68 & 0.20 & 0.10 & 0.39 & 1.55 & 0.32 & 0.17 & 0.40 & 4.26 & 4.85 & 0.93 & &&&&&&& \\ 0.21 & 0.10 & 0.10 & 0.05 & 0.40 & 0.10 & 0.08 & 0.05 & 0.68 & 1.06 & 2.12 & 0.09 & 1.19 & &&&&&& \\ 1.44 & 0.68 & 0.20 & 0.42 & 0.11 & 0.93 & 0.68 & 0.24 & 0.70 & 0.10 & 0.42 & 0.56 & 0.17 & 0.16 & &&&&& \\ 3.37 & 1.22 & 3.97 & 1.07 & 1.41 & 1.03 & 0.70 & 1.34 & 0.74 & 0.32 & 0.34 & 0.97 & 0.49 & 0.55 & 1.61 & &&&& \\ 2.12 & 0.55 & 2.03 & 0.37 & 0.51 & 0.86 & 0.82 & 0.23 & 0.47 & 1.46 & 0.33 & 1.39 & 1.52 & 0.17 & 0.80 & 4.38 & &&& \\ 0.11 & 1.16 & 0.07 & 0.13 & 0.72 & 0.22 & 0.16 & 0.34 & 0.26 & 0.21 & 0.67 & 0.14 & 0.52 & 1.53 & 0.14 & 0.52 & 0.11 & && \\ 0.24 & 0.38 & 1.09 & 0.33 & 0.54 & 0.23 & 0.20 & 0.10 & 3.87 & 0.42 & 0.40 & 0.13 & 0.43 & 6.45 & 0.22 & 0.79 & 0.29 & 2.49 & & \\ 2.01 & 0.25 & 0.20 & 0.15 & 1.00 & 0.30 & 0.59 & 0.19 & 0.12 & 7.82 & 1.80 & 0.31 & 2.06 & 0.65 & 0.31 & 0.23 & 1.39 & 0.37 & 0.31 & \\ \\ 8.66 & 4.40 & 3.91 & 5.70 & 1.93 & 3.67 & 5.81 & 8.33 & 2.44 & 4.85 & 8.62 & 6.20 & 1.95 & 3.84 & 4.58 & 6.95 & 6.10 & 1.44 & 3.53 & 7.09 \\ \end{tabular} } \end{center} The entry on the $i$-th row and $j$-th column of this matrix corresponds to the rate of substitutions between amino-acids $i$ and $j$. The last line in the file gives the stationary frequencies and must be separated from the rate matrix by one line. The ordering of the amino-acids is alphabetical, i.e, Ala, Arg, Asn, Asp, Cys, Gln, Glu, Gly, His, Ile, Leu, Lys, Met, Phe, Pro, Ser, Thr, Trp, Tyr and Val. \subsection{Topological constraint file}\label{sec:topoconstraints} PhyML can perform phylogenetic tree estimation under user-specified topological constraints. In order to do so, one should use the \x{--constraint\_file} \x{file\_name} command-line option where \x{file\_name} lists the topological constraints. Such constraints are straightforward to define. For instance, the following constraints: \vspace{0.2cm} \begin{Verbatim} ((A,B),C,(D,E,F)); \end{Verbatim} indicate that taxa D, E and F belong to the same clade. A, B and C also belong to the same clade and the two clades hence defined should not overlap. Under these two constraints, the tree ((A,B),D,((E,F),C)) is not valid. From the example above, you will notice that the constraints are defined using a multifurcating tree in NEWICK format. Note that this tree does not need to display the whole list of taxa. For instance, while the only taxa involved in specifying topological constraints above are A, B, C, D, E \& F, the actual data set could include more than these six taxa only. PhyML tree topology search algorithms all rely on improving a starting tree. By default, BioNJ is the method of choice for building this tree. However, there is no guarantee that the phylogeny estimated with PhyML does comply with the topological constraints. While it is probably possible to implement BioNJ with topological constraints, we have not done so yet. Instead, the same multifurcating tree that defines the topological constraints should also be used as starting tree using the \x{-u} (\x{--inputtree}) option. Altogether, the command line should look like the following: \x{-u}=\x{file\_name} \x{--constraint\_file}=\x{file\_name}. It is not possible to use as input tree a non-binary phylogeny that is distinct from that provided in the constraint tree file. However, any binary tree compatible with the constraint one can be used as input tree. \subsection{Output files} \begin{table} Sequence file name~: `{\x seq}'\\ \begin{center} \begin{tabular}{ll} \hline Output file name & Content \\ \hline \x{seq\_phyml\_tree.txt} & ML tree\\ \x{seq\_phyml\_stats.txt} & ML model parameters\\ \x{seq\_phyml\_boot\_trees.txt} & ML trees -- bootstrap replicates\\ \x{seq\_phyml\_boot\_stats.txt} & ML model parameters -- bootstrap replicates \\ \x{seq\_phyml\_rand\_trees.txt} & ML trees -- multiple random starts\\ \x{seq\_phyml\_ancestral\_seq.txt} & ancestral sequences\\ \x{seq\_phyml\_ancestral\_tree.txt} & ML tree with node labels as in ancestral sequence file\\ \hline \end{tabular} \end{center} \caption{{\bf Standard output files}}\label{tab:output} \end{table} Table \ref{tab:output} presents the list of files resulting from an analysis. Basically, each output file name can be divided into three parts. The first part is the sequence file name, the second part corresponds to the extension `\x{\_phyml\_}' and the third part is related to the file content. When launched with the default options, PhyML only generates two files: the tree file and the model parameter file. The estimated maximum likelihood tree is in standard Newick format (see Figure \ref{fig:trees}). The model parameters file, or statistics file, displays the maximum likelihood estimates of the substitution model parameters, the likelihood of the maximum likelihood phylogenetic model, and other important information concerning the settings of the analysis (e.g., type of data, name of the substitution model, starting tree, etc.). Two additional output files are created if bootstrap supports were evaluated. These files simply contain the maximum likelihood trees and the substitution model parameters estimated from each bootstrap replicate. Such information can be used to estimate sampling errors around each parameter of the phylogenetic model. When the random tree option is turned on, the maximum likelihood trees estimated from each random starting trees are printed in a separate tree file (see last row of Table \ref{tab:output}). PhyML estimates ancestral sequences by calculating the {\em marginal} (as opposed to the {\em joint}) probability of each character state at each internal node of the phylogeny. These probabilities are given in the file \x{seq\_phyml\_ancestral\_seq.txt}. The bulk of this file is a table where each row corresponds to a site in the original alignment and a number corresponding labeling each internal node. The different columns of that file give the probability of each character state given the data observed at the corresponding sites and the estimated phylogeny. PhyML also outputs an extra column called MPEE\index{MPEE}\index{ancestral reconstruction}, which stands for Minimum Posterior Expected Error. This column gives the state, which can be a single nucleotide/amino-acid or a combination of more than one nucleotide/amino-acid, that is optimal under the MPEE criterion \cite{oliva19}. Also, it is relatively straightforward to identify which number corresponds to which node in the tree by examining the information provided in the Newick-formatted file \x{seq\_phyml\_ancestral\_tree.txt}. \subsection{Treatment of invariable sites with fixed branch lengths} PhyML allows users to give an input tree with fixed topology and branch lengths and find the proportion of invariable sites that maximise the likelihood (option \x{-o r}). These two options can be considered as conflicting since branch lengths depend on the proportion of invariants. Hence, changing the proportion of invariants implies that branch lengths are changing too. More formally, let $l$ denote the length of a branch, i.e., the expected number of substitutions per site, and $p$ be the proportion of invariants. We have $l = (1-p)l'$, where $l'$ is the expected number of substitutions {\em at variable sites}. When asked to optimize $p$ but leave $l$ unchanged, PhyML does the following: \begin{enumerate} \item Calculate $l' = l/(1-p)$ and leave $l'$ unchanged throughout the optimization. \item Find the value of $p$ that maximises the likelihood. Let $p^{*}$ denote this value. \item Set $l^{*} = (1-p^{*})l'$ and print out the tree with $l^{*}$ (instead of $l$). \end{enumerate} PhyML therefore assumes that the users wants to fix the branch lengths measured at {\em variable} sites only (i.e., $l^{*}$ is fixed). This is the reason why the branch lengths in the input and output trees do differ despite the use of the the \x{-o r} option. While we believe that this approach relies on a sound rationale, it is not perfect. In particular, the original transformation of branch lengths ($l' = l/(1-p)$) relies on a default value for $p$ with is set to 0.2 in practice. It is difficult to justify the use of this value rather than another one. One suggestion proposed by Bart Hazes is to avoid fixing the branch lengths altogether and rather estimate the value of a scaling factor applied to each branch length in the input tree (option \x{--contrained\_lens}). We agree that this solution probably matches very well most users expectation, i.e., ``find the best value of $p$ while constraining the ratio of branch lengths to be that given in the input tree''. Please feel free to send us your suggestions regarding this problem by posting on the forum (\url{http://groups.google.com/group/phyml-forum}). \section{Inputs \& outputs for the XML interface }\label{sec:xmlio}\index{XML} \subsection{Mixture models in PhyML}\index{mixture models}\label{sec:mixtures} PhyML implements a wide range of mixture models. The discrete gamma model \cite{yang94b} is arguably the most popular of these models in phylogenetics. However, in theory, mixture models are not restricted to the description of the variation of substitution rates across sites. For instance, if there are good reasons to believe that the relative rates of substitution between nucleotides vary along the sequence alignments, it makes sense to use a mixture of GTR models. Consider the case where substitutions between $A$ and $C$ occur at high rate in some regions of the alignment and low rate elsewhere, a mixture with two classes, each class having its own GTR rate matrix, would be suitable. The likelihood at any site of the alignment is then obtained by averaging the likelihoods obtained for each GTR rate matrix, with the same weight given to each of these matrices. PhyML implements a generic framework that allows users to define mixtures on substitution rates, rate matrices and nucleotide or amino-acid equilibrium frequencies. Each class of the mixture model is built by assembling a substitution rate, a rate matrix\footnote{the rate matrix corresponds here the symmetrical matrix giving the so-called ``echangeability rates''} and a vector of equilibrium frequencies. For instance, let $\{R_1,R_2,R_3\}$ be a set of substitution rates, $\{M_1,M_2\}$ a set of rate matrices and $\{F_1,F_2\}$ a set of vectors of equilibrium frequencies. One could then define the first class of the mixture model as $\mathcal{C}_1 = \{R_1,M_1,F_1\}$, a second class as $\mathcal{C}_2 = \{R_2,M_1,F_1\}$, and a third class as $\mathcal{C}_3 = \{R_3,M_2,F_2\}$. If $R_1$, $R_2$ and $R_3$ correspond to slow, medium and fast substitution rates, then this mixture model allows the fast evolving rates to have their own vector of equilibrium frequencies and rate matrix, distinct from that found at the medium or slow evolving sites. The likelihood at any given site $D_s$ of the alignment is then: \begin{eqnarray*} \Pr(D_s) = \sum_{c=1}^{3} \Pr(D_s | \mathcal{C}_s=c) \Pr(\mathcal{C}_s=c), \label{equ:mixtlk} \end{eqnarray*} where $\Pr(\mathcal{C}_s=c)$ is obtained by multiplying the probability (density) of the three components (i.e., rate, matrix, frequencies). For instance, $\Pr(\mathcal{C}_1=\{R_1,M_1,F_1\}) = \Pr(R_1)\times \Pr(M_1) \times \Pr(F_1)$. We therefore assume here that substitution rates, rate matrices and equilibrium frequencies are independent from one another. Note that, using the same substitution rates, rate matrices and vector of equilibrium frequencies, it is possible to construct many other mixture models. For instance, the mixture model with the largest number of classes can be created by considering all the combinations of these three components. We would then get a mixture of $3\times 2 \times 2=12$ classes, corresponding to all the possible combinations of 3 rates, 2 matrices and 2 vectors of frequencies. % : $\mathcal{C}_1 = % \{R_1,M_1,F_1\}$ $\mathcal{C}_2 = \{R_1,M_1,F_2\}$, $\mathcal{C}_3 = \{R_1,M_2,F_1\}$, % $\mathcal{C}_4 = \{R_1,M_2,F_2\}$, $\mathcal{C}_5 = \{R_2,M_1,F_1\}$, $\mathcal{C}_6 = % \{R_2,M_1,F_2\}$, $\mathcal{C}_7 = \{R_2,M_2,F_1\}$, $\mathcal{C}_8 = \{R_2,M_2,F_2\}$, % $\mathcal{C}_9 = \{R_3,M_1,F_1\}$, $\mathcal{C}_{10} = \{R_3,M_1,F_2\}$, $\mathcal{C}_{11} = % \{R_3,M_2,F_1\}$ and $\mathcal{C}_{12} = \{R_3,M_2,F_2\}$. \subsection{Partitionned (i.e., multiple-gene) analyses}\index{partitionned analysis}\index{data partitions}\index{multiple-gene analysis} We first introduce some terms of vocabulary that have not been presented before. A partitionned data set, also referred to as partition, is a set of partition elements. Typically, a partitionned data set will be made of a set of distinct gene alignments. A partition element will then correspond to one (or several) of these gene alignments. Note that the biology litterature often uses the term partition to refer to an element of a partitionned data. We thus use here instead the mathematical definition of the terms `partition' and `partition element'. Phylogenetics models usually assume individual columns of an alignment to evolve independently from one another. Codon-based models (e.g., \cite{yang98,yang00b,yang02,guindon04}) are exceptions to this rule since the substitution process applies here to triplets of consecutive sites of coding sequences. The non-independence of the substitution process at the three coding positions (due to the specificities of the genetic code), can therefore be accounted for. Assuming that sites evolve independently does not mean that a distinct model is fitted to each site of the alignment. Estimating the parameters of these models would not make much sense in practice due to the very limited amount of phylogenetic signal conveyed by individual sites. Site independence means instead that the columns of the observed alignment were sampled randomly from the same ``population of columns''. The stochasticity of the substitution process running along the tree is deemed responsible to the variability of site patterns. Some parameters of the phylogenetic model are considered to be common to all the sites in the alignment. The tree topology is typically one such parameter. The transition/transversion ratio is also generally assumed to be the same for all columns. Other parameters can vary from site to site. The rate at which substitutions accumulate is one of these parameters. Hence, different sites can have distinct rates. However, such rates are all ``drawn'' from the same probabilitic distribution (generally a discrete Gamma density). Hence, while different sites may have distinct rates of evolution, they all share the same {\em distribution} of rates. This reasonning also applies on a larger scale. When analysing multiple genes, one can indeed assume that the same mechanism generated the different site patterns observed for every gene. Here again, we can assume that all the genes share the same underlying tree topology (commonly refered to as the ``species tree''). Other parameters of the phylogenetic model, such as branch lengths for instance, might be shared across genes. However, due to the specificities of the gene evolution processes, some model parameters need to be adjusted for each gene separately. To sum up, the phylogenetic analysis of partitionned data requires flexible models with parameters, or distribution of parameters, shared across several partition elements and other parameters estimated separately for each element of the partition. The likelihood of a data set made of the concatenation of $n$ sequence alignments noted $D^{(1)}$, $D^{(2)}, \ldots, D^{(n)}$ is then obtained as follows: \begin{eqnarray*} \Pr(D^{(1)},D^{(2)},\ldots,D^{(n)}) &=& \prod_{i=1}^{n} \Pr(D^{(i)}) \\ &=& \prod_{i=1}^{n} \prod_{s=1}^{L_i} \Pr(D^{(i)}_s), \end{eqnarray*} where $L_i$ is the number of site columns in partition element $i$. $\Pr(D^{(i)}_s)$ is then obtained using Equation \ref{equ:mixtlk}, i.e., by summing over the different classes of the mixture model that applies to site $s$ for partition element $i$. Hence, the joint probability of all the partition elements is here broken down into the product of likelihood of every site for each partition element. As noted just above, any given component of the mixture model at a given particular site is shared by the other sites that belong to the same partition element and, for some of them, by sites in other partition elements (e.g., the same tree topology is shared by all the sites, throughout all the partition elements). PhyML implements a wide variety of partition models. The only parameter that is constrained to be shared by all the partition elements is the tree topology. This constraint makes sense when considering distantly related taxa, typically inter-species data. For closely related taxa, i.e., when analysing intra-species or population-level data, not all the genes might have the same evolutionary history. Recombination events combined to the incomplete lineage sorting phenomenon can generate discrepancies between the gene trees and the underlying species tree (see \cite{degnan09} for a review). The phylogenetic softwares BEST \cite{best}\index{BEST}, STEM \cite{stem}\index{STEM} and *BEAST \cite{startbeast}\index{*BEAST} are dedicated to the estimation of species tree phylogenies from the analysis of multi-gene data and allow gene-tree topologies to vary across genes. Aside from the tree topology that is common to all the sites and all the partition elements, other parameters of the phylogenetic model can be either shared across partition elements or estimated separately for each of these. When analysing three partition elements, $A$, $B$ and $C$ for instance, PhyML can fit a model where the same set of branch lengths applies to $A$ and $B$ while $C$ has its own estimated lengths. The same goes for the substitution model: the same GTR model, with identical parameter values, can be fitted to $A$ and $C$ and JC69 for instance can be used for $B$. The sections below give more detailed information on the range of models available and how to set up the corresponding XML configuration files to implement them. \subsection{Combining mixture and partitions in PhyML: the theory} The rationale behind mixture models as implemented in PhyML lies in (1) the definition of suitable rate matrices, equilibrium frequency vectors and relative rates of substitution and (2) the assembly of these components so as to create the classes of a mixture. The main idea behind partitionned analysis in PhyML lies in (1) the hypothesis of statistical independance of the different data partition elements and (2) distinct data partition can share model components such as rate matrices, equilibrium frequencies or distribution of rates across sites. More formally, the likelihood of a data set made of $n$ partition elements is written as follows: \begin{eqnarray*} \Pr(D^{(1)},D^{(2)},\ldots,D^{(n)}) &=& \prod_{i=1}^{n} \prod_{s=1}^{L_i} \Pr(D^{(i)}_s) \\ &=& \prod_{i=1}^{n} \prod_{s=1}^{L_i} \sum_{c=1}^{K_i} \Pr(D^{(i)}_s|\mathcal{C}=c) \Pr(\mathcal{C}=c), \end{eqnarray*} where $L_i$ is the number of sites in partition element $i$ and $K_i$ is the number of classes in the mixture model that applies to this same partition element. Each class of a mixture is made of a rate matrix $M$, a vector of equilibrium frequencies $F$ and a relative rate of substitution $R$. Branch lengths, $L$ and tree topology $\tau$ are also required for the calculation of the likelihood. Hence we have: \begin{eqnarray*} && \Pr(D^{(1)},D^{(2)},\ldots,D^{(n)}) \\ &&= \prod_{i=1}^{n} \prod_{s=1}^{L_i} \sum_{c=1}^{K_i} \Pr(D^{(i)}_s|\mathcal{C}=c) \Pr(\mathcal{C}=c) \\ &&= \prod_{i=1}^{n} \prod_{s=1}^{L_i} \sum_{m}^{\mathcal{M}_i} \sum_{f}^{\mathcal{F}_i} \sum_{r}^{\mathcal{R}_i} \Pr(D^{(i)}_s|M_m^{(i)},F_f^{(i)},R_r^{(i)},L^{(i)},\tau) \Pr(M_m^{(i)},F_f^{(i)},R_r^{(i)}) \mathcal{I}(m,f,r,i) % &&= \prod_{i=1}^{n} \prod_{s=1}^{L_i} \sum_{m}^{\mathcal{M}_i} \sum_{f}^{\mathcal{F}_i} \sum_{r}^{\mathcal{R}_i} % \Pr(D^{(i)}_s|M_m^{(i)},F_f^{(i)},R_r^{(i)},L^{(i)},\tau) \Pr(M_m^{(i)}) \Pr(F_f^{(i)}) \Pr(R_r^{(i)}) \end{eqnarray*} where $\mathcal{M}_i$, $\mathcal{F}_i$ and $\mathcal{R}_i$ are the number of rate matrices, vector of equilibrium frequencies and relative rates that apply to partition element $i$ respectively. $\mathcal{I}(m,f,r,i)$ is an indicator function that takes value 1 if the combination $M_m$, $F_f$ and $R_r$ is acually defined in the model for this particular partition element $i$. Its value is 0 otherwise. In the example given in section \ref{sec:mixtures} $\{R_1,R_2,R_3\}$ is the set of substitution rates, $\{M_1,M_2\}$ the set of rate matrices and $\{F_1,F_2\}$ the set of vectors of equilibrium frequencies. We then define the first class of the mixture model as $\mathcal{C}_1 = \{R_1,M_1,F_1\}$, a second class as $\mathcal{C}_2 = \{R_2,M_1,F_1\}$ and the third as $\mathcal{C}_3 = \{R_3,M_2,F_2\}$. Hence, we have $\mathcal{I}(1,1,1,i)$, $\mathcal{I}(1,1,2,i)$ and $\mathcal{I}(2,2,3,i)$ equal to one while the nine other values that this indicator function takes, corresponding to the possible combinations of two vectors of frequencies, two matrices and three rates, are all zero. As stated before, our implementation assumes that the different components of a mixture are independant. In other words, we have $\Pr(M_m^{(i)},F_f^{(i)},R_r^{(i)}) = \Pr(M_m^{(i)}) \times \Pr(F_f^{(i)}) \times \Pr(R_r^{(i)})$. In practice, the joint probability $\Pr(M_m^{(i)},F_f^{(i)},R_r^{(i)})$ is obtained as follows: \begin{eqnarray*} \Pr(M_m^{(i)},F_f^{(i)},R_r^{(i)}) = \frac{\Pr(M_m^{(i)}) \Pr(F_f^{(i)}) \Pr(R_r^{(i)})}{ \sum_{m,f,r} \Pr(M_m^{(i)}) \Pr(F_f^{(i)}) \Pr(R_r^{(i)}) \mathcal{I}(m,f,r,i)} \label{equ:weights} \end{eqnarray*} The probabilities $\Pr(M_m^{(i)})$, $\Pr(F_f^{(i)})$ and $\Pr(R_r^{(i)})$, also called `weights', can be fixed or estimated from the data. \subsection{The XML format and its use in PhyML}\label{sec:XML format} The few paragraphs below are largely inspired from the Wikipedia page that describes the XML format (\url{http://en.wikipedia.org/wiki/XML}). XML (eXtensible Markup Language) is a markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. An XML document is divided into {\em markup} and {\em content}, which may be distinguished by the application of simple syntactic rules. Generally, strings that constitute markup either begin with the character `\x{<}' and end with a `\x{>}'. Strings of characters that are not markup are content: \vspace{0.2cm} \begin{Verbatim}[frame=single, label=XML markup and content example, samepage=true, baselinestretch=0.5, fontsize=\small] content \end{Verbatim} A markup construct that begins with `\x{<}' and ends with `\x{>}' is called a {\em tag}. Tags come in three flavors: (1) start-tags (e.g, \x{

}), end-tags (e.g., \x{
}) and empty-element tags (e.g., \x{}). A {\em component} either begins with a start-tag and ends with a matching end-tag or consists only of an empty-element tag. The characters between the start- and end-tags, if any, are the element's content, and may contain markup, including other elements, which are called child elements. In the following example, the element \x{img} has two {\em attributes}, \x{src} and \x{alt}: \x{Foligno Madonna, by Raphael}. Another example would be \x{Connect A to B.} where the name of the attribute is ``\x{number}" and the value is ``\x{3}". In practice, building a mixture model in a XML file readable by PhyML is relatively straightforward. The first step is to define the different components of each class of the mixture. Consider for instance that the fitted model will have a Gamma distribution with four classes plus a proportion of invariants. The rate component of the mixture can then be specified using the following XML code: \vspace{0.2cm} \begin{Verbatim}[frame=single, label=$\Gamma4$+I rates, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} In the example above, the \x{} component completely defines a model of substitution rate variation across sites. This component has a particular identity, i.e., a name associated to it (``\x{SiteRates1}'' here), which is not mandatory. This \x{} component has six sub-components. The first is the \x{} component, followed by five \x{} components. The \x{} component defines the type of distribution that characterizes the variation of rates across sites. A discrete Gamma plus invariants is used here. Two parameters specify this distribution: the gamma shape and the proportion of invariant parameters. Their initial values are set by using the corresponding attributes and attribute values (\x{alpha="0.1"} and \x{pinv="0.4"}). Also, PhyML can optimise these parameters so as to maximise the likelihood of the whole phylogenetic model (\x{optimise.pinv="yes"} and \x{optimise.alpha="yes"}). The following five \x{} components define the rate classes themselves. The \x{id} attribute is here mandatory and must be unique to each class. Note that one of the initial (relative) rate (\x{init.value} attribute) is set to zero. The corresponding rate class (the third in this example) will then correspond to the invariant site category. Having specified the part of the phylogenetic model that describes the variation of rates across sites, we can now move on to build the rest of the model. The component below defines two substitution models: \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Rate matrices, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} This \x{} component sets out a list of substitution models (HKY85 and GTR here). Here again, the different elements in this list correspond to the \x{} sub-components. Each instance must have a unique \x{id} attribute for a reason that will become obvious shortly. The remaining attributes and their functions are described in Section \ref{sec:xmlratematrices}. The next ``ingredient'' in our phylogenetic model are vectors of nucleotide frequencies. The \x{} component below specifies two of such vectors: \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Equilibrium frequencies, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} Now, we need to assemble these three components (rate variation across sites, rate matrices and vectors of equilibrium frequencies) into a mixture model. The \x{} component below defines one such model: \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Mixture model, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} The \x{} component defines a particular partition element. In this example, the partition element corresponds to the sequence file called \x{nucleic.txt}, which is an alignment of nucleotide sequences (see the \x{data.type} attribute value). The \x{} are sub-components of the \x{} component. Each \x{} has a \x{list} atrribute. Each such \x{list} gives the ID of components that have been defined before. For instance, the first \x{} refers to the five classes of the \x{} component. The ordering of the different term in these list matters a lot since it is directly related to the elements in each class of the mixture model. Hence, the first element in the \x{} attribute of the first \x{} added to the first element in the \x{} attribute of the second \x{} plus the the first element in \x{} attribute of the third \x{} defines the first class of the mixture model. Therefore, the mixture model defined above has five classes: $\mathcal{C}_1 = \{R_1,M_1,F_1\}$, $\mathcal{C}_2 = \{R_2,M_1,F_2\}$, $\mathcal{C}_3 = \{R_3,M_1,F_1\}$, $\mathcal{C}_4 = \{R_4,M_2,F_2\}$ and $\mathcal{C}_5 = \{R_5,M_2,F_2\}$. % Going back to the different components of this model, the XML code dealing with the substitution % rates defines five classes with names {\tt R1} to {\tt R5}. The initial values of these rates are % set to 1.0, except for {\tt R5}, which is set to 0 and will therefore correspond to the invariable % site class. The {\tt } tag that follows indicate that these rates define a $\Gamma 4$+Inv % model, with initial gamma shape parameter set to 0.1 and initial proportion of invariants set to % 0.4. These two parameters will be estimated in the analysis ({\tt optimise.alpha} and {\tt % optimise.pinv} attributes set to {\tt yes}). The two rate matrices have names {\tt M1} and {\tt % M2}. {\tt M1} corresponds to a HKY85 model, with transition/transversion ratio set to 4.0 and set % to be optimised in the analysis. {\tt M2} is a GTR model, which parameters are also set to be % optimised. {\tt F1} and {\tt F2} are two vectors of nucleotide frequencies at equilibrium. These two % sets of frequencies will therefore be estimated during the analysis. \subsection{Setting up mixture and partition models in PhyML: the basics}\index{mixture models}\index{partitionned analysis}\index{data partitions} Mixture models are particularly relevant to the analysis of partitionned data. Indeed, some features of evolution are gene-specific (e.g., substitution rates vary across genes). Models that can accomodate for such variation, as mixture models do, are therefore relevant in this context. However, other evolutionary features are shared across loci (e.g., genes located in the same genomic region usually have similar GC contents). As a consequence, some components of mixture models need to be estimated separately for each partition element while others should be shared by different partition elements. Below is a simple example with a partitionned data set made of two elements, corresponding to the sequence alignment files \x{nucleic1.txt} and \x{nucleic2.txt}. Importantly, the number and names of sequences in these two alignments have to match exactly. \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Two sets of branch lengths (one per partition element), samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} Mixture elements with names \x{R1},$\ldots$, \x{R5} refer to the $\Gamma4+$I model defined previsouly (see Section \ref{sec:XML format}). The \x{} XML component defines a mixture element that had not been introduced before. It defined vectors of branch lengths that apply to the estimated phylogeny. Two instances of such vectors are defined: \x{L1} and \x{L2}. When examining the two partition elements (\x{} component), it appears that \x{L1} is associated with \x{Part1} while \x{L2} is associated with \x{Part2}. Hence, branch lengths will be estimated separately for these two partition elements. Note that a given partition element can only have one {\tt branchlengths} instance associated to it. For instance, the example given below is not valid: \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Invalid mixture, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} In other words, mixture of branch lengths are forbidden. One reason for this restriction is that mixture of edge lengths sometimes lead to non-identifiable models (i.e., models with distinct sets of branch lengths have the same likelihood) \cite{matsen07}. But mostly, combining mixture of branch lengths with mixture of rates appears like a deadly combination. Consider for instance the following model: \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Invalid mixture, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} It is here impossible to tell apart branch lengths and substitution rates. Such model is strongly non-identifiable and therefore not relevant. In the example given above, the same $\Gamma4+$I model (i.e. the same gamma shape parameter and proportion of invariant ) applies to the two partition elements. It is possible to use two distinct $\Gamma4+$I models instead using the following XML code: \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Two distinct $\Gamma4+$I models, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} \x{SiteRates1} and \x{SiteRates2} here define two distinct $\Gamma4+$I models. Each of these models apply to one of the two partition elements (\x{nucleic1.txt} and \x{nucleic2.txt}), allowing them to display different patterns of rate variation across sites. \subsection{XML options} \subsubsection{{\tt phyml} component}\index{XML options!{\tt phyml} component} Options: \begin{itemize} \item \x{output.file="filename"}. The main output files of PhyML analysis will be named \x{filename\_phyml\_tree} and \x{filename\_phyml\_stats}. \item \x{bootstrap="nreplicates"}. Run \x{nreplicates} replicates for the non-parametric bootstrap analysis. \item \x{run.id="idstring"}. PhyML will append the string \x{idstring} to each output file. \item \x{print.json.trace="yes|true|no|false"}. PhyML will print the estimated trees, the corresponding loglikelihoods and various model parameters at multiple stages of the estimation process. This option is useful for monitoring the progress of the analysis when processing large data sets. \item \x{print.trace="yes|true|no|false"}. PhyML will print the estimated trees (and the corresponding loglikelihoods) at multiple stages of the estimation process. This option is also useful for monitoring the progress of the analysis when processing large data sets. \item \x{branch.test="aBayes|aLRT|SH|no"}. Calculate fast branch support using the aBayes method \cite{anisimova11}, aLRT \cite{anisimova06} or SH \cite{shimodaira99} tests. These branch statistics are much faster to estimate than the bootrap proportions and usually provide good estimates of the probabilities that the corresponding edges are correctly inferred (see Anisimova et al. 2011 for more precision). By default and if no bootstrap analysis is performed, branch supports are estimated using the aBayes approach. \item \x{quiet="yes|no"}. Runs PhyML in quiet mode when \x{quiet=yes}. The program will not pause if the memory required to run the analysis exceeds 256MB and will not output the progresssion of the log-likelihood scores on the standard output. \item \x{memory.check="yes|no"}. By default, when processing a large data set, PhyML will pause and ask the user to confirm that she/he wants to continue with the execution of the analysis despite the large amount of memory required. Setting \x{memory.check=no} skips this question. It is especially useful when running PhyML in batch mode. \end{itemize} \subsubsection{{\tt topology} component}\index{XML options!{\tt topology} component} Each instance of the \x{topology} component (there should be only one...) has the following options: \begin{itemize} \item \x{init.tree="bionj"|"user"|"random"}. Starting tree. Default is \x{bionj}. \item \x{n.rand.starts="X"}. Number of random starting trees. Default is 5. \item \x{file.name="name\_of\_tree\_file"}. In case \x{init.tree="user"}, this attribute is mandatory. \x{name\_of\_tree\_file} is a text file containing a tree in NEWICK format. \item \x{optimise.tree="yes"|"true"|"no"|"false"}. The starting tree topology as defined by \x{init.tree} is to be optimised (or not) so as to maximise the likelihood function. \item \x{search="nni"|"spr"|"none"}. Tree topology search is conducted using NNI (fast), SPR (a bit slower but more accurate) or no moves. \end{itemize} \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Example of `topology' component, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} \subsubsection{{\tt ratematrices} component}\index{XML options!{\tt ratematrices} component}\label{sec:xmlratematrices} Each instance of a \x{ratematrices} component have the following options: \begin{itemize} \item \x{model="JC69"|"K80"|"F81"|"F84"|"HKY85"|"TN93"|"GTR"|"custom"} for nucleotide data. The default is \x{"HKY85"}.\\ \x{model="LG"|"WAG"|"JTT"|"MtREV"|"Dayhoff"|"DCMut"|"RtREV"|"CpREV"|"VT"}\\\x{|"Blosum62"|"MtMam"|"MtArt"|"HIVw"|"HIVb"|"customaa"} for amino-acid sequences. The default is \x{"LG"}. \item \x{model.code="012345"}. For \x{custom} model applied to nucleotide sequences: set the string of digits that define a custom substitution model. See Table \ref{tab:modelcode} on page \pageref{tab:modelcode} for more information about the model codes. \item \x{ratematrix.code="filename"}. When used in conjunction with \x{model="customaa"}, \x{filename} is the name of the file that gives the rates of substitution between amino-acids as well as their frequences at equilibrium using PAML rate matrix format. An example of such file is provided in {phyml/examples/X1.mat}. \item \x{optimise.rr="yes"|"true"|"no"|"false"}. For \x{custom} and \x{GTR} nucleotide models only: optimise the substitution rate model parameters. \item \x{optimise.tstv="yes"|"true"|"no"|"false"}. For \x{K80}, \x{F84}, \x{HKY85} and \x{TN93} models only: optimise the transition/transversion rate ratio. \item \x{tstv="value"}. For \x{K80}, \x{HKY85} and \x{TN93} models only: set the transition/transversion to a given value. \end{itemize} An instance of a \x{ratematrices} component where a GTR or a custom model of substitutions between nucleotides is used can have a \x{rr} component associated to it in order to specificy the relative rates of substitutions (see example below). Also, the {\tt ratematrices} component has the attribute {\tt optimise.weights=yes/no} (default is {\tt no}). If {\tt optimise.weights=yes}, then the probabilities (or weights) or each matrix in the set of matrices defined by this component (see Equation \ref{equ:weights}), will be estimated from the data. \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Example of `ratematrices' component, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} \subsubsection{{\tt equfreqs} component}\index{XML options!{\tt equfreqs} component} Each instance of a \x{equfreqs} component has the following options: \begin{itemize} \item \x{base.freqs="a,b,c,d"} where \x{a-d} are nucleotide frequencies. Make sure that these frequencies are separated by comas and no space character is inserted. \item \x{freqs="empirical|model|user|optimized"}. Character frequencies are derived from counting the number of occurence of each amino-acid in the alignment (\x{freqs="empirical"}), given by the substitution model (\x{freqs="model"}), user-defined (\x{freqs="user"}, only available for nucleotide data) or estimated using maximum likelihood (\x{freqs="optimized"}). \end{itemize} The {\tt equfreqs} component has the attribute {\tt optimise.weights=yes/no} (default is {\tt no}). If {\tt optimise.weights=yes}, then the probabilities (or weights) or each vector of equilibrium frequencies in the set of vectors defined by this component (see Equation \ref{equ:weights}), will be estimated from the data. \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Example of `equfreqs' component, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} \subsubsection{{\tt branchlengths} component}\index{XML options!{\tt branchlengths} component} Options: \begin{itemize} \item \x{optimise.lens="yes"|"true"|"no"|"false"}: branch lengths are optimised or not. The default is set to \x{"yes"}. \end{itemize} \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Example of `branchlengths' component, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} \subsubsection{{\tt siterates} component}\index{XML options!{\tt siterates} component} Each instance defines a class of relative rate of substitution. It has the following options: \begin{itemize} \item \x{value="val"}, where \x{"val"} is the relative substitution rate for the corresponding class. \end{itemize} A \x{siterates} component generally includes a \x{weights} element that specifies the probabilitic distribution of the relative rates. The available options for such element are: \begin{itemize} \item \x{family="gamma|gamma+inv|freerates"}. \x{gamma} indicates that the distribution of the relative rates is set to be a discrete Gamma density. \x{gamma+inv} indicates that the relative rate model is a mixture of Gamma and invariant sites (this is the common $\Gamma+$I model). FreeRate is a model that does not use any parametric function to describe the distribution of the relative rates (see \cite{soubrier12}). Under this option, relative rates and the corresponding frequencies of these classes are directly estimated from the data. While such approach is slightly more computationally demanding than the $\Gamma$ (or $\Gamma$+I) model, it often provides a significantly better fit to the data. \item \x{alpha="value|optimised"}, where \x{value} is a real positive number. Use this option to set the gamma shape parameter to the selected value. \x{optimised}: the parameter is estimated from the data (see also next option). \item \x{optimise.alpha="yes|true|no|false"}. Optimise the shape of the Gamma distribution of relative rates (or not). \item \x{pinv="value|optimised"}, where \x{value} is in $[0,1]$. Use this option to set the proportion of invariants to the selected value. \x{optimised}: the parameter is estimated from the data (see also next option). \item \x{optimise.pinv="yes|true|no|false"}. Optimise the proportion of invariable sites (or not). \item \x{optimise.freerates="yes|true|no|false"}. Optimise the parameters of the FreeRate model, i.e., the relative rates and the corresponding frequencies. \end{itemize} \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Example of `siterates' component (discrete gamma model), samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} Setting up an analysis using the FreeRate\index{FreeRate} mixture model of rate variation across sites \cite{soubrier12} requires a bit more work. As in the discrete gamma model, the FreeRate model relies on definies classes of (relative) rates. In the discrete gamma model, each class has the same frequency. This is no longer the case with FreeRate. It is thus necessary to specify the frequency, or weight, of each class of rate in the XML file. The example below illustrates how these weights are defined in practice: \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Example of `siterates' component (FreeRate model), samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} Note that the weights do not have to sum to one. PhyML rescales them before starting the analysis such that these weights are transformed into proper probabilities. The relative rates themselves are rescaled too such that the weighted average (relative) rate is equal to one after rescaling. \subsubsection{{\tt partitionelem} and {\tt mixtureelem} components}\index{XML options!{\tt partitionelem} component}\index{XML options!{\tt mixtureelem} component} Options: \begin{itemize} \item \x{file.name="inputfilename"}, where \x{inputfilename} is the name of the input sequence file (in PHYLIP format) to be analysed. \item \x{data.type="nt|aa"}. Specify the type of sequences to be processed (nucleotide of amino-acid sequences). \item \x{interleaved="yes|true|no|false"}. Interleaved (\x{yes|true}) or sequential format (\x{no|false}) for the sequence alignment. \item \x{optimise.tree.scale="yes|true|no|false"}. The sum of edge length (or tree size) is optimized. This option is relevant when different data partition elements point to the same set of edge lengths so that setting \x{optimise.tree.scale="yes"} will find the optimal ratio of tree sizes considering the two elements. In other words, the different trees all share the same (relative) edge lengths but the corresponding partition elements have different mean rates of substitution. \item \x{tree.scale="val"}. \x{val} is the value of the (relative) substitution rate. By default, its value is set to 1.0. \item \x{print.site.lk="yes|true|no|false"}. The likelihood at each site (and other information) will be written in a file named \x{inputfilename\_phyml\_lk}. \end{itemize} Each \x{partitionelem} element should include exactly four \x{mixtureelem} elements, corresponding to branch lengths, equilibrium frequencies, substitution rate model and tree topology. The ordering of in which the \x{mixtureelem} elements are given does not matter, though exceptions apply for the $\Gamma+I$ model (see below). The $n$-th element in the \x{list} attribute of each \x{mixtureelem} defines the $n$-th class of the mixture model. In the example given below, the first class of the mixture is made of the following elements: \x{T1}, \x{F1}, \x{R1} and \x{L1}, the second class is made of \x{T1}, \x{F1}, \x{R2} and \x{L1}, etc. \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Example of `partitionelem' component, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} In general, the ordering of the \x{mixtureelem} elements does not matter. However, when the model has invariable sites, then the corresponding class should be first in the list of classes provided by \x{mixtureelem}. For instance, in the example above, if the rates are defined as follows: \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Example of `siterates' component, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} then \x{R1} corresponds to the invariable rate class (as \x{init.value="0.0"}). As \x{R1} is first in the \x{mixtureelem} (see line 6 in the example of \x{`partionelem'} given above), PhyML will print out an explicit error message and bail out. One way to avoid this shortcoming is to define \x{mixtureelem} as \x{R4, R2, R3, R1} instead. \subsection{Example: GTR + $\Gamma$4 + I} The example below provides all the required options to fit a $\Gamma$4+I model to a single alignment of nucleotide sequences under the GTR model of substitution using a SPR search for the best tree topology. The \x{phyml} component sets the name for the analysis to \x{simple.example}, meaning that each output file will display this particular string of characters. Also, the tree and statistics file names will begin with \x{p1.output}. The tree topology will be estimated so as to maximise the likelihood and the topology search algorithm used here is SPR, as indicated by the value of the corresponding attribute (i.e., \x{search="spr"}). Only one vector of branch lengths will be used here since only one partition element will be processed. Hence, the \x{} component only has one \x{} sub-component. Also, a single GTR model will apply to all the classes for the mixture model -- the \x{} component has only one \x{} sub-component, corresponding to this particular substitution model. The next component, \x{}, indicates that a single vector of equilibrium frequencies will apply here. Next, the \x{} component has five \x{} sub-components. Four of these correspond to the non-zero relative rates of evolution a defined by a discrete Gamma distribution. The last one (\x{}) defines the class of the mixture corresponding to invariable sites. The \x{} component indicates that a $\Gamma+$I model will be fitted here. The shape parameter of the Gamma distribution and the proportion of invariants will be estimated from the data. The \x{} gives information about the sequence alignment (the corresponding file name, the type of data and the alignment format). The \x{} components next define the mixture model. Each class of the fitted model corresponds to one column, with the first column made of the following elements: \x{T1, M1, F1, R1} and \x{L1}. The second class of the mixture is made of \x{T1, M1, F1, R2, L1} and so forth. \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Simple PhyML XML example, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} \subsection{Example: LG4X}\index{lg4x} The example below shows how to fit the LG4X model \cite{lg4x} to a given alignment of amino-acid sequences (file \x{M587.nex.Phy}). LG4X is a mixture model with four classes. Each class has its own rate and corresponding frequencies (hence the use of the FreeRate model below, see the \x{} component). In the particular example given here, the rate values and frequencies are set by the users. These parameters will then be optimized by PhyML (\x{optimise.freerates="yes"}). Each class also has its own rate matrix and vector of equilibrium frequencies, which need to be provided by the user (Note that these matrices can be downloaded from the following web address: \url{http://www.atgc-montpellier.fr/download/datasets/models/lg4x/LG4X_4M.txt}. They are also provided in the PhyML package \x{example/lg4x/} directory.) \vspace{0.2cm} \begin{Verbatim}[frame=single, label=LG4X, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} In order to fit the LG4X model to the \x{proteic} sequence file provided in the \x{examples/} directory, simply type \x{./phyml --xml=../examples/lg4x/lg4x.xml} (assuming the PhyML binary is installed in the \x{src/} directory). You can of course slightly tweak the file \x{../examples/lg4x/lg4x.xml} and use it as a template to fit this model to another data set. \subsection{Example: CAT-like model}\index{CAT model} The CAT model \cite{lartillot04} is a mixture model whereby each site may have its own exchangeability rate matrix and vector of state frequencies. In its original exposition, the number of classes in the mixture was also a parameter to be estimated from the data. PhyML uses a fixed number of classes instead. This lesser degree of sophistication facilitates the interpretation of parameter estimates. Examination of the statistics and site-likelihood files produced by PhyML under the CAT-like model makes it indeed straightforward to spot sites with peculiar substitution pattern (e.g., sites that sustained only certain types of substitutions, for instance between purines or between pyrimidines). In the example that follows, the CAT-like model implemented has six rate classes goverened by a FreeRate model. The first and second class share the same GTR matrix (M1) and vector of nucleotide frequencies (F1) (see the two leftmost columns in the assemblage matrix). The third and fourth also share the same GTR matrix (M2) and vector of frequencies (F2) which are estimated independantly from M1 and F1. Finally, the last two classes of the mixture share M3 and F3, which are independant from the other rate matrices and state frequencies. \vspace{0.2cm} \begin{Verbatim}[frame=single, label=CAT, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} \subsection{Example: multiple partition elements} The example below gives the complete XML file to specify the analysis of three partition elements, corresponding to the nucleotide sequence files \x{small\_p1\_pos1.seq}, \x{small\_p1\_pos2.seq} and \x{small\_p1\_pos3.seq} in interleaved PHYLIP format. Importantly, the number and names of sequences in these three alignments match exactly. \x{small\_p1\_pos1.seq} is fitted with the HKY85 model of substitution (with the transition/transversion ratio being estimated from the data), combined to a $\Gamma4$ model of rate variation across sites (with the gamma shape parameter being estimated from the data). \x{small\_p1\_pos2.seq} is fitted to a custom substitution model with the constraint $A\leftrightarrow G$=$C\leftrightarrow T$. The nucleotide frequencies are set to $\frac{1}{4}$ here. The model does not allow substitution rates to vary across sites. \x{small\_p1\_pos3.seq} is fitted using a GTR model conbined to a $\Gamma4+$I model of rate variation across sites. Note that the equilibrium nucleotide frequencies for the fourth and fifth class of the mixture are set to be equal to that estimated from the first partition element (i.e., \x{F1}) . The initial phylogeny is built using BioNJ and the tree topology is to be estimated using a NNI search algorithm. \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Example of PhyML XML file, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Example of PhyML XML file (ctnd), samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} \subsection{Branch lengths with invariants and partionned data} Accommodating for models with invariable sites applying to some elements of a partitioned data, with these elements sharing the same set of edge lengths can lead to inconsistencies. Consider for instance a partitioned data set with two elements. Assume that these two elements share the same set of edge lengths. Also, consider that GTR+I applies to the first element and HKY applies to the second. Now, the expected number of substitutions per site for the first element of the partition is equal to $(1-p)l$, where $p$ is the estimated proportion of invariants and $l$ is the maximum-likelihood estimate for the length of that specific edge. For the second element of the partition, the expected number of substitutions per site is equal to $l$, rather than $(1-p)l$. While $l$ are common to the two elements, matching the specification of the input model, the actual edge lengths do differ across the two partition elements. Please be aware that, due to the programming structure implemented in PhyML, the program will only return one value here, which will be equal to $(1-p)l$. \section{Citing PhyML} The ``default citation'' for PhyML is: \begin{itemize} \item ``New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0''. Guindon S., Dufayard J.F., Lefort V., Anisimova M., Hordijk W., Gascuel O. 2010, {\it Systematic Biology}, 59(3):307-321 \end{itemize} The ``historic citation'' for PhyML is: \begin{itemize} \item ``A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood'' Guindon S., Gascuel O. 2003, {\it Systematic Biology}, 52(5):696-704 \end{itemize} \section{Other programs} PhyML is software package that provides tools to tackle problems other than estimating maximum likelihood phylogenies. Installing these tools and processing data sets is explained is the following sections. \subsection{PhyTime}\index{PhyTime} PhyTime is a program that uses Bayesian sampling techniques to infer phylogenies from the analysis of genetic and fossil data. Edge lengths (and node heights) are expressed in calendar time units as opposed to expected number of substitutions as in PhyML. PhyTime can thus be used to date past evolutionary events. The main original features compared to other software for molecular dating are: \begin{itemize} \item a model of rate evolution whereby rates can vary along each branch (other software consider that the rates of evolution vary from one branch to another but stay constant along any given branch) \cite{guindon13}. \item the opportunity to accomodate for uncertainty in calibration information. A single time constraint can apply to multiple clades with corresponding probabilities as determined from prior analysis of fossil data \cite{guindon18}. \end{itemize} \subsubsection{Installing PhyTime} Compiling PhyTime is straightforward on Unix-like machines (i.e., linux and MacOS systems). PhyTime is not readily available for Windows machines but compilation should be easy on this system too. In the `phyml' directory, where the `src/' and `doc/' directories stand, enter the following commands: {\setlength{\baselineskip}{0.5\baselineskip} \begin{verbatim} ./configure --enable-phytime; make clean; make; \end{verbatim} } This set of commands generates a binary file called \x{phytime} which can be found in the `src/' directory. \subsubsection{Running PhyTime} Passing options and running PhyTime on your data set is quite similar to running PhyML using an XML parameter input file, i.e., a typical run would be launched using the following command: \x{./phytime --xml=./dating\_example.xml}, assuming that the \x{phytime} binary file is in the same directory as the XML control file \x{dating\_example.xml}, which happens to be the current directory here. The main differences between PhyML and PhyTime are explained below: \begin{itemize} \item Unlike PhyML, PhyTime requires calibration (along with genetic sequence) data as input. The format for defining the relevant time intervals is described below. \item PhyTime does not allow partitionned (i.e., multigene) analysis yet. \end{itemize} \subsubsection{PhyTime input} As stated above, PhyTime takes as input an XML file very similar to those compatible with PhyML. An example is given thereafter. The first part of the input file (up to the \x{partitionelem} block) corresponds to a standard XML input file for PhyML. Note however that the tag name ``\x{phytime}'' replaces ``\x{phyml}''. The second part is PhyTime-specific. Three new tags can be found here: \x{lineagerates}, \x{clade} and \x{calibration}\index{PhyTime!calibration}. The \x{lineagerates}\index{PhyTime!lineagerates} element is used to define the model of rate variation across lineages. Its only attribute is \x{model} which can take the following values: \begin{itemize} \item \x{model=strictclock} or \x{model=clock} implements the strict clock model, i.e., all lineages evolve at the same instantaneous rate, at any point in time. \item \x{model=lognormal} or \x{model=normal}. The logarithm of the average rate along each branch is distributed as a normal distribution. The mean and variance of this distribution are the same for all branches. More precisely, let $R_b$ be the average rate of substitution along edge $b$. We have $R_b=X_b\cdot \mu$, where $\mu$ is the ``baseline'' (or average over lineages) rate of substitution and $X_b$ is thus the branch-specific {\em relative} rate of substitution. The lognormal model has $\log(X_b) \sim \mathcal{N}(1,\nu)$, i.e., the logarithm of the relative rate is normally distributed with mean (mode and median) set to 1.0 and standard deviation equal to $\nu$. \item \x{model=geometricbrownian} or \x{model=brownian} or \x{model=geo}. The logarithm of the rate of substitution evolves according to a Brownian motion process along the tree. More precisely, the logarithm of the rate of evolution at the end of edge $b$, noted as $Y_b^{\text{stop}}=\log(R_b^{\text{stop}})$, is normally distributed with variance $v := \nu \cdot \Delta t_b$, where $\Delta t_b$ is the (calendar) time elapsed along edge $b$, and mean equal to $Y_b^{\text{start}}-\frac{1}{2} v$, so that $\Ex(R_b^{\text{stop}})=\Ex(R_b^{\text{start}})$. Under the geometric Brownian motion model, the average rate of evolution is also random. This variability is taken into account in the calculation of the transition between nucleotides or amino-acids along edges. See Guindon, 2012, {\em Syst. Biol.} for more information. \end{itemize} The \x{clade}\index{PhyTime!clade} element is used to define a subset of taxa. Each of these taxa is given in a \x{taxon} element. The value that each taxon takes is a string corresponding to the name of one of the sequences in the alignment file. In the example that follows, \x{Gymno\_Araucaria}, \x{Gymno\_Ginko}, \x{Gymno\_Juniperus} and \x{Gymno\_Juniperus} are three taxa that define a clade called \x{Gymno1}. Note that this clade may not be monophyletic. In fact, it is not considered as such (i.e., monophyletic) during the inference. The second important element is \x{calibration}. It defines time intervals corresponding to the time of diversification (i.e., the time of the most recent common ancestor) of the set of taxa the calibration points to. In other words, a given time interval defines the calibration for the timing of the crown node of a given clade. Each interval is defined using the \x{upper} and \x{lower} tags. The upper (resp. lower) bound for each interval is a date expressed in any unit you fancy. \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Example of PhyTime XML file, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} \begin{Verbatim}[frame=single, label=Example of PhyTime XML file, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] 40 60 50 200 \end{Verbatim} \subsubsection{Accounting for calibration uncertainty} It is not always obvious to determine with full precision and accuracy where in the tree a given fossil branches in {\em a priori}. For instance, a first morphological feature of an ancestral (fossilized) species might be shared by species A but not B nor C, while a second feature might be displayed by A and B but not C. Assuming for simplicity that the true tree topology is ((A,B),C), then the first feature calibrates the most recent common ancestor (MRCA)\index{MRCA} of A and B. Indeed, this ancestor cannot be younger than the fossil itself because, if this was the case, then A {\em and} B would display the morphological feature of interest. In a similar fashion, the second feature suggests that the calibration should instead apply to the MRCA of A, B and C. Therefore, assuming that the fossil was discovered in a well-defined geological layer which age range is known without ambiguity, there is still uncertainty around the node in the tree this time interval calibrates. In the particular example given above, it is not clear whether the calibration constraints applies to the node corresponding to the MRCA of A and B, or the root of the tree. PhyTime accomodates for this uncertainty by giving a probability to the two scenari: with probability $\alpha$ the calibration time interval applies to the MRCA of species A and B and with probability $1-\alpha$, this interval calibrates the age of the MRCA of A, B and C. It is fairly straightforward to set up an analysis that incorporates probabilistic distributions on calibrations in PhyTime. The section of the XML file below gives a simple example whereby the calibration ``\x{cal1}'' applies to clade ``\x{Gymno1}'' (i.e., the smallest clade in the tree that displays the four corresponding taxa) with probability 0.8 and to clade ``\x{Gymno2}'' with probability 0.2. Also, calibration \x{cal2} applies to ``\x{Gymno1}'' with probability 1.0, which is implicit here and thus does not need to be specified in the XML file. \begin{Verbatim}[frame=single, label=Calibrating with uncertainty, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] 40 60 50 200 \end{Verbatim} Note that in this example, it can be the case that both \x{cal1} and \x{cal2} apply to the same clade (\x{Gymno1}). In that particular situation, the actual calibration interval is conservative and derives from the overlap of the two calibration time intervals (one corresponding to \x{cal1}, the other to \x{cal2}). The MRCA of \x{Gymno\_Araucaria}, \x{Gymno\_Ginko}, \x{Gymno\_Juniperus} and \x{Gymno\_Juniperus} has an age that falls here in the $[50,60]$ interval. \subsubsection{MCMC settings}\label{sec:phytimesettings} PhyTime estimates the joint posterior distribution of the phylogenetic model parameters using an MCMC algorithm. This algorithm relies on sampling values of these parameters iteratively. The default number of iterations is fixed to 1E+07 and values of parameters are recorded every 1E+03 iteration of the MCMC algorithm. The burn-in period, during which tuning parameters of the MCMC as adjusted, lasts for 1E+04 iterations. These three parameters can be modified by setting the values of the attributes \x{mcmc.chain.len}, \x{mcmc.sample.every} and \x{mcmc.burnin} in the XML parameter file accordingly. For instance, the first line of this file could look as follows: \vspace{0.2cm} \begin{Verbatim}[frame=single, label=MCMC settings, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} The majority of analyses will not warrant 1E+07 iterations to converge to the target distribution (i.e., converge to the solution). I thus recommend monitoring each analysis by loading on a regular basis the statistics output file produced by PhyTime and interrupt the analysis once the effective sample sizes of all parameters have reached 200. The MCMC algorithm implemented in PhyTime relies on multiple operators that update different parameters, or the same parameter in different ways. The weights of these operators, i.e., the frequency with which they are applied, were adjusted for the analysis of the plant data set described in \cite{guindon18}. If you think these default values are not suitable for your own analysis, please feel free to have a look at the function \x{MCMC\_Complete\_MCMC} in the file \x{mcmc.c} to make the appropriate changes to the values of \x{mcmc->move\_weight[XXX]}, where \x{XXX} is the name of the operator. Alternatively, you might want to send me an email (\url{guindon@lirmm.fr}) if you are unsure about all this. It is generally very useful to compare the posterior estimates of model parameters to that obtained from a sampler that ignores sequence data (i.e., the Bayesian sampler only takes as input data the calibration information). Such analysis helps quantifying how sensitive are the marginal posterior estimates to the estimates one would get prior collecting and analysing genetic sequences. The attribute \x{ignore.sequences} used in the \x{phytime} tag permits to run such analysis. The first lines of a corresponding XML file would then look as follows: \vspace{0.2cm} \begin{Verbatim}[frame=single, label=Sampling from the prior in PhyTime, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] \end{Verbatim} \subsubsection{PhyTime output} The program PhyTime generates two output files. The statistics file is called `\x{outputfile\_phytime\_stats\_runid.txt}'. It lists the node times and (relative) substitution rates on edges sampled during the estimation process. It also gives the sampled values for other parameters, such as the autocorrelation of rates (parameter `nu'), and the rate of evolution (parameter `clock') amongst others. This output file can be analysed with the program Tracer\index{Tracer} from the BEAST\index{BEAST} package (\url{http://beast.bio.ed.ac.uk/Main_Page}) or Icylog (\url{http://tgvaughan.github.io/icylog/}\index{Icylog}). It is also possible to process this file using R here. Section \ref{sec:phytimeexample} of this document gives more information about the statistics output file. The second file is called `\x{outputfile\_phytime\_trees\_runid.txt}'. It is the list of rooted trees that were collected during the estimation process, i.e., phylogenies sampled from the posterior density of trees. This file can be processed using the software TreeAnnotator, also part of the BEAST package (see \url{http://beast.bio.ed.ac.uk/Main_Page}) in order to generate confidence sets for the node time estimates. \subsubsection{An example of PhyTime input and output files}\label{sec:phytimeexample} The directory \x{phyml/examples/phytime/} provides a sequence alignment (\x{seq.txt}) and an XML input file (\x{dating\_example.xml}) that can be used to run an analysis (using the following command: \x{phytime --xml=dating\_example.xml}). The XML file is very similar to that described above and will thus not be discussed further. The columns of the statistics output file are as follows: \begin{itemize} \item \x{sample}: the index of the iteration in the MCMC algorithm. For instance, a sample value of 100 means that 100 ``moves'' in the MCMC have been applied so far, one such move can correspond to an attempt to modify the tree topology, or height of some internal nodes, etc. \item \x{lnL(posterior)}: the logarithm of the posterior density of the current model parameters. It is this function that the MCMC attempts to get samples from. \item \x{lnL(seq)}: the logarithm of the conditional probability of the sequence alignment given the current model parameters. \item \x{lnL(times)}: the logarithm of the probability density of the phylogeny evaluated under the birth and death branching process. \item \x{lnL(rates)}: the logarithm of the probability density of the relative substitution rates along every edge of the current phylogeny. \item \x{birth}: sampled values of the birth parameter. Histogram of these values provide an estimate of the marginal posterior distribution for this parameter. \item \x{death}: death parameter for the birth-death branching process. \item \x{clock}: mean rate of substitution. \item \x{root}: age of the root node, i.e. the MRCA of all taxa in the sample. \item \x{tstv}: transition/transversion ratio (the substitution model used here is HKY, as selected in the \x{param.xml} file). \item \x{nu}: rate autocorrelation parameter. \item \x{rr0}, \x{rr1}, \x{rr2}, \x{pr0}, \x{pr1}, \x{pr2}: relative rate of substitution and the corresponding frequencies. The mixture model of rate variation used in this analysis is the FreeRate model \cite{soubrier12} with three rate classes. These columns give the values of these six parameters (though there are only four free parameters) sampled from the target (posterior) distribution. \item \x{t(calib:cal1\_clade:Gymno1)}: sampled age of the MRCA of \x{Gymno1} when calibration \x{cal1} applies to this particular node. \item \x{t(calib:cal1\_clade:Gymno2)}: sampled age of the MRCA of \x{Gymno2} when calibration \x{cal1} applies to this node. \item \x{t(calib:cal2\_clade:Gymno1)}: sampled age of the MRCA of \x{Gymno1} when calibration \x{cal12} applies to this node. \item \x{clade(calib:cal1)}: the clade id (\x{Gymno1} or \x{Gymno2}) calibration \x{cal1} applies to (0 for \x{Gymno1} and 1 for \x{Gymno2}). \item \x{clade(calib:cal2)}: the clade id calibration \x{cal2} applies to. \item \x{br0}, $\ldots$, \x{br41}: relative rate on all edges in the tree. \item \x{t22}, $\ldots$, \x{t42}: ages of all internal nodes. \item The remaining columns provides information about the acceptance rate of various operators in the MCMC and the corresponding tuning parameters. \end{itemize} The script below can be copied and pasted into R in order to produce the plots in Figure \ref{fig:phytimetrace} (assuming R was launched from the \x{phyml/examples/phytime} directory). The two plots at the top left and center give the trace of the node age of the MRCA of \x{Gymno1} when calibration \x{cal1} applies to it (left) and that of \x{Gymno2} when the same calibration \x{cal1} applies to it (centre). The plot on the top right gives the clade (which of \x{Gymno1} or \x{Gymno2}) the calibration \x{cal1} applies to. The analysis of these plots shows the impact of applying the calibration constraint \x{cal1} to \x{Gymno1} and \x{Gymno2} alternatively. When \x{cal1} applies to \x{Gymno2} (i.e., ({\em Gymno Araucaria}, {\em Gymno Juniperus}, {\em Gymno Sciadopitys})) the age of the MRCA of \x{Gymno1} ((i.e., ({\em Gymno\_Ginkgo}, {\em Gymno Araucaria}, {\em Gymno Juniperus}, {\em Gymno Sciadopitys}) is free to wander towards older values compared to the situation where both \x{cal1} and \x{cal2} apply to \x{Gymno1}. The age of the MRCA of \x{Gymno2} is here bound to fall in the $[40,60]$ time interval. When \x{cal1} applies to \x{Gymno1} instead, then the MRCA of that clade has to fall in the $[50,60]$ interval, while that of \x{Gymno2} has to be younger than the MRCA of \x{Gymno1}. \begin{Verbatim}[frame=single, label=R script to produce traces from the PhyTime statistics file, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] file=paste("out_phytime_stats_June2018.txt",sep=""); d=read.table(file,header=T); idx=floor(0.1*length(d$sample)):length(d$sample) par(mfrow=c(2,3),mar=c(5,4,2,2)); plot(d$sample[idx],d$t.calib.cal1_clade.Gymno1.[idx],type="l", col="red",ylab="Time",xlab="Sample",main="Time Gymno1") plot(d$sample[idx],d$t.calib.cal1_clade.Gymno2.[idx],type="l", col="red",ylab="Time",xlab="Sample",main="Time Gymno2") plot(d$sample[idx],d$clade.calib.cal1.[idx],type="l",col="black", ylab="Clade",xlab="Sample",main="(0->Gymno1, 1->Gymno2)") plot(d$sample[idx],d$lnL.seq.[idx],type="l",col="orange", ylab="log(Probability)",xlab="Sample",main="Likelihood sequences") plot(d$sample[idx],d$lnL.posterior.[idx],type="l",col="orange", ylab="log(density)",xlab="Sample",main="Joint posterior") plot(d$sample[idx],d$lnL.times.[idx],type="l",col="orange", ylab="log(density)",xlab="Sample",main="Branching process") \end{Verbatim} \begin{figure} \begin{center} \includegraphics[width=13.8cm]{./fig/phytimetrace} \end{center} \caption{{\bf Traces from the statistics output file produced by PhyTime.}} \label{fig:phytimetrace} \end{figure} % Important information is also displayed on the standard output of PhyTime (the standard output % generally corresponds to the terminal window from which PhyTime was launched). The first column of % this output gives the current generation, or run, of the chain. It starts at 1 and goes up to 1E+7 % by default. The second column gives the time elapsed in seconds since the sampling began. The % third column gives the log likelihood of the phylogenetic model (i.e., `Felsenstein's % likelihood'). The fourth column gives the logarithm of the joint prior probability of substitution % rates along the tree and node heights. The fifth column gives the current sampled value of the % EvolRate parameter along with the corresponding Effective Sample Size (ESS) for this parameter. % The sixth column gives the tree height and the corresponding ESS. The seventh column gives % the value of the autocorrelation parameter followed by the corresponding ESS. The eightth column % gives the values of the birth rate parameter that governs the birth-rate model of species % divergence dates. The last column of the standard output gives the minimum of the ESS values % taken over the whole set of node height estimates. It provides useful information when one has % to decide whether or not the sample size is large enough to draw valid conclusion, i.e., decide % whether the chain was run for long enough (see Section \ref{sec:recomphytime} for more detail about % adequate chain length). % \subsubsection{ClockRate vs. EvolRate} % The average rate of evolution along a branch is broken into two components. One is called ClockRate % and is the same throughout the tree. The other is called EvolRate and corresponds to a weighted % average of branch-specific rates. The model of rate evolution implemented in PhyTime forces the % branch-specific rate values to be greater than one. As a consequence, ClockRate is usually smaller % EvolRate. % In more mathematical terms, let $\mu$ be the value of ClockRate, $r_i$ be the value of the relative % rate along branch $i$ and $\Delta_i$ the time elapsed along branch $i$. The value of EvolRate is % then given by: % \begin{eqnarray*} % \mathrm{EvolRate} = \mu \frac{\sum_{i}^{2n-3} r_i \Delta_i}{\sum_{i}^{2n-3} \Delta_i}. % \end{eqnarray*} It is clear from this equation that multiplying each $r_i$ by a constant and % dividing $\mu$ by the same constant does not change the value of EvolRate. The $r_i$s and $\mu$ % are then confounded, or non-identifiable, and only the value of EvolRate can be estimated from % the data. {\color{red}{Please make sure that you use the value of EvolRate rather than that of % ClockRate when referring to the estimate of the substitution rate}}. % \subsubsection{Effective sample size}\label{sec:ess} % The MCMC technique generates samples from a target distribution (in our case, the joint posterior % density of parameters). Due to the Markovian nature of the method, these samples are not % independent. The ESS is the estimated number of independent measurements obtained from a set of % (usually dependent) measurements. It is calculated using the following formula: % \begin{eqnarray*} % \mathrm{ESS} = N\left(\frac{1-r}{1+r}\right), % \end{eqnarray*} % where $N$ is the length of the chain (i.e., the `raw' or `correlated' sample size) and $r$ is the % autocorrelation value, which is obtained using the following formula: % \begin{eqnarray*} % r = \frac{1}{(N-k)\sigma_x^2} \sum_{i=1}^{N-k} (X_i - \mu_x)(X_{i+k}-\mu_x), % \end{eqnarray*} where $\mu_x$ and $\sigma_x$ are the mean and standard deviation of the $X_i$ values % respectively and $k$ is the lag. The value of $r$ that is used in PhyTime corresponds to the case where $k=1$, % which therefore gives a first order approximation of the `average' autocorrelation value (i.e., the % autocorrelation averaged over the set of possible values of the lag). % \subsubsection{Prior distributions of model parameters}\label{sec:prior} % Any Bayesian analysis requires specifying a prior distribution of model parameters. The outcome of % the data analysis, i.e., the posterior distribution, is influenced by the priors. It is especially % true if the signal conveyed by the data is weak. While some have argued that the specification of % priors relies more on arbitrary decisions than sound scientific reasoning, choosing relevant prior % distributions is in fact fully integrated in the process of building model that generates the % observed data. In particular, the problem of estimating divergence times naturally lends itself to % hierarchical Bayesian modelling. Based on the hypothesis that rates of evolution are conserved (to % some extant) throughout the course of evolution, the hierarchical Bayesian approach provides an % adequate framework for inferring substitution rates and divergence dates separately. Hence, in this % situation, it makes good sense to use what is known about a relatively well-defined feature of the % evolution of genetic sequences (the ``molecular clock'' hypothesis combined to stochastic variations of % rates across lineages) to build a prior distribution on rates along edges. \subsubsection{Citing PhyTime}\label{sec:citephytime} The ``default citation'' is: \begin{itemize} \item Guindon S. ``From trajectories to averages: an improved description of the heterogeneity of substitution rates along lineages'', {\it Systematic Biology}, 2013. 62(1): 22-34. \end{itemize} If you are using mutliple calibrations on a given clade, please cite the following article: \begin{itemize} \item Guindon S. ``Accounting for calibration uncertainty: Bayesian molecular dating as a ``doubly intractable'' problem'', {\it Systematic Biology}, 2018. 67(4): 651-661. \end{itemize} An earlier article also describes some of the methods implemented in PhyTime: \begin{itemize} \item Guindon S. ``Bayesian estimation of divergence times from large data sets'', {\it Molecular Biology and Evolution}, 2010, 27(8):1768:81. \end{itemize} \subsection{PhyloGeo}\index{PhyloGeo} PhyloGeo is a program that implements the competition-dispersal phylogeography model described in Ranjard, Welch, Paturel and Guindon ``Modelling competition and dispersal in a statistical phylogeographic framework''. Accepted for publication in {\it Systematic Biology}. It implements a Markov Chain Monte Carlo approach that samples from the posterior distribution of the three parameters of interest in this model, namely the competition intensity $\lambda$, the dispersal bias parameter $\sigma$ and the overal dispersal rate $\tau$. The data consist in a phylogeny with node heights proportional to their ages and geographical locations for evety taxon in this tree. An important assumption of the model is that each node in the phylogeny corresponds to a speciation {\em and} a dispersal event. As a consequence, this model does not authorize a given taxon to occupy more than one locations. Note however that the converse is not true: a given location can be occupied by several different taxa. \subsubsection{Installing PhyloGeo} Compiling PhyloGeo is straightforward on Unix-like machines (i.e., linux and MacOS systems). PhyloGeo is not readily available for Windows machines but compilation should be easy on this system too. In the `phyml' directory, where the `src/' and `doc/' directories stand, enter the following commands: {\setlength{\baselineskip}{0.5\baselineskip} \begin{verbatim} ./configure --enable-geo; make clean; make; \end{verbatim} } This set of commands generates a binary file called \x{phylogeo} which can be found in the `src/' directory. \subsubsection{Running PhyloGeo} PhyloGeo takes as input a rooted tree file in Newick format and a file with geographical locations for all the tips of the phylogeny. Here is an example of valid tree and the corresponding spatial locations just below: \begin{scriptsize} \begin{Verbatim}[frame=single, label=Valid PhyloGeo input tree, samepage=true, baselinestretch=0.5] (((unicaA:1.30202,unicaB:1.30202):1.34596,(((nitidaC:0.94617,(nitidaA:0.31497, nitidaB:0.31497):0.63120):0.18955,(((mauiensisA:0.00370,mauiensisB:0.00370):0.20068, (pilimauiensisA:0.05151,pilimauiensisB:0.05151):0.15287):0.78769,(brunneaA:0.10582, brunneaB:0.10582):0.88625):0.14365):0.80126,(((molokaiensisA:0.03728, molokaiensisB:0.03728):0.71371,(deplanataA:0.01814,deplanataB:0.01814):0.73285):0.34764, ((parvulaA:0.20487,parvulaB:0.20487):0.40191,(kauaiensisA:0.24276, kauaiensisB:0.24276):0.36401):0.49186):0.83835):0.71099):1.38043, (nihoaA:0.05673,nihoaB:0.05673):3.97168); \end{Verbatim} \begin{Verbatim}[frame=single, label=Valid PhyloGeo spatial location file, samepage=true, baselinestretch=0.5] nihoaA 23.062222 161.926111 nihoaB 23.062222 161.926111 kauaiensisA 22.0644445 159.5455555 kauaiensisB 22.0644445 159.5455555 unicaA 21.436875 158.0524305 unicaB 21.436875 158.0524305 parvulaA 21.436875 158.0524305 parvulaB 21.436875 158.0524305 molokaiensisA 20.90532 156.6499 molokaiensisB 20.90532 156.6499 deplanataA 20.90532 156.6499 deplanataB 20.90532 156.6499 brunneaA 20.90532 156.6499 brunneaB 20.90532 156.6499 mauiensisA 20.90532 156.6499 mauiensisB 20.90532 156.6499 pilimauiensisA 20.90532 156.6499 pilimauiensisB 20.90532 156.6499 nitidaA 19.7362 155.6069 nitidaB 19.7362 155.6069 nitidaC 19.7362 155.6069 \end{Verbatim} \end{scriptsize} In order to run PhyloGeo, enter the following command: \x{./phylogeo ./tree\_file ./spatial\_location\_file > phylogeo\_output}. PhyloGeo will then print out the sampled values of the model parameters in the file \x{phylogeo\_output}. This file can then be used to generate the marginal posterior densities of the model parameters. In particular, evidence for competition corresponds to value of $\lambda$ smaller than 1.0. Please see the original article for more information on how to interpret the model parameters. \subsubsection{Citing PhyloGeo}\label{sec:citephylogeo} Ranjard, L., Welch D., Paturel M. and Guindon S. ``Modelling competition and dispersal in a statistical phylogeographic framework''. 2014. Systematic Biology. \subsection{PhyREX} \index{PhyREX}PhyREX is a program that is part of the PhyML package which implements phylogeographic models, i.e., models that analyse geo-referenced genetic sequences so as to infer the rate of diffusion of lineages in space along with other parameters of biological interest. Previous versions of PhyREX implemented the {\bf spatial $\Lambda$-Fleming-Viot model} \cite{etheridge2008,berestycki2009,barton2010,barton2010b,veber2012,barton2013} or \sfv\ for short. This model can be thought of as a structured-coalescent where space is considered as continuous rather than organized into separate demes. Under the \sfv\ model, the spatial distribution of individuals in a population is uniform and does not change during the course of evolution, as opposed to other models such as the very popular isolation by distance model proposed by Wright and Mal\'ecot. The \sfv\ model does not suffer from the ``pain in the torus'' \cite{felsenstein1975} and clumps of individuals with increasing densities do not arise. Also, estimates of migration parameters are not sensitive to variation in sampling intensities across regions, unlike ``mugration'' or ``discrete trait analysis'' models \cite{lemey2009}. PhyREX implements an original data augmentation technique embedded in a Bayesian sampler to estimate two important biological parameters: the neighorhood size ($\mathcal{N}$) and the dispersal intensity ($\sigma^2$). These two parameters are closely related to the effective size of the population of interest per unit area since $\mathcal{N}$ is defined as follows: $\mathcal{N} := 4\pi \rho_e \sigma^2$, where $\rho_e$ is the effective population density. Instead of the \sfv\ process, more recent releases of PhyREX implement various versions of the {\bf Relaxed Random Walk} (RRW) model \cite{lemey2010}\index{RRW (model)}. This approach relies on a Brownian diffusion process running along the phylogeny that governs the random fluctuations of 2d-coordinates during the course of evolution. As opposed to the \sfv\ model, the RRW approach is not mechanistic in the sense that it is not built on biological hypotheses about the migration and reproduction process at at the individual level. Yet, it defines a flexible framework to model the dispersal of organisms and is able, at least in principle, to capture important features of the underlying processes. \subsubsection{Installing PhyREX} Compiling PhyREX is straightforward on Unix-like machines (i.e., linux and MacOS systems). PhyREX is not readily available for Windows machines but compilation should be easy on this system too. In the `phyml' directory, where the `src/' and `doc/' directories stand, enter the following commands: {\setlength{\baselineskip}{0.5\baselineskip} \begin{verbatim} ./configure --enable-phyrex; make clean; make; \end{verbatim} } This set of commands generates a binary file called \x{phyrex} which can be found in the `src/' directory. \subsubsection{Running PhyREX} Example input files for PhyREX can be found in the \x{examples/phyrex/} directory. PhyREX takes as input a XML file (See Section \ref{sec:xmlio}), in a PhyML-compatible format. This file defines the models and various parameter settings for conducting an analysis. We give more information below about this file. The XML file gives the names of a sequence alignment file in the standard PHYLIP (or NEXUS) format along with a file providing the spatial coordinates of the corresponding sequences. An example of the coordinates file is given below: \begin{Verbatim}[frame=single, label=Valid PhyREX spatial location file, samepage=true, baselinestretch=0.5] # state.name lat lon |SouthWest| 0 0 |NorthEast| 10 10 levosl 5.082206 4.133893 kmgcwv 5.914294 4.603446 uhfwja 4.990937 4.445124 ndmwkc 5.178017 4.442268 jpadex 3.747484 4.571090 lqcdcw 7.081925 5.133123 wnsbtg 4.164588 4.720346 ptwgnn 5.711159 4.462993 jhqdsm 3.539525 4.537706 vlnoes 4.613251 4.470530 pfrnpk 4.117791 4.489819 elwdvr 5.649958 4.824092 lptxiv 4.563302 4.005124 \end{Verbatim} This first column in this file gives the sequence names, the second is the latitude and the third column gives the longitude. The first row in this file gives the names of the columns. It starts with the `\#' character signaling a comment. The second and third rows define the limits of the population's habitat. At the moment, this habitat is assumed to be a rectangle. The position in space of that rectangle are determined by the coordinates of the bottom-left corner (\x{|SouthWest| 0 0}) and the top-right one (\x{|NorthEast| 10 10}). The `\x{|}' characters help identify the terms in the list of coordinate corresponding indeed to these two particular points and should thus not be omitted. The geographical limits of the habitat are considered as data under the \sfv\ model while they are ignored under the RRW model (although one needs to define these coordinates such that all samples are located within the corresponding rectangle). The following rows give the coordinates of each taxon. Every taxon in the sequence file should also be listed in the coordinate file. Below is an excerpt of a XML file that can be used as input to run a PhyREX analysis. The phylogeographic model parameters are estimated using a Bayesian sampler which hyperparameters (number of samples, frequency at which samples are collected, length of the ``burnin'' phase, etc.) are set through the corresponding XML attributes (see \ref{sec:phytimesettings}). Below is the list of the main options : \begin{itemize} \item {\em Evolution of spatial coordinates along the tree.} \x{} component. This is the part where the RRW model is parametrized. Users need to specify what probabilistic distribution is to be used in order to model the variation of the spatial dispersal rates across lineages. Using \x{name="rw"} implements a ``strict'' random walk, i.e., the dispersal rate is the same throughout the whole tree. \x{name="rrw+gamma"} and \x{name="rrw+lognormal"} implements the relaxed random walk model using a gamma or a lognormal distribution of the relative rate of dispersal respectively. The modes of these distributions is set to one and the standard deviation is fixed to 2.0. The dispersal rate itself is a composite parameter that is estimated from the data. Each spatial dimension, corresponding to the North-South and East-West axes, has its own dispersal parameter. The prior distribution that applies to each of these two parameter is an exponential distribution with mean defined by the user through the \x{dispersal.prior.mean} attribute. Finally, the \x{sampling} attribute is either \x{survey} or \x{detection} (default). This attribute specifies the spatial sampling scheme. \x{detection} corresponds to the case where sampling is proportional to the underlying population spatial density (i.e., the most densely populated areas are also the most sampled ones). \x{survey} corresponds to the situation where sampling and population densities are decoupled, corresponding to situations where some areas of the habitat are easier to access for instance. \item {\em Substitution rate (a.k.a., clock rate).} \x{} component. In case all sequences were collected at (approximately) the same time, it is recommended to set the average rate of evolution to a specific value (\x{}). It is also possible to apply a lognormal prior on the clock rate with mean parameter and variance defined as in the following example: \x{}. With serially-sampled data, the average rate of evolution can be estimated directly from the data and setting it to a particular value or using an informative prior is not mandatory. \item {\em Variation of rates across lineages (a.k.a. relaxed clock\index{relaxed clock (model)}).} \x{} component. PhyREX is similar to PhyTime in the sense that substitution rates and times are separate parameters. The rates can vary across lineages (using for instance a log-normal model: \x{}) or not (strict clock: \x{}). Finally, it is necessary to give information about the time at which each sequence was collected. This is done through seeting appropriate calibration constraints. From a technical perspective, these constraints apply to clades where each clade here is a single tip. \item {\em Tree generating model.} \x{} component. Using \x{model="coalescent"} fits a coalescent model to the data. If no other options are given, Kingman's coalescent is used. Using \x{powgrowth="yes"} fits a coalescent where the population size varies according to a power function $N(t) = N_e(t+1)^{-g}$ where $N_e$ is the effective population size at time $t=0$ and $g$ is the growth parameter, which is to be estimated from the data. Using \x{expgrowth="yes"} fits a coalescent where the population size varies according to an exponential function $N(t) = N_ee^{-gt}$. The prior distribution on the growth parameter $g$ is exponential with mean set through the \x{neff.prior.mean} attribute value. \end{itemize} \begin{Verbatim}[frame=single, label=Example (excerpt) of PhyREX XML file, samepage=true, baselinestretch=0.5, fontsize=\small, numbers=left] <-- File where 2-D spatial coordinates are found --> 0 0 10 10 \end{Verbatim} PhyREX generates correlated samples from the posterior distribution of the \sfv\ model parameters. The estimated distributions and related summary statistics can be monitored during the analysis using the MCMC vizualization software Icylog or Tracer. Most of the names of the sampled parameters are self-explanatory but some of them (listed below) require more explanations: \begin{itemize} \item \texttt{lnPost}: log-posterior density. \item \texttt{lnAlgn}: logarithm of the probability of the sequence alignment given the current tree. \item \texttt{lnSpac}: logarithm of the probability density of the location data given the current tree. \item \texttt{lnRate}: logarithm of the probability density of the substitution rates along the tree. \item \texttt{lnTime}: logarithm of the probability density of the tree given the tree-generating model. \item \texttt{substRate}: substitution rate. \item \texttt{siqSqLon}: diffusion rate along the East-West axis. \item \texttt{siqSqLat}: diffusion rate along the North-South axis. \item \texttt{nEff}: effective population size. \item \texttt{growth}: coalescent population growth parameter. \item \texttt{dispDistAlt}: effective diffusion rate (average number of kilometers covered in one unit of time). \item \texttt{rootTime}: calendar time corresponding to the MRCA node. \item \texttt{rootLon}: longitude of the root node location. \item \texttt{rootLat}: latitude of the root node location. \end{itemize} If you are unsure about the precise definition of one of these parameters or have any questions regarding how to interpret the results returned by PhyREX, please do not hesitate to contact me (\url{guindon@lirmm.fr}). % \begin{figure} % \begin{center} % \includegraphics[width=13.8cm]{./fig/phyrexlog} % \end{center} % \caption{{\bf Statistics file generated by PhyREX and loaded on Icylog % (\url{http://tgvaughan.github.io/icylog/icylog.html}).} The traces for the neighborhood size % (top) and % the dispersal intensity (bottom) are shown here.} % \label{fig:phyrextrace} % \end{figure} \subsubsection{Citing PhyREX} The ``default citation'' is: \begin{itemize} \item Guindon S., H Guo, D Welch. ``Demographic inference under the coalescent in a spatial continuum'', {\it Theoretical Population Biology}, 2016, 111:43-50. \end{itemize} \section{Recommendations on program usage}\label{sec:progusage} \subsection{PhyML} The choice of the tree searching algorithm among those provided by PhyML is generally a tough one. The fastest option relies on local and simultaneous modifications of the phylogeny using NNI moves. More thorough explorations of the space of topologies are also available through the SPR options. As these two classes of tree topology moves involve different computational burdens, it is important to determine which option is the most suitable for the type of data set or analysis one wants to perform. Below is a list of recommendations for typical phylogenetic analyses. \begin{enumerate} \item {\em Single data set, unlimited computing time.} The best option here is probably to use a SPR search (i.e., straight SPR of best of SPR and NNI). If the focus is on estimating the relationships between species, it is a good idea to use more than one starting tree to decrease the chance of getting stuck in a local maximum of the likelihood function. Using NNIs is appropriate if the analysis does not mainly focus on estimating the evolutionary relationships between species (e.g. a tree is needed to estimate the parameters of codon-based models later on). Branch supports can be estimated using bootstrap and approximate likelihood ratios. \item {\em Single data set, restricted computing time.} The three tree searching options can be used depending on the computing time available and the size of the data set. For small data sets (i.e., $<$ 50 sequences), NNI will generally perform well provided that the phylogenetic signal is strong. It is relevant to estimate a first tree using NNI moves and examine the reconstructed phylogeny in order to have a rough idea of the strength of the phylogenetic signal (the presence of small internal branch lengths is generally considered as a sign of a weak phylogenetic signal, specially when sequences are short). For larger data sets ($>$ 50 sequences), a SPR search is recommended if there are good evidence of a lack of phylogenetic signal. Bootstrap analysis will generally involve large computational burdens. Estimating branch supports using approximate likelihood ratios therefore provides an interesting alternative here. \item {\em Multiple data sets, unlimited computing time.} Comparative genomic analyses sometimes rely on building phylogenies from the analysis of a large number of gene families. Here again, the NNI option is the most relevant if the focus is not on recovering the most accurate picture of the evolutionary relationships between species. Slower SPR-based heuristics should be used when the topology of the tree is an important parameter of the analysis (e.g., identification of horizontally transferred genes using phylogenetic tree comparisons). Internal branch support is generally not a crucial parameter of the multiple data set analyses. Using approximate likelihood ratio is probably the best choice here. \item {\em Multiple data sets, limited computing time.} The large amount of data to be processed in a limited time generally requires the use of the fastest tree searching and branch support estimation methods Hence, NNI and approximate likelihood ratios rather than SPR and non-parametric bootstrap are generally the most appropriate here. \end{enumerate} Another important point is the choice of the substitution model. While default options generally provide acceptable results, it is often warranted to perform a pre-analysis in order to identify the best-fit substitution model. This pre-analysis can be done using popular software such as Modeltest \cite{posada98} or ProtTest \cite{abascal05} for instance. These programs generally recommend the use of a discrete gamma distribution to model the substitution process as variability of rates among sites is a common feature of molecular evolution. The choice of the number of rate classes to use for this distribution is also an important one. While the default is set to four categories in PhyML, it is recommended to use larger number of classes if possible in order to best approximate the patterns of rate variation across sites \cite{galtier04}. Note however that run times are directly proportional to the number of classes of the discrete gamma distribution. Here again, a pre-analysis with the simplest model should help the user to determine the number of rate classes that represents the best trade-off between computing time and fit of the model to the data. \subsection{PhyTime}\label{sec:recomphytime} Analysing a data set using PhyTime should involve three steps based on the following questions: (1) do the priors seem to be adequate (2) can I use the fast approximation of the likelihood and (3) how long shall I run the program for? I explain below how to provide answers to these questions. \begin{itemize} \item {\em Are the priors adequate?} Bayesian analysis relies on specifiying the joint prior density of model parameters. In the case of node age estimation, these priors essentially describe how rates of substitution vary across lineages and the probabilistic distribution that node ages have when ignoring the information provided by the genetic sequences. These priors vary from tree to tree. It is therefore essential to check the adequacy of priors for each user-defined input tree. In order to do so, PhyTime needs to be run with the \x{--no\_data} option. When this option is required, the sequence data provided as input will be ignored and the rest of the analysis will proceed normally. The prior distribution of model parameters, essentially edge rates and node heights, can then be checked using the program Tracer as one would do for the standard `posterior' analysis. \item {\em Can I use the fast approximation to the likelihood?} The suface of the log-likelihood function can be approximated using a multivariate normal density. This technique is saving very substantial amounts of computation time. However, like most approximations, there are situations where it does not provide a good fit to the actual function. This usually happens when the phylogeny displays a lot of short branches, i.e., the signal conveyed by the sequences is weak. It is therefore important to first check whether using the approximate likelihood is reasonable. In order to do so, it is recommended to first run the program without the approximation, i.e., using the default settings. Once the minimum value of the ESS of node ages (the last column on the right of the standard output) has reached 40-50, open the \x{phytime.XXXX} output file with Tracer and examine the correlation between the exact and approximate likelihood values. If the correlation is deemed to be good enough, PhyTime can be re-run using the \x{--fast\_lk} option, which uses the fast normal approximation to the likelihood function. % Figure \ref{fig:approxbad} gives an example where the correlation is too weak and the approximation % of the likelihood should be avoided. Figure \ref{fig:approxbad} gives an example where the % approximation is good enough. The current execution of PhyTime can be terminated and then % re-launched using the \x{--fast\_lk} option. % \begin{figure} % \begin{center} % \resizebox{14cm}{8cm}{\includegraphics{./fig/approx_bad.eps}} % \caption{{\bf Exact vs. approximate likelihoods.} The correlation between the normally approximated % (Y-axis) and the exact (X-axis) likelihoods is weak here. The exact likelihood should be used (option \x{fastlk=no}).} % \label{fig:approxbad} % \end{center} % \end{figure} % \begin{figure} % \begin{center} % \resizebox{14cm}{8cm}{\includegraphics{./fig/approx_good.eps}} % \caption{{\bf Exact vs. approximate likelihoods.} The correlation between the normally approximated % (Y-axis) and the exact (X-axis) likelihoods is good. The approximation of the likelihood can be used (option \x{fastlk=yes}).} % \label{fig:approxgood} % \end{center} % \end{figure} \item {\em How long shall I run the program for?} PhyTime should be run long enough such that the ESS of each parameter is `large enough'. The last column on the right handside of the standard output gives the minimum ESS across all internal node heights. It is recommended to run the program so that this number reaches at least 100. \end{itemize} \section{Frequently asked questions} \begin{enumerate} \item {\it PhyML crashes before reading the sequences. What's wrong ?}\\ \begin{itemize} \item The format of your sequence file is not recognized by PhyML. See Section \ref{sec:input_output} \item The carriage return characters in your sequence files are not recognized by PhyML. You must make sure that your sequence file is a plain text file, with standard carriage return characters (i.e., corresponding to ``$\backslash$\x{n}'', or ``$\backslash$\x{r}'') \end{itemize} \item {\it The program crashes after reading the sequences. What's wrong ?}\\ \begin{itemize} \item You analyse protein sequences and did not enter the \x{-d aa} option in the command-line. \item The format of your sequence file is not recognized by PhyML. See Section \ref{sec:input_output} \end{itemize} \item {\it Does PhyML handle outgroup sequences ?}\\ \begin{itemize} \item Yes, it does. Outgroup taxa are identified by adding the `*' sign at the end of each corresponding sequence name (see Section \ref{sec:outgroupspecify}) \end{itemize} \item {\it Does PhyML estimate clock-constrained trees ?}\\ \begin{itemize} \item No, the PhyML program does not estimate clock-contrained trees. One can however use the program PhyTime to perform such analysis but the tree topology will not be estimated. \end{itemize} \item {\it Can PhyML analyse partitioned data, such as multiple gene sequences ?}\\ \begin{itemize} \item We are currently working on this topic. Future releases of the program will provide options to estimate trees from phylogenomic data sets, with the opportunity to use different substitution models on the different data partitions (e.g., different genes). PhyML will also include specific algorithms to search the space of tree topologies for this type of data. \end{itemize} \end{enumerate} \section{Acknowledgements} The development of PhyML since 2000 has been supported by the Centre National de la Recherche Scientifique (CNRS) and the Minist\`ere de l'\'Education Nationale. \bibliographystyle{./naturemag} \bibliography{./ref.bib} \printindex \end{document} phyml-3.3.20220408/doc/ref.bib000077500000000000000000005431141422404706600154270ustar00rootroot00000000000000@article{lemey2010, title = {Phylogeography takes a relaxed random walk in continuous space and time}, volume = {27}, number = {8}, journal = {Molecular Biology and Evolution}, author = {Lemey, Philippe and Rambaut, Andrew and Welch, John J and Suchard, Marc A}, year = {2010}, note = {Publisher: SMBE}, pages = {1877--1885}, } @article{oliva19, title={Accounting for ambiguity in ancestral sequencereconstruction}, author={Oliva, A and Pulicani, S and Lefort, V and Br\'eh\'elin, L. and Gascuel, O. and Guindon, S}, journal={Bioinformatics}, pages={https://www.biorxiv.org/content/early/2018/09/05/409029}, year={2019} } @article{etheridge2008, title={Drift, draft and structure: some mathematical models of evolution}, author={Etheridge, Alison M}, journal={Banach Center Publ.}, volume={80}, pages={121--144}, year={2008} } @inproceedings{berestycki2009, title={Survival, extinction and ergodicity in a spatially continuous population model}, author={Berestycki, N and Etheridge, A and Hutzenthaler, M}, booktitle={Markov Proc. 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Tilman}, journal={The American Naturalist}, volume={178}, number={3}, pages={355--371}, year={2011}, publisher={JSTOR} } @article{gernhard08, title={The conditioned reconstructed process}, author={Gernhard, Tanja}, journal={Journal of Theoretical Biology}, volume={253}, number={4}, pages={769--778}, year={2008}, publisher={Elsevier} } @article{ayres12, title={BEAGLE: an application programming interface and high-performance computing library for statistical phylogenetics}, author={Ayres, Daniel L and Darling, Aaron and Zwickl, Derrick J and Beerli, Peter and Holder, Mark T and Lewis, Paul O and Huelsenbeck, John P and Ronquist, Fredrik and Swofford, David L and Cummings, Michael P and others}, journal={Systematic Biology}, volume={61}, number={1}, pages={170--173}, year={2012}, publisher={Oxford University Press} } @article{ho11, title={Time-dependent rates of molecular evolution}, author={Ho, Simon YW and Lanfear, Robert and Bromham, Lindell and Phillips, Matthew J and Soubrier, Julien and Rodrigo, Allen G and Cooper, Alan}, journal={Molecular Ecology}, volume={20}, number={15}, pages={3087--3101}, year={2011}, publisher={Wiley Online Library} } @article{fletcher10, title={The effect of insertions, deletions, and alignment errors on the branch-site test of positive selection}, author={Fletcher, William and Yang, Ziheng}, journal={Molecular Biology and Evolution}, volume={27}, number={10}, pages={2257--2267}, year={2010}, publisher={SMBE} } @article{degnan09, title={Gene tree discordance, phylogenetic inference and the multispecies coalescent}, author={Degnan, J.H. and Rosenberg, N.A.}, journal={Trends in Ecology \& Evolution}, volume={24}, number={6}, pages={332--340}, year={2009}, publisher={Elsevier} } @article{best, title={{BEST}: Bayesian estimation of species trees under the coalescent model}, author={Liu, L.}, journal={Bioinformatics}, volume={24}, number={21}, pages={2542--2543}, year={2008}, publisher={Oxford Univ Press} } @article{lg4x, title={Modeling protein evolution with several amino acid replacement matrices depending on site rates}, author={Le, Si Quang and Dang, Cuong Cao and Gascuel, Olivier}, journal={Molecular Biology and Evolution}, volume={29}, number={10}, pages={2921--2936}, year={2012}, publisher={SMBE} } @article{stem, title={{STEM}: species tree estimation using maximum likelihood for gene trees under coalescence}, author={Kubatko, L.S. and Carstens, B.C. and Knowles, L.L.}, journal={Bioinformatics}, volume={25}, number={7}, pages={971--973}, year={2009}, publisher={Oxford Univ Press} } @article{startbeast, title={Bayesian inference of species trees from multilocus data}, author={Heled, J. and Drummond, A.J.}, journal={Molecular Biology and Evolution}, volume={27}, number={3}, pages={570--580}, year={2010}, publisher={SMBE} } @article{guindon10, title={Bayesian estimation of divergence times from large sequence alignments}, author={Guindon, St{\'e}phane}, journal={Molecular Biology and Evolution}, volume={27}, number={8}, pages={1768--1781}, year={2010}, publisher={SMBE} } @article{guindon18, title={Accounting for Calibration Uncertainty: Bayesian Molecular Dating as a ``Doubly Intractable'' Problem}, author={Guindon, St{\'e}phane}, journal={Systematic Biology}, volume={67}, number={}, pages={651-661}, year={2018}, } %% Created for guindon at 2009-04-21 13:11:50 +1200 @article{soubrier12, title={The influence of rate heterogeneity among sites on the time dependence of molecular rates}, author={Soubrier, J. and Steel, M. and Lee, M.S.Y. and Sarkissian, C.D. and Guindon, S. and Ho, S.Y.W. and Cooper, A.}, journal={Molecular Biology and Evolution}, year={2012}, publisher={SMBE} } %% Saved with string encoding Unicode (UTF-8) @article{matsen07, title={Phylogenetic mixtures on a single tree can mimic a tree of another topology}, author={Matsen, F.A. and Steel, M.}, journal={Systematic Biology}, volume={56}, number={5}, pages={767--775}, year={2007}, publisher={Oxford University Press} } @book{bailey64, title={The elements of stochastic processes with applications to the natural sciences}, author={Bailey, Norman TJ}, publisher={Wiley}, year={1964} } @article{lemey2009, title={Bayesian phylogeography finds its roots}, author={Lemey, Philippe and Rambaut, Andrew and Drummond, Alexei J and Suchard, Marc A}, journal={PLoS Comput Biol}, volume={5}, number={9}, pages={e1000520}, year={2009}, publisher={Public Library of Science} } @article{gillespie2011, title={Long-distance dispersal: a framework for hypothesis testing}, author={Gillespie, R.G. and Baldwin, B.G. and Waters, J.M. and Fraser, C.I. and Nikula, R. and Roderick, G.K.}, journal={Trends in Ecology \& Evolution}, year={2011}, vol=27, pages={47,56} } @article{guindon13, title={From trajectories to averages: an improved description of the heterogeneity of substitution rates along lineages.}, author={Guindon, S.}, journal={Systematic Biology}, year={2013}, volume={62}, pages={22-34}, publisher={Oxford University Press} } @article{arnold01, Author = {Barry C. Arnold and Enrique Castillo and Jose Maria Sarabia}, Title = {Conditionally Specified Distributions: An Introduction}, Journal = {Statistical Science}, Year = 2001, Volume = 16, Pages = {249,265} } @article{arnold07, Author = {Barry C. Arnold and Enrique Castillo and Jose Maria Sarabia}, Date-Added = {2009-04-21 13:08:07 +1200}, Date-Modified = {2009-04-21 13:11:32 +1200}, Journal = {Journal of Statistical Planning and Inference}, Pages = {3249-3260}, Read = {Yes}, Title = {Variations on the classical multivariate normal theme}, Volume = {137}, Year = {2007}, Bdsk-File-1 = {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}} @article{boussau06, title={Efficient likelihood computations with nonreversible models of evolution}, author={Boussau, Bastien and Gouy, Manolo}, journal={Systematic biology}, volume={55}, number={5}, pages={756--768}, year={2006}, publisher={Oxford University Press} } @article{boussau08, Abstract = {Fossils of organisms dating from the origin and diversification of cellular life are scant and difficult to interpret, for this reason alternative means to investigate the ecology of the last universal common ancestor (LUCA) and of the ancestors of the three domains of life are of great scientific value. It was recently recognized that the effects of temperature on ancestral organisms left 'genetic footprints' that could be uncovered in extant genomes. Accordingly, analyses of resurrected proteins predicted that the bacterial ancestor was thermophilic and that Bacteria subsequently adapted to lower temperatures. As the archaeal ancestor is also thought to have been thermophilic, the LUCA was parsimoniously inferred as thermophilic too. However, an analysis of ribosomal RNAs supported the hypothesis of a non-hyperthermophilic LUCA. Here we show that both rRNA and protein sequences analysed with advanced, realistic models of molecular evolution provide independent support for two environmental-temperature-related phases during the evolutionary history of the tree of life. In the first period, thermotolerance increased from a mesophilic LUCA to thermophilic ancestors of Bacteria and of Archaea-Eukaryota; in the second period, it decreased. Therefore, the two lineages descending from the LUCA and leading to the ancestors of Bacteria and Archaea-Eukaryota convergently adapted to high temperatures, possibly in response to a climate change of the early Earth, and/or aided by the transition from an RNA genome in the LUCA to organisms with more thermostable DNA genomes. This analysis unifies apparently contradictory results into a coherent depiction of the evolution of an ecological trait over the entire tree of life.}, Address = {Laboratoire de Biometrie et Biologie Evolutive, CNRS, Universite de Lyon, Universite Lyon I, 43 Boulevard du 11 Novembre, 69622 Villeurbanne, France.}, Au = {Boussau, B and Blanquart, S and Necsulea, A and Lartillot, N and Gouy, M}, Author = {Boussau, Bastien and Blanquart, Samuel and Necsulea, Anamaria and Lartillot, Nicolas and Gouy, Manolo}, Crdt = {2008/11/28 09:00}, Da = {20081218}, Date-Added = {2009-01-30 16:18:39 +1300}, Date-Modified = {2009-01-30 16:18:57 +1300}, Dcom = {20090113}, Dep = {20081126}, Doi = {10.1038/nature07393}, Edat = {2008/11/28 09:00}, Issn = {1476-4687 (Electronic)}, Jid = {0410462}, Journal = {Nature}, Jt = {Nature}, Language = {eng}, Mh = {Adaptation, Physiological/genetics/*physiology; Archaea/genetics/*physiology; Evolution, Molecular; Genes, rRNA/genetics; *Hot Temperature; Phylogeny}, Mhda = {2009/01/14 09:00}, Number = {7224}, Own = {NLM}, Pages = {942--945}, Phst = {2008/03/05 {$[$}received{$]$}; 2008/09/01 {$[$}accepted{$]$}; 2008/11/26 {$[$}aheadofprint{$]$}}, Pii = {nature07393}, Pl = {England}, Pmid = {19037246}, Pst = {ppublish}, Pt = {Journal Article; Research Support, Non-U.S. Gov't}, Sb = {IM}, So = {Nature. 2008 Dec 18;456(7224):942-5. Epub 2008 Nov 26. }, Stat = {MEDLINE}, Title = {Parallel adaptations to high temperatures in the Archaean eon.}, Volume = {456}, Year = {2008 Dec 18}, Bdsk-Url-1 = {http://dx.doi.org/10.1038/nature07393}} @article{dereeper08, Abstract = {Phylogenetic analyses are central to many research areas in biology and typically involve the identification of homologous sequences, their multiple alignment, the phylogenetic reconstruction and the graphical representation of the inferred tree. The Phylogeny.fr platform transparently chains programs to automatically perform these tasks. It is primarily designed for biologists with no experience in phylogeny, but can also meet the needs of specialists; the first ones will find up-to-date tools chained in a phylogeny pipeline to analyze their data in a simple and robust way, while the specialists will be able to easily build and run sophisticated analyses. Phylogeny.fr offers three main modes. The 'One Click' mode targets non-specialists and provides a ready-to-use pipeline chaining programs with recognized accuracy and speed: MUSCLE for multiple alignment, PhyML for tree building, and TreeDyn for tree rendering. All parameters are set up to suit most studies, and users only have to provide their input sequences to obtain a ready-to-print tree. The 'Advanced' mode uses the same pipeline but allows the parameters of each program to be customized by users. The 'A la Carte' mode offers more flexibility and sophistication, as users can build their own pipeline by selecting and setting up the required steps from a large choice of tools to suit their specific needs. Prior to phylogenetic analysis, users can also collect neighbors of a query sequence by running BLAST on general or specialized databases. A guide tree then helps to select neighbor sequences to be used as input for the phylogeny pipeline. Phylogeny.fr is available at: http://www.phylogeny.fr/}, Author = {Dereeper A, Guignon V, Blanc G, Audic S, Buffet S, Chevenet F, Dufayard JF, Guindon S, Lefort V, Lescot M, Claverie JM, Gascuel O.}, Date-Added = {2009-01-19 17:02:10 +1300}, Date-Modified = {2009-01-19 17:03:31 +1300}, Journal = { Nucleic Acids Res.}, Title = {Phylogeny.fr: robust phylogenetic analysis for the non-specialist.}, Volume = {36}, Year = {2008}} @article{lartillot06, Author = {Nicolas Lartillot}, Date-Added = {2008-12-18 09:36:03 +1300}, Date-Modified = {2008-12-18 09:39:29 +1300}, Journal = {Journal of Computational Biology}, Pages = {1701-1722}, Rating = {5}, Title = {Conjugate Gibbs Sampling for Bayesian Phylogenetic Models }, Volume = {13}, Year = {2006}, Bdsk-File-1 = {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}} @book{aarts97, Address = {Chichester}, Author = {E. 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03 03 03 01 01 03 03 00 02 03 00 02 01 02 02 01 02 00 02 01 02 00 00 01 00 02 02 00 00 00 02 tax2 01 ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? 00 00 00 02 00 00 01 03 00 00 01 01 00 02 02 00 03 03 03 01 01 03 03 00 02 03 00 02 01 02 02 01 02 00 02 01 02 00 00 01 00 02 02 00 00 00 02 tax3 02 ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? 00 02 02 00 03 03 03 01 01 03 03 00 02 03 00 02 01 02 02 01 02 00 02 01 02 00 00 01 00 02 02 00 00 00 02 tax4 03 ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? 02 03 00 02 01 02 02 01 02 00 02 01 02 00 00 01 00 02 02 00 00 00 02 tax5 00 ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? 01 01 00 02 02 00 03 03 03 01 01 03 03 00 02 03 00 02 01 02 02 01 02 00 02 01 02 00 00 01 00 02 02 00 00 00 02 tax6 01 ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? 00 00 00 02 00 00 00 01 03 00 00 01 01 00 02 02 00 03 03 03 01 01 03 03 00 02 03 00 02 01 02 02 01 02 00 02 01 02 00 00 01 00 02 02 00 00 00 02 tax7 02 ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? 02 00 03 03 03 01 01 03 03 00 02 03 00 02 01 02 02 01 02 00 02 01 02 00 00 01 00 02 02 00 00 00 02 tax8 03 ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? tax9 00 ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? 02 02 00 03 03 03 01 03 03 01 02 03 00 02 01 02 02 01 02 00 02 01 02 00 00 01 00 02 02 00 00 03 00 tax10 01 ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? 00 02 01 02 02 01 02 00 02 01 02 00 00 01 00 02 02 00 00 00 03; END; phyml-3.3.20220408/examples/nucleic000077500000000000000000001665431422404706600166220ustar00rootroot0000000000000054 886 tax1 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C---CC tax2 ATTGCCTCAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-TCAC tax3 ?????????? TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TG----TGAT tax4 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T-CC tax5 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ATAGCTCAAA T-TTGAAAGC TGG-C----- tax6 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax7 ?T-GCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA --TTGAAATC TGGGC-T--C tax8 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax9 ?????????? TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax10 ????????AG TA-CGGCGAG TGAAGCGGCA ACAGCTCCA- T-TTGAAATC TGG-C-T--C tax11 ?TTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax12 ATTGCCCTAG TA-CGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax13 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax14 ?TTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax15 ?TTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax16 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax17 ATTGCCTCAG TAACGGCGAG TGAAGCGGCA TCAGCTCAAA T-TTGAAATC TGG-C-T--C tax18 ATTGCCCCAG TAACGGCGAG TGAAGCGGCA ATAGCTCAAA T-TTGAAATC TGG-C--CCC tax19 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax20 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax21 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax22 ATTGCCTTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-CCTCTT tax23 ATTGCCTCAG TAACGGCGAG TGAAGCGGCA AAAGCTCAGA T-TTGAAATC TGG-CGTCTT tax24 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-TCTG tax25 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-CGT--C tax26 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-CGT--C tax27 ATTGCCTCAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T-CC tax28 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-CCT-CC tax29 ?TTGCCCTAG TA-CGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-CGT--C tax30 ATTGCCCTAG TA-CGGCGAG TGA-GCGGCA ACAGCTCAAA T-TTGAAATC TGG-CGT--C tax31 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-CGT--C tax32 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-CGT--C tax33 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax34 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ATAGCTCAAA T-TTAAAATC TGG-C-TCTG tax35 ????????AG TA-CGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-TCTC tax36 ATTGCCTCAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-CCG-TT tax37 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ATAGCTCAAA T-TTGAAAGC TGG-C----- tax38 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ATAGCTCAAA T-TTGAAAGC TGG-C----- tax39 ATTGCCTTAG TAACGGCGAG T-AAGC-GCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax40 ?????????? ?????????? ?????????A ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax41 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax42 ATTGCCCTAG TAACGGCGAG T-AAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax43 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax44 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax45 ??TGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax46 ?TTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax47 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax48 ATTGCCCTAG TAACGGCGAG T-AAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax49 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCTCAAA T-TTGAAATC TGG-C-T--C tax50 ATAGCCCTAG TAACGGCGAG TGAAGCGGCT ACAGCTCAAA T-TTGAAATC TGG-CCTC-C tax51 ATTGCCCTAG TAACGGCGAG TGAAGCGGCA ACAGCACAAA T-TTGAAATC TGG-C---TC tax52 ATTGCCCCAG TAACGGCGAG TGAAGCGGCA ATAGCTCAAA T-TTGAAATC TGG-C---CC tax53 TTCGCCCCAG TAACGGCGAG TGAAGCGGCA ATAGCTCAAA T-TTGAAATC TGG-C---CC tax54 ???GCCCCAG TAACGGCGAG TGAAGCGGCA ATAGCTCAAA T-TTGAAATC TGG-C---CC TCTCAG--GG TCCGAGTTGT AATTTGTAGA GGGTGCTTTG GCATTGGTTG TGGTCTAAGT ---------G CCCGAGTTGT AATTTGTAGA GGATGTTTCG GGTGAAGCCG CGGTCCAAGT ---GAAA--G CCCGAGTTGT AATTTGCAGA GGATGTTTCG GGCGAGGCCG CGGTCCAAGT TTT-GG--GG TCCGAGTTGT AATTTGTAGA GGGTGCTTTG GCGTTGGCTG TGGTCTAAGT TT----ATGG CCCGCATTGT AATTTGTAGA GGATGCTTTT AGGCAGCCGC CGGTCTAAGT TTTCAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTCAG--GG TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GTGTCGGCAG CGGTCCAAGT TTTTAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTTAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTCAG--GG TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTCAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTTAG--GG TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTTAG--GG TCCGAGTTGT AATTTGCAGA GGGTGCTTTG GCAAAGGTTG TGGTCTAAGT TTTTAG--GG TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTTAG--GG TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTTAG--GG TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTTAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCATAGGCAG CGATTCAAGT TTT-GG--GG TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GAGTTGGCTG CAGCCTAAGT TTTTAG--GG TCCGAGTTGT AATTTGCAGA GGGTGCTTTG GCTTTGGCAG CGGTCCAAGT TTTTAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCGTTGGGAG CGGTCCAAGT TTTTAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCGTTGGCAG CGGTCCAAGT T---GG--GG TCCGAATTGT AATTTGGAGA GGATGTTTTT GGGTGTCCGC CGGCCTAAGT C---GG-C-G TCCGAGTTGT AATCTGTAGA GGATGCCTTT GGGTAGCCAC CGGTCTAAGT TAAAAGA--G TCCGAGTTGT AATTTGTAGA GGGTGCTTTG GTGTTGGTAG CGGTCTAAGT TTT-GG-C-G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCATTGGCAG CGGTCCAAGT TTT-GG-C-G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCATTGGCAG CGGTCCAAGT TTT-GGA--G TCCGAGTTGT AATTTGTAGA GGGTGCTTTG GCGTTGGTTG TGGTCTAAGT TTTGGG--GG TCCGAGTTGT AATTTGCAGA GGGTGCTTTG GCATTGGCGG CGGTCTAAGT TTT-GG-C-G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCATTGGCAG CGGTCCAAGT TTT-GG-C-G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCATTGGCAG CGGTCCAAGT TTT-GG-C-G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCATTGGCAG CGGTCCAAGT TTT-GG-C-G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCATTGGCAG CGGTCCAAGT TTTTAG--GG TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCGTTGGCAG CGGTCCAAGT CTCTAG--GG TCCGAGTTGT AATTTGTAGA GGGTGCTTTG GCACTGATGG CGGTCTAAGT TTTGGG--GG TCCGAGTTGT AATTTGCAGA GGATGCTTTG GCATTGGCGG CGGTCTAAGT TA-----CGG TCCGAGTTGT AATTTGTAGA GGATGTTTTG GGTACCGCCC CGGTTTAAAT TT----ATGG TCCGCATTGT AATTTGTAGA GGATGATTTT AGGCAGCCGC CGGTCTAAGT TT----ATGG CCCGCATTGT AATTTGTAGA GGATGCTTTT AGGCAGCCGC CGGTCTAAGT TTTTAG--GG TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTCAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTCAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTCAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTCAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTCAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTCAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTCAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTCAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTCAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TTTTAGA--G TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCTTTGGCAG CGGTCCAAGT TCTGGG--GG TCCGAGTTGT AATTTGCAGA GGATGCTTTG GCGTTGGCGG CGGTCTAAGT TTTTA---GG TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GCGTTGGCAG CGGTCCAAGT TTTCAG--GG TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GTGTCGGCAG CAGCCTAAGT TTTCAG--GG TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GTGTCGGCAG CAGCCTAAGT TTTCAG--GG TCCGAGTTGT AATTTGCAGA GGGCGCTTTG GAGTCGGCAG CAGCCTAAGT TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGCCGCCA ACCTTC-GCC CCCTTGGAAC AGGACGTCAT AGAGGGTGAG AACCCCGTAC CTGGCCGCCG GTGCCC--CC CCCTTGGAAC AGGACGTCAT AGAGGGTGAG AACCCCGTAC CTGGCCGCCG GCTCCCC-CC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGCCGCCA GCCTCC-GCC TCCTTGGAAC AGGACGTCAT AGAGGGTGAG AATCCCGTAT GTGACCGGCT -CTGGC-ACC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCCTTC-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGCCGCCA ACCCGC-GCT TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATC-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATC-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTCTT-GCC TCCTTGGAAC AGGTCATCAT AGAGGGTGAG AATCCCGTAT GTGGTTGCAT GCCTTC-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATC-GCC TCTTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA TCCTTC-GCC TCTTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCCTTC-GCC CCCTTGGAAC AGGGCGTCAT AGAGGGTGAG AATCCCGTAT GTGGCCGGAA AGGTACC-CT CCCCTGGAAC GGGGTGTCAC AGAGGGTGAG AATCCCGTAT GTGACCGGAA GGGCGCC-CT TCCTTGGAAC AGGACATCAC AGAGGGTGAG AATCCCGTAT GTGGTCGCTA GCCTTC-GCT TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCCTTT-ACC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCCTTT-ACC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGCCGCCA GCCTCC-GCC TCCTTGGAAC AGGACATCGC AGAGGGTGAG AATCCCGTAC GTGGGCGCCT GCCTTT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCCTTT-ACC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCCTTT-ACC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCCTTT-ACC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCCTTT-ACC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTG GCCTTC-GCC TCCTTGGAAC AGGACATCAC AGAGGGTGAG AATCCCGTAT GTGGTCGCTG ATCTTT-GCC TCCTTGGAAC AGGACATCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCCT GCCTTT-GCC TTCTTGGAAC AGAATGTCAA AGAGGGTGAG AGTCCCGTCT TGGACCGCGG TAGGGC-CT- TCCTTGGAAC AGGACGTCAT AGAGGGTGAG AATCCCGTAT GTGACCGGCT -CTGGC-ACC TCCTTGGAAC AGGACGTCAT AGAGGGTGAG AATCCCGTAT GTGACCGGCT -CTGGC-ACC TCCTTGGAAC AGGACGTCAC A-AGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC A-AGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC A-AGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTA GCTATT-GCC TCCCTGGAAC AGGACATCGC AGAGGGTGAG AATCCCGTAC GTGGGCGCCT GCCTTC-GCC TCCTTGGAAC AGGACGTCAC AGAGGGTGAG AATCCCGTAC GTGGTCGCTG GCCTTC-GCC TCCTTGGAAC AGGTCATCAT AGAGGGTGAG AATCCCGTAT GTGGCTGCTT GTCTTC-ACC TCCTTGGAAC AGGTCATCAT AGAGGGTGAG AATCCCGTAT GTGGCTGCTT GTCTTC-ACC TCCTTGGAAC AGGTCATCAT AGAGGGTGAG AATCCCGTAT GTGGCTGCTT GTCTTC-GCC --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --GCTGTGAA ACTCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATTGG-TGG --GCTGTGAA ACTCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATTGG-TGG --G-TGTAAA GCCCCTTCGA TGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG TTA-TGTAAA GCTCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --A-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA TGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG C--C-GTAAA ACTCCTTCGA CGAGTCGGGT TGTTTGGGAA TGCAGCCCTA AATGGG-AGG --A-TACATG GCTCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG ---CTGTAAA GCCCCTTCAA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA TGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCAA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCTCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA ACTCCTTCGA CGAGTCGACT TGTTTGGGAA TGCAGGTCAA AATGGG-TGG TTA-TGTAAA GCTCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG TTA-TGTAAA GCTCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGGGAGG C-G-TGTAAA GCCCCTTCGA CGAGTCGAGT -GTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCTCCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA ATTGGG-AGG --G-TGTAAA GCCTCTTCGA CGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA TGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA TGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG --G-TGTAAA GCCCCTTCGA TGAGTCGAGT TGTTTGGGAA TGCAGCTCTA AATGGG-AGG TAAATTCCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTCCAT CTAAAGCTAA ATACCGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTCCAT CTAAAGCTAA ATACCGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTTGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCGA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACCGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTCCTT CTAAAGCTAA ATACCGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTTGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACCGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCAT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCGA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCGA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAA-GCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACCGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATACTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG TAAATTTCTT CTAAAGCTAA ATATTGGCCA GAGACCGATA GCGCACAAGT AGAGTGATCG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGA AAAGAGAGTT AAAA-GTACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGA AAAGAGAGTT AAAAAGTACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTT AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAATAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AAAACTTTGG AAAGAGAGTT AAACAGCATG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAATAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAATAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTT AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTT AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTT AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTT AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGTACTTTGG AAAGAGAGTC AAATAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTT AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTT AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTT AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTT AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAATAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGTACTTTGG AAAGAGAGTC AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGA AAAGAGAGTT AAACAGTATG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTT AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTT AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAACAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGTACTTTGG AAAGAGAGTC AAATAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAATAGCACG TGAAATTGTT AAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGCACTTTGG AAAGAGAGTC AAAAAGCACG TGAAATTGTT GAAAGGGAAG AAAGATGAAA AGTACTTTGG AAAGAGAGTC AAAAAGCACG TGAAATTGTT GAAAGGGAAG CGCTTGCAGC CAGACTTGCC TGTAGTTGCT CACCTCGGC- -TTCTG--CC TTGGGTACTC CGCTTGCGGC CAGACTTGCC TGCGGTTGCT CATCCCGCC- -TTTTG--GC GGGTGCACTC CGCTTGCGGC CAGACTTGCC CGCGGTTGCT CAGCCCGCC- -TTTTG--GC GGGTGCACTC CGCTTGCAGC TAGACGTGCC TTGGGTTGAT CAGCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAAT CAGACTTGGA CTTGGCTGTT CAACAGGTC- -TTCTG--AC CTGCCTATTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCC TGTAGTTGCT TATCCGGAC- -TTTTG--TC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CACCCGGGC- -CTCTGTGCC CGGTGCATTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CACCCGGGC- -CTCTGTGCC CGGTGCATTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGAC- -TTTTG--TC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCCGC CAGACTTGCT TGCAGTTGCT CACCCGGGCC CTTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCC CGTAGTTGCT CATCCGGGC- -TCTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCC TGTAGTTGCT CATCCGGGC- -TCTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCC CGCAGTTGCT CATCCGGGG- -TTCT-C-CC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TTCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCC TGTAGTTGCT TATCTGGAC- -TTTTG--TC CAGTGCACTC CGCTTGCAGC CAGACTTGCC TGTAGTTGCT TATCCGGAC- -TTTTG--TC CGGTGCACTC CGCTTGCGAT CAGTCTCGAC GGCGGCCGTT CGGCCTCTC- -TTCTG--AG TGGTTTATTC CGCTTGCAAC CAGACTTGTT GGCGGTGTTC C-GCCGGTC- -TTCTG--AC CGGTCTACTC CGCTTGCCGC CAGACTTGCC TGCAGTTGCT CATCTGGGCA -TTGTG--CC CTGTGCATTC CGCTTGCAGC CAGACTTGCC TGTAGTTGCT CATCCGGGT- -TTTTA--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCC TGTAGTTGCT CATCCGGGT- -TTTTA--CC CGGTGCACTC CGCTTGCAGC TAGACGTGCC TTGGGTTGAT CAGCCGGAC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCC CGCAGTTGCT CACCTAGGC- -TTTCG--CC TGGGGCACTC CGCTTGCAGC CAGACTTGCC TGTAGTTGCT CATCCGGGT- -TTCTA--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCC TGTAGTTGCT CATCCGGGT- -TTCTA--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCC TGTAGTTGCT CATCCGGGT- -TTCTA--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCC TGTAGTTGCT CATCCGGGT- -TTCTA--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCC TGTAGTTGCT CATCTGGGC- -TTTTG--TC CAGTGCACTC CGCTTGCCGC CAGACTTGCC TGCAGTTGCT CACCTAGGCG -TTCAG--CC TGGGGCACTC CGCTTGCAGC CAGACTTGCC CGCAGTTGCT CACCCAGGC- --TTTG-GCC CGGGGCACTC CGCTGGCAAC CAGACTCGTG CGCGGG-GTT CCCCCTTGC- -TTCTG--CT TGGGTCACTT CGCTTGCAAT CAGACTTGGA CTTGGCTGTT CAACAGGTC- -TTCTG--AC CTGCCTATTC CGCTTGCAAT CAGACTTGGA CTTGGCTGTT CAACAGGTC- -TTCTG--AC CTGCCTATTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTTCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTT---CC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACTTGCT TGCAGTTGCT CATCCGGGC- -TTTTG--CC CGGTGCACTC CGCTTGCAGC CAGACGTGCC CGCAGTTGCT CACCCAGGC- --TCCGG-CC TGGGGCACTC CGCTTGCAGC CAGACTTGCC CGTAGTTGCT CATCCAGGC- -TTTTG---C CTGTGCACTC CGCTTGCAGC CAGACGTGCC CGTAGTTGCT CATCTGGGG- -TTTTC--CC CAGTGTATTC CGCTTGCAGC CAGACGTGCC CGTAGTTGCT CATTCGGGG- -TTCTC--CC CGATGTACTC CGCTTGCAGC CAGACGTGCC CGCAGTTGCT CATCCGGGG- -ATTTC--CC CGGTGCACTC TTCTACGGGC AGGCCAGCAT CAGTCCGGGC GGTTGGATAA ATGCCTGCTA AATGTACCTC TTCCGCAGTC AGGCCAGCAT CGGTTTGGGC GGTCGGATAA AGGCGCTGGG AACGTGGCCT TTCCGCGGTC AGGCCAGCAT CGGTTCGGGC GGTCGGATAA AGGCGACGGG AACGTGGCCT TTCCTTTGGC AGGCCAGCAT CAGTTTGGGC GGCTGGATAA AGGTCTGTTA AACGTGACTC AGTCTTGTCC AGGCCAGCAT CAGTTTCGGC GGCCGGATAA AGGCCCTAGG AATGTGGCTT TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGT CATGTACCTC TTCTGCGGGC AGGCCAGCAT CAGTTTGGGC GGTTGGATAA AGGTCTCTGT CATGTACCTC TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGT CACGTACCTC TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGT CACGTACCTC TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGT CACGTACCTC TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGT CACGTACCTC TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGT CACGTACCTC TTCTATGGGC AGGCCAGCAT CAGTCTTGGC GGTCGGATAA ATGCGTGCTA AACGTACCTC TTCTGTAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGT CACGTACCTC TTCTGTAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGT CACGTACCTC TTCTGTAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGT CACGTACCTC TTCTGTAGGC AGGCCAGCAT CAGTTTGGGC GGTTGGATAA AGGTCTCTGT CATGTACCTC TTCTGTGGGC AGGCCAGCAT CAGTTCGGGC GGTTGGAGAA AGACCTGTGT CATGTAGCTG TTCTGTAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGCTTTTGG AATGTGGCTC TTCTGCGGGC AGGCCAGCAT CAGTTTGGGC GGTTGGATAA AGGTCTCTGT CATGTACCTC TTCTGCGGGC AGGCCAGCAT CAGTTTAGGC GGTTGGATAA AGGTCTCTAT CACGTACCTC GGTCGCCGCC GGGCCAGCAT CAGTTTCGGC GGTTGGATAA AGGTTGTGGG AATGTGGCCC ACC-GTCTGC AGGCCAGCAT CATCTGGGAC CGCTGGATAA AAGCGGAGGG AATGTGGCTC TTCTGCAAGC AGGCCAGCAT CAGTTTAGAC AGTCAGATAA GGGTCTCTGT CATGTATCTT TTCTATAGGC AGGCCAGCAT CAGTTTGGGC GGTTGGATAA AGGTCTCTGT CATGTACCTC TTCTATAGGC AGGCCAGCAT CAGTTTGGGC GGTTGGATAA AGGTCTCTGT CATGTACCTC TTCCTTTGGC AGGCCAGCAT CAGTTTGGGC GGCTGGATAA AGGTCTGTTA AACGTGACTT TTCTGCGGGC AGGCCAGCAT CAGTTTGGGC GGTTGGATAA AGGCCTCTGT CACGTATCTC TTCTATAGGC AGGCCAGCAT CAGTTTGGGC GGTTGGATAA AGGTCTCTGT CATGTACCTC TTCTATAGGC AGGCCAGCAT CGGGGTGGGC GGTTGGATAA AGGTCTCTGT CATGTACCTC TTCTATAGGC AGGCCAGCAT CAGTTTGGGC GGTTGGATAA AGGTCTCTGT CATGTACCTC TTCTATAGGC AGGCCAGCAT CAGTTTGGGC GGTTGGATAA AGGTCTCTGT CATGTACCTC TTCTACAGGC AGGCCAGCAT CAGTTTGGGC GGTTGGATAA AGGTCTCTGT CACGTACCTC TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC AGCCGGATAA AGGTCTCTGA CACGTTCCTA TTCTGCGGGC AGGCCAGCAT CAGTTTGGGC GGTTGGATAA AGGCCTCTGT CACGTATCTC CCCCGTGTCC GGGCCATCAT CAGTTTCGGG GGCCGGTTAA AGGCCTTGGG AATGTATCCA AGTCTTGTCC AGGCCAGCAT CAGTTTGGGC GGCCGGATAA AGGCTTTGGG AATGTGGCTT AGTCTTGTCC AGGCCAGCAT CAGTTTCGGC GGCCGGATAA AGGCCCTAGG AATGTGGCTT TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGA CACGTTCCTT TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGT CACGTACCTC TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGGTAA AGGTCTCTGT CACGT-CCTC CTCTGCAGGC AG-CCAGCAT CAGTTTGG-C GGTGGGATAA AGGTCTTTGA CACGTTCCTT TTCTGTAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGT CACGTACCTC TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGA CACGTTCCTT TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGA CACGTTCCTT TTCTGTAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGT CACGTACCTC TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGT CACGTACCAT TTCTGTAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCT-TGT CACGTACCTC TTCTGCAGGC AGGCCAGCAT CAGTTTGGGC GGTGGGATAA AGGTCTCTGA CACGTTCCTT TTCTGCGGGC AGGCCAGCAT CGGTTCGGGC GGTCGGATAA AGGTCTCTGT CACGTACCTC TTCTATGGGC AGGCCAGCAT CAGTTTGGGC GGTTGGATAA AGGTCTCTGT CA???????? TTCTATGGGC AGGCCAGCAT CAGTTCGGGC GGTTGGAGAA AGACCTGTGT CACGTAGCTC TTCTATGGGC AGGCCAGCAT CAGTTCGGGC GGTTGGAGAA AGACCTGTGT CACGTAGCTC TTCTGTGGGC AGGCCAGCAT CAGTTCGGGC GGTTGGAGAA AGACCTGTGT CACGTAGCTC TC--CTCGGG G----A--GG A-CTTATAGG GTAGGC-GGC ATACAACCAG CCTGGACTGA CCC-CTCGGG G--AG---GT G--TTATAGC CCGGCGCGCA ATGCGACCAG CCCGGACCGA CCC-CTCGGG G--AG---GT G--TTATAGC CCGGCGCGCA ATGCGGCCAG CCCGGACCGA CC--TTCGGG G----A--GA G-CTTATAGG GCAGAC-GAC ATGCAGCCAG TCCGAACTGA TCCCTTCGGG GG-A-A--GT G--TTATAGC CTAGGGTGTA ATACGGCCAG CTGGGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGGC-GAC ATACCACCAG CCTAGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGACGGAC ATGCAACCAG CCTGGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGGC-GAC ATACCACCAG CCTGGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGGC-GAC ATACCACCAG CCTGGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGAC-GAC ATACCACCAG CCTGGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGGC-GAC ATACCACCAG CCTGGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGAC-GAC ATACCACCAG CCTGGACTGA TC--TTCGGG G----A--GG A-CTTATAGG GCACGC-GGC ATACGACCAG CCGGGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGAC-GAC ATACCACCAG CCTAGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGAC-GAC ATACCACCAG CCTAGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGAC-GAC ATACCACCAG CCTAGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGGC-GAC ATACAACCAG CCTAGACTGA TT--CTCGGG C----A--GT G--TTATAGG GCAGGTTGGA ATGCAACCAG CTCGAACTGA TC--TTCGGG G----A--GG -CCTT-TAGG GGAAGGTGTA ATACCACCAG CTGGGACTGA CT--TTCGGG G----A--GG -CCTTATAGG GGAGAC-GAC ATGCAACCAG CCTGGACTGA CC--TTCGGG GTT-----GG -CCTTATAGG GGAGAC-GAC ATGCAACCAG CCTGAACTGA C---CTCGGG GG-------T G--TTATAGC CCACTTCGTA ATACAACCAG CTGGGACTGA C---TCCGGG ---A----GT G--TTATAGC CCTCTGTGTA ATACAGCGAG TCCCGGGTGA TC--TTCGGA A------TGA -CCTTATAGG GGGGGC-GTA GTATGGCTAG TCTAGACTGA TC--TTCGGG G----A--GA A-CTTATAGG GGAGAC-GAC ATGCAACCAG CCCGGACTGA TC--TTCGGG G----A--GA A-CTTATAGG GGAGAC-GAC ATGCAACCAG CCCGGACTGA CC--TTCGGG G----A--GA G-CTTATAGG GTAGAC-GAC ATGCAGCCAG CCTGAACTGA CC--TTCGGG GT------GA -CCTTATAGG GGAGGC-GCA ATGCAACCAG CCCGGACTGA CT--TTCGGG G----A--GA A-CTTATAGG GGAGAC-GAC ATGCAACCAG CCCGGACTGA CT--TTCGGG G----A--GA A-CTTATAGG GGAGAC-GAC ATGCAACCAG CCCGGACTGA CT--TTCGGG G----A--GA A-CTTATAGG GGAGAC-GAC ATGCAACCAG CCTGGACTGA CT--TTCGGG G----A--GA A-CTTATAGG GGAGAC-GAC ATGCAACCAG CCTGGACTGA CC--TTCGGG G----A--GG -CCTTATAGG GGAGAC-GAC ATGCAACCAG CCTGGACTGA CC--TTTGTG -T---A--GG -CCATATAGG GGAGAC-GTC ATGCGGTTAG CCTGGACTGA CC--TTCGGG GT------GA -CCTTATAGG GGAGGC-GCA ATGCAACCAG CCCGGACTGA CGCCTTCGGG GG-----TG- GACTTATAGC CCAGGGTGTC ATGCGGCCAC CCGGGACTGA TCT-TCCGGG GG-A-A--GT ---TTATAGG --AAGGTGTA ATACGGCCAG CTGGGACTGA TCCCTTCGGG GG-A-A--GT G--TTATAGC CTAGGGTGTA ATACGGCCAG CTGGGACTGA CC--TTCGGG -TT-----GG -CCATATAGG GGAGAC-GTC ATACCACCAG CCTGGACTGA TC--TTCGGG G----A--GG A-CTTATAGG GGAGGC-GAC ATACCACCAG CCTGGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGGCC-AC ATACCACCAG CCTGGACTGA CC--TTCGGG -TT-----G- -CCATATAGG G-AGAC-GTC ATACCACCAG CCTGGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGAC-GAC ATACCACCAG CCTAGACTGA CC--TTCGGG -TT-----GG -CCATATAGG GGAGAC-GTC ATACCACCAG CCTGGACTGA CC--TTCGGG -TT-----GG -CCATATAGG GGAGAC-GTC ATACCACCAG CCTGGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGAC-GAC ATACCACCAG CCTAGACTGA CC--TTCGGG -----AT-GG -CCATATAGG GGAGAC-GTC ATACCACCAG CCTGGACTGA TC--TTCGGG G----A--GG -CCTTATAGG GGAGAC-GAC ATACCACCAG CCTAGACTGA CC--TTCGGG -TT-----GG -CCATATAGG GGAGAC-GTC ATACCACCAG CCTGGACTGA CC--TTCGGG GT------GG -CCTTATAGG GGAGAC-GCA ATGCGACCAG CCCGGACCGA ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? CT--TTCGGG G----A--GT G--TTATAGG GCAGGT-GGA ATGCAACCAG CCTGAACTGA TC--TTCGGG G----A--GT G--TTATAGG GCAGGT-GGA ATGCAACCAG CCTGAACTGA TC--TTCGGG G----A--GT G--TTATAGG GCAGGT-GGA ATGCAACCAG CCTGAACTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTTCGCGC- TTT----GCT AGGATGCTGG CGTAATGGCC GT-AAGC-GG CCCGTCTTGA GGTTCGCGC- TCT----GCT AGGATGCTGG CGTAATGGCC GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATAGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCT TC---G-GCT AGGATGCTGG CGTAATGGTT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA T-T----GCT AGGATGCTGG CGTAAGGGCT GT--AGC-GG -CCGTCTTGA GGTCCGCGCA TTT--ATGCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TTT--ATGCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TTT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TTTTT-TGCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TTC--GTGCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCC-CGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCT TCT----GCT AGGATGCTGG CGTAATGGCT GTCAAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCT TC---G-GCT AGGATGCTGG CAAAATGGTC GT-AAGC-GG CCCGTCTTGA GGTCCGCGCT TC---G-GCT AGGATGCTGG CGTAATGGTC GT-AAGC-GG CCCGTCTTGA GGTCCGCGCT TTT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CATAATAGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AA-CGGG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TT-----GCT AGGATGCTGG CGTAATGGCT G-CAAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGAACGCGCT TC---G-GCG AGGATGATGG CGTAATGGTT GTC-AGC-GG CCCGTCTTGA GGTCCGCGCT TC---G-GCT AGGATGCTGG CGTAATGGTT GTCAAGC-GG CCCGTCTTGA GGTCCGCGCT TC---G-GCT AGGATGCTGG CGTAATGGTT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGAGCA TCT----GCT AGGA-GCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTG- CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GG-CCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCC AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA GGTCCGCGCA TCT----GCT AGGATGCTGG CGTAATGGCT GT-AAGC-GG CCCGTCTTGA AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTAAAG CCCGGACGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAA CCCGTGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAA CCCGTGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGGGAC CAAGGAGTC- TAACATCTAT GCGAGTGTTA GGGTGTCAAA CCCTTACGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC C-----GT-- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCAGACGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAACGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAACGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAACGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAACGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCAGACGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCAGACGCG AACACGG-AC CAAGGAGTC- TAACATCTAC GCGAGTGTTC GGGTGTCAAA CCCGTGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTC GGGTGTCAAA CCCCTACGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGGGCGCA AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAACGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGGGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGGGAC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGGACGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTC GGGTGTCAAA CCCTTGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTA GGGTGTCAAA CCCTTACGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTA GGGTGTCAAA CCCTTACGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTAAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGGGCGCG ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG AACACGG-AC CAAGGAGTC- TAACATCTAT GCGAGTGTTT GGGTGTCAAG CCCGAGCGCG CAATGAAAGT GAACGGAGG- TGGGAACCCC TC---GGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCGC -----AAGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACC-G CA---A-GGT GCACCATC-G ACCGATCCTG TAATGAAAGT AAACGGAGG- TGGGAACCCT TT----GGGT GCACCACC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGAGAACCCG CA---AGGGT GCATCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCT TT----GGGT GCACCATCCG ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCC TC---GGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCC TC---GGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCC TC---GGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCTT TT---A-GGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCTT TT---A-GGT GCACCATC-G ACCGATCCTG CAATGAAAGT GAACGGAGG- TGGGATCCCG CA---AGGGC GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCTT TT---A-GGT GCACCATC-G ACCGATCCTG TAATAAAAGT GAACGGAGG- TGGGAACCTT TTT----GGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCTT TC---GGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCTT TC---GGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT AAACGTAGG- TGGGAACCTT TT---A-GGT GCACCATC-G ACCGATCTTG CAATGAAAGT GAACGGAGG- TGGGAACC-G CA---A-GGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCTT TC---GGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGGG TGGGAACCTT TC---G???? ?????????? ?????????? TAATGAAAGT GAACGGAGG- TGGGAACCTT TC---GGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCTT TC---GGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGACCCCT TTT--AGGGT GCACCATC-G ACCGATCCTG CAATAAACGT GAACGGAGG- TGGGAACCCT CTTTGGGGGT GCACCATC-G ACCGATCCTG CAATGAAAGT GAACGGAGG- TGGGAACCCT TTT---GGGT GCACCATC-G ACCGATCCTG GAATGAAAGT GAACGGAGG- TAGGAAGGCT TA---AGCCT GCACTATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGAGAACCCG CA---AAGGT GCATCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGAGAACCCG CA---AGGGT GCATCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGC GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGC GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGC GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCG CA---AGGGT GCACCATC-G ACCGATCCTG CAATGAAAGT GAACGGAGG- TGGGAACCCC TC---GGGGC GCACCATC-G ACCGATCCTG ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? TAATGAAAGT GAACGGAGG- TGGGAACCCC TC---GGGGC GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCC TC---GGGGT GCACCATC-G ACCGATCCTG TAATGAAAGT GAACGGAGG- TGGGAACCCC TC---GGGGT GCACCATC-G ACCGATCCTG AAGTTTACGG ATGGATTTGA GTATGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG AAGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG AAGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTACGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTTCGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTTCGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTTCGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTTTTCGG ATGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG AAGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCGT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTACGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTACGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTACGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTACGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTACGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTACGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTACGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTACGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTACGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTACGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA AAGTTTACGG AAGGATTTGA GTAAGAGCAT GGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA ATGTCTTCGG ATGGATTTGA GTAAGAGCAT AGCTGTTGGG ACCCGAAAGA TGGTGAACTA TGCCTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCGTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCGTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCCTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCCTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA -GGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTCCGCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCCTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCCTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCCTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCCTGGATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCCTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCGTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCCTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCCTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCA?? ?????????? TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAGGC CAGAGGAAAC TCTGGTGGAG GCT??????? ?????????? TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCT??????? ?????????? TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG?? ?????????? ?????????? ?????????? ?????????? TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCTTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? TGCCTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCCTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCCTGAATA GGGTGAAG-C CAGAGGAAAC TCTGGTGGAG GCTC-GCAGC GGTTCTGACG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGCGTATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAA?? ?????????? ?????????? ?????? TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGTATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAA??? TGCAAATCGA TCGTCAAATT TGGGC????? ?????????? ?????? TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAAT?? TGCAAATCGA TCGTCAAATT TGGGCATAGG G-CGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGA? ?????? TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGA? ?????? TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGTATAGG GGCGAAAG?? ?????? TGCAAATCGA TCGTCAAATT TGGGTATAGG GGCGAAAG?? ?????? TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAAT?? TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAAT?? TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG G-CGAAAGAC TAATCG ?????????? ?????????? ?????????? ?????????? ?????? TGCAAATCGA TCGTCAAATT TGGGCATAGG G-CGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG G-CGAAAGAC TAAT?? TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG G????????? ?????? TGCAAATCGA TCGTCAAATT TGCGTATAGG GGCGAAAG?? ?????? TGCAAATCGA TCGTCAAATT TGGGTATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGTATAGG GGCGAAAGAC TAATCG ?????????? ?????????? ?????????? ?????????? ?????? TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG ?????????? ?????????? ?????????? ?????????? ?????? ?????????? ?????????? ?????????? ?????????? ?????? TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG G-CGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG G-CGAAAGAC TAAT?? TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG ?????????? ?????????? ?????????? ?????????? ?????? TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG ?????????? ?????????? ?????????? ?????????? ?????? TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG TGCAAATCGA TCGTCAAATT TGGGCATAGG GGCGAAAGAC TAATCG phyml-3.3.20220408/examples/phyrex/000077500000000000000000000000001422404706600165525ustar00rootroot00000000000000phyml-3.3.20220408/examples/phyrex/flu.xml000066400000000000000000000216331422404706600200670ustar00rootroot00000000000000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 phyml-3.3.20220408/examples/phyrex/h1n1.nxs000077500000000000000000002112461422404706600200640ustar00rootroot00000000000000#NEXUS BEGIN DATA; DIMENSIONS NTAX=36 NCHAR=1434; FORMAT DATATYPE=DNA INTERLEAVE; MATRIX CY130177|South_Carolina|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF647978|Idaho|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648268|New_Mexico|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648064|North_Carolina|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF647995|New_Hampshire|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648000|Colorado|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648133|Florida|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648145|Georgia|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648155|Vermont|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAACCT KF648158|Utah|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648159|Louisiana|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648008|Rhode_Island|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648011|New_York|12_13|H1N1 ---------ATGAATCCAAA CCGAAAGATAATAACCATTG GTTCGGTCTGCATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF647921|Maryland|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF647926|Missouri|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF647928|Texas|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF647936|Wyoming|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCCAACTTAATATT ACAAATTGGAAACATAATCT KF648197|Indiana|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAATATAACCT KF648228|Mississippi|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648239|Tennessee|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648034|Arizona|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648037|Kentucky|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648038|Kansas|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF647919|Alabama|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF647931|Wisconsin|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF647932|Pennsylvania|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF647958|Nebraska|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF647971|Oklahoma|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF647902|Iowa|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648002|Minnesota|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648040|New_Jersey|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648101|Ohio|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT KF648260|Washington|12_13|H1N1 ---------ATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT CY170697|California|12_13|H1N1 GAGTTTAAAATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT CY168881|Massachusetts|12_13|H1N1 GAGTTTAAAATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGGTCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT CY171161|Illinois|12_13|H1N1 GAGTTTAAAATGAATCCAAA CCAAAAGATAATAACCATTG GTTCGATCTGTATGACAATT GGAATGGCTAACTTAATATT ACAAATTGGAAACATAATCT CY130177|South_Carolina|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTAGGAATCAAAA TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF647978|Idaho|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAAGTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648268|New_Mexico|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648064|North_Carolina|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF647995|New_Hampshire|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648000|Colorado|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGAAATCAAAG TCAGATTGAAGCATGCAATC AAAGCGTCATTACTTACGAA AACAACACTTGGGTAAATCA KF648133|Florida|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648145|Georgia|12_13|H1N1 CAATATGGGTTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAGCATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648155|Vermont|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAGCATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648158|Utah|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648159|Louisiana|12_13|H1N1 CAATATGGGTTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648008|Rhode_Island|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAAGTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648011|New_York|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAAGTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF647921|Maryland|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF647926|Missouri|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF647928|Texas|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAA TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF647936|Wyoming|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTATGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648197|Indiana|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAGCATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648228|Mississippi|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648239|Tennessee|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648034|Arizona|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648037|Kentucky|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAGCATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648038|Kansas|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAGCATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF647919|Alabama|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF647931|Wisconsin|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAAGTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF647932|Pennsylvania|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAGCATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF647958|Nebraska|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAGCATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF647971|Oklahoma|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAAGTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF647902|Iowa|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648002|Minnesota|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648040|New_Jersey|12_13|H1N1 CAATATGGGTTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAACGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648101|Ohio|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAAGTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA KF648260|Washington|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA CY170697|California|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAAGTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA CY168881|Massachusetts|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAAGTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA CY171161|Illinois|12_13|H1N1 CAATATGGATTAGCCACTCA ATTCAACTTGGGAATCAAAG TCAGATTGAAACATGCAATC AAAGCGTCATTACTTATGAA AACAACACTTGGGTAAATCA CY130177|South_Carolina|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF647978|Idaho|12_13|H1N1 GACATATGTTAACATCAGCA ATACCAACTTTGCTGTTGGG CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648268|New_Mexico|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648064|North_Carolina|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF647995|New_Hampshire|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648000|Colorado|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648133|Florida|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648145|Georgia|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648155|Vermont|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648158|Utah|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648159|Louisiana|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648008|Rhode_Island|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGG CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648011|New_York|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGG CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF647921|Maryland|12_13|H1N1 GACATATGTTAACATCAGTA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF647926|Missouri|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAATTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF647928|Texas|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF647936|Wyoming|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648197|Indiana|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648228|Mississippi|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648239|Tennessee|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGG CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648034|Arizona|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648037|Kentucky|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648038|Kansas|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF647919|Alabama|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF647931|Wisconsin|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGG CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF647932|Pennsylvania|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF647958|Nebraska|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF647971|Oklahoma|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGG CAGCCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF647902|Iowa|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648002|Minnesota|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648040|New_Jersey|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGTGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648101|Ohio|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGG CAGCCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG KF648260|Washington|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGA CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG CY170697|California|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGG CAGCCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG CY168881|Massachusetts|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCTGCTGGG CAGTCAGTGGTTTCCGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGATGG CY171161|Illinois|12_13|H1N1 GACATATGTTAACATCAGCA ACACCAACTTTGCAGCTGGA CAGTCAGTGGTTTCTGTGAA ATTAGCGGGCAATTCCTCTC TCTGCCCTGTTAGTGGGTGG CY130177|South_Carolina|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTATAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF647978|Idaho|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648268|New_Mexico|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648064|North_Carolina|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF647995|New_Hampshire|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648000|Colorado|12_13|H1N1 GCTATATACAGCAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648133|Florida|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648145|Georgia|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648155|Vermont|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648158|Utah|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648159|Louisiana|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648008|Rhode_Island|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648011|New_York|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF647921|Maryland|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF647926|Missouri|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF647928|Texas|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTATAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF647936|Wyoming|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTATAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648197|Indiana|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648228|Mississippi|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648239|Tennessee|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648034|Arizona|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648037|Kentucky|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648038|Kansas|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF647919|Alabama|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF647931|Wisconsin|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF647932|Pennsylvania|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF647958|Nebraska|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF647971|Oklahoma|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF647902|Iowa|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648002|Minnesota|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648040|New_Jersey|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648101|Ohio|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA KF648260|Washington|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA CY170697|California|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA CY168881|Massachusetts|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTGTAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA CY171161|Illinois|12_13|H1N1 GCTATATACAGTAAAGACAA CAGTATAAGAATCGGTTCCA AGGGGGATGTGTTTGTCATA AGGGAACCATTCATATCATG CTCCCCCTTGGAATGCAGAA CY130177|South_Carolina|12_13|H1N1 CCTTCTTCTTGACCCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF647978|Idaho|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648268|New_Mexico|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648064|North_Carolina|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF647995|New_Hampshire|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCATTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648000|Colorado|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648133|Florida|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648145|Georgia|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648155|Vermont|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648158|Utah|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648159|Louisiana|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648008|Rhode_Island|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648011|New_York|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAGG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF647921|Maryland|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF647926|Missouri|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF647928|Texas|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC TTAATGAGCTGTCCTATTGG KF647936|Wyoming|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648197|Indiana|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648228|Mississippi|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648239|Tennessee|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648034|Arizona|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648037|Kentucky|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648038|Kansas|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCTTATTGG KF647919|Alabama|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF647931|Wisconsin|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF647932|Pennsylvania|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF647958|Nebraska|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF647971|Oklahoma|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF647902|Iowa|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648002|Minnesota|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648040|New_Jersey|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648101|Ohio|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG KF648260|Washington|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG CY170697|California|12_13|H1N1 CCTTTTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG CY168881|Massachusetts|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG CY171161|Illinois|12_13|H1N1 CCTTCTTCTTGACTCAAGGG GCCTTGCTAAATGACAAACA TTCCAATGGAACCATTAAAG ACAGGAGCCCATATCGAACC CTAATGAGCTGTCCTATTGG CY130177|South_Carolina|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF647978|Idaho|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648268|New_Mexico|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648064|North_Carolina|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF647995|New_Hampshire|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648000|Colorado|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATTAATTGGC TAACAATTGGAATTTCTGGC KF648133|Florida|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648145|Georgia|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648155|Vermont|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648158|Utah|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648159|Louisiana|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648008|Rhode_Island|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648011|New_York|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF647921|Maryland|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF647926|Missouri|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF647928|Texas|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF647936|Wyoming|12_13|H1N1 TGAAGTTCCCTCCCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648197|Indiana|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648228|Mississippi|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648239|Tennessee|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648034|Arizona|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648037|Kentucky|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648038|Kansas|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF647919|Alabama|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF647931|Wisconsin|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF647932|Pennsylvania|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF647958|Nebraska|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF647971|Oklahoma|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF647902|Iowa|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648002|Minnesota|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648040|New_Jersey|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648101|Ohio|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC KF648260|Washington|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCGGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC CY170697|California|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC CY168881|Massachusetts|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC CY171161|Illinois|12_13|H1N1 TGAAGTTCCCTCTCCATACA ACTCAAGATTTGAGTCAGTC GCTTGGTCAGCAAGTGCTTG TCATGATGGCATCAATTGGC TAACAATTGGAATTTCTGGC CY130177|South_Carolina|12_13|H1N1 CCAGACAATGGGGCAGTGGC TGTGTTAAAGTACAACGGCA TAATAACAGACACTATCAAG AGTTGGAGAAATAATATATT GAGAACACAAGAATCTGAAT KF647978|Idaho|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAACGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACGATATATT GAGAACACAAGAGTCTGAAT KF648268|New_Mexico|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATAGT GAGAACACAAGAGTCTGAAT KF648064|North_Carolina|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF647995|New_Hampshire|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF648000|Colorado|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAATCTGAAT KF648133|Florida|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF648145|Georgia|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGRAAAATAATATATT GAGAACACAAGAGTCTGAAT KF648155|Vermont|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAAAAATAATATATT GAGAACACAAGAGTCTGAAT KF648158|Utah|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF648159|Louisiana|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF648008|Rhode_Island|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAACGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF648011|New_York|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAACGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF647921|Maryland|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF647926|Missouri|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF647928|Texas|12_13|H1N1 CCAGACAATGGGGCAGTTGC TGTGTTAAAGTACAACGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF647936|Wyoming|12_13|H1N1 CCAGACAATGGGGCAGTGGC TGTGTTAAAGTACAACGGCA TAATAACAGACACTATCAAG AGTTGGAGAAATAATATATT GAGAACACAAGAATCTGAAT KF648197|Indiana|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAAAAATAATATATT GAGAACACAAGAGTCTGAAT KF648228|Mississippi|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF648239|Tennessee|12_13|H1N1 CCAGACAGTGGGGCAGTTGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF648034|Arizona|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF648037|Kentucky|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAAAAATAATATATT GAGAACACAAGAGTCTGAAT KF648038|Kansas|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAAAAATAATATATT GAGAACACAAGAGTCTGAAT KF647919|Alabama|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF647931|Wisconsin|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAACGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF647932|Pennsylvania|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAAAAATAATATATT GAGAACACAAGAGTCTGAAT KF647958|Nebraska|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAAAAATAATATATT GAGAACACAAGAGTCTGAAT KF647971|Oklahoma|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAACGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACGATATATT GAGAACACAAGAGTCTGAAT KF647902|Iowa|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF648002|Minnesota|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF648040|New_Jersey|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT KF648101|Ohio|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAACGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACGATATATT GAGAACACAAGAGTCTGAAT KF648260|Washington|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTATTAAAGTACAATGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT CY170697|California|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAACGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACGATATATT GAGAACACAAGAGTCTGAAT CY168881|Massachusetts|12_13|H1N1 CCAGACAGTGGGGCAGTGGC TGTGTTAAAGTACAACGGCA TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT CY171161|Illinois|12_13|H1N1 CCAGACAATGGGGCAGTGGC TGTGTTAAAGTACAACGGCG TAATAACAGACACTATCAAG AGTTGGAGAAACAATATATT GAGAACACAAGAGTCTGAAT CY130177|South_Carolina|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF647978|Idaho|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648268|New_Mexico|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648064|North_Carolina|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF647995|New_Hampshire|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648000|Colorado|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648133|Florida|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648145|Georgia|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGACGGACAGG CCTCATACAAGATCTTCCGA ATAGAAAAGGGAAAGATAGT KF648155|Vermont|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGACGGACAGG CCTCATACAAGATCTTCCGA ATAGAAAAGGGAAAGATAGT KF648158|Utah|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648159|Louisiana|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648008|Rhode_Island|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648011|New_York|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF647921|Maryland|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF647926|Missouri|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF647928|Texas|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAAA ATAGAAAAGGGAAAGATAGT KF647936|Wyoming|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648197|Indiana|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGACGGACAGG CCTCATACAAGATCTTCCGA ATAGAAAAGGGAAAGATAGT KF648228|Mississippi|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648239|Tennessee|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648034|Arizona|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648037|Kentucky|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGACGGACAGG CCTCATACAAGATCTTCCGA ATAGAAAAGGGAAAGATAGT KF648038|Kansas|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGACGGACAGG CCTCATACAAGATCTTCCGA ATAGAAAAGGGAAAGATAGT KF647919|Alabama|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF647931|Wisconsin|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF647932|Pennsylvania|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGACGGACAGG CCTCATACAAGATCTTCCGA ATAGAAAAGGGAAAGATAGT KF647958|Nebraska|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGACGGACAGG CCTCATACAAGATCTTCCGA ATAGAAAAGGGAAAGATAGT KF647971|Oklahoma|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF647902|Iowa|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648002|Minnesota|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648040|New_Jersey|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648101|Ohio|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT KF648260|Washington|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT CY170697|California|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACTGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT CY168881|Massachusetts|12_13|H1N1 GTGCATGTGTAAATGGTTCT TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT CY171161|Illinois|12_13|H1N1 GTGCATGTGTAAATGGTTCA TGCTTTACCATAATGACCGA TGGACCAAGTGATGGACAGG CCTCATACAAGATCTTCAGA ATAGAAAAGGGAAAGATAGT CY130177|South_Carolina|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF647978|Idaho|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648268|New_Mexico|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTACCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648064|North_Carolina|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF647995|New_Hampshire|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648000|Colorado|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648133|Florida|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648145|Georgia|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648155|Vermont|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCATATGTG TGTGCAGGGATAACTGGCAT KF648158|Utah|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648159|Louisiana|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648008|Rhode_Island|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAATTGGCAT KF648011|New_York|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCAAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF647921|Maryland|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF647926|Missouri|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF647928|Texas|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF647936|Wyoming|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648197|Indiana|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648228|Mississippi|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648239|Tennessee|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648034|Arizona|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648037|Kentucky|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648038|Kansas|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF647919|Alabama|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF647931|Wisconsin|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF647932|Pennsylvania|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF647958|Nebraska|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF647971|Oklahoma|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF647902|Iowa|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648002|Minnesota|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648040|New_Jersey|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648101|Ohio|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT KF648260|Washington|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT CY170697|California|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT CY168881|Massachusetts|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAATTGGCAT CY171161|Illinois|12_13|H1N1 CAAATCAGTCGAAATGAATG CCCCTAATTATCACTATGAG GAATGCTCCTGTTATCCTGA TTCTAGTGAAATCACATGTG TGTGCAGGGATAACTGGCAT CY130177|South_Carolina|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCCAATGATA KF647978|Idaho|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF648268|New_Mexico|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF648064|North_Carolina|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF647995|New_Hampshire|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF648000|Colorado|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF648133|Florida|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF648145|Georgia|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGGTTTTCGGAGA CAATCCACGCCCTAATGATA KF648155|Vermont|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGGTTTTCGGAGA CAATCCACGCCCTAATGATA KF648158|Utah|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF648159|Louisiana|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGGTTTTCGGAGA CAATCCACGCCCTAATGATA KF648008|Rhode_Island|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF648011|New_York|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGAAAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF647921|Maryland|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF647926|Missouri|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF647928|Texas|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF647936|Wyoming|12_13|H1N1 GGCTCGAATCGACCGTGGGT TTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGTCCTAATGATA KF648197|Indiana|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGGTTTTCGGAGA CAATCCACGCCCTAATGATA KF648228|Mississippi|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF648239|Tennessee|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATAAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF648034|Arizona|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF648037|Kentucky|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGGTTTTCGGAGA CAATCCACGCCCTAATGATA KF648038|Kansas|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGGTTTTCGGAGA CAATCCACGCCCTAATGATA KF647919|Alabama|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF647931|Wisconsin|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF647932|Pennsylvania|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGGTTTTCGGAGA CAATCCACGCCCTAATGATA KF647958|Nebraska|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGGTTTTCGGAGA CAATCCACGCCCTAATGATA KF647971|Oklahoma|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF647902|Iowa|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAATCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF648002|Minnesota|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATR TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF648040|New_Jersey|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGGTTTTCGGAGA CAATCCACGCCCTAATGATA KF648101|Ohio|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA KF648260|Washington|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA CY170697|California|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA CY168881|Massachusetts|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA CY171161|Illinois|12_13|H1N1 GGCTCGAATCGACCGTGGGT GTCTTTCAACCAGAATCTGG AATATCAGATAGGATACATA TGCAGTGGGATTTTCGGAGA CAATCCACGCCCTAATGATA CY130177|South_Carolina|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF647978|Idaho|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648268|New_Mexico|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648064|North_Carolina|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF647995|New_Hampshire|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648000|Colorado|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648133|Florida|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648145|Georgia|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648155|Vermont|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648158|Utah|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648159|Louisiana|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648008|Rhode_Island|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648011|New_York|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF647921|Maryland|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF647926|Missouri|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF647928|Texas|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTGTCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF647936|Wyoming|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648197|Indiana|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648228|Mississippi|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648239|Tennessee|12_13|H1N1 AGACAGGTAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648034|Arizona|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTCTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648037|Kentucky|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648038|Kansas|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF647919|Alabama|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTCTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF647931|Wisconsin|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF647932|Pennsylvania|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF647958|Nebraska|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF647971|Oklahoma|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF647902|Iowa|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648002|Minnesota|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648040|New_Jersey|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648101|Ohio|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG KF648260|Washington|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG CY170697|California|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG CY168881|Massachusetts|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG CY171161|Illinois|12_13|H1N1 AGACAGGCAGTTGTGGTCCA GTATCGTCTAATGGAGCAAA TGGAGTAAAAGGATTTTCAT TCAAATACGGCAATGGTGTT TGGATAGGGAGAACTAAAAG CY130177|South_Carolina|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AACGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF647978|Idaho|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648268|New_Mexico|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648064|North_Carolina|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF647995|New_Hampshire|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648000|Colorado|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648133|Florida|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648145|Georgia|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648155|Vermont|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648158|Utah|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648159|Louisiana|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCG AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648008|Rhode_Island|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648011|New_York|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF647921|Maryland|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF647926|Missouri|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF647928|Texas|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AACGGATGGACTGGGACAGA CAATAGCTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF647936|Wyoming|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AACGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648197|Indiana|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648228|Mississippi|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATGAAT KF648239|Tennessee|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648034|Arizona|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648037|Kentucky|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648038|Kansas|12_13|H1N1 CATTGGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF647919|Alabama|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF647931|Wisconsin|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF647932|Pennsylvania|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF647958|Nebraska|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF647971|Oklahoma|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF647902|Iowa|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648002|Minnesota|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648040|New_Jersey|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCG AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648101|Ohio|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT KF648260|Washington|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT CY170697|California|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT CY168881|Massachusetts|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AATGGATGGACTGGGACAGA CAATAACTTCTCAATAAAGC AAGATATCGTAGGAATAAAT CY171161|Illinois|12_13|H1N1 CATTAGTTCAAGAAAAGGTT TTGAGATGATTTGGGATCCA AACGGATGGACTGGGACAGA CAATAACTTCTCAACAAAGC AAGATATCGTAGGAATAAAT CY130177|South_Carolina|12_13|H1N1 GAGTGGTCAGGATACAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGACGAC KF647978|Idaho|12_13|H1N1 GAGTGGACAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAATTAATCAGAGGGCGAC KF648268|New_Mexico|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648064|North_Carolina|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF647995|New_Hampshire|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648000|Colorado|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648133|Florida|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648145|Georgia|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648155|Vermont|12_13|H1N1 GAATGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648158|Utah|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648159|Louisiana|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648008|Rhode_Island|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648011|New_York|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF647921|Maryland|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF647926|Missouri|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF647928|Texas|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGACTGT ATAAGACCTTGTTTCTGGGT TGAACTAATCAGAGGGCGAC KF647936|Wyoming|12_13|H1N1 GAGTGGTCAGGATACAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGACGAC KF648197|Indiana|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648228|Mississippi|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648239|Tennessee|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648034|Arizona|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648037|Kentucky|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648038|Kansas|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF647919|Alabama|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF647931|Wisconsin|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAATTAATCAGAGGGCGAC KF647932|Pennsylvania|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF647958|Nebraska|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF647971|Oklahoma|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF647902|Iowa|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648002|Minnesota|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648040|New_Jersey|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648101|Ohio|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC KF648260|Washington|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC CY170697|California|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC CY168881|Massachusetts|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGCGAC CY171161|Illinois|12_13|H1N1 GAGTGGTCAGGATATAGCGG GAGTTTTGTTCAGCATCCAG AACTAACAGGGCTGGATTGT ATAAGACCTTGCTTCTGGGT TGAACTAATCAGAGGGAGAC CY130177|South_Carolina|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF647978|Idaho|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGTGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648268|New_Mexico|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648064|North_Carolina|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF647995|New_Hampshire|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648000|Colorado|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648133|Florida|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648145|Georgia|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648155|Vermont|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648158|Utah|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGTGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648159|Louisiana|12_13|H1N1 CCGAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648008|Rhode_Island|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGTGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648011|New_York|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGTGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF647921|Maryland|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF647926|Missouri|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF647928|Texas|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGTGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG GCACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF647936|Wyoming|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC TTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648197|Indiana|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648228|Mississippi|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648239|Tennessee|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648034|Arizona|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648037|Kentucky|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648038|Kansas|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF647919|Alabama|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF647931|Wisconsin|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGTGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF647932|Pennsylvania|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF647958|Nebraska|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCCTGG CCAGACGGTGCTGAGTTGCC KF647971|Oklahoma|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGTGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF647902|Iowa|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648002|Minnesota|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648040|New_Jersey|12_13|H1N1 CCGAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTTGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648101|Ohio|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGTGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC KF648260|Washington|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC CY170697|California|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGTGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC CY168881|Massachusetts|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGTGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC CY171161|Illinois|12_13|H1N1 CCAAAGAGAACACAATCTGG ACTAGCGGGAGCAGCATATC CTTTTGTGGTGTAAACAGTG ACACTGTGGGTTGGTCTTGG CCAGACGGTGCTGAGTTGCC CY130177|South_Carolina|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF647978|Idaho|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648268|New_Mexico|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648064|North_Carolina|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF647995|New_Hampshire|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648000|Colorado|12_13|H1N1 ATTTGCCATTGACAAGTAA- -------------- KF648133|Florida|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648145|Georgia|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648155|Vermont|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648158|Utah|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648159|Louisiana|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648008|Rhode_Island|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648011|New_York|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF647921|Maryland|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF647926|Missouri|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF647928|Texas|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF647936|Wyoming|12_13|H1N1 ATTTATCATTGACAAGTAA- -------------- KF648197|Indiana|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648228|Mississippi|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648239|Tennessee|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648034|Arizona|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648037|Kentucky|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648038|Kansas|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF647919|Alabama|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF647931|Wisconsin|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF647932|Pennsylvania|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF647958|Nebraska|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF647971|Oklahoma|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF647902|Iowa|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648002|Minnesota|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648040|New_Jersey|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648101|Ohio|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- KF648260|Washington|12_13|H1N1 ATTTACCATTGACAAGTAA- -------------- CY170697|California|12_13|H1N1 ATTTACCATTGACAAGTAAT TTGTTCA------- CY168881|Massachusetts|12_13|H1N1 ATTTACCATTGACAAGTAAT TTGTTCAAAAAACT CY171161|Illinois|12_13|H1N1 ATTTACCATTGACAATTAAT TTGTTCAAAAAACT ; END; phyml-3.3.20220408/examples/phyrex/usa_coord.txt000077500000000000000000000033701422404706600212770ustar00rootroot00000000000000# state.name lon lat |SouthWest| -122 25 |NorthEast| -66 49 |Alabama| -86.902298 32.3182314 |Alaska| -149.4936733 64.2008413 |Arizona| -111.0937311 34.0489281 |Arkansas| -91.8318334 35.20105 |California| -119.4179324 36.778261 |Colorado| -105.7820674 39.5500507 |Connecticut| -73.087749 41.6032207 |Delaware| -75.5276699 38.9108325 |Florida| -81.5157535 27.6648274 |Georgia| -82.9000751 32.1656221 |Hawaii| -155.5827818 19.8967662 |Idaho| -114.7420408 44.0682019 |Illinois| -89.3985283 40.6331249 |Indiana| -86.1349019 40.2671941 |Iowa| -93.097702 41.8780025 |Kansas| -98.4842465 39.011902 |Kentucky| -84.2700179 37.8393332 |Louisiana| -91.9623327 30.9842977 |Maine| -69.4454689 45.253783 |Maryland| -76.6412712 39.0457549 |Massachusetts| -71.3824374 42.4072107 |Michigan| -85.6023643 44.3148443 |Minnesota| -94.6858998 46.729553 |Mississippi| -89.3985283 32.3546679 |Missouri| -91.8318334 37.9642529 |Montana| -110.3625658 46.8796822 |Nebraska| -99.9018131 41.4925374 |Nevada| -116.419389 38.8026097 |New_Hampshire| -71.5723953 43.1938516 |New_Jersey| -74.4056612 40.0583238 |New_Mexico| -105.8700901 34.5199402 |New_York| -74.0059413 40.7127837 |North_Carolina| -79.0192997 35.7595731 |North_Dakota| -101.0020119 47.5514926 |Ohio| -82.907123 40.4172871 |Oklahoma| -97.5164276 35.4675602 |Oregon| -120.5542012 43.8041334 |Pennsylvania| -77.1945247 41.2033216 |Rhode_Island| -71.4774291 41.5800945 |South_Carolina| -81.1637245 33.836081 |South_Dakota| -99.9018131 43.9695148 |Tennessee| -86.5804473 35.5174913 |Texas| -99.9018131 31.9685988 |Utah| -111.0937311 39.3209801 |Vermont| -72.5778415 44.5588028 |Virginia| -78.6568942 37.4315734 |Washington| -77.0368707 38.9071923 |West_Virginia| -80.4549026 38.5976262 |Wisconsin| -88.7878678 43.7844397 |Wyoming| -107.2902839 43.0759678 phyml-3.3.20220408/examples/phytime/000077500000000000000000000000001422404706600167125ustar00rootroot00000000000000phyml-3.3.20220408/examples/phytime/dating_example.xml000066400000000000000000000043101422404706600224130ustar00rootroot00000000000000 40 60 50 200 phyml-3.3.20220408/examples/phytime/seq.txt000066400000000000000000003035131422404706600202500ustar00rootroot0000000000000022 4533 Nymphaeales_Cabomba tcaaagattaagccatgcatgtgtaagtatgaacaagttcagaccgtgaaactgcgaatggctcattaaatcagttataatttgtttgatggtact-tactactcggataaccgtagtaaagctagagctaatacgtgcaccaaaccccgac-ttctggaaggggcgcatttattagataaaaggccgatgcgggctt-tgcccgatttattggtgattcatgataactcgacggatcgcacggccctcgtgccggcgacgcatcattcaaatttctgccctatcaactttcgatggtaggatagaggcctaccatggtagtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaatacataacaataccgggctc-tttgagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggatcttgggttggtccggccggtccgcctcc--ggtgtgcaccggtcgtctcgtcccttctaccggcgtcgcgttcctggccttaactggccgggtcgtgccttcggtgctgttactttgaagaaattagagtgctcaaagcaagcttacgctgtgcatacattagcatgggataacatcacaggatttcggtcctattgtgttggcctttgggatcggagtaatgattaagagggacagtcgggggcattcgtatttcatagtcagaggtgaaattcttggatttatgaaagacgaacaactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgactagggatcggcggatgttgctcttaggactccgccggcaccttatgagaaatcaaagtttttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagactgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctacgtggaggt-acccttccacggccagcttcttagacggactatggccgtttaggccacggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagtt-tatagccttggccgacaggtccgggtaatcttgg-aaactacatcgtgatggggatagatcattgcaattgttggtcttcaacgaggaattcctagtaagcgtgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttcggatcgcggcgacgggggcggtccgccgcctacgacgtcgcgagaagttcattgaaccttatcattgagarg-----------------------------------------------------------------------------------------------------aattaatgtgacttgtgaggtacagcaattattaggaaataatcgagttagagctgtagctatgagtgctacagatgggctgaggagaggaatggaagtgattgacacaggagctgctctaagtgttccagtaggtggagccactctaggacgaattttcaatgttcttggagaacccgttgataatttaggtcctgtagatactcgcacaacatcccccattcatagatccgcacccgcttttatacagttagataccaaattatcaatctttgaaacggggataaaagtagtggaccttttagcgccttatcgacgtgggggaaaaatcggattatttgggggagccggagtgggtaaaacagtactcatcatggaattaatcaacaacattgccaaagctcatgggggtgtatccgtattcggcggagtaggagaacgcactcgtgagggaaatgatctttacatggaaatgaaagaatccggggtgattaatgaagaaaatattgcagaatctaaagtagctctagtatatggacagatgaatgaaccgccgggagctcgtatgagagtcggtttgactgccctaaccatggcggaatatttccgtgatgttaatgaacaagacgtccttctatttatcgacaatatctttcgcttcgtacaagcggggtcagaagtatctgccttattaggtagaatgccttccgccgtgggttatcagcctacccttagtacagaaatgggttctttgcaagaaagaattacttctacaaaagagggatccataacttccattcaagcagtttatgtacccgcggacgatttgactgatcctgctcctgccacgacatttgcacatttagatgctactaccgtactatcaagaggattagctgccaaaggtatctatccagcagtggatcctttagattcaacgtcaactatgctccaacctcggatcgttggcgaagaacattacgaaacagcgcaaagagttaagcaaactttacaacgttacaaagaacttcaggacattatagcgattcttggactggacgaattatctgaagaggatcgtttaaccgtagcaagagcgcgaaaaattgaacgttttctatcacaacccttcttcgtagcggaagtatttacaggttctccagggaaatatgttggtctcgcagaaacaattagggggtttcaactgatcctttccggagaatttgatagttttcctgagcaggccttttatttggtaggtaatatcgatgaagctacctctaaggctattaacttagaagtcgagagaagtgttggattcaaagctggtgttaaagattacagattgacttattacactcctgagtatgaaacccttgctactgatatcttggcagcattccgagtaactcctcaacctggagttccacctgaggaagcaggagctgcggtggctgccgaatcttccactggtacatggacaactgtgtggaccgatggacttaccagcctcgatcgttacaaaggacgatgctaccacatcgagccagttgctggggaggaaaatcaatatattgcttatgtagcttaccctttggatctttttgaagaaggttctgttactaacatgtttacttccattgtgggcaatgtatttggattcaaagccctacgagctctacgcttggaggatctgagaattcctcctgcttattctaaaactttccagggcccacctcatggaatccaagttgagagagataaattgaacaagtatggtcgtcccctattgggatgtactattaaaccaaaattggggttatccgcaaaaaactatggtagagcagtttatgagtgtctacgtggtggacttgattttaccaaggatgatgagaacgtaaactcccagccgtttatgcgttggagagatcgtttcttattttgcgccgaagctatttataaagcacaggccgaaacaggtgaaattaaaggacattacttgaatgctactgcaggtacatctgaagaaatgatgaaaagagcagcatgcgcccgagagttgggagttcctatcgtcatgcatgactacttaacagggggattcactgcaaatactagcttgtctcattattgccgagacaatggcctacttcttcacatccaccgcgcaatgcatgcagttattgatagacagaagaatcatggtattcacttccgtgtactagctaaagcgttgcgtatgtctgggggggatcatattcactctggtaccgtagtaggtaaactggaaggggaacgagatgtcactttgggctttgttgatttactacgtgatgattttattgaaaaagaccggagtcgcggtatttatttcactcaagattgggtatctatgcctggtgttttgcccgtggcttcaggcggtattcacgtttggcatatgcctgccctgaccgagatatttggggatgattccgtgctacagttcgg--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Amborella tcaaagattaagccatgcatgtgtaagtatgaactaattcagactgtgaaactgcgaatggctcattaaatcagttatagtttgtttgatggtacc-tgctactcggataaccgtagtaattctagagctaatacgtgcaagaaatcccgac-ctctggaagggatgcatttattagataaaaggccagcgcgggctt-tgcccgatgctttggtgaatcatgataactcgacggatcgcacggccattgtgctggcgacgcatcattcaaatttctgccctatcaactttcgatggtaggataggggcctaccatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaataccgggctc-caagagtctggtaattggaatgagtacaatctaaaccccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggatcttgggctgggccgaccggtccgccttcacggtgtgcaccggccgtcccgtcccttcacccggcgatgcgttcctggccttaactggccgggtcgtgcctccggcaccgttactttgaagaaattagagtgctcaaagcaagcctacgctctgaatacattagcatgggataacatcacaggatttcggtcctattgtgttggccttcgggatcggagtaatgattaagagggacagtcgggggcattcgtatttcatagtcagaggtgaaattcttggatttatgaaagacgaaccactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggatcggcggatgttgctcttaggactccgtcggcaccttatgagaaatcaaagtctttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagactgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctacgtggaggt--atcctccacggccagcttcttagagggactatggccgtttaggccacggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagta-tatagccttggccgacaggcccgggtaatctgtc-aaatttcatcgtgatggggatagatcattgcaattgttggtcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttcggatcgcggcgacggaggcggtccgccgctggcgacgtcgcgagaagtccactgaaccttatcatttagaggaaggtcattggtccggtactggatgtagcctttcccccgggcaagatgcccaatatttacaactctntggtagttaaaggtcgagatactgccggtcagcaaattaatgtgacttgtgaggtacaacaattattaggaaataatcgagttagaacggtagctatgagtgctacagatggtctaatgagaggaatggaagtgattgacacgggagctccttaagtgttcccggttggcgaagctactctcggacgaattttcaacgttcttggagaaccagttgataatttaggtcctgtagatactcgcacaacatctcctattcatagatctgcgcctgcctttatacagttagataccaaattatcaatctttgaaacaggcattaaagtggtggatcttttagctccttatcgtcgtggaggaaaaatcgggctattcgggggagccggagtgggtaaaacagtactcatcatggaattgatcaataacattgccaaagctcacggaggtgtatctgtatttggcggagtaggagaacgtactcgtgaaggaaatgatctttacatggaaatgaaagaatccggagtgattaatgaacaaaatattgcagaatcaaaagtcgctctagtctatggtcagatgaatgaaccgccgggagctcggatgagagttgttttaactgccctaacaatggcggaatatttccgtgatgttaatgagcaagacgtacttctatttattgacaatatctctcgcttcgttcaagcaggatcagaagtctctgccttattaggcagaatgccttctgccgtgggttatcaacctacccttagtacagaaatgggttctttgcaagaaagaattacttctactaaagagggatccataacttccattcaagcagtttatgtacctgcagacgatttgaccgaccctgctcctgccacgacatttgcacatttagatgctactaccgtactatcaagaggattagccgccaaaggtatctatccagcagtagatcctttagattcaacgtcaactatgctccaacctcggatcgttggcgaggaacattacgaaactgcgcaaagagttaagcaaactttacaacgttacaaagaacttcaagacattatagctattcttggaccagatgaattatccgaagaggatcgtttaaccgtagcaagagcacgaaaaattgagcgtttcttatcacaacccttcttcgtagcagaggtatttactggttctccaggaaaatatgttggtctcgcagaaacaattagggggtttcaactgatcctttccggagaattagatggtcttcccgaacaggctttttatctggtaggtaacatcgatgaagctaccgcgaaggctattaacttagaagtagagag--gcgctggattcaaagccggtgttaaagattacagattgacttattatactcctgactatgaaaccctagctactgatatcttggcagcattccgagtaactcctcaacctggagttccacctgaggaagcaggagctgcagtagctgccgaatcttccactggtacatggacaactgtgtggaccgatggacttaccagccttgatcgttacaaaggtcgatgctaccacatcgaacccgttgctggggaacaaaatcaatatattgcttatgtagcttatcctttggacctttttgaggaaggttctgttactaacatgtttacttccatcgtaggtaatgtatttgggttcaaagccctacgagctctacgtctagaggatctgcgaattcctcctgcttattccaaaagtttccaaggcccacctcatggcatccaagtcgagagagataaattgaacaagtatggtcgtcccctattaggatgtactattaaaccaaaattggggttatctgccaaaaactacggtagagcggtttatgaatgtctccgtggtgggcttgattttaccaaggatgatgagaatgtgaactcccaaccatttatgcgttggagggaccgtttcttattttgtgccgaagctctttataaagcacaggcagaaacaggtgaaatcaaaggacattacttgaatgctactgcaggtacatgcgaagaaatgatgaaaagggccgtatttgccagagaattgggagttcctatcgtaatgcatgactacttaacaggaggattcactgcaaatactagtttggcccattattgccgagacaatggcctacttcttcacattcatcgcgcaatgcatgcagttattgatagacaaaggaatcatggtatacactttcgtgtactagctaaagcattgcgtatgtctggtggggatcatattcacgctggtaccgtagtaggtaaactggaaggggaacgggatgtcactttgggttttgttgatctactacgtgatgattttattgaaaaagaccgaagtcgcggtatttatttcactcaagattgggtatctatgccaggtgttctgcccgtggcttcaggtggtattcacgtttggcatatgcctgccctgaccgagatctttggggatgattccgtattacagttcggcggaggaactctgggacacccttggggaaatgcacccggtgcagtagctaatcgggtggctttagaagcgtgcgtacaagctcgtaatgagggacgtgatcttgctcgtgaaggtaatgaagttatccgtgaagctagcaggtggagcccagaactagctgctgcttgtgaggtatggaaggagatcaaattcgaattcgaagcaatggatgt Austrobaileyales_Austrobaileya tcaaagattaagccatgcatgtgtaagtatgaaccaattcagactgtgaaactgcgaatggctcattaaatcagttatagtttgtttgatggtatc-tcctactcggataaccgtagtaattctagagctaatacgtgcaacaaaccccgac-tttgggaaggggcgcatttattagataaaaggccgatnncggctc-tgcccgatgctccggtgattcatgataactcgacggatcgcacggccttcgtgccggcgacgcatcattcaaatttctgccctatcaactttcgatggtaggatagtggcctactatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaataccgggctc-ttcgagtctggtaattggaatgagtacaatctaaaccccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggatcttgggtcgggtcggccggtccgcctttc-ggtgtgcaccggtcgtcttgacccttctaccggcgatgcgctcctggccttaattggccgggtcgtgcctccggtgctgttactttgaagaaattagagtgctcaaagcaagcccaagctctgcatacattagcatgggataacatcacaggatttcggtcctattttgttggccttcgggatcggagtaatgattaatagggacagtcgggggcattcgtatttcatagtcagaggtgaaattcttggatttatgaaagacgaaccactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggatcggcggatgttgcttttaggactccgccggcaccttatgagaaatcaaagtttttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagactgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctatgtggaggc-gaccctccacggccagcttcttagagggactatggccgtttaggccacggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagtt-cctagccctggccgacaggcccgggtaatcttgt-aaatttcatcgtgatggggatagatcattgcaattgttggtcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttcggatcgcggcgacnngggcggttcgccacccgcgacgtcgcgagaagtccactgaaccttatcatttagaggaaggtcattggtccggtactggaagtagcctttccccccggcaagatgcctaatatttacaacttcttggtagttaagggtcaagatactgtcggtcagccaattaatgtgacttgtgaggtacagcaattattaggaaataatcgagttagagctgtagctatgagtgctacggatggtctgatgagaggaatggaagtaattgatacgggagctcctctaagtgttccagtcggtggagctactctgggacgaattttcaacgttcttggggaacctgttgataatttaggtcctgtagatactcgcacaacatctcccattcatagacctgcgcctgcctttatacagttagataccaaattatcaatctttgaaacagggattaaagtggtggatcttttagctccttatcgccgtggaggaaaaatcggactattcgggggagccggggtgggtaaaacagtactcatcatggaattgatcaacaacattgccaaagctcatggaggtgtatccgtatctggcggagtaggcgaacgtactcgtgaaggaaatgatctttacatggaaatgaaagaatccggagtgattaatgaacaaaatattgcagaatccaaagtagctctagtctatggtcagatgaatgaaccgccgggagctcgtatgagagttggtttgactgccctaaccatggcagaatatttccgggatgttaatgaacaagacgtacttctatttatcgacaatatctcccgcttcgttcaagcaggatcagaagtatctgccttattaggcagaatgccttccgccgtaggttatcagcctacccttagtacagaaatgggttctttgcaagaaagaattacttccaccaaagagggatctataacttccattcaagcagtttatgtacctgcggacgatttgaccgaccctgctcctgccacgacatttgcacatttagatgctactaccgtactatcaagagggttagctgccaaaggtatctatccagcagtggatcctttagattcaacgtcaactatgctccaacctcggatcgttggcgaggaacattatgaaactgcacaaagagttaagcaaacttcacaacgttacaaagaacttcaggacattatagctatccttgggttggacgaattatccgaagaagatcgtttaaccgtagcaagagcacgcaaaattgagcgtttcttatcacaacccttcttcgtagcagaagtatttactggttctccagggaaatatgttggtctcgcagaaacaattagggggtttcaactgatcctttccggagaattagatggtcttcccgagcaggccttttatttggtaggtaacatcgatgaagctaccgcgaaggctatgaacttagaagttgagag--gtgttggattcaaggctggtgttaaagattacagattgacttattatactcctgactatgaaactaaaatgactgatatcttggcagcattccgagtaactcctcaacccggagttccacctgaggaagcgggggctgcggtagctgcagaatcttctactggtacatggacaactgtgtggaccgatggacttaccagcctcgatcgttacaaaggtcgatgctaccacatcgagcctgttgctggggaggaaaatcaatatattgcttatgtagcttaccctttagacctttttgaagaaggttctgttactaacatgtttacttccattgtgggtaatgtatttgggttcaaagccctacgagctctgcgtctggaagatctgcgaattcctcctgcttattccaaaactttccaaggcccgcctcatggcatccaagttgagagagataaattgaacaagtatgggcgtcccctattgggatgtactattaaaccaaaattaggtttatctgccaagaactacggtagagcggtttatgaatgtctccgcggtggacttgattttaccaaggatgatgagaacgtgaactcccaaccgtttatgcgttggagggaccgtttcgtattttgtgccgaagaagtttataaagcgcaggcagaaacaggtgaaatcaaaggacattacttgaatgctaccgcaggtacatgcgaagaaatgatcaaaagggccgtatttgccagagaattgggagttcctatcgtaacgcatgactacttaacagggggattcactgcaaatactagcttggctcattattgccgagacaacggcctacttcttcacatccatcgcgcaatgcatgcagttattgatagacagaggaatcatggtatacactttcgtgtactagctaaagcgttgcgtatgtctggtggagatcatgttcactctggtaccgtagtaggcaaactggaaggggaacgggacgtcactttgggttttgttgatttactacgtgatgattttattgaaaaagaccgaagtcgcggtatttattttactcaagattgggtatctatgccaggtgttttacccgtggcttcaggaggtattcacgtttggcatatgcctgccctgaccgagatctttggggatgattccgtactacagttcggtggaggaactttagggcacccttggggaaatgcacctgatgcagtagccaatcgggtggctttagaagcgtgtgtacaagctcggaatgagggacgtgatcttgctcgtgaaggtaatgaggttatccgtgaagcgagcaaatggagccctgaactagctgctgcttgtgaggtatggaaggagatcaaattcgaattcgaagcaatggatgt Austrobaileyales_Schisandra tcaaagattaagccatgcatgtgtaagtatgaactaattcagactgtgaaactgcgaatggctcattaaatcagttatagtttgtttgatggtacc-tgctactcggataaccgtagtaattctagagctaatacgtgcaacaaaccccgac-ttttggaagggatgcatttattagataaaaggccgacgcgggcta-tgcccgatgctccggtgattcatgataactcgacggatcgcacggccttagtgccggcgacgcatcattcaaatttctgccctatcaactttcgatggtaggatagtggcctactatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaataccgggctc-tttgagtctggtaattggaatgagtacaatctaaaccccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggatcttgggttgggccggccggtccgccttcc-ggtgtgcaccggtcgtcttgacccttctaccggcgatgcgctcctggtcttaactggccgggtcgtgcctccggtgctgttactttgaagaaattagagtgctcaaagcaagcctacgctctgtatacattagcatgggataacatcacaggatttcggtcctattttgttggccttcgggatcggagtaatgattaatagggacagtcgggggcattcgtatttcatagtcagaggtgaaattcttggatttatgaaagacgaaccactgcgaaagcatttggcaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggatcggcggatgttgcttttaggactccgccggcaccttatgagaaatcaaagtttttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcngcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagactgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctacgtggaggt-gaccctccacggccagcttcttagagggactatggccgtttaggccacggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagtc-catagccttggccgacaggcccgggtaatcttgt-aaatttcatcgtgatggggatagatcattgcaattgttggtcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttcggatcgcggcgacgggggcggttc----------------------------------------------------tcattggcccggtactggatgtagcttttcccccgggcaagatgcctaatattaacaactctttgntcgttaaaggtcgagatactgttggtcagcaaattaatgtgacttgtgangtacaacaattattaggaaataatcgagttagagctgtagctatgagtgctacggatggtctgatgagaggaatggaagtgattgatacgggagctcctctaagtgttccagttggtggatccactctcggacgcattttcaacgttcttggagagcctgttgataatttaggtcccgtggatactcgcacaacatctcctattcatagatctgcgcctgcctttatacagttagataccaaattatcaatctttgaaacagggattaaagtggtggatcttttagctccctatcgccgtggagggaaaatcggactattcgggggagccggggtgggtaaaacagtactgatcatggaattgatcaacaacattgccaaagctcatgggggtgtatctgtatttggtggagtaggtgaacgtactcgtgaaggaaatgatctttacatggaaatgaaagaatccggagtgattaatgaacaaaatattgcagaatcaaaagtggctctagtctacggtcagatgaatgaaccgcccggagctcgtatgagagttggtttgactgccctaaccatggcggaatatttccgggatgttaatgaacaagacgtacttctatttatcgacaatatctcccgcttcgtccaagcaggatcagaagtatctgccttattaggtagaatgccttctgccgtgggttatcagcctacccttagtacagaaatgggttctttgcaagaaagaattacttctaccaaagagggatctataacttccattcaagcagtttatgtacctgcagatgatttgaccgaccctgctcctgccacgacatttgcacatttagatgctactaccgtactatcaagagggttagctgccaaaggtatctatccagcagtggatcctttagattcaacgtcaactatgctccaacctaggatcgttggcgaagagcattatgaaactgcgcaaagagttaagcaaacttcacaacgttacaaagaacttcaggacattatagctatccttgggttggacgaattatccgaagaggatcgtttaaccgtagcaagagcacgaaaaattgagcgtttcttatcacaacccttcttcgtagcagaagtatttactggttctcccgggaaatatgttggtctcgcagaaaccattagggggtttcaactgatcctttccggagaattagatggtcttcccgagcaggccttttatttggtaggtaacatcgatgaagctaccgcgaaggctatgaacttagaagtagagag--gtgttggattcaaggctggtgttaaagattacagattgacttattatactcctgaatatgaaacgaaagatactgatatcttggcagcattccgagtaactcctcaacccggagttccgcccgaggaagcgggagctgcggtagctgcggaatcttctactggtacctggactactgtgtggactgatggacttaccagcctcgatcgttataaagggcgatgctaccacattgagcccgttgctggggaggaaaatcaatatattgcttatgtagcttaccctttagacctttttgaagaaggctctgttactaacatgtttacttctattgtgggtaatgtatttgggttcaaagccctacgagctctgcgtctggaagatttgcgaattcctcctgcttattccaaaactttccaaggcccacctcatggcatccaagttgagagagataaattgaacaagtatggtcgtcccctattgggatgtactattaaaccaaaattagggttatctgccaagaactacggtagagcggtttatgaatgtctccgcggtggacttgattttaccaaggatgatgagaacgtgaactcccaaccgtttatgcgttggagggaccgtttcttattttgtgccgaagctctttataaagcgcaggccgaaacaggtgaaattaaaggacattacttgaatgctactgcaggtacatgcgaagaaatgatgaaaagggctgtatttgccagagaattgggagttcctatcgtaatgcatgactacttaacagggggattcactgcaaatactagcttggctcattattgccgagacaacggcctacttcttcacatccatcgcgcaatgcatgcagttattgatagacagaggaatcatggtatccactttcgtgtactagctaaagcgttgcgtatgtctggtggagatcatattcactctggtaccgtagtaggtaaactggaaggggaacgggacgtcactttgggttttgttgatttactacgtgatgattttattgaaaaagaccgaagtcgcggcatttatttcactcaagattgggtatctatgccaggtgttctgcccgtggcttcagggggtattcacgtttggcatatgcctgccctgaccgagatctttggggatgattccgtactacagttcggtggaggaactttaggacacccttggggaaatgcgcctggtgcagtagctaatcgtgtggctttagaggcgtgtgtacaagctcgtaatgaggggcgtgatcttgctcgtgaaggtaatgaagttatccgtgaagctagcaaatggagccctgaactagctgctgcttgtgaggtctggaaggagatcaaattcgaattcgaagcaatggatgt Oxalidales_Davidsonia tcaaagattaanccatgcatgtgtaagtatgaactaattcagactgtgaaactgcgaatggctcattaaatcagttatagtttgtttgatggtatt-tgctactcggataaccgtagtaattctagagctaatacgtgcaacaaaccccgac-ctctggaagggatgcatttattagataaaaggtcgacgcgggctt-tgcccgttgcttcggtgattcatgataactcgacggatcgcacggccatcgtgccggcgacgcatcattcaaatttctgccctatcaactttcgatggtaggatagtggcctaccatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaataccgggctc-atcgagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggaccttgggttgggtcggccggtccgcctc-a-ggtgtgcaccggccggctygtcccttctgccggcgatgcgctcctggccttaattggccgggtcgtgcctccggcgctgttactttgaagaaattagagtgctcaaagcaagcctacgctctggatacattagcatgggataacatcataggattccggtcctattgtgttggccttcgggatcggagtaatgattaacagggacagtcgggggcattcgtatttcatagtcagaggtgaaattcttggatttatgaaagacgaacaactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggatcggcggatgttgctttaaggactccgccggcaccttatgagaaatcaaagtctttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagactgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctatgcggaggt-gaccctccgcggccagcttcttagagggactatggccgctcaggccaaggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagtc-tatagccttggccgacaggcccgggtaatctttg-aaatttcatcgtgatggggatagatcattgcaattgttggtcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttcggatcgcggcgacgtgggcggttcgccgccggcgacgtcgcgagaagtccactgaaccttatcatttagaggaagg--------------------------------------------------------------------------------------------------------------------------------------------------------gtagctatgagtgctacagatggtctaatgagaggaatggaagtgattgacacgggagctcctctaagtgttccagttggcggagcgactctaggacgaattttcaacgtacttggagagcctattgataatttaggtcctgtagatactcgcacaacatctcctattcatagatctgcgcccgcctttatacagttagatacaaaattatctatttttgaaacaggaattaaagtagtagatcttttagccccttatcgccgtggtggaaaaatcggactattcgggggagctggagtaggtaaaacagtgctcattatggaattaatcaacaatattgccaaagctcatggaggtgtatccgtatttggcggagtcggtgaacgtactcgtgaaggaaatgatctttacatggaaatgaaagaatctggagtaattaatgaagaaaatattgcggaatcaaaagtggctctagtctacggtcagatgaatgaaccgccgggggctcgtatgagagttggtttgactgccctaactatggcggaatatttccgagatgttaatgaacaagacgtacttctctttatcgacaatatcttccgtttcgtccaagcgggatccgaagtatccgccttattgggtagaatgccttctgctgtgggttatcaacccacccttagtaccgaaatgggttctttacaagaaagaattacttctaccaaagaagggtccataacttctattcaagcagtttatgtacctgcggacgatttgaccgaccctgctccggccacgacatttgcacatttagatgctactactgtactatcaagaggattagctgccaaaggtatctacccagcagtagatcctttagattcaacgtcaactatgctccaacctcgaattgttggtgaggaacattatgaaactgcgcaaagagttaaacaaactttacaacgttacaaagaacttcaggacattatagctatccttgggttggacgaattatccgaagaggatcgcttacccgtagcaagagcacgaaaaattgagcgtttcttatcacaaccctttttcgtagcagaagtatttacgggttctccggggaaatatgttggtctagcagaaacaattagagggtttaaattgatcctttccggagaattagacggtcttcctgaacaggccttttatttggtaggtaacatcgatgaagct-------------------------------------------------------------------------------atactcctgactatgaaaccaaagatactgatatcttggcagcattccgngtatctcctcttcctggagttccgcctgaggaagccgggcgagcggtagctgctgaatcttctactggtacatggacaactgtctggaccgacgggcttaccagtcttgatcgttacaaaggacgatgctacgacatcgagcctgttgcgggagaagaaaatcaatatattgnttatgtagcttaccccttagacctttttgaagaaggttctgttactaacatgtttacttccattgtaggtaatgtgtttgggttcaaggcgctgcgggctctacgtctggaggatttgcgaatccctactgcttatgttaaaactttccaaggtccgcctcatggtatccaagttgagagagataaattgaacaagtatggccgccccctattgggatgtactattaaacctaaattggggttatccgctaagaattatggtagggcagtttatgaatgtctccgcggtggacttgattttaccaaagatgatgaaaacgtgaactcccaaccatttatgngttggagagaccgtttcttatttttggccgaagcactttataaagcacaggccgaaacgggcgaaatcaaagggcattacttgaatgctactgcaggtacatgcgaagaaatgatgaaaagggctgtatttgctagagaattgggagttcccatcgtaatgcatgactacttaacaggaggattcactgcaaatactagcttggctgattattgccgcgataatggtctacttcttcacatccatcgtgcaatgcatgccgttnttgatngacagaagaatnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnntgtctggtggagatcatatncatgccggtaccgtngtcggcnaacttgaaccccaaagagacatcactttnggctttgttgatttactacgtgatgattttattgaaaaagatcgaagccgcggtatttntttcactcaagattgggtctctctaccnggcgttctgcccgtngcttcaggaggtattcatgtttggcatatnccggctttgaccgagatctttggagatgattccnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnccgtngctaatcgagtagctctngaagcatgtgtacaagctcgtaatgaggggcgtgatcttgctcgtgagggtaatgaaattatccntnaggctagcaaatggagtccnnnnnnnnnnnctgcttgtgaagtatggaaggagatcaaatttgaattccnnnnnatggatac Celastales_Lepuropetalon tcaaagattaagccatgcatgtgcaagtatgaacgaattcaaactgtgaaactgcgaatggctcattaaatcagttatagtttgtttgatggtatc-tactactcggataaccgtagtaattctagagctaatacgtgcaacatatcccgac-ttccggaagggatgcatttattagagaaaaggtcaacgcttgct--cgctcgttgctcagatgattcatgataactcgacggatcgcatggccattgtgctggcgacgcatcattcaaatttctgccctatcaactttcgatggtaggatagaggcctaccatggtggtgacgggtaacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctaacaaggggaggtagtgacaataaataacaataccgggctctcaagagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttgtannttgggttgggtcgaccggtccgcttca--ggtgagcaccatcagtcctgtccttattgccggtgatgcgctcctagccttaattggttgggtcgtccctctggtgctgttactttgaagaaattagagtgctcaaagcccgcctacgctctggatacattagcatgggataacgtcataggatttcgatcctatttagttgg-cttcgggatcggagtaatgattaacagggacagtcgggggcattcgtatttcatagtcagaggtgaaattcttggatttatgaaagacgaacaactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagagatcagcggatgttgctcttaggactccgctggcatcttatgagaaatcaaagtttttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagnaaggattgacagattgagagctctttcttgattcnatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaaatagctacgcggaggg-ttccctctgcggccagcttcttagagggac-acggccgcttaggtcgaaggagtttgaggcaataacagctgtgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagtc-tataaccttggccgacaggcccgggtaatctttg-aaatttcatcgtgatggggatagatcattgcaattgttggtcttcaacgaggaattcctagtaagcgcag--catcaactcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttcggatcgcggcgacgttggcggttccctgctggcgatgtcgcgagaagtccactgaaccttatcatttagaggaagg---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ctgtagatacccgtacaacatctcctattcatagatcagcgcccgcctttatacagttagacacaaaattatccatttttgaaacaggaattaaagttgtagaccttttagctccttatcgccgtggaggtaaaatagggctattcgggggggctggagtgggtaaaacagtactaattatggaattgatcaacaacattgccaaagctcatggaggtgtatccgtatttggcggagtgggtgaacgtactcgtgaaggaaatgacctttacatggaaatgaaagagtccggagtaattaatgaacaaaatattgcagaatcaaaagtggctctagtctatggccagatgaatgaaccgccgggagctcgtatgcgagttggtttgaccgccctaactatggccgaatattttcgagatgttaatgaacaagacgtacttctttttatcgacaatatattccgtttcgtccaagcgggatctgaagtatccgccttattgggtagaatgccctccgctgtgggttatcaacccacccttagtactgaaatgggttctttacaagaaagaattacttctaccaaaaaagggtccataacttctattcaagctgtttatgtaccggcagacgatttgactgaccctgctccggccacgacatttgcgcatttagatgccactactgtattatcgcgaggactagctgccaagggtatctacccagcagtagatcctttagattcaacgtcaactatgcttcaaccccgcattgttggtgaagaacattatgaaaccgcgcaaagagtgaagcaaactttacaacgttacaaagaacttcaggacattatagctatcctggggttggacgaattgtccgaagaggaccgcttaaccgtagcaagggcacgaaaaatcgagcgtttcttatcacaaccctttttcgtagcagaagtatttacgggttctccggggaaatatgttggtctagcagaaactattagagggtttaaattgatcctttcgggggaattagatggtcttcctgagcaggcsttttatttggtaggtaacattgatgaggctactgcgaaggctacgaattt----------------gttggattcaaggctggcgttaaagattataaattgacttattatactcctgactatgaaaccaaagatactgatatcttggcagcatttcgaatgactcctcaacctggagttccgcctgaagaagctggagctgcggtagctgctgaatcttctactggtacctggacaactgtgtggaccgatggacttaccagtcttgatcgttacaaaggacgatgctaccacatcgagcccgtggctggagaagacaatcaatatattgcttatgtagcttatcctttagacctttttgaagaaggttctgttactaatatgtttacttctattgtgggtaatgtttttgggttcaaagccctgcgcgctctacgtctggaagatttgcgaatcccccctgcttattctaaaactttccaaggcccgccgcatggcatccaagttgagagagataaattgaacaagtatggacgccctctattgggctgtactattaaacctaaattgggattatccgctaagaattatggtagagccgtttatgaatgtctacgcggtggacttgattttaccaaagatgatgagaacgtaaactcccaaccatttatgcgttggagagaccgtttcttattttgcgctgaagccatttttaaagcacaagctgaaacaggtgaaatcaaaggacattacttgaatgctactgcgggtacgtgcgaagaaatgatcaaacgggctgtatttgccagagaactaggagtccctatcgtaatgcacgactacttaacggggggattcactgcaaatactaccttggctcattattgccgagataatggtttacttcttcacatccaccgtgcaatgcatgcagttattgatagacagaaaaatcatggtatacactttcgtgtactagcgaaggccctacgtatgtctggtggagaccatattcatgctggtaccgtagtaggtaaacttgaaggcgaaagagatatcactctaggctttgttgatttattacgcgatgattttattgaaaaagatcgaagccgcggtatttatttcactcaagattgggtctctcttccaggtgttctgcccgtggcttccggaggtattcacgtttggcatatgcctgctctgaccgagatctttggtgatgattccgtactacaattcggtggaggaactttagggcatccttggggaaatgcgccaggtgctgtagcgaatcgagtagctctagaagcatgtgtacaagctcgtaatgagggacgtgatcttgctcgtgaaggtaatgaaattatccgcgaggcctgcaaatggagtcctgagttagctgctgcttgtgaagtatggaaggagatcaaatttgaattcgaagcaatggatac Celastales_Plagiopteron tcaaagattaagccatgcatgtctaagtatgaactaattcagactgtgaaactgcgaatggctcattaaatcagttatagtttgtttgatggtat--tactactcggataaccgtagtaattctagagctaatacgtgcaacaaaccccgac-ttctggaagggatgcatttattggataaaaggtcaacgcgggctc-tgcccgttgctctgttgattcatgataacttgacggatcgcacggccatcgtgccggcgacgcatcattcaaatttctgccctatcaactttcgatggtaggatagaggcctaccatggtagtgacgggtaacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaataccgggctc-tgcgagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggatcttgggttgggtcgagtggtccgccat-t-ggtgtgcacctttcgtcccgtcccttctgccggcgatgcgctcctggccttaactggccgggtcgttcctccggcgctgttactttgaagaaattagagtgctcaaagcaagcctacgctctggatacattagcatgggataacatcataggatttcggtcctattctgttggccttcgggatcggagtaatgattaacagggacagtcgggggcattcgtatttcatagtcagaggtgaaattcttggatttatgaaagacgaacaactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggatcagcggatgttgcttttaggactccgctggcaccttatgagaaatcaaagtttttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagactgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctacgcggagga-atccctccgcggccagcttcttagagggactacggccgcttaggccgaggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagtc-tatagccttggccgacaggcctgggtaatctttg-aaatttcatcgtgatggggatagatcattgcaattgttggtcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttcggatcgcggcgacgtgggcggtttactgccgacaacgtcgcgagaagtccactgaaccttatcatttagaggaaggtcattggtccagtactggatgtagctttttccccgggcaagatgcctaatatttacaacgctctagtggttaagggtcgagatacgtccggtcacacaattaatgtgatttgcgaagtacagcaattattaggaaataaccaagttcgggctgtagctatgagtgctacagatggtctaatgagagggatggacgtgattgacacgggagctcctctaagtgttccagtcggcggagcgactctcggacgaattttcaacgtacttggtgagcctgttgataatttaggtcctgtagatactcgtacaacctctcctattcataaatcagcacccgcctttatacagttagagacaaaattgtctatttttgaaacaggaattaaagtagtagaccttttagctccttatcgccgtggaggaaaaatcggactattcgggggggctggagtgggtaaaacggtcctaattatggaattgatcaacaacattgccaaagctcatggaggtgtatccgtctttggcggagtgggtgaacgtacgcgcgaaggaaatgatctttacatggaaatgaaagaatctggagtaattaatgaacaaaatattgcagaatcaaaagtggctttagtttatggccagatgaatgaaccgccaggagctcgtatgagagttggtttraccgctctaactatggcggaatattttcgagatgttaatgaacaagacgtacttctatttatcgacaacatcttccgtttcgtccaagctggatccgaagtatccgccttattggggagaatgccttccgctgtgggttatcaacccacccttagtactgaaatgggtgctttacaagaaagaattacttctaccaaaaaagggtccataacttctattcaagctgtttatgtaccagcggatgatttgactgaccctgctccggccacgacctttgcacacttagatgctactactgtactatcacgaggactagctgccaagggtatctacccggcagtagatcctttagattcaacgtcaactatgctacaacctcgaattgttggtgaggagcattatgaaactgcgcaaagagtgaagcaaactttacaacgttacaaagagcttcaggacattatagctatcctggggttggacgaattgtccgaagaggaccgcttaaccgtagcaagagcacgaaaaatagagcgtttcttatcacaacccttttttgtcgccgaagtttttaccggttctccggggaaatatgtcggtctagcagaaacaattagagggtttaaattgatcctttccggagaattagatggtcttcctgagcaggccttttatttggtaggtaacatcgatgaagccactactaaggctacgaatttagaaatggag--nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnntgatcgttttaaaggacgatgctaccacatcgagcccgttgctggaggagaaaatcaatatattgcttatgtagcttactccttagaccttttcgaagaaggttctgttactaacatggttacttccattgtgggtaatgttcttgggttcaaagccctgcgcgctctacgtctggaggatttgcgagtcccccctgcttattctaaaacttttcaaggcccgccgcatggtatcccagttgagagagataaattgaacaagtatggacgccctctattgggatgtactatttaacctaaattgggattatctgctaagaattatggtagagcggtttatgaatgtctccgcggtggacttgattttacccaagatgatgagaacgtaaactcccaaccatttatgcgttggagagaacgtctcttaatttgtgccgaggcgctttataaagcacaggctgaaacaggtgaaatcaaagggcattacttgaatgctactgccggtacatgtgaagaaatgatcaaaagggctgtatttgctagagaactgggcgtacctatcgtaatgcatgactacttaacggggggattcactgcaaatactagcttggctcattattgccgagataatggtttacttcttcacatacaacgtgcaatgcatgcagttattgatagacagaaaaatcatggtatgcactttcgtgtactaggcgaggccctacgtatgtctggtggagaccatattcatgccggtaccgtagtaggtaaacttgagggtgaaagagatatcactttaggctttgttgatttattacgtgatgattttattgaaaaagaccgaagccgcggtatttatttcactcaagattgggtctctctaccaggtgttcttgctgtggcttccggaggtattcacgtttggcatatgcctgctctgaccgagatctttggtgatgattccgtactacaattcggtggaggaactttagggcatccttggggaaatgcaactggtgctgtagctaatcgagtagctctagaagcatgtgttacagctcgtaatgagggacgtgatcttgctcgtgaaggtaatgaaattatccgcgaggctagcaaatggagtcctgaactagctgctgcttgtgaagtatggaaggagatccaaattgaattcgcagcaatggatac Malpighiales_Populus tcaaagattaagccatgcatgtgtaagtatgaactaattcagactgtgaaactgcgaatggctcattaaatcagttatagtttgtttgatggtatt-tgctactcggataaccgtagtaattctagagctaatacgtgcaacaaaccccgac-ttctggaagggacgcatttattagataaaaggtcgacgcgggctc-tgcccgttgctctgatgattcatgataactcgacggatcgcacggccatcgtgctggcgacgcatcattcaaatttctgccctatcaactttcgatggtaggatagaggcctaccatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaataccgggctc-ttcgagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggactttgggttgggtcggccggtccgcctc-a-ggtgtgcaccggtcgcctcgtccyttctaccggcgatgcgctcctggccttaactggccgggtcgtgcctccggtgctgttactttgaagaaattagagtgctcaaagcaagcctacgctctggatacattagcatgggataacatcataggatttcgatcctattgtgttggccttcgggatcggagtaatgattaacagggacagtcgggggcattcgtatttcatagtcagaggtgaaattcttggatttatgaaagacgaacaactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggattggcggatgttgcttctaggactccgccagcaccttatgagaaatcaaagtttttgggttctggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagactgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctatgcggaggt-gaccctccgcggccagcttcttagagggactatggccttccaggccaaggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagtc-tatagccttggccgacaggcccgggtaatctttg-aaatttcatcgtgatggggatagatcattgcaattgttggtcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttcggatcgcggcgacgtgggcggttcgccgccggcgacgtcgcgagaagtccactgaaccttatcatttagaggaaggtcattggcccagtgctagatgtagtttttcccccgggcaagatgcctaatatttacaacgctctggtagttaagggtcgagatactgtcagtcaacaaattaatgtcacttgtgaagtacagcaattattaggaaataatcgagttcgagctgtagctatgagtgctacagagggtctaatgagaggaatggaagtgattgacacgggagctcccctaagtgttccagtcggcggggcgactctaggacgaattttcaacgtgcttggagaacctgttgatgatttacgtcctgtagatactgggacaacatcccctattcatagatctgcgcctgcctttatacagttagatacaaaattatctatttttgaaacaggaattaaagtcgtagatcttttagccccttatcgccgtggaggaaaaatcggactatttgggggcgctggagtaggtaaaacagtactaattatggaattaatcaacaacattgcgaaagctcatgggggtgtatccgtatttggcggagtaggtgaacgtactcgtgaaggaaacgatctttacatggaaatgaaagaatccggagtaattaatgaagaaaatattgcagaatcaaaagtggctctagtttatggtcagatgaacgaaccaccgggagctcgtatgagagttggtttgactgccctaactatggcggaatatttccgagatgttaatgaacaagacgtacttctatttatcgacaatatctttcgtttcgtccaagcaggatccgaagtatccgccttattgggtagaatgccctctgctgtgggttatcaacccacccttagtaccgaaatgggtactttacaagaaagaattacttctaccaaagaagggtccataacttctattcaagcagtttatgtacctgcggacgatttaaccgaccctgctcctgccacgacatttgcgcatttagatgctactactgtactatcaagaggattagctgctaaaggtatctatccagcagtcgaccctttagattcaacatcaactatgctccaacctcagatcgttggtgaggaacattatgaaactgcgcaaagagttaagcaaactttacaacgttacaaagaacttcaagacattatagctatccttgggttggacgaattatccgaagaggatcgcttaactgtagcaagagcacgaaaaattgagcgtttcttatcacaaccctttttcgtagccgaagtatttaccggttccccgggaaaatatgtcggtctagcagaaacaattagagggtttaaattgatcctttccggagaattagatagtcttcctgagcaggccttttatttggtaggtaacattgatgaagctactgcgaaggctacgaacttagaaatggagaaaggtgttggattcaaggctggtgttaaagagtataaattgaattattatactcctgaatatgaaaccaaagatactgatatcttggcagcattccgagtaactcctcaacctggagttccgcccgaggaagcaggggccgcagtagctgctgaatcttctactggtacatggacaactgtgtggaccgacgggcttaccagtcttgatcgttataaaggacgatgctacgacatcgagcccgttgctggagaagaaaatcaatttattgcttatgtagcttaccccttagacctttttgaagaaggttctgttactaacatgtttacttccattgtgggtaatgtatttgggttcaaagccctacgcgctctacgtctggaggatttgcgaattcctcctgcttatactaagacttttcaaggcccacctcatggtatccaagttgaaagagataaattgaacaagtatggtcgccccctattgggatgtactattaaacctaaattggggttatccgctaagaattacggtagagcagtttatgaatgtctccgcggtggacttgattttaccaaagatgatgagaacgtgaactcccaaccatttatgcgttggagagaccgtttcttattttgtgccgaagcactttataaagcacagactgaaacaggtgaaatcaaagggcattatttgaatgctactgcaggtacatgcgaagaaatgatgaaaagggctatatttgccagagaattgggagttcctatcgtaatgcatgactacttaacagggggattcaccgcaaacactagtttggctcattattgccgagataatggtttacttcttcacatccatcgcgcaatgcatgcagttattgatagacagaaaaatcatggtatacactttcgtgtactagctaaggcattacgtatgtctggtggagatcatattcactctggtaccgtagtaggtaaacttgaaggggaaagagacataactttgggttttgttgatttactacgtgatgattttgttgaaaaagatcgaagccgcggtatttatttcactcaagattgggtctctctaccgggtgttctgcccgtggcttcggggggtattcacgtttggcatatgcctgctctgaccgagatctttggagatgattccgtactncaattcggtggaggaactttagggcacccttggggaaatgcacctggtgctgtagctaatcgagtagctctagaagcatgtgtacaagctcgtaatgagggacgtgatcttgctcgtgagggtaatgaaattatccgtgaagctagcaaatggagccctgaactagctgctgcttgtgaagtatggaaggagattaaatttgaattcgaagcaatggatac Malpighiales_Linum tcaaagattaagccatgcatgtgtgagtatgaacaaattcagactgtgaaactgcgaatggctcattaaatcagttatagtttgtttgatggtacc-tgctactcggataaccgtagtaattctagagctaatacgtgcaacaaaccccgac-ttttggaaggggcgcatttattagataaaaggtcaacgcgggctc-tgctcgttgttttgatgattcatgataactcgacggatcgcaaggccttcgtgccggcgacgcatcattcaaatttctgccctatcaactttcgatggtaggatagaggcctaccatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaataccgggctc-attgagtctggtaattggaatgagtacaatctaaaccccttaacgagtacccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggaccttgggttgggtcggccggtccgctcac--ggtgtgcaccggccgtcttgtcccttctgccggcgatgcgctcctggtcttaattggccgggtcgtgcctccggcgcagttactttgaagaaattagagtgctcaaagcaagcctacgctctgaatacattagcatgggataacatcacaggatttcggtcctattgtgttggccttcgggatcggagtaatgattaacagggacagtcgggggcattcgtatttcatagtcagaggtgaaattcttggatttatgaaagacgaacaactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggatcggcggatgttgctttttggactccgccggcaccttatgagaaatcaaagtctttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagactgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctatgcggaggc--accctccgtggccagcttcttagagggactatggccttttaggccaaggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagtc-tatagccttggccgacaggtctgggtaatctttg-aaatttcatcgtgatggggatagatcattgcaattattggtcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttcggattgcggcgacgtgggtggttcgctgcccgcgacgtcgcgagaagttcnctgaaccttatc-------------tcatcngnccggtactagatgtagcttttcccccggggaagatgcctaatatttacaatgctctcctagttaagggtcaagaccccgccggtcaacaaattaatgtgacttgtgaagtacagcaattattaggaaataatcgagttcgggctgtagctatgagtgctacagatggtttaatgagaggaatggaagtgattgacacgggaactcctctaagtgttccagtcggcggagcaactctaggacgaattttcaacgtgcttggagaacctattgatgatttaggtccggtagatactcgcacaacatcccctattcatagagctgcacccgcctttatacagttagatacaaaattatctatttttgaaacaggaattaaagtagtagatcttttagccccttatcgtcgtggtggaaaaatcggattattcgggggagctggggtaggtaaaacagtactaattatggaattaattaacaacattgcgaaagctcatgggggtgtatccgtctttggcggagtaggcgaacgtactcgtgaaggaaatgatctttacatggaaatgaaagaatccggtgtaattaatgaagaaaatattgcagaatccaaagtagctctagtctatggtcagatgaacgaaccgccgggagctcgtatgagagttggattgactgccctaaccatggcagaatatttccgagatgttaatgagcaagatgtacttctatttattgacaatatcttccgtttcgtccaagccggatccgaagtatccgctctattgggtagaatgccttccgctgtgggttatcaacccacccttagtaccgaaatgggttctttacaagaaagaattacttctaccaaagaagggtcgataacttccattcaagcggtttatgtacctgcagatgatttgactgaccctgctcctgccacgacatttgcacatttagatgctactactgtactatcaagaggattagccgctaaagggatctatcctgcagtagatcctttagattcaacatccactatgctccaacctcagatcgttggtgaggaacattatgaaactgcacaaagagttaagcaaactttacaacgttacaaagaactccaggacattatagccatcctggggttggacgaattatccgaagaggatcg-ttaactgtagcaagagcacgaaaaattgagcgtttcttatcacaaccctttttcgtagcagaagtatttacgggttctccgggaaaatatgtcggtctagcagaaactataagagggtttaaattgatcctttccggagaattagatagtcttcctgagcaggccttttatttggtaggtaatattgatgaagctactgcgaaggctacgaacttagaaatggagaa--gtgttggattccaggctggtgttaaagattataaattaacttattatactcctgaatatgaaaccaaagatactgatatcttggcagcattccgagtaactcctcaaccaggagttccgcctgaggaagcgggggctgcggtagctgctgaatcttctactggtacgtggacaactgtgtggaccgatgggcttaccagtcttgatcgttataaaggacgatgctaccacattgaacccgttcccggagaagaaagtcaatttattgcttatgtagcgtaccccttagacctttttgaagaaggttctgttactaacatgtttacttccattgtaggtaatgtatttgggttcaaagccctgcgcgccctacgtctggaggatttgcgaatccctcctgcttattctaaaactttccaaggcccgcctcacgggattcaagttgagagagataaattgaacaagtatggtcgccccctattgggatgtactattaaaccgaaactgggcttatccgctaagaattatggtagagctgtttatgaatgtcttcgtggtggacttgattttaccaaagatgatgaaaacgtgaactcccaaccatttatgcgttggagagaccgtttcttattttgtgccgaagctatttttaaatcacaggccgaaacaggggaaatcaaagggcattatttgaatgctaccgcgggtacatgtgaagaaatgatcaaaagggctgtatttgccagagagttgggagttcctatcgtaatgcacgactacttaacagggggattcaccgctaatactagtttggctcattattgccgagataatggtttacttcttcacatccaccgtgcaatgcatgcagttattgatagacagaaaaatcatggtatacactttcgtgtactagctaaagcgttacgtatgtctggtggagatcatattcactcaggtaccgtagtaggtaaacttgaaggggaaagagatatcactttgggctttgttgatttactgcgtgatgattttgttgaaaaagatagaagccgtggtatttatttcactcaagattgggtctctctaccaggcgtcttacccgtagcttcggggggtattcacgtttggcatatgcctgctctgacagagatctttggagatgattccgtattacaatttggcggtggaactttgggtcacccttggggaaatgcacctggtgccgtagctaatcgagtagctctagaagcatgtgtacaagctcggaatgagggacgtgatcttgctcgtgagggtaatgaaattattcgtgaggctagcaaatggagtcctgaattggctgctgcttgtga------------------------------------------ Nymphaeales_Barclaya tcaaagattaagccatgcatgtgtaagtatgaactatttcagactgtgaaactgcgaatggctcattaaatcagttatagtttgtttgatggtact-tgctactcggataaccgtagtaaagctagagctaatacgtgcaccacaccccgac-ttctggaagggacgcatttattagataaaaggccgacgcgggctc-tgcccgatgttttggtgattcatgataactcgacggatcgcacggcctttgtgccggcgacgcatcattcaaatttctgccctatcaactttcgatggtaggatagtggcctaccatggtagtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaatacataacaataccgggctc-ttcgagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggactttgggttgggccgaccggtccgcctct--ggtgtgcaccggtcgtctcgtcccttctgccggcgatgcgctcctggccttaactggccgggtcgtgcctccggcgctgttactttgaagaaattagagtgctcaaagcaagcctacgctctgcatacattagcatgggataacatcacaggatttcggtcctattgtgttggccttcgggatcggagtaatgattaagagggacagtcgggggcattcgtatttcatagtcagaggtgaaattcttggatttatgaaagacgaacaactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgactagggatcggcggatgttgcttttaggactccgccggcaccttatgagaaatcaaagtttttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagactgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctacgcggaggt-acccctccgcggccagcttcttagagggactatggccgtttaggccacggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagtt-tatagccttggccgacaggcccgggtaatcttgg-aaactacatcgtgatggggatagatcattgcaattgttggtcttaaacgaggaattcctagtaagcgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttcggatcgcggcgacggaggcggttcgccgcttgcgacgtcgcgagaagttcattgaaccttatcatt----------tcattggtccggtactggatgtagcttttcccccggggaagatgcctaatatttacaactctttggtagttaagggtcgagataccacaggtcaggaaattaatgtgacttgtgaggtacagcaattattaggaaataatcgagttagagctgtagctatgagtgctacagatgggctgatgagaggaatggaagtgattgacacgggagctcctctaagtgttccagtcggtggagccactctaggacgaattttcaatgttcttggagaacctgttgataatttgggtcctgtagatactcgtacaacatctcctattcatagacccgcacccgcttttatacaactagatacaaaattatcaatctttgaaacgggcattaaagtggtggatcttttagccccttatcggcgtgggggaaaaataggactattcgggggagccggagtgggtaaaacagtactcatcatggaattaatcaacaacattgccaaagctcatgggggtgtatccgtattcggcggagtaggagaacgcactcgtgaaggaaatgatctttacatggaaatgaaagaatccggggtgattaatgaagaaaatattgcagaatccaaagtagctctagtatatgggcagatgaatgaaccgccgggagctcgtatgagagtcggtttgactgccctaaccatggcggaatatttccgtgatgttaatcaacaagacgtacttctatttattgacaatatctttcgcttcgtccaagcggggtcagaagtatccgccttattaggtagaatgccttccgccgtgggttatcagcctacccttagtacagaaatgggttctttgcaagaaagaattacttctactaaagagggatctataacttccattcaagcagtttatgtacctgcggatgatttgactgatcctgctcctgccacgacatttgcacatttagatgctactaccgtactatcaagaggattagctgccaaaggtatctatccagcagtagatcctttagattcaacgtcaactatgctccaacctcggatcgttggcgaagaacattacgaaactgcgcaaagagttaaacaaacttta--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------aagtgttggattcaaagctggtgttaaagattacagattgacttattacactcctgattatgaaacccttgctactgatatcttggcagcattccgagtaactcctcaacctggagttccgcctgaggaagcaggagctgcggtggctgccgaatcttccactggtacatggacaactgtgtggaccgatggacttaccagccttgatcgttacaaaggacgatgctaccacatcgagccagttgctggggaggaaaatcaatatattgcttatgtagcttaccctttggacctttttgaagaaggttctgttactaacatgtttacttccattgtgggtaatgtatttgggttcaaagccctacgagctctacgtctggaggatctgagaattcctcctgcttattctaaaactttccagggcccacctcatggaatccaagttgagagagataaattgaacaagtatggtcgtcccctattgggatgtacaattaaaccaaaattggggttatccgcaaagaactatgggagagcggtttatgagtgtctccgtggtggacttgattttaccaaggatgatgagaacgtgaactcccaaccgtttatgcgttggagagaccgtttcttattttgcgccgaagctatttataaagcgcaggccgaaacaggtgaaattaaaggacattacttgaatgctactgcgggtacatccgaagaaatgatcaaaagggcggtatgtgcccgagagttgggagttcctatcgtaatgcatgactacttaacagggggattcaccgcaaatactagcttggctcattattgccgagacaatggcctacttcttcacatccaccgcgcaatgcatgcagttattgatagacagaggaatcatggtattcactttcgtgtactagctaaagcgttgcgtatgtctgggggggatcatattcactctggtaccgtagtaggtaaactggaaggggagcgagatgtcactttgggctttgttgatttactacgtgatgattttattgaaaaagaccggagtcgcggtatttatttcactcaagattgggtatctatgccaggtgttctgcccgtggcttcagggggtattcacgtttggcatatgcctgccctgaccgatatatttggggatgattccgtgctacagttcgg--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Gymno_Ginkgo tcaaagattaagccatgcatgtgtaagtatgaactctttcagactgtgaaactgcgaatggctcattaaatcagttatagtttctttgatggtacc-tactactcggataaccgtagtaattctagagctaatacgtgcaccaaatcccgac-ttctggaagggacgcatttattagataaaaggccgacgcgggc---cgcccgctgcttcggtgattcatgataactcgacggatcgcacggccctggtgccggcgacgcttcattcaaatttctgccctatcaactttcgatggtaggatagaggcctaccatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaatactgggctc-atcgagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggatcttgggccgggtcggccggtccgccttttcggtgtgcaccggccgctccgtcccttctgccggcggcgcgctcctggccttaattggctgggtcgcggctccggcgccgttactttgaaaaaattagagtgctcaaagcaagcctacgctctgaatacattagcatggaataacgcgataggagtctggtcctattgtgttggccttcgggaccggagtaatgattaatagggacggtcgggggcattcgtatttcattgtcagaggtgaaattcttggatttatgaaagacgaaccactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgactagggatcggcggatgttgctttaaggactccgccggcaccttgtgagaaatcaaagtttttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagattgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctatgcggaggt-tg-ccttcgtggccagcttcttagagggactatggcccttcaggccatggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagt--ctatacctgggccgagaggcccgggaaatctgc-caaatttcatcgtgatggggatagatcattgcaattattgatcttaaacgaggaattcctagtaagcgcgagtcatcaactcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatgatccggtgaagtgttcggatcgcgccgacgacggcggttcgccgccggcgacgtcgcgagaagttcattgaaccttatcatttagaggaaggtcattggcccggtactggatgtaacttttcccccgggcaatatgcctaatattttcaattctttgatagtgaagggtcaagatacagctggtcaggaaattcatgttacttgtgaggtacaacaattattaggaaataatagagttagagctgtagctatgaatgctacagatggtctgatgagaggaatgaaagtgattgacacaggagctcctctgagtgttccagttggtggagctactcttggacgaattttcaatgttcttggagaacctgtcgataatttaggtcctgtagatgctcgcacaacatcccctattcatagacctgctcctgcctttacgcagttggataccaaattatcaatcttcgaaacaggcattaaagtggtggatcttttagctccttatcgacgtggaggaaagatcggactattcgggggagctggagtgggtaaaacagtactcatcatggagttaatcaacaacattgctaaggctcatggaggtgtatctgtatttggcggagtaggggaacgtactcgtgaagggaatgatctttacgtggaaatgaaagaatcaggagtaattaatgagcaaaacatctcagaatcaaaagtagctctggtctatggtcagatgaatgaaccaccaggagctcgtatgagagttggtttaactgccttaaccatggctgaatattcccgagatgtcaatgagcaagacgtactcttatttatcgacaatatcttccgctttgtccaagcaggatcagaagtatccgccttattaggtagaatgccttctgctgtgggttatcagccaactcttagcacggaaatgggttctttgcaagagagaattacttctactaaagagggatctataacctccattcaagcagtttatgtacctgcggacgatttgaccgaccctgctcctgctacgacatttgcacatttagatgctactaccgtactatcgagaggattagcttccaaaggtatctatccagcagtagatcctttagattcaacgtcgactatgctccaaccttggattgtgggcgaggaacattacgaaaccgcacaaggagttaagcaaacttcacaacgttataaagaacttcaagacattatagctattcttggactggacgaattatcagaagaagatcgtttaaccgtagcaagagcacgaaaaattgaacgttttttatcacaaccctttttcgtagcagaggtcttcaccggttccccgggcaaatatgtcagtctcgtagaaacaattagaggttttaaaatgatcctttctggagaattagatggtctccccgaacaggctttttatttggtaggtaacattgatgaagctaccgcgaaggctatgaacttcaaagtggagagaagtgtcggatttaaagctggtgttaaagattacagattgacttattatactcctgaatatcaaaccaaagatactgatatcttggcagcattccgagtaactcctcaacctggagtgccacctgaggaagcgggagctgcagtagctgccgaatcttccactggtacatggaccactgtttggaccgatggacttaccagtcttgatcgttacaagggaagatgctatgacatcgagcccgttcctggggaggaaaatcaatttattgcctatgtagcttaccctttggatcttttcgaggaaggttctgttactaacctgttcacttccattgtagggaatgtatttggattcaaagccctacgagctctacgtctggaagatctgcgaattcctcctgcttattccaaaacttgccagggtccacctcatggtatccaagttgnaagggataaattgaataaatatggccgtcccctattgggatgtactatcaagccaaaattgggtttatctgccaaaaattatggtagagcagtttacgaatgtcttcgtggtggacttgattttactaaagatgatgagaacgtaaattcccaaccattcatgcgctggagagatcgtttcttgttttgtgcagaagcaatttataaagctcagaccgagacgggtgaaattaagggacattacttgaatgctactgcaggtacatgcgaagaaatgatgaaaagggcagtatttgccagagaattgggagttcctatcgtcatgcatgactatctgacgggaggttttactgcaaatactagcttggctcattattgccgagacaatggcctacttcttcacattcaccgcgcaatgcatgcagttattgacagacaaagaaatcatggtatgcatttccgtgtactanntaaagcattgcgtatgtctggtggagatcatattcacgccggtactgtagtaggtaaacttgaaggagaacgagaagtcactctgggttttgttgatctactgcgtgatgattttattgaaaaagaccgaagtcgtggtatttatttcactcaagattgggtatctatgccaggtgttctgcccgtagcttcagggggtattcacgtttggcatatgcctgctctgaccgagatctttggggatgattctgtactacagtttggtgggggaactttgggacacccttggggnnnnncacctggnnntgtagctaattgngtnnnnctagaagcttgtgtacaagctcgtaatgaaggacgtgatcttgctcgtgaaggtaatgaagtgatccgtgaagctagtaaatnnnnnnn---------------------------------------------------------n----- Gymno_Ephedra tcaaagattaagccatgcatgtctaagtatgaactaattcaaacggtgaaactgcggatggctcattaaatcagttatagtttctttgatggtacc-tactactcggataaccgtagtaattctagagctaatacgtgcagcaaatcccgac-ctctggaagggatgcatttattagataaaaggccgacgcgggc---tgcccgcttgttcggtgaatcatgataacttgtcggatcgcaaggcctcagtgccggcgacgcttcattcaaatttctgccctatcaactttcgacggtaggatagaggcctaccgtggttgtgacgggtgacggggaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgatacggggaggtagtgacaataaataacaataccgggctc-attgagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggatcttgggtcggcatggtcggtccgccttttcggtgtgcatcggccatcccgatccttctgtcggcggcgtgctcctggccttaactggctgggtcacggctccgacgcagttactttgaaaaaattagagtgctcaaagcaagcctatgctctgaatacattagcatggaataacgcgataggatttcggtcctgttgtgttggccttcgggaccggagtaatgattaatagggactgtcgggggcattcgtatttcattgtcagaggtgaaattcttggatttatgaaagacgaactactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggatcggcggatgttgcttttaggactccgccggcaccttccgagaaatcagagtttttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgacccaacacgggaaaacttaccaggtccagacatagtaaggattgacagactgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagtgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctacgtcgagg--atccctcggcggacagcttcttagagggactatggccgtttagaccatggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgcattcagcgagct-cattccctggtccgaaaggcccggggaatcttgt-aaaatgcatcgtgatggggatagatctttgcaattatcggtcttgaacgaggaattcctagtaagcgcgagtcattagctcgtgctgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaacgatccggtgaaatgttcggatcgcggcgacggcgacggtccgccgtgcccgacgtcgcgagaagttcattgaaccttatcgtttagaggaaggtnattggtccggnactggatgtatcctntccctctggggntatacanaatatttacaattctttgatngtgaaaggtcnanatannnccgnnnnggnnattaatattacttgtgaggnacaacanttattgggagacantacagtnagagctgtagatatgagcgcgacagacggtntgatgagaggantgaaantnattganacaggagctcntctaagagtacccntcggtnnaatgactcngggacgaatnttntanntttttggagaacnngttgataatttaggtcttatagacacacgtacaacatttcctattcatcgacccgcccctgccttttcacaattagatattaatttggcaatttttgaaacaggcattaaagtcgtggaccttttagcaccttaccgacgtggtggcaaaattggtctctttgggggagcaggagtgggtaaaacagtgctaattatggagttaatcaataacatagctaaagctcatggtggtgtttccgtttttggtggcgtaggagaacgtactcgtgaaggaaatgatctttacatggaaatgaaggaatccggagtaatcaatgaaaaaaatataatagaatccaaagtagctctggtctatggtcaaatgaatgaaccaccaggagctcgtatgagagttggtttaaccgccctaactatggcagaatatttccgagatgtgaacgaacaagatgtacttttatttatagataatattttccgctttgttcaagctggatctgaagtatccgctctattgggcagaatgccctctgctgtaggttatcaaccaacccttagtacagaaatgggttctttgcaagagagaataacttctactaaaaaggggtctataacctctatccaagcagtttatgtacctgcggacgacttgactgatcccgctcctgctacaacattcgcacatttggatgctactactgtactttctagaggattagctgccaaaggaatctacccagcagttgacccattagattccacatctactatgcttcaacctaggattgtaggtgaaaaacattatgaaattgcacaagaagtgaaacaaaccttgcaacgttacaaagaacttcaagatattatagctattcttggactagatgaattatcagaagaagaccgattaactgtagccagagcacgaaaaattgaacgttttttatcacagcccttttttgtagcagaggtttttacgggttccccagggaaatatgttagtcttattgaaacaacaagaggttttcaaatgattcttgccggagatttagatggtctccctgaacaatccttttacttgattggtaatatcgatgaagttataaaatgataagagaaatccgaaanaagtaa---cgatggatttaaagctggtgttaaagattatagattaacttattatactccagagtatcagactaaggacactgatatcttggcagcattccgagtaactcctcaacccggagtaccgcccgaggaagcaggctgggcggtagctgctgaatcttctacaggtacatggaccacagtttggactgatggtcttactagtcttgatcgttacaaaggacgatgctgtgatatcgaacctgttccgggagaagacaatcaatattttgcttatgtagcatatcctttggacctttttgaagaaggttctgttactaacatgtttacttctattgtggggaatgtttttggttttaaagccctacgcgctctacgcctagaagatttgcgaattcctactgcttatattcaaacatttcaaggtccacctcatgggatccaagtagagagagataaattaaacaaatacggacgtcctttgttgggatgtaccatcaaacctaaattaggtctatctgctaaaaactatggtagagcagtttatgaatgtcttcgtggcggactagattttactaaagatgatgaaaatgtaaattctcgagcctttatgcgttggagagatcgctttgttttttgcgcggaagctatttataaagcacaagctgaaacaggtgaaattaagggacattacttaaatgctacgtcgggtacatgtgaagaaatgataaaaagagcagtattcgcaagagaattaggggttccgattgttatgcatgattatttaacaggaggtttcactgcaaataccactttagctcattattgccgagataatggcttacttcttcatattcatcgtgcaatgcatgcagttattgatagacaaaaaaatcatggtatgcacttccgtgtactggctaaagcattacgaatgtccggtggagatcatattcatgccggtactgtcgtaggtaaacttgaaggagaacgagaaattactttaggttttgtggatttacttcgtgatgactttattgaaaaagatcgaagtcgtggtatttatttcacgcaagattgggtatctatgccaggtgttctgcctgttgcttcaggaggtattcacgtttggcatatgcctgctttgactgagatctttggagatgacgctgtattacaatttggtggaggaaccttaggacatccttggggaaatgctcccggtgccgtagctaatcgggttgctttagaagcttgtgtccaagctcgtaatgaaggacgtgatcttgctcgtgaggggaatgaagtaattcgtgaagcttgcaagtggagtcctgaactagctgctgcttgtgaagtatggaaagaaatcaagtttgatgctacagtggat----- Marattia ----agattaagccatgcatgtgtaagtataaactctcttgtactgtgaaactgcgaatggctcattaaatcagttatagtttctttgatggtaccttactactcggataaccgtagtaattctagagctaatacgtgcaccgactcccgactttccggaagggacgcatttattagataaaaggccgatgcgggc-t-tgcccggtgac-tggcgaatcatgataacttcccgaatcgcacggccctggcgccggcgatgtttcattcaaatttctgccctatcaactttcgatggtaggatagaggcctaccatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcccgacacggggaggtagtgacaataaataacaatactgggcttttacaagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgctgcagttaaaaagctcgtagttggatctcggggcggggcgagcggtccgccccttcggtgtgcactggtcgcccgtccttttctgtcggggacgcgctccttgccttcgctggccgggacgcggattcggcgatgttactttgaaaaaattagagtgctcaaagcaagcctatgctctgaatacattagcatggaataacgtgataggactctggtcctattgtgttggtcttcgggaccggagtaatgattaatagggatggttgggggcattcgtatttcattgtcagaggtgaaattcttggatttatgaaagacgaactactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgactagggatttgcggatgttacttcgatgactccgccagcaccttatgagaaatcaaagtttttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagattgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagtgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagttacgcgaagga-tcctcttcgtggccaacttcttagagggactatggccgtctaggccatggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgaattcaacgagtt-taacacctgggccgagaggcccgggtaatcttctgaaatttcatcgtgatggggatagatcattgcaattattgatcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgctgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagttttcggatcgcggcgacgccggcggttcgccgccggcgacgttgtgagaagttcattaaaccttatcat------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------tcctgtcggtgaaattactcttggacgaatttttaatgttcttggagaaccagttgataatttaggacctgtcgatattggcacaacatcacctattcacaaatctgctcctgcttttacacaactagatactaaactatctatatttgaaacgggaattaaagtagtagatcttttagctccttatcgtcgtggaggaaaaattggattattcggaggagccggagtgggaaaaacagttcttattatggaattgattaataacattgccaaagctcatggaggtgtatcagtatctggtggcgtaggagaacggacccgtgaaggtaatgatctctacatggaaatgaaagaatcaaaggtaattaatcaagaaaacatttcagaatcaaaagtagctttggtatatggtcagatgaatgaaccaccaggagctcgtatgagagtcggtttaacagctccgacaatggctgaatatttcagagatgttaataaacaagatgtactattattcattgacaatatatttcgctttgttcaagcaggatcagaggtttccgctttattgggtagaatgccttctgctgtaggttatcaaccaacccttgctacagaaatgggttccttacaggaaaggattacttccactaaagaaggttctataacttccattcaagcagtttatgtaccggcagacgatttgactgatccggctcctgctacaacatttgcacattcagatgcgactactgtattatccaggggattagcagccaaaggcatttacccggctgtagatcccttagattcaacttcgactatgctacaaccctggattgtgggtgaagagcattatgagaccgcacagggggtgaaacaaactttacaacgttataaagaacttcaagatattatagctattcttggactggatgaattatcggaagaagaccgtttaacagtggctagagcacgaaaaattgaacgcttcttatcacaaccatttttcgtagcagaagtatttactggttctccaggaaaatatgttagtctgatagaaacaattaagggatttcaaatgattctttccggagaattagatagccttcctgaacaagctttttatttggtaggcaatatcgatgaagcgaccgcaaaggctgcaactttgcaagtggagaa----------------------------------------------tatactcctcagtatgagactaaggatactgatatcctggcagcattccgaatgactcctcaacctggagtaccgcctgaagaagcgggagctgcagtagctgccgaatcttctaccggtacttggactactgtatggacggatgggcttactagccttgatcgttacaaaggtcgatgctatgatatcgaacccgttgctggcgaagaaaatcaatatattgcttatgtagcttatcctttggatctattcgaagaaggttctgtcaccaatatgtttacctctatcgtaggtaacgtatttggattcaaagctttacgagctttacgattagaggacttaagaattcctcctgcttattcgaagaccttcctaggtccgcctcacggtatccaagttgaaagagataaattaaacaagtatggtcgtccactactaggatgtactatcaaaccaaaattaggtttatctgctaaaaactatggtagagctgtttatgaatgtcttcgtggtggacttgatttcacaaaagatgatgagaacgtaaattctcaaccatttatgcgttggagagatcgtttcttattcgtagcagaagctctttttaagtcccaagctgagacaggcgaagtaaaagggcactacttaaatgctactgcaggtacgtgtgaagaaatgatgaaaagagcaatctttgctagagaattgggcgcacccattgttatgcacgattatctgacaggaggatttactgcaaatacctcattggctcattattgtagagataatggtcttcttcttcatattcaccgtgcaatgcatgctgttattgacagacaacgaaatcacggtatacatttccgtgtattagctaaagcattgcgtatgtccggtggagatcatattcatgctggtacggtagtaggtaaacttgaaggagaacgtgaagtaactttaggctttgttgattcacttcgtgatgactatatcgaaaaagatcgaagtcgtggtatctatttcacccaggattgggtatctatgccgggtgtattccctgtagcttcaggtggtattcatgtttggcatatgcctgctctaactgagatttttggagatgattccgtattacaatttggtggaggaactctaggacacccttgggggaatgcacctggtgcagtagctaatcgagttgcttcagaggc---------------t-------------------------------------------------------------------------------------------------------------------------------- Angiopteris ----agattaagccatgcatgtgtaagtataaactctcttgtactgtgaaactgcgaatggctcattaaatcagttatagtttctttgatggtaccttactactcggataaccgtagtaattctagagctaatacgtgcaccgactcccgac-tcctggaagggacgcatttattagataaaaggccgatgcgggctt-tgcccggtgac-tggcgaatcatgataacttcccgaatcgcacggccctggcgccggcgatgtttcattcaaatttctgccctatcaactttcgatggtaggatagaggcctaccatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcccgacacggggaggtagtgacaataaataacaatactgggctt-tacaagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgctgcagttaaaaagctcgtagttggatctcggggcggggcgagcggtctgcctcctcggtgtgcactggtcgctccgtcctttctgtcggggacgcgctccttgcctttgctggccgggacgcggattcggcgatgttactttgaaaaaattagagtgctcaaagcaagcctatgctctgaatacattaacatggaataacgtgataggaccctggtcctattgtgttggtcttcgggaccggggtaatgattaatagggatggttgggggcattcgtatttcattgtcagaggtgaaattcttggatttatgaaagacgaactactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgactagggatttgcggatgttacttcgatgactccgccagcaccttatgagaaatcaaagtttttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgacccaacacggggaaacttaccaggtccagacatagtaaggattgacagattgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagtgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagttacgcgaagga-tcctcttcgtggccaacttcttagagggactatggccgtctaggccatggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgaattcaacgagtt-taacacctgggccgagaggtccgggtaatcttctgaaatttcatcgtgatggggatagatcattgcaattattgatcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgctgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagttttcggatcgcggcgacgccggcggtccgccgccggcgacgttgtgagaagttcattaaaccttatcat------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------tcctgtcggtgaagttactctcggacgaatttttaatgttcttggagaaccagttgataatttaggacctgtcgatgctggcacaacatcacctattcacaaatctgctcctgctttcacacaattagatacgaaactatctatatttgaaacgggaattaaagtagtagatctttcagctccttatcgtcgtggaggaaaaattggattattcggaggggccggagtgggaaaaacagttcttattatggaattgattaataacattgccaaagctcatggaggtgtatcagtttttggtggcgtaggagaacggacccgtgaaggtaatgatctttacatggaaatgaaagaatcaaaggtgattaatcaagaaaacatttcagaatcgaaagtagctttggtatatggtcagatgaatgaaccaccaggagctcgtatgagagtcggtttaacagctctgacaatggctgaatattttcgagatgttaataaacaagatgtactattattcattgacaatatatttcgctttgttcaagcaggttcagaggtttccgcttcatcaggtagaatgccttctgctgtgggttatcaaccaacccttgctacagaaatgggttccttacaagaaaggattacttccacaaaagaaggttctataacttccattcaagcagtttatgtaccggcagacgatttgactgatccggctcctgctacaacatttgcacattcagatgcgactactgtactatccagaggattagcagccaaaggcatctacccggctgtagatcctttagattcaacttcaactatgctacaaccctggattgtgggtgaagaacattatgagaccgcacaaggggtgaaacaaactttacaacgttataaagaacttcaagatattatagcgattcttggactggatgaattatcagaagaggaccgtttaacagtggctagagcacgaaaaattgaacgcttcttgtcacaaccatttttcgtagcagaagtatttactggttctccagggaaatatgttagtctgatagaaacaattaagggatttcaaatgattctttccggagaattagatagccttcctgaacaagctttttatttggtaggcaatattgatgaagcgaccgcaaaggctgcaactttgcaagtggagaa----------------------------------------------tacactcctgagtatgagactaaggatactgatatcctggcagcattccgaatgactcctcaacctggagtaccgcctgaagaagcgggagctgcagtagctgccgaatcttctaccggtacttggactactgtatggacagatgggcttactagccttgatcgttacaaaggtcgatgctatgatatcgaacctgttgctggcgaagaaaatcaatatattgcttatgtagcttatcctttggatctattcgaagaaggttctgtcaccaatatgtttacctctatcgtaggtaacgtattcggattcaaagctttacgagctttgcgattagaggacttaagagttcctcctgcttattcgaaaactttccaaggtccgcctcacggtatccaagctgaaagagataaattaaacaagtatggtcgtccactactaggatgtactatcaaaccaaaattaggtttatctgctaaaaactatggtagagctgtttatgaatgtcttcgtggtggacttgattttacgaaagatgatgagaacgtcaattctcaaccatttatgcgttggagagatcgtttcttattcgtagcagaagctctttttaagtctcaagctgagacaggcgaagtaaaagggcactacttaaatgctactgcaggtacgtgtgaagaaatgatgaaaagagcaatctttgctagagaattgggcgcacccattgtcatgcacgactatctgacaggaggatttactgcaaatacttcattggctcattattgtagagataatggtcttcttcttcatattcaccgtgcaatgcatgctgttattgacagacaacgaaatcacggtatgcatttccgtgtattagctaaagctttgcgtatgtccggtggagatcatgttcacgccggtacggtagtgggtaaacttgagggagaacgtgaagtaactttaggcttcgttgattcacttcgtgatgactatatcgaaaaagatcgaagtcgtggtatctatttcactcaggattgggtatctatgccgggtgtattccccgtagcttcaggtggtattcatgtctggcatatgcctgctctaactgagattttcggagatgattctgtattacaatttggtggaggaactctaggacacccttgggggaatgcacctggtgcagtagctaatcgagttgcttcagaggc---------------t-------------------------------------------------------------------------------------------------------------------------------- Ophioglossum ----agattaagccatgcatgtgtaagtataaactctcttgtactgtgaaactgcgaatggctcattaaatcagttatagtttctttgatggtaccttactactcggataaccgtagtaattctagagctaatacgtgcaccgactcccgac-ttccggaagggacgcatttattagataaaaggccgatgcgggc-t-tgcccggtgatgcggtgactcatgataactgtgcggatcgcacggcccttgcgccggcgacgcttcattcaaatttctgccctatcaactttcgatggtaggatagaggcctaccatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcccgacacggggaggtagtgacaataaataacaataccgggcttttacaagtctggtaattggaatgagtacaatctaaacaccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggatctcggggcggggcgggcggtccgcccctcgggtgtgcactgcgcgctccgccctttctgccggggacgcgctcctggccttaggtggctgggacgcggattcggcgatgttactttgaaaaaattagagtgctcaaagcaagcctacgctctgaatacgttagcatggaataacgcgacaggactctggtcctattgtgttggtcttcgggaccggagtaatgattaatagggacggttgggggcattcgtatttcattgtcagaggtgaaattcttggatttatgaaagacgaactactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgactagggatcggcggatgttacttcgatgactccgccagcaccttatgagaaatcaaagtctttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagattgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagtgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagttacgcgaagct-cagccttcgcggcgaacttcttagagggactgtggccgtctaggccacggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgaattcaacgagtc-cgccgcctgggccgagaggcccgggtaatcttctgaaatttcatcgtgatggggatagatcattgcaattattgatcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgctgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagttttcggatcgcggcgacgccggcggtccgtccgccgcgacgttgtgagaagttcattaaaccttaccat------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------tcccgttggtgaatccactcttggacgaatatttaatgttctaggagaacctgtcgatgagttgggtcccgtagatgctgacgaaacatctcctattcacaaacctgctcctgctttcacacagttggataccaaattatccatatttgaaaccgggattaaagtggtggatctcttagctccttatcgtcgtggtggaaaaatcggattgtttgggggagccggagttggaaaaacggttcttatcatggaattgattaacaacattgctaaagcccacggaggtgtatctgtatttggtggggtaggggaacgtacacgcgaaggaaatgatctctatatggaaatgaaagaatcaaaagttattaatgaacagaatgtctcggaatcaaaagtagctctggtatatggtcaaatgaatgaatcgcccggagctcgtatgagggttggtttaaccgcgttaacaatggccgagtattttcgggatgttaataaacaagatgtactcctatctattgataatattttccgtttcgtacaagcaggatccgaggtttctgccttattggggagaatgccctccgctgtaggttaccaaccgactcttagtacagagatgggttccttacaagaaaggatcacttctaccaaggaaggatccataacttccattcaagcggtttatgtacctgccgacgatttgactgacccagcccctgccacaacatttgcacatctagatgctaccactgttctctcaagaggattagctgcgaagggtatttatccggccgtggatccactagattcaacttcaaccatgttacaacccttgattgtgggtgaggaacattataagactgcgcagggagttaaacagactctgcaacgttataaggaactccaagacattatagctattctcggactggatgaattatcggaagaggaccgtttaacggtagctagagcacgaaagattgaacgtttcttatcacagcctttctttgtagcagaagtattcacgggctctccgggcaaatatgttagtctcgtaaaaacaatcaaaggtttccaaatgattctttccggagaattagacaaccttcccgaacaagctttttattcagtgggtgacatcgatgaagcagctgcaaaagctgcaatttcgcaagcggggaa----------------------------------------------tacactcccgattatgagcctaaggataccgatatcctagcagcctttcgaatgactccccaacctggagtgccggccgaggaagcaggagctgcagtagctgctgaatcctccacgggtacgtggactaccgtatggaccgatgggcttaccagtctcgatcgttacaaaggtcgatgctatgaaatcgaacctgttgctggggaggaagatcaatacattgcttatgtagcgtatcccttagacctttttgaagaaggttccgtcaccaacatgttcacctccattgtaggtaacgtattcggattcaaggcattgaaagctctacggttggaggatttacgagttcctcctgcttattctaagactttcatgggccctccccacggtatccaagtcgaaagggataaattaaacaaatatggtcgtcccttactggggtgtaccatcaaacccaaattgggattatctgccaagaattatggtagagccgtttatgaatgcttacgcggtgggctcgacttcaccaaagatgatgaaaacgtaaattctcaaccatttatgcgttggagagatcgtttcgtatttgtggcggaagctcttttcaaatctcaggcggaaacgggagagattaaggggcattacttaaatgctaccgcaggtaattgtgatgaaatgatgaaaagagcagtatttgctcgagaattgggagtacccattgtcatgcacgattatttgacgggaggatttactgcaaatactaccttagcttcctactgccgagacaatggtttgctccttcacattcaccgtgcaatgcatgctgttatcgataggcaaaaaaatcacggtatgcacttccgtgtattggccaaagcattgcgcatgtctggcggggatcacattcatgccggtaccgtggttggtaaactcgagggcgaacgcgagatcactttaggtttcgttgatctacttcgtgacgattatattgataaagatcgaagtcgaggtatctatttcacccaggattgggtatccatgccgggtgtatttcctgtagcttcgggaggtattcacgtttggcatatgcctgctctaaccgagatcttcggggatgattccgtacttcaatttggtggggggactctgggacacccatggggtaatgcacccggtgcagtagctaatcgagtcgctctggaagc---------------t-------------------------------------------------------------------------------------------------------------------------------- Fagales_Juglans tcnaagattaagccatgcatgtgtaagtatgaactaattcagactgtgaaactgcgaatggctcattaaatcagttatagtttgtttgatggtatc-tgctactcggataaccgtagtaattctagagctaatacgtgcaacaaaccccgac-ttctggaagggatgcatttattagataaaaggtcgacgcgggctt-tgcccgttgctctgatgattcatgataactcgacggatcgcacggccatcgtgccggcgacgcatcattcaaatttctgccctatcaactttcgattgtaggatagaggcctacaatggtggtgacgggtaacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaataccgggctcttacgagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggatcttgggttgggcagagcggtccgcccc-t-ggtgtgcaccggtctgctcgtcccttctaccggcgatgcgctcctggccttaactggccgggtcgtgcctccggtgctgttactttgaagaaattagagtgctcaaagcaagcctacgctctgtatacattagcatgggataacatcataggatttcggtcctattgtgttggccttcgggatcggagtaatgattaacaggaacagtcgggggcattcgtatttcatagtcagaggtgaaattcttggatttatgaaagacgaacaactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggatcggcggatgttgctttaaggactccgccggcaccttatgagaaatcaaagtctttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagactgagagctctttcttgattctatgggtggtggtncatgnccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctatgcggaggt-gaccttccgcggccagcttcttagagggactatggccgcttaggccaaggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagtt-tatagccttggccgacaggcccgggtaatctttg-aaatttcatcgtgatggggatagatcattgcaattgttggtcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttcggatcgaggcgatgtgggcggttcgctgccggcaacgtcgcgagaagtccactgnaccttatcatntagngg----ttattggtccggtactggatgtatcttttccccccggcaaaatgcctaatatttacaacgctctagtagttaagggtcgagatactaccggtcaacaaattaatgtgacttgtgaagtacagcaattattaggaaataatcgcgttagagctgtagctatgagtgctacagatggtctaatgagaggaatggaagtgattgacacgggagcttctttaagcgttccagtcggcggagcaactctaggacgaattttcaacgtgcttggagagcctgttgacaatttaggtcctgtagatactcgcacaacatctcctattcatagatctgcgcctgcctttatacagttagatacaaaattatctatttttgaaacaggaattaaagtagtagatcttttagctccttatcgccgtggaggaaaaataggactattcgggggagctggagtgggtaaaacagtactcattatggaattgatcaacaacattgccaaagctcatgggggtgtatccgtatttggcggagtaggtgaacgtactcgtgaaggaaatgatctttacatggaaatgaaagaatctggagtaattaatgaacaaaatattgcagaatcaaaagtggctctagtatatggtcagatgaatgaaccgcctggagctcgtatgagagttggtttaactgccctaactatggcagaatatttccgagatgttaatgaacaagacgtacttctatttatcgacaatatcttccggttcgtccaagcaggatccgaagtatctgccttattgggtcgaatgccttccgctgtgggttatcaacccactcttagtaccgaaatgggttctttacaagaaagaattacttctaccaaagcgggatccataacctctattcaagcagtttatgtacctgcagacgatttgaccgatcctgctcctgccacgacatttgcacatttagatgctactaccgtactatcaagaggattagctgccaaaggtatctatccagcagtagatcctttagattcaacgtcaacgatgctccaacctcggatcgttggtgaagaacattatgaaactgcgcaaagagttaaacaaactttacagcggtataaagaacttcaggacattatagctattcttggattggacgagttatcagaagaggatcgcttaaccgtagcaagagcacgaaaaattgagcgtttcttatcacaaccttttttcgtagcagaagtatttacgggttccccgggaaaatatgtcggtttagccgaaaccattagagggtttcaattgatcctttccggagaattagacggtcttcctgagcaggctttttatttggtaggtaacattgatgaagctactgcgaaggctacaaacttagaaatggagagaagtgttggattcaaagctggtgttaaagattataaattgacttattatactcctgaatatcaaaccaaagatactgatatcttggcagcgttccgagtaagccctcaacctggagttccgcctgaggaagcaggggcagcagtagctgctgaatcttctactggtacatggacaactgtgtggaccgatgggcttactagtcttgatcgttacaaaggacgatgctaccacatcgagccagttgctggagaagaaaatcaatttattgcttatgtagcttaccccttagacctttttgaagaaggttctgttactaacatgtttacttccattgtgggtaatgtatttggattcaaggccctgcgcgctctacgtctggaggatttgcgaatccctcctgcttattctaaaactttccaaggcccgcctcatggaatccaagttgagagagataaattaaacaagtatggccgccctctattgggatgtactattaaacctaaattgggattatccgctaagaattacggtagagcagtttatgaatgtctccgcggtgggcttgattttaccaaagatgatgagaacgtgaattcccaaccatttatgcgttggagagaccgtttcctattttgtgccgaagcaatttataaagcgcaggctgaaaccggtgaaatcaaagggcattacttgaatgctactgcaggtacatgcgaagaaatgatcaaaagggctgtatttgccagagaattgggagttcctatcgtaatgcatgactacttaaccgggggatttactgcaaatactagcttggctcattattgccgagataatggtctacttcttcacatccatcgtgcaatgcatgcagttattgatagacagaagaatcatggtatacactttcgtgtactagctaaagcgttacgtatgtctggtggagatcatattcacgctggtaccgtagtaggtaaacttgaaggggaaagagaaatcactttaggctttgttgatttactacgtgatgattttgttgaaaaagatcgaagccgcggtatttatttcactcaagattgggtctctctacctggtgttctgcccgtggcttcagggggtattcacgtttggcatatgcctgctctgaccgaaatctttggagatgattccgtactacaattcggtggaggaactttagggcacccttggggaaatgcacccggtgccgtagctaatcgagtagctctagaagcatgtgtacaagctcgtaatgagggacgtgatcttgctcgtgagggtaatgaaattattcgtgaggctagtaaatggagtcctgagctagcggctgcttgtgaagtatggaaggagatcaaatttgaattcccagcaatggatac Rosales_Morus tcaaagattaagccatgcatgtgtaagtatgaactaattcagactgtgaaactgcgaatggctcattaaatcagttatagtttgtttgatggtact-tgctactcggataaccgtagtaattctagagctaatacgtgcaacaaaccccgac-ttctggaagggatgcatttattagataaaaggtcgac---ggctc-tgcccgttgctctgatgattcatgataactcgacggatcgcacggccattgtgccggtgacgcatnattcaaatatctgccctatcaactttcgatggtaggatagtggcctactatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaataccgggctc-tacgagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggaccttgggttgggttgatcggtccgcctc-c-ggtgtgcaccggtcgactcgtcccttctgccggcgatgcgctcctggccttaattggccgggtcgtgcctccggcgctgttactttgaagaaattagagtgctcaaagcaagcctacgctctgtatacattagcatgggataacatcataggatttcggtcctattctgttggccttcgggatcggagtaatgattaacagggacagtcgggggcattcgtatttcatagtcagaggtgaaattcttggatttatgaaagacgaacaactgcgaaacagtttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggatcggcggatgttgcttttaggactctgccggcaccttatgagaaatcaaagtttttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagactgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctat-cggagga-taccctccgcggccagcttcttagagggactatggccgcttaggccaaggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagtc-tatagccttggccgataggcccgggtaatctttg-aaatttcatcgtgatggggatagatcattgcaattgttggtcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttcggatcgaggcgacatgggcggttcgctgcctgtgacgttgcgagaagtccactgaaccttatcatttngaggaaggtcattggtccggtactagatgtagcttttcccccgggcaagatgcctaatatttacaacgctctggtagttwaaggtcgagatactgttggtcaacaaattaatgtgacttgtgaagtacagcaattattaggaaataatcgagttagagctgtagctatgagtgctacagatggtctaatgagaggaatggaagtgattgacacaggagctcctataagtgttccagtcggcggagcgacgctaggacgaattttcaacgtgcttggagagcccattgataatttaggtcctgtagatactcgcacaacatctcctattcatagatctgcgcctgcctttatacaattagatacaaanttatctatttttgaaacaggaattaaagtagtagatcttttagccccttatcgtcgtggaggaaaaataggactattcgggggggctggagtaggtaaaactgtactcattatggaattgatcaacaacattgccaaagctcatggaggtgtatccgtatttggcggagtaggtgagcgtacccgtgaaggaaatgatctttacatggaaatgaaagaatccggcgtaattaatgaacaaaatattgcagaatcaaaagtggctctagtctacggtcagatgaatgaaccgccgggggctcgtatgagagttggtttaactgccctaactatggcggagtattttagagatgttaatgaacaagacgtacttctatttatcgacaatatctttcgttttgtccaagcgggatccgaagtatccgccttattgggtagaatgccttctgctgtgggttatcaacccactcttagtaccgaaatgggttctttacaagaaagaattacttctaccaaagaggggtccataacttctattcaagcagtttatgtacctgcggacgatttgaccgatccagctccggccacgacatttgcacatttagatgctactaccgtactatcaagaggattggccgccaaaggcatctatccagcagtagatcctttagattcaacgtcaactatgctccaacctcggattgttggtgaagaacattatgaaactgcccaaagagttaaacaaactttacaacgttacaaagaacttcaggacattatagctatccttgggttggacgaattatccgaagaagatcgcttaaccgtagcaagagcacgaaaaattgagcgtttcctgtcacaacctttttttgtagcagaagtatttaccggttccccagggaaatatgttggtctagcagaaacaattagagggtttaaattgatcctttctggagaattagatggtcttcctgaacaggctttttatttggtaggtaacattgatgaagctactgcgaaggc-------------------------gttggattcaaagctggtgttaaagattataaattgacttattatactcctgactatgaaaccaaagatactgatatcttggcagcatttcgagtaactcctcaacctggagttccacctgaagaagcaggggcagcggtagctgctgaatcttctactggtacatggacaactgtatggactgacgggcttactagtcttgatcgttacaaaggtcgatgctaccacatcgagcccgttgctggagaagaaagtcaatttattgcttatgtagcttaccccttagacctttttgaagagggttctgttactaacatgtttacttccattgtaggtaatgtgtttgggttcaaggccctgcgcgctctacgtctggaggatttgcgaatccctactgcttatgttaaaactttccaaggcccgcctcatgggatccaagttgagagagataaattgaacaagtatggccgccccctattgggatgtactattaaacctaaattggggttatccgctaagaattacggtagagcagtttatgaatgtctccgcggtggacttgattttaccaaagatgatgagaatgttaattcccaaccatttatgcgttggagagaccgtttcttattttgtgccgaagcaatttataaagcacaggctgaaacaggtgaaatcaaagggcattacttgaacgctactgcaggtacatgcgaagatatgatgaaaagagctgtatttgccagagaattgggggttcctatcgtaatgcatgattacttaacagggggattcactgcaaatactagcttggctcattattgccgagataatggtttacttcttcacatccaccgtgcaatgcatgcagttattgatagacagaagaatcatggtatgcactttcgtgtactagctaaagcgttacgtatgtctggtggagatcatatacacgctggtaccgtagtaggtaaacttgagggggaaagggagatcactttaggctttgttgatttactacgtgatgattttgttgaaaaagatcgaagccgcggtatttatttcactcaagattgggtctctatgccaggtgttttgcctgtagcttcagggggtattcacgtttggcatatgcctgctctgaccgagatctttggagatgattctgtactacaatttggcggcggaactttaggacacccttggggaaatgcacctggtgccgtagctaatcgagtagctctagaagcatgtgtacaagctcgtaatgagggacgtgatcttgctcgtgagggtaatgaaattattcgcgaggctagtaaatggagtcctgaactagctgctgcttgtgaagtatggaaggagatcaaatttgaatt--------------- Rosales_Humulus tcaaagattaagccatgcatgtgtaagtatgaactaattcagactgtgaaactgcgaatggctcattaaatcagttatagtttgtttgatggtatc-tgctactcggataaccgtagtaattctagagctaatacgtgcaacaaaccccgac-ttctggaagggatgcatttattagataaaaggtcgacgcgggctc-tgcccgttgctctgatgattcatgataactcgacggatcgcacggccttcgtgccggcgacgcatcattcaaatttctgccctatcaactttcgatggtaggatagtggcctactatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaataccgggctc-tacgagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggaccttgggttgggtcgatcggtccgcctc-c-ggtgtgcaccggtcggctcgtcccttctaccggcgatgcgctcctggccttaattggccgggtcgtgcctccggtgctgttactttgaagaaattagagtgctcaaagcaagcctacgctctgtatacattagcatgggataacatcataggatttcggtcctattctgttggccttcgggatcggagtaatgattaacagggacagtcgggggcattcgtatttcatagtcagaggtgaaattcttggatttatgaaagacgaacaactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggattggcggatgttgcttttaggactccgccagcaccttatgagaaatcaaagtttttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagattgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctatgcggagga-tttcctccgcggccagcttcttagagggactatggccgcttaggccaaggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagtc-tatagccttggccgacaggcccgggtaatctttg-aaatttcatcgtgatggggatagatcattgcaattgttggtcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttcggatcgaggcgacgtgggcggttcgctgcccgcgacgtagcgagaagtccactgaaccttatcatttagaggaaggtcattggtccggtactggatgtagcttttccgccggggaaaatgcctaatatttacaatgctctggtagttaagggtcgagatactgttggtcaacaaattaatgtgacttgtgaagtacagcaattattaggaaataatcgagttagagctgtagctatgagtgctacagatggtctaatgagaggaatggacgtgattgacacaggagctcctttaagtgttccagtcggcggagcgacactaggacgaattttcaacgtacttggggagcccgttgataatttaggtccggtagatactcgcacaacatctcctattcatagatctgcacctgcctttatacaattagatacaaaattatcgatttttgaaacaggaattaaagtagtagatcttttagccccttatcgccgtggaggaaaaataggactcttcgggggtgctggagtaggtaaaacagtactcattatggaattgatcaacaacattgccaaagctcatgggggcgtatccgtatttggcggagtaggtgaacgtacccgtgaaggaaatgatctttacatggaaatgaaagaatctggcgtaattaatgaacaaaatattgccgaatcaaaagtggctctagtctatggtcagatgaatgaaccaccaggggctcgtatgagagttggtttaactgctctaactatggcggagtatttccgagatgttaatgaacaagacgtacttctatttatcgacaatatctttcgttttgtccaagcaggatccgaagtatccgccttattaggcagaatgccttccgctgtgggttatcaacccactcttagtactgaaatgggttctttacaagaaagaattacttctaccaaagaggggtccataacttctattcaagcagtttatgtacccgcggacgatttgaccgatccagctccggccacgacatttgcacatttagatgcgactaccgtactatcaagaggattggccgccaaaggcatctatccagcagtagatcctttagattcaacgtcaacgatgctccaacctcggattgttggtgaagaacattatgaaactgcgcaaagagttaaagaaactttacaacgttacaaagaacttcaggacattatagctattcttgggttggacgaattatccgaagacgatcgcttaaccgtagcaagagcacgaaaaattgagcgtttcttgtcacaaccttttttcgtagcagaagtatttacaggttccccggggaaatatgttggtctagcagaaacaattaaagggtttaaattgattctttctggagaattagatggtcttcctgaacaggctttttatttggtaggtaacattgatgaagctactgcgaaggctacgaacttagaaatggagaaaagtgttggattcaaagctggtgttaaagattataaattgacttattacactccggactatgaaaccaaagatactgatatcttggcagcatttcgagtaactcctcaacctggagttccccctgaagaagctggggctgcggtagctgctgaatcttctactggtacatggacaactgtatggactgacgggcttaccagccttgatcgctacaaaggtcgatgctaccacatcgagcccgttgctggagaagaaaatcaatatattgcttatgtagcttatcctttagacctttttgaagaaggttctgttactaacatgtttacttccattgtgggtaatgtatttgggttcaaagccctgcgcgctctacgtctggaagatttgagaatccctacttcttatactaaaactttccaaggtccgcctcatgggatccaagttgagagagataaattgaacaagtatggccgcccactattgggatgtactattaaacctaaattggggttatccgctaagaattacggtagagcagtttatgaatgtcttcgtggtggacttgattttaccaaagatgatgagaacgtgaattcccaaccatttatgcgttggagagaccgtttcttattttgtgcagaagcaatttataaatcacagtctgaaacaggggaaatcaaaggacattacttgaatgctactgcaggtacatgtgaagaaatgatgaaaagggctgtatttgccagagaattgggagttcctattgtaatgcatgattacttaacaggaggattcactgcaaatactagcctggctcattattgtcgagataatggtctacttcttcacatccaccgtgcaatgcatgcagttattgatagacaaaagaatcatggtatacactttcgtgtactagctaaagcgttacgtatgtctggtggagatcatatccatgcgggtactgtagtaggtaaacttgaaggggaaagagaaatcactttaggctttgttgatttactacgtgatgattttattgaaaaagatcgaagccgtggtatttatttcactcaagattgggtctctctaccaggtgttctgcccgtggcttcagggggtattcacgtttggcatatgcccgctttgaccgagatctttggagatgattccgtactacaatttggtggaggaactttaggacatccttggggaaatgcacccggtgctgtcgctaatcgagtagctctagaagcatgtgtacaagctcgtaatgagggacgtgatcttgctcgtgagggtaatgaaattattcgtgaggcttgtaaatggagtcctgaactagctgctgcttgtgaagtttggaaggaaatcaaatttgaatttgaagcaatggatac Gymno_Cycas tcaaagattaagccatgcatgtgttggtatgaactatgtcggactgtgaaactgcgaatggctcattaaatcagttatagtttatttgatggattcatgttactcggataaccgtagtaattctagagctaatacgtgtaccaaatcctgac-ttttggaagggaggcatctattagataaaaggccgatgcgggctc-tcgccggtcgttcggtgattcatgataactcgacggatcgcatggccctagtgccggcgacgtttcattcaaatttctgccctatcaactttcgatggtaggatagaggcctaccatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaatactgggctc-atcgagtctggtaattggaatgagtacaatttaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggatcttgggacggcccgaccggtctgcctttc-ggtgtgcatcgatcgtttcgtcctttttgtcggcggcgcgttcctggacttagttgcctgggttgcggctctggcgttgttactttgaaaaaattagagtgctcaaggcaagcccacgctctgaatacattagcatggaataacacgataggattctggtcctattacgttggccttcgggaccggagtaatgattaatagggacagtcgggggcattcgtatttcattgtcagaggtgaaattcttggatttatgaaagacgaaccactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgactagggatcggtggatgttgctctaaggactccgccggcaccttatgagaaatcaaagtttttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagattgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctacgcagaggg-tttcctttgcggccagcttcttagagggactatggccgttcaggccatggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgtattcaacgagtc-tataaccttggccgagaggttggggtaatctgtggaaatttcatcgtgatggggatagatcattgcaattattgatcttcaacgaggaattcctagtaagcgcgagtcatcaacacgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatgatccggtgaagtgttcggatcgcggcgacgacggcggtttgctgctgccgacgtcgcgagaagttcattgaaccttattatttagaggaagg-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------tccggttggtggagctactctcggacgaattttcaatgttattggagaacctgtcgataatttaggtcctgtagatgctcgcacaacatctcctattcatagacccgctcctgcctttatacagttggatacaaagttatcaatcttcgaaacaggcattaaagtggtggatcttttagctccttaccgccgtggaggaaaaataggactattcgggggagctggggtaggtaaaacagtgctcatcatggaattaatcaacaatattgctaaggctcatggaggtgtatctgtatttggcggagtaggagaacgcacccgtgagggaaatgatctttacgttgaaatgaaagaatcgggagtaattaatgaacaagatatctcgggatcgaaagtagctctggtctatggccagatgaatgaaccaccaggagctcgtatgagagttgggttaaccgccctaaccatggctgaatatttccgagatgtcaatgagcaagacgtacttttatttatcgacaatatcttccgttttgtccaagcgggatcagaagtatctgcattattaggcagaatgccttccgctgtgggttatcagccaactcttagtacagaaatgggttctttgcaggaaagaattacttctaccaaagagggatctataacctccattcaagcagtttatgtacctgcagacgatttgaccgaccccgctcctgctacaacatttgcacatttagatgctactaccgtattatcgagaggattagctgccaaaggcatctatccagcagtagatcctttagattcaacatcgaccatgctccaaccttggatcgtgggcgaagaacattatgaaactgcacaaggagtcaagcaaactttacagcgttataaagaacttcaagacattatagctattcttggactggacgaattatcagaagaagatcgtttaaccgtagcaagagcacgaaagattgaacgtttcttatcacaacccttttttgtagcagaggtattcactggttccccgggtaaatatgtcggtctcgtagaaacaattagaggttttaaaatgatcctttccggagaattagatggtcttcccgaacaggccttttatttggtgggtaacattgatga----------------------------------ag----gttggatttaaagctggtgttaaagattacagattaacttattacactcctgaatatcaaaccaaagataccgatatcttggcagcgttccgagtaactcctcaacctggagtgccgcctgaggaagcgggagctgcagtagccgctgaatcttccactggtacatggaccactgtttggaccgatggacttaccagtctcgatcgttacaaggggcgatgctatgacatcgagcccgttcctggggagaaaaatcaatttattgcctatgtagcttaccccttagacctctttgaagaaggttctgttactaacatgttcacttccattgtaggtaatgtatttggattcaaagccctacgagctctacgcctagaagatttgcgagttcctcctgcttattccaaaactttccaaggtccacctcatggtatccaagttgaaagagataaattaaacaaatatggccgtcctctattgggatgtactattaaacccaaattgggtttatctgccaaaaactatggtagagcagtttatgaatgtcttcgtggtggacttgattttaccaaagatgatgagaacgtaaattcccaaccatttatgcgctggagagatcgtttctgcttctgtgcagaagcaatttataaagctcaggctgagacgggtgaaattaagggacattacttgaatgctactgcaggtacatgcgaagaaatgatcaaaagggcagtatttgccagagaattgggggttcctatcgtcatgcatgactacctgacgggaggatttactgcaaataccagcttggctcattattgccgagacaatggcctacttcttcacattcaccgcgcaatgcatgcagttattgacagacaaagaaatcatggtatgcacttccgtgtgctagctaaagcattgcgaatgtccggtggagatcatattcacggcggtactgtagtaggtaaacttgaaggagaacgagacgtaactttgggttttgttgatctactgcgtgacgattttattgaaagagaccgaagtcgtggtatttatttcacccaagattgggtatctatgccaggtgttctgcccgtagcttcggggggtattcacgtttggcatatgcctgctctaaccgagatctttggggatgattccgtactacagttcggtgggggaactttgggacacccttggggaaatgcacctggtgcagtagcgaatcgagttgccttagaagcttgtgtacaagctcgtaatgaaggacgtgatcttgctcgtgaaggtaatgaagtgatccgcgaagctagtaaatggagtcc---------------------------------------------------------c----- Gymno_Araucaria tcaaagattaagccatgcatgtctaagtatgaactatttcagactgtgaaactgcggatggctcattaaatcagttatagtttctttgatggtaccttgctactcggataaccgtagtaattctagagctaatacgtgcaccaagtcccaac-tttcggaagggatgcatttattagataaaaggccggcgcgggctt-cggcccgcgttccggtgattcatgataactcgacggatcgcacggcccttgtgccggcgacgcttcattcaaatttctgccctatcaactttcgatggtaggatagaggcctaccatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaatactgggctc-atcgagtctggtaattggaatgagtacaatctaaaccccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggatcttgggtcgtcacggtcggtccgccttcttggtgtgcaccggccctcacgtcccttctgccggcggcgtgttcctgtccttaactggttgggtcgcggcaccggcgctgttactttgaaaaaattagagtgctcaaagcaagcctacgctctgaatacattagcatggaataacgtgataggagtctggtcctattccgttggccttcgggaccggagtaatgattaatagggactgtcgggggcattcgtatttcattgtcagaggtgaaattcttggatttatggaagacgaaccactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggatcggcggatgttgctctaaggactccgccagcaccttctgagaaatcagagtgtttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagattgacagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgataacgaacgagacctcagcctgctaactagctacgcggaggt-tccccttcgcggccagcttcttagagggactatggccgtttaggccatggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgcaatcaacgagctttctgccctggttcgataggcccgggaaatcttgtgaaattgcatcgtgatggggatagaccattgcaattattgatcttcaacgaggaattcctagtaaacgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatggtccggtgaagtgttccgatcgcgacgacgatggcggtcctctgccggcgacgtcgcgagaagttcattgaaccttatcatttagaggaagg-----------------------------------------------------------------------------------------------------------------------------------------------gttagagctgtagctatgagtgctacagatggtttgatgagaggaatgaaagtgtttgatacaggagctcctctgagtgttccagttggtggagctactctcggacgaatttttaatgttcttggagaacctgtcgatgatttaggtcctgtagatgctcgcgcaaaatcccctattcatagagctgctcctgcctttacacagttggataccaaattttcaatttttgaaacaggcattaaagtagtggatcttttagctccttatcgccgcgggggaaaaataggattatttgggggagctggagtgggtaaaacagtactcattatggagttaatcaacaacattgctaaggctcatgggggtgtatctgtatttagcggggtaggagagcgtacccgtgaaggaaatgatctttatatggaaatgaaagaatcgggagtcattcaggaacaaaatatctcagaatccaaagtagctctggtctatggtcagatgaacgaaccaccaggagctcgtatgagagtcgggttaactgctttaacgatggcggaatatttccgagatgtaaataagcaagatgtactattatttattgacaatatcttccgctttgtccaagcaggatcagaggtatccgccttattaggaagaatgccttctgctgtgggttatcagccgactcttggcacggaaatgggttgtttgcaagagagaataacttctaccaaagagggatctataacctccattcaagcagtttatgtacctgcggacgacttgactgatcctgctcctgctacaacatttgcacatttagacgctactaccgtgctatcacgaggattagctgccaagggtatctatccagcggtagatcctttagattcaacatcaactatgctccaaccttcgatcgttggcgaagaacattatgaaattgcacaaggagttaaacaaactttgcaacgttataaggaacttcaagacattatagctattcttggactggacgaattatcagaagaggatcgtttaacagtagcaagagcacgaaaaattgaacgtttcttgtcacaaccctttttcgtagcagaggtattcactggttccccgggtaaatatgttagtctcacagaaacaattagaggctttcaaatgattctttccggagaattagacggtctccctgaacagtctttttatttggtgggtaacattgatga----------------------------------ag----gttggatttaaagctggtgttagagattatagattaacttattatactccggaatatcagacccttgatacggatatcttggcagcattccgagtcactcctcaacccggagtgccccccgaggaagcaggagcagcagtagcggctgaatcttctaccggtacatggacaactgtttggaccgatggacttaccagtcttgatcgttacaaggggcgatgctatggcatcgagcctgttcctggagaggaaactcaatttattgcctatgtagcttaccccttagacctttttgaagaaggttctgttaccaacctgttcacttccattgtaggtaatgtatttggattcaaagccctacgggctctacgtctggaagatctgcgaattcctcctgcttattccaaaactttccaaggtccaccccatggtattcaagttgaaagagataaattaaacaaatatggccgtcccttgttggggtgtactatcaaaccaaaattgggtctatctgccaaaaattatggtagagcagtttacgaatgtctccgcggtggacttgattttaccaaggatgacgagaacgtgaattcccaaccattcatgcgctggagagatcgttttgtcttttgtgcagaagcaatttataaagctcaggcggagacgggtgaaattaaggggcattacttgaatgctactgcaggtacatgtgaagaaatgatgaaaagagtaagtttcgcctatgaattgggagttcccatcgtcatgcatgactatctgacgggaggttttacggcaaatacttcgttggctcattattgccgagatcatggcctacttcttcatattcaccgcgcaatgcatgcagttattgacagacaaaaaattcatggtatgcactttcgtgtgctggctaaagcattgcgtatgtctggcggagatcatgttcacgctggtactgtagtaggtaaacttgaaggtgaacgagaagttactttaggttttgttgatttactacgtgatgattttattgaaaaagaccgaagtcgtggtatttattttactcaagattgggtatctatgccaggtgttttgcctgtagcttcagggggtattcacgtttggcatatgcctgctctgaccgagatctttggggatgattctgtattacagtttggtgggggaactttgggacacccttggggaaatgcacctggtgcagtagctaatcgggtcgctttagaagcttgtgtacaagctcgtaatgaaggacgtgatcttgctcgtgaaggtaatgaaattatccgtgaagcagctaaatggagtcc---------------------------------------------------------t----- Gymno_Sciadopitys tcaaagattaagccatgcatgtctaagtatgaactatttcaaactgtgaaactgcggatggctcattaaatcagttatagtttctttgatggtaccttactactcggataaccgtagtaattctagagctaatacgtgcaccaactcccgac-ttctggaagggacgcatttattagatacaaggccggcgcgggc-t-tgcccgctactccggtgattcatgataactcgacggatcgcacggccctggtgccggcgacgcttcattcaaatttctgccctatcaactttcgatggtaggatagaggcctaccatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaatactgggctc-atcgagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggatcttgggccgttacggtcggtccgcctactcggtgtgcactggccatgacgtcccttctgccggcggcgtgctcctggccttagttgactgggtcgcggttccggcgccgttactttgaaaaaattagagtgctcaaagcaagcctacgctctgaatacattagcatggaataacgcgataggagtctggtcctgttccgttggccttcgggaccggagtaatgattaatagggactgtcgggggcattcgtatttcattgtcagaggtgaaattcttggatttatggaagacgaaccactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggatcggcggatgttgctctaaggactccgccagcaccttctgagaaatcagagtgtttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagattgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctacgcggaggt-tctccttcgcggccagcttcttagagggactatggccgtttaggccatggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgcaaccaacgagttctttgccctggttcgaaaggcccgggaaatcttgccaaattgcatcgtgatggggatagaccattgcaattattgatcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatgatccggtgaagtgttcagatcgcgccgacggcggcggttctctgccggcgacgtcgcgagaagttcattgaaccttatcatttagaggaagg-----------------------------------------------------------------------------------------------------------------------------------------------gttagagctgtagctatgagtgctacagatggtttgaaaagaggaatgagagtgattgatacaggagctcctcttagtgttcctgtcggtgaagctactctcggaagaatttttaatgttcttggagaacctgtcgatgatttaggtcctgtagatactcctaaaacatctcccattcatagatctgctcctgcttttacacagttggataccagattttcaattttcgaaacaggcattaaagtagtggatcttttagctccttaccgtcgtgggggaaaaattgggctatttgggggagcaggagtgggtaaaacagtactgattatggagttaatcaacaacattgctaaggctcatgggggtgtatctgtatttggtggagtaggagagcgtacccgtgaaggaaatgatctttacaaggaaatgaaagaatcgggagtaattaatgaacaaaatatctcagaatccaaagtagctctggtctatggtcagatgaatgaaccaccaggagctcgtatgagagtcgggttaactgccctaactatggctgaatatttcagagatgttaataagcaaaacgtactattatttattgataatatcttccgctttgtccaagcagggtcagaggtatctgccttattaggcagaatgccttccgccgttggttatcaaccaactcttagtacggaaatgggttctttgcaagagagaatcacctctaccaaagatggatctataacttccatccaagcagtttatgtacctgcagacgacttgaccgaccctgctcctgctacaacatttgcacatttagatgctactaccgtactatcgagaggattagctgccaagggcatctatccagcggtcgaccctttagattcaacgtcgactatgctccaaccttcgatcgtgggcaaagaacattatgaaactgcacaaggggttaagcaaacttt--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------g----gttggattcaaagctggtgttaaagattacagattaacttattatacccctgaatatcagaccaaagacactgatatcttggcagcattccgagtcactcctcaacccggagtgcctcctgaggaggcaggannngcagtagctgccgaatcttccactggtacatggaccactgtttggaccgatggactcaccagtcttgatcgttacaaggggcgatgctatgacatcgagcccgttcctggagaggaaagtcaatttattgcctatgtagcttatcccttagatctttttgaggaaggttctgttactaacatgttcacttctattgtaggtaatgtatttggattcaaagccctacgggctctacgtctggaagatctgcgaattcctcctgcttattcaaaaactttccaaggtccgccccatggtatccaagttgaaagagataaattaaacaaatatggtcgccctttattggggtgtactatcaaaccaaaatgtggcctatctgccaaaaattatggcagagcagtttacgaatgtcttcgcggtggacttgattttaccaaagatgatgagaacgtgaattcccaaccattcatgcgctggagagaccgtttttgcttttgtgctgaagcaatttataaagctcaagctgaaacgggtgaaattaagggacattatttgaatgctactgcaggtacatgtgaagaaatgatcaaaagagcagtattcgccagagaattgggagtccccatagtcatgcatgactatctgacgggaggttttacggcaaatacttcgttgtctcattattgccgagacaacggcctacttcttcacattcaccgcgcgatgcatgcagttattgacagacaaagaaatcatggtatgcatttccgtgtgctggctaaagcactacgtatgtctggtggagatcatattcacgctggtactgtagtaggtaaacttgaaggagaacgagaagtcactttgggttttgttgatttactgcgtgatgattttattgaaaaagatcgaagccgtggtatttatttcactcaagattgggtatctatgccaggtgttttgcctgtggcttcagggggtattcacgtttggcatatgcctgctctgactgagatctttggggacgattctgtattacagttcggtgggggaactttggggcatccgtggggaaatgcgcctggtgcagtagctaatcgagtcgctttagaagcttgtgtacaagctcgtaatgaaggacgcgatcttgctcgtgaaggtaatgaagtgatccgtgaagcttgtaaatggagtcc---------------------------------------------------------t----- Gymno_Juniperus tcaaagattaagccatgcatgtctaagtatgaactatttcagactgtgaaactgcggatggctcattaaatcagttatagtttctttgatggtactttgctactcggataaccgtagtaattctagagctaatacgtgcaccaaatcccgac-tcttggaagggatgcatttattagataaaaggccggcgcgggc-t-cgcccgctattccggtgattcatgataactcgacggatcgcacggcctttgtgctggcgacgcttcattcaaatttctgccctatcaactttcgatggtaggatagaggcctaccatggtggtgacgggtgacggagaattagggttcgattccggagagggagcctgagaaacggctaccacatccaaggaaggcagcaggcgcgcaaattacccaatcctgacacggggaggtagtgacaataaataacaatactgggctc-atcgagtctggtaattggaatgagtacaatctaaatcccttaacgaggatccattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatatttaagttgttgcagttaaaaagctcgtagttggaccttgggtcgtcacggtcggtccgcctttttggtgtgcactggccctcacgtcccttctgccggcggcgtgttcctggccttaattggctgggtcgcggttccggcgccgttactttgaaaaaattagagtgctcaaagcaagcctacgctctgaatacattagcatggaataacgcgataggagtctggtcctgttccgttggccttcgggaccggagtaatgattaatagggactgtcgggggcattcgtatttcattgtcagaggtgaaattcttggatttatggaagacgaaccactgcgaaagcatttgccaaggatgttttcattaatcaagaacgaaagttgggggctcgaagacgatcagataccgtcctagtctcaaccataaacgatgccgaccagggatcggcggatgttgctctaaggactccgccagcaccttctgagaaatcagagtgtttgggttccggggggagtatggtcgcaaggctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacggggaaacttaccaggtccagacatagtaaggattgacagattgagagctctttcttgattctatgggtggtggtgcatggccgttcttagttggtggagcgatttgtctggttaattccgttaacgaacgagacctcagcctgctaactagctacgcggaggt-tccccttcgcggccagcttcttagagggactatggccttctaggccatggaagtttgaggcaataacaggtctgtgatgcccttagatgttctgggccgcacgcgcgctacactgatgcaaccaacgagtttctctccctggcccgaaaggtttgggaaatcttgccaaattgcatcgtgatggggatagatcattgcaattattgatcttcaacgaggaattcctagtaagcgcgagtcatcagctcgcgttgactacgtccctgccctttgtacacaccgcccgtcgctcctaccgattgaatgatccggtgaagtgttcggatcgcggcgacgacggcggtt-cctgtcggcgacgtcgcgagaagttcattgaaccttatcatttagaggaagg-----------------------------------------------------------------------------------------------------------------------------------------------gttagagctgtcgctatgagtgctacagacggtttgatgagaggaatgaaagtaattgatacaggagggccacttagtgttccagttggtgaaactactctcgggagaatttttaatgttcttggggaacccgttgataacttaggtcctgtagatgctctcacaagatctcctattcatagatctgctcccgcctttacacagttggataccaaattttcaatctttgaaacaggcattaaagtagtggatcttttagctccttatcgccgtggggggaaaattggattattcgggggagctggagtgggtaaaacagtgttgattatggaattaatcaacaacattgctaaggctcatggaggggtttctgtgtttggtggagtaggagaacgtacccgtgaaggaaatgatctttacaaggaaatgaaagaatcgggagtcattaatgaacaaaatatatccgaatccaaagtagctctggtctatggtcagatgaatgaaccaccaggagctcgtatgagagtaggtttaactgctttaactatggctgaatatttccgagatgtcaataaacaagatgtacttctatttattgacaatatcttccgctttgtccaagcaggatcagaggtatccgcattattaggcagaatgccttccgcagtgggttatcagcccactcttagcacggaaatgggttctttacaagagagaataacttctacgaaagaaggatctataacctccattcaagcagtttatgtacctgcagatgacttgactgatcccgctcctgctacaacatttgcacatttagatgctactactgtactatcgagaggattagctgccaaaggcatctatccagcggtagatccgctggattccacgtcaactatgctccaaccttcgatcgtaggcgaacaacattatgaaactgcgcaaggagttaaacaaactttgcaacgttataaggaacttcaagatattatagctattcttggattagatgaattatcagaagatgatcgtttacttgtagcaagagcaagaaaaattgaacggtttttgtcacaacccttttttgtagcagaggtattcaccggttcccccggtaaatatgttagtctaatagagacaattagaggttttcaaatgatcctttccggagaattagatgctctacccgagcagtctttttatttggtgggtaacattgatga----------------------------------ag-------------aaagcgggtgttaaagattacagattaacttattatactccggaatatcagaccaaagatactgatatcttggcagcattccgagtcactcctcaacctggagtgccccccgaagaagcgggagcagcggtagctgccgaatcttccactggtacgtggaccactgtttggaccgatggacttaccagtcttgatcgctacaaggggcgatgctatgatattgaacccgttcctggagaagaaactcaatttattgcctatgtagcttaccctttagacctttttgaagaaggctctgtgactaacctgtttacttctattgtaggtaatgtatttggattcaaagctttacgggctvtacgtctggaagatttacgaattcctcctgcttattcaaaaacttttcaaggcccdccacatggtattcaagtagaaagagataaattaaacaaatatggtcgtcctttattgggatgtactatcaaaccaaaattgggtctatctgmcaagaattatggtagagcggtttatgaatgtctccgtggtggacttgattttaccaaggatgatgaaaacgtgaattcccagccatttatgcgctggagagatcgtttctgcttttgtgcagaagcactttataaagctcaggctgagacgggtgagattaagggacattacctgaatgcnactgcaggtacatgtgaagaaatgattaaaagagcagtattcgccagagaattgggagttcctatagtcatgcatgactatctgactggaggttttacggcaaatacttcgttggctcattattgccgagataacggcctacttcttcacattcaccgcgcaatgcacgcagttattgacagacaaagaaatcatggtatgcacttccgtgtactggctaaagcactacgtatgtctggtggagatcatattcatgctggtactgtagtaggtaaacttgaaggagaacgagaagtcactttgggttttgttgatctattgcgtgatgattttattgaaaaagaccgaagtcgtggtatttatttcactcaagattgggtctctatgccgggtgtcctgcctgtagcttcaggaggtattcacgtttggcatatgcctgctctgaccgagatctttggagatgattccgtattacagtttggtggagggactttggggcacccttggggaaatgcacctggtgcagtggctaatcgggtcgctttagaagcttgtgtacaagctcgtaatgaaggacgtgatcttgcgcgtgaagg--------------------------------------------------------------------------------------------------- phyml-3.3.20220408/examples/proteic000077500000000000000000000626061422404706600166400ustar00rootroot0000000000000037 547 tax1 ALSDPSKLES DKDLRIDITP DKENKTLTIR DTGIGMTKAD LVNNLGTIAR SGTKQFMEAL tax2 SLSDPSKLDT GKDLRIDIIP DKENKTLTIQ DTGIGMTKAD LVNNLGTIAR SGTKQFMEAL tax3 SLTDASVLES KTELEIKIIP DKTAKTLTLI DSGIGMTKTD MVKNLGTIAR SGTKNFMEQL tax4 SLTDKSKLDA QPELFIRIIP DKATNTLTLI DSGIGMTKSD LVNNLATIGR SGTKDFMEAL tax5 SLTDKSKLDA QPELFIRLVP DKASKTLSII DSGVGMTKSD LVNNLGTIAR SGTKEFMEAL tax6 SLTDKSKLDG QPELFIHIIP DKANNTLTII DSGIGMTKAD LVNNLGTIAR SGTKEFMEAL tax7 SLSDKSILDT EAAMEIRIIA DKEAKTLTLQ DTGVGMTKGD LVNNLGMIAN SGTKSYMEAL tax8 SLTDSSVLDN EPKLEIRILT DKNNKSLTLI DTGIGMTKDD LIQNLGTIAK SGTKSFMEAL tax9 SLTDKSKLDA QPELFIHIVP DKASNTLSII DSGIGMTKSD LVNNLGTIAR SGTKEFMEAL tax10 SLTDKSKLDG QPELFIHIIP DKTNNTLTII DSGIGMTKAD LVNNLGTIAR SGTKEFMEAL tax11 SLTDKSKLDG QPELFIHIIP DKTNNTLTII DSGIGMTKAD LVNNLGTIAR SGTKEFMEAL tax12 SLTDKSKLDG QPELFIHIIP DKTNNTLTII DSGIGMTKAD LVNNLGTIAR SGTKEFMEAL tax13 SLTDKSKLDA QPELFIRLVP DKTNKTLSII DSGVGMAKAD LVNNLGTIAR SGTKEFMEAL tax14 SLTDPSVLGE SPRLCIRVVP DKENKTLTVE DNGIGMTKAD LVNNLGTIAR SGTKAFMEAL tax15 SLTDPSVLGD ATRLCVRVVP DKENKTLTVE DNGIGMTKAD LVNNLGTIAR SGTKAFMEAL tax16 SLTNQSVLGD EPHLRIRVIP DRVNKTLTVE DSGIGMTKAD LVNNLGTIAR SGTKSFMEAL tax17 SLTNQAVLGD ESHLRIRVVP DKANKTLTVE DTGIGMTKAE LVNNLGTIAR SGTKAFMEAL tax18 ALTEPSELDT GKELFIKITP NKEEKTLTIM DTGIGMTKAD LVNNLGTIAK SGTKAFMEAL tax19 ALTEPAELET GKELYIKITP NKADKTLTIM DTGIGMTKAD LVNNLGTIAK SGTKAFMEAL tax20 SLTDPTKLDS GKDLKIDIIP NVQERTLTLI DTGIGMTKAD LINNLGTIAK SGTKAFMEAL tax21 SLTDPSKLDS GKELHINIIP NKQDRTLTIV DTGIGMTKAD LINNLGTIAK SGTKAFMEAL tax22 SLTDPTKLDS GKELFIKIIP NKEAGTLTLI DTGIGMTKAD LVNNLGTIAK SGTKAFMEAL tax23 SLTDPSKLDS GKELYIKLIP NKTAGTLTII DTGIGMTKSD LVNNLGTIAK SGTKAFMEAL tax24 SLTDPTKLDN GKELKIDVIP NVEERTLTLI DTGIGMTKAD LINNLGTIAK SGTKAFMEAL tax25 SLTDPSKLDS GKELKIDIIP NPQEATLTLV DTGIGMTKAD LINNLGTIAK SGTKAFMEAL tax26 SLTDPSKLDS GKELHINLIP SKQDRTLTIV DTGIGMTKAD LINNLGTIAK SGTKAFMEAL tax27 SLTDPSKLDS GKELKIDIIP NPQERTLTLV DTGIGMTKAD LINNLGTIAK SGTKAFMEAL tax28 VCSDPSKPGL QDHSSIDIIP DKANKTLTIR DTGIGMTKAD LVNNLGTIAR SGTKQFMEAL tax29 ALSDPSQLES EPELFIRITP HKDQKVLEIR DSGIGMTKAD LVNNLGTIAK SGTKSFMEAL tax30 ALSDPSQLES EPELFIRIIP QKDQKVLEIR DSGIGMTKAD LVNNLGTIAK SGTKSFMEAL tax31 SLSDPHALDA EKDLFIRITP DKENKILSIR DTGIGMTKND LINNLGVIAK SGTKQFMEAA tax32 SLSDPKQLET EPDLFIRITP KPEQKVLEIR DSGIGMTKAE LINNLGTIAK SGTKAFMEAL tax33 AIKDPKQVED FPEYQISLSA DKTNKTLTIE DTGIGMTKTD LINNLGTIAK SGTKAFMEAI tax34 SITDTQKLSA EPEFFIRIIP DKTNNTLTIE DSGIGMTKND LINNLGTIAR SGTKAFMEAI tax35 SITDSEKAKL EPNFRIRIIP DKANNTLTLW DTGIGMTKKE LINNLGTIAK SGTKAFMEAL tax36 AITEPEKLKT KPELFIRLIP DKANNTLTIE NSGIGMTKAD LVNNLGTIAR SGTKAFMEAL tax37 AIKDPKQIED QPDYYIRLYA DKNNNTLTIE DSGIGMTKAD LVNNLGTIAK SGTRAFMEAL TSMIGQFGVG FYSAYLVADK VTVISKSNDD QYIWESNAGG TFKVTDGRAI GRMILHLDEQ TSMIGQFGVG FYSAYLVADR VTVVSKNNDD QYIWESSAGG TFNISDGPSI GRIILHLDEQ QSMIGQFGVG FYSAYLVADT VIVHSKNNDD QYVWESSAGG EFTIADTEPL GRIVLHMEDQ ASMIGQFGVG FYSAYLVAER VIVTSKHNDD QHVWESQAGG SFTVTDGEPL GRITLYLDDQ ASMIGQFGVG FYSAYLVADR VMVTTKHNDD QYVWESQAGG SFTVTDGEQL GRITLFLDDQ ASMIGQFGVG FYSAYLVAEK VVVTTKHNDD QYVWESQAGG SFTVTDGENL GRMVLYLEDQ SSMIGQFGVG FYSAYLAADN VVVHTKHNDD QYVWESSAGG SFTIRKGPAL TRVVLHLDDQ QSMIGQFGVG FYSAYLVADR VVVETKNNND QYIWESSAGG SFTINDITDL ARITLFLDDQ ASMIGQFGVG FYSAYLVAER VVVTTKHNDD QYVWESQAGG SFTVTDGEQL GRITLYLDDQ ASMIGQFGVG FYSAYLVADK VVVTTKHNDD QYVWESQAGG SFTVTDGEAL GRMVLYLEDQ ASMIGQFGVG FYSAYLVADK VVVTTKHNDD QYVWESQAGG SFTVTDGETL GRMVLYLEDQ ASMIGQFGVG FYSAYLVADK VVVTTKHNDD QYVWESQAGG SFTVTDGEAL GRMILYLEDQ QSMIGQFGVG FYSAYLVAEK VIVTTKHNDD QYIWESQAGG SFTVTDGEQL GRITLFLEDQ ESMIGQFGVG FYSAYLVADR VTVTSKNNSD SYVWESSACG TFTITTESDM KRITLHLEDQ ESMIGQFGVG FYSAYLVADR VTVTSKNNSD VYVWESSAGG TFTITAESDM KLITLHLEDQ ESMIGQFGVG FYSAYLVADR VTVVSKNNED AYTWESSAGG TFTVTTDCDL KRIVLHLEDQ ESMIGQFGVG FYSAYLVADR VTVVSKNNDD AYTWESSAGG TFTVTTDCDL KRIVLHLEDQ QSMIGQFGVG FYSAFLVADK VVVTSKNNDD SYQWESSAGG SFVVRFDPEV TRIVMHIEDQ QSMIGQFGVG FYSAFLVADK VVVASKHNDD CYQWESSAGG SFIIRVDPEL TRITLYIEDQ QSMIGQFGVG FYSAYLVAEK VTVITKHNDD QYAWESSAGG SFTVKDGEPI GRVILHLEDQ QSMIGQFGVG FYTAYLVAEK VTVITKHNDD QYAWESSAGG SFTVRDGEPM GRVILHLEDQ QSMIGQFGVG FYSAYLVADK VVVTSKNNDD QYVWESSAGG SFTVRDGEPL GRIVLHIEDQ QSMIGQFGVG FYSAYLVADK VTVTSKNNDD QYVWESSAGG SFTVRDSEPL GRIVLYIEDQ QSMIGQFGVG FYSAYLVAER VTVITKHNDD QYIWESSAGG SFTVKDGEPM LRVILHMEDQ QSMIGQFGVG FYSAYLVAEK VVVITKHNDD QYAWESSAGG SFTVRDGEPI GRVILHLEDQ QSMIGQFGVG FYSAYLVAEK VTVITKHNDD QYAWESSAGG SFTVRDGEPM GRVILHLEDQ QSMIGQFGVG FYSAYLVAEK VVVITKHNDD QYAWESSAGG SFTVRDGEPI GRVILHLEDQ TSMIGQFGVG FYSAYLVADR VTVVSKHNDD QYIWESSAGG TFTIRDGEPL AKIILHLEEQ SSMIGQFGVG FYSLFLVADH VQVVSKHNDD QYIWESNAGG KFTVTDNERL GRLRLFLEDQ SSMIGQFGVG FYSLFLVADH VQVISKHNDD QYVWESNAGG KFTVTDNERL GRLRLFLEDQ ASMIGQFGVG FYSAYLVADK VQVVSKHNDD QYIWESSAGG SFTVTDGPRL LRIRLFMEDQ SSMIGQFGVG FYSLFLVADR VQVISKSNDD QYIWESNAGG SFTVTDNERI GRLRLFLDDQ QSMIGQFGVG FYSAYLVADK VTVVSKNNND QYVWESNASG HFTVTDEDQL KRLILHLDDQ QSMIGQFGVG FYSAYLVADH VVVISKNNDD QYVWESAAGG SFTVTDNEKL GRIILHLEDQ SSMIGQFGVG FYSAYLVAEK VEVISKSNDD QWRWESSAGG TFTVVDNPEK LRIILHMNDN QSMIGQFGVG FYSAYLVADS VTVVSKHNDD QYVWESAAGG SFTVQDYEPL GRIILHLEDQ QSMIGQFGVG FYSAYLVADK VTVVSKNNAD QYVWESTASG HFTVKDHEPL KRLILHLEDQ TEYLNESKIK EVVKKQSEFI FYPIYLHVLK EEKEVPDAKK TKKIKENKIE EEELNKTKPI TDYLNESKIK EVIKKHSEFI SYPIYLHVQK EEVEVPDKPK TKKVKEVKTE EEELNKQKPI LDYLDETKIK NLVKKHSEFI QYPISLLTIK EDEETTAEKE KKKVKVQEKE WDVLNKTKPL LEYLEERRLK DLVKKHSEFI SYPISLWTEK TEKEISDEKK KKKIKEVSHE WNLINKQKPI LEYLEERRLK DLVKKHSEFI SYPIYLWTEK TEKEISDSKK KKKVKEVSHE WVQINKQKPI LEYLEERRLK DLIKKHSEFI SYPISLWVEK TEKEISDEKK KKKVKEVSNE WSLVNKQKPI QEWLEERRIK DLVKKHSEFI QYPIKLWVEK EEKEVEVKKK TKKIKEKEHE WQLLNKNKPI LEYLEERRLK DLVKKHSEFI QYPINLWVEK EEKEVDAEKK KKKIKEISHE WQFLNKNKPI LEYLEERRLK DLIKKHSEFI SYPISLWTEK TEKEISDEKK KKKIKEVSHE WSLVNKQKPI MEYIEERRLK DLVKKHSEFI SMGISLWIEK TEKEISDEKK KKKIKEVSHE WDLVNKQKPI LEYLEERRLK DLVKKHSEFI SYPISLWIEK TEKEISDEKK KKKIKEVSHE WDLVNKQKPI MEYIEERRLK DLVKKHSEFI SYPISLWIEK TEKEISDEKK KKKIKEVTHE WDLVNKQKPI LEYLEERRIK DLVKKHSEFI SYPIYLWTEK TEKEISDGKK KKKIKEVSHE WQLINKQKPI MEYLEPRRLK ELIKKHSEFI GYDIELMVEK TEKEVTDKKK TKKVKEVTKE YEVQNKHKPL LEYLEARRLK ELIKKHSEFI GYDIELMVEK TEKEVTDKKK TKKVKEVTKE YEVQNKHKPL QEYLEERRLK DLIKKHSEFI GYDIELMVEN TEKEVTDKKK TKKVKEVKQE FVVQNKHKPL QEYLEERRLK DLIKKHSEFI GYDIELMVEK AEKEVTDKKK TKKVKEVTQE FVVQNKHKPL IDFLEERKIK EIVKKHSQFI GYPIKLVVEK EEKEVEDKKK TKKIKEKYFE DEELNKTKPI TDYLEERRIK EIVKKHSQFI GYPIKLTVEK EDKEVSDDKK KKKIKEKYHE DEELNKTKPI TEYIEEKRVK EVVKKHSQFI GYPITLYVEK EDKEISDKKK KKKIKEKYID QEELNKTKPI TEYMEERRIK EIVKKHSQFI GYPITLFVEK EDKEVSDKKK KKKIKEKYID QEELNKTKPI LEYLEESKIK QIVNKHSQFI GYPIKLLVEK EEKEVSDDKK KKTVKVKYTE DEELNKTKPI TDYLEESKIK EIVNKHSQFI GYPIKLLVEK EEKEVSDAKK KKTIKEKYTE DEELNKTKPI TEYVEEKRVK EVVKKHSQFI GYPITLFVEK EEKEISDKKK TKKIKEKYID QEELNKTKPI TEYLEERRVK EVVKKHSQFI GYPITLYLEK EEKEISDKKK TKKIKEKYID QEELNKTKPI TEYLEERRIK EIVKKHSQFI GYPITLFVEK EDKEVSDKKK KKKIKEKYID QEELNKTKPI TEYLEERRVK EVVKKHSQFI GYPITLYLEK EEKEISDKKK TKKIKEKYID QEELNKTKPI LDYLNESRIK EVVKKHSEFI SYPIYLHVQI SEVTEEGRRR SRTIKETRSE EEELNKQKPI LEYLEEKRII EVVKKHSEFV AYPIQLVVTK EEKEVPEEKK TKKIKEEVTE TEELNKTKPL LEYLEEKRIK EVVKKHSEFV AYPIQLVVTK EEKEVPEEKK TKTVKEEVTE TEELNKTKPL LQYLEEKTIK DTVKKHSEFI SYPIQLVVTR EEKEVPEKKE KKKVKETTTE TEELNKTKPI LEYLEEKRIK EVIKRHSEFV AYPIQLVVTK EEKEVPIKPK TKKVKEEVQE IEELNKTKPL SEYLEERRLK ELVKKHSEFI SFPIRLSVEK TETEVTDEKK KRKVTNVTRE WEMLNKQKPI LEYLEEKRIK DLVKKHSEFI SFPIKLYCER QEKEITAEKR KKKIHTVEHE WEELNKQKPL LEFLEERRIK DLIKKHSEFI AFPIELQVEK TEKEETDDKK KKKVKVVHTE FEEQNKNKPL GEYLEERRLK DLVKKHSEFI SFPIELAVEK TEREVTETGK TKKVQEVTRE WEQLNKQKPL TEYLEERRLK ELVKKHSEFI SFPISLSVEK TETEVTDKKK KRKVTNVTRE WEMLNKQKPI WTRNPADITQ EEYASFYKTN DWEDHLAVKH FSVEGQLEFR AILFVPKRAP FDLFETKTKN WTRNPQDITQ EEYAAFYKSN DWEDHLAVKH FSVEGQLEFK AILFVPKRAP FDLFETKTKN WTRNPSDVTK EEYNSFYKSN DWEEPLAVKH FSVEGQLEFK AILFVPKKAP FDLFESKKAN WMRKPEEITK DEFAAFFKTN DWEEHLGVKH FSVEGQLEFK AVLFVPKRAP FDLFDTRKLN WLRKPEEITR DEYASFYKTN DWEDHLAVKH FSVEGQLEFK AILFVPRRAP FDLFDTRKLN WMRKPEEITK EEYAAFYKTN DWEEHLAVKH FSVEGQLEFK AVLFVPKRAP FDLFDTKKPN WTRKPEEVTK EEYASFYKTN DWEEHLTVKH FSLEGQLEFR AILFCPKRAP FDLFEPRKLN WTRKPEEISK EEYSSFYKSN DWEDHLAVKH FSVEGQLEFK ALLFVPKRAP FDLFEPRKNN WMRKPEEITK EEYAAFYKTN DWEEHLAVKH FSVEGQLEFK AVLFVPKRAP FDLFDTRKLN WMRKPEEINK EEYAAFYKSN DWDKDLAVKH FSVEGQLEFK AILFVPKRRP FDLLYTKKPN WMRKPEEINK EEYAAFYKSN DWEEHLAVKH FSVEGQLEFK AILFVPKRAP FDLFDTKKPN WMRKPEEINK EEYAAFYKSN DWEEHLAVKH FSVEGQLEFK AILFVPKRAP FDLFDTKKPN WLRKPEEITK EEYASFYKTN DWEDHLAVKH FSVEGQLEFK AILFVPKRAP FDLFDTRKMN WTRDPKDVTK EEYAAFYKSN DWEDPRATKH FSVEGQLEFR SIMFVPKRAP FDMFEPNKRN WTRDPKDVTK EEYAAFYKSN DWEDPPATKH FSVEGQLEFR AIMFVPKRAP FDMLEPNKRN WTRDPKDVTK EEYASFYKSN DWEEQLSTKH FSVEGQLEFR AILFLPKRAP FDMFEPNKRN WTRDPKDVTK EEYAAFYKSN DWEEPLSTKH FSVEGQLEFR AILFVPKRAP FDMFEPSKRN WTRNPDDISN EEYAEFYKSN DWEDHLAVKH FSVEGQLEFR ALLFVPQRAP FDLFENKSKN WTRNPDDISN EEYAEFYKSN DWEDHLAVKH FSVEGQLEFR ALLFVPQRAP FDLFENKTKN WTRNPDDISN EEYGEFYKTN DWEDHLAVKH FSVEGQLEFR ALLFIPRRAP FDLFENKKKN WTRNPDDITN EEYGEFYKTN DWEEHLAVKH FSVEGQLEFR ALLFVPRRAP FDLFENRKKN WTRNADDISQ EEYGEFYKTN DWEDHLAVKH FSVEGQLDFR ALLFVPRRMP FDLFENKKKN WTRNPDDISQ EEYGEFYKTN DWEDHLAVKH FSVEGQLEFR ALLFIPRRTP FDLFENQKRN WTRNPDDITM EEYGEFYKTN DWEEHLAVKH FSVEGQLEFR ALLFIPRRAP FDLFENKKKN WTRNPDDITQ EEYGEFYKTN DWEDHLAVKH FSVEGQLEFR ALLFIPRRAP FDLFENKKKN WTRNPDDITN EEYGEFYKTN DWEEHLAVKH FSVEGQLEFR ALLFVPRRAP FDLFENRKKN WTRNPDDITQ EEYGEFYKTN DWEDHLAVKH FSVEGQLEFR ALLFIPRRAP FDLFENKKKN WTRNPQDITQ EEYAAFYKSN DWEDHLAVKH FSVEGQLEFR AILFVPKRAP FDLFETKTKS WTRNPSDITQ EEYNAFYKSN DWEDPLAVKH FSVEGQLEFR AILFVPKRAP FDAFESKKKS WTRNPSDITQ DEYNAFYKSN DWEDPLAVKH FSVEGQLEFR AILFVPKRAP FDAFESKKKN WTRNPSEVTK EEYASFYKTN DWEDHLAVKH FSVEGQLEFR AILFVPRRAP MDLFEAKKKN WTRNPSDITQ EEYNAFYKSN DWEDPLYVKH FSVEGQLEFR AILFIPKRAP FDLFESKKKN WMRLPTEVTN EEYASFYKSN DWEDHLAVKH FSVEGQLEFK AILFVPKRAP FDMFENRKKN WMRKPEEVTN EEYASFYKTN DWEDHLAVKH FSVEGQLEFK ALLFIPKRAP FDMFENRKRN WMRKPEEITK EEYVNFYKTN DWEEHQAVKQ FSVEGQLEFR AILFIPKRAP FDLFETKKKN WMRKPEEVTE EEYASFYKSN DWEEHLAVKH FSVEGQLEFK ALLFVPKRAP FDLFETRKRN WMRLPSEVTN EEYAAFYKTN DWEDHLAVKH FSVEGQLEFK ALLFVPRRAP FDMFESRKKN NIKLYVRRVF ITDDATDLIP EWLSFIKGVV DSEDLPLGLS RETLQQNKIM KVIKNIVKKT NIKLYVRRVF ITDDATDLIP EWLSFVKGVV DSEDLPLNLS RETLQQNKIM KVIKNIVKKA NIKLYVKRVF IMDNCADIIP EYLNFVRGIV DSEDLPLNIS RETLQQNKIL TVIKNLVKKC NIKLYVRRVF IMDNCEELIP EWLSFVKGIV DSEDLPLNIS RETLQQNKIL KVIKNLVKKC NIKLYVRRVF IMDNCEELIP EWLGFVKGVV DSDDLPLNIS RETLQQNKIL KVIKNLVKKC NIKLYVRRVF IMDNCDELIP EYLSFVKGIV DSEDLPLNIS RETLQQNKIL KVIKNLVKKC NIKLYVKRVF IMDNCEDIIP EYLNFVKGVV DSEDLPLNLS REMLQQNKIL KVIKNLVKKC NIKLYVKRVF IMDNCDEIIP EYLNFIKGVV DSEDLPLNIS RETLQQNKVV KVIKNIVKKC NIKLYVRRVF IMDNCEELIP EWLSFVKGIV DSEDLPLNIS REMLQQNKIL KVIKNLVKKC NISLYVRRVF IMTYCEDIIP EYLGFVKGIV DSEDLPLNIS RETLQQNKIS MVIKNLVKKC NIKLYVRRVF IMDNCEDIIP EYLGFVKGIV DSEDLPLNIS RETLQQNKIL KVIKNLVKKC NIKLYVRRVF IMDNCEDIIP DYLGFVKGIV DSEDLPLNIS RETLQQNKIL KVIKNLVKKC NIKLYVRRVF IMDNCEELIP EYLGFVKGVV DSDDLPLNIS REMLQQNKIL KVIKNLVKKC NIKLYVRRVF IMDNCEDLCP DWLGFVKGVV DSEDLPLNIS RENLQQNKIL KVIKNIVKKC NIKLYVRRVF IMDNCEDLCP DWLGFVKGVV DSEDLPLNIS RENLQQNKIL KVIKNIVKKC NIKLYVRRVF IMDNCEDLCP EWLGFLRGVV DSEDLPLNIS RENLQQNKIL KVIKNIVKKA NIKLYVRRVF IMDNCEDLCP EWLAFVRGVV DSEDLPLNIS RENLQQNKIL KVIKNIVKKA SIKLYVRRVF IMENCEELMP EYLNFIKGVV DSEDLPLNIS REMLQQSKIL KVIKNLVKKC AIKLYVRRVF IMENCDELMP EYLNFIKGVV DSEDLPLNIS REMLQQSKIL KVIKNLVKKC NIKLYVRRVF IMDNCEELIP EYLNFIRGVV DSEDLPLNIS REMLQQSKIL KVIKNIVKKC NIKLYVRRVF IMDNCEELFP EYLNFIRGVV DSEDLPLNIS REILQQSKIL KVIKNLVRKC NIKLYVRRVF IMDNCEELIP DYLNFMKGVV DSEDLPLNIS REMLQQNKIL KVIKNLVKKC NIKLYVRRVF IMDNCEDLIP EYLNFMKGVV DSEDLPLNIS REMLQQNKVL KVIKNLVKKT NIKLYVRRVF IMDSCEELIP EYLNFVRGVV DSEDLPLNIS REMLQQSKIL KVIKNIVKKC NIKLYVRRVF IMDSCDDLIP EYLNFIRGVV DSEDLPLNIS REMLQQSKIL KVIKNIVKKC NIKLYVRRVF IMDNCEELIP EYLNFIRGVV DSEDLPLNIS REMLQQSKIL KVIKNLVKKC NIKLYVRRVF IMDSCDELIP EYLNFIRGVV DSEDLPLNIS REMLQQSKIL KVIKNIVKKC NIKLYVRRVF ITEHATDLVP EWLSFIKGVV DSEDLPLNLS RETLQQNKIM KVIKNIVKKS NSKLYVRRVF ITDDAEELIP EWLSFVKGVV DSEDLPLNLS REMLQQNKIL KVIKNISKKM NIKLYVRRVF ITDDAEELIP EWLSFIKGVV DSEDLPLNLS REMLQQNKIL KVIKNIVKKM NIKLYVRRVF ITDDCEELIP EWLGFIKGVV DSEDLPLNLS REMLQQNKIM KVIKNLVRRC NIKLYVRRVF ITDEAEDLIP EWLSFVKGVV DSEDLPLNLS REMLQQNKIM KVIKNIVKKL NIKLYVRRVF IMDDCDELIP EWLGFVKGVV DSEDLPLNIS REVLQQNKIL KVIKNLVKKC NIKLYVRRVF IMDDCEEIIP EWLNFVKGVV DSEDLPLNIS RESLQQNKIL KVIKNLIKKC NIKLYVRRVF IMDDCEELIP EYLNFIKGVV DSEDLPLNIS REFLQHNKIL KVIKNIVKKC NIKLYVRRVF IMDDCEDIIP EWLNFVKGVV DSEDLPLNIS RESLQQNKIL KVIKNLVKKC NIKLYVRRVF IMDDCEELIP EWLSFVKGVV DSEDLPLNIS RETLQQNKIL KVIKNLVKKC LELFNEIAED REQFKFYSAF SKNIKLGVHE DAQNRPALAK LLRYNSTTTS LTDYVTRMPE LELFTEIAED KEQFKFYTAF SKNIKLGIHE DSQNRNTLAK LLRFNSTQTS LSDYVTRMPE IELFNEIAEN SEDYKFYEAF SKNLKLGVHE DSQNREKFAD LLRYQTSLVT LKEYVGRMKE IELFFEIAEN KEDYKFYEAF SKNLKLGVHE DSTNRTKLAE LLRYHSTLTS LKDYVTRMKE IEMFFEIAEN KDDYKFYDAF SKNIKLGIHE DSQNRAKLAD LLRYHSTTTS LKDYVTRMKE VELFFEIAEN KEDYKFYEAF SKNLKLGIHE DSQNRAKFAE LLRYHSTMTS LKDYVTRMKE MDMFAEAAEN KEDYVFYEQF SKNLKLGIHE DAQNREKLAE LLRYNTTLTS LKEYVSRMKE LELFLEISEN KDDVIFYDQY SKNIKLGIHE DSQNRSKLAD LLRYKSTTTT LKEYVSRMKE VELFFEIAEN KEDYKFYEAF SKNLKLGIHE DSTNRNKIAE LLRYHSTLTS LKDYVTRMKE LELFFEIAEN KEDYKFYEAF SKNLKPGIHE DSKIRTKIAE LLRYHSTLTS LKDYVTRMKE LELFFEIAEN KEDYKFYEAF SKNLKLGIHE DSQNRTKIAE LLRYHSTLTS LKDYVTRMKE LELFFEIAEN KEDYKFYEAF SKNLKLGIHE DSQNRTKIAE LLRYHSTLTS LKDYVTRMKE IEMFNEIAEN KDDYKFYEAF SKNLKLGIHE DSQNRAKLAD LLRYYSTLTS LKDYVTRMKE LEMFDEVAEN KEDYQFYEQF GKNIKLGIHQ DTANRKKLME FVRFYSSMTT LKDYVTRMKA LEMFEEVAEN KEDYQFYEQF GKNIKLGIHE DTANRKKLME LLRFYSTMTT LKDYVTRMKA LELFEELAGN KEDYKFYEQF SKNVKLGIHE DSTNRKKLME LLRFHSSMTT LKDYVTRMKE LELFEEIAEN KEDYKFYEQF GKNVKLGIHE DSANRKKLME LLRFHSSMTT LKDYVTRMKE MELIDEVAED KDNFKFYEQF GKNLKLGIHE DSTNRKKLSD FLRYSTSPTS LKEYVSRMKE LELFDEIAED KDNFKFYEQF SKNIKLGIHE DSTNRKKLSE FLRFYTSMTS LKDYVSRMKE LELFAELAED KDNYKFYDAF SKNLKLGIHE DCQNRKKLSE LLRYQSSMTS LTEYVSRMKE LELFHELAED KENYKFYEQF SKNIKLGIHE DSQNRKKLSE LLRYYTSMVS LKDYCTRMKE MELFEELAED KETYKFYDQF SKNLKLGVHE DSQNRQKLAD LLRFNTSYCS LNDYVGRMKE MELIEELTED KENYKFYDQF SKNLKLGVHE DSNNRAKLAD FLRFHTSFCS LADYVSRMKD MELFGELAED RENYKFYDGF SKNLKLGIHE DSQNRKKLSE LLRYHSSLTS LTEYLTRMKD LELFSELAED KENYKFYEAF SKNLKLGIHE DSTNRRRLSE LLRYHTSMTS LSEYVSRMKE LELFTELAED KENYKFYEQF SKNIKLGIHE DSQNRKKLSE LLRYYTSMVS LKDYCTRMKE LELFSELAED KENYKFYEAF SKNLKLGIHE DSTNRRRLSE LLRYHTSMTS LSEYVSRMKE IELFNEIAED KEQFKFYSAF SKNIKLGIHE DAQNRSALAK LLRFHSTMTS LSDYVARMPE IETFNEISED QEQFQFYTAF SKNIKLGIHE DAQNRQALAK LLRFYSTMTS LSDYVTRMPE IETFNEISED QEQFQFYTAF SKNIKLGIHE DAQNRQSLAK LLRFYSTMTS LSDYVTRMPE LDMFNEIAED KENFTFYDAF SKNLKLGIHE DAANRPALAK LLRYNSLLIS LEDYITKMPE IEAFNEIAED SEQFKFYSAF SKNIKLGVHE DTQNRAALAK LLRYNSTLTS LTDYVTRMPE LELFSELTEK KEDFKFYEQF SKNLKLGIHE DNTNRNKISE LLRYETSAIS LKEYVDRMKP LDMFSELAEN KENYKFYEQF SKNLKLGIHE DNANRTKITE LLRFQTSMIG LKEYVDRMKE LDLIQEVADN EEDFKFYEQF GKNLKLGIHE DSANREKLSS FLRYHSSLTT LKDYVSRMKE LEMFAEIEEK KENYKFYEQF SKNLKLGIHE DSANRAKIAE LLRFHSSMVS FKEYVDRMKE LELFNELTEK KEDFKFYEQF SKNLKLGIHE DNANRSKIAE LLRFETTLVS LKEYVDRMKS HHKTIYYITG ESLKAVQKSP FLDTLKEKNF EVLFLVDPND EYAMTQLKEF DGKKLVDITK HQKNMYYITG ESIKAVSKSP FLDSLKEKGF EVLFLVDPID EYAMTQLKEF EGKKLVDITK GQNEIYYITG ESKKAVENSP FIEGLKKKNL EVIYMCDPID EYAVQQLKEY DGKKLVSITK GQNDIYYITG ESKKAVENSP FLEKLKKKGY EVLYMVDAID EYSIGQLKEF EGKKLVSATK GQKDIYYITG ESRKAVENSP FLERLKKKGY EVLFMVDAID EYAVGQLKEY DGKKLVSATK GQNDIYYITG ESKKAVENSP FLEKLKKKGY EVLYMVDAID EYSIGQLKEF EGKKLVSATK GQKNIYYITG ASKKAVENAP FLEKLKKKGF EVIYMTEPID EYCVQQLKEY DGKKLVCTTK NQNQIYYITG ESQKSVENSP FLEKLKQKGF EVLFMIEPID EYCVQQLKEY EGKKLVCATK GQNDIYYITG ESKKAVENSP FLEKLKKKGY EVLYMVDAID EYAVGQLKEF EGKKLVSATK GQNDIFYITG ESKKAVENSP FLEKLKKKGI EVLYMVDAID EYAIGQLKEF EGKKLVSATK GQNDIFYITG ESKKAVENSP FLEKLKKKGI EVLYMVDAID EYAIGQLKEF EGKKLVSATK GQNEIFYITG ESKKAVENSP FLEKLKKKGY EVLYMVDAID EYAIGQLKEF EGKKLVSATK GQKDIYYITG ESKKAVENSP FLERLKKKGY EVLFMVDAID EYAVGQLKEY DGKKLVSATK GQKSIYYITG DSKKKLESSP FIEQAKRRGL EVLFMTEPID EYVMQQVKDF EDKKFACLTK EQNSIYYITG DSKKKLESSP FIEQAKRRGF EVLFMTEPYD EYVMQQVKDF EDKKFACLTK GQKCIYYVTG DSKKKLETSP FIEQARRRGM EVLFMTDPID EYVMQQVKDF EDKKFACLTK GQKCIYYVTG DSKKKLETSP FIEQARRRGF EVLFMTEPID EYVMQQVKDF EDKKFACLTK NQTQIYYITG ESKDVVAASA FVERVKSRGF EVLYMCDPID EYCVQQLKEY DGKKLVSVTK NQKQIYFITG ESREAVASSA FVERVKRRGF EVIYMTDPID EYCVQQLKEY DGKKLVSVTK NQKSIYYITG ESKDQVAHSA FVERVCKRGF EVLYMTEPID EYCVQQLKDF DGKSLVSVTK NQKHIYFITG ETKDQVANSA FVERLRKHGL EVIYMIEPID EYCVQQLKEF EGKTLVSVTK NQTQIYFITG ESIEQVKNSA FVERVKKRGF EVIYMTEAID EYVIQQLKEY KGKQLVCVTK NQKHVYFITG ESKDQVSNSA FVERVKARGF EVVYMTEPID EYVIQHLKEY KGKQLVSVTK NQKSIYYITG ESKDQVANSA FVERVRKRGF EVLYMTEPID EYCVQQLKEF DGKTLVSVTK TQKSIYYITG ESKEQVANSA FVERVRKRGF EVVYMTEPID EYCVQQLKEF DGKSLVSVTK NQKHIYFITG ETKDQVANSA FVERLRKHGL EVIYMIEPID EYCVQQLKEF EGKTLVSVTK TQKSIYYITG ESKEQVANSA FVERVRKRGF EVVYMTEPID EYCVQQLKEF DGKSLVSVTK HQKNIYYITG ESIKAVSKSP FLDALKEKGF EVLFLVDPID EYAMTQLKEF EGKKLVDITK HQKNIYYITG ESIKAVEKSP FLDALKAKNF EVLFMVDPID EYAMTQLKEF EDKKLVDITK HQKNIYYITG ESIKAVEKSP FLDALKAKNF EVLFMVDPID EYAMTQLKEF EDKKLVDITK HQKNIYFITG ESKQAVENSP FLEIFRAKKF DVLFMVDPID EYAVTQLKEF EGKKLVNITK HQKNIYYITG ESLKAVEKSP FLDALKAKNF EVLFLTDPID EYAFTQLKEF EGKTLVDITK EQKYIYYITG ESKQSVANSP FLECLRSRGI EVIYMTDPID EYAVQQIKEF EGKKLKCCTK NQKDIYYITG ESINAVSNSP FLEALTKKGF EVIYMVDPID EYAVQQLKDF DGKKLKCCTK GQKDIFFITG ESRAAVAASP FVESLRKRGY EVLYMVDPID EYVIQQLKEY DGKKLKNCSK GQKDIYYITG ESRQTVANSP FLEKLTKKGY EVLYMTDPID EYAVQQLKEF DNHKLRCCTK DQKYVYYITG ESKQSVASSP FLETLRSRDY EVLYMTDPID EYAVQQIKEF EGKKLKCCTK DFELEETEEE KKDREAEEKE YEGLAKSLEN ILGDKVEKVV VSHKLIGSPC AIRTGQFGWS DFELEETEEE KKQREAEEKE YDGLAKALKN VLGDKVEKVV VSHKLVGAPC AIRTGQFGWS ELKLDETEDE KKKAEQDKAA NEELLKQVKD VLGDKVEKVV LSTRLANSPC VLVTSEYGWS ELKLDDSEEE KKRKEELKEK FEGLCKVIKE VLGDRVEKVI VSDRVVDSPC CLVTGEYGWT ELKLDDEDDE EKKREERKKR FEELCKVIKD ILGDRVEKVV VSDRIVDSPC CLVTGEYGWT ELKLDESEDE KKKQEELKEK FEGLCKVMKD VLGDKVEKVI VSDRVVDSPC CLVTGEYGWT EMQLEESEEE KAAREAEAKA CETLCEVIKE NLGEKVEKVV VSDRLADSPC ILVTGEYGWS ELDLGDSEND KKVKENEKEQ FDELCKVIKE TLNDKVEKVV ISDRLSDSPC ILVTGEYGWS ELKLDESEDE KKRKEELKEK FEGLCKVIKE VLGDKVEKVV VSDRVVDSPC CLVTGEYGWT ELKLDETEDE KKKKEELNEK FEGLCKVIKD VLGDKVEKVI VSDRVLDSPC CLVTGEYGWT ELKLDETEDE KKKKEELKEK FEGLCKVIKD VLGDKVEKVI VSDRVVDSPC CLVTGEYGWT ELKLEETDDE KKKKEELKEK FEGLCKVIKD VLGDKVEKVI VSDRVVDSPC CLVTGEYGWT ELKLEDDDEE EKKKREEKKS FENLCKIIKD ILGDKVEKVV VSDRIVDSPC CLVTGEYGWT EVHFEESEEE KQQREEEKAA CEKLCKTMKE VLGDKVEKVI VSECLSTSPC ILVTSEFGWS EVHFEESEEE KRQREEEKAT CEKLCKTMKE VLGDKVEKVT VSERLSTSPC ILVTSEFGWS EVHFEETEEE KKQREEEKAS YERLCKAMKE VLGDKVEKVV VSDRLATSPC ILVTSEFGWS EVHFEETEEE KKQREEEKTA YERLCKAMKD VLGDKVEKVV VSERLATSPC ILVTSEFGWS ELELPETEEE KKKFEEDKVA YENLCKVIKD ILEKKVEKVG VSNRLVSSPC CIVTSEYGWS ELELPESEEE KKKFEEDKVK FENLCKVMKD ILEKKVEKVA VSNRLVSSPC CIVTSEYGWS ELELPEDEDE KKKMEEDKAK FENLCKLMKE ILDKKVEKVT VSNRLVSSPC CIVTSTYGWT ELELPEDEEE KKKQEEKKTK FENLCKIMKD ILEKKVEKVV VSNRLVTSPC CIVTSTYGWT ELELPEDEAE KKKREEDKAK FENLCKVMKS VLESKVEKVV VSNRLVDSPC CIVTSQYGWS ELELPEDESE KKKREEDKAK FESLCKLMKS ILDNKVEKVV VSNRLVDSPC CIVTSQFGWS ELELPEDEEE KKKMDEDKTK FENLCKLMKE ILDKKVEKVT VSNRLVSSPC CIVTSTYGWT ELELPEDEEE KKKMEESKAR FENLCKLMKE ILDKKVEKVT ISNRLVSSPC CIVTSTYGWT ELELPEDEEE KKKQEEKKTK FENLCKIMKD ILEKKVEKVV VSNRLVTSPC CIVTSTYGWT ELELPEDEEE KKKMEESKAK FENLCKLMKE ILDKKVEKVT ISNRLVSSPC CIVTSTYGWT DFELEETEEE KKQREQEEKE YEDLCKALKN ILGDKVEKVV VSHKLVGSPC AIRTGQFGWS DFDLEETEEE KSTREKEIKE FEPLTKALKD ILGDQVEKVV VSYKLVDAPA AIRTGQFGWS DFELEESDEE KAAREKEIKE YEPLTKALKD ILGDQVEKVV VSYKLVDAPA AIRTGQFGWS DLELEETDEE KAAREKLEKE YEEFAKQLKT ILGDKVEKVV VSNKIVGSPC LLTTGQYGWS DFELEETDEE KAEREKEIKE YEPLTKALKE ILGDQVEKVV VSYKLLDAPA AIRTGQFGWS ELELEDTEEE RKNFETLEKE MEPLCRLIKE ILHDKVEKVV CGKRFTESPC ALVTSEFGWS ELDIDDSEEA KKDFETLKAE YEGLCKVIKD VLHEKVEKVV VGQRITDSPC VLVTSEFGWS ELELEQTEDE KKKFEEKKAA YEPLCKQIKE VLGDKVEKVV VGQRLDESPC VLVTGEYGWS ELEIDESEEE KKKFEELKAE FEPLLKLIKE VLHDKVDKVV LSNRITDSPC VLVTTEFGWS ELDLDEGEDE KKSFEALKEE MEPLCKHIKE VLHDKVEKVV CGTRFTDSPC ALVTSEFGWS ANMERIMKAQ ALRDTSMSSR RSSRPTENRT VKSITQLLFE TSLLSGFTIE EPSFAGRIHK ANMERIMKAQ ALRDTSMSSY MSSKPKENKT VKSIVQLLFE TSLLSGFTIE EPAFAERIHK ANMERIMKAQ ALRDSSMSSY MSSKPRDSKT FKDFVYLLYE TALLSGFSLD EPSFASRIHR ANMERIMKAQ ALRDTSMGGY MSSKAEENKS VKDLVMLLFE NSLLSGFSLD DPNFGTRIHR ANMERIMKAQ ALRDSSMSAY MSSKGEENKS VKDLVLLLFE TALLSGFSLD DPNFAARIHR ANMERIMKAQ ALRDSSMAGY MSSKSDENKS VKDLVLLLFE TALLSGFSLE EPNFGNRIHR ANMERIMSSQ ALRDNSLSTY MSSRSRETKT VKDLVSLLFD TAMLSGFSLE EPHFAGRIQR ANMERIMKAQ ALRDSSLSTY MSSRSNENKT VKDLVNLLFD TSLLSGFSLD EPHFAERIHR ANMERIMKAQ ALRDSSMAGY MSSKAEENKS VKDLVLLLFE TALLSGFSLD DPNFGSRIHR ANMERIMKAQ ALRDSSMGGY MSSKLDENKS VKDLVLLLFE TALLSGFSLD EPNFGSRIHR ANMERIMKAQ ALRDSSMAGY MSSKSDENKS VKDLVLLLFE TALLSGFSLD EPNFGSRIHR ANMERIMKAQ ALKDSNTGGY MSSKSDENKS VKDLVLLLFE TALLSGFSLD EPNFGSRIHR ANMERIMKAQ ALRDSSMSSY MSSKGEENKS VKDLVLLLFE TALLSGFSLD DPNFGARIHR AHMEQIMRNQ ALRDSSMAQY MMSKPKENKA VKDLVFLLFD TSLLSGFQLE DPTYAERINR AHMEQMMRNQ ALRDSSMAQY MMSKPKENKA VKDLVFLLFD TSLLSGFQLE DPTYAERINR AHMEQIMRNQ ALRDSSMSAY MMRKAKENKA AKDLIFLLFD TSLLSGFTLD DPTYADRIHR AHMEQIMRNQ ALRDSSMSAY MMSKPKENKA VKDLVYLLFD TALLSGFTLD DPTYAERIHR ANMERIMKAQ ALRDSSTMGY MAAKAKTNKT VKDLVVLLFE TALLSGFSLE EPQHASRIYR ANMERIMKAQ ALRDSSTMGY MAAKSKANKT VKDLVVLLFE TALLSGFSLE DPQHASRIYR ANMERIMKAQ ALRDNSTMGY MMANPETNKA VKDLVILLFE TALLSGFSLD DPQHSNRIYR ANMERIIKAQ ALRDNSTMGY MAAKSETNKS VKDLVILLYE TALLSGFSLE DPQHANRIYR ANMERIMKAQ ALRDSSAMGY MAGKAETNKA VKDLVILLFE TALLSGFSLD EPGHAARIYR ANMERIMKAQ ALRDTATMGY MAGKPETNKA VKDLVILLFE TSLLSGFSLD SPQHASRIYR ANMERIMKAQ ALRDNSTMGY MMAKPETNKA VKDLVILLFE TALLSGFSLD DPQHSNRIYR ANMERIMKAQ ALRDNSTMGY MMAKPETNKA VKDLVVLLFE TALSLASHFR RPKHSNRIYR ANMERIMKAQ ALRDNSTMGY MAAKSETNKS VKDLVILLYE TALLSGFSLE DPQHANRIYR ANMERIMKAQ ALRDNSTMGY MMAKPETNKA VKDLVVLLFE TALLSGFSLE DPQHSNRIYR ANMERIMKAQ ALRDTSMSSY MSSKPKENKT VKSIVQLLYE TSLLSGFTID EPALPRGIHK ANMERIMKAQ ALRDTTMSSY TSSKPKEEKT VKDLTTLLFD TALLSGFTLD EPSFAHRINR ANMERIMKAQ ALRDTTMSSY MSSKPKEEKT VKDLTTLLFD TALLSGFTLD EPSFAHRINR ANMERIMKAQ ALRDTSMSAY MSSRPAEERS VKDLATILYE TALLSGFTLD DPSYAQRINR ANMERIMKAQ ALRDSSMSSY MSSKPKEQKT VKDLTKLLYE TALLSGFSLD EPTFASRINR ANMERIMKAQ ALRDSSFGSF MISKSKESKT LKDLVWLLYD TAMLSGFNLD DPTFGGRIYR ANMERIMKAQ ALRDNSMTSY MLSKPSASKT VKDLIWLLFD TSLLSGFALE EPTFSKRIHR ANMERIMKAQ ALRDASMSTY MISKAQEAKT VKDLIWLLFE TSLLSGFSLD DPSFANRIHR ANMERIMKAQ ALRDNSMTSY MVSKSIESKT VKDLIWLLYD TALLSGFSLE EPTFAARIHR ANMERIMKAQ ALRDSSITSY MLSKSKETKT VKDLVWLLYD TALLSGFNLD EPTFGNRIYR LVSLGLN LVALGLN MIKLGLS MLKLGLS MLKLGLN MLKLGLS MLMLGLQ MIKLGLS MLKLGLS MLKLGLS MLKLGLS MLKLGLS MLKLGLS MIKLGLS MIKLGLS MIKLGLS MIKLGLS MIKLGLD MIKLGLD MIKLGLG MIKLGLG MVKLGLG MIKLGLG MIKLGLG MIKLGLG MIKLGLG MIKLGLG LVSLGQN LIALGLN LIALGLN LISLGLS LISLGLN MIKLGLS MIKLGLS MIKLGLQ MIKLGLS MIKLGLS phyml-3.3.20220408/src/000077500000000000000000000000001422404706600142045ustar00rootroot00000000000000phyml-3.3.20220408/src/Makefile.am000077500000000000000000000157321422404706600162530ustar00rootroot00000000000000# EXTRA_DIST = doc bin examples DEFS = $(REVISION) if WANT_PHYML bin_PROGRAMS = phyml PROG = PHYML else if WANT_PHYTIME bin_PROGRAMS = phytime PROG = PHYTIME else if WANT_RF bin_PROGRAMS = rf PROG = RF else if WANT_MPI bin_PROGRAMS = phyml-mpi PROG = PHYML else if WANT_WIN bin_PROGRAMS = phyml-windows PROG = PHYML else if WANT_TEST bin_PROGRAMS = test PROG = TEST else if WANT_EVOLVE bin_PROGRAMS = evolve PROG = EVOLVE else if WANT_PHYREX bin_PROGRAMS = phyrex PROG = PHYREX else if WANT_PHYREXSIM bin_PROGRAMS = phyrexsim PROG = PHYREXSIM else if WANT_DATE bin_PROGRAMS = date PROG = DATE else bin_PROGRAMS = phyml PROG = PHYML endif endif endif endif endif endif endif endif endif endif if WANT_PHYTIME phytime_SOURCES = main.c \ utilities.c utilities.h\ optimiz.c optimiz.h\ lk.c lk.h\ bionj.c bionj.h\ models.c models.h\ free.c free.h\ help.c help.h\ simu.c simu.h\ eigen.c eigen.h\ pars.c pars.h\ alrt.c alrt.h\ interface.c interface.h\ cl.c cl.h\ spr.c spr.h\ times.c times.h\ draw.c draw.h\ rates.c rates.h\ mcmc.c mcmc.h\ stats.c stats.h\ mg.c mg.h\ io.c io.h\ make.c make.h\ mixt.c mixt.h\ init.c init.h\ nexus.c nexus.h\ date.c date.h\ tbe.c tbe.h\ sse.c sse.h\ avx.c avx.h\ ancestral.c ancestral.h\ xml.c xml.h phytime_LDADD = -lm else # if WANT_RWRAP # lib_LTLIBRARIES = librwrap.la # librwrap_la_SOURCES = main.c \ # utilities.c utilities.h\ # optimiz.c optimiz.h\ # lk.c lk.h\ # bionj.c bionj.h\ # models.c models.h\ # free.c free.h\ # help.c help.h\ # simu.c simu.h\ # eigen.c eigen.h\ # pars.c pars.h\ # alrt.c alrt.h\ # interface.c interface.h\ # cl.c cl.h\ # spr.c spr.h\ # draw.c draw.h\ # stats.c stats.h\ # rates.c rates.h\ # mcmc.c mcmc.h\ # times.c times.h\ # tiporder.c tiporder.h\ # mg.c mg.h\ # io.c io.h\ # make.c make.h\ # nexus.c nexus.h\ # init.c init.h\ # xml.c xml.h\ # mixt.c mixt.h\ # rwrapper.c rwrapper.h # librwrap_la_LIBADD = -lm # librwrap_la_LDFLAGS = -I/usr/share/R/include -shared -module -flat_namespace # librwrap_la_CFLAGS=-std=gnu99 -fPIC -Wl,-z,defs # else if WANT_RF rf_SOURCES = main.c \ utilities.c utilities.h\ optimiz.c optimiz.h\ lk.c lk.h\ bionj.c bionj.h\ models.c models.h\ free.c free.h\ help.c help.h\ simu.c simu.h\ eigen.c eigen.h\ pars.c pars.h\ alrt.c alrt.h\ interface.c interface.h\ cl.c cl.h\ spr.c spr.h\ draw.c draw.h\ stats.c stats.h\ mg.c mg.h\ io.c io.h\ make.c make.h\ nexus.c nexus.h\ init.c init.h\ xml.c xml.h\ mixt.c mixt.h\ tbe.c tbe.h \ ancestral.c ancestral.h\ sse.c sse.h\ avx.c avx.h rf_LDADD = -lm else if WANT_MPI phyml_mpi_SOURCES = main.c \ utilities.c utilities.h\ optimiz.c optimiz.h\ lk.c lk.h\ bionj.c bionj.h\ models.c models.h\ free.c free.h\ help.c help.h\ simu.c simu.h\ eigen.c eigen.h\ pars.c pars.h\ alrt.c alrt.h\ interface.c interface.h\ cl.c cl.h\ spr.c spr.h\ draw.c draw.h\ stats.c stats.h\ io.c io.h\ make.c make.h\ nexus.c nexus.h\ init.c init.h\ xml.c xml.h\ mixt.c mixt.h\ tbe.c tbe.h \ sse.c sse.h\ avx.c avx.h\ ancestral.c ancestral.h\ mpi_boot.c mpi_boot.h phyml_mpi_LDADD = -lm else if WANT_WIN phyml_windows_SOURCES = main.c \ utilities.c utilities.h\ optimiz.c optimiz.h\ lk.c lk.h\ bionj.c bionj.h\ models.c models.h\ free.c free.h\ help.c help.h\ simu.c simu.h\ eigen.c eigen.h\ pars.c pars.h\ alrt.c alrt.h\ interface.c interface.h\ cl.c cl.h\ spr.c spr.h\ draw.c draw.h\ tbe.c tbe.h \ stats.c stats.h\ rates.c rates.h\ mcmc.c mcmc.h\ times.c times.h\ mg.c mg.h\ io.c io.h\ make.c make.h\ nexus.c nexus.h\ init.c init.h\ xml.c xml.h\ mixt.c mixt.h\ sse.c sse.h\ avx.c avx.h\ ancestral.c ancestral.h\ date.c date.h phyml_windows_LDADD = -lm else if WANT_TEST test_SOURCES = main.c \ utilities.c utilities.h\ optimiz.c optimiz.h\ lk.c lk.h\ bionj.c bionj.h\ models.c models.h\ free.c free.h\ help.c help.h\ simu.c simu.h\ eigen.c eigen.h\ pars.c pars.h\ alrt.c alrt.h\ interface.c interface.h\ cl.c cl.h\ spr.c spr.h\ draw.c draw.h\ tbe.c tbe.h \ stats.c stats.h\ rates.c rates.h\ mcmc.c mcmc.h\ times.c times.h\ mg.c mg.h\ io.c io.h\ make.c make.h\ nexus.c nexus.h\ init.c init.h\ xml.c xml.h\ mixt.c mixt.h\ sse.c sse.h\ avx.c avx.h\ ancestral.c ancestral.h\ date.c date.h test_LDADD = -lm else if WANT_EVOLVE evolve_SOURCES = main.c\ utilities.c utilities.h\ optimiz.c optimiz.h\ lk.c lk.h\ bionj.c bionj.h\ models.c models.h\ free.c free.h\ help.c help.h\ simu.c simu.h\ eigen.c eigen.h\ pars.c pars.h\ alrt.c alrt.h\ interface.c interface.h\ cl.c cl.h\ spr.c spr.h\ draw.c draw.h\ tbe.c tbe.h \ stats.c stats.h\ rates.c rates.h\ mcmc.c mcmc.h\ times.c times.h\ mg.c mg.h\ io.c io.h\ make.c make.h\ nexus.c nexus.h\ init.c init.h\ xml.c xml.h\ mixt.c mixt.h evolve_LDADD = -lm else if WANT_PHYREX phyrex_SOURCES = main.c\ utilities.c utilities.h\ optimiz.c optimiz.h\ lk.c lk.h\ bionj.c bionj.h\ models.c models.h\ free.c free.h\ help.c help.h\ simu.c simu.h\ eigen.c eigen.h\ pars.c pars.h\ alrt.c alrt.h\ interface.c interface.h\ cl.c cl.h\ spr.c spr.h\ draw.c draw.h\ stats.c stats.h\ rates.c rates.h\ mcmc.c mcmc.h\ times.c times.h\ io.c io.h\ make.c make.h\ nexus.c nexus.h\ init.c init.h\ xml.c xml.h\ mixt.c mixt.h\ date.c date.h\ tbe.c tbe.h \ sse.c sse.h\ avx.c avx.h\ ancestral.c ancestral.h\ phyrex.c phyrex.h\ location.c location.h\ slfv.c slfv.h\ rw.c rw.h\ rrw.c rrw.h phyrex_LDADD = -lm else if WANT_PHYREXSIM phyrexsim_SOURCES = main.c\ utilities.c utilities.h\ optimiz.c optimiz.h\ lk.c lk.h\ bionj.c bionj.h\ models.c models.h\ free.c free.h\ help.c help.h\ simu.c simu.h\ eigen.c eigen.h\ pars.c pars.h\ alrt.c alrt.h\ interface.c interface.h\ cl.c cl.h\ spr.c spr.h\ draw.c draw.h\ stats.c stats.h\ rates.c rates.h\ mcmc.c mcmc.h\ times.c times.h\ mg.c mg.h\ io.c io.h\ make.c make.h\ nexus.c nexus.h\ init.c init.h\ xml.c xml.h\ mixt.c mixt.h\ date.c date.h\ tbe.c tbe.h \ sse.c sse.h\ avx.c avx.h\ ancestral.c ancestral.h\ phyrex.c phyrex.h phyrexsim_LDADD = -lm else if WANT_DATE date_SOURCES = main.c \ utilities.c utilities.h\ optimiz.c optimiz.h\ lk.c lk.h\ bionj.c bionj.h\ models.c models.h\ free.c free.h\ help.c help.h\ simu.c simu.h\ eigen.c eigen.h\ pars.c pars.h\ alrt.c alrt.h\ interface.c interface.h\ cl.c cl.h\ spr.c spr.h\ draw.c draw.h\ stats.c stats.h\ rates.c rates.h\ mcmc.c mcmc.h\ times.c times.h\ mg.c mg.h\ io.c io.h\ make.c make.h\ nexus.c nexus.h\ init.c init.h\ xml.c xml.h\ tbe.c tbe.h \ mixt.c mixt.h\ date.c date.h date_LDADD = -lm else phyml_SOURCES = main.c \ utilities.c utilities.h\ optimiz.c optimiz.h\ lk.c lk.h\ bionj.c bionj.h\ models.c models.h\ free.c free.h\ help.c help.h\ simu.c simu.h\ eigen.c eigen.h\ pars.c pars.h\ alrt.c alrt.h\ interface.c interface.h\ cl.c cl.h\ spr.c spr.h\ draw.c draw.h\ stats.c stats.h\ io.c io.h\ make.c make.h\ nexus.c nexus.h\ init.c init.h\ xml.c xml.h\ sse.c sse.h\ avx.c avx.h\ tbe.c tbe.h \ ancestral.c ancestral.h \ mixt.c mixt.h phyml_LDADD = -lm endif endif endif endif endif endif endif endif endif all-am: intro $(bin_PROGRAMS) @echo "" @echo "Done." intro: @echo "" @echo "" @echo ".: Building [$(bin_PROGRAMS)]. Version $(VERSION) :." @echo "" @echo "" phyml-3.3.20220408/src/Makefile_mc.am000077500000000000000000000015671422404706600167330ustar00rootroot00000000000000# AM_CFLAGS=-mpowerpc # AM_CFLAGS=-O3 -fomit-frame-pointer -funroll-loops -Wall -m64 # AM_CFLAGS=-O2 -Wall # AM_CFLAGS=-m64 # AM_CFLAGS=-g -Wall # AM_LDFLAGS=-lm # DEFS = -DUNIX -D$(PROG) -DDEBUG -DBATCH -DQUIET # DEFS=-DUNIX -D$(PROG) -DDEBUG # DEFS=-DUNIX -D$(PROG) # DEFS = -DUNIX -D$(PROG) AM_MAKEFLAGS = -f Makefile_mc bin_PROGRAMS = mc PROG = MC DEFS=-DUNIX -D$(PROG) -DDEBUG mc_SOURCES = main.c utilities.c optimiz.c lk.c bionj.c models.c free.c\ help.c simu.c eigen.c pars.c alrt.c interface.c cl.c spr.c mc.c m4.c draw.c rates.c mcmc.c numeric.c # mc_LDADD = -lm # SCOMPILE = $(CC) $(SDEFS) $(DEFS) $(INCLUDES) $(AM_CFLAGS) $(SCFLAGS) # SCCLD = $(CC) $(LIBS) # SLINK = $(SCCLD) $(AM_CFLAGS) $(SLDFLAGS) $(LDFLAGS) $(INCLUDES) $(AM_LDFLAGS) all-am: intro mc$(EXEEXT) @echo "" @echo "Done." intro: @echo "" @echo "** Building MC version $(VERSION) **" @echo "" phyml-3.3.20220408/src/Makefile_r.am000077500000000000000000000035621422404706600165720ustar00rootroot00000000000000bin_PROGRAMS = rwrapper PROG = RWRAP rwrapper_SOURCES = main.c utilities.c optimiz.c lk.c bionj.c models.c free.c\ help.c simu.c eigen.c pars.c alrt.c interface.c cl.c spr.c draw.c rates.c rwrapper.c numeric.c mcmc.c rwrapper_LDADD = main.o utilities.o optimiz.o lk.o bionj.o models.o free.o\ options.o simu.o eigen.o pars.o alrt.o interface.o cl.o spr.o draw.o rates.o rwrapper.o numeric.o mcmc.o AM_CFLAGS=-fPIC -Wall AM_LDFLAGS=-lm DEFS=-DUNIX -D$(PROG) -DDEBUG # SCOMPILE = $(CC) $(SDEFS) $(DEFS) $(INCLUDES) $(AM_CFLAGS) $(SCFLAGS) SCOMPILE = $(CC) $(SDEFS) $(DEFS) $(INCLUDES) $(AM_CFLAGS) $(SCFLAGS) -I/Library/Frameworks/R.framework/Versions/2.8/Resources/include/ SCCLD = $(CC) $(LIBS) SLINK = $(SCCLD) $(AM_CFLAGS) $(SLDFLAGS) $(LDFLAGS) $(INCLUDES) $(AM_LDFLAGS) -bundle -flat_namespace -undefined suppress rwrapper$(EXEEXT): $(rwrapper_LDADD) $(rwrapper_SOURCES) $(SLINK) $(rwrapper_LDADD) -o $@.so main.o: main.c $(SCOMPILE) -c main.c utilities.o: utilities.c utilities.h $(SCOMPILE) -c utilities.c optimiz.o: optimiz.c optimiz.h $(SCOMPILE) -c optimiz.c lk.o: lk.c lk.h $(SCOMPILE) -c lk.c bionj.o: bionj.c bionj.h $(SCOMPILE) -c bionj.c models.o: models.c models.h $(SCOMPILE) -c models.c free.o: free.c free.h $(SCOMPILE) -c free.c options.o: help.c help.h $(SCOMPILE) -c help.c simu.o: simu.c simu.h $(SCOMPILE) -c simu.c eigen.o: eigen.c eigen.h $(SCOMPILE) -c eigen.c pars.o: pars.c pars.h $(SCOMPILE) -c pars.c alrt.o: alrt.c alrt.h $(SCOMPILE) -c alrt.c interface.o: interface.c interface.h $(SCOMPILE) -c interface.c cl.o: cl.c cl.h $(SCOMPILE) -c cl.c spr.o: spr.c spr.h $(SCOMPILE) -c spr.c mg.o: mg.c $(SCOMPILE) -c mg.c draw.o: draw.c $(SCOMPILE) -c draw.c rates.o: rates.c $(SCOMPILE) -c rates.c rwrapper.o: rwrapper.c $(SCOMPILE) -c rwrapper.c numeric.o: numeric.c $(SCOMPILE) -c numeric.c mcmc.o: mcmc.c $(SCOMPILE) -c mcmc.c phyml-3.3.20220408/src/alrt.c000066400000000000000000001132121422404706600153120ustar00rootroot00000000000000/* PHYML : a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences Copyright (C) Stephane Guindon. Oct 2003 onward All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ /* Implementation of aLRT branch tests, and 5-branchs NNI research optimization. Authors : Jean-Francois Dufayard & Stephane Guindon. */ #include "alrt.h" ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* * Check every testable branch of the tree, * check for NNIs, optimizing 5 branches, * param tree : the tree to check */ int Check_NNI_Five_Branches(t_tree *tree) { int best_edge; phydbl best_gain; int best_config; int i; int better_found; /* = 1 if a phylogeny with greater likelihood than current one was found */ int result; phydbl init_lnL; init_lnL = UNLIKELY; better_found = YES; //While there is at least one NNI to do... while(better_found == YES) { Update_Dirs(tree); //Interface output if(tree->verbose > VL0 && tree->io->quiet == NO) PhyML_Printf("\n\n. Checking for NNIs, optimizing five branches...\n"); better_found = 0; result = -1; best_edge = -1; best_gain = tree->mod->s_opt->min_diff_lk_local; best_config = -1; MIXT_Set_Alias_Subpatt(YES,tree); Set_Both_Sides(YES,tree); init_lnL = Lk(NULL,tree); MIXT_Set_Alias_Subpatt(NO,tree); // For every branch for(i=0;i<2*tree->n_otu-3;++i) { //if this branch is not terminal if((!tree->a_edges[i]->left->tax) && (!tree->a_edges[i]->rght->tax)) { result = -1; //Try every NNIs for the tested branch result = NNI_Neigh_BL(tree->a_edges[i],tree); //Look for possible NNI to do, and check if it is the best one switch(result) { case 1 : /* lk1 > lk0 > lk2 */ { if((tree->a_edges[i]->nni->lk0 - tree->a_edges[i]->nni->lk1) < best_gain) { better_found = YES; best_edge = i; best_gain = tree->a_edges[i]->nni->lk0-tree->a_edges[i]->nni->lk1; best_config = 1; } break; } case 2 : /* lk2 > lk0 > lk1 */ { if((tree->a_edges[i]->nni->lk0 - tree->a_edges[i]->nni->lk2) < best_gain) { better_found = YES; best_edge = i; best_gain = tree->a_edges[i]->nni->lk0-tree->a_edges[i]->nni->lk2; best_config = 2; } break; } case 3 : /* lk1 > lk2 > lk0 */ { if((tree->a_edges[i]->nni->lk2 - tree->a_edges[i]->nni->lk1) < best_gain) { better_found = YES; best_edge = i; best_gain = tree->a_edges[i]->nni->lk2-tree->a_edges[i]->nni->lk1; best_config = 1; } break; } case 4 : /* lk2 > lk1 > lk0 */ { if((tree->a_edges[i]->nni->lk1 - tree->a_edges[i]->nni->lk2) < best_gain) { better_found = YES; best_edge = i; best_gain = tree->a_edges[i]->nni->lk1-tree->a_edges[i]->nni->lk2; best_config = 2; } break; } default : /* lk2 = lk1 = lk0 */ { if(best_gain > .0) best_gain = .0; break; } } } } if((tree->c_lnL < init_lnL - tree->mod->s_opt->min_diff_lk_local) || (tree->c_lnL > init_lnL + tree->mod->s_opt->min_diff_lk_local)) { PhyML_Fprintf(stderr,"\n\n== tree->c_lnL = %f init_lnL = %f.",tree->c_lnL,init_lnL); PhyML_Fprintf(stderr,"\n== Err. in file %s at line %d\n\n.\n",__FILE__,__LINE__); Warn_And_Exit("\n"); } //Don't do any NNI if the user doesn't want to optimize topology if(!tree->mod->s_opt->opt_topo) better_found = NO; /* if(FABS(best_gain) <= tree->mod->s_opt->min_diff_lk_move) better_found = 0; */ //If there is one swap to do, make the best one. if(better_found) { Make_Target_Swap(tree,tree->a_edges[best_edge],best_config); MIXT_Set_Alias_Subpatt(YES,tree); Lk(NULL,tree); MIXT_Set_Alias_Subpatt(YES,tree); if(tree->c_lnL < init_lnL) { PhyML_Fprintf(stderr,"\n\n== tree->c_lnL = %f init_lnL = %f.",tree->c_lnL,init_lnL); PhyML_Fprintf(stderr,"\n== Err. in file %s at line %d\n\n.\n",__FILE__,__LINE__); Exit("\n"); } if(tree->verbose > VL0 && tree->io->quiet == NO) Print_Lk(tree,"[Topology ]"); if(FABS(tree->c_lnL - init_lnL) < tree->mod->s_opt->min_diff_lk_move) return 0; return 1; } } return 0; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* Compute aLRT supports */ void aLRT(t_tree *tree) { int i; char *method; // Print_All_Edge_Likelihoods(tree); Unscale_Br_Len_Multiplier_Tree(tree); Br_Len_Not_Involving_Invar(tree); // Print_All_Edge_Likelihoods(tree); /* aLRT support will label each internal branch */ tree->io->print_support_val = YES; /* The topology will not be modified when assessing the branch support. We make sure that it will not be modified afterwards by locking the topology */ method = (char *)mCalloc(100,sizeof(char)); switch(tree->io->ratio_test) { case ALRTCHI2: { strcpy(method,"aLRT"); break; } case MINALRTCHI2SH: { strcpy(method,"aLRT"); break; } case ALRTSTAT: { strcpy(method,"aLRT"); break; } case SH: { strcpy(method,"SH"); break; } case ABAYES: { strcpy(method,"aBayes"); break; } default : return; } if(tree->io->quiet == NO) PhyML_Printf("\n\n. Calculating fast branch supports (using '%s').",method); Free(method); MIXT_Set_Alias_Subpatt(YES,tree); Set_Both_Sides(YES,tree); // Print_All_Edge_Likelihoods(tree); Lk(NULL,tree); // Print_All_Edge_Likelihoods(tree); MIXT_Set_Alias_Subpatt(NO,tree); Update_Dirs(tree); for(i=0;i<2*tree->n_otu-3;++i) { if((!tree->a_edges[i]->left->tax) && (!tree->a_edges[i]->rght->tax)) { /* Compute likelihoods for each of the three configuration */ NNI_Neigh_BL(tree->a_edges[i],tree); /* Compute the corresponding statistical support */ Compute_Likelihood_Ratio_Test(tree->a_edges[i],tree); } } tree->lock_topo = YES; Br_Len_Involving_Invar(tree); Rescale_Br_Len_Multiplier_Tree(tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* * Launch one branch testing, * analyse the result * and convert supports as wished by the user. * param tree : the tree to check * param tested_t_edge : the tested t_edge of the tree * param old_loglk : the initial likelihood, before any aLRT analysis * param isBoot : 1 if used from the Bootstrap procedure, 0 if not * return an integer, informative to analyse the results and potential NNIs to do */ int Compute_Likelihood_Ratio_Test(t_edge *tested_edge, t_tree *tree) { int result=0; tested_edge->ratio_test = 0.0; tested_edge->alrt_statistic = -1.0; if((tested_edge->nni->lk0 > tested_edge->nni->lk1) && (tested_edge->nni->lk0 > tested_edge->nni->lk2)) { if(tested_edge->nni->lk1 < tested_edge->nni->lk2) { //lk0 > lk2 > lk1 tested_edge->alrt_statistic = 2*(tested_edge->nni->lk0 - tested_edge->nni->lk2); } else { //lk0 > lk1 >= lk2 tested_edge->alrt_statistic = 2*(tested_edge->nni->lk0 - tested_edge->nni->lk1); } if (tested_edge->alrt_statistic < 0.0) { tested_edge->alrt_statistic = 0.0; tested_edge->ratio_test = 0.0; } else { //aLRT statistic is valid, compute the branch support if (tree->io->ratio_test == ALRTCHI2) { tested_edge->ratio_test = Statistics_To_Probabilities(tested_edge->alrt_statistic); } else if(tree->io->ratio_test == MINALRTCHI2SH) { phydbl sh_support; phydbl param_support; sh_support = Statistics_To_SH(tree); param_support = Statistics_To_Probabilities(tested_edge->alrt_statistic); if(sh_support < param_support) tested_edge->ratio_test = sh_support; else tested_edge->ratio_test = param_support; } else if(tree->io->ratio_test == ALRTSTAT) { tested_edge->ratio_test=tested_edge->alrt_statistic; } else if(tree->io->ratio_test == SH) { tested_edge->ratio_test = Statistics_To_SH(tree); } else if(tree->io->ratio_test == ABAYES) { phydbl Kp0,Kp1,Kp2,logK; logK = 1. - MAX(MAX(tested_edge->nni->lk0,tested_edge->nni->lk1),tested_edge->nni->lk2); Kp0 = exp(tested_edge->nni->lk0+logK); Kp1 = exp(tested_edge->nni->lk1+logK); Kp2 = exp(tested_edge->nni->lk2+logK); tested_edge->ratio_test = Kp0/(Kp0+Kp1+Kp2); } } } //statistic is not valid, give the negative statistics if wished, or a zero support. else if((tested_edge->nni->lk1 > tested_edge->nni->lk0) && (tested_edge->nni->lk1 > tested_edge->nni->lk2)) { /* tested_edge->ratio_test = 2*(tested_edge->nni->lk0-tested_edge->nni->lk1); */ tested_edge->ratio_test = 0.0; if(tree->io->ratio_test > 1) tested_edge->alrt_statistic = 0.0; } else if((tested_edge->nni->lk2 > tested_edge->nni->lk0) && (tested_edge->nni->lk2 > tested_edge->nni->lk1)) { /* tested_edge->ratio_test = 2*(tested_edge->nni->lk0-tested_edge->nni->lk2); */ tested_edge->ratio_test = 0.0; if(tree->io->ratio_test > 1) tested_edge->alrt_statistic = 0.0; } else // lk0 ~ lk1 ~ lk2 { tested_edge->ratio_test = 0.0; if(tree->io->ratio_test > 1) tested_edge->ratio_test = 0.0; } return result; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* * Test the 3 NNI positions for one branch. * param tree : the tree to check * param tested_t_edge : the tested t_edge of the tree * param old_loglk : the initial likelihood, before any aLRT analysis * param isBoot : 1 if used from the Bootstrap procedure, 0 if not */ int NNI_Neigh_BL(t_edge *b_fcus, t_tree *tree) { /*! syntax : (node) [edge] (left_1) . .(right_1) \ (left) (right) / \._____________./ / [b_fcus] \ / \ (left_2) . .(right_2) */ int site; t_node *v1,*v2,*v3,*v4; t_edge *e1,*e2,*e3,*e4; scalar_dbl *len_e1,*len_e2,*len_e3,*len_e4; scalar_dbl *var_e1,*var_e2,*var_e3,*var_e4; phydbl lk0, lk1, lk2; scalar_dbl *l_init,*v_init; phydbl lk_init, lk_temp; int i; int result; void *buff; result = 0; /*! Initialization */ l_init = Duplicate_Scalar_Dbl(b_fcus->l); v_init = Duplicate_Scalar_Dbl(b_fcus->l_var); lk_init = tree->c_lnL; lk_temp = UNLIKELY; lk0 = lk1 = lk2 = UNLIKELY; v1 = v2 = v3 = v4 = NULL; Init_NNI_Score(0.0,b_fcus,tree); /*! vertices */ v1 = b_fcus->left->v[b_fcus->l_v1]; v2 = b_fcus->left->v[b_fcus->l_v2]; v3 = b_fcus->rght->v[b_fcus->r_v1]; v4 = b_fcus->rght->v[b_fcus->r_v2]; if(v1->num < v2->num) { PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d\n\n",__FILE__,__LINE__); Exit(""); } if(v3->num < v4->num) { PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d\n\n",__FILE__,__LINE__); Exit(""); } /*! edges */ e1 = b_fcus->left->b[b_fcus->l_v1]; e2 = b_fcus->left->b[b_fcus->l_v2]; e3 = b_fcus->rght->b[b_fcus->r_v1]; e4 = b_fcus->rght->b[b_fcus->r_v2]; /*! record initial branch lengths */ len_e1 = Duplicate_Scalar_Dbl(e1->l); len_e2 = Duplicate_Scalar_Dbl(e2->l); len_e3 = Duplicate_Scalar_Dbl(e3->l); len_e4 = Duplicate_Scalar_Dbl(e4->l); var_e1 = Duplicate_Scalar_Dbl(e1->l_var); var_e2 = Duplicate_Scalar_Dbl(e2->l_var); var_e3 = Duplicate_Scalar_Dbl(e3->l_var); var_e4 = Duplicate_Scalar_Dbl(e4->l_var); /*! Optimize branch lengths and update likelihoods for the original configuration. */ do { lk0 = lk_temp; for(i=0;i<3;i++) //a branch has three nodes if(b_fcus->left->v[i] != b_fcus->rght) //Only consider left_1 and left_2 { Update_Partial_Lk(tree,b_fcus->left->b[i],b_fcus->left); lk_temp = Br_Len_Opt(&(b_fcus->left->b[i]->l->v),b_fcus->left->b[i],tree); } Update_Partial_Lk(tree,b_fcus,b_fcus->left); lk_temp = Br_Len_Opt(&(b_fcus->l->v),b_fcus,tree); for(i=0;i<3;i++) if(b_fcus->rght->v[i] != b_fcus->left) //Only consider right_1 and right_2 { Update_Partial_Lk(tree,b_fcus->rght->b[i],b_fcus->rght); lk_temp = Br_Len_Opt(&(b_fcus->rght->b[i]->l->v),b_fcus->rght->b[i],tree); } Update_Partial_Lk(tree,b_fcus,b_fcus->rght); lk_temp = Br_Len_Opt(&(b_fcus->l->v),b_fcus,tree); if(lk_temp < lk0 - tree->mod->s_opt->min_diff_lk_local) { PhyML_Fprintf(stderr,"\n. lk_temp = %f lk0 = %f\n",lk_temp,lk0); PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d\n\n",__FILE__,__LINE__); Exit("\n"); } } while(fabs(lk_temp-lk0) > tree->mod->s_opt->min_diff_lk_global); //until no significative improvement is detected lk0 = tree->c_lnL; buff = (t_tree *)tree; do { for(site=0;siten_pattern;site++) tree->log_lks_aLRT[0][site] = tree->c_lnL_sorted[site]; tree = tree->next_mixt; } while(tree); tree = (t_tree *)buff; /*! Go back to initial branch lengths */ Copy_Scalar_Dbl(len_e1,e1->l); Copy_Scalar_Dbl(len_e2,e2->l); Copy_Scalar_Dbl(len_e3,e3->l); Copy_Scalar_Dbl(len_e4,e4->l); Copy_Scalar_Dbl(l_init,b_fcus->l); Copy_Scalar_Dbl(var_e1,e1->l_var); Copy_Scalar_Dbl(var_e2,e2->l_var); Copy_Scalar_Dbl(var_e3,e3->l_var); Copy_Scalar_Dbl(var_e4,e4->l_var); Copy_Scalar_Dbl(v_init,b_fcus->l_var); Update_PMat_At_Given_Edge(e1,tree); Update_PMat_At_Given_Edge(e2,tree); Update_PMat_At_Given_Edge(e3,tree); Update_PMat_At_Given_Edge(e4,tree); Update_PMat_At_Given_Edge(b_fcus,tree); /* Sanity check */ MIXT_Set_Alias_Subpatt(YES,tree); Update_Partial_Lk(tree,b_fcus,b_fcus->rght); Update_Partial_Lk(tree,b_fcus,b_fcus->left); lk_temp = Lk(b_fcus,tree); MIXT_Set_Alias_Subpatt(NO,tree); if((lk_temp > lk_init + tree->mod->s_opt->min_diff_lk_local) || (lk_temp < lk_init - tree->mod->s_opt->min_diff_lk_local)) { PhyML_Fprintf(stderr,"\n. lk_temp = %f lk_init = %f",lk_temp,lk_init); PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d\n",__FILE__,__LINE__); Exit("\n"); } /*! Do first possible swap */ Swap(v2,b_fcus->left,b_fcus->rght,v3,tree); MIXT_Set_Alias_Subpatt(YES,tree); Set_Both_Sides(YES,tree); Update_Partial_Lk(tree,b_fcus,b_fcus->left); Update_Partial_Lk(tree,b_fcus,b_fcus->rght); lk1 = Lk(b_fcus,tree); MIXT_Set_Alias_Subpatt(NO,tree); for(i=0;i<3;i++) if(b_fcus->left->v[i] != b_fcus->rght) Update_Partial_Lk(tree,b_fcus->left->b[i],b_fcus->left); for(i=0;i<3;i++) if(b_fcus->rght->v[i] != b_fcus->left) Update_Partial_Lk(tree,b_fcus->rght->b[i],b_fcus->rght); /*! Optimize branch lengths and update likelihoods */ lk_temp = UNLIKELY; do { lk1 = lk_temp; for(i=0;i<3;i++) if(b_fcus->left->v[i] != b_fcus->rght) { Update_Partial_Lk(tree,b_fcus->left->b[i],b_fcus->left); lk_temp = Br_Len_Opt(&(b_fcus->left->b[i]->l->v),b_fcus->left->b[i],tree); } Update_Partial_Lk(tree,b_fcus,b_fcus->left); lk_temp = Br_Len_Opt(&(b_fcus->l->v),b_fcus,tree); for(i=0;i<3;i++) if(b_fcus->rght->v[i] != b_fcus->left) { Update_Partial_Lk(tree,b_fcus->rght->b[i],b_fcus->rght); lk_temp = Br_Len_Opt(&(b_fcus->rght->b[i]->l->v),b_fcus->rght->b[i],tree); } Update_Partial_Lk(tree,b_fcus,b_fcus->rght); lk_temp = Br_Len_Opt(&(b_fcus->l->v),b_fcus,tree); if(lk_temp < lk1 - tree->mod->s_opt->min_diff_lk_local) { PhyML_Fprintf(stderr,"\n. lk_temp = %f lk1 = %f\n",lk_temp,lk1); PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d\n\n",__FILE__,__LINE__); Exit(""); } } while(FABS(lk_temp-lk1) > tree->mod->s_opt->min_diff_lk_global); /*! until no significative improvement is detected */ lk1 = tree->c_lnL; /*! record likelihood of each site, in order to compute branch supports */ buff = (t_tree *)tree; do { for(site=0;siten_pattern;site++) tree->log_lks_aLRT[1][site]= tree->c_lnL_sorted[site]; tree = tree->next_mixt; } while(tree); tree = (t_tree *)buff; Swap(v3,b_fcus->left,b_fcus->rght,v2,tree); /*! Go back to initial branch lengths */ Copy_Scalar_Dbl(len_e1,e1->l); Copy_Scalar_Dbl(len_e2,e2->l); Copy_Scalar_Dbl(len_e3,e3->l); Copy_Scalar_Dbl(len_e4,e4->l); Copy_Scalar_Dbl(l_init,b_fcus->l); Copy_Scalar_Dbl(var_e1,e1->l_var); Copy_Scalar_Dbl(var_e2,e2->l_var); Copy_Scalar_Dbl(var_e3,e3->l_var); Copy_Scalar_Dbl(var_e4,e4->l_var); Copy_Scalar_Dbl(v_init,b_fcus->l_var); Update_PMat_At_Given_Edge(e1,tree); Update_PMat_At_Given_Edge(e2,tree); Update_PMat_At_Given_Edge(e3,tree); Update_PMat_At_Given_Edge(e4,tree); Update_PMat_At_Given_Edge(b_fcus,tree); /* Sanity check */ MIXT_Set_Alias_Subpatt(YES,tree); Update_Partial_Lk(tree,b_fcus,b_fcus->rght); Update_Partial_Lk(tree,b_fcus,b_fcus->left); for(i=0;i<3;i++) if(b_fcus->left->v[i] != b_fcus->rght) Update_Partial_Lk(tree,b_fcus->left->b[i],b_fcus->left); for(i=0;i<3;i++) if(b_fcus->rght->v[i] != b_fcus->left) Update_Partial_Lk(tree,b_fcus->rght->b[i],b_fcus->rght); lk_temp = Lk(b_fcus,tree); MIXT_Set_Alias_Subpatt(NO,tree); if((lk_temp > lk_init + tree->mod->s_opt->min_diff_lk_local) || (lk_temp < lk_init - tree->mod->s_opt->min_diff_lk_local)) { PhyML_Fprintf(stderr,"\n== lk_temp = %f lk_init = %f",lk_temp,lk_init); PhyML_Fprintf(stderr,"\n== Err. in file %s at line %d\n",__FILE__,__LINE__); Warn_And_Exit(""); } /*! do the second possible swap */ Swap(v2,b_fcus->left,b_fcus->rght,v4,tree); Set_Both_Sides(YES,tree); MIXT_Set_Alias_Subpatt(YES,tree); Update_Partial_Lk(tree,b_fcus,b_fcus->left); Update_Partial_Lk(tree,b_fcus,b_fcus->rght); lk2 = Lk(b_fcus,tree); MIXT_Set_Alias_Subpatt(NO,tree); for(i=0;i<3;i++) if(b_fcus->left->v[i] != b_fcus->rght) Update_Partial_Lk(tree,b_fcus->left->b[i],b_fcus->left); for(i=0;i<3;i++) if(b_fcus->rght->v[i] != b_fcus->left) Update_Partial_Lk(tree,b_fcus->rght->b[i],b_fcus->rght); /*! Optimize branch lengths and update likelihoods */ lk_temp = UNLIKELY; do { lk2 = lk_temp; for(i=0;i<3;i++) if(b_fcus->left->v[i] != b_fcus->rght) { Update_Partial_Lk(tree,b_fcus->left->b[i],b_fcus->left); lk_temp = Br_Len_Opt(&(b_fcus->left->b[i]->l->v),b_fcus->left->b[i],tree); if(lk_temp < lk2 - tree->mod->s_opt->min_diff_lk_local) { PhyML_Fprintf(stderr,"\n== l: %f var:%f",b_fcus->left->b[i]->l->v,b_fcus->left->b[i]->l_var->v); PhyML_Fprintf(stderr,"\n== lk_temp = %f lk2 = %f",lk_temp,lk2); PhyML_Fprintf(stderr,"\n== Err. in file %s at line %d",__FILE__,__LINE__); Exit("\n"); } } Update_Partial_Lk(tree,b_fcus,b_fcus->left); lk_temp = Br_Len_Opt(&(b_fcus->l->v),b_fcus,tree); if(lk_temp < lk2 - tree->mod->s_opt->min_diff_lk_local) { PhyML_Fprintf(stderr,"\n== l: %f var:%f",b_fcus->l->v,b_fcus->l_var->v); PhyML_Fprintf(stderr,"\n== lk_temp = %f lk2 = %f",lk_temp,lk2); PhyML_Fprintf(stderr,"\n== Err. in file %s at line %d",__FILE__,__LINE__); Exit("\n"); } for(i=0;i<3;i++) if(b_fcus->rght->v[i] != b_fcus->left) { Update_Partial_Lk(tree,b_fcus->rght->b[i],b_fcus->rght); lk_temp = Br_Len_Opt(&(b_fcus->rght->b[i]->l->v),b_fcus->rght->b[i],tree); if(lk_temp < lk2 - tree->mod->s_opt->min_diff_lk_local) { Set_Both_Sides(YES,tree); Lk(b_fcus,tree); Check_Lk_At_Given_Edge(YES,tree); PhyML_Fprintf(stderr,"\n== l: %f var:%f",b_fcus->rght->b[i]->l->v,b_fcus->rght->b[i]->l_var->v); PhyML_Fprintf(stderr,"\n== lk_temp = %f lk2 = %f",lk_temp,lk2); PhyML_Fprintf(stderr,"\n== Err. in file %s at line %d",__FILE__,__LINE__); Exit("\n"); } } Update_Partial_Lk(tree,b_fcus,b_fcus->rght); lk_temp = Br_Len_Opt(&(b_fcus->l->v),b_fcus,tree); } while(FABS(lk_temp-lk2) > tree->mod->s_opt->min_diff_lk_global); //until no significative improvement is detected lk2 = tree->c_lnL; /*! save likelihood of each sites, in order to compute branch supports */ buff = (t_tree *)tree; do { for(site=0;siten_pattern;site++) tree->log_lks_aLRT[2][site]= tree->c_lnL_sorted[site]; tree = tree->next_mixt; } while(tree); tree = (t_tree *)buff; /*! undo the swap */ Swap(v4,b_fcus->left,b_fcus->rght,v2,tree); /***********/ /*! restore the initial branch length values */ Copy_Scalar_Dbl(len_e1,e1->l); Copy_Scalar_Dbl(len_e2,e2->l); Copy_Scalar_Dbl(len_e3,e3->l); Copy_Scalar_Dbl(len_e4,e4->l); Copy_Scalar_Dbl(l_init,b_fcus->l); Copy_Scalar_Dbl(var_e1,e1->l_var); Copy_Scalar_Dbl(var_e2,e2->l_var); Copy_Scalar_Dbl(var_e3,e3->l_var); Copy_Scalar_Dbl(var_e4,e4->l_var); Copy_Scalar_Dbl(v_init,b_fcus->l_var); /*! recompute likelihoods */ Update_PMat_At_Given_Edge(e1,tree); Update_PMat_At_Given_Edge(e2,tree); Update_PMat_At_Given_Edge(e3,tree); Update_PMat_At_Given_Edge(e4,tree); Update_PMat_At_Given_Edge(b_fcus,tree); MIXT_Set_Alias_Subpatt(YES,tree); Update_Partial_Lk(tree,b_fcus,b_fcus->left); Update_Partial_Lk(tree,b_fcus,b_fcus->rght); for(i=0;i<3;i++) if(b_fcus->left->v[i] != b_fcus->rght) Update_Partial_Lk(tree,b_fcus->left->b[i],b_fcus->left); for(i=0;i<3;i++) if(b_fcus->rght->v[i] != b_fcus->left) Update_Partial_Lk(tree,b_fcus->rght->b[i],b_fcus->rght); MIXT_Set_Alias_Subpatt(NO,tree); lk_temp = Lk(b_fcus,tree); if((lk_temp > lk_init + tree->mod->s_opt->min_diff_lk_local) || (lk_temp < lk_init - tree->mod->s_opt->min_diff_lk_local)) { PhyML_Fprintf(stderr,"\n== lk_temp = %f lk_init = %f",lk_temp,lk_init); PhyML_Fprintf(stderr,"\n== Err. in file %s at line %d\n",__FILE__,__LINE__); Warn_And_Exit(""); } //save likelihoods in NNI structures b_fcus->nni->lk0 = lk0; b_fcus->nni->lk1 = lk1; b_fcus->nni->lk2 = lk2; b_fcus->nni->score = lk0 - MAX(lk1,lk2); if((b_fcus->nni->score < tree->mod->s_opt->min_diff_lk_local) && (b_fcus->nni->score > -tree->mod->s_opt->min_diff_lk_local)) { b_fcus->nni->score = .0; b_fcus->nni->lk1 = b_fcus->nni->lk2 = b_fcus->nni->lk0; } if((b_fcus->nni->lk1 > b_fcus->nni->lk0) && (b_fcus->nni->lk1 > b_fcus->nni->lk2)) { if(b_fcus->nni->lk0 > b_fcus->nni->lk2) result = 1; //lk1 > lk0 > lk2 else result = 3; //lk1 > lk2 > lk0 } else if((b_fcus->nni->lk2 > b_fcus->nni->lk0) && (b_fcus->nni->lk2 > b_fcus->nni->lk1)) { if(b_fcus->nni->lk0 > b_fcus->nni->lk1) result = 2; //lk2 > lk0 > lk1 else result = 4; //lk2 > lk1 > lk0 } Free_Scalar_Dbl(len_e1); Free_Scalar_Dbl(len_e2); Free_Scalar_Dbl(len_e3); Free_Scalar_Dbl(len_e4); Free_Scalar_Dbl(l_init); Free_Scalar_Dbl(var_e1); Free_Scalar_Dbl(var_e2); Free_Scalar_Dbl(var_e3); Free_Scalar_Dbl(var_e4); Free_Scalar_Dbl(v_init); return result; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* * Make one target swap, optimizing five branches. * param tree : the tree to check * param tested_t_edge : the swaping t_edge of the tree * param swapToDo : 1 or 2, to select the first or the second swap to do */ void Make_Target_Swap(t_tree *tree, t_edge *b_fcus, int swaptodo) { t_node *v1,*v2,*v3,*v4; phydbl lktodo; phydbl lk_init, lk_temp; int i; if(swaptodo < 0) { PhyML_Fprintf(stderr,"\n== Err in file %s at line %d\n\n",__FILE__,__LINE__); Warn_And_Exit(""); } //Initialization lk_init = tree->c_lnL; b_fcus->nni->score = .0; lktodo = UNLIKELY; v1 = v2 = v3 = v4 = NULL; v1 = b_fcus->left->v[b_fcus->l_v1]; v2 = b_fcus->left->v[b_fcus->l_v2]; v3 = b_fcus->rght->v[b_fcus->r_v1]; v4 = b_fcus->rght->v[b_fcus->r_v2]; if(v1->num < v2->num) { PhyML_Fprintf(stderr,"\n== Err in file %s at line %d\n\n",__FILE__,__LINE__); Warn_And_Exit(""); } if(v3->num < v4->num) { PhyML_Fprintf(stderr,"\n. Err in file %s at line %d\n\n",__FILE__,__LINE__); Warn_And_Exit(""); } /***********/ //make the selected swap if(swaptodo==1) { Swap(v2,b_fcus->left,b_fcus->rght,v3,tree); if(!Check_Topo_Constraints(tree,tree->io->cstr_tree)) Swap(v3,b_fcus->left,b_fcus->rght,v2,tree); } else { Swap(v2,b_fcus->left,b_fcus->rght,v4,tree); if(!Check_Topo_Constraints(tree,tree->io->cstr_tree)) Swap(v4,b_fcus->left,b_fcus->rght,v2,tree); } MIXT_Set_Alias_Subpatt(YES,tree); Set_Both_Sides(YES,tree); lktodo = Update_Lk_At_Given_Edge(b_fcus,tree); for(i=0;i<3;i++) if(b_fcus->left->v[i] != b_fcus->rght) Update_Partial_Lk(tree,b_fcus->left->b[i],b_fcus->left); for(i=0;i<3;i++) if(b_fcus->rght->v[i] != b_fcus->left) Update_Partial_Lk(tree,b_fcus->rght->b[i],b_fcus->rght); MIXT_Set_Alias_Subpatt(NO,tree); //Optimize branch lengths and update likelihoods lk_temp = UNLIKELY; do { lktodo = lk_temp; for(i=0;i<3;i++) if(b_fcus->left->v[i] != b_fcus->rght) { Update_Partial_Lk(tree,b_fcus->left->b[i],b_fcus->left); lk_temp = Br_Len_Opt(&(b_fcus->left->b[i]->l->v),b_fcus->left->b[i],tree); } Update_Partial_Lk(tree,b_fcus,b_fcus->left); lk_temp = Br_Len_Opt(&(b_fcus->l->v),b_fcus,tree); for(i=0;i<3;i++) if(b_fcus->rght->v[i] != b_fcus->left) { Update_Partial_Lk(tree,b_fcus->rght->b[i],b_fcus->rght); lk_temp = Br_Len_Opt(&(b_fcus->rght->b[i]->l->v),b_fcus->rght->b[i],tree); } Update_Partial_Lk(tree,b_fcus,b_fcus->rght); if(lk_temp < lktodo - tree->mod->s_opt->min_diff_lk_local) { PhyML_Fprintf(stderr,"\n== Edge %3d lk_temp = %f lktodo = %f\n",b_fcus->num,lk_temp,lktodo); PhyML_Fprintf(stderr,"\n== Err. in file %s at line %d\n\n",__FILE__,__LINE__); Warn_And_Exit(""); } } while(FABS(lk_temp-lktodo) > tree->mod->s_opt->min_diff_lk_global); //until no significative improvement is detected /* PhyML_Printf("\n.<< [%3d] l=%f lk_init=%f tree->c_lnL=%f score=%12f v1=%3d v2=%3d v3=%3d v4=%3d >>", */ /* b_fcus->num, */ /* b_fcus->l->v, */ /* lk_init, */ /* tree->c_lnL, */ /* lk_init-tree->c_lnL, */ /* v1->num,v2->num,v3->num,v4->num); */ if(tree->c_lnL < lk_init - tree->mod->s_opt->min_diff_lk_global) { PhyML_Fprintf(stderr,"\n== [%3d] v1=%d v2=%d v3=%d v4=%d",b_fcus->num,v1->num,v2->num,v3->num,v4->num); PhyML_Fprintf(stderr,"\n== tree->c_lnL = %f lk_init = %f\n",tree->c_lnL,lk_init); PhyML_Fprintf(stderr,"\n== Err. in file %s at line %d\n\n",__FILE__,__LINE__); Warn_And_Exit(""); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /** * Convert an aLRT statistic to a non-parametric support * param in: the statistic */ phydbl Statistics_To_Probabilities(phydbl in) { phydbl rough_value=0.0; phydbl a=0.0; phydbl b=0.0; phydbl fa=0.0; phydbl fb=0.0; if(in>=0.000000393 && in<0.00000157) { a=0.000000393; b=0.00000157; fa=0.0005; fb=0.001; } else if(in>=0.00000157 && in<0.0000393) { a=0.00000157; b=0.0000393; fa=0.001; fb=0.005; } else if(in>=0.0000393 && in<0.000157) { a=0.0000393; b=0.000157; fa=0.005; fb=0.01; } else if(in>=0.000157 && in<0.000982) { a=0.000157; b=0.000982; fa=0.01; fb=0.025; } else if(in>=0.000982 && in<0.00393) { a=0.000982; b=0.00393; fa=0.025; fb=0.05; } else if(in>=0.00393 && in<0.0158) { a=0.00393; b=0.0158; fa=0.05; fb=0.1; } else if(in>=0.0158 && in<0.0642) { a=0.0158; b=0.0642; fa=0.1; fb=0.2; } else if(in>=0.0642 && in<0.148) { a=0.0642; b=0.148; fa=0.2; fb=0.3; } else if(in>=0.148 && in<0.275) { a=0.148; b=0.275; fa=0.3; fb=0.4; } else if(in>=0.275 && in<0.455) { a=0.275; b=0.455; fa=0.4; fb=0.5; } else if(in>=0.455 && in<0.708) { a=0.455; b=0.708; fa=0.5; fb=0.6; } else if(in>=0.708 && in<1.074) { a=0.708; b=1.074; fa=0.6; fb=0.7; } else if(in>=1.074 && in<1.642) { a=1.074; b=1.642; fa=0.7; fb=0.8; } else if(in>=1.642 && in<2.706) { a=1.642; b=2.706; fa=0.8; fb=0.9; } else if(in>=2.706 && in<3.841) { a=2.706; b=3.841; fa=0.9; fb=0.95; } else if(in>=3.841 && in<5.024) { a=3.841; b=5.024; fa=0.95; fb=0.975; } else if(in>=5.024 && in<6.635) { a=5.024; b=6.635; fa=0.975; fb=0.99; } else if(in>=6.635 && in<7.879) { a=6.635; b=7.879; fa=0.99; fb=0.995; } else if(in>=7.879 && in<10.828) { a=7.879; b=10.828; fa=0.995; fb=0.999; } else if(in>=10.828 && in<12.116) { a=10.828; b=12.116; fa=0.999; fb=0.9995; } if (in>=12.116) { rough_value=0.9999; } else if(in<0.000000393) { rough_value=0.0001; } else { rough_value=(b-in)/(b-a)*fa + (in - a)/(b-a)*fb; } rough_value=rough_value+(1.0-rough_value)/2.0; rough_value=rough_value*rough_value*rough_value; return rough_value; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /** * deprecated * Compute a RELL support, using the latest tested branch * param tree: the tested tree */ phydbl Statistics_to_RELL(t_tree *tree) { int i; int occurence=10000; phydbl nb=0.0; phydbl res,*pi; int site; phydbl lk0=0.0; phydbl lk1=0.0; phydbl lk2=0.0; int position = -1; t_tree *buff_tree; int* samples; /*! 1000 times */ for(i=0;idata->crunch_len,sizeof(phydbl)); for(site=0;siten_pattern;site++) pi[site] = tree->data->wght[site] / (phydbl)tree->data->init_len; samples = Sample_n_i_With_Proba_pi(pi,tree->n_pattern,tree->data->init_len); For(site, tree->data->init_len) { position = samples[site]; lk0+=tree->log_lks_aLRT[0][position]; lk1+=tree->log_lks_aLRT[1][position]; lk2+=tree->log_lks_aLRT[2][position]; } if (lk0>=lk1 && lk0>=lk2) nb++; tree = tree->next_mixt; Free(samples); Free(pi); } while(tree); tree = buff_tree; } res= nb/(phydbl)occurence; return res; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /*! Compute a SH-like support, using the latest tested branch param tree: the tested tree */ phydbl Statistics_To_SH(t_tree *tree) { int i; int occurence=10000; phydbl nb=0.0; phydbl res; int site; phydbl lk0=0.0; phydbl lk1=0.0; phydbl lk2=0.0; phydbl c0=0.0; phydbl c1=0.0; phydbl c2=0.0; int position=-1; phydbl delta_local=-1.; phydbl delta=0.0; t_tree *buff_tree; phydbl *pi; int* samples; /*! Compute the total log-lk of each NNI position */ buff_tree = tree; do { For(site, tree->n_pattern) { c0+=tree->log_lks_aLRT[0][site] * tree->data->wght[site]; c1+=tree->log_lks_aLRT[1][site] * tree->data->wght[site]; c2+=tree->log_lks_aLRT[2][site] * tree->data->wght[site]; } tree = tree->next_mixt; } while(tree); tree = buff_tree; if (c0>=c1 && c0>=c2) { if (c1>=c2) { delta=c0-c1; } else { delta=c0-c2; } } else if(c1>=c0 && c1>=c2) { if (c0>=c2) { delta=c1-c0; } else { delta=c1-c2; } } else { if (c1>=c0) { delta=c2-c1; } else { delta=c2-c0; } } /*! 1000 times */ for(i=0;idata->crunch_len,sizeof(phydbl)); for(site=0;siten_pattern;site++) pi[site] = tree->data->wght[site] / (phydbl)tree->data->init_len; samples = Sample_n_i_With_Proba_pi(pi,tree->n_pattern,tree->data->init_len); /*! Shuffle the data */ For(site, tree->data->init_len) { position = samples[site]; lk0+=tree->log_lks_aLRT[0][position]; lk1+=tree->log_lks_aLRT[1][position]; lk2+=tree->log_lks_aLRT[2][position]; } tree = tree->next_mixt; Free(samples); Free(pi); } while(tree); tree = buff_tree; /*! return to null hypothesis */ lk0=lk0-c0; lk1=lk1-c1; lk2=lk2-c2; /*! compute results and increment if needed */ delta_local=0.0; if (lk0>=lk1 && lk0>=lk2) { if (lk1>=lk2) { delta_local=lk0-lk1; } else { delta_local=lk0-lk2; } } else if(lk1>=lk0 && lk1>=lk2) { if (lk0>=lk2) { delta_local=lk1-lk0; } else { delta_local=lk1-lk2; } } else { if (lk1>=lk0) { delta_local=lk2-lk1; } else { delta_local=lk2-lk0; } } if (delta>(delta_local+0.1)) { nb++; } } res= nb/occurence; return res; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /** * deprecated * Compute one side likelihood * param b_fcus : concerned edge * param tree : b_fcus tree * param exclude : side to exclude for computation */ phydbl Update_Lk_At_Given_Edge_Excluding(t_edge *b_fcus, t_tree *tree, t_node *exclude) { if((!b_fcus->left->tax) && (exclude == NULL || exclude != b_fcus->left)) Update_Partial_Lk(tree,b_fcus,b_fcus->left); if((!b_fcus->rght->tax) && (exclude == NULL || exclude != b_fcus->rght)) Update_Partial_Lk(tree,b_fcus,b_fcus->rght); tree->c_lnL = Lk(b_fcus,tree); return tree->c_lnL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phyml-3.3.20220408/src/alrt.h000066400000000000000000000016631422404706600153250ustar00rootroot00000000000000/* PHYML : a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences Copyright (C) Stephane Guindon. Oct 2003 onward All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include #ifndef ALRT_H #define ALRT_H #include "utilities.h" #include "lk.h" #include "optimiz.h" #include "models.h" #include "free.h" #include "simu.h" void aLRT(t_tree *tree); int Check_NNI_Five_Branches(t_tree *tree); int Compute_Likelihood_Ratio_Test(t_edge *tested_edge, t_tree *tree); int NNI_Neigh_BL(t_edge *b_fcus, t_tree *tree); void Make_Target_Swap(t_tree *tree, t_edge *b_fcus, int swaptodo); phydbl Statistics_To_Probabilities(phydbl in); phydbl Statistics_To_RELL(t_tree *tree); phydbl Statistics_To_SH(t_tree *tree); phydbl Update_Lk_At_Given_Edge_Excluding(t_edge *b_fcus, t_tree *tree, t_node *exclude); #endif phyml-3.3.20220408/src/ancestral.c000066400000000000000000001060271422404706600163320ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include "ancestral.h" void Sample_Ancestral_Seq(int fullmutmap, int fromprior, t_tree *tree) { int rate_cat; int i,j,k,l; phydbl *probs; phydbl sum; int n_mut; FILE *fp; phydbl *muttime; int *muttype,*muttax; char *s,*mut_string; int *ordering; if(tree->is_mixt_tree == YES) { MIXT_Sample_Ancestral_Seq(fullmutmap,fromprior,tree); return; } probs = (phydbl *)mCalloc(tree->mod->ras->n_catg,sizeof(phydbl)); muttime = (phydbl *)mCalloc((2*tree->n_otu-3)*5, // At most 5 mutations per branch on average sizeof(phydbl)); muttype = (int *)mCalloc((2*tree->n_otu-3)*5, // At most 5 mutations per branch on average sizeof(int)); muttax = (int *)mCalloc((2*tree->n_otu-3)*5, // At most 5 mutations per branch on average sizeof(int)); ordering = (int *)mCalloc((2*tree->n_otu-3)*5, // At most 5 mutations per branch on average sizeof(int)); s = (char *)mCalloc(T_MAX_NAME,sizeof(char)); for(i=0;i<2*tree->n_otu-1;++i) if(tree->a_nodes[i]->tax == NO) { tree->a_nodes[i]->c_seq_anc = (align *)mCalloc(1,sizeof(align));; tree->a_nodes[i]->c_seq_anc->state = (char *)mCalloc(tree->n_pattern,sizeof(char)); } /* Update P(D_x|X=i) for each state i and node X */ Set_Both_Sides(YES,tree); Lk(NULL,tree); for(i=0;in_pattern;++i) { /* Sample the rate class from its posterior density */ for(j=0;jmod->ras->n_catg;j++) { if(fromprior == NO) probs[j] = tree->unscaled_site_lk_cat[i*tree->mod->ras->n_catg+j]* tree->mod->ras->gamma_r_proba->v[j]; else probs[j] = tree->mod->ras->gamma_r_proba->v[j]; } /* Scale probas. */ sum = .0; for(j=0;jmod->ras->n_catg;j++) sum += probs[j]; for(j=0;jmod->ras->n_catg;j++) probs[j]/=sum; rate_cat = Sample_i_With_Proba_pi(probs,tree->mod->ras->n_catg); n_mut = 0; if(tree->n_root != NULL) { Sample_Ancestral_Seq_Pre(tree->n_root,tree->n_root->v[1],tree->n_root->b[1], i,rate_cat, muttype,muttime,muttax,&n_mut, fullmutmap,fromprior,tree); Sample_Ancestral_Seq_Pre(tree->n_root,tree->n_root->v[2],tree->n_root->b[2], i,rate_cat, muttype,muttime,muttax,&n_mut, fullmutmap,fromprior,tree); } else { Sample_Ancestral_Seq_Pre(tree->a_nodes[0],tree->a_nodes[0]->v[0],tree->a_nodes[0]->b[0], i,rate_cat, muttype,muttime,muttax,&n_mut, fullmutmap,fromprior,tree); } for(j=0;j muttime[j]) ordering[k]++; else ordering[j]++; } } strcpy(s,"mutmap."); sprintf(s+strlen(s),"%d.",tree->io->r_seed); sprintf(s+strlen(s),"%d",i); fp = fopen(s,"a"); PhyML_Fprintf(fp,"\n-1 -1 -1.0 -1 %d",tree->mixt_tree ? tree->mixt_tree->mcmc->run : tree->mcmc->run); Reverse_Muttime(muttime,n_mut,tree); for(j=0;jdata->init_len;l++) if(tree->data->sitepatt[l] == i) break; mut_string = Mutation_Id(muttype[k],tree); PhyML_Fprintf(fp,"\n%4d %s %g %4d %s",j,mut_string,muttime[k],l,tree->a_nodes[muttax[k]]->name); Free(mut_string); break; } } } for(j=0;j> a: %d d: %d b->left: %d b->rght: %d",a?a->num:-1,d?d->num:-1,b?b->left->num:-1,b?b->rght->num:-1); */ ns = tree->mod->ns; probs = (phydbl *)mCalloc(ns,sizeof(phydbl)); if(a->tax == TRUE) // Sample state at tip if observed state is ambiguous { assert(b); if(a == b->left) for(i=0;ip_lk_tip_l[site*ns+i]; else for(i=0;ip_lk_tip_r[site*ns+i]; for(i=0;imod->e_frq->pi->v[i]; sum = 0.0; for(i=0;in_root) { sa = Sample_Ancestral_Seq_Core(NULL,tree->n_root,NULL,r_cat,site,tree); } else { sa = Assign_State(a->c_seq_anc->state + site, tree->mod->io->datatype, tree->mod->io->state_len); } sd = Sample_Ancestral_Seq_Core(a,d,b,r_cat,site,tree); if(fullmutmap == YES) Map_Mutations(a,d,sa,sd,b,site,r_cat,muttype,muttime,muttax,n_mut,tree); if(d->tax) return; else { for(i=0;i<3;++i) { if(d->v[i] != a && d->b[i] != tree->e_root) { Sample_Ancestral_Seq_Pre(d,d->v[i],d->b[i],site,r_cat,muttype,muttime,muttax,n_mut,fullmutmap,fromprior,tree); } } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int Sample_Ancestral_Seq_Core(t_node *a, t_node *d, t_edge *b, int r_cat, int site, t_tree *tree) { int i,j; int ns; ns = tree->mod->ns; int dim1 = tree->mod->ras->n_catg * ns; int dim2 = ns; int dim3 = ns * ns; phydbl *probs,*Pij,sum; int state; probs = (phydbl *)mCalloc(ns,sizeof(phydbl)); state = -1; if(d->tax == YES) { assert(b); if(d == b->left) for(i=0;ip_lk_tip_l[site*ns+i]; else if(d == b->rght) for(i=0;ip_lk_tip_r[site*ns+i]; else assert(FALSE); for(i=0;imod->e_frq->pi->v[i]; sum = 0.0; for(i=0;in_root); phydbl r,l; Update_PMat_At_Given_Edge(tree->n_root->b[1],tree); Update_PMat_At_Given_Edge(tree->n_root->b[2],tree); for(i=0;ie_root->left == tree->n_root->v[1]) Pij = tree->n_root->b[1]->Pij_rr; else Pij = tree->n_root->b[2]->Pij_rr; l = 0.0; for(j=0;je_root->left->tax == NO) l += tree->e_root->p_lk_left[site*dim1+r_cat*dim2+j] * Pij[r_cat*dim3+i*dim2+j]; else l += tree->e_root->p_lk_tip_l[site*dim2+j] * Pij[r_cat*dim3+i*dim2+j]; } if(tree->e_root->rght == tree->n_root->v[1]) Pij = tree->n_root->b[1]->Pij_rr; else Pij = tree->n_root->b[2]->Pij_rr; r = 0.0; for(j=0;je_root->rght->tax == NO) r += tree->e_root->p_lk_rght[site*dim1+r_cat*dim2+j] * Pij[r_cat*dim3+i*dim2+j]; else r += tree->e_root->p_lk_tip_r[site*dim2+j] * Pij[r_cat*dim3+i*dim2+j]; } probs[i] = r*l*tree->mod->e_frq->pi->v[i]; } sum = 0.0; for(i=0;ic_seq_anc->state[site] = Reciproc_Assign_State(state,tree->io->datatype); } else { assert(b); // State (already) sampled at node a state = Assign_State(a->c_seq_anc->state + site, tree->mod->io->datatype, tree->mod->io->state_len); Pij = b->Pij_rr; for(i=0;ileft) probs[i] = b->p_lk_left[site*dim1+r_cat*dim2+i] * Pij[r_cat*dim3+state*dim2+i]; else if(d == b->rght) probs[i] = b->p_lk_rght[site*dim1+r_cat*dim2+i] * Pij[r_cat*dim3+state*dim2+i]; else assert(FALSE); } sum = 0.0; for(i=0;ic_seq_anc->state[site] = Reciproc_Assign_State(state,tree->io->datatype); } } Free(probs); return state; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Map_Mutations(t_node *a, t_node *d, int sa, int sd, t_edge *b, int site, int rcat, int *muttype, phydbl *muttime, int *muttax, int *n_mut, t_tree *tree) { int i,j; phydbl *probs,*all_probs; int slast,snew; // Last state visited phydbl tlast; phydbl *Q; phydbl u,sum; int *mut; // Array of mutations int thismut; int n_mut_branch; int n_iter; int first_mut; phydbl T; int ns,tax_idx; phydbl rr; ns = tree->mod->ns; all_probs = (phydbl *)mCalloc(ns*ns,sizeof(phydbl)); mut = (int *)mCalloc(ns*ns,sizeof(int)); // Site relative rate rr = tree->mod->ras->gamma_rr->v[rcat]; // Rate matrix Q = tree->mod->r_mat->qmat->v; // Length of the 'time' interval considered. T = 0.0; #ifdef PHYTIME T = tree->rates->cur_l[d->num]*rr; #else T = b->l->v*rr; #endif /* PhyML_Printf("\n. Mutmap: a:%d d:%d ta:%G td:%G cr:%G rr:%G l:%G", */ /* a?a->num:-1,d?d->num:-1, */ /* tree->times->nd_t[a->num], */ /* tree->times->nd_t[d->num], */ /* cr,rr, */ /* fabs(tree->times->nd_t[a->num]-tree->times->nd_t[d->num])*cr*rr); */ // Matrix of change probabilities for(i=0;ii)=0.0; all_probs[i*ns+i] = 0.0; // Normalise so that \sum_j p(i->j) = 1.0; sum = 0; for(j=0;j sample a new mutation event if(tlast < T) { first_mut = NO; n_mut_branch++; if(n_mut_branch > 5 && n_mut_branch%10 == 0) PhyML_Printf("\n. # of mutations on edge %d (length: %g) exceeds 5 (%d) ! The program will probably crash soon...:(", b->num, tree->rates ? tree->rates->cur_l[d->num] : b->l->v, n_mut_branch); snew = Sample_i_With_Proba_pi(probs,ns); // Record mutation type mut[slast*ns+snew]++; // Record mutation type in the site mutation array thismut = slast*ns+snew; muttype[(*n_mut)+n_mut_branch-1] = thismut; // Record time of mutation muttime[(*n_mut)+n_mut_branch-1] = tlast; #ifdef PHYTIME // Transform into time in calendar units muttime[(*n_mut)+n_mut_branch-1] /= tree->rates->cur_l[d->num]; muttime[(*n_mut)+n_mut_branch-1] *= fabs(tree->times->nd_t[a->num]-tree->times->nd_t[d->num]); #endif tax_idx = -1; Random_Tax_Idx(a,d,&tax_idx,tree); muttax[(*n_mut)+n_mut_branch-1] = tax_idx; // Update the last state slast = snew; } else { if(slast == sd) break; else { // Restart from the beginning for(i=0;i 0) */ /* { */ /* thismut = MIN(i,j) * ns + MAX(i,j) - (MIN(i,j)+1+(int)POW(MIN(i,j)+1,2))/2; */ /* if(tree->mixt_tree != NULL) */ /* tree->mixt_tree->mutmap[thismut*(tree->n_pattern)*(2*tree->n_otu-3) + b->num*(tree->n_pattern) + site]++; */ /* else */ /* tree->mutmap[thismut*(tree->n_pattern)*(2*tree->n_otu-3) + b->num*(tree->n_pattern) + site]++; */ /* } */ /* } */ /* } */ Free(all_probs); Free(mut); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void Ancestral_Sequences(t_tree *tree, int print) { int i; if(print == YES) { PhyML_Printf("\n\n. Estimating ancestral sequences..."); strcpy(tree->io->out_ancestral_seq_file,tree->io->out_file); strcat(tree->io->out_ancestral_seq_file,"_phyml_ancestral_"); if(tree->io->append_run_ID) { strcat(tree->io->out_ancestral_seq_file,tree->io->run_id_string); strcat(tree->io->out_ancestral_seq_file,"_"); } strcat(tree->io->out_ancestral_seq_file,"seq.txt"); tree->io->fp_out_ancestral_seq = Openfile(tree->io->out_ancestral_seq_file,1); strcpy(tree->io->out_ancestral_tree_file,tree->io->out_file); strcat(tree->io->out_ancestral_tree_file,"_phyml_ancestral_"); if(tree->io->append_run_ID) { strcat(tree->io->out_ancestral_tree_file,tree->io->run_id_string); strcat(tree->io->out_ancestral_tree_file,"_"); } strcat(tree->io->out_ancestral_tree_file,"tree.txt"); tree->io->fp_out_ancestral_tree = Openfile(tree->io->out_ancestral_tree_file,1); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n\n\n"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n. Printing marginal probabilities of ancestral sequences at each site"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n. of the alignment and each node of the tree. The tree in Newick format"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n. with internal nodes labels corresponding to those given below can be"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n. found in the file '%s'.",tree->io->out_ancestral_tree_file); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n. Ancestral reconstruction is conducted based on the \"Minimum Posterior Expected"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n. Error\" (MPEE) criterion, which accomodates for uncertainty in the selection of "); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n. ancestral character states."); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n. Recommended citation:"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n. Oliva A., Pulicani S., Lefort V., Brehelin L., Gascuel O. and S. Guindon,"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n. \"Accounting for ambiguity in ancestral sequence reconstruction\","); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n. Bioinformatics, Volume 35, Issue 21, 1 November 2019."); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n\n"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"Site\tNodeLabel\t"); for(i=0;imod->ns;i++) PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"%10c\t",Reciproc_Assign_State(i,tree->io->datatype)); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"MPEE\t"); PhyML_Fprintf(tree->io->fp_out_ancestral_seq,"\n"); short int bck_support = tree->io->print_support_val; tree->io->print_node_num = YES; tree->io->print_support_val = NO; char *s = Write_Tree(tree); PhyML_Fprintf(tree->io->fp_out_ancestral_tree,"%s",s); tree->io->print_node_num = NO; tree->io->print_support_val = bck_support; Free(s); fclose(tree->io->fp_out_ancestral_tree); } for(i=0;i<2*tree->n_otu-2;i++) if(tree->a_nodes[i]->tax == NO) Ancestral_Sequences_One_Node(tree->a_nodes[i],tree,print); if(tree->n_root) Ancestral_Sequences_One_Node(tree->n_root,tree,print); fclose(tree->io->fp_out_ancestral_seq); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Ancestral_Sequences_One_Node(t_node *d, t_tree *tree, int print) { if(d->tax) return; else { if(tree->is_mixt_tree) { MIXT_Ancestral_Sequences_One_Node(d,tree,print); } else { t_node *v0,*v1,*v2; // three neighbours of d t_edge *b0,*b1,*b2; int i,j; int catg; phydbl p0, p1, p2; phydbl *p; int site,csite; phydbl *p_lk0, *p_lk1, *p_lk2; int *sum_scale0, *sum_scale1, *sum_scale2; phydbl sum_probas; phydbl *Pij0, *Pij1, *Pij2; phydbl inc,sum_scale; FILE *fp; unsigned const int ncatg = tree->mod->ras->n_catg; unsigned const int ns = tree->mod->ns; unsigned const int nsns = ns*ns; unsigned const int ncatgns = ns*ncatg; if(tree->scaling_method == SCALE_RATE_SPECIFIC) { PhyML_Fprintf(stderr,"\n. Likelihood rescaling method not compatible with the calculation of ancestral state probabilities."); Exit("\n"); } if(!d) return; fp = tree->io->fp_out_ancestral_seq; assert(fp != NULL); p = (phydbl *)mCalloc(ns,sizeof(phydbl)); for(site=0;sitedata->init_len;site++) // For each site in the current partition element { csite = tree->data->sitepatt[site]; for(i=0;iv[0]; v1 = d->v[1]; v2 = d->v[2]; b0 = d->b[0]; b1 = d->b[1]; b2 = d->b[2]; Pij0 = b0->Pij_rr; Pij1 = b1->Pij_rr; Pij2 = b2->Pij_rr; sum_scale = 0.0; if(v0 == b0->left) { p_lk0 = b0->p_lk_left; sum_scale0 = b0->sum_scale_left; } else { p_lk0 = b0->p_lk_rght; sum_scale0 = b0->sum_scale_rght; } if(v1 == b1->left) { p_lk1 = b1->p_lk_left; sum_scale1 = b1->sum_scale_left; } else { p_lk1 = b1->p_lk_rght; sum_scale1 = b1->sum_scale_rght; } if(v2 == b2->left) { p_lk2 = b2->p_lk_left; sum_scale2 = b2->sum_scale_left; } else { p_lk2 = b2->p_lk_rght; sum_scale2 = b2->sum_scale_rght; } for(catg=0;catgtax) { for(j=0;jb[0]->p_lk_tip_r[csite*ns+j] * Pij0[catg*nsns+i*ns+j]; if(isinf(p0) || isnan(p0)) { PhyML_Fprintf(stderr,"\n. p0: %G v0->b[0]->p_lk_tip_r[csite*ns+j]: %G Pij0[catg*nsns+i*ns+j]: %G\n", p0, v0->b[0]->p_lk_tip_r[csite*ns+j], Pij0[catg*nsns+i*ns+j]); Exit("\n"); } } } else { for(j=0;jtax) { for(j=0;jb[0]->p_lk_tip_r[csite*ns+j] * Pij1[catg*nsns+i*ns+j]; if(isinf(p1) || isnan(p1)) { PhyML_Fprintf(stderr,"\n. p1: %G v1->b[0]->p_lk_tip_r[csite*ns+j]: %G Pij1[catg*nsns+i*ns+j]: %G\n", p1, v1->b[0]->p_lk_tip_r[csite*ns+j], Pij1[catg*nsns+i*ns+j]); Exit("\n"); } } } else { for(j=0;jtax) { for(j=0;jb[0]->p_lk_tip_r[csite*ns+j] * Pij2[catg*nsns+i*ns+j]; } } else { for(j=0;jmod->e_frq->pi->v[i] * tree->mod->ras->gamma_r_proba->v[catg]; p[i] += inc; if(isinf(p[i]) || isnan(p[i])) { PhyML_Fprintf(stderr,"\n. site: %4d p0: %G p1: %G p2: %G tree->mod->e_frq->pi->v[i]: %G tree->cur_site_lk[csite]: %G tree->mod->ras->gamma_r_proba->v[catg]: %G tree->c_lnL_sorted[csite]: %G", csite, p0,p1,p2, tree->mod->e_frq->pi->v[i] , tree->cur_site_lk[csite] , tree->mod->ras->gamma_r_proba->v[catg], tree->c_lnL_sorted[csite]); Exit("\n"); } } /* printf("\n. site: %d || %d %d %d", */ /* csite, */ /* v0->tax ? -1 : sum_scale0[csite*ncatg+catg], */ /* v1->tax ? -1 : sum_scale1[csite*ncatg+catg], */ /* v2->tax ? -1 : sum_scale2[csite*ncatg+catg]); */ } if(tree->mod->ras->invar == YES) { int num_prec_issue = NO; phydbl inv_site_lk = Invariant_Lk(sum_scale,csite,&num_prec_issue,tree); switch(num_prec_issue) { case YES : { assert(isinf(inv_site_lk)); inv_site_lk = Invariant_Lk(0,csite,&num_prec_issue,tree); for(i=0;imod->ras->pinvar->v * tree->mod->e_frq->pi->v[i]; break; } case NO : { for(i=0;imod->ras->pinvar->v) + inv_site_lk * tree->mod->ras->pinvar->v * tree->mod->e_frq->pi->v[i]; break; } } } for(i=0;ic_lnL_sorted[csite]; for(i=0;inum); for(i=0;i highest_count) { highest_count = count[i]; highest_count_idx = i; } } most_frequent_state = best_state[highest_count_idx]; Free(alpha); Free(best_state); Free(idx); Free(count); return(most_frequent_state); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ int MPEE_Score(const phydbl *alpha, int *idx, const phydbl *p, const int ns) { unsigned int i; phydbl *mpee_score,*cdf_sorted; int min_idx,res; phydbl a,b; phydbl min; mpee_score = (phydbl *)mCalloc(ns,sizeof(phydbl)); cdf_sorted = (phydbl *)mCalloc(ns,sizeof(phydbl)); cdf_sorted[0] = p[idx[0]]; for(i=1;i #ifndef ANCESTRAL_H #define ANCESTRAL_H #include "utilities.h" void Sample_Ancestral_Seq(int fullmutmap, int fromprior, t_tree *tree); void Sample_Ancestral_Seq_Pre(t_node *a, t_node *d, t_edge *b, int site, int r_cat, int *muttype, phydbl *muttime, int *muttax, int *n_mut, int fullmutmap, int fromprior, t_tree *tree); int Sample_Ancestral_Seq_Core(t_node *a, t_node *d, t_edge *b, int r_cat, int site, t_tree *tree); void Map_Mutations(t_node *a, t_node *d, int sa, int sd, t_edge *b, int site, int rcat, int *muttype, phydbl *muttime, int *muttax, int *n_mut, t_tree *tree); void Ancestral_Sequences(t_tree *tree, int print); void Ancestral_Sequences_One_Node(t_node *d, t_tree *tree, int print); int MPEE_Score(const phydbl *alpha, int *idx, const phydbl *p, const int ns); int MPEE_Infer(const phydbl *p, const int ns); void Reverse_Muttime(phydbl *muttime, int n_mut, t_tree *tree); #endif phyml-3.3.20220408/src/avx.c000066400000000000000000000501521422404706600151510ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include "assert.h" #include "avx.h" #if (defined(__AVX__) || defined(__AVX2__)) ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void AVX_Update_Eigen_Lr(t_edge *b, t_tree *tree) { unsigned int site,catg; unsigned int i,j; unsigned const int npattern = tree->n_pattern; unsigned const int ncatg = tree->mod->ras->n_catg; unsigned const int ns = tree->mod->ns; unsigned const int sz = (int)BYTE_ALIGN / 8; unsigned const int nblocks = ns / sz; unsigned const int ncatgns = ncatg*ns; const phydbl *p_lk_left,*p_lk_rght,*pi; phydbl *dot_prod,*p_lk_left_pi; phydbl *l_ev,*r_ev; __m256d *_l_ev,*_r_ev,*_prod_left,*_prod_rght; p_lk_left_pi = tree->p_lk_left_pi; l_ev = tree->l_ev; _l_ev = tree->_l_ev; _r_ev = tree->_r_ev; _prod_left = tree->_prod_left; _prod_rght = tree->_prod_rght; assert(sz == 4); assert(tree->update_eigen_lr == YES); r_ev = tree->mod->eigen->r_e_vect; /* Copy transpose of matrix of left eigen vectors */ for(i=0;imod->eigen->l_e_vect[j*ns+i]; /* Load into AVX registers */ for(i=0;ileft->tax ? b->p_lk_tip_l : b->p_lk_left; p_lk_rght = b->rght->tax ? b->p_lk_tip_r : b->p_lk_rght; pi = tree->mod->e_frq->pi->v; dot_prod = tree->dot_prod; for(site=0;sitedata->wght[site] > SMALL) { for(catg=0;catgleft->tax == NO) p_lk_left += ns; if(b->rght->tax == NO) p_lk_rght += ns; } if(b->left->tax == YES) p_lk_left += ns; if(b->rght->tax == YES) p_lk_rght += ns; } else { if(b->left->tax == YES) p_lk_left += ns; else p_lk_left += ncatgns; if(b->rght->tax == YES) p_lk_rght += ns; else p_lk_rght += ncatgns; dot_prod += ncatgns; } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl AVX_Lk_Core_One_Class_No_Eigen_Lr(const phydbl *p_lk_left, const phydbl *p_lk_rght, const phydbl *Pij, const phydbl *tPij, const phydbl *pi, const int ns, const int ambiguity_check, const int observed_state) { const unsigned int sz = (int)BYTE_ALIGN / 8; const unsigned nblocks = ns/sz; if(nblocks == 1) { __m256d _plk_r,_plk_l; if(ambiguity_check == NO) // tip case. { Pij += observed_state*ns; _plk_r = _mm256_mul_pd(_mm256_load_pd(Pij),_mm256_load_pd(p_lk_left)); return pi[observed_state] * AVX_Vect_Norm(_plk_r); } else { unsigned int i; __m256d _pijplk,_pij; _plk_r = _mm256_mul_pd(_mm256_load_pd(p_lk_rght),_mm256_load_pd(pi)); _pijplk = _mm256_setzero_pd(); for(i=0;in_pattern; const unsigned int ns = tree->mod->ns; const unsigned int ncatg = tree->mod->ras->n_catg; const unsigned int ncatgns = ncatg * ns; const unsigned int nsns = ns * ns; const unsigned int sz = (int)BYTE_ALIGN / 8; const unsigned nblocks = ns/sz; __m256d *_tPij1,*_tPij2,*_pmat1plk1,*_pmat2plk2,*_plk0; _tPij1 = tree->_tPij1; _tPij2 = tree->_tPij2; _pmat1plk1 = tree->_pmat1plk1; _pmat2plk2 = tree->_pmat2plk2; _plk0 = tree->_plk0; sum_scale_v1_val = 0; sum_scale_v2_val = 0; n_v1 = n_v2 = NULL; plk0 = plk1 = plk2 = NULL; Pij1 = Pij2 = NULL; tPij1 = tPij2 = NULL; sum_scale_v1 = sum_scale_v2 = NULL; p_lk_loc = NULL; state_v1 = state_v2 = -1; Set_All_Partial_Lk(&n_v1,&n_v2, &plk0,&sum_scale,&p_lk_loc, &Pij1,&tPij1,&plk1,&sum_scale_v1, &Pij2,&tPij2,&plk2,&sum_scale_v2, d,b,tree); /* PhyML_Printf("\n. b: %d b->left:%d b->rght:%d d:%d [%p,%p]", */ /* b->num, */ /* b->left->num, */ /* b->rght->num, */ /* d->num, */ /* plk0, */ /* b->p_lk_rght); */ // Copy transpose of transition matrices into AVX registers for(i=0;imod->augmented == YES) { PhyML_Printf("\n== AVX version of the Update_Partial_Lk function does not"); PhyML_Printf("\n== allow augmented data."); assert(FALSE); } /* For every site in the alignment */ for(site=0;sitedata->wght[site] > SMALL) { state_v1 = state_v2 = -1; ambiguity_check_v1 = ambiguity_check_v2 = YES; if(n_v1 && n_v1->tax) { ambiguity_check_v1 = n_v1->c_seq->is_ambigu[site]; if(ambiguity_check_v1 == NO) state_v1 = n_v1->c_seq->d_state[site]; } if(n_v2 && n_v2->tax) { ambiguity_check_v2 = n_v2->c_seq->is_ambigu[site]; if(ambiguity_check_v2 == NO) state_v2 = n_v2->c_seq->d_state[site]; } for(catg=0;catgtax) ? 0 : ns; plk2 += (n_v2->tax) ? 0 : ns; } _tPij1 -= ncatg * nsns / sz; _tPij2 -= ncatg * nsns / sz; plk1 += (n_v1->tax) ? ns : 0; plk2 += (n_v2->tax) ? ns : 0; if(tree->scaling_method == SCALE_FAST) { sum_scale_v1_val = (sum_scale_v1)?(sum_scale_v1[site]):(0); sum_scale_v2_val = (sum_scale_v2)?(sum_scale_v2[site]):(0); sum_scale[site] = sum_scale_v1_val + sum_scale_v2_val; if(sum_scale[site] >= 1024) { /* plk0 -= ncatgns; */ /* plk1 -= (n_v1->tax) ? ns : ncatgns; */ /* plk2 -= (n_v2->tax) ? ns : ncatgns; */ /* PhyML_Fprintf(stderr,"\n. PARTIAL site: %d plk0: %p [%g %g %g %g] plk1: %p [%g %g %g %g] plk2: %p [%g %g %g %g]", */ /* site, */ /* plk0, */ /* plk0[0], */ /* plk0[1], */ /* plk0[2], */ /* plk0[3], */ /* plk1, */ /* plk1[0], */ /* plk1[1], */ /* plk1[2], */ /* plk1[3], */ /* plk2, */ /* plk2[0], */ /* plk2[1], */ /* plk2[2], */ /* plk2[3] */ /* ); */ /* PhyML_Fprintf(stderr,"\n. PARTIAL site: %d d: %d n_v1: %d n_v2: %d",site,d->num,n_v1->num,n_v2->num); */ /* PhyML_Fprintf(stderr,"\n. PARTIAL site: %d sum n: %d sum n_v1: %d sum n_v2: %d",site,sum_scale[site],sum_scale_v1_val,sum_scale_v2_val); */ /* plk0 += ncatgns; */ /* plk1 += (n_v1->tax) ? ns : ncatgns; */ /* plk2 += (n_v2->tax) ? ns : ncatgns; */ /* Exit("\n"); */ } plk0 -= ncatgns; largest_p_lk = -BIG; for(i=0;i largest_p_lk) largest_p_lk = plk0[i]; if(largest_p_lk < INV_TWO_TO_THE_LARGE && tree->mod->augmented == NO && tree->apply_lk_scaling == YES) { for(i=0;itax) ? ns : ncatgns; plk2 += (n_v2->tax) ? ns : ncatgns; } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void AVX_Partial_Lk_Exex(const __m256d *_tPij1, const int state1, const __m256d *_tPij2, const int state2, const int ns, __m256d *plk0) { unsigned const int sz = (int)BYTE_ALIGN / 8; unsigned const int nblocks = ns / sz; unsigned int i; _tPij1 = _tPij1 + state1 * nblocks; _tPij2 = _tPij2 + state2 * nblocks; for(i=0;i plk0: %f",x[i]); */ /* _mm256_store_pd(x,_tPij1[0]); */ /* for(int i=0;i<4;++i) PhyML_Printf("\n> Pij1: %f",x[i]); */ /* _mm256_store_pd(x,_tPij2[0]); */ /* for(int i=0;i<4;++i) PhyML_Printf("\n> Pij2: %f",x[i]); */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void AVX_Partial_Lk_Exin(const __m256d *_tPij1, const int state1, const __m256d *_tPij2, const phydbl *_plk2, __m256d *_pmat2plk2, const int ns, __m256d *_plk0) { unsigned const int sz = (int)BYTE_ALIGN / 8; unsigned const int nblocks = ns / sz; unsigned int i; _tPij1 = _tPij1 + state1 * nblocks; AVX_Matrix_Vect_Prod(_tPij2,_plk2,ns,_pmat2plk2); for(i=0;i 1.0 || plk1[i] < 1.0 || plk2[i] > 1.0 || plk2[i] < 1.0) break; if(i != ns) { AVX_Matrix_Vect_Prod(_tPij1,plk1,ns,_pmat1plk1); AVX_Matrix_Vect_Prod(_tPij2,plk2,ns,_pmat2plk2); for(i=0;i #ifndef AVX_H #define AVX_H #include "utilities.h" #include "optimiz.h" #include "models.h" #include "free.h" #include "times.h" #include "mixt.h" #if (defined(__AVX__) || defined(__AVX2__)) void AVX_Update_Partial_Lk(t_tree *tree,t_edge *b_fcus,t_node *n); void AVX_Update_Eigen_Lr(t_edge *b, t_tree *tree); phydbl AVX_Lk_Core_One_Class_Eigen_Lr(const phydbl *dot_prod, const phydbl *expl, const unsigned int ns); void AVX_Lk_dLk_Core_One_Class_Eigen_Lr(const phydbl *dot_prod, const phydbl *expl, const unsigned int ns, phydbl *lk, phydbl *dlk); phydbl AVX_Lk_Core_One_Class_No_Eigen_Lr(const phydbl *p_lk_left, const phydbl *p_lk_rght, const phydbl *Pij, const phydbl *tPij, const phydbl *pi, const int ns, const int ambiguity_check, const int observed_state); phydbl AVX_Vect_Norm(__m256d _z); phydbl AVX_Lk_Core(int state, int ambiguity_check, t_edge *b, t_tree *tree); phydbl AVX_Lk_Core_Nucl(int state, int ambiguity_check, t_edge *b, t_tree *tree); phydbl AVX_Lk_Core_AA(int state, int ambiguity_check, t_edge *b, t_tree *tree); void AVX_Partial_Lk_Exex(const __m256d *_tPij1, const int state1, const __m256d *_tPij2, const int state2, const int ns, __m256d *plk0); void AVX_Partial_Lk_Exin(const __m256d *_tPij1, const int state1, const __m256d *_tPij2, const phydbl *_plk2, __m256d *_pmat2plk2, const int ns, __m256d *_plk0); void AVX_Partial_Lk_Inin(const __m256d *_tPij1, const phydbl *plk1, __m256d *_pmat1plk1, const __m256d *_tPij2, const phydbl *plk2, __m256d *_pmat2plk2, const int ns, __m256d *_plk0); void AVX_Matrix_Vect_Prod(const __m256d *_m_transpose, const phydbl *_v, const int ns, __m256d *res); __m256d AVX_Horizontal_Add(const __m256d x[4]); phydbl AVX_Lk_Core_One_Class_No_Eigen_Lr_Block(const phydbl *p_lk_left, const phydbl *p_lk_rght, const phydbl *Pij, const phydbl *tPij, const phydbl *pi, const int ns, const int ambiguity_check, const int observed_state); phydbl AVX_Lk_Core_One_Class_No_Eigen_Lr_No_Block(const phydbl *p_lk_left, const phydbl *p_lk_rght, const phydbl *Pij, const phydbl *tPij, const phydbl *pi, const int ns, const int ambiguity_check, const int observed_state); #endif #endif phyml-3.3.20220408/src/beagle_utils.c000066400000000000000000000416211422404706600170130ustar00rootroot00000000000000/* * author: Imran Fanaswala */ #ifndef BEAGLE_UTILS_CPP #define BEAGLE_UTILS_CPP #include #include "beagle_utils.h" #define CLEAN_BEAGLE_API /* Attempting to remove unnecessary communication with BEAGLE device */ double* int_to_double(const int* src, int num_elems) { double* dest = (double*)malloc(num_elems*sizeof(double)); if (NULL==dest) Warn_And_Exit("\n. Couldn't allocate memory.\n"); int i; for(i=0; ilength; i++) { fprintf(stdout, "\t\tResource %i:\n\t\tName : %s\n", i, rList->list[i].name); fprintf(stdout, "\t\t\tDesc : %s\n", rList->list[i].description); fprintf(stdout, "\t\t\tFlags:"); print_beagle_flags(rList->list[i].supportFlags); fprintf(stdout, "\n"); } fflush(stdout); } void print_beagle_instance_details(BeagleInstanceDetails *inst) { int rNumber = inst->resourceNumber; fprintf(stdout, "\tUsing resource %i:\n", rNumber); fprintf(stdout, "\t\tRsrc Name : %s\n", inst->resourceName); fprintf(stdout, "\t\tImpl Name : %s\n", inst->implName); fprintf(stdout, "\t\tImpl Desc : %s\n", inst->implDescription); fprintf(stdout, "\t\tFlags:"); fflush(stdout); print_beagle_flags(inst->flags); fflush(stdout); } int create_beagle_instance(t_tree *tree, int quiet, option* io) { if(UNINITIALIZED != tree->b_inst){ // fprintf(stdout,"\n\tWARNING: Creating a BEAGLE instance on a tree with an existing BEAGLE instance:%d\n",tree->b_inst); } if(!quiet){ // print_beagle_resource_list(); } int i; BeagleInstanceDetails inst_d; int num_rate_catg = tree->mod->ras->n_catg; int num_branches = 2*tree->n_otu-1; //rooted tree //Recall that in PhyML, each edge has a "left" and "right" partial vectors. Therefore, //in BEAGLE we have 2*num_branches number of partials. //BEAGLE's partials buffer = [ tax1, tax2, ..., taxN, b1Left, b2Left, b3Left,...,bMLeft, b1Rght, b2Rght, b3Rght,...,bMRght] (N taxa, M branches) int num_partials = 2*(tree->n_otu + num_branches); /* TODO: This does not seem correct; suspect poor indexing elsewhere */ /* In update_operations, indexes range from 0 to almost 2 (n_otu + num_branches), but not all integers are used */ int resourceList[1]; resourceList[0] = io->beagle_resource; // DUMP_I(tree->n_otu, num_rate_catg, num_partials, num_branches, tree->mod->ns, tree->n_pattern, tree->mod->whichmodel); int beagle_inst = beagleCreateInstance( tree->n_otu, /**< Number of tip data elements (input) */ num_partials, /**< Number of partial buffer (input) */ /* PhyML uses partials */ 0, /**< Number of compact state representation buffers to create (input) */ tree->mod->ns, /**< Number of states in the continuous-time Markov chain (input) */ tree->n_pattern, /**< Number of site patterns to be handled by the instance (input) */ 1, /**< Number of rate matrix eigen-decomposition,state freqs, and category weight buffers*/ num_branches, /**< Number of rate matrix buffers (input) */ num_rate_catg, /**< Number of rate categories (input) */ -1, /**< Number of scaling buffers. Unused because we use SCALING_ALWAYS */ resourceList, /**< List of potential resource on which this instance is allowed (input, NULL implies no restriction */ 1, /**< Length of resourceList list (input) */ BEAGLE_FLAG_FRAMEWORK_CPU | BEAGLE_FLAG_PROCESSOR_CPU | BEAGLE_FLAG_SCALING_ALWAYS | BEAGLE_FLAG_EIGEN_REAL | ((sizeof(float)==sizeof(phydbl)) ? BEAGLE_FLAG_PRECISION_SINGLE:BEAGLE_FLAG_PRECISION_DOUBLE), 0, /**< Bit-flags indicating required implementation characteristics, see BeagleFlags (input) */ &inst_d); if (beagle_inst < 0){ fprintf(stderr, "beagleCreateInstance() failed:%i\n\n",beagle_inst); return beagle_inst; } if(!quiet){ fprintf(stdout, "\n\tUnique BEAGLE instance id:%i\n", beagle_inst); print_beagle_instance_details(&inst_d); } //Set the tips for(i=0; i<2*tree->n_otu-1; ++i) //taxa+internal nodes { // Print_Tip_Partials(tree, tree->a_nodes[i]); if(tree->a_nodes[i]->tax) { assert(tree->a_nodes[i]->c_seq->len == tree->n_pattern); // number of compacts sites == number of distinct site patterns double* tip = short_to_double(tree->a_nodes[i]->b[0]->p_lk_tip_r, tree->n_pattern*tree->mod->ns); //The tip states are stored on the branch leading to the tip //Recall we store tip partials on the branch leading to the tip, rather than the tip itself. int ret = beagleSetTipPartials(beagle_inst, tree->a_nodes[i]->b[0]->p_lk_tip_idx, tip); if(ret<0){ fprintf(stderr, "beagleSetTipPartials() on instance %i failed:%i\n\n",beagle_inst,ret); Free(tip); return ret; } Free(tip); } } //Set the pattern weights double* pwts = int_to_double(tree->data->wght,tree->n_pattern); //BTW, These weights are absolute counts, and not freqs int ret = beagleSetPatternWeights(beagle_inst, pwts); if(ret<0){ fprintf(stderr, "beagleSetPatternWeights() on instance %i failed:%i\n\n",beagle_inst,ret); Free(pwts); return ret; } Free(pwts); tree->mod->b_inst=beagle_inst; update_beagle_ras(tree->mod); update_beagle_efrqs(tree->mod); return beagle_inst; } /* Update partial likelihood on edge b on the side of b where node d lies. */ void update_beagle_partials(t_tree* tree, t_edge* b, t_node* d) { /* | |<- b | d / \ b1 / \ b2 / \ n_v1 n_v2 */ if(d->tax) //Partial likelihoods are only calculated on internal nodes { PhyML_Printf("\n== t_node %d is a leaf...",d->num); PhyML_Printf("\n== Err. in file %s at line %d (function '%s')\n",__FILE__,__LINE__,__FUNCTION__); Warn_And_Exit("\n"); } //Determine d's "left" and "right" neighbors. t_node *n_v1, *n_v2;//d's "left" and "right" neighbor nodes phydbl *p_lk,*p_lk_v1,*p_lk_v2; phydbl *Pij1,*Pij2; int *sum_scale, *sum_scale_v1, *sum_scale_v2; int *p_lk_loc; int dest_p_idx, child1_p_idx, child2_p_idx, Pij1_idx, Pij2_idx; n_v1 = n_v2 = NULL; p_lk = p_lk_v1 = p_lk_v2 = NULL; Pij1 = Pij2 = NULL; sum_scale_v1 = sum_scale_v2 = NULL; p_lk_loc = NULL; dest_p_idx = child1_p_idx = child2_p_idx = Pij1_idx = Pij2_idx = UNINITIALIZED; Set_All_P_Lk(&n_v1,&n_v2, &p_lk,&sum_scale,&p_lk_loc, &Pij1,&p_lk_v1,&sum_scale_v1, &Pij2,&p_lk_v2,&sum_scale_v2, d,b,tree, &dest_p_idx, &child1_p_idx, &child2_p_idx, &Pij1_idx, &Pij2_idx); // fprintf(stdout, "\nUpdating partials on Branch %d (on the side where Node %d lies)\n",b->num,d->num);fflush(stdout); // double* p_lk_v1_b = (double*)malloc(tree->mod->ras->n_catg*tree->mod->ns*tree->n_pattern*sizeof(double));if(NULL==p_lk_v1_b) Warn_And_Exit("Couldnt allocate memory"); // beagleGetPartials(tree->b_inst, child1_p_idx, BEAGLE_OP_NONE, (double*)p_lk_v1_b); // double* p_lk_v2_b = (double*)malloc(tree->mod->ras->n_catg*tree->mod->ns*tree->n_pattern*sizeof(double));if(NULL==p_lk_v2_b) Warn_And_Exit("Couldnt allocate memory"); // beagleGetPartials(tree->b_inst, child2_p_idx, BEAGLE_OP_NONE, (double*)p_lk_v2_b); // fprintf(stdout, "Left partials :");fflush(stdout); // Dump_Arr_D(p_lk_v1_b, tree->mod->ras->n_catg*tree->mod->ns*tree->n_pattern); // fprintf(stdout, "Right partials:");fflush(stdout); // Dump_Arr_D(p_lk_v2_b, tree->mod->ras->n_catg*tree->mod->ns*tree->n_pattern); // Free(p_lk_v1_b); // Free(p_lk_v2_b); //Create the corresponding BEAGLE operation // fprintf(stderr,"%d, %d, %d, ", dest_p_idx, child1_p_idx, child2_p_idx); BeagleOperation operations[1] = {{dest_p_idx, BEAGLE_OP_NONE, BEAGLE_OP_NONE, child1_p_idx, Pij1_idx, child2_p_idx, Pij2_idx}}; //Compute the partials int ret = beagleUpdatePartials(tree->b_inst, operations, 1, BEAGLE_OP_NONE); if(ret<0){ fprintf(stderr, "beagleUpdatePartials() on instance %i failed:%i\n\n",tree->b_inst,ret); Exit(""); } //Load the computed/updated partial partials #ifndef CLEAN_BEAGLE_API ret = beagleGetPartials(tree->b_inst, dest_p_idx, BEAGLE_OP_NONE, (double*)p_lk); if(ret<0){ fprintf(stderr, "beagleGetPartials() on instance %i failed:%i\n\n",tree->b_inst,ret); Exit(""); } #endif // fprintf(stdout, "Updated partials:");fflush(stdout); // Dump_Arr_D(p_lk, tree->mod->ras->n_catg*tree->mod->ns*tree->n_pattern); } int finalize_beagle_instance(t_tree *tree) { if(tree->b_inst >= 0) { int ret = beagleFinalizeInstance(tree->b_inst); if(ret<0) fprintf(stderr, "\nFailed to finalize BEAGLE instance %i: %i\n\n", tree->b_inst, ret); return ret; } return 0; } void update_beagle_ras(t_mod* mod) { assert(UNINITIALIZED != mod->b_inst); int ret=-1; if((sizeof(float)==sizeof(phydbl))) //Do we need to convert? { double* catg_rates = float_to_double(mod->ras->gamma_rr->v, mod->ras->n_catg); ret = beagleSetCategoryRates(mod->b_inst, catg_rates); Free(catg_rates); if(ret<0){ fprintf(stderr, "beagleSetCategoryRates() on instance %i failed:%i\n\n",mod->b_inst,ret); Exit(""); } double* catg_wts = float_to_double(mod->ras->gamma_r_proba->v, mod->ras->n_catg); if(!mod->optimizing_topology) { ret = beagleSetCategoryWeights(mod->b_inst, 0, catg_wts); Free(catg_wts); if(ret<0){ fprintf(stderr, "beagleSetCategoryWeights() on instance %i failed:%i\n\n",mod->b_inst,ret); Exit(""); } } } else { ret = beagleSetCategoryRates(mod->b_inst, mod->ras->gamma_rr->v); if(ret<0){ fprintf(stderr, "beagleSetCategoryRates() on instance %i failed:%i\n\n",mod->b_inst,ret); Exit(""); } if(!mod->optimizing_topology) { ret = beagleSetCategoryWeights(mod->b_inst, 0, mod->ras->gamma_r_proba->v); if(ret<0){ fprintf(stderr, "beagleSetCategoryWeights() on instance %i failed:%i\n\n",mod->b_inst,ret); Exit(""); } } } } void update_beagle_efrqs(t_mod* mod) { assert(UNINITIALIZED != mod->b_inst); int ret=-1; if((sizeof(float)==sizeof(phydbl))) { double* efrqs = float_to_double(mod->e_frq->pi->v, mod->ns); ret = beagleSetStateFrequencies(mod->b_inst, 0, efrqs); Free(efrqs); } else { ret = beagleSetStateFrequencies(mod->b_inst, 0, mod->e_frq->pi->v); } if(ret<0){ fprintf(stderr, "beagleSetStateFrequencies() on instance %i failed:%i\n\n",mod->b_inst,ret); Exit(""); } } void calc_edgelks_beagle(t_edge *b, t_tree *tree) { assert(UNINITIALIZED != tree->b_inst); //Compute the edge likelihood int parents[1] = {b->p_lk_left_idx}; int children[1] = {b->rght->tax?b->p_lk_tip_idx:b->p_lk_rght_idx}; int pmats[1] = {b->Pij_rr_idx}; int other[1] = {0};//Category Weights and State Frequencies both have a single buffer, hence they are both indexed at 0 double lnL[1] = {UNINITIALIZED}; // DUMP_I(parents[0], children[0], pmats[0], b->num, b->rght->tax); int ret=beagleCalculateEdgeLogLikelihoods(tree->b_inst, parents, children, pmats, NULL, NULL, other, other, NULL, 1, lnL, NULL, NULL); int i; if(ret<0){ fprintf(stderr, "beagleCalculateEdgeLogLikelihoods() on instance %i failed:%i\n\n",tree->b_inst,ret); Exit(""); } tree->c_lnL = sizeof(phydbl)==sizeof(float)?(float)lnL[0]:lnL[0]; //Retrieve the site likelihoods that were computed during the previous edge likelihood computation ret = beagleGetSiteLogLikelihoods(tree->b_inst,tree->cur_site_lk);//TODO: Handle when cur_site_lk is float if(ret<0){ fprintf(stderr, "beagleGetSiteLogLikelihoods() on instance %i failed:%i\n\n",tree->b_inst,ret); Exit(""); } //Transform for(i=0;in_pattern;++i) tree->cur_site_lk[i]=exp(tree->cur_site_lk[i]); } void update_beagle_eigen(t_mod* mod) { assert(UNINITIALIZED != mod->b_inst); int whichmodel = mod->whichmodel; //We use Eigen Decomposition only for GTR models and AA datasets if((mod->io->datatype == AA || whichmodel==GTR || whichmodel==CUSTOM) && mod->use_m4mod == NO) { //Beagle expects untransformed eigen-values (i.e. recall e_val is exp() scaled, so we undo that) phydbl* evals = (phydbl*)malloc(mod->ns * sizeof(phydbl)); int i; for(i=0;ins;++i) evals[i]=log(mod->eigen->e_val[i]); int ret=-1; if((sizeof(float)==sizeof(phydbl)))//Need to convert to doubles? { double* eigen_vects = float_to_double(mod->eigen->r_e_vect, mod->eigen->size*mod->eigen->size); double* eigen_vects_inv = float_to_double(mod->eigen->l_e_vect, mod->eigen->size*mod->eigen->size); double* eigen_vals = float_to_double(evals,mod->eigen->size); ret = beagleSetEigenDecomposition(mod->b_inst,0,eigen_vects,eigen_vects_inv,eigen_vals); Free(eigen_vects);Free(eigen_vects_inv);Free(eigen_vals); } else { ret = beagleSetEigenDecomposition(mod->b_inst,0,mod->eigen->r_e_vect,mod->eigen->l_e_vect,evals); } if(ret<0){ fprintf(stderr, "beagleSetEigenDecomposition() on instance %i failed:%i\n\n",mod->b_inst,ret); Free(evals); Exit(""); } Free(evals); } } #endif // BEAGLE_UTILS_CPP phyml-3.3.20220408/src/beagle_utils.h000066400000000000000000000066111422404706600170200ustar00rootroot00000000000000/* * author: Imran Fanaswala */ #ifndef BEAGLE_UTILS_H #define BEAGLE_UTILS_H #include "assert.h" #include "libhmsbeagle/beagle.h" #include "utilities.h" #define UNINITIALIZED -42 /* BEAGLE is a library that abstracts the core computations common to most phylogenetic packages. The following paragraphs serve as a "design document" for the implementation details of how BEAGLE has been integrated with PhyML. The reader is expected to be familiar with phylogenetics, skimmed the BEAGLE API, and seen a couple of its examples. PARTIAL LIKELIHOODS: - For each internal edge, PhyML maintains a vector of partials that lie to the "right" and "left", to faciliate both post-order and pre-order tree traversal. Therefore each t_edge has a `p_lk_right` and `p_lk_left` field - PhyML represents a tip as a vector of partials (where one bit is "1", and the rest are "0"). Therefore, a t_edge connected to a tip has `p_lk_tip` field. Also by convention, the tip always lies to the right of the edge its connected to. In other words, if b->right->tax is True, then b->p_lk_tip holds the tip partials. - Therefore for BEAGLE, we allocate N+(2*num_edges) partial buffers. - Recall that BEAGLE requires each partial buffer to be uniquely indexed. Thus each edge also has an additional `p_lk_rght_idx` and `p_lk_left_idx` field. These fields are used to create a BeagleOperation struct during the call to beagleUpdatePartials(). - In PhyML, Update_P_Lk() updates the partials for a specific edge lieing along a specific "direction". The "direction" depends on which tree-traversal is being employed. In other words, the tree-traversal dictates whether we are computing "up"/"right" or "down"/"left" partials relative to the t_node*. Specifically, Set_All_P_Lk() handles the nitty gritty of determining whether we are computing "right" or "left" partials. Therefore this function has been modified to also set the appropriate BEAGLE indices. P-MATS and EDGELIKELIHOODS: - (1) Observe that PhyML does eigen decomposition for only GTR and AA models and thus the P-matrix is computed using Beagle. This implies the usage of SetEigenDecomposition() and UpdateTransitionMatricies() calls. - (2) On the other hand, in case of simpler models (JC69, F81, etc), the P-Matrix is explicitly set in BEAGLE thus implying the usage of SetTransitionMatrix(). - The choice between (1) and (2) is made in Update_PMat_At_Given_Edge(). BTW, upon reading Update_PMat_At_Given_Edge(), it may appear that the P-Matrix is being computed in PhyML *and* in BEAGLE; afterall because PMat() function in all cases. However, when BEAGLE is enabled, observe that in PMat() essentially does nothing. - Lk_Core() computes the edgelikelihoods, and calc_edgelks_beagle() is the corresponding BEAGLE function that does the same. */ #define TODO_BEAGLE "TODO. This codepath has not been implemented in PhyML-X, please post your usecase on the PhyML discussion list" int create_beagle_instance(t_tree* tree, int quiet, option* io); int finalize_beagle_instance(t_tree* tree); void update_beagle_partials(t_tree* tree, t_edge* b, t_node* d); void update_beagle_ras(t_mod* mod); void update_beagle_efrqs(t_mod* mod); void update_beagle_eigen(t_mod* mod); void calc_edgelks_beagle(t_edge* b, t_tree* tree); double* int_to_double(const int* src, int num_elems); double* short_to_double(const short* src, int num_elems); double* float_to_double(const phydbl *src, int num_elems); #endif // BEAGLE_UTILS_H phyml-3.3.20220408/src/bionj.c000066400000000000000000000254741422404706600154650ustar00rootroot00000000000000/* PHYML : a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences Copyright (C) Stephane Guindon. Oct 2003 onward All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ /* The code below is an implementation of the building tree algorithm described in "BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data." (1997) O. Gascuel. Mol Biol Evol. 14:685-95. */ #include "bionj.h" void Bionj(matrix *mat) { int x,y,i; phydbl vxy,lx,ly,lamda,score; Clean_Tree_Connections(mat->tree); for(i=0;itree->n_otu;i++) mat->tip_node[i] = mat->tree->a_nodes[i]; mat->tree->num_curr_branch_available = mat->tree->n_otu; while(mat->r > 3) { x = y = 0; vxy = .0; score = .0; Compute_Sx(mat); Best_Pair(mat,&x,&y,&score); vxy=BioNJ_Variance(mat,x,y); lx=Br_Length(mat,x,y); ly=Br_Length(mat,y,x); lamda=Lamda(mat,x,y,vxy); Update_Mat(mat,x,y,lx,ly,vxy,lamda); Update_Tree(mat,x,y,lx,ly,score); } Finish(mat); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Finish(matrix *mat) { phydbl dxy,dxz,dyz; int x,y,z; t_node *nx,*ny,*nz,*new; int i; dxy = dxz = dyz = -1.; x = y = z = -1; for(i=0;in_otu;i++) { if(mat->on_off[i]) { if(x < 0) x=i; else if(y < 0) y = i; else if(z < 0) z = i; } } dxy = Dist(mat,x,y); dxz = Dist(mat,x,z); dyz = Dist(mat,y,z); nx = mat->tip_node[x]; ny = mat->tip_node[y]; nz = mat->tip_node[z]; new = mat->tree->a_nodes[mat->curr_int]; new->num = mat->curr_int; new->v[0] = nx; new->v[1] = ny; new->v[2] = nz; nx->v[0] = new; ny->v[0] = new; nz->v[0] = new; if(nx->tax) Connect_One_Edge_To_Two_Nodes(new,nx,mat->tree->a_edges[nx->num],mat->tree); else Connect_One_Edge_To_Two_Nodes(new,nx,mat->tree->a_edges[mat->tree->num_curr_branch_available],mat->tree); if(ny->tax) Connect_One_Edge_To_Two_Nodes(new,ny,mat->tree->a_edges[ny->num],mat->tree); else Connect_One_Edge_To_Two_Nodes(new,ny,mat->tree->a_edges[mat->tree->num_curr_branch_available],mat->tree); if(nz->tax) Connect_One_Edge_To_Two_Nodes(new,nz,mat->tree->a_edges[nz->num],mat->tree); else Connect_One_Edge_To_Two_Nodes(new,nz,mat->tree->a_edges[mat->tree->num_curr_branch_available],mat->tree); nx->b[0]->l->v = .5*(dxy-dyz+dxz); ny->b[0]->l->v = .5*(dyz-dxz+dxy); nz->b[0]->l->v = .5*(dxz-dxy+dyz); new->b[0]->l->v = nx->b[0]->l->v; new->b[1]->l->v = ny->b[0]->l->v; new->b[2]->l->v = nz->b[0]->l->v; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Update_Mat(matrix *mat, int x, int y, phydbl lx, phydbl ly, phydbl vxy, phydbl lamda) { int i; int a,b; a = b = -1; for(i=0;in_otu;i++) { if((mat->on_off[i]) && (i != x) && (i != y)) { if(x > i) { a=x; b=i; } else { a=i; b=x; } mat->dist[a][b]=Dist_Red(mat,x,lx,y,ly,i,lamda); mat->dist[b][a]=Var_Red(mat,x,y,i,lamda,vxy); } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Update_Tree(matrix *mat, int x, int y, phydbl lx, phydbl ly, phydbl score) { t_node *new, *nx, *ny; nx = mat->tip_node[x]; ny = mat->tip_node[y]; new = mat->tree->a_nodes[mat->curr_int]; nx->v[0] = new; ny->v[0] = new; new->v[1] = nx; new->v[2] = ny; new->num = mat->curr_int; if(nx->tax) Connect_One_Edge_To_Two_Nodes(new,nx,mat->tree->a_edges[nx->num],mat->tree); else Connect_One_Edge_To_Two_Nodes(new,nx,mat->tree->a_edges[mat->tree->num_curr_branch_available],mat->tree); if(ny->tax) Connect_One_Edge_To_Two_Nodes(new,ny,mat->tree->a_edges[ny->num],mat->tree); else Connect_One_Edge_To_Two_Nodes(new,ny,mat->tree->a_edges[mat->tree->num_curr_branch_available],mat->tree); nx->b[0]->l->v = lx; ny->b[0]->l->v = ly; new->b[1]->l->v = lx; new->b[2]->l->v = ly; new->score[0] = score; nx->b[0]->l->v = lx; ny->b[0]->l->v = ly; mat->tip_node[x] = new; mat->on_off[y] = 0; mat->curr_int++; mat->r--; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Best_Pair(matrix *mat, int *x, int *y,phydbl *score) { int i,j/* ,n_ties */; phydbl Qmin,Qmin2; phydbl *t_Qij; /* int *ties; */ t_Qij = (phydbl *)mCalloc(mat->n_otu * mat->n_otu,sizeof(phydbl )); /* ties = (int *)mCalloc(mat->n_otu * mat->n_otu,sizeof(int )); */ Qmin = 1.e+10; for(i=0;in_otu;i++) { if(mat->on_off[i]) { for(j=0;jon_off[j]) { t_Qij[mat->n_otu*i+j] = Q_Agglo(mat,i,j); if(t_Qij[mat->n_otu*i+j] < Qmin - 1.E-05) { *x = i; *y = j; Qmin = t_Qij[mat->n_otu*i+j]; } } } } } /* n_ties = 0; */ /* for(i=0;in_otu;i++) */ /* { */ /* if(mat->on_off[i]) */ /* { */ /* for(j=0;jon_off[j]) */ /* { */ /* if((t_Qij[mat->n_otu*i+j] < Qmin + 1.E-05) && (t_Qij[mat->n_otu*i+j] > Qmin - 1.E-05)) */ /* { */ /* ties[n_ties] = mat->n_otu*i+j; */ /* n_ties++; */ /* } */ /* } */ /* } */ /* } */ /* } */ /* if(!n_ties) */ /* { */ /* PhyML_Printf("\n. Err in file %s at line %d\n\n",__FILE__,__LINE__); */ /* Warn_And_Exit(""); */ /* } */ /* /\* Useful if some pairwise distances are null *\/ */ /* if(n_ties > 1) */ /* { */ /* int cand; */ /* *x = *y = -1; */ /* cand = (int)RINT(rand()/(phydbl)(RAND_MAX) * (n_ties-1)); */ /* *x = (int)(ties[cand] / mat->n_otu); */ /* *y = (int)(ties[cand] % mat->n_otu); */ /* } */ Qmin2 = 1e+10; for(i=0;in_otu;i++) { if((i != *y) && (i != *x) && (t_Qij[mat->n_otu*(*x)+i] < Qmin2)) Qmin2 = t_Qij[mat->n_otu*(*x)+i]; } for(i=0;in_otu;i++) { if((i != *y) && (i != *x) && (t_Qij[mat->n_otu*i+(*y)] < Qmin2)) Qmin2 = t_Qij[mat->n_otu*i+(*y)]; } *score = FABS(Qmin2 - Qmin)/FABS(Qmin); Free(t_Qij); /* Free(ties); */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Compute_Sx(matrix *mat) { int i,j; for(i=0;in_otu;i++) { mat->dist[i][i] = .0; if(mat->on_off[i]) { for(j=0;jn_otu;j++) { if((i != j) && (mat->on_off[j])) { mat->dist[i][i] += Dist(mat,i,j); } } } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Sum_S(matrix *mat, int i) { return mat->dist[i][i]; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Dist(matrix *mat, int x, int y) { if(x > y) return(mat->dist[x][y]); else return(mat->dist[y][x]); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl BioNJ_Variance(matrix *mat, int x, int y) { if(x > y) { return(mat->dist[y][x]); } else { return(mat->dist[x][y]); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Br_Length(matrix *mat, int x, int y) { return .5*(Dist(mat,x,y)+ (Sum_S(mat,x)-Sum_S(mat,y))/(phydbl)(mat->r-2.)); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Dist_Red(matrix *mat, int x, phydbl lx, int y, phydbl ly, int i, phydbl lamda) { phydbl Dui; Dui=lamda*(Dist(mat,x,i)-lx) +(1.-lamda)*(Dist(mat,y,i)-ly); return(Dui); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Var_Red(matrix *mat, int x, int y, int i, phydbl lamda, phydbl vxy) { phydbl Vui; Vui=lamda*(BioNJ_Variance(mat,x,i)) +(1.-lamda)*(BioNJ_Variance(mat,y,i)) -lamda*(1.-lamda)*vxy; return(Vui); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Lamda(matrix *mat, int x, int y, phydbl vxy) { phydbl lamda=0.0; int i; if(mat->method == 0) /* NJ (Saitou & Nei, 1987) */ lamda = 0.5; else /* BioNJ (Gascuel, 1997) */ { if(vxy < SMALL && vxy > -SMALL) lamda=0.5; else { for(i=0;in_otu;i++) { if((x != i) && (y != i) && (mat->on_off[i])) lamda = lamda + BioNJ_Variance(mat,y,i) - BioNJ_Variance(mat,x,i); } lamda = 0.5 + lamda/(2.*(mat->r-2)*vxy); } if(lamda > 1.0) lamda = 0.5;/*1.0;*/ else if(lamda < 0.0) lamda = 0.5;/*0.0;*/ } return(lamda); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Q_Agglo(matrix *mat, int x, int y) { phydbl Qxy; Qxy = .0; Qxy=(mat->r-2.)*Dist(mat,x,y)-Sum_S(mat,x)-Sum_S(mat,y); return(Qxy); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Bionj_Br_Length(matrix *mat) { int x; x = Bionj_Br_Length_Post(mat->tree->a_nodes[0], mat->tree->a_nodes[0]->v[0], mat); mat->tree->a_nodes[0]->b[0]->l->v = Dist(mat,0,x); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int Bionj_Br_Length_Post(t_node *a, t_node *d, matrix *mat) { int i; if(d->tax) { return d->num; } else { int d_v1, d_v2; phydbl lx, ly, vxy,lamda; int x,y; d_v1 = d_v2 = -1; for(i=0;i<3;i++) if(d->v[i] != a) {(d_v1 < 0)?(d_v1 = i):(d_v2 = i);} x = Bionj_Br_Length_Post(d,d->v[d_v1],mat); y = Bionj_Br_Length_Post(d,d->v[d_v2],mat); vxy = .0; Compute_Sx(mat); vxy=BioNJ_Variance(mat,(x),(y)); lx=Br_Length(mat,(x),(y)); ly=Br_Length(mat,(y),(x)); lamda=Lamda(mat,(x),(y),vxy); Update_Mat(mat,(x),(y),lx,ly,vxy,lamda); d->b[d_v1]->l->v = lx; d->b[d_v2]->l->v = ly; mat->on_off[y] = 0; mat->r--; return x; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phyml-3.3.20220408/src/bionj.h000066400000000000000000000024561422404706600154650ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include #ifndef NJ_H #define NJ_H #include "utilities.h" #include "optimiz.h" #include "free.h" void Bionj(matrix *mat); void Finish(matrix *mat); void Compute_Sx(matrix *mat); phydbl Sum_S(matrix *mat, int i); phydbl Dist(matrix *mat, int x, int y); phydbl Q_Agglo(matrix *mat, int x, int y); phydbl BioNJ_Variance(matrix *mat, int x, int y); phydbl Br_Length(matrix *mat, int x, int y); void Update_Dist(matrix *mat, int x, int y); phydbl Lamda(matrix *mat, int x, int y, phydbl vxy); void Best_Pair(matrix *mat, int *x, int *y, phydbl *score); phydbl Var_Red(matrix *mat, int x, int y, int i, phydbl lamda, phydbl vxy); void Update_Tree(matrix *mat, int x, int y, phydbl lx, phydbl ly, phydbl score); void Update_Mat(matrix *mat, int x, int y, phydbl lx, phydbl ly, phydbl vxy, phydbl lamda); phydbl Dist_Red(matrix *mat, int x, phydbl lx, int y, phydbl ly, int i, phydbl lamda); int Bionj_Br_Length_Post(t_node *a, t_node *d, matrix *mat); void Bionj_Br_Length(matrix *mat); #endif phyml-3.3.20220408/src/checkpoint.c000066400000000000000000000021311422404706600164740ustar00rootroot00000000000000#include "checkpoint.h" void CHECK_Main(int argc,char **argv) { // Up to you Salva... } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phyml-3.3.20220408/src/checkpoint.h000066400000000000000000000002101422404706600164750ustar00rootroot00000000000000#include #ifndef CHECKPOINT_H #define CHECKPOINT_H #include "utilities.h" void CHECK_Main(int argc, char **argv); #endif phyml-3.3.20220408/src/cl.c000066400000000000000000001416731422404706600147620ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include "cl.h" /*********************************************************/ /** * Fill the Option fields, with the argc array */ int Read_Command_Line(option *io, int argc, char **argv) { int c; int idx; int i; int writemode; short int opt_m; writemode = WRITE; opt_m = 0; if(argc == 1) Exit("\n. No argument was passed to the program. Please check the documentation. \n"); PhyML_Printf("",writemode); struct option longopts[] = { {"n_rgrft", required_argument,NULL,0}, {"n_globl", required_argument,NULL,1}, {"max_dist", required_argument,NULL,2}, {"n_optim", required_argument,NULL,3}, {"n_best", required_argument,NULL,4}, {"model", required_argument,NULL,5}, {"search", required_argument,NULL,6}, {"datatype", required_argument,NULL,7}, {"multiple", required_argument,NULL,8}, {"input", required_argument,NULL,9}, {"bootstrap", required_argument,NULL,10}, {"ts/tv", required_argument,NULL,11}, {"nclasses", required_argument,NULL,12}, {"pinv", required_argument,NULL,13}, {"alpha", required_argument,NULL,14}, {"inputtree", required_argument,NULL,15}, {"min_diff_lk_local", required_argument,NULL,16}, {"min_diff_lk_global",required_argument,NULL,17}, {"steph_spr", no_argument,NULL,18}, {"brent_it_max", required_argument,NULL,19}, {"rand_start", no_argument,NULL,20}, {"n_rand_starts", required_argument,NULL,21}, {"sequential", no_argument,NULL,22}, {"inside_opt", no_argument,NULL,23}, {"p_moves", required_argument,NULL,24}, {"fast_nni", no_argument,NULL,25}, {"g_pars", no_argument,NULL,26}, {"r_seed", required_argument,NULL,27}, {"collapse_boot", required_argument,NULL,28}, {"random_boot", required_argument,NULL,29}, {"print_trace", no_argument,NULL,30}, {"print_site_lnl", no_argument,NULL,31}, {"print_site_lk", no_argument,NULL,31}, {"cov", no_argument,NULL,32}, {"cov_delta", required_argument,NULL,33}, {"cov_alpha", required_argument,NULL,34}, {"cov_ncats", required_argument,NULL,35}, {"ps", no_argument,NULL,36}, {"cov_free", no_argument,NULL,37}, {"no_gap", no_argument,NULL,38}, {"n_rr_branch", required_argument,NULL,39}, {"append", no_argument,NULL,40}, {"no_five_branch", no_argument,NULL,41}, {"pars_thresh", required_argument,NULL,42}, {"min_diff_lk_move", required_argument,NULL,43}, {"hybrid", no_argument,NULL,44}, {"use_median", no_argument,NULL,45}, {"run_id", required_argument,NULL,46}, {"pars", no_argument,NULL,47}, {"quiet", no_argument,NULL,48}, {"version", no_argument,NULL,49}, {"calibration_file", required_argument,NULL,50}, {"calibration", required_argument,NULL,50}, {"clade_file", required_argument,NULL,50}, {"boot_progress_every", required_argument,NULL,51}, {"aa_rate_file", required_argument,NULL,52}, {"chain_len", required_argument,NULL,53}, {"sample_freq", required_argument,NULL,54}, {"burnin", required_argument,NULL,55}, {"no_memory_check", no_argument,NULL,56}, {"no_colalias", no_argument,NULL,57}, {"alias_subpatt", no_argument,NULL,58}, {"no_sequences", no_argument,NULL,59}, {"prior", no_argument,NULL,59}, {"fastlk", no_argument,NULL,60}, {"free_rates", no_argument,NULL,61}, {"freerates", no_argument,NULL,61}, {"freerate", no_argument,NULL,61}, {"free_rate", no_argument,NULL,61}, {"is", no_argument,NULL,62}, // no 63 since it corresponds to character '?' {"rate_model", required_argument,NULL,64}, {"ratemodel", required_argument,NULL,64}, {"log_l", no_argument,NULL,65}, {"gamma_lens", no_argument,NULL,66}, {"il", no_argument,NULL,66}, {"codpos", required_argument,NULL,67}, {"constraint_file", required_argument,NULL,68}, {"constraint_tree", required_argument,NULL,68}, {"help", no_argument,NULL,69}, {"mutmap", no_argument,NULL,70}, {"parvals", required_argument,NULL,71}, {"constrained_lens", no_argument,NULL,72}, {"xml", required_argument,NULL,73}, {"l_var", required_argument,NULL,74}, #ifdef BEAGLE {"beagle_resource", required_argument,NULL,75}, #endif {"ancestral", no_argument,NULL,76}, {"anc", no_argument,NULL,76}, {"coord_file", required_argument,NULL,77}, {"json_trace", no_argument,NULL,78}, {"weights", required_argument,NULL,79}, {"tbe", no_argument,NULL,80}, {"leave_duplicates", no_argument,NULL,81}, {"precision", required_argument,NULL,82}, {"l_min", required_argument,NULL,83}, {"print_mat_and_exit", no_argument,NULL,84}, {0,0,0,0} }; io->datatype = UNDEFINED; #ifndef PHYML int open_ps_file = 0; #endif idx=-1; do { c = getopt_long(argc,argv,"qi:d:t:m:b:n:f:zk:v:c:a:u:ho:s:x:g:l:ep",longopts,&idx); switch(c) { case 84 : { io->print_mat_and_exit = YES; break; } case 83 : { io->mod->l_min = String_To_Dbl(optarg); break; } case 82 : { if ((!atoi(optarg)) || (atoi(optarg) < 1) || (atoi(optarg) > DECIMAL_DIG -3)) { PhyML_Printf("\n. The number of digits must be [1 - %d]\n", DECIMAL_DIG -3); Exit("\n"); } else { io->precision = atoi(optarg); } break; } case 81 : { io->leave_duplicates = YES; break; } case 80 : { io->do_tbe = YES; io->do_boot = NO; io->do_alrt = NO; break; } case 79: { io->has_io_weights = YES; strcpy(io->weight_file, optarg); break; } case 78: { io->print_json_trace = YES; break; } case 77: { char *tmp; tmp = (char *)mCalloc(T_MAX_FILE, sizeof(char)); if(strlen(optarg) > T_MAX_FILE -11) { char choix; strcpy (tmp, "\n. The file name'"); strcat (tmp, optarg); strcat (tmp, "' is too long.\n"); PhyML_Printf("%s",tmp); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else if (!Filexists (optarg)) { char choix; strcpy (tmp, "\n. The file '"); strcat (tmp, optarg); strcat (tmp, "' doesn't exist.\n"); PhyML_Printf("%s",tmp); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else { strcpy(io->in_coord_file, optarg); io->fp_in_coord = Openfile(io->in_coord_file,READ); } Free(tmp); break; } case 76: { io->ancestral = YES; break; } #ifdef BEAGLE case 75: { io->beagle_resource = (int)atoi(optarg); break; } #endif case 74: { io->mod->l_var_sigma = String_To_Dbl(optarg); break; } case 73: { #ifdef INVITEE Free_Optimiz(io->mod->s_opt); Free_Model_Basic(io->mod); Free_Input(io); PhyTime_XML(optarg); return 0; #elif defined(PHYML) Free_Optimiz(io->mod->s_opt); Free_Model_Basic(io->mod); Free_Input(io); io = PhyML_XML(optarg); Free(io); return 0; #elif defined(PHYTIME) Free_Optimiz(io->mod->s_opt); Free_Model_Basic(io->mod); Free_Input(io); DATE_XML(optarg); return 0; #elif defined(PHYREX) Free_Optimiz(io->mod->s_opt); Free_Model_Basic(io->mod); Free_Input(io); PHYREX_XML(optarg); return 0; #endif break; } case 72: { io->mod->s_opt->constrained_br_len = YES; break; } case 71: { io->mcmc->in_fp_par = fopen(optarg,"r"); io->mcmc->randomize = NO; break; } case 70: { io->mutmap = YES; break; } case 68: { char *tmp; tmp = (char *)mCalloc(T_MAX_FILE, sizeof(char)); if(strlen(optarg) > T_MAX_FILE -11) { char choix; strcpy (tmp, "\n. The file name'"); strcat (tmp, optarg); strcat (tmp, "' is too long.\n"); PhyML_Printf("%s",tmp); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else if (!Filexists (optarg)) { char choix; strcpy (tmp, "\n. The file '"); strcat (tmp, optarg); strcat (tmp, "' doesn't exist.\n"); PhyML_Printf("%s",tmp); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else { strcpy(io->in_constraint_tree_file, optarg); io->fp_in_constraint_tree = Openfile(io->in_constraint_tree_file,0); } Free(tmp); break; } case 67: { phydbl pos; pos = atof(optarg); io->codpos = (int)pos; if(io->codpos < 1 || io->codpos > 3) { char choix; PhyML_Printf("\n. Coding position must be set to 1, 2 or 3.\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } break; break; } case 66: { io->mod->gamma_mgf_bl = YES; io->mod->s_opt->opt_gamma_br_len = YES; break; } case 65: { io->mod->log_l = YES; break; } case 64: { char *s; int i; s = (char *)mCalloc(T_MAX_NAME,sizeof(char)); i = 0; while(optarg[i++]) s[i]=tolower(optarg[i]); if(!strcmp(optarg,"gbd")) io->rates->model_id = THORNE; else if(!strcmp(optarg,"autocorrelated")) io->rates->model_id = THORNE; else if(!strcmp(optarg,"autocorr")) io->rates->model_id = THORNE; else if(!strcmp(optarg,"geom")) io->rates->model_id = GUINDON; else if(!strcmp(optarg,"integrated")) io->rates->model_id = GUINDON; else if(!strcmp(optarg,"uncorrelated")) io->rates->model_id = LOGNORMAL; else if(!strcmp(optarg,"uncorr")) io->rates->model_id = LOGNORMAL; else if(!strcmp(optarg,"lognorm")) io->rates->model_id = LOGNORMAL; else if(!strcmp(optarg,"clock")) io->rates->model_id = STRICTCLOCK; else if(!strcmp(optarg,"strictclock")) io->rates->model_id = STRICTCLOCK; else if(!strcmp(optarg,"strict_clock")) io->rates->model_id = STRICTCLOCK; else { PhyML_Printf("\n. rate_model should be 'autocorrelated', 'uncorrelated', 'integrated' or 'clock'."); Exit("\n"); } Free(s); break; } case 62: { io->mcmc->is = YES; break; } case 61: { io->mod->ras->free_mixt_rates = YES; io->mod->s_opt->opt_free_mixt_rates = YES; break; } case 60: { io->lk_approx = NORMAL; break; } case 59: { break; } case 58: { io->do_alias_subpatt = YES; break; } case 57: { io->colalias = NO; break; } case 56: { io->mem_question = NO; break; } case 55: { phydbl len; len = atof(optarg); io->mcmc->chain_len_burnin = (int)len; if(io->mcmc->chain_len_burnin < 1) { char choix; PhyML_Printf("\n. chain_len_burnin must be an integer greater than 0.\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } break; } case 54: { phydbl len; len = atof(optarg); io->mcmc->sample_interval = (int)len; if(io->mcmc->sample_interval < 1) { char choix; PhyML_Printf("\n. sample_interval must be an integer greater than 0.\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } break; } case 53: { phydbl len; len = atof(optarg); io->mcmc->chain_len = (int)len; if(io->mcmc->chain_len < 1) { char choix; PhyML_Printf("\n. chain_len must be an integer greater than 0.\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } break; } case 52: { char *s; s = (char *)mCalloc(T_MAX_FILE, sizeof(char)); strcpy(s,optarg); io->mod->fp_aa_rate_mat = Openfile(s,0); strcpy(io->mod->aa_rate_mat_file->s,s); Free(s); break; } case 51: { io->boot_prog_every = atoi(optarg); if(io->boot_prog_every < 1) { char choix; PhyML_Printf("\n. boot_progress_every must be an integer greater than 0.\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } break; } case 50: { strcpy(io->clade_list_file,optarg); break; } case 49: { PhyML_Printf("\n. This is PhyML version %s.\n\n",VERSION); Exit(""); break; } case 48 : { io->quiet = 1; break; } case 'p' : case 47 : { io->in_tree = 1; break; } case 46 : { if(strlen(optarg) > 0) { io->append_run_ID = YES; strcpy(io->run_id_string,optarg); } break; } case 45 : { io->mod->ras->gamma_median = 1; break; } case 44 : { io->mod->s_opt->hybrid_thresh = 0; break; } case 43 : { io->mod->s_opt->min_diff_lk_move = atof(optarg); if(io->mod->s_opt->min_diff_lk_move < 0) { char choix; PhyML_Printf("\n. Min_diff_lk_move must be a double greater than 0.\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } break; } case 42 : { io->mod->s_opt->pars_thresh = (int)atoi(optarg); if(io->mod->s_opt->pars_thresh < 0) { PhyML_Printf("\n. The parsimony threshold must be an integer greater than 0.\n"); PhyML_Printf("\n. Type any key to exit.\n"); Exit("\n"); } break; } case 41 : { io->mod->s_opt->opt_five_branch = 0; break; } case 40 : { writemode = APPEND; break; } case 39 : { break; } case 38 : { io->rm_ambigu = 1; break; } case 37 : { io->mod->s_opt->opt_cov_free_rates = YES; #ifdef M4 io->mod->m4mod->use_cov_alpha = NO; io->mod->m4mod->use_cov_free = YES; #endif break; } case 36 : { #ifndef PHYML open_ps_file = 1; #endif break; } case 35 : { #ifdef M4 io->mod->m4mod->n_h = (int)atoi(optarg); if(io->mod->m4mod->n_h < 1) { char choix; PhyML_Printf("\n. The number of classes must be greater than 0.\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } #endif break; } case 34 : { #ifdef M4 io->mod->m4mod->use_cov_alpha = YES; io->mod->m4mod->use_cov_free = NO; if(!strcmp(optarg,"e") || !strcmp(optarg,"E") || !strcmp(optarg,"estimated") || !strcmp(optarg,"ESTIMATED")) { io->mod->s_opt->opt_cov_alpha = YES; io->mod->m4mod->alpha = 1.0; } else { io->mod->m4mod->alpha = (phydbl)atof(optarg); if(io->mod->m4mod->alpha < 1.E-5) { char choix; PhyML_Printf("\n. The value of alpha must be greater than 1.E-5.\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } } #endif break; } case 33 : { #ifdef M4 if(!strcmp(optarg,"e") || !strcmp(optarg,"E") || !strcmp(optarg,"estimated") || !strcmp(optarg,"ESTIMATED")) { io->mod->s_opt->opt_cov_delta = YES; io->mod->m4mod->delta = 1.0; } else { io->mod->m4mod->delta = (phydbl)atof(optarg); if(atof(optarg) < 1.E-10) { char choix; PhyML_Printf("\n. The value of delta must be larger than 1.E-10.\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } } #endif break; } case 32 : { io->mod->use_m4mod = YES; break; } case 31 : { io->print_site_lnl = YES; break; } case 30 : { io->print_trace = YES; break; } case 29 : { io->random_boot_seq_order = (int)atoi(optarg); break; } case 28 : { io->collapse_boot = (int)atoi(optarg); break; } case 27 : { io->r_seed = (int)atoi(optarg); break; } case 26 : { io->mod->s_opt->general_pars = YES; break; } case 25 : { io->mod->s_opt->fast_nni = YES; break; } case 24 : { io->mod->s_opt->p_moves_to_examine = (phydbl)atof(optarg); break; } case 23 : { io->mod->s_opt->wim_inside_opt = 1; break; } case 0 : { io->mod->s_opt->wim_n_rgrft = atoi(optarg); break; } case 1 : { io->mod->s_opt->wim_n_globl = atoi(optarg); break; } case 2 : { io->mod->s_opt->wim_max_dist = atoi(optarg); break; } case 3 : { io->mod->s_opt->wim_n_optim = atoi(optarg); break; } case 4 : { io->mod->s_opt->wim_n_best = atoi(optarg); break; } case 16 : { io->mod->s_opt->min_diff_lk_local = atof(optarg); break; } case 17 : { io->mod->s_opt->min_diff_lk_global = atof(optarg); break; } case 18 : { io->mod->s_opt->steph_spr = NO; io->mod->s_opt->greedy = YES; break; } case 19 : { io->mod->s_opt->brent_it_max = atoi(optarg); break; } case 20 : { io->mod->s_opt->random_input_tree = YES; break; } case 21 : { io->mod->s_opt->random_input_tree = YES; io->mod->s_opt->n_rand_starts = atoi(optarg); if(io->mod->s_opt->n_rand_starts < 1) Exit("\n. Number of random starting trees must be > 0.\n\n"); } case 's':case 6: { if((!strcmp(optarg,"spr")) || (!strcmp(optarg,"SPR"))) { io->mod->s_opt->topo_search = SPR_MOVE; io->mod->s_opt->greedy = (io->mod->s_opt->steph_spr)?(0):(1); } else if((!strcmp(optarg,"nni")) || (!strcmp(optarg,"NNI"))) { PhyML_Printf("\n. The NNI option is deprecated. PhyML now uses a mix of SPRs and NNIs."); io->mod->s_opt->topo_search = NNI_MOVE; io->mod->s_opt->random_input_tree = 0; } else if((!strcmp(optarg,"best")) || (!strcmp(optarg,"BEST"))) { PhyML_Printf("\n. The BEST option is deprecated. PhyML now uses a mix of SPRs and NNIs."); io->mod->s_opt->topo_search = BEST_OF_NNI_AND_SPR; io->mod->s_opt->greedy = (io->mod->s_opt->steph_spr)?(0):(1); } break; } case 'd':case 7: { if(!strcmp(optarg,"nt")) { io->datatype = NT; io->mod->ns = 4; #ifdef M4 io->mod->m4mod->n_o = 4; #endif if((io->mod->whichmodel == LG) || (io->mod->whichmodel == WAG) || (io->mod->whichmodel == DAYHOFF) || (io->mod->whichmodel == JTT) || (io->mod->whichmodel == BLOSUM62) || (io->mod->whichmodel == MTREV) || (io->mod->whichmodel == RTREV) || (io->mod->whichmodel == CPREV) || (io->mod->whichmodel == DCMUT) || (io->mod->whichmodel == VT) || (io->mod->whichmodel == MTMAM) || (io->mod->whichmodel == MTART) || (io->mod->whichmodel == HIVW) || (io->mod->whichmodel == HIVB) || (io->mod->whichmodel == AB) || (io->mod->whichmodel == CUSTOMAA) ) { io->mod->whichmodel = HKY85; strcpy(io->mod->modelname->s, "HKY85\0"); } } else if (!strcmp(optarg,"aa")) { io->datatype = AA; io->mod->s_opt->opt_kappa = NO; io->mod->ns = 20; #ifdef M4 io->mod->m4mod->n_o = 20; #endif if( (io->mod->whichmodel == JC69) || (io->mod->whichmodel == K80) || (io->mod->whichmodel == F81) || (io->mod->whichmodel == HKY85) || (io->mod->whichmodel == F84) || (io->mod->whichmodel == TN93) || (io->mod->whichmodel == GTR) || (io->mod->whichmodel == CUSTOM) ) { io->mod->whichmodel = LG; strcpy(io->mod->modelname->s, "LG\0"); } } else if ((!strcmp(optarg,"generic")) || (!strcmp(optarg,"gen"))) { io->datatype = GENERIC; } else { char choix; PhyML_Printf("\n. Unknown argument to -d option: please use `nt' for DNA or `aa' for Amino-Acids\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } break; } case 'm': case 5 : { opt_m = 1; if (!isalpha(optarg[0])) { if(strchr(optarg,',') == NULL) { strcpy(io->mod->custom_mod_string->s,optarg); if (strlen(io->mod->custom_mod_string->s) != 6) { Warn_And_Exit("\n. The custom model string should be of length 6.\n"); } io->datatype = NT; io->mod->whichmodel = CUSTOM; strcpy(io->mod->modelname->s, "custom"); io->mod->s_opt->opt_kappa = NO; io->mod->s_opt->opt_rr = YES; } else { phydbl v; int n_rr; const char *d = ","; char *tok = strtok(optarg,d); io->datatype = NT; io->mod->ns = 4; io->mod->whichmodel = GTR; io->mod->s_opt->opt_rr = NO; io->mod->r_mat = (t_rmat *)Make_Rmat(io->mod->ns); Init_Rmat(io->mod->r_mat); Make_Custom_Model(io->mod); n_rr = 0; while(tok && n_rr < 6) { v = strtod(tok,NULL); if (v != 0) { io->mod->r_mat->rr->v[n_rr] = v; io->mod->r_mat->rr_val->v[n_rr] = log(v); } else { PhyML_Printf("\n. Invalid relative rate parameter value: '%s'.\n", tok); Exit("\n"); } tok = strtok (NULL,d); n_rr++; } assert(n_rr <= 6); } } else { char *s = To_Upper_String(optarg); if (strcmp(s, "JC69") == 0) { io->datatype = NT; io->mod->ns = 4; io->mod->whichmodel = JC69; } else if(strcmp(s, "K80") == 0) { io->datatype = NT; io->mod->ns = 4; io->mod->whichmodel = K80; } else if(strcmp(s, "F81") == 0) { io->datatype = NT; io->mod->ns = 4; io->mod->whichmodel = F81; } else if (strcmp(s, "HKY85") == 0) { io->datatype = NT; io->mod->ns = 4; io->mod->whichmodel = HKY85; } else if(strcmp(s, "F84") == 0) { io->datatype = NT; io->mod->ns = 4; io->mod->whichmodel = F84; } else if (strcmp(s,"TN93") == 0) { io->datatype = NT; io->mod->ns = 4; io->mod->whichmodel = TN93; } else if(strncmp(s, "GTR", 3) == 0) { io->datatype = NT; io->mod->ns = 4; io->mod->whichmodel = GTR; io->mod->s_opt->opt_rr = YES; } else if(strcmp(s, "DAYHOFF") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = DAYHOFF; } else if(strcmp(s, "JTT") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = JTT; } else if(strcmp(s, "MTREV") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = MTREV; } else if(strcmp(s, "LG") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = LG; } else if(strcmp(s, "WAG") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = WAG; } else if(strcmp(s, "DCMUT") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = DCMUT; } else if(strcmp(s, "RTREV") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = RTREV; } else if(strcmp(s, "CPREV") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = CPREV; } else if(strcmp(s, "VT") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = VT; } else if(strcmp(s, "BLOSUM62") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = BLOSUM62; } else if(strcmp(s, "MTMAM") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = MTMAM; } else if (strcmp(s,"MTART") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = MTART; } else if (strcmp(s,"HIVW") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = HIVW; } else if(strcmp(s, "HIVB") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = HIVB; } else if(strcmp(s, "AB") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = AB; } else if (strcmp(s, "CUSTOM") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = CUSTOMAA; } else if(strcmp(s, "FLU") == 0) { io->datatype = AA; io->mod->ns = 20; io->mod->whichmodel = FLU; } else { PhyML_Printf("\n. The model name is incorrect. Please see the documentation.\n"); Exit("\n"); } Free(s); } Set_Model_Name(io->mod); break; } case 'a':case 14 : { if ((strcmp (optarg, "e") == 0) || (strcmp (optarg, "E") == 0) || (strcmp (optarg, "estimated") == 0) || (strcmp (optarg, "ESTIMATED") == 0)) { io->mod->s_opt->opt_alpha = YES; } else if (atof(optarg) < 1.E-10) { char choix; PhyML_Printf("\n. Alpha must be > 1.E-10.\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else { io->mod->ras->alpha->v = (phydbl)atof(optarg); io->mod->s_opt->opt_alpha = NO; } break; } case 'b':case 10: { if ((int)String_To_Dbl(optarg) < -5) { char choix; PhyML_Printf("\n. Branch test value must be a positive integer for bootstrap, or between -1 and -4 for aLRT branch test\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else { if((int)String_To_Dbl(optarg) > 0) { io->do_alrt = NO; io->ratio_test = 0; io->n_boot_replicates = (int)atoi(optarg); io->print_boot_trees = YES; if(io->n_data_sets > 1) { char choix; PhyML_Printf("\n. Bootstrap option is not allowed with multiple data sets\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } } else if (atoi(optarg)==0) { io->do_alrt = NO; io->do_tbe = NO; io->do_boot = NO; io->ratio_test = 0; } else { io->do_alrt = YES; io->do_tbe = NO; io->do_boot = NO; io->ratio_test = -(int)atoi(optarg); } } break; } case 'c':case 12: { if ((!atoi(optarg)) || (atoi(optarg) < 0)) { char choix; PhyML_Printf("\n. Unknown argument to -c option: the number of rate categories must be a positive integer\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else { io->mod->ras->n_catg = atoi(optarg); if(io->mod->ras->n_catg < 1) { PhyML_Printf("\n. The number of rate categories must be a positive integer\n"); Exit("\n"); } } break; } case 'f': { if(!strcmp(optarg,"e")) // empirical state frequencies { io->mod->s_opt->state_freq = EMPIRICAL; } else if(!strcmp(optarg,"o")) // ML estimation { io->mod->s_opt->state_freq = ML; } else if(!strcmp(optarg,"m")) // Given by the model { if(io->datatype == AA) io->mod->s_opt->state_freq = MODEL; else if(io->datatype == NT) io->mod->s_opt->state_freq = ML; } else if(!isalpha(optarg[0])) { phydbl sum; double val1,val2,val3,val4; io->mod->e_frq = (t_efrq *)Make_Efrq(4); Init_Efrq(NULL,io->mod->e_frq); io->mod->s_opt->state_freq = USER; sscanf(optarg,"%lf,%lf,%lf,%lf",&val1,&val2,&val3,&val4); io->mod->e_frq->user_b_freq->v[0] = (phydbl)val1; io->mod->e_frq->user_b_freq->v[1] = (phydbl)val2; io->mod->e_frq->user_b_freq->v[2] = (phydbl)val3; io->mod->e_frq->user_b_freq->v[3] = (phydbl)val4; sum = (io->mod->e_frq->user_b_freq->v[0] + io->mod->e_frq->user_b_freq->v[1] + io->mod->e_frq->user_b_freq->v[2] + io->mod->e_frq->user_b_freq->v[3]); io->mod->e_frq->user_b_freq->v[0] /= sum; io->mod->e_frq->user_b_freq->v[1] /= sum; io->mod->e_frq->user_b_freq->v[2] /= sum; io->mod->e_frq->user_b_freq->v[3] /= sum; if(io->mod->e_frq->user_b_freq->v[0] < .0 || io->mod->e_frq->user_b_freq->v[1] < .0 || io->mod->e_frq->user_b_freq->v[2] < .0 || io->mod->e_frq->user_b_freq->v[3] < .0 || io->mod->e_frq->user_b_freq->v[0] > 1. || io->mod->e_frq->user_b_freq->v[1] > 1. || io->mod->e_frq->user_b_freq->v[2] > 1. || io->mod->e_frq->user_b_freq->v[3] > 1.) { Warn_And_Exit("\n. Invalid base frequencies.\n"); } } break; } case 'h':case 69: { Usage(); break; } case 'i':case 9: { char *tmp; tmp = (char *) mCalloc (T_MAX_FILE, sizeof(char)); if (strlen (optarg) > T_MAX_FILE -16) { char choix; strcpy (tmp, "\n. The file name'"); strcat (tmp, optarg); strcat (tmp, "' is too long.\n"); PhyML_Printf("%s",tmp); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else if (!Filexists (optarg)) { char choix; strcpy (tmp, "\n. The file '"); strcat (tmp, optarg); strcat (tmp, "' does not exist.\n"); PhyML_Printf("%s",tmp); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else { strcpy(io->in_align_file, optarg); io->fp_in_align = Openfile(io->in_align_file,0); strcpy(io->out_file, optarg); strcpy(io->out_tree_file,optarg); #ifdef PHYML strcat(io->out_tree_file,"_phyml_tree"); #elif M4 strcat(io->out_tree_file,"_m4_tree"); #elif PHYREX strcat(io->out_tree_file,"_phyrex_tree"); #endif strcpy(io->out_stats_file,optarg); #ifdef PHYML strcat(io->out_stats_file,"_phyml_stats"); #elif M4 strcat(io->out_stats_file,"_m4_stats"); #elif PHYREX strcat(io->out_stats_file,"_phyrex_stats"); #endif #ifdef PHYREX strcpy(io->out_summary_file,optarg); strcat(io->out_summary_file,"_phyrex_summary"); #endif } Free (tmp); break; } case 't':case 11: { if(opt_m == 0) { PhyML_Fprintf(stderr,"\n. Please use the -m option before -t in the command line."); Exit("\n"); } if((io->mod->whichmodel != JC69) && (io->mod->whichmodel != F81) && (io->mod->whichmodel != GTR)) { if ((strcmp(optarg, "e") == 0) || (strcmp(optarg, "E") == 0) || (strcmp(optarg, "estimated") == 0) || (strcmp(optarg, "ESTIMATED") == 0)) { io->mod->kappa->v = 4.0; io->mod->s_opt->opt_kappa = YES; if(io->mod->whichmodel == TN93) io->mod->s_opt->opt_lambda = YES; } else { io->mod->s_opt->opt_kappa = NO; io->mod->s_opt->opt_lambda = NO; // Added the 2 TsTv ratios for TN93 // lambda is the ratio of both TsTv ratios // kappa is the mean of both TsTv ratios if (io->mod->whichmodel == TN93) { double TsTvPur, TsTvPyr; TsTvPur = TsTvPyr = -1.; if(!isalpha(optarg[0])) { sscanf(optarg,"%lf,%lf",&TsTvPur,&TsTvPyr); } else { PhyML_Fprintf(stderr,"\n. The TN93 model requires two ts/tv ratios.\n"); Exit("\n"); } if ( (TsTvPur < .0) || (TsTvPyr < .0) ) { PhyML_Fprintf(stderr,"\n. ts/tv for purines: %f",TsTvPur); PhyML_Fprintf(stderr,"\n. ts/tv for pyrimidines: %f",TsTvPyr); PhyML_Fprintf(stderr,"\n. The TN93 model requires two ts/tv ratios"); PhyML_Fprintf(stderr,"\n. The command-line option should look as follows: ... -t 3.5,4.3 ... "); PhyML_Fprintf(stderr,"\n. The ts/tv ratio must be a positive number.\n"); Exit("\n"); } io->mod->lambda->v = (phydbl)(TsTvPur / TsTvPyr); io->mod->kappa->v = (phydbl)((TsTvPur + TsTvPyr)/2.); } else { // -- End TN93 rates for purines & pyrimidines if (atof(optarg) < .0) { char choix; PhyML_Printf("\n. The ts/tv ratio must be a positive number\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else { io->mod->kappa->v = (phydbl)atof(optarg); } } } } break; } case 'n':case 8: { if ((!atoi(optarg)) || (atoi(optarg) < 0)) { char choix; PhyML_Printf("\n. The number of alignments must be a positive integer\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else io->n_data_sets = atoi (optarg); break; } case 'q':case 22: { io->interleaved = NO; break; } case 'u':case 15: { char *tmp; tmp = (char *)mCalloc(T_MAX_FILE, sizeof(char)); if(strlen(optarg) > T_MAX_FILE -11) { char choix; strcpy (tmp, "\n. The file name'"); strcat (tmp, optarg); strcat (tmp, "' is too long.\n"); PhyML_Printf("%s",tmp); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else if (! Filexists (optarg)) { char choix; strcpy (tmp, "\n. The file '"); strcat (tmp, optarg); strcat (tmp, "' doesn't exist.\n"); PhyML_Printf("%s",tmp); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else { strcpy(io->in_tree_file, optarg); io->in_tree = 2; io->fp_in_tree = Openfile(io->in_tree_file,READ); } Free(tmp); break; } case 'v':case 13: { if ((strcmp (optarg, "e") == 0) || (strcmp (optarg, "E") == 0) || (strcmp (optarg, "estimated") == 0) || (strcmp (optarg, "ESTIMATED") == 0)) { io->mod->s_opt->opt_pinvar = YES; io->mod->ras->invar = YES; } else if ((atof(optarg) < 0.0) || (atof(optarg) > 1.0)) { char choix; PhyML_Printf("\n. The proportion of invariable site must be a number between 0.0 and 1.0\n"); PhyML_Printf("\n. Type any key to exit."); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } else { io->mod->ras->pinvar->v = (phydbl)atof(optarg); if (io->mod->ras->pinvar->v > 0.0+SMALL) io->mod->ras->invar = 1; else io->mod->ras->invar = 0; io->mod->s_opt->opt_pinvar = 0; } break; } case 'o': { if(!strcmp(optarg,"tlr")) { io->mod->s_opt->opt_topo = YES; io->mod->s_opt->opt_bl = YES; io->mod->s_opt->opt_subst_param = YES; } else if(!strcmp(optarg,"tl")) { io->mod->s_opt->opt_topo = YES; io->mod->s_opt->opt_bl = YES; io->mod->s_opt->opt_subst_param = NO; } else if(!strcmp(optarg,"t")) { Warn_And_Exit("\n. You can't optimize the topology without adjusting branch length too...\n"); } else if(!strcmp(optarg,"lr")) { io->mod->s_opt->opt_topo = NO; io->mod->s_opt->opt_bl = YES; io->mod->s_opt->opt_subst_param = YES; } else if(!strcmp(optarg,"l")) { io->mod->s_opt->opt_topo = NO; io->mod->s_opt->opt_bl = YES; io->mod->s_opt->opt_subst_param = NO; } else if(!strcmp(optarg,"r")) { io->mod->s_opt->opt_topo = NO; io->mod->s_opt->opt_bl = NO; io->mod->s_opt->opt_subst_param = YES; } else if(!strcmp(optarg,"none") || !strcmp(optarg,"n")) { io->mod->s_opt->opt_topo = NO; io->mod->s_opt->opt_bl = NO; io->mod->s_opt->opt_subst_param = NO; } else { char choix; PhyML_Printf ("\n. The optimization parameter must be 'tlr' or 'tl' or 'lr' or 'l' or 'r' or 'n'."); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } break; } case '?': { Exit("\n"); break; } case -1: { break; } default: { Usage(); break; } } }while(c != -1); /* if((io->mod->whichmodel == K80) || (io->mod->whichmodel == JC69)) */ /* { */ /* if(io->mod->s_opt->opt_state_freq) */ /* { */ /* char c; */ /* PhyML_Printf("\n. WARNING: nucleotide frequencies must be set to 1/4 with this model.\n"); */ /* PhyML_Printf("\n. Type the enter key to resume the analysis.\n"); */ /* scanf("%c",&c); */ /* } */ /* io->mod->s_opt->opt_state_freq = 0; */ /* } */ if(io->mod->s_opt->constrained_br_len == YES) { io->mod->s_opt->opt_topo = NO; /* io->mod->s_opt->opt_bl = NO; */ } if(io->do_tbe == YES) { io->do_alrt = NO; io->do_boot = NO; } else { if(io->do_alrt == NO && io->n_boot_replicates > 0) io->do_boot = YES; } #ifndef PHYML if((open_ps_file) || (io->m4_model == YES)) { strcpy(io->out_ps_file,io->in_align_file); strcat(io->out_ps_file, "_mc_tree.ps"); io->fp_out_ps = Openfile(io->out_ps_file,WRITE); } #endif if(io->datatype == UNDEFINED) io->datatype = NT; if ((io->datatype == NT) && (io->mod->whichmodel > 10)) { char choix; PhyML_Printf("\n. Err.: model incompatible with the data type. Please use JC69, K80, F81, HKY, F84, TN93 or GTR\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Warn_And_Exit("\n"); } else if ((io->datatype == AA) && (io->mod->whichmodel < 11)) { char choix; PhyML_Printf("\n. Err.: model incompatible with the data type. Please use LG, Dayhoff, JTT, MtREV, WAG, DCMut, RtREV, CpREV, VT, Blosum62, MtMam, MtArt, HIVw, HIVb or AB.\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } if(io->mod->use_m4mod == NO) { io->mod->s_opt->opt_cov_delta = 0; io->mod->s_opt->opt_cov_alpha = 0; io->mod->s_opt->opt_cov_free_rates = 0; } if((io->mod->s_opt->opt_cov_free_rates) && (io->mod->s_opt->opt_cov_alpha)) { io->mod->s_opt->opt_cov_free_rates = 1; #ifdef M4 io->mod->m4mod->use_cov_alpha = 0; io->mod->m4mod->use_cov_free = 1; #endif } if(io->print_site_lnl && io->fp_in_align != NULL) { strcpy(io->out_lk_file,io->in_align_file); strcat(io->out_lk_file, "_phyml_lk"); if(io->append_run_ID) { strcat(io->out_lk_file,"_"); strcat(io->out_lk_file,io->run_id_string); } strcat(io->out_lk_file, ".txt"); io->fp_out_lk = Openfile(io->out_lk_file,1); } if(io->print_trace && io->fp_in_align != NULL) { strcpy(io->out_trace_file,io->in_align_file); strcat(io->out_trace_file,"_phyml_trace"); if(io->append_run_ID) { strcat(io->out_trace_file,"_"); strcat(io->out_trace_file,io->run_id_string); } strcat(io->out_trace_file,".txt"); io->fp_out_trace = Openfile(io->out_trace_file,WRITE); } if(io->print_json_trace && io->fp_in_align != NULL) { strcpy(io->out_json_trace_file,io->in_align_file); strcat(io->out_json_trace_file,"_phyml_trace"); if(io->append_run_ID) { strcat(io->out_json_trace_file,"_"); strcat(io->out_json_trace_file,io->run_id_string); } strcat(io->out_json_trace_file,".json"); io->fp_out_json_trace = Openfile(io->out_json_trace_file,READWRITE); } if(io->mod->s_opt->random_input_tree && io->fp_in_align != NULL) { strcpy(io->out_trees_file,io->in_align_file); strcat(io->out_trees_file,"_phyml_rand_trees"); if(io->append_run_ID) { strcat(io->out_trees_file,"_"); strcat(io->out_trees_file,io->run_id_string); } strcat(io->out_trees_file,".txt"); io->fp_out_trees = Openfile(io->out_trees_file,1); } if((io->print_boot_trees) && (io->do_boot == YES || io->do_tbe == YES) && (io->fp_in_align != NULL)) { strcpy(io->out_boot_tree_file,io->in_align_file); strcat(io->out_boot_tree_file,"_phyml_boot_trees"); if(io->append_run_ID) { strcat(io->out_boot_tree_file,"_"); strcat(io->out_boot_tree_file,io->run_id_string); } strcat(io->out_boot_tree_file,".txt"); io->fp_out_boot_tree = Openfile(io->out_boot_tree_file,1); strcpy(io->out_boot_stats_file,io->in_align_file); strcat(io->out_boot_stats_file,"_phyml_boot_stats"); if(io->append_run_ID) { strcat(io->out_boot_stats_file,"_"); strcat(io->out_boot_stats_file,io->run_id_string); } strcat(io->out_boot_stats_file,".txt"); io->fp_out_boot_stats = Openfile(io->out_boot_stats_file,1); } if(io->append_run_ID && io->fp_in_align != NULL) { strcat(io->out_tree_file,"_"); strcat(io->out_stats_file,"_"); strcat(io->out_tree_file,io->run_id_string); strcat(io->out_stats_file,io->run_id_string); } if(io->fp_in_align != NULL) { strcat(io->out_tree_file,".txt"); strcat(io->out_stats_file,".txt"); } #ifdef PHYREX strcat(io->out_summary_file,".txt"); #endif if(io->mod->ras->n_catg == 1) io->mod->s_opt->opt_alpha = 0; if(io->mod->s_opt->opt_subst_param == NO) { io->mod->s_opt->opt_alpha = NO; io->mod->s_opt->opt_kappa = NO; io->mod->s_opt->opt_lambda = NO; io->mod->s_opt->opt_pinvar = NO; io->mod->s_opt->opt_rr = NO; } if(io->mod->whichmodel != K80 && io->mod->whichmodel != HKY85 && io->mod->whichmodel != F84 && io->mod->whichmodel != TN93) { io->mod->s_opt->opt_kappa = NO; } if(io->datatype == AA && io->mod->whichmodel == CUSTOMAA && !io->mod->fp_aa_rate_mat) { PhyML_Printf("\n. Custom model option with amino-acid requires you to specify a rate matrix file through the '--aa_rate_file' option.\n"); Exit("\n"); } #if !defined(PHYTIME) // Make sure you don't erase the input file... if(!strcmp(io->out_tree_file,io->in_align_file) || !strcmp(io->out_stats_file,io->in_align_file)) { PhyML_Fprintf(stderr,"\n. The alignment file '%s' does not seem to exist...",io->in_align_file); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } if(io->fp_in_align != NULL) { io->fp_out_tree = Openfile(io->out_tree_file,writemode); io->fp_out_stats = Openfile(io->out_stats_file,writemode); } #endif #if defined(PHYREX) if(io->fp_in_align != NULL) io->fp_out_summary = Openfile(io->out_summary_file,writemode); #endif if(io->quiet == NO) { PhyML_Printf("\n\n. Command line: "); for(i=0;i #ifndef CL_H #define CL_H #include #include #include "utilities.h" #include "help.h" #include "models.h" #include "free.h" #include "interface.h" #include "invitee.h" #include "date.h" int Read_Command_Line(option *input, int argc, char **argv); #endif phyml-3.3.20220408/src/date.c000066400000000000000000001402301422404706600152650ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ /* Routines for molecular dating */ #include "date.h" ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int DATE_Main(int argc, char **argv) { option *io; io = Get_Input(argc,argv); Free(io); return(0); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DATE_XML(char *xml_filename) { FILE *fp_xml_in; xml_node *xnd,*xroot; t_tree *mixt_tree,*tree; phydbl *res; int seed; char *dum_string; mixt_tree = XML_Process_Base(xml_filename); assert(mixt_tree); mixt_tree->rates = RATES_Make_Rate_Struct(mixt_tree->n_otu); RATES_Init_Rate_Struct(mixt_tree->rates,NULL,mixt_tree->n_otu); mixt_tree->times = TIMES_Make_Time_Struct(mixt_tree->n_otu); TIMES_Init_Time_Struct(mixt_tree->times,NULL,mixt_tree->n_otu); tree = mixt_tree; do { // All rate stuctures point to the same object tree->rates = mixt_tree->rates; tree = tree->next; } while(tree); fp_xml_in = fopen(xml_filename,"r"); if(!fp_xml_in) { PhyML_Fprintf(stderr,"\n. Could not find the XML file '%s'.\n",xml_filename); Exit("\n"); } /* xroot = XML_Load_File(fp_xml_in); */ xroot = mixt_tree->xml_root; if(xroot == NULL) { PhyML_Fprintf(stderr,"\n. Encountered an issue while loading the XML file.\n"); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } xnd = XML_Search_Node_Name("phytime",NO,xroot); if(xnd == NULL) { PhyML_Fprintf(stderr,"\n. Cound not find the \"root\" of the XML file (it should have \'phytime\' as tag name).\n"); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } dum_string = XML_Get_Attribute_Value(xnd,"mcmc.chain.len"); if(dum_string != NULL) mixt_tree->io->mcmc->chain_len = (int)String_To_Dbl(dum_string); dum_string = XML_Get_Attribute_Value(xnd,"mcmc.sample.every"); if(dum_string != NULL) mixt_tree->io->mcmc->sample_interval = (int)String_To_Dbl(dum_string); dum_string = XML_Get_Attribute_Value(xnd,"mcmc.print.every"); if(dum_string != NULL) mixt_tree->io->mcmc->print_every = (int)String_To_Dbl(dum_string); dum_string = XML_Get_Attribute_Value(xnd,"mcmc.burnin"); if(dum_string != NULL) mixt_tree->io->mcmc->chain_len_burnin = (int)String_To_Dbl(dum_string); dum_string = XML_Get_Attribute_Value(xnd,"ignore.sequences"); if(dum_string != NULL) mixt_tree->eval_alnL = NO; dum_string = XML_Get_Attribute_Value(xnd,"ignore.seq"); if(dum_string != NULL) mixt_tree->eval_alnL = NO; dum_string = XML_Get_Attribute_Value(xnd,"ignore.data"); if(dum_string != NULL) mixt_tree->eval_alnL = NO; dum_string = XML_Get_Attribute_Value(xnd,"mutmap"); if(dum_string != NULL) { int select = XML_Validate_Attr_Int(dum_string,6, "true","yes","y", "false","no","n"); if(select < 3) mixt_tree->io->mutmap = YES; else mixt_tree->io->mutmap = NO; } // Looking for XML node with rate-across-lineage info xnd = XML_Search_Node_Name("lineagerates",YES,xroot); if(xnd == NULL) { PhyML_Fprintf(stdout,"\n. The model of rate variation across lineages is not specified."); PhyML_Fprintf(stdout,"\n. Using the geometric Brownian model (see Guindon, 2012, Syst. Biol.).\n"); mixt_tree->rates->model_id = GUINDON; mixt_tree->mod->gamma_mgf_bl = YES; strcpy(mixt_tree->rates->model_name,"geometric Brownian"); } else { char *model_name; model_name = XML_Get_Attribute_Value(xnd,"model"); if(model_name == NULL) { PhyML_Fprintf(stderr,"\n. Please specify a model of rate variation across lineages,"); PhyML_Fprintf(stderr,"\n. e.g., ."); PhyML_Fprintf(stderr,"\n. See the manual for more options."); assert(FALSE); } else { if(!strcmp(model_name,"geometricbrownian")) { mixt_tree->rates->model_id = GUINDON; mixt_tree->mod->gamma_mgf_bl = YES; strcpy(mixt_tree->rates->model_name,"integrated"); } else if(!strcmp(model_name,"geometric")) { mixt_tree->rates->model_id = GUINDON; mixt_tree->mod->gamma_mgf_bl = YES; strcpy(mixt_tree->rates->model_name,"integrated"); } else if(!strcmp(model_name,"integrated")) { mixt_tree->rates->model_id = GUINDON; mixt_tree->mod->gamma_mgf_bl = YES; strcpy(mixt_tree->rates->model_name,"integrated"); } else if(!strcmp(model_name,"geo")) { mixt_tree->rates->model_id = GUINDON; mixt_tree->mod->gamma_mgf_bl = YES; strcpy(mixt_tree->rates->model_name,"integrated"); } else if(!strcmp(model_name,"lognormal")) { mixt_tree->rates->model_id = LOGNORMAL; mixt_tree->mod->gamma_mgf_bl = NO; strcpy(mixt_tree->rates->model_name,"lognormal (uncorrelated)"); } else if(!strcmp(model_name,"normal")) { mixt_tree->rates->model_id = LOGNORMAL; mixt_tree->mod->gamma_mgf_bl = NO; strcpy(mixt_tree->rates->model_name,"lognormal (uncorrelated)"); } else if(!strcmp(model_name,"strictclock")) { mixt_tree->rates->model_id = STRICTCLOCK; mixt_tree->mod->gamma_mgf_bl = NO; strcpy(mixt_tree->rates->model_name,"strict clock"); } else if(!strcmp(model_name,"clock")) { mixt_tree->rates->model_id = STRICTCLOCK; mixt_tree->mod->gamma_mgf_bl = NO; strcpy(mixt_tree->rates->model_name,"strict clock"); } else if(!strcmp(model_name,"thorne")) { mixt_tree->rates->model_id = THORNE; mixt_tree->mod->gamma_mgf_bl = NO; strcpy(mixt_tree->rates->model_name,"autocorrelated"); } else if(!strcmp(model_name,"autocorrelated")) { mixt_tree->rates->model_id = THORNE; mixt_tree->mod->gamma_mgf_bl = NO; strcpy(mixt_tree->rates->model_name,"autocorrelated"); } else if(!strcmp(model_name,"autocorr")) { mixt_tree->rates->model_id = THORNE; mixt_tree->mod->gamma_mgf_bl = NO; strcpy(mixt_tree->rates->model_name,"autocorrelated"); } else { assert(FALSE); } } } // Looking for XML node with rate-across-lineage info xnd = XML_Search_Node_Name("clockrate",YES,xroot); if(xnd != NULL) { char *clock_r; clock_r = XML_Get_Attribute_Value(xnd,"value"); if(clock_r == NULL) clock_r = XML_Get_Attribute_Value(xnd,"clock.val"); if(clock_r == NULL) clock_r = XML_Get_Attribute_Value(xnd,"val"); if(clock_r != NULL) { mixt_tree->rates->clock_r = String_To_Dbl(clock_r); for(int i=0;i<2*mixt_tree->n_otu-1;++i) { mixt_tree->rates->br_r[i] = mixt_tree->rates->clock_r; mixt_tree->rates->nd_r[i] = mixt_tree->rates->clock_r; } } char *opt_clock; opt_clock = XML_Get_Attribute_Value(xnd,"optimise.clock"); if(opt_clock == NULL) opt_clock = XML_Get_Attribute_Value(xnd,"optimize.clock"); if(opt_clock == NULL) opt_clock = XML_Get_Attribute_Value(xnd,"optimize.rate"); if(opt_clock == NULL) opt_clock = XML_Get_Attribute_Value(xnd,"opt.clock"); if(opt_clock != NULL) { int select = XML_Validate_Attr_Int(opt_clock,6, "true","yes","y", "false","no","n"); if(select < 3) mixt_tree->mod->s_opt->opt_clock_r = YES; else mixt_tree->mod->s_opt->opt_clock_r = NO; } } // Looking for calibration info xnd = XML_Search_Node_Name("calibration",YES,xroot); if(xnd == NULL) { PhyML_Fprintf(stderr,"\n. No calibration information seems to be provided."); PhyML_Fprintf(stderr,"\n. Please amend your XML file. \n"); assert(FALSE); } else { assert(xnd->child); if(XML_Search_Node_Name("upper",NO,xnd->child) == NULL && XML_Search_Node_Name("lower",NO,xnd->child) == NULL) { PhyML_Fprintf(stderr,"\n. There is no calibration information provided. \n"); PhyML_Fprintf(stderr,"\n. Please check your data. \n"); assert(FALSE); } } /* MIXT_Check_Model_Validity(mixt_tree); */ /* MIXT_Init_Model(mixt_tree); */ /* Print_Data_Structure(NO,stdout,mixt_tree); */ /* tree = MIXT_Starting_Tree(mixt_tree); */ /* Copy_Tree(tree,mixt_tree); */ /* Free_Tree(tree); */ /* MIXT_Connect_Cseqs_To_Nodes(mixt_tree); */ /* MIXT_Init_T_Beg(mixt_tree); */ /* MIXT_Chain_Edges(mixt_tree); */ /* MIXT_Chain_Nodes(mixt_tree); */ /* MIXT_Make_Tree_For_Pars(mixt_tree); */ /* MIXT_Make_Tree_For_Lk(mixt_tree); */ /* MIXT_Make_Spr(mixt_tree); */ /* MIXT_Chain_All(mixt_tree); */ /* Add_Root(mixt_tree->a_edges[0],mixt_tree); */ /* MIXT_Check_Edge_Lens_In_All_Elem(mixt_tree); */ /* MIXT_Turn_Branches_OnOff_In_All_Elem(ON,mixt_tree); */ /* MIXT_Check_Invar_Struct_In_Each_Partition_Elem(mixt_tree); */ /* MIXT_Check_RAS_Struct_In_Each_Partition_Elem(mixt_tree); */ /* XML_Read_Calibration(xroot,mixt_tree); */ /* MIXT_Chain_Cal(mixt_tree); */ seed = (mixt_tree->io->r_seed < 0)?(time(NULL)):(mixt_tree->io->r_seed); srand(seed); mixt_tree->io->r_seed = seed; MIXT_Check_Model_Validity(mixt_tree); MIXT_Chain_Models(mixt_tree); Init_Model(mixt_tree->mod->io->cdata,mixt_tree->mod,mixt_tree->mod->io); Set_Model_Parameters(mixt_tree->mod); Print_Data_Structure(NO,stdout,mixt_tree); tree = MIXT_Starting_Tree(mixt_tree); Add_Root(tree->a_edges[0],tree); Copy_Tree(tree,mixt_tree); Free_Tree(tree); Copy_Tree(mixt_tree,mixt_tree->next); Connect_CSeqs_To_Nodes(mixt_tree->mod->io->cdata,mixt_tree->mod->io,mixt_tree); Init_T_Beg(mixt_tree); Make_Tree_For_Lk(mixt_tree); Make_Tree_For_Pars(mixt_tree); Make_Spr(mixt_tree); MIXT_Chain_All(mixt_tree); MIXT_Check_Edge_Lens_In_All_Elem(mixt_tree); MIXT_Turn_Branches_OnOff_In_All_Elem(ON,mixt_tree); MIXT_Check_Invar_Struct_In_Each_Partition_Elem(mixt_tree); MIXT_Check_RAS_Struct_In_Each_Partition_Elem(mixt_tree); XML_Read_Calibration(xroot,mixt_tree); MIXT_Chain_Cal(mixt_tree); res = DATE_MCMC(mixt_tree); // Cleaning up... RATES_Free_Rates(mixt_tree->rates); RATES_Free_Rates(mixt_tree->aux_tree[0]->rates); TIMES_Free_Times(mixt_tree->times); TIMES_Free_Times(mixt_tree->aux_tree[0]->times); MCMC_Free_MCMC(mixt_tree->mcmc); MCMC_Free_MCMC(mixt_tree->aux_tree[0]->mcmc); Free_Mmod(mixt_tree->mmod); Free_Spr_List_One_Edge(mixt_tree); Free_Tree_Pars(mixt_tree); Free_Tree_Lk(mixt_tree); if(mixt_tree->io->fp_out_trees) fclose(mixt_tree->io->fp_out_trees); if(mixt_tree->io->fp_out_tree) fclose(mixt_tree->io->fp_out_tree); if(mixt_tree->io->fp_out_stats) fclose(mixt_tree->io->fp_out_stats); if(mixt_tree->io->fp_out_json_trace) fclose(mixt_tree->io->fp_out_json_trace); Free_Input(mixt_tree->io); tree = mixt_tree; do { Free_Calign(tree->data); tree = tree->next_mixt; } while(tree); tree = mixt_tree; do { Free_Optimiz(tree->mod->s_opt); tree = tree->next; } while(tree); Free_Model_Complete(mixt_tree->mod); Free_Model_Basic(mixt_tree->mod); Free_Tree(mixt_tree->aux_tree[0]); Free(mixt_tree->aux_tree); Free_Tree(mixt_tree); Free(res); XML_Free_XML_Tree(xroot); fclose(fp_xml_in); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Update t_prior_min and t_prior_max on a given ranked tree // given (primary and secondary) calibration information. // Make sure secondary and primary calibration are up-to-date void DATE_Update_T_Prior_MinMax(t_tree *tree) { int i,j; for(i=0;i<2*tree->n_otu-1;++i) // All nodes { tree->times->t_prior_max[i] = +INFINITY; tree->times->t_prior_min[i] = -INFINITY; if(tree->a_nodes[i]->n_cal > 0) // Primary calibration found on that node { for(j=0;ja_nodes[i]->n_cal;++j) { tree->times->t_prior_max[i] = MIN(tree->times->t_prior_max[i],tree->a_nodes[i]->cal[j]->upper); tree->times->t_prior_min[i] = MAX(tree->times->t_prior_min[i],tree->a_nodes[i]->cal[j]->lower); } } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DATE_Assign_Primary_Calibration(t_tree *tree) { int i,j,idx,node_num; t_clad *clade; t_cal *cal; clade = NULL; cal = NULL; for(i=0;itimes->n_cal;++i) { cal = tree->times->a_cal[i]; if(cal->clade_list != NULL) { clade = cal->clade_list[cal->current_clade_idx]; clade->target_nd = NULL; } } for(i=0;i<2*tree->n_otu-1;++i) for(j=0;ja_nodes[i]->cal[j] = NULL; tree->a_nodes[i]->n_cal = 0; } for(i=0;itimes->n_cal;++i) { cal = tree->times->a_cal[i]; if(cal->clade_list != NULL) { clade = cal->clade_list[cal->current_clade_idx]; node_num = Find_Clade(clade->tax_list, clade->n_tax, tree); clade->target_nd = tree->a_nodes[node_num]; idx = tree->a_nodes[node_num]->n_cal; tree->a_nodes[node_num]->cal[idx] = tree->times->a_cal[i]; tree->a_nodes[node_num]->n_cal++; if(tree->a_nodes[node_num]->n_cal == MAX_N_CAL) { PhyML_Fprintf(stderr,"\n. A node cannot have more than %d calibration",MAX_N_CAL); PhyML_Fprintf(stderr,"\n. constraints attached to it. Feel free to increase the"); PhyML_Fprintf(stderr,"\n. value of the variable MAX_N_CAL in utilities.h if"); PhyML_Fprintf(stderr,"\n. necessary."); Exit("\n"); } } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Return splitted calibration intervals. Make sure all primary // and secondary calibration intervals are up-to-date. phydbl *DATE_Splitted_Calibration(t_tree *tree) { phydbl *minmax,*splitted_cal,buff; int i,len,done; // One t_prior_min and one t_prior_max per internal nodes except root, so // 2 x # of internal nodes boundaries in total at most. minmax = (phydbl *)mCalloc(2*(tree->n_otu-2),sizeof(phydbl)); For(i,2*(tree->n_otu-2)) minmax[i] = +INFINITY; splitted_cal = (phydbl *)mCalloc((int)(4*tree->n_otu-10),sizeof(phydbl)); len = 0; for(i = tree->n_otu; i < 2*tree->n_otu-1; i++) { if(tree->a_nodes[i] != tree->n_root) { minmax[len] = MAX(tree->times->t_prior_min[i],tree->times->nd_t[tree->n_root->num]); minmax[len+1] = tree->times->t_prior_max[i]; len+=2; } } // Bubble sort of all these times in increasing order do { done = YES; for(i=0;i minmax[i+1]) { buff = minmax[i]; minmax[i] = minmax[i+1]; minmax[i+1] = buff; done = NO; } } } while(done == NO); for(i=0;i minmax[i+1])); // Remove ties for(i=0;i minmax[i+1]) { buff = minmax[i]; minmax[i] = minmax[i+1]; minmax[i+1] = buff; done = NO; } } } while(done == NO); splitted_cal[0] = minmax[0]; len = 1; for(i = 1; i < 2*(tree->n_otu-2); i++) { splitted_cal[len] = minmax[i]; if(len+1 < 4*tree->n_otu-10) splitted_cal[len+1] = minmax[i]; len+=2; } /* For(i,4*tree->n_otu-10) PhyML_Printf("\n. split -- %3d %12f",i,splitted_cal[i]); */ Free(minmax); return splitted_cal; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl DATE_J_Sum_Product(t_tree *tree) { phydbl prod, total,*splitted_cal; int fact,idx,ans,rk; DATE_Assign_Primary_Calibration(tree); DATE_Update_T_Prior_MinMax(tree); splitted_cal = DATE_Splitted_Calibration(tree); ans = 0; total = 0.0; idx = 0; rk = 1; do { prod = 1.0; fact = 1; ans = DATE_J_Sum_Product_Pre(tree->a_nodes[tree->times->t_rank[rk]], // Oldest node after root (as rk=1) idx, -1, prod,fact,&total,splitted_cal,rk,tree); idx+=2; } while(ans != 1); Free(splitted_cal); return(total); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int DATE_J_Sum_Product_Pre(t_node *d, int split_idx_d, int split_idx_a, phydbl prod, int fact, phydbl *total, phydbl *splitted_cal, int rk, t_tree *tree) { int ans,idx; ans = DATE_Is_Split_Accessible(d,split_idx_d,splitted_cal,tree); switch(ans) { case 1 : // split interval is younger than t_prior_max. No need to go further. { return ans; break; } case 0 : // split interval is within [t_prior_min,t_prior_max] { int local_ans; // Calculate J for this time interval prod *= DATE_J(tree->times->birth_rate, tree->times->death_rate, FABS(splitted_cal[split_idx_d+1]), FABS(splitted_cal[split_idx_d])); // Remove factorial term from current product prod *= fact; if(split_idx_d == split_idx_a) fact++; else fact = 1; prod /= fact; if(tree->times->t_rank[tree->n_otu-2] == d->num) // Youngest internal node { (*total) += prod; return 0; } idx = split_idx_d; do { local_ans = DATE_J_Sum_Product_Pre(tree->a_nodes[tree->times->t_rank[rk+1]], idx, split_idx_d, prod,fact,total,splitted_cal,rk+1,tree); idx+=2; } while(local_ans == 0); break; } case -1 : // split interval is older than t_prior_min. Move forward. { int local_ans; // Advance to younger split intervals and stop once you're in idx = split_idx_d+2; do { local_ans = DATE_J_Sum_Product_Pre(d, idx, idx+1, prod,fact,total,splitted_cal,rk,tree); idx+=2; } while(local_ans == -1); break; } } return ans; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int DATE_Is_Split_Accessible(t_node *d, int which, phydbl *splitted_cal, t_tree *tree) { phydbl eps; assert(d->tax == NO); eps = FABS(tree->times->t_prior_min[d->num]) / 1.E+6; assert(eps > MDBL_MIN); // Upper and lower bound of splitted calibration interval are equal to zero if(Are_Equal(splitted_cal[which],0.0,eps) && Are_Equal(splitted_cal[which+1],0.0,eps)) return +1; if(Are_Equal(tree->times->t_prior_min[d->num],splitted_cal[which],eps) || Are_Equal(tree->times->t_prior_max[d->num],splitted_cal[which+1],eps) || (tree->times->t_prior_min[d->num] < splitted_cal[which] && tree->times->t_prior_max[d->num] > splitted_cal[which+1])) return 0; // splitted interval is within [t_prior_min,t_prior_max] else if(Are_Equal(tree->times->t_prior_max[d->num],splitted_cal[which],eps) || splitted_cal[which] > tree->times->t_prior_max[d->num]) return +1; // splitted interval is younger than [t_prior_min,t_prior_max] else if(Are_Equal(tree->times->t_prior_min[d->num],splitted_cal[which+1],eps) || splitted_cal[which+1] < tree->times->t_prior_min[d->num]) return -1; // splitted interval is older than [t_prior_min,t_prior_max] else { PhyML_Printf("\n. d->num: %d d->tax: %d",d->num,d->tax); PhyML_Printf("\n. t_prior_min: %f t_prior_max: %f", tree->times->t_prior_min[d->num], tree->times->t_prior_max[d->num]); PhyML_Printf("\n. splitted_cal_min: %f splitted_cal_max: %f", splitted_cal[which], splitted_cal[which+1]); PhyML_Printf("\n"); assert(FALSE); // splitted interval cannot be partially overlapping [t_prior_min,t_prior_max] } return(0); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl DATE_J(phydbl birth_r, phydbl death_r, phydbl t_min, phydbl t_pls) { phydbl d,b,J; assert(t_pls > t_min); d = death_r; b = birth_r; J = (b-d)*(exp(t_min*d+t_pls*b) - exp(t_min*b+t_pls*d)); J /= ((b*exp(t_min*b)-d*exp(t_min*d)) * (b*exp(t_pls*b)-d*exp(t_pls*d))); /* printf(" J : %f",J); */ return(J); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int DATE_Check_Calibration_Constraints(t_tree *tree) { int i,j; phydbl lower,upper; lower = upper = -1.; For(i,2*tree->n_otu-1) { if(tree->a_nodes[i]->n_cal > 1) { lower = tree->a_nodes[i]->cal[0]->lower; upper = tree->a_nodes[i]->cal[0]->upper; for(j=1; j < tree->a_nodes[i]->n_cal; j++) { lower = MAX(lower,tree->a_nodes[i]->cal[j]->lower); upper = MIN(upper,tree->a_nodes[i]->cal[j]->upper); if(upper < lower) { /* PhyML_Printf("\n. Inconsistency detected on node %d",i); */ return 0; } } } } return 1; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Check that time constraints are satisfied. Note: it is a // requirement to verify that the age of the root node is also // within correct boundaries int DATE_Check_Time_Constraints(t_tree *tree) { for(int i=0;i<2*tree->n_otu-1;++i) { if(tree->a_nodes[i]->tax == NO) { if(tree->times->nd_t[i] > tree->times->t_prior_max[i] || tree->times->nd_t[i] < tree->times->t_prior_min[i]) { /* PhyML_Printf("\n!!! Node %d t: %f min:%f max:%f", */ /* i, */ /* tree->times->nd_t[i], */ /* tree->times->t_prior_min[i], */ /* tree->times->t_prior_max[i]); */ return 0; } } } return 1; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl *DATE_MCMC(t_tree *tree) { t_mcmc *mcmc; char *s_tree; int move, n_vars, i, j, adjust_len; phydbl u; phydbl *res; FILE *fp_stats,*fp_tree; int t_beg; t_beg = (int)time(NULL); fp_stats = tree->io->fp_out_stats; fp_tree = tree->io->fp_out_tree; if(tree->io->mutmap == YES) Make_MutMap(tree); TIMES_Randomize_Tree_With_Time_Constraints(tree->times->a_cal[0],tree); MIXT_Propagate_Tree_Update(tree); mcmc = MCMC_Make_MCMC_Struct(); tree->mcmc = mcmc; MCMC_Init_MCMC_Struct(NULL,NULL,mcmc); MCMC_Complete_MCMC(mcmc,tree); MCMC_Randomize_Birth(tree); MCMC_Randomize_Death(tree); MCMC_Randomize_Clock_Rate(tree); MCMC_Randomize_Rate_Across_Sites(tree); MCMC_Randomize_Rates(tree); n_vars = 10; adjust_len = tree->io->mcmc->chain_len_burnin; mcmc->sample_interval = tree->io->mcmc->sample_interval; mcmc->print_every = tree->io->mcmc->print_every; mcmc->chain_len = tree->io->mcmc->chain_len; mcmc->chain_len_burnin = tree->io->mcmc->chain_len_burnin; tree->rates->bl_from_rt = YES; res = (phydbl *)mCalloc(tree->mcmc->chain_len / tree->mcmc->sample_interval * n_vars,sizeof(phydbl)); Set_Both_Sides(YES,tree); Set_Update_Eigen(YES,tree->mod); Lk(NULL,tree); Set_Update_Eigen(NO,tree->mod); RATES_Lk(tree); DATE_Assign_Primary_Calibration(tree); TIMES_Lk(tree); /* Time_To_Branch(tree); */ /* tree->bl_ndigits = 1; */ /* printf("\n. Random init tree: %s",Write_Tree(tree)); */ /* tree->bl_ndigits = 7; */ RATES_Update_Edge_Lengths(tree); PhyML_Printf("\n. AVX enabled: %s", #if defined(__AVX__) "yes" #else "no" #endif ); PhyML_Printf("\n. SSE enabled: %s", #if defined(__SSE3__) "yes" #else "no" #endif ); PhyML_Printf("\n\n. Seed: %d",tree->io->r_seed); PhyML_Printf("\n. Ignore sequences: %s",tree->eval_alnL == YES ? "no" : "yes"); PhyML_Printf("\n. Model of variation of rates across lineages: %s",tree->rates->model_name); PhyML_Printf("\n. log(Pr(Seq|Tree)) = %f",tree->c_lnL); PhyML_Printf("\n. log(Pr(Tree)) = %f",tree->times->c_lnL); tree->aux_tree = (t_tree **)mCalloc(1,sizeof(t_tree *)); tree->aux_tree[0] = Make_Tree_From_Scratch(tree->n_otu,tree->data); tree->aux_tree[0]->mod = tree->mod; Copy_Tree(tree,tree->aux_tree[0]); tree->aux_tree[0]->rates = RATES_Make_Rate_Struct(tree->n_otu); RATES_Init_Rate_Struct(tree->aux_tree[0]->rates,NULL,tree->n_otu); RATES_Copy_Rate_Struct(tree->rates,tree->aux_tree[0]->rates,tree->n_otu); tree->aux_tree[0]->rates->model_id = LOGNORMAL; tree->aux_tree[0]->times = TIMES_Make_Time_Struct(tree->n_otu); TIMES_Init_Time_Struct(tree->aux_tree[0]->times,NULL,tree->n_otu); TIMES_Copy_Time_Struct(tree->times,tree->aux_tree[0]->times,tree->n_otu); RATES_Duplicate_Calib_Struct(tree,tree->aux_tree[0]); MIXT_Chain_Cal(tree->aux_tree[0]); DATE_Assign_Primary_Calibration(tree->aux_tree[0]); TIMES_Randomize_Tree_With_Time_Constraints(tree->aux_tree[0]->times->a_cal[0],tree->aux_tree[0]); TIMES_Lk(tree->aux_tree[0]); PhyML_Printf("\n. log(Pr(extra tree)) = %f",tree->aux_tree[0]->times->c_lnL); mcmc = MCMC_Make_MCMC_Struct(); tree->aux_tree[0]->mcmc = mcmc; MCMC_Init_MCMC_Struct(NULL,NULL,mcmc); MCMC_Complete_MCMC(mcmc,tree->aux_tree[0]); PhyML_Fprintf(fp_stats,"\n%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t", "sample", "lnL(posterior)", "lnL(seq)", "lnL(times)", "lnL(rates)", "birth", "death", "clock", "root", "tstv", "nu"); for(i=0;imod->ras->n_catg;i++) PhyML_Fprintf(fp_stats,"rr%d\t",i); for(i=0;imod->ras->n_catg;i++) PhyML_Fprintf(fp_stats,"pr%d\t",i); for(i=0;itimes->n_cal;++i) { t_cal *cal = tree->times->a_cal[i]; for(j=0;jclade_list_size;++j) { t_clad *clade = cal->clade_list[j]; PhyML_Fprintf(fp_stats,"t(calib:%s_clade:%s)\t",cal->id,clade->id); } } for(i=0;itimes->n_cal;++i) { t_cal *cal = tree->times->a_cal[i]; PhyML_Fprintf(fp_stats,"clade(calib:%s)\t",cal->id); } if(tree->rates->model_id == THORNE || tree->rates->model_id == LOGNORMAL || tree->rates->model_id == STRICTCLOCK) for(i=0;i<2*tree->n_otu-2;++i) PhyML_Fprintf(fp_stats,"br%d\t",i); else for(i=0;i<2*tree->n_otu-1;++i) PhyML_Fprintf(fp_stats,"nr%d\t",i); for(i=0;i<2*tree->n_otu-1;++i) if(tree->a_nodes[i]->tax == NO) PhyML_Fprintf(fp_stats,"t%d\t",i); PhyML_Fprintf(fp_stats,"accRT\t"); PhyML_Fprintf(fp_stats,"tuneRT\t"); PhyML_Fprintf(fp_stats,"accT\t"); PhyML_Fprintf(fp_stats,"tuneT\t"); PhyML_Fprintf(fp_stats,"accClock\t"); PhyML_Fprintf(fp_stats,"tuneClock\t"); PhyML_Fprintf(fp_stats,"accTCr\t"); PhyML_Fprintf(fp_stats,"tuneTCr\t"); PhyML_Fprintf(fp_stats,"accTreeRates\t"); PhyML_Fprintf(fp_stats,"tuneTreeRates\t"); PhyML_Fprintf(fp_stats,"accSprW\t"); PhyML_Fprintf(fp_stats,"tuneSprW\t"); PhyML_Fprintf(fp_stats,"accSpr\t"); PhyML_Fprintf(fp_stats,"tuneSpr\t"); PhyML_Fprintf(fp_stats,"accSprLoc\t"); PhyML_Fprintf(fp_stats,"tuneSprLoc\t"); fflush(NULL); PhyML_Printf("\n\n"); PhyML_Printf("\n. MCMC settings. Chain length: %g steps.",(phydbl)tree->mcmc->chain_len); PhyML_Printf("\n. MCMC settings. Burnin: %g steps.",(phydbl)tree->mcmc->chain_len_burnin); PhyML_Printf("\n. MCMC settings. Sample every %g steps.",(phydbl)tree->mcmc->sample_interval); PhyML_Printf("\n. MCMC settings. Print out every %g steps.",(phydbl)tree->mcmc->print_every); PhyML_Printf("\n\n"); for(i=0;imcmc->n_moves;i++) tree->mcmc->start_ess[i] = YES; Set_Both_Sides(NO,tree); tree->mcmc->always_yes = NO; move = -1; // Get in the range of sensible values for clock_r i = 0; do { MCMC_Clock_R(tree); } while(i++ < 100); do { if(tree->mcmc->run > adjust_len) for(i=0;imcmc->n_moves;i++) tree->mcmc->adjust_tuning[i] = NO; if(tree->c_lnL < UNLIKELY + 0.1 && tree->eval_alnL == YES) { PhyML_Printf("\n. Move '%s' failed\n",tree->mcmc->move_name[move]); assert(FALSE); } u = Uni(); for(move=0;movemcmc->n_moves;move++) if(tree->mcmc->move_weight[move] > u-1.E-10) break; assert(!(move == tree->mcmc->n_moves)); if(!strcmp(tree->mcmc->move_name[move],"clock")) MCMC_Clock_R(tree); else if(!strcmp(tree->mcmc->move_name[move],"birth_rate")) MCMC_Birth_Rate(tree); else if(!strcmp(tree->mcmc->move_name[move],"death_rate")) MCMC_Death_Rate(tree); else if(!strcmp(tree->mcmc->move_name[move],"birth_death_updown")) MCMC_Birth_Death_Updown(tree); else if(!strcmp(tree->mcmc->move_name[move],"tree_height")) MCMC_Tree_Height(tree); else if(!strcmp(tree->mcmc->move_name[move],"times")) MCMC_Times_All(tree); else if(!strcmp(tree->mcmc->move_name[move],"times_and_rates")) MCMC_Times_And_Rates_All(tree); else if(!strcmp(tree->mcmc->move_name[move],"spr")) MCMC_Prune_Regraft(tree); else if(!strcmp(tree->mcmc->move_name[move],"spr_local")) MCMC_Prune_Regraft_Local(tree); else if(!strcmp(tree->mcmc->move_name[move],"spr_weighted")) MCMC_Prune_Regraft_Weighted(tree); else if(!strcmp(tree->mcmc->move_name[move],"updown_t_cr")) MCMC_Updown_T_Cr(tree); else if(!strcmp(tree->mcmc->move_name[move],"kappa")) MCMC_Kappa(tree); else if(!strcmp(tree->mcmc->move_name[move],"ras")) MCMC_Rate_Across_Sites(tree); else if(!strcmp(tree->mcmc->move_name[move],"nu")) MCMC_Nu(tree); else if(!strcmp(tree->mcmc->move_name[move],"subtree_height")) MCMC_Subtree_Height(tree); else if(!strcmp(tree->mcmc->move_name[move],"time_slice")) MCMC_Time_Slice(tree); else if(!strcmp(tree->mcmc->move_name[move],"br_rate")) MCMC_Rates_All(tree); else if(!strcmp(tree->mcmc->move_name[move],"tree_rates")) MCMC_Tree_Rates(tree); else if(!strcmp(tree->mcmc->move_name[move],"clade_change")) MCMC_Clade_Change(tree); else continue; phydbl cur_lk = tree->c_lnL; phydbl new_lk = Lk(NULL,tree); if(Are_Equal(cur_lk,new_lk,1.E-5) == NO) { PhyML_Printf("\n. move: %s",tree->mcmc->move_name[move]); PhyML_Printf("\n. new: %f cur: %f",new_lk,cur_lk); assert(FALSE); } if(!RATES_Check_Edge_Length_Consistency(tree)) { PhyML_Fprintf(stderr,"\n. Issue detected by RATES_Check_Edge_Length_Consistency(tree)."); PhyML_Fprintf(stderr,"\n. Move: %s",tree->mcmc->move_name[move]); if(tree->eval_alnL == YES) assert(FALSE); } if(!TIMES_Check_Node_Height_Ordering(tree)) { PhyML_Fprintf(stderr,"\n. Issue detected by TIMES_Check_Node_Height_Ordering(tree)."); PhyML_Fprintf(stderr,"\n. Move: %s",tree->mcmc->move_name[move]); assert(FALSE); } if(!(tree->times->c_lnL > UNLIKELY)) { PhyML_Fprintf(stderr,"\n. move: %s",tree->mcmc->move_name[move]); PhyML_Fprintf(stderr,"\n. glnL=%f",tree->times->c_lnL); assert(FALSE); } (void)signal(SIGINT,MCMC_Terminate); if(!(tree->mcmc->run%tree->mcmc->print_every)) { phydbl mean_r,post; mean_r = RATES_Average_Substitution_Rate(tree); post = Get_Lk(tree) + tree->times->c_lnL + tree->rates->c_lnL; if(tree->mcmc->run < adjust_len) PhyML_Printf("\nx"); else PhyML_Printf("\n."); PhyML_Printf(" %10d lnL: [%12.2f -- %12.2f -- %12.2f -- %12.2f] root age: %12f [time: %7d sec] clock: %15f %20s", tree->mcmc->run, post, Get_Lk(tree), tree->times->c_lnL, tree->rates->c_lnL, fabs(tree->times->nd_t[tree->n_root->num]), (int)time(NULL) - t_beg, mean_r, tree->mcmc->move_name[move]); } if(!(tree->mcmc->run%tree->mcmc->sample_interval)) { phydbl mean_r,post; mean_r = RATES_Average_Substitution_Rate(tree); post = Get_Lk(tree) + tree->times->c_lnL + tree->rates->c_lnL; PhyML_Fprintf(fp_stats,"\n%6d\t%9.1f\t%9.1f\t%9.1f\t%9.1f\t%12G\t%12G\t%12G\t%12G\t%12G\t%12G\t", tree->mcmc->run, post, Get_Lk(tree), tree->times->c_lnL, tree->rates->c_lnL, tree->times->birth_rate, tree->times->death_rate, mean_r, fabs(tree->times->nd_t[tree->n_root->num]), tree->next->mod->kappa->v, tree->rates->nu); for(i=0;imod->ras->n_catg;i++) PhyML_Fprintf(fp_stats,"%G\t",tree->mod->ras->gamma_rr->v[i]); for(i=0;imod->ras->n_catg;i++) PhyML_Fprintf(fp_stats,"%G\t",tree->mod->ras->gamma_r_proba->v[i]); for(i=0;itimes->n_cal;i++) { t_cal *cal = tree->times->a_cal[i]; for(j=0;jclade_list_size;++j) { t_clad *clade = cal->clade_list[j]; PhyML_Fprintf(fp_stats,"%G\t",fabs(tree->times->nd_t[clade->target_nd->num])); } } for(i=0;itimes->n_cal;++i) { t_cal *cal = tree->times->a_cal[i]; PhyML_Fprintf(fp_stats,"%d\t",cal->current_clade_idx); /* PhyML_Fprintf(fp_stats,"%s\t",cal->clade_list[cal->current_clade_idx]->id); */ } if(tree->rates->model_id == THORNE || tree->rates->model_id == LOGNORMAL || tree->rates->model_id == STRICTCLOCK) for(i=0;i<2*tree->n_otu-2;++i) PhyML_Fprintf(fp_stats,"%G\t",tree->rates->br_r[i]); else for(i=0;i<2*tree->n_otu-1;++i) PhyML_Fprintf(fp_stats,"%G\t",tree->rates->nd_r[i]); for(i=0;i<2*tree->n_otu-1;++i) if(tree->a_nodes[i]->tax == NO) PhyML_Fprintf(fp_stats,"%G\t",fabs(tree->times->nd_t[i])); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->acc_rate[tree->mcmc->num_move_times_and_rates_root]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->tune_move[tree->mcmc->num_move_times_and_rates_root]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->acc_rate[tree->mcmc->num_move_root_time]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->tune_move[tree->mcmc->num_move_root_time]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->acc_rate[tree->mcmc->num_move_clock_r]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->tune_move[tree->mcmc->num_move_clock_r]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->acc_rate[tree->mcmc->num_move_updown_t_cr]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->tune_move[tree->mcmc->num_move_updown_t_cr]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->acc_rate[tree->mcmc->num_move_tree_rates]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->tune_move[tree->mcmc->num_move_tree_rates]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->acc_rate[tree->mcmc->num_move_spr_weighted]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->tune_move[tree->mcmc->num_move_spr_weighted]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->acc_rate[tree->mcmc->num_move_spr]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->tune_move[tree->mcmc->num_move_spr]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->acc_rate[tree->mcmc->num_move_spr_local]); PhyML_Fprintf(fp_stats,"%G\t",tree->mcmc->tune_move[tree->mcmc->num_move_spr_local]); if(tree->mcmc->sample_num == 0) { PhyML_Fprintf(fp_tree,"\n#NEXUS"); PhyML_Fprintf(fp_tree,"\nBEGIN TREES;"); } else { fseek(fp_tree,-5,SEEK_CUR); } TIMES_Time_To_Bl(tree); tree->bl_ndigits = 3; /* RATES_Update_Edge_Lengths(tree); */ s_tree = Write_Tree(tree); tree->bl_ndigits = 7; PhyML_Fprintf(fp_tree,"\ntree %d [&lnP=%f] = [&R] %s",tree->mcmc->sample_num,tree->c_lnL,s_tree); Free(s_tree); PhyML_Fprintf(fp_tree,"\nEND;"); fflush(NULL); RATES_Update_Edge_Lengths(tree); if(tree->mcmc->run > tree->mcmc->chain_len_burnin && tree->io->mutmap == YES) Sample_Ancestral_Seq(YES,NO,tree); tree->mcmc->sample_num++; } tree->mcmc->run++; MCMC_Get_Acc_Rates(tree->mcmc); } while(tree->mcmc->run < tree->mcmc->chain_len); return(res); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Update the list of nodes that are younger than lim void DATE_List_Of_Nodes_Younger_Than(t_node *a, t_node *d, phydbl lim, t_ll **list, t_tree *tree) { if(tree->times->nd_t[d->num] > lim) Push_Bottom_Linked_List(d,list,YES); if(d->tax == YES) return; else { int i; if(d == tree->n_root) { DATE_List_Of_Nodes_Younger_Than(d,d->v[1],lim,list,tree); DATE_List_Of_Nodes_Younger_Than(d,d->v[2],lim,list,tree); } else { for(i=0;i<3;i++) if(d->v[i] != a && d->b[i] != tree->e_root) DATE_List_Of_Nodes_Younger_Than(d,d->v[i],lim,list,tree); } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Update the list of nodes that are younger than lim with direct ancestors // not younger than lim void DATE_List_Of_Nodes_And_Ancestors_Younger_Than(t_node *a, t_node *d, phydbl lim, t_ll **list, t_tree *tree) { if(tree->times->nd_t[d->num] > lim && a != NULL && tree->times->nd_t[a->num] > lim) Push_Bottom_Linked_List(d,list,YES); if(d->tax == YES) return; else { int i; if(d == tree->n_root) { DATE_List_Of_Nodes_And_Ancestors_Younger_Than(d,d->v[1],lim,list,tree); DATE_List_Of_Nodes_And_Ancestors_Younger_Than(d,d->v[2],lim,list,tree); } else { for(i=0;i<3;i++) if(d->v[i] != a && d->b[i] != tree->e_root) DATE_List_Of_Nodes_And_Ancestors_Younger_Than(d,d->v[i],lim,list,tree); } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // List of valid regraft nodes, taking into account calibration // constraints. The subtree (defined by prune and prune_daughter // will be re-attached *on top of* one of the nodes in this list // (as opposed to *on one of the sister edges below*). t_ll *DATE_List_Of_Regraft_Nodes(t_node *prune, t_node *prune_daughter, phydbl *t_min, phydbl *t_max, int verbose, t_tree *tree) { t_node *n,*m; int i,j; t_ll *out,*in,*ll; int is_clade_affected; t_clad *clade; t_cal *cal; cal = NULL; clade = NULL; n = NULL; m = NULL; *t_min = -INFINITY; in = NULL; out = NULL; is_clade_affected = NO; // Find the oldest LCA of calibrated sets among the nodes between // prune and root. These clades might see the position of their // LCA change. if(prune != tree->n_root) { n = prune; while(n) { for(i=0;itimes->n_cal;++i) { // That node is the LCA of calibration a_cal[i] cal = tree->times->a_cal[i]; clade = cal->clade_list[cal->current_clade_idx]; if(n == clade->target_nd) { is_clade_affected = NO; for(j=0;jn_tax;++j) { m = clade->tip_list[j]; do { if(m == prune_daughter) { is_clade_affected = YES; break; } m = m->anc; } while(m); if(is_clade_affected == YES) break; } // Maximum of the lower bounds for calibration intervals /* if(is_clade_affected == YES) *t_min = MAX(*t_min,tree->times->a_cal[i]->lower); */ if(is_clade_affected == YES) *t_min = MAX(*t_min,tree->times->t_prior_min[n->num]); } } n = n->anc; } } // Find the oldest internal node within intervals defined by // calibrations affected by the pruning. n = prune_daughter; while(n->anc && !(tree->times->nd_t[n->anc->num] < *t_min)) { n = n->anc; assert(n); } if(verbose) { PhyML_Printf("\n. Apical: %d @ time %f min: %f",n->num,tree->times->nd_t[n->num],*t_min); fflush(NULL); } // List all nodes younger than this apical node DATE_List_Of_Nodes_Younger_Than(n->anc,n,-INFINITY,&in,tree); assert(in != NULL); if(verbose) { ll = in->head; t_node *x; do { x = (t_node *)ll->v; PhyML_Printf("\nx Inlist %d @ %f",x->num,tree->times->nd_t[x->num]); ll = ll->next; } while(ll != NULL); } // Remove from that list the nodes that are too young to be suitable regraft points. n = prune_daughter; out = NULL; while(n) { for(i=0;itimes->n_cal;i++) { cal = tree->times->a_cal[i]; clade = cal->clade_list[cal->current_clade_idx]; if(n->anc && n->anc == clade->target_nd) { for(j=0;jn_tax;++j) { m = clade->tip_list[j]; do { if(m == prune_daughter) break; if(m == prune) break; m = m->anc; } while(m); if(m == prune) break; // Prune-regraft anywhere below calibrated node will not change that node. } if(m != prune) { for(j=0;j<3;++j) { if(n->anc->v[j] != n->anc->anc && n->anc->b[j] != tree->e_root && n->anc->v[j] != n) { DATE_List_Of_Nodes_And_Ancestors_Younger_Than(n->anc, n->anc->v[j], tree->times->a_cal[i]->upper, &out, tree); break; } } } } } n = n->anc; } // Remove nodes that are `strictly' younger than prune_daughter DATE_List_Of_Nodes_And_Ancestors_Younger_Than(tree->n_root,tree->n_root->v[1],tree->times->nd_t[prune_daughter->num],&out,tree); DATE_List_Of_Nodes_And_Ancestors_Younger_Than(tree->n_root,tree->n_root->v[2],tree->times->nd_t[prune_daughter->num],&out,tree); // Remove nodes that are below prune_daughter (prune_daughter included) DATE_List_Of_Nodes_Younger_Than(prune,prune_daughter,-INFINITY,&out,tree); // Add prune node to the list of node that can't be targeted for regraft Push_Bottom_Linked_List(prune,&out,YES); // Add root node as one cannot regraft above it /* Push_Bottom_Linked_List(tree->n_root,&out); */ if(verbose) { printf("\nx outlist: %p",(void *)out); fflush(NULL); ll = out->head; do { t_node *x = (t_node *)ll->v; PhyML_Printf("\nx Outlist %d @ %f",x->num,tree->times->nd_t[x->num]); ll = ll->next; } while(ll != NULL); } /* Print_List(in); */ ll = out->head; do { if(verbose) { t_node *x = (t_node *)ll->v; printf("\nx Remove %d",x->num); } Remove_From_Linked_List(NULL,ll->v,&in); if(verbose) PhyML_Printf("\n. List in (in->head:%p in->tail:%p):",in?in->head:NULL,in?in->tail:NULL); /* Print_List(in); */ ll = ll->next; } while(ll != NULL); Free_Linked_List(out); if(verbose) { ll = in->head; do { t_node *x; x = (t_node *)ll->v; printf("\n. In1: %d",x->num); fflush(NULL); ll = ll->next; } while(ll != NULL); } return(in); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl DATE_Lk_Calib(t_tree *tree) { phydbl lnL; int i; t_cal *cal; lnL = 0.0; for(i=0;itimes->n_cal;++i) { cal = tree->times->a_cal[i]; lnL += LOG(cal->alpha_proba_list[cal->current_clade_idx]); } return lnL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phyml-3.3.20220408/src/date.h000066400000000000000000000033341422404706600152750ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include #ifndef DATE_H #define DATE_H #include "utilities.h" int DATE_Main(int argc, char **argv); void DATE_XML(char *xml_filename); void DATE_Update_Secondary_Cal(t_tree *tree); void DATE_Update_Secondary_Cal_Post(t_node *a, t_node *d, t_tree *tree); void DATE_Update_Secondary_Cal_Pre(t_node *a, t_node *d, t_tree *tree); phydbl *DATE_Splitted_Calibration(t_tree *tree); void DATE_Assign_Primary_Calibration(t_tree *tree); void DATE_Update_T_Prior_MinMax(t_tree *tree); phydbl DATE_J(phydbl birth_r, phydbl death_r, phydbl t_min, phydbl t_pls); int DATE_Is_Split_Accessible(t_node *d, int which, phydbl *splitted_cal, t_tree *tree); phydbl *DATE_Splitted_Calibration(t_tree *tree); phydbl DATE_J_Sum_Product(t_tree *tree); int DATE_J_Sum_Product_Pre(t_node *d, int split_idx_d, int split_idx_a, phydbl prod, int fact, phydbl *total, phydbl *splitted_cal, int rk, t_tree *tree); void DATE_Chain_Cal(t_tree *mixt_tree); int DATE_Check_Calibration_Constraints(t_tree *tree); int DATE_Check_Time_Constraints(t_tree *tree); phydbl *DATE_MCMC(t_tree *tree); void DATE_List_Of_Nodes_Younger_Than(t_node *a, t_node *d, phydbl lim, t_ll **list, t_tree *tree); void DATE_List_Of_Nodes_And_Ancestors_Younger_Than(t_node *a, t_node *d, phydbl lim, t_ll **list, t_tree *tree); t_ll *DATE_List_Of_Regraft_Nodes(t_node *prune, t_node *prune_daughter, phydbl *t_min, phydbl *t_max, int verbose, t_tree *tree); phydbl DATE_Lk_Calib(t_tree *tree); #endif phyml-3.3.20220408/src/draw.c000066400000000000000000000461151422404706600153140ustar00rootroot00000000000000/* PHYML : a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences Copyright (C) Stephane Guindon. Oct 2003 onward All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include "draw.h" void DR_Draw_Tree(char *file_name, t_tree *tree) { FILE *ps_tree; ps_tree = (FILE *)fopen(file_name,"w"); DR_Print_Postscript_Header(1,ps_tree); tree->ps_tree = DR_Make_Tdraw_Struct(tree); DR_Init_Tdraw_Struct(tree->ps_tree); DR_Get_Tree_Box_Width(tree->ps_tree,tree); Dist_To_Root(tree); tree->ps_tree->max_dist_to_root = DR_Get_Max_Dist_To_Root(tree); DR_Get_X_Coord(NO,tree->ps_tree,tree); DR_Get_Y_Coord(NO,tree->ps_tree,tree); DR_Print_Tree_Postscript(1,NO,ps_tree,tree); DR_Print_Postscript_EOF(ps_tree); fclose(ps_tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DR_Get_Tree_Coord(t_tree *tree) { DR_Init_Tdraw_Struct(tree->ps_tree); DR_Get_Tree_Box_Width(tree->ps_tree,tree); if(!tree->n_root) { PhyML_Printf("\n. Adding root before rendering the tree."); Add_Root(tree->a_edges[0],tree); } Dist_To_Root(tree); tree->ps_tree->max_dist_to_root = DR_Get_Max_Dist_To_Root(tree); DR_Get_X_Coord(NO,tree->ps_tree,tree); DR_Get_Y_Coord(NO,tree->ps_tree,tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DR_Print_Postscript_Header(int n_pages, FILE *fp) { if(!fp) { PhyML_Printf("\n== Failed to open the postscript file."); PhyML_Printf("\n== Did you forget the '--ps' option ?."); Exit("\n"); } PhyML_Fprintf(fp,"%%!PS-Adobe-3.0\n"); PhyML_Fprintf(fp,"%%%%BoundingBox: 0 0 595.28 841.89\n"); PhyML_Fprintf(fp,"%%%%DocumentFonts: Times-Roman Times-Roman\n"); PhyML_Fprintf(fp,"%%%%Creator: Stephane Guindon\n"); PhyML_Fprintf(fp,"%%%%Title: tree\n"); PhyML_Fprintf(fp,"%%%%EndComments\n"); PhyML_Fprintf(fp,"%%%%Pages: %d\n",n_pages); PhyML_Fprintf(fp,"/lt {lineto} bind def\n"); PhyML_Fprintf(fp,"/mt {moveto} bind def\n"); PhyML_Fprintf(fp,"/sc {setrgbcolor} bind def\n"); PhyML_Fprintf(fp,"/ct {curveto} bind def\n"); PhyML_Fprintf(fp,"/np {newpath} bind def\n"); PhyML_Fprintf(fp,"/cp {closepath} bind def\n"); PhyML_Fprintf(fp,"/gs {gsave} bind def\n"); PhyML_Fprintf(fp,"/gr {grestore} bind def\n"); PhyML_Fprintf(fp,"/Times-Roman findfont\n"); PhyML_Fprintf(fp,"12 scalefont\n"); PhyML_Fprintf(fp,"setfont\n"); PhyML_Fprintf(fp,"/clipbox\n"); PhyML_Fprintf(fp,"{\n"); PhyML_Fprintf(fp,"newpath\n"); PhyML_Fprintf(fp,"20 20 mt\n"); PhyML_Fprintf(fp,"580 20 lt\n"); PhyML_Fprintf(fp,"580 820 lt\n"); PhyML_Fprintf(fp,"20 820 lt\n"); PhyML_Fprintf(fp,"20 20 lt\n"); PhyML_Fprintf(fp,"closepath\n"); PhyML_Fprintf(fp,"clip\n"); PhyML_Fprintf(fp,"} bind def\n"); /* PhyML_Fprintf(fp,"gs\n"); */ /* PhyML_Fprintf(fp,"newpath\n"); */ /* PhyML_Fprintf(fp,"20 20 mt\n"); */ /* PhyML_Fprintf(fp,"580 20 lt\n"); */ /* PhyML_Fprintf(fp,"580 820 lt\n"); */ /* PhyML_Fprintf(fp,"20 820 lt\n"); */ /* PhyML_Fprintf(fp,"20 20 lt\n"); */ /* PhyML_Fprintf(fp,"closepath\n"); */ /* PhyML_Fprintf(fp,"stroke\n"); */ /* PhyML_Fprintf(fp,"gr\n"); */ /* PhyML_Fprintf(fp,"0 0 0 sc\n"); */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DR_Print_Postscript_EOF(FILE *fp) { PhyML_Fprintf(fp,"%%%%Trailer\n"); PhyML_Fprintf(fp,"%%%%EOF\n"); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DR_Print_Tree_Postscript(int page_num, int render_name, FILE *fp, t_tree *tree) { tdraw *draw; t_node *n_root; draw = tree->ps_tree; /* DR_Get_Tree_Coord(tree); */ n_root = tree->n_root; /* PhyML_Fprintf(fp,"%%%%Page: %d %d\n",page_num,page_num); */ /* PhyML_Fprintf(fp,"0.001 setlinewidth\n"); */ /* PhyML_Fprintf(fp,"0.5 0.5 0.4 sc\n"); */ /* PhyML_Fprintf(fp,"0 0 0 sc\n"); */ /* PhyML_Fprintf(fp,"clipbox\n"); */ /* PhyML_Fprintf(fp,"stroke\n"); */ PhyML_Fprintf(fp,"20 20 translate\n"); PhyML_Fprintf(fp,"newpath\n"); draw->ycoord[n_root->num] = (draw->ycoord[n_root->v[2]->num] + draw->ycoord[n_root->v[1]->num])/2. + 20; draw->xcoord[n_root->num] = 0.0; DR_Print_Tree_Postscript_Pre(n_root,n_root->v[2],n_root->b[2],render_name,fp,draw,tree); DR_Print_Tree_Postscript_Pre(n_root,n_root->v[1],n_root->b[1],render_name,fp,draw,tree); PhyML_Fprintf(fp,"closepath\n"); PhyML_Fprintf(fp,"0 0 translate\n"); PhyML_Fprintf(fp,"stroke\n"); PhyML_Fprintf(fp,"showpage\n"); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DR_Print_Tree_Postscript_Pre(t_node *a, t_node *d, t_edge *b, int render_name, FILE *fp, tdraw *w, t_tree *tree) { int i; phydbl R, G, B; R = G = B = 0.0; PhyML_Fprintf(fp,"gs\n"); PhyML_Fprintf(fp,"%.1f %.1f mt\n",w->xcoord[a->num],w->ycoord[a->num]); /* PhyML_Fprintf(fp,"%.1f %.1f lt\n",w->xcoord[a->num],w->ycoord[d->num]); */ /* PhyML_Fprintf(fp,"%.1f %.1f lt\n",w->xcoord[d->num],w->ycoord[d->num]); */ phydbl min,max,step,val; min = 0.0; max = 5.; step = (max-min)/13.; /* val = tree->rates->mean_r[d->num] / (phydbl)(tree->mcmc->run/tree->mcmc->sample_interval+1.); */ /* val = tree->rates->mean_r[d->num]; */ /* val = tree->times->has_survived[d->num]; */ /* if(val > 0.5) {R=1.; G=.0; B=0.;} */ /* val = tree->geo->ldscape[tree->geo->idx_loc[d->num]*tree->geo->n_dim+0] + 2.5; */ val = tree->geo->coord_loc[tree->geo->idx_loc[d->num]]->lonlat[0] + 2.5; /* val = 0.; */ if(val <= min+1.*step) {R=.0; G=1.; B=1.;} else if(val > min+1.*step && val <= min+2.*step) {R=.0; G=1.; B=.8;} else if(val > min+2.*step && val <= min+3.*step) {R=.0; G=1.; B=.5;} else if(val > min+3.*step && val <= min+4.*step) {R=.0; G=1.; B=.3;} else if(val > min+4.*step && val <= min+5.*step) {R=.0; G=1.; B=.0;} else if(val > min+5.*step && val <= min+6.*step) {R=.25; G=1.; B=0.;} else if(val > min+6.*step && val <= min+7.*step) {R=.5; G=1.; B=0.;} else if(val > min+7.*step && val <= min+8.*step) {R=.75; G=1.; B=.0;} else if(val > min+8.*step && val <= min+9.*step) {R=1.; G=1.; B=.0;} else if(val > min+9.*step && val <= min+10.*step) {R=1.; G=.75; B=.0;} else if(val > min+10.*step && val <= min+11.*step) {R=1.; G=.5; B=.0;} else if(val > min+11.*step && val <= min+12.*step) {R=1.; G=.25; B=.0;} else if(val > min+12.*step) {R=1.; G=.0; B=0.;} /* R = 0.; G = 0.; B = 0.; */ PhyML_Fprintf(fp,"2 setlinewidth\n"); /* PhyML_Fprintf(fp,"%.1f %.1f lt\n",w->xcoord[a->num],w->ycoord[d->num]); */ /* PhyML_Fprintf(fp,"%.1f %.1f lt\n",w->xcoord[d->num],w->ycoord[d->num]); */ phydbl xa = w->xcoord[a->num]; phydbl xd = MIN(w->xcoord[a->num] + 5,w->xcoord[d->num]); phydbl ya = w->ycoord[a->num]; phydbl yd = w->ycoord[d->num]; PhyML_Fprintf(fp,"%.1f %.1f %.1f %.1f %.1f %.1f ct\n", xa + (xd-xa)/2.,(ya+yd)/2., xd - (xd-xa)/5.,yd, xd,yd); PhyML_Fprintf(fp,"%.1f %.1f lt\n",w->xcoord[d->num],w->ycoord[d->num]); /* PhyML_Fprintf(fp,"%.1f %.1f %.1f %.1f %.1f %.1f ct\n", */ /* w->xcoord[a->num], */ /* w->ycoord[d->num], */ /* w->xcoord[d->num], */ /* w->ycoord[d->num], */ /* w->xcoord[d->num], */ /* w->ycoord[d->num]); */ if(tree->rates && tree->times->has_survived[d->num] == YES) { PhyML_Fprintf(fp," /Helvetica findfont 16 scalefont\n"); PhyML_Fprintf(fp,"setfont\n"); PhyML_Fprintf(fp,"%.1f %.1f mt\n",w->xcoord[d->num]-5,w->ycoord[d->num]); PhyML_Fprintf(fp,"0 0 0 sc\n"); PhyML_Fprintf(fp,"(*) show \n"); } PhyML_Fprintf(fp,"%f %f %f sc\n",R,G,B); if(d->tax) { PhyML_Fprintf(fp,"stroke\n"); PhyML_Fprintf(fp,"0 setgray\n"); PhyML_Fprintf(fp,"2 setlinewidth\n"); PhyML_Fprintf(fp,"np %.1f %.1f 1 0 360 arc cp\n",w->xcoord[d->num],w->ycoord[d->num]); PhyML_Fprintf(fp,"%.1f %.1f %.1f sc fill\n",R,G,B); /* PhyML_Fprintf(fp,"%f setgray fill\n",greylevel); */ PhyML_Fprintf(fp,"0 0 0 sc\n"); PhyML_Fprintf(fp," /Helvetica findfont 10 scalefont\n"); PhyML_Fprintf(fp,"setfont\n"); PhyML_Fprintf(fp,"%.1f %.1f mt\n",w->xcoord[d->num]+2,w->ycoord[d->num]-6); /* PhyML_Fprintf(fp,"(%d) show \n",d->num); */ /* PhyML_Fprintf(fp,"(%s) show \n",d->name); */ PhyML_Fprintf(fp," /Helvetica findfont 14 scalefont\n"); PhyML_Fprintf(fp,"setfont\n"); PhyML_Fprintf(fp,"%.1f %.1f mt\n",w->xcoord[d->num] - (w->xcoord[d->num] - w->xcoord[a->num])/2.,w->ycoord[d->num]); PhyML_Fprintf(fp," /Helvetica findfont 10 scalefont\n"); PhyML_Fprintf(fp,"setfont\n"); #if (defined GEO) /* PhyML_Fprintf(fp,"([%4.4f,%4.4f]) show \n", */ /* tree->geo->ldscape[tree->geo->loc[d->num]*tree->geo->n_dim+0], */ /* tree->geo->ldscape[tree->geo->loc[d->num]*tree->geo->n_dim+1]); */ #endif /* PhyML_Fprintf(fp,"%.1f %.1f mt\n",w->xcoord[d->num]+5,w->ycoord[d->num]); */ /* PhyML_Fprintf(fp,"(%.10s) show \n",d->name); */ /* if(render_name) */ /* { */ /* if(tree->io->long_tax_names) */ /* PhyML_Fprintf(fp,"(%s) show \n",tree->io->long_tax_names[d->num]); */ /* else */ /* PhyML_Fprintf(fp,"(%s) show \n",d->name); */ /* } */ PhyML_Fprintf(fp,"stroke\n"); PhyML_Fprintf(fp,"gr\n"); PhyML_Fprintf(fp,"0 0 0 sc\n"); return; } else { PhyML_Fprintf(fp,"stroke\n"); PhyML_Fprintf(fp,"0 setgray\n"); PhyML_Fprintf(fp,"2 setlinewidth\n"); PhyML_Fprintf(fp,"np %.1f %.1f 1 0 360 arc cp\n",w->xcoord[d->num],w->ycoord[d->num]); PhyML_Fprintf(fp,"%.1f %.1f %.1f sc fill\n",R,G,B); /* PhyML_Fprintf(fp,"%f setgray fill\n",greylevel); */ PhyML_Fprintf(fp,"0 0 0 sc\n"); PhyML_Fprintf(fp," /Helvetica findfont 10 scalefont\n"); PhyML_Fprintf(fp,"setfont\n"); PhyML_Fprintf(fp,"%.1f %.1f mt\n",w->xcoord[d->num]+2,w->ycoord[d->num]); /* PhyML_Fprintf(fp,"(%d) show \n",b->num); */ PhyML_Fprintf(fp,"%.1f %.1f mt\n",w->xcoord[d->num],w->ycoord[d->num]); PhyML_Fprintf(fp," /Helvetica findfont 14 scalefont\n"); PhyML_Fprintf(fp,"setfont\n"); PhyML_Fprintf(fp,"%.1f %.1f mt\n",w->xcoord[d->num] - (w->xcoord[d->num] - w->xcoord[a->num])/2.,w->ycoord[d->num]); PhyML_Fprintf(fp," /Helvetica findfont 10 scalefont\n"); PhyML_Fprintf(fp,"setfont\n"); #if (defined GEO) /* PhyML_Fprintf(fp,"([%4.4f,%4.4f]) show \n", */ /* tree->geo->ldscape[tree->geo->loc[d->num]*tree->geo->n_dim+0], */ /* tree->geo->ldscape[tree->geo->loc[d->num]*tree->geo->n_dim+1]); */ #endif PhyML_Fprintf(fp,"stroke\n"); PhyML_Fprintf(fp,"gr\n"); PhyML_Fprintf(fp,"0 0 0 sc\n"); for(i=0;i<3;i++) if(d->v[i] != a && d->b[i] != tree->e_root) DR_Print_Tree_Postscript_Pre(d,d->v[i],d->b[i],render_name,fp,w,tree); } return; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DR_Get_X_Coord_Pre(t_node *a, t_node *d, t_edge *b, tdraw *w, int fixed_tips, t_tree *tree) { int i; if(!(d->tax && fixed_tips == YES)) w->xcoord[d->num] = d->dist_to_root * (phydbl)w->tree_box_width/w->max_dist_to_root; if(d->tax) return; else { for(i=0;i<3;i++) if((d->v[i] != a) && (d->b[i] != tree->e_root)) DR_Get_X_Coord_Pre(d,d->v[i],d->b[i],w,fixed_tips,tree); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DR_Get_X_Coord(int fixed_tips, tdraw *w, t_tree *tree) { if(!(tree->n_root->v[2]->tax && fixed_tips == YES)) w->xcoord[tree->n_root->v[2]->num] = tree->n_root->v[2]->dist_to_root * (phydbl)w->tree_box_width/w->max_dist_to_root; if(!(tree->n_root->v[1]->tax && fixed_tips == YES)) w->xcoord[tree->n_root->v[1]->num] = tree->n_root->v[1]->dist_to_root * (phydbl)w->tree_box_width/w->max_dist_to_root; DR_Get_X_Coord_Pre(tree->n_root,tree->n_root->v[2],NULL,w,fixed_tips,tree); DR_Get_X_Coord_Pre(tree->n_root,tree->n_root->v[1],NULL,w,fixed_tips,tree); w->xcoord[tree->n_root->num] = 0; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DR_Get_Y_Coord(int fixed_tips, tdraw *w, t_tree *tree) { int next_y_slot; next_y_slot = 0; DR_Get_Y_Coord_Post(tree->n_root,tree->n_root->v[2],NULL,&next_y_slot,fixed_tips,w,tree); DR_Get_Y_Coord_Post(tree->n_root,tree->n_root->v[1],NULL,&next_y_slot,fixed_tips,w,tree); w->ycoord[tree->n_root->num] = (int)((w->ycoord[tree->n_root->v[2]->num] + w->ycoord[tree->n_root->v[2]->num]) / 2.) + 20; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DR_Get_Y_Coord_Post(t_node *a, t_node *d, t_edge *b, int *next_y_slot, int fixed_tips, tdraw *w, t_tree *tree) { int i; if(d->tax) { if(!fixed_tips) { /* w->ycoord[d->num] = *next_y_slot + (int)(w->page_height / (2.*tree->n_otu)); */ w->ycoord[d->num] = *next_y_slot + 20; (*next_y_slot) += (int)(w->page_height / (tree->n_otu-1)); printf("\n. %s %f",d->name,w->ycoord[d->num]); } } else { int d1, d2; d1 = d2 = -1; for(i=0;i<3;i++) { if(d->v[i] != a && d->b[i] != tree->e_root) { DR_Get_Y_Coord_Post(d,d->v[i],d->b[i],next_y_slot,fixed_tips,w,tree); if(d1<0) d1 = i; else d2 = i; } } w->ycoord[d->num] = (w->ycoord[d->v[d1]->num] + w->ycoord[d->v[d2]->num])/2.; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// tdraw *DR_Make_Tdraw_Struct(t_tree *tree) { tdraw *w; w = (tdraw *)mCalloc(1,sizeof(tdraw)); w->xcoord = (phydbl *)mCalloc(2*tree->n_otu-1,sizeof(phydbl)); w->ycoord = (phydbl *)mCalloc(2*tree->n_otu-1,sizeof(phydbl)); w->xcoord_s = (phydbl *)mCalloc(2*tree->n_otu-1,sizeof(phydbl)); w->ycoord_s = (phydbl *)mCalloc(2*tree->n_otu-1,sizeof(phydbl)); w->cdf_mat = (int *)mCalloc((2*tree->n_otu-2)*(2*tree->n_otu-2),sizeof(int)); w->cdf_mat_x = (phydbl *)mCalloc(2*tree->n_otu-1,sizeof(phydbl)); w->cdf_mat_y = (phydbl *)mCalloc(2*tree->n_otu-1,sizeof(phydbl)); return w; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DR_Init_Tdraw_Struct(tdraw *w) { w->page_width = 580-20; w->page_height = 820-20; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DR_Get_Tree_Box_Width(tdraw *w, t_tree *tree) { int i; int max_name_len, curr_len; max_name_len = curr_len = 0; for(i=0;in_otu;i++) { curr_len = (int)strlen(tree->a_nodes[i]->name); if(curr_len > max_name_len) max_name_len = curr_len; } w->tree_box_width = w->page_width - max_name_len * 8.66667; /* w->tree_box_width = w->page_width - max_name_len * 10.; */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl DR_Get_Max_Dist_To_Root(t_tree *tree) { phydbl mx; int i; mx = .0; for(i=0;in_otu;i++) { if(tree->a_nodes[i]->dist_to_root > mx) { mx = tree->a_nodes[i]->dist_to_root; } } return mx; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DR_Get_Tree_Coord_Scaled(tdraw *w, t_tree *tree) { int i; int max_x,min_x; int max_y,min_y; max_x = -INT_MAX; min_x = INT_MAX; For(i,2*tree->n_otu-1) { if(w->xcoord[i] > max_x) max_x = w->xcoord[i]; if(w->xcoord[i] < min_x) min_x = w->xcoord[i]; } max_y = -INT_MAX; min_y = INT_MAX; For(i,2*tree->n_otu-1) { if(w->ycoord[i] > max_y) max_y = w->ycoord[i]; if(w->ycoord[i] < min_y) min_y = w->ycoord[i]; } For(i,2*tree->n_otu-1) { w->xcoord_s[i] = (phydbl)(w->xcoord[i] - min_x) / (max_x - min_x); w->ycoord_s[i] = (phydbl)(w->ycoord[i] - min_y) / (max_y - min_y); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void DR_Get_Cdf_Mat(t_tree *tree) { int i,j,k; phydbl min_x,max_x,y; phydbl x_mat,y_mat; t_node *d, *a; phydbl eps; eps = 1.E-6; For(i,2*tree->n_otu-1) tree->ps_tree->cdf_mat_x[i] = tree->ps_tree->xcoord_s[i]; For(i,2*tree->n_otu-1) tree->ps_tree->cdf_mat_y[i] = tree->ps_tree->ycoord_s[i]; Qksort(tree->ps_tree->cdf_mat_x,NULL,0,2*tree->n_otu-2); Qksort(tree->ps_tree->cdf_mat_y,NULL,0,2*tree->n_otu-2); For(i,2*tree->n_otu-2) /* x coordinates */ { For(j,2*tree->n_otu-2) /* y coordinates */ { For(k,2*tree->n_otu-2) /* all nodes in the tree */ { d = tree->a_nodes[k]; a = tree->a_nodes[k]->anc; min_x = tree->ps_tree->xcoord_s[a->num]; max_x = tree->ps_tree->xcoord_s[d->num]; y = tree->ps_tree->ycoord_s[d->num]; x_mat = tree->ps_tree->cdf_mat_x[i]; y_mat = tree->ps_tree->cdf_mat_y[j]; /* printf("\n. x_mat=%.1f ymat=%.1f min=%.1f max=%.1f y=%.1f", */ /* x_mat,y_mat,min_x,max_x,y); */ if((min_x < x_mat + eps) && (max_x > x_mat) && (y > y_mat)) { tree->ps_tree->cdf_mat[j*(2*tree->n_otu-2)+i] += 1; /* PhyML_Printf("\n. Add 1 to [%.1f,%.1f]", */ /* tree->ps_tree->cdf_mat_x[i], */ /* tree->ps_tree->cdf_mat_y[j]); */ } } } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phyml-3.3.20220408/src/draw.h000066400000000000000000000022641422404706600153160ustar00rootroot00000000000000#include #ifndef DRAW_H #define DRAW_H #include "utilities.h" void DR_Dist_To_Root_Pre(t_node *a, t_node *d, t_edge *b, t_tree *tree); void DR_Dist_To_Root(t_node *n_root, t_tree *tree); void DR_Get_X_Coord_Pre(t_node *a, t_node *d, t_edge *b, tdraw *w, int fixed_tips, t_tree *tree); void DR_Get_X_Coord(int fixed_tips, tdraw *w, t_tree *tree); tdraw *DR_Make_Tdraw_Struct(t_tree *tree); void DR_Init_Tdraw_Struct(tdraw *d); void DR_Get_Tree_Box_Width(tdraw *w, t_tree *tree); void DR_Get_Y_Coord_Post(t_node *a, t_node *d, t_edge *b, int *next_y_slot, int fixed_tips, tdraw *w, t_tree *tree); void DR_Get_Y_Coord(int fixed_tips, tdraw *w, t_tree *tree); void DR_Get_Tree_Coord(t_tree *tree); phydbl DR_Get_Max_Dist_To_Root(t_tree *tree); void DR_Print_Tree_Postscript(int tree_num, int render_name,FILE *fp, t_tree *tree); void DR_Print_Tree_Postscript_Pre(t_node *a, t_node *d, t_edge *b, int render_name, FILE *fp, tdraw *w, t_tree *tree); void DR_Print_Postscript_EOF(FILE *fp); void DR_Print_Postscript_Header(int n_pages, FILE *fp); void DR_Get_Tree_Coord_Scaled(tdraw *w, t_tree *tree); void DR_Get_Cdf_Mat(t_tree *tree); void DR_Draw_Tree(char *file_name, t_tree *tree); #endif phyml-3.3.20220408/src/eigen.c000066400000000000000000000621301422404706600154410ustar00rootroot00000000000000/*********************************************************** * This eigen() routine works for eigenvalue/vector analysis * for real general square matrix A * A will be destroyed * rr,ri are vectors containing eigenvalues * vr,vi are matrices containing (right) real and imaginary eigenvectors * * A * [vr+vi*i] = [vr+vi*i] * diag{rr+ri*i} * * Algorithm: Handbook for Automatic Computation, vol 2 * by Wilkinson and Reinsch, 1971 * most of source codes were taken from a public domain * solftware called MATCALC. * Credits: to the authors of MATCALC * * return -1 not converged * 0 no complex eigenvalues/vectors * 1 complex eigenvalues/vectors * Tianlin Wang at University of Illinois * Thu May 6 15:22:31 CDT 1993 ***************************************************************/ #include "eigen.h" #define BASE 2 /* base of floating point arithmetic */ /* no. of digits to the base BASE in the fraction */ #define DIGITS 40 /* #define DIGITS 53 */ #define MAXITER 10000 /* max2. no. of iterations to converge */ #define pos(i,j,n) ((i)*(n)+(j)) /* rr/vr : real parts of eigen values/vectors */ /* ri/vi : imaginary part s of eigen values/vectors */ int Eigen(int job, phydbl *A, int n, phydbl *rr, phydbl *ri, phydbl *vr, phydbl *vi, phydbl *work) { /* job=0: eigen values only 1: both eigen values and eigen vectors phydbl w[n*2]: work space */ int low,hi,i,j,k, it, istate=0; phydbl tiny, t; for(i=0;itiny) istate=1; } return (istate) ; } /* complex functions */ complex compl (phydbl re,phydbl im) { complex r; r.re = re; r.im = im; return(r); } complex _conj (complex a) { a.im = -a.im; return(a); } complex cplus (complex a, complex b) { complex c; c.re = a.re+b.re; c.im = a.im+b.im; return (c); } complex cminus (complex a, complex b) { complex c; c.re = a.re-b.re; c.im = a.im-b.im; return (c); } complex cby (complex a, complex b) { complex c; c.re = a.re*b.re-a.im*b.im ; c.im = a.re*b.im+a.im*b.re ; return (c); } complex cdiv (complex a,complex b) { phydbl ratio, den; complex c; if (fabs(b.re) <= fabs(b.im)) { ratio = b.re / b.im; den = b.im * (1 + ratio * ratio); c.re = (a.re * ratio + a.im) / den; c.im = (a.im * ratio - a.re) / den; } else { ratio = b.im / b.re; den = b.re * (1 + ratio * ratio); c.re = (a.re + a.im * ratio) / den; c.im = (a.im - a.re * ratio) / den; } return(c); } /* complex local_cexp (complex a) */ /* { */ /* complex c; */ /* c.re = exp(a.re); */ /* if (fabs(a.im)==0) c.im = 0; */ /* else { c.im = c.re*sin(a.im); c.re*=cos(a.im); } */ /* return (c); */ /* } */ complex cfactor (complex x, phydbl a) { complex c; c.re = a*x.re; c.im = a*x.im; return (c); } int cxtoy (complex *x, complex *y, int n) { int i; For (i,n) y[i]=x[i]; return (0); } int cmatby (complex *a, complex *b, complex *c, int n,int m,int k) /* a[n*m], b[m*k], c[n*k] ...... c = a*b */ { int i,j,i1; complex t; For (i,n) for(j=0;j=n */ int i,j,k, *irow=(int*) space; phydbl xmaxsize, ee=1e-20; complex t,t1; for(i=0;i=0; i--) { if (irow[i] == i) continue; for(j=0;j= 0; k--) { for (j = k; j >= 0; j--) { for (i = 0; i <= k; i++) { if (i != j && fabs(mat[pos(j,i,n)]) > SMALL) break; } if (i > k) { scale[k] = j; if (j != k) { for (i = 0; i <= k; i++) { c = mat[pos(i,j,n)]; mat[pos(i,j,n)] = mat[pos(i,k,n)]; mat[pos(i,k,n)] = c; } for (i = 0; i < n; i++) { c = mat[pos(j,i,n)]; mat[pos(j,i,n)] = mat[pos(k,i,n)]; mat[pos(k,i,n)] = c; } } break; } } if (j < 0) break; } /* search for columns isolating an eigenvalue and push them left */ for (l = 0; l <= k; l++) { for (j = l; j <= k; j++) { for (i = l; i <= k; i++) { if (i != j && fabs(mat[pos(i,j,n)]) > SMALL) break; } if (i > k) { scale[l] = j; if (j != l) { for (i = 0; i <= k; i++) { c = mat[pos(i,j,n)]; mat[pos(i,j,n)] = mat[pos(i,l,n)]; mat[pos(i,l,n)] = c; } for (i = l; i < n; i++) { c = mat[pos(j,i,n)]; mat[pos(j,i,n)] = mat[pos(l,i,n)]; mat[pos(l,i,n)] = c; } } break; } } if (j > k) break; } *hi = k; *low = l; /* balance the submatrix in rows l through k */ for (i = l; i <= k; i++) { scale[i] = 1; } do { for (done = 1,i = l; i <= k; i++) { for (c = 0,r = 0,j = l; j <= k; j++) { if (j != i) { c += fabs(mat[pos(j,i,n)]); r += fabs(mat[pos(i,j,n)]); } } /* if (c != 0 && r != 0) { */ if ((fabs(c) > SMALL || fabs(c) < -SMALL) && (fabs(r) > SMALL || fabs(r) < -SMALL)) { g = r / BASE; f = 1; s = c + r; while (c < g) { f *= BASE; c *= BASE * BASE; } g = r * BASE; while (c >= g) { f /= BASE; c /= BASE * BASE; } if ((c + r) / f < 0.95 * s) { done = 0; g = 1 / f; scale[i] *= f; for (j = l; j < n; j++) { mat[pos(i,j,n)] *= g; } for (j = 0; j <= k; j++) { mat[pos(j,i,n)] *= f; } } } } } while (!done); } /* * Transform back eigenvectors of a balanced matrix * into the eigenvectors of the original matrix */ void unbalance(int n,phydbl *vr,phydbl *vi, int low, int hi, phydbl *scale) { int i,j,k; phydbl tmp; for (i = low; i <= hi; i++) { for (j = 0; j < n; j++) { vr[pos(i,j,n)] *= scale[i]; vi[pos(i,j,n)] *= scale[i]; } } for (i = low - 1; i >= 0; i--) { if ((k = (int)scale[i]) != i) { for (j = 0; j < n; j++) { tmp = vr[pos(i,j,n)]; vr[pos(i,j,n)] = vr[pos(k,j,n)]; vr[pos(k,j,n)] = tmp; tmp = vi[pos(i,j,n)]; vi[pos(i,j,n)] = vi[pos(k,j,n)]; vi[pos(k,j,n)] = tmp; } } } for (i = hi + 1; i < n; i++) { if ((k = (int)scale[i]) != i) { for (j = 0; j < n; j++) { tmp = vr[pos(i,j,n)]; vr[pos(i,j,n)] = vr[pos(k,j,n)]; vr[pos(k,j,n)] = tmp; tmp = vi[pos(i,j,n)]; vi[pos(i,j,n)] = vi[pos(k,j,n)]; vi[pos(k,j,n)] = tmp; } } } } /* * Reduce the submatrix in rows and columns low through hi of real matrix mat to * Hessenberg form by elementary similarity transformations */ void elemhess(int job,phydbl *mat,int n,int low,int hi, phydbl *vr, phydbl *vi, int *work) { /* work[n] */ unsigned int i,j,m; phydbl x,y; for (m = low + 1; m < hi; m++) { for (x = 0,i = m,j = m; j <= hi; j++) { if(fabs(mat[pos(j,m-1,n)]) > fabs(x)) { x = mat[pos(j,m-1,n)]; i = j; } } if ((work[m] = i) != m) { for (j = m - 1; j < n; j++) { y = mat[pos(i,j,n)]; mat[pos(i,j,n)] = mat[pos(m,j,n)]; mat[pos(m,j,n)] = y; } for (j = 0; j <= hi; j++) { y = mat[pos(j,i,n)]; mat[pos(j,i,n)] = mat[pos(j,m,n)]; mat[pos(j,m,n)] = y; } } if (fabs(x) > SMALL) { for (i = m + 1; i <= hi; i++) { if (fabs(y = mat[pos(i,m-1,n)]) > SMALL) { y = mat[pos(i,m-1,n)] = y / x; for (j = m; j < n; j++) { mat[pos(i,j,n)] -= y * mat[pos(m,j,n)]; } for (j = 0; j <= hi; j++) { mat[pos(j,m,n)] += y * mat[pos(j,i,n)]; } } } } } if (job) { for (i=0; i low; m--) { for (i = m + 1; i <= hi; i++) { vr[pos(i,m,n)] = mat[pos(i,m-1,n)]; } if ((i = work[m]) != m) { for (j = m; j <= hi; j++) { vr[pos(m,j,n)] = vr[pos(i,j,n)]; vr[pos(i,j,n)] = 0.0; } vr[pos(i,m,n)] = 1.0; } } } } /* * Calculate eigenvalues and eigenvectors of a real upper Hessenberg matrix * Return 1 if converges successfully and 0 otherwise */ int realeig(int job,phydbl *mat,int n,int low, int hi, phydbl *valr, phydbl *vali, phydbl *vr,phydbl *vi) { complex v; phydbl p=.0,q=.0,r=.0,s=.0,t,w,x,y,z=0,ra,sa,norm,eps; int niter,en,i,j,k,l,m; phydbl precision = POW((phydbl)BASE,(phydbl)(1-(int)DIGITS)); // Won't pass testiphy... /* phydbl precision = SMALL; */ eps = precision; for (i=0; i hi) valr[i] = mat[pos(i,i,n)]; } t = 0; en = hi; while (en >= low) { niter = 0; for (;;) { /* look for single small subdiagonal element */ for (l = en; l > low; l--) { s = fabs(mat[pos(l-1,l-1,n)]) + fabs(mat[pos(l,l,n)]); if (fabs(s) < SMALL) s = norm; if (fabs(mat[pos(l,l-1,n)]) <= eps * s) break; } /* form shift */ x = mat[pos(en,en,n)]; if (l == en) { /* one root found */ valr[en] = x + t; if (job) mat[pos(en,en,n)] = x + t; en--; break; } y = mat[pos(en-1,en-1,n)]; w = mat[pos(en,en-1,n)] * mat[pos(en-1,en,n)]; if (l == en - 1) { /* two roots found */ p = (y - x) / 2; q = p * p + w; z = SQRT(fabs(q)); x += t; if (job) { mat[pos(en,en,n)] = x; mat[pos(en-1,en-1,n)] = y + t; } if (q < 0) { /* complex pair */ valr[en-1] = x+p; vali[en-1] = z; valr[en] = x+p; vali[en] = -z; } else { /* real pair */ z = (p < 0) ? p - z : p + z; valr[en-1] = x + z; valr[en] = (fabs(z) < SMALL) ? x + z : x - w / z; if (job) { x = mat[pos(en,en-1,n)]; s = fabs(x) + fabs(z); p = x / s; q = z / s; r = SQRT(p*p+q*q); p /= r; q /= r; for (j = en - 1; j < n; j++) { z = mat[pos(en-1,j,n)]; mat[pos(en-1,j,n)] = q * z + p * mat[pos(en,j,n)]; mat[pos(en,j,n)] = q * mat[pos(en,j,n)] - p*z; } for (i = 0; i <= en; i++) { z = mat[pos(i,en-1,n)]; mat[pos(i,en-1,n)] = q * z + p * mat[pos(i,en,n)]; mat[pos(i,en,n)] = q * mat[pos(i,en,n)] - p*z; } for (i = low; i <= hi; i++) { z = vr[pos(i,en-1,n)]; vr[pos(i,en-1,n)] = q*z + p*vr[pos(i,en,n)]; vr[pos(i,en,n)] = q*vr[pos(i,en,n)] - p*z; } } } en -= 2; break; } if (niter == MAXITER) return(-1); if (niter != 0 && niter % 10 == 0) { t += x; for (i = low; i <= en; i++) mat[pos(i,i,n)] -= x; s = fabs(mat[pos(en,en-1,n)]) + fabs(mat[pos(en-1,en-2,n)]); x = y = 0.75 * s; w = -0.4375 * s * s; } niter++; /* look for two consecutive small subdiagonal elements */ for (m = en - 2; m >= l; m--) { z = mat[pos(m,m,n)]; r = x - z; s = y - z; p = (r * s - w) / mat[pos(m+1,m,n)] + mat[pos(m,m+1,n)]; q = mat[pos(m+1,m+1,n)] - z - r - s; r = mat[pos(m+2,m+1,n)]; s = fabs(p) + fabs(q) + fabs(r); p /= s; q /= s; r /= s; if (m == l || fabs(mat[pos(m,m-1,n)]) * (fabs(q)+fabs(r)) <= eps * (fabs(mat[pos(m-1,m-1,n)]) + fabs(z) + fabs(mat[pos(m+1,m+1,n)])) * fabs(p)) break; } for (i = m + 2; i <= en; i++) mat[pos(i,i-2,n)] = 0; for (i = m + 3; i <= en; i++) mat[pos(i,i-3,n)] = 0; /* phydbl QR step involving rows l to en and columns m to en */ for (k = m; k < en; k++) { if (k != m) { p = mat[pos(k,k-1,n)]; q = mat[pos(k+1,k-1,n)]; r = (k == en - 1) ? 0 : mat[pos(k+2,k-1,n)]; if (fabs(x = fabs(p) + fabs(q) + fabs(r)) < SMALL) continue; p /= x; q /= x; r /= x; } s = SQRT(p*p+q*q+r*r); if (p < 0) s = -s; if (k != m) { mat[pos(k,k-1,n)] = -s * x; } else if (l != m) { mat[pos(k,k-1,n)] = -mat[pos(k,k-1,n)]; } p += s; x = p / s; y = q / s; z = r / s; q /= p; r /= p; /* row modification */ for (j = k; j <= (!job ? en : n-1); j++){ p = mat[pos(k,j,n)] + q * mat[pos(k+1,j,n)]; if (k != en - 1) { p += r * mat[pos(k+2,j,n)]; mat[pos(k+2,j,n)] -= p * z; } mat[pos(k+1,j,n)] -= p * y; mat[pos(k,j,n)] -= p * x; } j = MIN(en,k+3); /* column modification */ for (i = (!job ? l : 0); i <= j; i++) { p = x * mat[pos(i,k,n)] + y * mat[pos(i,k+1,n)]; if (k != en - 1) { p += z * mat[pos(i,k+2,n)]; mat[pos(i,k+2,n)] -= p*r; } mat[pos(i,k+1,n)] -= p*q; mat[pos(i,k,n)] -= p; } if (job) { /* accumulate transformations */ for (i = low; i <= hi; i++) { p = x * vr[pos(i,k,n)] + y * vr[pos(i,k+1,n)]; if (k != en - 1) { p += z * vr[pos(i,k+2,n)]; vr[pos(i,k+2,n)] -= p*r; } vr[pos(i,k+1,n)] -= p*q; vr[pos(i,k,n)] -= p; } } } } } if (!job) return(0); if (fabs(norm) > SMALL) { /* back substitute to find vectors of upper triangular form */ for (en = n-1; en >= 0; en--) { p = valr[en]; if ((q = vali[en]) < 0) { /* complex vector */ m = en - 1; if (fabs(mat[pos(en,en-1,n)]) > fabs(mat[pos(en-1,en,n)])) { mat[pos(en-1,en-1,n)] = q / mat[pos(en,en-1,n)]; mat[pos(en-1,en,n)] = (p - mat[pos(en,en,n)]) / mat[pos(en,en-1,n)]; } else { v = cdiv(compl(0.0,-mat[pos(en-1,en,n)]), compl(mat[pos(en-1,en-1,n)]-p,q)); mat[pos(en-1,en-1,n)] = v.re; mat[pos(en-1,en,n)] = v.im; } mat[pos(en,en-1,n)] = 0; mat[pos(en,en,n)] = 1; for (i = en - 2; i >= 0; i--) { w = mat[pos(i,i,n)] - p; ra = 0; sa = mat[pos(i,en,n)]; for (j = m; j < en; j++) { ra += mat[pos(i,j,n)] * mat[pos(j,en-1,n)]; sa += mat[pos(i,j,n)] * mat[pos(j,en,n)]; } if (vali[i] < 0) { z = w; r = ra; s = sa; } else { m = i; if (fabs(vali[i]) < SMALL) { v = cdiv(compl(-ra,-sa),compl(w,q)); mat[pos(i,en-1,n)] = v.re; mat[pos(i,en,n)] = v.im; } else { /* solve complex equations */ x = mat[pos(i,i+1,n)]; y = mat[pos(i+1,i,n)]; v.re = (valr[i]- p)*(valr[i]-p) + vali[i]*vali[i] - q*q; v.im = (valr[i] - p)*2*q; if (fabs(v.re) + fabs(v.im) < SMALL) { v.re = eps * norm * (fabs(w) + fabs(q) + fabs(x) + fabs(y) + fabs(z)); } v = cdiv(compl(x*r-z*ra+q*sa,x*s-z*sa-q*ra),v); mat[pos(i,en-1,n)] = v.re; mat[pos(i,en,n)] = v.im; if (fabs(x) > fabs(z) + fabs(q)) { mat[pos(i+1,en-1,n)] = (-ra - w * mat[pos(i,en-1,n)] + q * mat[pos(i,en,n)]) / x; mat[pos(i+1,en,n)] = (-sa - w * mat[pos(i,en,n)] - q * mat[pos(i,en-1,n)]) / x; } else { v = cdiv(compl(-r-y*mat[pos(i,en-1,n)], -s-y*mat[pos(i,en,n)]),compl(z,q)); mat[pos(i+1,en-1,n)] = v.re; mat[pos(i+1,en,n)] = v.im; } } } } } else if (fabs(q) < SMALL) { /* real vector */ m = en; mat[pos(en,en,n)] = 1; for (i = en - 1; i >= 0; i--) { w = mat[pos(i,i,n)] - p; r = mat[pos(i,en,n)]; for (j = m; j < en; j++) { r += mat[pos(i,j,n)] * mat[pos(j,en,n)]; } if (vali[i] < 0) { z = w; s = r; } else { m = i; if (fabs(vali[i]) < SMALL) { if (fabs(t = w) < SMALL) t = eps * norm; mat[pos(i,en,n)] = -r / t; } else { /* solve real equations */ x = mat[pos(i,i+1,n)]; y = mat[pos(i+1,i,n)]; q = (valr[i] - p) * (valr[i] - p) + vali[i]*vali[i]; t = (x * s - z * r) / q; mat[pos(i,en,n)] = t; if (fabs(x) <= fabs(z)) { mat[pos(i+1,en,n)] = (-s - y * t) / z; } else { mat[pos(i+1,en,n)] = (-r - w * t) / x; } } } } } } /* vectors of isolated roots */ for (i = 0; i < n; i++) { if (i < low || i > hi) { for (j = i; j < n; j++) { vr[pos(i,j,n)] = mat[pos(i,j,n)]; } } } /* multiply by transformation matrix */ for (j = n-1; j >= low; j--) { m = MIN(j,hi); for (i = low; i <= hi; i++) { for (z = 0,k = low; k <= m; k++) { z += vr[pos(i,k,n)] * mat[pos(k,j,n)]; } vr[pos(i,j,n)] = z; } } } /* rearrange complex eigenvectors */ for (j = 0; j < n; j++) { if (fabs(vali[j]) > SMALL) { for (i = 0; i < n; i++) { vi[pos(i,j,n)] = vr[pos(i,j+1,n)]; vr[pos(i,j+1,n)] = vr[pos(i,j,n)]; vi[pos(i,j+1,n)] = -vi[pos(i,j,n)]; } j++; } } return(0); } #define LUDCMP_TINY 1.0e-20; int ludcmp(phydbl **a, int n, phydbl *d) { int i,imax,j,k; phydbl big,dum,sum,temp; phydbl *vv; imax = 0; vv = (phydbl *)mCalloc(n,sizeof(phydbl)); *d=1.0; for (i=0;i big) big=temp; if (fabs(big) < SMALL) Exit("\n. Singular matrix in routine LUDCMP"); vv[i]=1.0/big; } for (j=0;j= big) { big=dum; imax=i; } } if (j != imax) { for (k=0;k big) big=temp; if (fabs(big) < SMALL) Exit("\n. Singular matrix in routine LUDCMP"); vv[i]=1.0/big; } for (j=0;j= big) { big=dum; imax=i; } } if (j != imax) { for (k=0;k #ifndef EIGEN_H #define EIGEN_H #include "utilities.h" #include "free.h" #ifdef RWRAPPER #include #endif int ludcmp_1D(phydbl *a, int n, phydbl *d); void det_1D(phydbl *a, int n, phydbl *d); int Eigen(int job, phydbl *A, int n, phydbl *rr, phydbl *ri, phydbl *vr, phydbl *vi, phydbl *w); void balance(phydbl *mat, int n, int *low, int *hi, phydbl *scale); void unbalance(int n, phydbl *vr, phydbl *vi, int low, int hi, phydbl *scale); int realeig(int job, phydbl *mat, int n,int low, int hi, phydbl *valr, phydbl *vali, phydbl *vr, phydbl *vi); void elemhess(int job, phydbl *mat, int n, int low, int hi, phydbl *vr, phydbl *vi, int *work); int ludcmp(phydbl **a, int n, phydbl *d); void det(phydbl **a, int n, phydbl *d); /* complex functions */ typedef struct { phydbl re, im; } complex; #define csize(a) (FABS(a.re)+FABS(a.im)) complex compl (phydbl re,phydbl im); complex _conj (complex a); complex cplus (complex a, complex b); complex cminus (complex a, complex b); complex cby (complex a, complex b); complex cdiv (complex a,complex b); /* complex local_cexp (complex a); */ complex cfactor (complex x, phydbl a); int cxtoy (complex *x, complex *y, int n); int cmatby (complex *a, complex *b, complex *c, int n,int m,int k); int cmatout (FILE * fout, complex *x, int n, int m); int cmatinv( complex *x, int n, int m, phydbl *space); phydbl *Cholesky_Decomp(phydbl *A, int dim); #endif phyml-3.3.20220408/src/free.c000066400000000000000000001064211422404706600152750ustar00rootroot00000000000000/* PHYML : a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences Copyright (C) Stephane Guindon. Oct 2003 onward All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include "free.h" ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Clade(t_clad *this) { Free(this); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_All_Nodes_Light(t_tree *tree) { if(tree->a_nodes != NULL) { for(int i=0;i<2*tree->n_otu-1;++i) if(tree->a_nodes[i] != NULL) Free_Node(tree->a_nodes[i]); Free(tree->a_nodes); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_All_Edges_Light(t_tree *tree) { if(tree->a_edges != NULL) { for(int i=0;i<2*tree->n_otu-1;++i) if(tree->a_edges[i] != NULL) Free_Edge(tree->a_edges[i]); Free(tree->a_edges); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_All_Edges_Lens(t_tree *tree) { if(tree->a_edges != NULL) { for(int i=0;i<2*tree->n_otu-1;++i) if(tree->a_edges[i] != NULL) Free_Edge_Length(tree->a_edges[i]); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Edge_Length(t_edge *b) { if(b->l != NULL) Free_Scalar_Dbl(b->l); if(b->l_old != NULL) Free_Scalar_Dbl(b->l_old); if(b->l_var != NULL) Free_Scalar_Dbl(b->l_var); if(b->l_var_old != NULL) Free_Scalar_Dbl(b->l_var_old); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Edge(t_edge *b) { Free_Label(b->label); Free_Edge_Core(b); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Edge_Core(t_edge *b) { Free(b); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Node(t_node *n) { Free(n->b); Free(n->v); Free(n->score); Free(n->s_ingrp); Free(n->s_outgrp); Free(n->cal); Free_Label(n->label); if(n->c_seq_anc != NULL) { Free(n->c_seq_anc->state); Free(n->c_seq_anc); } if(n->ori_name) { Free(n->ori_name); n->ori_name = NULL; } Free(n); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Mat(matrix *mat) { int i; for(i=0;in_otu;i++) { Free(mat->P[i]); Free(mat->Q[i]); Free(mat->dist[i]); Free(mat->name[i]); } Free(mat->P); Free(mat->Q); Free(mat->dist); Free(mat->name); Free(mat->tip_node); Free(mat->on_off); Free(mat); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Partial_Lk(phydbl *p_lk, int len, int n_catg) { Free(p_lk); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Tree(t_tree *tree) { if(tree->is_mixt_tree == YES) { MIXT_Free_Tree(tree); } else { if(tree->mat) Free_Mat(tree->mat); if(tree->t_dir) Free(tree->t_dir); if(tree->short_l) Free(tree->short_l); if(tree->mutmap) Free(tree->mutmap); Free_Bip(tree); Free(tree->curr_path); Free_All_Edges_Lens(tree); Free_All_Edges_Light(tree); Free_All_Nodes_Light(tree); Free(tree); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Bip(t_tree *tree) { int i,j; if(tree->has_bip) { For(i,2*tree->n_otu-2) { Free(tree->a_nodes[i]->bip_size); for(j=0;j<3;j++) Free(tree->a_nodes[i]->bip_node[j]); Free(tree->a_nodes[i]->bip_node); } } tree->has_bip = NO; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Calign(calign *data) { int i; if(data->io_wght) Free_Scalar_Dbl(data->io_wght); Free(data->invar); Free(data->wght); Free(data->ambigu); Free(data->obs_state_frq); Free(data->sitepatt); for(i=0;in_otu;i++) { Free(data->c_seq[i]->name); if(data->c_seq[i]->state) { Free(data->c_seq[i]->state); Free(data->c_seq[i]->d_state); if(data->c_seq[i]->is_ambigu) Free(data->c_seq[i]->is_ambigu); } Free(data->c_seq[i]); } for(i=0;in_rm;i++) { Free(data->c_seq_rm[i]->name); if(data->c_seq_rm[i]->state) { Free(data->c_seq_rm[i]->state); Free(data->c_seq_rm[i]->d_state); if(data->c_seq_rm[i]->is_ambigu) Free(data->c_seq_rm[i]->is_ambigu); } Free(data->c_seq_rm[i]); } if(data->c_seq_rm != NULL) Free(data->c_seq_rm); if(data->c_seq != NULL) Free(data->c_seq); Free(data); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Seq(align **d, int n_otu) { int i; for(i=0;iname); Free(d[i]->state); Free(d[i]->d_state); if(d[i]->is_ambigu) Free(d[i]->is_ambigu); Free(d[i]); } Free(d); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_All(align **d, calign *cdata, t_tree *tree) { Free_Calign(cdata); Free_Seq(d,tree->n_otu); Free_Tree(tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_SubTree(t_edge *b_fcus, t_node *a, t_node *d, t_tree *tree) { int i; if(d->tax) return; else { for(i=0;i<3;i++) { if(d->v[i] != a) { Free_SubTree(d->b[i],d,d->v[i],tree); Free_Edge(d->b[i]); Free_Node(d->v[i]); } } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Tree_Ins_Tar(t_tree *tree) { return; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Tree_Pars(t_tree *tree) { int i; Free(tree->step_mat); Free(tree->site_pars); for(i=0;i<2*tree->n_otu-3;++i) Free_Edge_Pars(tree->a_edges[i]); if(tree->n_root) { Free_Edge_Pars_Left(tree->n_root->b[1]); Free_Edge_Pars_Left(tree->n_root->b[2]); } else { Free_Edge_Pars(tree->a_edges[2*tree->n_otu-3]); Free_Edge_Pars(tree->a_edges[2*tree->n_otu-2]); } if(tree->is_mixt_tree == YES) MIXT_Repeat_Task(Free_Tree_Pars,tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Edge_Pars_Left(t_edge *b) { if(b->pars_l) Free(b->pars_l); if(b->ui_l) Free(b->ui_l); if(b->p_pars_l) Free(b->p_pars_l); if(b->n_diff_states_l) Free(b->n_diff_states_l); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Edge_Pars_Rght(t_edge *b) { if(b->pars_r) Free(b->pars_r); if(b->ui_r) Free(b->ui_r); if(b->p_pars_r) Free(b->p_pars_r); if(b->n_diff_states_r) Free(b->n_diff_states_r); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Edge_Pars(t_edge *b) { Free_Edge_Pars_Left(b); Free_Edge_Pars_Rght(b); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Tree_Lk(t_tree *tree) { int i; Free(tree->big_lk_array); Free(tree->c_lnL_sorted); Free(tree->cur_site_lk); Free(tree->old_site_lk); Free(tree->site_lk_cat); Free(tree->fact_sum_scale); Free(tree->unscaled_site_lk_cat); Free(tree->expl); for(i=0;i<3;i++) Free(tree->log_lks_aLRT[i]); Free(tree->log_lks_aLRT); for(i=0;i<2*tree->n_otu-1;++i) Free_NNI(tree->a_edges[i]->nni); for(i=0;i<2*tree->n_otu-3;++i) Free_Edge_Lk(tree->a_edges[i]); for(i=0;i<2*tree->n_otu-3;++i) Free_Edge_Loc(tree->a_edges[i]); if(tree->is_mixt_tree == NO) { Free(tree->dot_prod); Free(tree->p_lk_left_pi); Free(tree->l_ev); #if (defined(__AVX__) || defined(__AVX2__) || defined(__SSE__) || defined(__SSE2__) || defined(__SSE3__)) Free(tree->_tPij1); Free(tree->_tPij2); Free(tree->_pmat1plk1); Free(tree->_pmat2plk2); Free(tree->_plk0); Free(tree->_l_ev); Free(tree->_r_ev); Free(tree->_prod_left); Free(tree->_prod_rght); #endif Free(tree->div_post_pred_extra_0); Free(tree->sum_scale_cat_extra_0); Free(tree->sum_scale_extra_0); Free(tree->patt_id_extra_0); Free(tree->p_lk_extra_0); Free(tree->p_lk_tip_extra_0); Free(tree->div_post_pred_extra_1); Free(tree->sum_scale_cat_extra_1); Free(tree->sum_scale_extra_1); Free(tree->patt_id_extra_1); Free(tree->p_lk_extra_1); Free(tree->p_lk_tip_extra_1); if(tree->n_root != NULL) { Free_Edge_Lk_Left(tree->n_root->b[1]); Free_Edge_Lk_Left(tree->n_root->b[2]); Free_Edge_Loc_Left(tree->n_root->b[1]); Free_Edge_Loc_Left(tree->n_root->b[2]); } else { Free_Edge_Lk(tree->a_edges[2*tree->n_otu-3]); Free_Edge_Lk(tree->a_edges[2*tree->n_otu-2]); Free_Edge_Loc(tree->a_edges[2*tree->n_otu-3]); Free_Edge_Loc(tree->a_edges[2*tree->n_otu-2]); } } if(tree->is_mixt_tree == YES) MIXT_Repeat_Task(Free_Tree_Lk,tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Node_Lk(t_node *n) { /* Free(n->n_ex_nodes); */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Extra_Edge_Lk(t_tree *tree) { if(tree->div_post_pred_extra_0) Free(tree->div_post_pred_extra_0); if(tree->sum_scale_cat_extra_0) Free(tree->sum_scale_cat_extra_0); if(tree->sum_scale_extra_0) Free(tree->sum_scale_extra_0); if(tree->p_lk_extra_0) Free(tree->p_lk_extra_0); if(tree->p_lk_tip_extra_0) Free(tree->p_lk_tip_extra_0); if(tree->patt_id_extra_0) Free(tree->patt_id_extra_0); if(tree->div_post_pred_extra_1) Free(tree->div_post_pred_extra_1); if(tree->sum_scale_cat_extra_1) Free(tree->sum_scale_cat_extra_1); if(tree->sum_scale_extra_1) Free(tree->sum_scale_extra_1); if(tree->p_lk_extra_1) Free(tree->p_lk_extra_1); if(tree->p_lk_tip_extra_1) Free(tree->p_lk_tip_extra_1); if(tree->patt_id_extra_1) Free(tree->patt_id_extra_1); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Edge_Lk_Rght(t_edge *b) { assert(b); Free(b->div_post_pred_rght); if(b->p_lk_rght) { if(b->sum_scale_rght) Free(b->sum_scale_rght); } if(b->p_lk_tip_r) Free(b->p_lk_tip_r); if(b->sum_scale_rght_cat) Free(b->sum_scale_rght_cat); if(b->patt_id_rght) Free(b->patt_id_rght); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Edge_Lk_Left(t_edge *b) { Free(b->div_post_pred_left); if(b->p_lk_left) { if(b->sum_scale_left) Free(b->sum_scale_left); } if(b->p_lk_tip_l) Free(b->p_lk_tip_l); if(b->sum_scale_left_cat) Free(b->sum_scale_left_cat); if(b->patt_id_left) Free(b->patt_id_left); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Edge_Lk(t_edge *b) { Free_Edge_Lk_Left(b); Free_Edge_Lk_Rght(b); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Edge_Loc_Rght(t_edge *b) { if(b->p_lk_loc_rght) Free(b->p_lk_loc_rght); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Edge_Loc_Left(t_edge *b) { if(b->p_lk_loc_left) Free(b->p_lk_loc_left); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Edge_Loc(t_edge *b) { Free_Edge_Loc_Left(b); Free_Edge_Loc_Rght(b); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Model_Complete(t_mod *mixt_mod) { Free_Eigen(mixt_mod->eigen); Free_Rmat(mixt_mod->r_mat); Free_Efrq(mixt_mod->e_frq); Free_Vect_Dbl(mixt_mod->Pij_rr); mixt_mod->r_mat = NULL; mixt_mod->e_frq = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Rmat_Weights(t_mod *mixt_mod) { t_mod *mod; mod = mixt_mod; do { Free(mod->r_mat_weight); mod = mod->next_mixt; } while(mod); if(mixt_mod->next) Free_Scalar_Dbl(mixt_mod->next->r_mat_weight); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Efrq_Weights(t_mod *mixt_mod) { t_mod *mod; mod = mixt_mod; do { Free(mod->e_frq_weight); mod = mod->next_mixt; } while(mod); if(mixt_mod->next) Free_Scalar_Dbl(mixt_mod->next->e_frq_weight); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Model_Basic(t_mod *mixt_mod) { t_mod *mod; Free_RAS(mixt_mod->ras); Free_Scalar_Dbl(mixt_mod->mr); Free_Scalar_Dbl(mixt_mod->kappa); Free_Scalar_Dbl(mixt_mod->lambda); Free_Scalar_Dbl(mixt_mod->br_len_mult); Free_Scalar_Dbl(mixt_mod->br_len_mult_unscaled); Free_Rmat_Weights(mixt_mod); Free_Efrq_Weights(mixt_mod); Free_String(mixt_mod->modelname); Free_String(mixt_mod->custom_mod_string); Free_String(mixt_mod->aa_rate_mat_file); mod = mixt_mod; do { if(mod->next) { mod = mod->next; Free(mod->prev); } else { Free(mod); break; } } while(mod); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Vect_Dbl(vect_dbl *v) { vect_dbl *next; assert(v); next = v->next; do { Free(v->v); Free(v); v = next; if(v) next = v->next; } while(v); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Vect_Int(vect_int *v) { vect_int *next; assert(v); next = v->next; do { Free(v->v); Free(v); v = next; if(v) next = v->next; } while(v); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Scalar_Dbl(scalar_dbl *v) { scalar_dbl *next; assert(v); next = v->next; do { Free(v); v = next; if(v) next = v->next; } while(v); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Scalar_Int(scalar_int *v) { scalar_int *next; assert(v); next = v->next; do { Free(v); v = next; if(v) next = v->next; } while(v); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Linked_List(t_ll *t) { t_ll *next,*ll; if(t == NULL) return; ll = t->head; next = ll->next; do { /* t_node *n = ll->v; */ /* printf("\n. free node %d",n?n->num:-1); */ /* printf(" ll: %p",ll); */ /* printf(" hd: %p",ll?ll->head:NULL); fflush(NULL); */ Free(ll); ll = next; if(ll) next = ll->next; } while(ll); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_String(t_string *ts) { t_string *next; next = ts->next; do { Free(ts->s); Free(ts); ts = next; if(ts) next = ts->next; } while(ts); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Custom_Model(t_mod *mod) { } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Efrq(t_efrq *e_frq) { Free(e_frq->pi->v); Free(e_frq->pi); Free(e_frq->pi_unscaled->v); Free(e_frq->pi_unscaled); Free(e_frq->user_b_freq->v); Free(e_frq->user_b_freq); if(e_frq->next) Free_Efrq(e_frq->next); Free(e_frq); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Rmat(t_rmat *r_mat) { Free(r_mat->rr->v); Free(r_mat->rr); Free(r_mat->rr_num->v); Free(r_mat->rr_val->v); Free(r_mat->rr_val); Free(r_mat->n_rr_per_cat->v); Free(r_mat->n_rr_per_cat); Free(r_mat->rr_num); Free(r_mat->qmat->v); Free(r_mat->qmat); Free(r_mat->qmat_buff->v); Free(r_mat->qmat_buff); if(r_mat->next) Free_Rmat(r_mat->next); Free(r_mat); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_RAS(t_ras *ras) { if(ras->gamma_r_proba->v) { Free(ras->gamma_r_proba->v); Free(ras->gamma_r_proba_unscaled->v); Free(ras->gamma_rr->v); Free(ras->gamma_rr_unscaled->v); } Free(ras->gamma_r_proba); Free(ras->skip_rate_cat); Free(ras->gamma_r_proba_unscaled); Free(ras->gamma_rr); Free(ras->gamma_rr_unscaled); Free_Scalar_Dbl(ras->pinvar); Free_Scalar_Dbl(ras->alpha); Free_Scalar_Dbl(ras->free_rate_mr); if(ras->next) Free_RAS(ras->next); Free(ras); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Model(t_mod *mod) { Free_Custom_Model(mod); Free_Model_Complete(mod); Free_Model_Basic(mod); /* Free(mod); */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free(void *p) { #if (defined(__AVX__) || defined(__SSE3__)) #ifndef WIN32 free(p); #else _aligned_free(p); #endif #else free(p); #endif p = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Input(option *io) { int i; do { RATES_Free_Rates(io->rates); TIMES_Free_Times(io->times); MCMC_Free_MCMC(io->mcmc); Free(io->in_align_file); Free(io->in_tree_file); Free(io->in_constraint_tree_file); Free(io->in_coord_file); Free(io->out_file); Free(io->out_tree_file); Free(io->out_trees_file); Free(io->out_boot_tree_file); Free(io->out_boot_stats_file); Free(io->out_stats_file); Free(io->weight_file); Free(io->out_lk_file); Free(io->out_summary_file); Free(io->out_ps_file); Free(io->out_trace_file); Free(io->out_json_trace_file); Free(io->out_ancestral_seq_file); Free(io->out_ancestral_tree_file); Free(io->nt_or_cd); Free(io->run_id_string); Free(io->clade_list_file); for(i=0;ialphabet[i]); Free(io->alphabet); if(io->short_tax_names) { for(i=0;isize_tax_names;i++) { Free(io->short_tax_names[i]); Free(io->long_tax_names[i]); } Free(io->long_tax_names); Free(io->short_tax_names); } Free_Tree_List(io->treelist); if(io->lon) Free(io->lon); if(io->lat) Free(io->lat); if(io->next) { io = io->next; Free(io->prev); } else { Free(io); break; } }while(1); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Tree_List(t_treelist *list) { Free(list->tree); Free(list); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_St(supert_tree *st) { int i; For(i,2*st->tree->n_otu-1) Free_NNI(st->tree->a_edges[i]->nni); for(i=0;in_part;i++) Free(st->match_st_node_in_gt[i]); Free(st->match_st_node_in_gt); Free_Tree(st->tree); Free(st); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Eigen(eigen *eigen_struct) { Free(eigen_struct->space_int); Free(eigen_struct->space); Free(eigen_struct->e_val); Free(eigen_struct->e_val_im); Free(eigen_struct->r_e_vect_im); Free(eigen_struct->l_e_vect); Free(eigen_struct->r_e_vect); Free(eigen_struct->dum); Free(eigen_struct->q); if(eigen_struct->next) Free_Eigen(eigen_struct->next); Free(eigen_struct); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_One_Spr(t_spr *this_spr) { Free(this_spr->path); if(this_spr->l0) Free_Scalar_Dbl(this_spr->l0); if(this_spr->l1) Free_Scalar_Dbl(this_spr->l1); if(this_spr->l2) Free_Scalar_Dbl(this_spr->l2); if(this_spr->v0) Free_Scalar_Dbl(this_spr->v0); if(this_spr->v1) Free_Scalar_Dbl(this_spr->v1); if(this_spr->v2) Free_Scalar_Dbl(this_spr->v2); if(this_spr->init_target_l) Free_Scalar_Dbl(this_spr->init_target_l); if(this_spr->init_target_v) Free_Scalar_Dbl(this_spr->init_target_v); Free(this_spr); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Spr_List_One_Edge(t_tree *tree) { int i; for(i=0;isize_spr_list_one_edge+1;++i) Free_One_Spr(tree->spr_list_one_edge[i]); if(tree->is_mixt_tree == YES) MIXT_Repeat_Task(Free_Spr_List_One_Edge,tree); Free(tree->spr_list_one_edge); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Spr_List_All_Edge(t_tree *tree) { int i; for(i=0;isize_spr_list_all_edge+1;++i) Free_One_Spr(tree->spr_list_all_edge[i]); if(tree->is_mixt_tree == YES) MIXT_Repeat_Task(Free_Spr_List_All_Edge,tree); Free(tree->spr_list_all_edge); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Best_Spr(t_tree *tree) { Free_One_Spr(tree->best_spr); if(tree->is_mixt_tree == YES) MIXT_Repeat_Task(Free_Best_Spr,tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Actual_CSeq(calign *data) { int i; for(i=0;in_otu;i++) { Free(data->c_seq[i]->state); Free(data->c_seq[i]->d_state); data->c_seq[i]->state = NULL; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Prefix_Tree(pnode *n, int size) { int i; for(i=0;inext[i]) { Free_Prefix_Tree(n->next[i],size); } } Free_Pnode(n); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Pnode(pnode *n) { Free(n->next); Free(n); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Optimiz(t_opt *s_opt) { Free(s_opt); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Nexus(option *io) { int i,j; for(i=0;inex_com_list[i]->nparm) Free_Nexus_Parm(io->nex_com_list[i]->parm[j]); Free(io->nex_com_list[i]->parm); Free(io->nex_com_list[i]->name); Free(io->nex_com_list[i]); } Free(io->nex_com_list); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Nexus_Com(nexcom **com) { int i; for(i=0;iparm); Free(com[i]->name); Free(com[i]); } Free(com); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Nexus_Parm(nexparm *parm) { Free(parm->value); Free(parm->name); Free(parm); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void XML_Free_XML_Tree(xml_node *node) { if(!node) return; if(node->child) XML_Free_XML_Tree(node->child); if(node->next) XML_Free_XML_Tree(node->next); XML_Free_XML_Node(node); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void XML_Free_XML_Node(xml_node *node) { Free(node->id); Free(node->name); Free(node->value); XML_Free_XML_Ds(node->ds); XML_Free_XML_Attr(node->attr); Free(node); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void XML_Free_XML_Attr(xml_attr *attr) { if(attr) { Free(attr->name); Free(attr->value); if(attr->next) XML_Free_XML_Attr(attr->next); Free(attr); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void XML_Free_XML_Ds(t_ds *ds) { if(ds->next) XML_Free_XML_Ds(ds->next); Free(ds); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void MCMC_Free_MCMC(t_mcmc *mcmc) { int i; Free(mcmc->move_type); Free(mcmc->adjust_tuning); Free(mcmc->out_filename); Free(mcmc->move_weight); Free(mcmc->move_prob); Free(mcmc->acc_move); Free(mcmc->run_move); Free(mcmc->prev_acc_move); Free(mcmc->prev_run_move); Free(mcmc->acc_rate); Free(mcmc->tune_move); for(i=0;in_moves;i++) Free(mcmc->move_name[i]); Free(mcmc->move_name); Free(mcmc->ess_run); Free(mcmc->start_ess); Free(mcmc->ess); Free(mcmc->sampled_val); Free(mcmc->mode); Free(mcmc); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void TIMES_Free_Times(t_time *times) { Free(times->buff_t); Free(times->nd_t); Free(times->true_t); Free(times->t_prior); Free(times->t_mean); Free(times->t_prior_min); Free(times->t_prior_max); Free(times->t_floor); Free(times->t_has_prior); Free(times->t_rank); Free(times->mean_t); Free(times->t_prior_min_ori); Free(times->t_prior_max_ori); Free(times->times_partial_proba); Free(times->calib_prob); Free(times->numb_calib_chosen); for(int i=0;in_cal;i++) Free_Calib(times->a_cal[i]); Free(times->a_cal); Free(times->n_jps); Free(times->t_jps); Free(times->time_slice_lims); Free(times->n_time_slice_spans); Free(times->curr_slice); Free(times->has_survived); Free(times); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void RATES_Free_Rates(t_rate *rates) { if(rates->is_allocated == YES) { Free(rates->nd_r); Free(rates->br_r); Free(rates->buff_nd_r); Free(rates->buff_br_r); Free(rates->true_r); Free(rates->dens); Free(rates->triplet); Free(rates->cond_var); Free(rates->invcov); Free(rates->ml_l); Free(rates->cur_l); Free(rates->u_ml_l); Free(rates->u_cur_l); Free(rates->cov_r); Free(rates->lca); Free(rates->trip_cond_cov); Free(rates->trip_reg_coeff); Free(rates->_2n_vect1); Free(rates->_2n_vect2); Free(rates->_2n_vect3); Free(rates->_2n_vect4); Free(rates->_2n_vect5); Free(rates->_2n2n_vect1); Free(rates->_2n2n_vect2); Free(rates->cov_l); Free(rates->mean_l); Free(rates->mean_r); Free(rates->grad_l); Free(rates->reg_coeff); Free(rates->br_do_updt); Free(rates->cur_gamma_prior_mean); Free(rates->cur_gamma_prior_var); Free(rates->n_tips_below); Free(rates->model_name); } Free(rates); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Calib(t_cal *cal) { if(!cal) return; else { int i; for(i=0;iclade_list_size;i++) Free(cal->clade_list[i]); Free(cal->clade_list); Free(cal->id); Free(cal->alpha_proba_list); Free(cal); } } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void Free_Geo(t_geo *t) { int i; Free(t->f_mat); Free(t->r_mat); Free(t->occup); Free(t->idx_loc); Free(t->sorted_nd); Free(t->cov); Free(t->idx_loc_beneath); for(i=0;ildscape_sz;i++) Free_Geo_Coord(t->coord_loc[i]); Free(t->coord_loc); Free(t); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void Free_Geo_Coord(t_geo_coord *t) { Free(t->cpy->lonlat); Free(t->cpy->id); Free(t->cpy); Free(t->lonlat); Free(t->id); Free(t); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void PHYREX_Free_Disk(t_dsk *t) { Free_Geo_Coord(t->centr); Free(t->ldsk_a); Free(t->id); Free(t); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void PHYREX_Free_Ldisk(t_ldsk *t) { if(t == NULL) return; else { Free(t->next); Free_Geo_Coord(t->coord); if(t->cpy_coord) Free_Geo_Coord(t->cpy_coord); Free(t); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void PHYREX_Free_Ldsk_Struct(t_tree *tree) { t_dsk *disk,*next; int i; assert(tree); assert(tree->n_root); assert(tree->n_root->ldsk); assert(tree->n_root->ldsk->disk); disk = tree->n_root->ldsk->disk; do { if(disk->ldsk) PHYREX_Free_Ldisk(disk->ldsk); next = disk->next; PHYREX_Free_Disk(disk); disk = next; } while(disk); for(i=0;in_otu;++i) PHYREX_Free_Ldisk(tree->a_nodes[i]->ldsk); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Poly(t_poly *p) { int i; for(i=0;in_poly_vert;i++) Free_Geo_Coord(p->poly_vert[i]); Free(p->poly_vert); Free(p); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Mmod(t_phyrex_mod *mmod) { if(mmod == NULL) return; Free_Geo_Coord(mmod->lim_up); Free_Geo_Coord(mmod->lim_do); Free(mmod->sigsq_scale); Free(mmod->sigsq); Free(mmod); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void JSON_Free_Array(json_a *a) { if(a->object) JSON_Free_Object(a->object); Free(a); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void JSON_Free_Object(json_o *o) { if(o->kv) JSON_Free_KeyVal(o->kv); if(o->next) JSON_Free_Object(o->next); Free(o); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void JSON_Free_KeyVal(json_kv *kv) { if(kv->key) Free(kv->key); if(kv->value) Free(kv->value); if(kv->object) JSON_Free_Object(kv->object); if(kv->array) JSON_Free_Array(kv->array); if(kv->next) JSON_Free_KeyVal(kv->next); Free(kv); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_NNI(t_nni *t) { if(t->init_l) Free_Scalar_Dbl(t->init_l); if(t->init_v) Free_Scalar_Dbl(t->init_v); if(t->best_l) Free_Scalar_Dbl(t->best_l); if(t->best_v) Free_Scalar_Dbl(t->best_v); if(t->l0) Free_Scalar_Dbl(t->l0); if(t->v0) Free_Scalar_Dbl(t->v0); if(t->l1) Free_Scalar_Dbl(t->l1); if(t->v1) Free_Scalar_Dbl(t->v1); if(t->l2) Free_Scalar_Dbl(t->l2); if(t->v2) Free_Scalar_Dbl(t->v2); Free(t); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* Matrices used in transfer bootstrap computation (tbe.c) */ void Free_TBE_Matrices(int n_otu, short unsigned*** i_matrix, short unsigned*** c_matrix, short unsigned*** hamming, short unsigned** min_dist, short unsigned** min_dist_edge, int** cluster_sizes){ int i; int nb_edges = 2*n_otu-3; for (i=0; ikey); Free(lab->val); if(lab->next != NULL) Free_Label(lab->next); Free(lab); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Free_Contrasts(t_ctrst *ctrst) { Free(ctrst->x); Free(ctrst->tprime); Free(ctrst); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phyml-3.3.20220408/src/free.h000066400000000000000000000066371422404706600153120ustar00rootroot00000000000000/* PHYML : a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences Copyright (C) Stephane Guindon. Oct 2003 onward All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include #ifndef FREE_H #define FREE_H #include "utilities.h" void Free_All_Nodes_Light(t_tree *tree); void Free_All_Edges_Light(t_tree *tree); void Free_Mat(matrix *mat); void Free_Partial_Lk(phydbl *p_lk,int len,int n_catg); void Free_Tree(t_tree *tree); void Free_Bip(t_tree *tree); void Free_Edge(t_edge *b); void Free_Node(t_node *n); void Free_Calign(calign *data); void Free_Seq(align **d,int n_otu); void Free_All(align **d,calign *cdata,t_tree *tree); void Free_SubTree(t_edge *b_fcus,t_node *a,t_node *d,t_tree *tree); void Free_Tree_Ins_Tar(t_tree *tree); void Free_Tree_Pars(t_tree *tree); void Free_Edge_Pars(t_edge *b); void Free_Edge_Pars_Left(t_edge *b); void Free_Edge_Pars_Rght(t_edge *b); void Free_Tree_Lk(t_tree *tree); void Free_Node_Lk(t_node *n); void Free_Edge_Lk(t_edge *b); void Free_Model_Complete(t_mod *mod); void Free_Model_Basic(t_mod *mod); void Free_Custom_Model(t_mod *mod); void Free_Efrq(t_efrq *e_frq); void Free_Rmat(t_rmat *r_mat); void Free_Model(t_mod *mod); void Free(void *p); void Free_Input(option *io); void Free_Tree_List(t_treelist *list); void Free_St(supert_tree *st); void Free_Eigen(eigen *eigen_struct); void Free_One_Spr(t_spr *this_spr); void Free_Spr_List_One_Edge(t_tree *tree); void Free_Spr_List_All_Edge(t_tree *mixt_tree); void Free_Actual_CSeq(calign *data); void Free_Prefix_Tree(pnode *n,int size); void Free_Pnode(pnode *n); void Free_Optimiz(t_opt *s_opt); void Free_Nexus(option *io); void Free_Nexus_Com(nexcom **com); void Free_Nexus_Parm(nexparm *parm); void Free_RAS(t_ras *ras); void XML_Free_XML_Tree(xml_node *node); void XML_Free_XML_Node(xml_node *node); void XML_Free_XML_Attr(xml_attr *attr); void XML_Free_XML_Ds(t_ds *ds); void Free_String(t_string *ts); void Free_Vect_Dbl(vect_dbl *v); void Free_Vect_Int(vect_int *v); void Free_Scalar_Dbl(scalar_dbl *v); void Free_Scalar_Int(scalar_int *v); void Free_Edge_Core(t_edge *b); void Free_Rates(t_rate *rates); void Free_Calib(t_cal *cal); void Free_Edge_Lk_Left(t_edge *b); void Free_Edge_Lk_Rght(t_edge *b); void Free_Geo(t_geo *t); void Free_Geo_Coord(t_geo_coord *t); void PHYREX_Free_Disk(t_dsk *t); void PHYREX_Free_Ldisk(t_ldsk *t); void PHYREX_Free_Ldsk_Struct(t_tree *tree); void Free_Poly(t_poly *p); void Free_Mmod(t_phyrex_mod *mmod); void Free_Efrq_Weights(t_mod *mixt_mod); void Free_Rmat_Weights(t_mod *mixt_mod); void JSON_Free_KeyVal(json_kv *kv); void JSON_Free_Object(json_o *o); void JSON_Free_Array(json_a *a); void Free_Edge_Loc_Rght(t_edge *b); void Free_Edge_Loc_Left(t_edge *b); void Free_Edge_Loc(t_edge *b); void Free_NNI(t_nni *t); void Free_Linked_List(t_ll *t); void Free_TBE_Matrices(int n_otu, short unsigned*** i_matrix, short unsigned*** c_matrix, short unsigned*** hamming, short unsigned** min_dist, short unsigned** min_dist_edge, int** cluster_sizes); void Free_Extra_Edge_Lk(t_tree *tree); void Free_Edge_Length(t_edge *b); void Free_Clade(t_clad *this); void Free_Label(t_label *lab); void Free_Contrasts(t_ctrst *ctrst); void TIMES_Free_Times(t_time *times); void Free_Best_Spr(t_tree *tree); void Free_All_Edges_Light(t_tree *tree); void Free_All_Edges_Lens(t_tree *tree); #endif phyml-3.3.20220408/src/geo.c000066400000000000000000001663631422404706600151410ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include "geo.h" ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int GEO_Main(int argc, char **argv) { /* GEO_Simulate_Estimate(argc,argv); */ GEO_Estimate(argc,argv); return(1); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int GEO_Estimate(int argc, char **argv) { t_geo *t; int seed; FILE *fp; t_tree *tree; phydbl *ldscp; int *loc_hash; int i,j; phydbl *probs; phydbl sum; // geo ./ban seed = getpid(); /* seed = 28224; */ /* seed = 21249; */ /* seed = 21596; */ printf("\n. Seed = %d",seed); srand(seed); t = GEO_Make_Geo_Basic(); GEO_Init_Geo_Struct(t); fp = Openfile(argv[1],0); /* Open tree file */ tree = Read_Tree_File_Phylip(fp); /* Read it */ Update_Ancestors(tree->n_root,tree->n_root->v[2],tree->n_root->b[2],tree); Update_Ancestors(tree->n_root,tree->n_root->v[1],tree->n_root->b[1],tree); tree->rates = RATES_Make_Rate_Struct(tree->n_otu); RATES_Init_Rate_Struct(tree->rates,NULL,tree->n_otu); tree->times = TIMES_Make_Time_Struct(tree->n_otu); TIMES_Init_Time_Struct(tree->times,NULL,tree->n_otu); Branch_To_Time(tree); tree->geo = t; GEO_Read_In_Landscape(argv[2],t,&ldscp,&loc_hash,tree); GEO_Make_Geo_Complete(t->ldscape_sz,t->n_dim,tree->n_otu,t); j = 0; for(i=0;ildscape_sz;i++) { t->coord_loc[i]->lonlat[0] = ldscp[j]; t->coord_loc[i]->lonlat[1] = ldscp[j+1]; PhyML_Printf("\n. Location %d: %13f %13f",i+1,t->coord_loc[i]->lonlat[0],t->coord_loc[i]->lonlat[1]); j+=2; } for(i=0;in_otu;i++) t->idx_loc[i] = loc_hash[i]; t->cov[0*t->n_dim+0] = t->sigma; t->cov[1*t->n_dim+1] = t->sigma; t->cov[0*t->n_dim+1] = 0.0; t->cov[1*t->n_dim+0] = 0.0; GEO_Get_Sigma_Max(t); t->max_sigma = t->sigma_thresh * 2.; PhyML_Printf("\n. Sigma max: %f",t->sigma_thresh); GEO_Get_Locations_Beneath(t,tree); probs = (phydbl *)mCalloc(tree->geo->ldscape_sz,sizeof(phydbl)); sum = 0.0; for(i=0;igeo->ldscape_sz;i++) sum += tree->geo->idx_loc_beneath[tree->n_root->num * tree->geo->ldscape_sz + i]; for(i=0;igeo->ldscape_sz;i++) probs[i] = tree->geo->idx_loc_beneath[tree->n_root->num * tree->geo->ldscape_sz + i]/sum; tree->geo->idx_loc[tree->n_root->num] = Sample_i_With_Proba_pi(probs,tree->geo->ldscape_sz); Free(probs); GEO_Randomize_Locations(tree->n_root,t,tree); GEO_Update_Occup(t,tree); GEO_Lk(t,tree); PhyML_Printf("\n. Init loglk: %f",tree->geo->c_lnL); /* DR_Draw_Tree("essai.ps",tree); */ GEO_MCMC(tree); fclose(fp); Free(ldscp); Free(loc_hash); return(1); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int GEO_Simulate_Estimate(int argc, char **argv) { t_geo *t; int n_tax; t_tree *tree,*ori_tree; int seed; phydbl *res; FILE *fp; int pid; char *s; int rand_loc; phydbl *probs,sum; int i; phydbl mantel_p_val; phydbl rf; s = (char *)mCalloc(T_MAX_NAME,sizeof(char)); strcpy(s,"geo.out"); pid = getpid(); sprintf(s+strlen(s),".%d",pid); fp = fopen(s,"w"); seed = getpid(); /* seed = 15520; */ /* seed = 5023; */ printf("\n. Seed = %d",seed); srand(seed); t = GEO_Make_Geo_Basic(); GEO_Init_Geo_Struct(t); /* t->tau = 3.0; */ /* t->lbda = 0.02; */ /* t->sigma = 10.; */ t->ldscape_sz = (int)atoi(argv[1]); t->n_dim = 2; n_tax = (int)atoi(argv[2]); GEO_Make_Geo_Complete(t->ldscape_sz,t->n_dim,n_tax,t); t->cov[0*t->n_dim+0] = t->sigma; t->cov[1*t->n_dim+1] = t->sigma; t->cov[0*t->n_dim+1] = 0.0; t->cov[1*t->n_dim+0] = 0.0; GEO_Simulate_Coordinates(t->ldscape_sz,t); GEO_Get_Sigma_Max(t); t->max_sigma = t->sigma_thresh * 2.; PhyML_Printf("\n. sigma max: %f threshold: %f",t->max_sigma,t->sigma_thresh); /* t->tau = Uni()*(t->max_tau/100.-t->min_tau*10.) + t->min_tau*10.; */ /* t->lbda = exp(Uni()*(log(t->max_lbda/100.)-log(t->min_lbda*10.)) + log(t->min_lbda*10.)); */ /* t->sigma = Uni()*(t->max_sigma-t->min_sigma) + t->min_sigma; */ t->lbda = 1.; t->sigma = 0.3; t->tau = 1.; PhyML_Fprintf(fp,"\n# SigmaTrue\t SigmaThresh\t LbdaTrue\t TauTrue\txTrue\t yTrue\t xRand\t yRand\t RF\t Sigma5\t Sigma50\t Sigma95\t ProbSigmaInfThresh\t Lbda5\t Lbda50\t Lbda95\t ProbLbdaInf1\t Tau5\t Tau50\t Tau95\t X5\t X50\t X95\t Y5\t Y50\t Y95\t RandX5\t RandX50\t RandX95\t RandY5\t RandY50\t RandY95\t Mantel\t"); PhyML_Fprintf(fp,"\n"); tree = GEO_Simulate(t,n_tax); ori_tree = Make_Tree_From_Scratch(tree->n_otu,NULL); Copy_Tree(tree,ori_tree); Random_SPRs_On_Rooted_Tree(tree); Free_Bip(ori_tree); Alloc_Bip(ori_tree); Get_Bip(ori_tree->a_nodes[0],ori_tree->a_nodes[0]->v[0],ori_tree); Free_Bip(tree); Alloc_Bip(tree); Get_Bip(tree->a_nodes[0],tree->a_nodes[0]->v[0],tree); Match_Tip_Numbers(tree,ori_tree); rf = (phydbl)Compare_Bip(ori_tree,tree,NO)/(tree->n_otu-3); PhyML_Printf("\n. rf: %f",rf); phydbl scale; scale = exp(Rnorm(0,0.2)); PhyML_Printf("\n. Scale: %f",scale); For(i,2*tree->n_otu-1) tree->times->nd_t[i] *= scale; phydbl *tree_dist,*geo_dist; int j; Time_To_Branch(tree); tree_dist = Dist_Btw_Tips(tree); geo_dist = (phydbl *)mCalloc(tree->n_otu*tree->n_otu,sizeof(phydbl)); for(i=0;in_otu;i++) { for(j=0;jn_otu;j++) { /* geo_dist[i*tree->n_otu+j] = */ /* FABS(t->ldscape[t->idx_loc[i]*t->n_dim+0] - */ /* t->ldscape[t->idx_loc[j]*t->n_dim+0])+ */ /* FABS(t->ldscape[t->idx_loc[i]*t->n_dim+1] - */ /* t->ldscape[t->idx_loc[j]*t->n_dim+1]); */ geo_dist[i*tree->n_otu+j] = FABS(t->coord_loc[t->idx_loc[i]]->lonlat[0] - t->coord_loc[t->idx_loc[j]]->lonlat[0])+ FABS(t->coord_loc[t->idx_loc[i]]->lonlat[1] - t->coord_loc[t->idx_loc[j]]->lonlat[1]); } } printf("\n. tau: %f lbda: %f sigma: %f",t->tau,t->lbda,t->sigma); mantel_p_val = Mantel(tree_dist,geo_dist,tree->n_otu,tree->n_otu); printf("\n. Mantel test p-value: %f",mantel_p_val); Free(tree_dist); Free(geo_dist); rand_loc = Rand_Int(0,t->ldscape_sz-1); PhyML_Printf("\n. sigma: %f\t lbda: %f\t tau:%f\t x:%f\t y:%f rand.x:%f\t rand.y:%f\t", t->sigma, t->lbda, t->tau, /* t->ldscape[t->idx_loc[tree->n_root->num]*t->n_dim+0], */ /* t->ldscape[t->idx_loc[tree->n_root->num]*t->n_dim+1], */ /* t->ldscape[rand_loc*t->n_dim+0], */ /* t->ldscape[rand_loc*t->n_dim+1]); */ t->coord_loc[t->idx_loc[tree->n_root->num]]->lonlat[0], t->coord_loc[t->idx_loc[tree->n_root->num]]->lonlat[1], t->coord_loc[rand_loc]->lonlat[0], t->coord_loc[rand_loc]->lonlat[1]); PhyML_Fprintf(fp,"%f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t", t->sigma, t->sigma_thresh, t->lbda, t->tau, t->coord_loc[t->idx_loc[tree->n_root->num]]->lonlat[0], t->coord_loc[t->idx_loc[tree->n_root->num]]->lonlat[1], t->coord_loc[rand_loc]->lonlat[0], t->coord_loc[rand_loc]->lonlat[1], rf); GEO_Get_Locations_Beneath(t,tree); probs = (phydbl *)mCalloc(tree->geo->ldscape_sz,sizeof(phydbl)); sum = 0.0; for(i=0;igeo->ldscape_sz;i++) sum += tree->geo->idx_loc_beneath[tree->n_root->num * tree->geo->ldscape_sz + i]; for(i=0;igeo->ldscape_sz;i++) probs[i] = tree->geo->idx_loc_beneath[tree->n_root->num * tree->geo->ldscape_sz + i]/sum; tree->geo->idx_loc[tree->n_root->num] = Sample_i_With_Proba_pi(probs,tree->geo->ldscape_sz); Free(probs); GEO_Randomize_Locations(tree->n_root,tree->geo,tree); GEO_Update_Occup(t,tree); GEO_Lk(t,tree); PhyML_Printf("\n. Init loglk: %f",tree->geo->c_lnL); res = GEO_MCMC(tree); PhyML_Fprintf(fp,"%f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f\t %f \n", /* Sigma5 */ Quantile(res+0*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.025), /* Sigma50*/ Quantile(res+0*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.50), /* Sigma95*/ Quantile(res+0*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.975), /* ProbInfThesh */ Prob(res+0*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval,t->sigma_thresh), /* Lbda5 */ Quantile(res+1*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.025), /* Lbda50 */ Quantile(res+1*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.50), /* Lbda95 */ Quantile(res+1*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.975), /* ProbInf1 */ Prob(res+1*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval,1.0), /* Tau5 */ Quantile(res+2*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.025), /* Tau50 */ Quantile(res+2*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.50), /* Tau 95 */ Quantile(res+2*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.975), /* X5 */ Quantile(res+4*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.025), /* X50 */ Quantile(res+4*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.50), /* X95 */ Quantile(res+4*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.975), /* Y5 */ Quantile(res+5*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.025), /* Y50 */ Quantile(res+5*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.50), /* Y95 */ Quantile(res+5*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.975), /* RandX5 */ Quantile(res+6*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.025), /* RandX50*/ Quantile(res+6*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.50), /* RandX95*/ Quantile(res+6*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.975), /* RandY5 */ Quantile(res+7*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.025), /* RandY50*/ Quantile(res+7*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.50), /* RandY95*/ Quantile(res+7*tree->mcmc->chain_len / tree->mcmc->sample_interval,tree->mcmc->run / tree->mcmc->sample_interval+1,0.975), mantel_p_val ); fflush(NULL); Free(s); Free(res); fclose(fp); return 1; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl *GEO_MCMC(t_tree *tree) { phydbl *res; int n_vars; int rand_loc; t_geo *t; t = tree->geo; tree->mcmc = MCMC_Make_MCMC_Struct(); MCMC_Complete_MCMC(tree->mcmc,tree); tree->mcmc->io = NULL; tree->mcmc->is = NO; tree->mcmc->run = 0; tree->mcmc->sample_interval = 1E+3; tree->mcmc->chain_len = 1E+6; tree->mcmc->chain_len_burnin = 1E+5; tree->mcmc->randomize = YES; tree->mcmc->norm_freq = 1E+3; tree->mcmc->max_tune = 1.E+20; tree->mcmc->min_tune = 1.E-10; tree->mcmc->print_every = 2; tree->mcmc->is_burnin = NO; tree->mcmc->nd_t_digits = 1; t->tau = 1.0; t->lbda = 1.0; t->sigma = 1.0; t->dum = 1.0; tree->mcmc->chain_len = 1.E+8; tree->mcmc->sample_interval = 50; MCMC_Complete_MCMC(tree->mcmc,tree); GEO_Update_Occup(t,tree); GEO_Lk(t,tree); tree->mcmc->start_ess[tree->mcmc->num_move_geo_sigma] = YES; tree->mcmc->start_ess[tree->mcmc->num_move_geo_lambda] = YES; tree->mcmc->start_ess[tree->mcmc->num_move_geo_tau] = YES; tree->mcmc->start_ess[tree->mcmc->num_move_geo_dum] = YES; n_vars = 10; res = (phydbl *)mCalloc(tree->mcmc->chain_len / tree->mcmc->sample_interval * n_vars,sizeof(phydbl)); PhyML_Printf("\n. Run Sigma [ESS] Lambda [ESS] Tau [ESS] Dummy [ESS] LogLk"); tree->mcmc->run = 0; do { MCMC_GEO_Lbda(tree); MCMC_GEO_Tau(tree); /* MCMC_Geo_Dum(tree); */ MCMC_GEO_Loc(tree); t->update_fmat = YES; MCMC_GEO_Sigma(tree); t->update_fmat = NO; /* t->update_fmat = YES; */ /* MCMC_Geo_Updown_Lbda_Sigma(tree); */ /* t->update_fmat = NO; */ /* MCMC_Geo_Updown_Tau_Lbda(tree); */ /* MCMC_Geo_Updown_Tau_Lbda(tree); */ /* MCMC_Geo_Updown_Tau_Lbda(tree); */ /* printf("\n"); */ /* int i; */ /* For(i,2*tree->n_otu-1) */ /* { */ /* if(tree->a_nodes[i]->tax == NO) */ /* { */ /* printf("%2d ",tree->geo->idx_loc[i]); */ /* } */ /* } */ if(tree->mcmc->run%tree->mcmc->sample_interval == 0) { MCMC_Copy_To_New_Param_Val(tree->mcmc,tree); MCMC_Update_Effective_Sample_Size(tree->mcmc->num_move_geo_lambda,tree->mcmc,tree); MCMC_Update_Effective_Sample_Size(tree->mcmc->num_move_geo_sigma,tree->mcmc,tree); MCMC_Update_Effective_Sample_Size(tree->mcmc->num_move_geo_tau,tree->mcmc,tree); /* printf("\n. lbda:%f,%f tau:%f,%f sigma:%f,%f", */ /* tree->mcmc->acc_rate[tree->mcmc->num_move_geo_lambda], */ /* tree->mcmc->tune_move[tree->mcmc->num_move_geo_lambda], */ /* tree->mcmc->acc_rate[tree->mcmc->num_move_geo_tau], */ /* tree->mcmc->tune_move[tree->mcmc->num_move_geo_tau], */ /* tree->mcmc->acc_rate[tree->mcmc->num_move_geo_sigma], */ /* tree->mcmc->tune_move[tree->mcmc->num_move_geo_sigma]); */ PhyML_Printf("\n. %6d %12f %4.0f %12f %4.0f %12f %4.0f %12f %4.0f %12f", tree->mcmc->run, t->sigma, tree->mcmc->ess[tree->mcmc->num_move_geo_sigma], t->lbda, tree->mcmc->ess[tree->mcmc->num_move_geo_lambda], t->tau, tree->mcmc->ess[tree->mcmc->num_move_geo_tau], t->dum, tree->mcmc->ess[tree->mcmc->num_move_geo_dum], t->c_lnL); /* PhyML_Printf("\n%12f %12f %12f %12f", */ /* t->sigma, */ /* t->lbda, */ /* t->tau, */ /* t->dum); */ rand_loc = Rand_Int(0,t->ldscape_sz-1); res[0 * tree->mcmc->chain_len / tree->mcmc->sample_interval + tree->mcmc->run / tree->mcmc->sample_interval] = t->sigma; res[1 * tree->mcmc->chain_len / tree->mcmc->sample_interval + tree->mcmc->run / tree->mcmc->sample_interval] = t->lbda; res[2 * tree->mcmc->chain_len / tree->mcmc->sample_interval + tree->mcmc->run / tree->mcmc->sample_interval] = t->tau; res[3 * tree->mcmc->chain_len / tree->mcmc->sample_interval + tree->mcmc->run / tree->mcmc->sample_interval] = t->c_lnL; /* res[4 * tree->mcmc->chain_len / tree->mcmc->sample_interval + tree->mcmc->run / tree->mcmc->sample_interval] = t->ldscape[t->idx_loc[tree->n_root->num]*t->n_dim+0]; */ /* res[5 * tree->mcmc->chain_len / tree->mcmc->sample_interval + tree->mcmc->run / tree->mcmc->sample_interval] = t->ldscape[t->idx_loc[tree->n_root->num]*t->n_dim+1]; */ res[4 * tree->mcmc->chain_len / tree->mcmc->sample_interval + tree->mcmc->run / tree->mcmc->sample_interval] = t->coord_loc[t->idx_loc[tree->n_root->num]]->lonlat[0]; res[5 * tree->mcmc->chain_len / tree->mcmc->sample_interval + tree->mcmc->run / tree->mcmc->sample_interval] = t->coord_loc[t->idx_loc[tree->n_root->num]]->lonlat[1]; /* res[6 * tree->mcmc->chain_len / tree->mcmc->sample_interval + tree->mcmc->run / tree->mcmc->sample_interval] = t->ldscape[rand_loc*t->n_dim+0]; */ /* res[7 * tree->mcmc->chain_len / tree->mcmc->sample_interval + tree->mcmc->run / tree->mcmc->sample_interval] = t->ldscape[rand_loc*t->n_dim+1]; */ res[6 * tree->mcmc->chain_len / tree->mcmc->sample_interval + tree->mcmc->run / tree->mcmc->sample_interval] = t->coord_loc[rand_loc]->lonlat[0]; res[7 * tree->mcmc->chain_len / tree->mcmc->sample_interval + tree->mcmc->run / tree->mcmc->sample_interval] = t->coord_loc[rand_loc]->lonlat[1]; } tree->mcmc->run++; if(tree->mcmc->ess[tree->mcmc->num_move_geo_sigma] > 200.) tree->mcmc->adjust_tuning[tree->mcmc->num_move_geo_sigma] = NO; if(tree->mcmc->ess[tree->mcmc->num_move_geo_tau] > 200.) tree->mcmc->adjust_tuning[tree->mcmc->num_move_geo_tau] = NO; if(tree->mcmc->ess[tree->mcmc->num_move_geo_lambda]> 200.) tree->mcmc->adjust_tuning[tree->mcmc->num_move_geo_lambda] = NO; MCMC_Get_Acc_Rates(tree->mcmc); if(tree->mcmc->ess[tree->mcmc->num_move_geo_sigma] > 1000. && tree->mcmc->ess[tree->mcmc->num_move_geo_tau] > 1000. && tree->mcmc->ess[tree->mcmc->num_move_geo_lambda]> 1000.) break; } while(tree->mcmc->run < tree->mcmc->chain_len); return(res); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Update F matrix. Assume a diagonal covariance matrix. void GEO_Update_Fmat(t_geo *t) { phydbl *loc1, *loc2; int i,j,k; int err; phydbl lognloc; for(i=0;in_dim;i++) t->cov[i*t->n_dim+i] = t->sigma; // Diagonal covariance matrix. Same variance in every direction lognloc = log(t->ldscape_sz); // Fill in F matrix; for(i=0;ildscape_sz;i++) { loc1 = t->coord_loc[i]->lonlat; for(j=i;jldscape_sz;j++) { loc2 = t->coord_loc[j]->lonlat; t->f_mat[i*t->ldscape_sz+j] = .0; // Calculate log(f(l_i,l_j)) - log(f(l_i,l_i) - log(m) for(k=0;kn_dim;k++) t->f_mat[i*t->ldscape_sz+j] += Log_Dnorm(loc2[k],loc1[k],t->cov[k*t->n_dim+k],&err); t->f_mat[i*t->ldscape_sz+j] -= lognloc; for(k=0;kn_dim;k++) t->f_mat[i*t->ldscape_sz+j] -= Log_Dnorm(loc1[k],loc1[k],t->cov[k*t->n_dim+k],&err); // Take the exponential t->f_mat[i*t->ldscape_sz+j] = exp(t->f_mat[i*t->ldscape_sz+j]); // Matrix is symmetric t->f_mat[j*t->ldscape_sz+i] = t->f_mat[i*t->ldscape_sz+j]; /* printf("\n. f[%d,%d] = %f (1:[%f;%f] 2:[%f;%f]) sigma=%f",i,j,t->f_mat[i*t->ldscape_sz+j],loc1[0],loc1[1],loc2[0],loc2[1],SQRT(t->cov[0*t->n_dim+0])); */ } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Sort node heights from oldest to youngest age. void GEO_Update_Sorted_Nd(t_geo *t, t_tree *tree) { int i; int swap; t_node *buff; buff = NULL; For(i,2*tree->n_otu-1) t->sorted_nd[i] = tree->a_nodes[i]; // Bubble sort of the node heights do { swap = NO; For(i,2*tree->n_otu-2) { if(tree->times->nd_t[t->sorted_nd[i+1]->num] < tree->times->nd_t[t->sorted_nd[i]->num]) { buff = t->sorted_nd[i]; t->sorted_nd[i] = t->sorted_nd[i+1]; t->sorted_nd[i+1] = buff; swap = YES; } } } while(swap == YES); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Update the set of vectors of occupation along the tree void GEO_Update_Occup(t_geo *t, t_tree *tree) { int i,j; t_node *v1, *v2; GEO_Update_Sorted_Nd(t,tree); For(i,t->ldscape_sz*(2*tree->n_otu-1)) t->occup[i] = 0; t->occup[tree->n_root->num*t->ldscape_sz + t->idx_loc[tree->n_root->num]] = 1; for(i=1;i<2*tree->n_otu-1;i++) { for(j=0;jldscape_sz;j++) { t->occup[t->sorted_nd[i]->num*t->ldscape_sz + j] = t->occup[t->sorted_nd[i-1]->num*t->ldscape_sz + j]; } if(t->sorted_nd[i-1]->tax == NO) { v1 = v2 = NULL; if(t->sorted_nd[i-1] == tree->n_root) { v1 = tree->n_root->v[1]; v2 = tree->n_root->v[2]; } else { for(j=0;j<3;j++) { if(t->sorted_nd[i-1]->v[j] != t->sorted_nd[i-1]->anc && t->sorted_nd[i-1]->b[j] != tree->e_root) { if(!v1) v1 = t->sorted_nd[i-1]->v[j]; else v2 = t->sorted_nd[i-1]->v[j]; } } } if(t->idx_loc[v1->num] != t->idx_loc[t->sorted_nd[i-1]->num]) { t->occup[t->sorted_nd[i]->num * t->ldscape_sz + t->idx_loc[v1->num]]++; } else { t->occup[t->sorted_nd[i]->num * t->ldscape_sz + t->idx_loc[v2->num]]++; } } } /* printf("\n"); */ /* For(i,2*tree->n_otu-1) */ /* { */ /* printf("\n. Node %3d: ",t->sorted_nd[i]->num); */ /* for(j=0;jldscape_sz;j++) */ /* { */ /* /\* printf("%3d [%12f;%12f] ", *\/ */ /* /\* t->occup[t->sorted_nd[i]->num*t->ldscape_sz + j], *\/ */ /* /\* t->ldscape[j*t->n_dim+0],t->ldscape[j*t->n_dim+1]); *\/ */ /* /\* printf("%3d ", *\/ */ /* /\* t->occup[t->sorted_nd[i]->num*t->ldscape_sz + j]); *\/ */ /* } */ /* } */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Calculate R mat at node n void GEO_Update_Rmat(t_node *n, t_geo *t, t_tree *tree) { int i,j; phydbl lbda_j; for(i=0;ildscape_sz;i++) { for(j=0;jldscape_sz;j++) { lbda_j = ((t->occup[n->num*t->ldscape_sz + j]==0) ? (1.0) : (t->lbda)); t->r_mat[i*t->ldscape_sz+j] = t->f_mat[i*t->ldscape_sz+j] * lbda_j * t->tau * t->dum; } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl GEO_Lk(t_geo *t, t_tree *tree) { int i,j; phydbl loglk; phydbl R; int dep,arr; // departure and arrival location indices; t_node *curr_n,*prev_n,*v1,*v2; phydbl sum; GEO_Update_Occup(t,tree); // Same here. if(t->update_fmat == YES) GEO_Update_Fmat(t); prev_n = NULL; curr_n = NULL; loglk = .0; for(i=1;in_otu-1;i++) // Consider all the time slices, from oldest to youngest. // Start at first node below root { prev_n = t->sorted_nd[i-1]; // node just above curr_n = t->sorted_nd[i]; // current node GEO_Update_Rmat(curr_n,t,tree); // NOTE: don't need to do that every time. Add check later. R = GEO_Total_Migration_Rate(curr_n,t); // Total migration rate calculated at node n /* if(i < 2) */ /* { */ /* printf("\n. t0: %f t1: %f R: %f tau: %f sigma: %f lbda: %f x1: %f y1: %f x2: %f y2: %f log0: %d loc1: %d rij: %G fij: %G", */ /* tree->times->nd_t[t->sorted_nd[i-1]->num], */ /* tree->times->nd_t[t->sorted_nd[i]->num], */ /* R, */ /* t->tau, */ /* t->lbda, */ /* t->sigma, */ /* t->ldscape[t->idx_loc[tree->geo->sorted_nd[i-1]->num]*t->n_dim+0], */ /* t->ldscape[t->idx_loc[tree->geo->sorted_nd[i-1]->num]*t->n_dim+1], */ /* t->ldscape[t->idx_loc[tree->geo->sorted_nd[i]->num]*t->n_dim+0], */ /* t->ldscape[t->idx_loc[tree->geo->sorted_nd[i]->num]*t->n_dim+1], */ /* t->idx_loc[tree->geo->sorted_nd[i-1]->num], */ /* t->idx_loc[tree->geo->sorted_nd[i]->num], */ /* t->r_mat[t->idx_loc[tree->geo->sorted_nd[i-1]->num] * t->ldscape_sz + t->idx_loc[tree->geo->sorted_nd[i]->num]], */ /* t->f_mat[t->idx_loc[tree->geo->sorted_nd[i-1]->num] * t->ldscape_sz + t->idx_loc[tree->geo->sorted_nd[i]->num]] */ /* ); */ /* printf("\n >> "); */ /* int j; */ /* for(j=0;jldscape_sz;j++) */ /* if(t->occup[t->sorted_nd[i]->num * t->ldscape_sz + j] > 0) */ /* printf("%f %f -- ", */ /* t->ldscape[j*t->n_dim+0], */ /* t->ldscape[j*t->n_dim+1]); */ /* } */ /* printf("\n. %d %d (%d) %f %p %p \n",i,curr_n->num,curr_n->tax,tree->times->nd_t[curr_n->num],curr_n->v[1],curr_n->v[2]); */ v1 = v2 = NULL; for(j=0;j<3;j++) if(curr_n->v[j] != curr_n->anc && curr_n->b[j] != tree->e_root) { if(!v1) v1 = curr_n->v[j]; else v2 = curr_n->v[j]; } dep = t->idx_loc[curr_n->num]; // departure location arr = // arrival location (t->idx_loc[v1->num] == t->idx_loc[curr_n->num] ? t->idx_loc[v2->num] : t->idx_loc[v1->num]); /* printf("\n%f\t%f", */ /* t->ldscape[arr*t->n_dim+0]-t->ldscape[dep*t->n_dim+0], */ /* t->ldscape[arr*t->n_dim+1]-t->ldscape[dep*t->n_dim+1]); */ loglk -= R * FABS(tree->times->nd_t[curr_n->num] - tree->times->nd_t[prev_n->num]); loglk += log(t->r_mat[dep * t->ldscape_sz + arr]); /* printf("\n <> %d %f %f %d %d v1:%d v2:%d anc:%d %d %d %d", */ /* curr_n->num, */ /* R * FABS(tree->times->nd_t[curr_n->num] - tree->times->nd_t[prev_n->num]), */ /* log(t->r_mat[dep * t->ldscape_sz + arr]), */ /* dep,arr,v1->num,v2->num,curr_n->anc->num,curr_n->v[0]->num,curr_n->v[1]->num,curr_n->v[2]->num); */ /* if(i<2) */ /* { */ /* printf("\n. R = %f r_mat = %f f_mat = %f dt = %f loglk = %f", */ /* R, */ /* t->r_mat[dep * t->ldscape_sz + arr], */ /* t->f_mat[dep * t->ldscape_sz + arr], */ /* FABS(t->sorted_nd[i] - t->sorted_nd[i-1]),loglk); */ /* Exit("\n"); */ /* } */ } // Likelihood for the first 'slice' (i.e., the part just below the root down to // the next node) GEO_Update_Rmat(tree->n_root,t,tree); loglk -= log(t->ldscape_sz); dep = t->idx_loc[tree->n_root->num]; arr = (t->idx_loc[tree->n_root->num] != t->idx_loc[tree->n_root->v[1]->num] ? t->idx_loc[tree->n_root->v[1]->num] : t->idx_loc[tree->n_root->v[2]->num]); /* printf("\n %f %f",t->ldscape[dep],t->ldscape[arr]); */ loglk += log(t->r_mat[dep * t->ldscape_sz + arr]); sum = .0; for(i=0;ildscape_sz;i++) sum += t->r_mat[dep * t->ldscape_sz + i]; loglk -= log(sum); tree->geo->c_lnL = loglk; return loglk; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void GEO_Init_Tloc_Tips(t_geo *t, t_tree *tree) { int i; // TO DO for(i=0;in_otu;i++) { t->idx_loc[i] = i; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Do not forget to call GEO_Update_Rmat (with node n) before calling this function phydbl GEO_Total_Migration_Rate(t_node *n, t_geo *t) { phydbl R; int i,j; R = .0; for(i=0;ildscape_sz;i++) { for(j=0;jldscape_sz;j++) { R += t->r_mat[i * t->ldscape_sz + j] * t->occup[n->num * t->ldscape_sz + i]; } } return R; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Find the arrival location for the migration leaving from n int GEO_Get_Arrival_Location(t_node *n, t_geo *t, t_tree *tree) { int i; t_node *v1, *v2; // the two daughters of n v1 = v2 = NULL; for(i=0;i<3;i++) { if(n->v[i] && n->v[i] != n->anc) { if(!v1) v1 = n->v[i]; else v2 = n->v[i]; } } if(t->idx_loc[v1->num] == t->idx_loc[v2->num]) // Migrated to the same location as that of n { if(t->idx_loc[n->num] != t->idx_loc[v1->num]) { PhyML_Printf("\n== Error detected in location labeling."); PhyML_Printf("\n== Err in file %s at line %d\n",__FILE__,__LINE__); Exit("\n"); } else return t->idx_loc[n->num]; } else // Migrated to a different spot { if((t->idx_loc[v1->num] != t->idx_loc[n->num]) && (t->idx_loc[v2->num] != t->idx_loc[n->num])) { PhyML_Printf("\n== Error detected in location labeling."); PhyML_Printf("\n== Err in file %s at line %d\n",__FILE__,__LINE__); Exit("\n"); } else { if(t->idx_loc[v1->num] == t->idx_loc[n->num]) return t->idx_loc[v2->num]; // v2 gets the new location, v1 inheritates from n else return t->idx_loc[v1->num]; // v1 gets the new location, v2 inheritates from n } } return -1; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// t_tree *GEO_Simulate(t_geo *t, int n_otu) { t_tree *tree; int n_branching_nodes; t_node **branching_nodes; // vector of nodes available for branching out phydbl *p_branch; // p_branch[i]: proba that the i-th node in branching_nodes branches out (p_x vector in the article) phydbl *p_mig; // p_branch[i]: proba of migrating to location i from the location of the edge branching out (q_i vector in the article) int hit; phydbl time; int dep, arr; int i,j; phydbl sum; phydbl R; int *occup; // occupation vector. Updated as we move down the tree int nd_idx; t_node *buff_nd; phydbl buff_t; int buff_l; int swap; tree = Make_Tree_From_Scratch(n_otu,NULL); tree->rates = RATES_Make_Rate_Struct(tree->n_otu); RATES_Init_Rate_Struct(tree->rates,NULL,tree->n_otu); tree->times = TIMES_Make_Time_Struct(tree->n_otu); TIMES_Init_Time_Struct(tree->times,NULL,tree->n_otu); tree->n_root = tree->a_nodes[2*tree->n_otu-2]; // Set the root node to the last element in the list of nodes tree->geo = t; For(i,2*tree->n_otu-2) tree->times->nd_t[i] = -1.; occup = (int *)mCalloc(t->ldscape_sz,sizeof(int)); GEO_Update_Fmat(t); branching_nodes = (t_node **)mCalloc(tree->n_otu,sizeof(t_node *)); branching_nodes[0] = tree->n_root; n_branching_nodes = 1; nd_idx = 0; p_branch = (phydbl *)mCalloc(tree->n_otu,sizeof(phydbl )); p_branch[0] = 1.0; p_mig = (phydbl *)mCalloc(t->ldscape_sz,sizeof(phydbl )); time = 0.0; // Time at the root node (this will be changed afterward) // Sample a location uniformly for the root t->idx_loc[tree->n_root->num] = Rand_Int(0,t->ldscape_sz-1); // Update the occupancy vector occup[t->idx_loc[tree->n_root->num]] = 1; dep = arr = -1; // total migration rate R = 0.0; for(i=0;ildscape_sz;i++) { R += t->f_mat[t->idx_loc[tree->n_root->num]*t->ldscape_sz+i] * ((occup[i] == 0) ? (1.0) : (t->lbda)) * t->tau * t->dum; } do { // Select the node that branches out hit = Sample_i_With_Proba_pi(p_branch,n_branching_nodes); /* printf("\n. [%d] Select node %d (location %d)",n_branching_nodes,branching_nodes[hit]->num,t->idx_loc[branching_nodes[hit]->num]); */ // Set the time for the branching node tree->times->nd_t[branching_nodes[hit]->num] = time; /* printf("\n. Set its time to %f",time); */ // Select the destination location dep = t->idx_loc[branching_nodes[hit]->num]; // Departure point sum = .0; for(i=0;ildscape_sz;i++) // Total rate of migration out of departure point { p_mig[i] = t->f_mat[dep*t->ldscape_sz+i] * ((occup[i] == 0) ? (1.0) : (t->lbda)) * t->tau * t->dum; sum += p_mig[i]; } for(i=0;ildscape_sz;i++) p_mig[i] /= sum; arr = Sample_i_With_Proba_pi(p_mig,t->ldscape_sz); /* printf("\n. Migrate from %d [%5.2f,%5.2f] to %d [%5.2f,%5.2f]", */ /* dep, */ /* t->ldscape[dep*t->n_dim+0], */ /* t->ldscape[dep*t->n_dim+1], */ /* arr, */ /* t->ldscape[arr*t->n_dim+0], */ /* t->ldscape[arr*t->n_dim+1]); */ /* printf("\n%f\t%f", */ /* t->ldscape[arr*t->n_dim+0]-t->ldscape[dep*t->n_dim+0], */ /* t->ldscape[arr*t->n_dim+1]-t->ldscape[dep*t->n_dim+1]); */ // Update vector of occupation occup[arr]++; /* printf("\n. Remove %d. Add %d and %d",branching_nodes[hit]->num,tree->a_nodes[nd_idx]->num,tree->a_nodes[nd_idx+1]->num); */ // Connect two new nodes to the node undergoing a branching event tree->a_nodes[nd_idx]->v[0] = branching_nodes[hit]; tree->a_nodes[nd_idx+1]->v[0] = branching_nodes[hit]; branching_nodes[hit]->v[1] = tree->a_nodes[nd_idx]; branching_nodes[hit]->v[2] = tree->a_nodes[nd_idx+1]; // update branching_nodes vector. Element 'hit' is being replaced so that the corresponding node can no longer branch out branching_nodes[hit] = tree->a_nodes[nd_idx]; branching_nodes[n_branching_nodes] = tree->a_nodes[nd_idx+1]; // Update t_loc vector. t->idx_loc[tree->a_nodes[nd_idx]->num] = dep; t->idx_loc[tree->a_nodes[nd_idx+1]->num] = arr; // Update total migration rate R = .0; for(i=0;ildscape_sz;i++) { if(occup[i] > 0) { for(j=0;jldscape_sz;j++) { R += occup[i] * t->f_mat[i*t->ldscape_sz+j] * ((occup[j] == 0) ? (1.0) : (t->lbda)) * t->tau * t->dum; } } } // Set the time until next branching event time = time + Rexp(R); // Update p_branch vector For(i,n_branching_nodes+1) { dep = t->idx_loc[branching_nodes[i]->num]; p_branch[i] = 0.0; for(j=0;jldscape_sz;j++) { p_branch[i] += t->f_mat[dep*t->ldscape_sz+j] * ((occup[j] == 0) ? (1.0) : (t->lbda)) * t->tau * t->dum / R; /* printf("\n. %f %f %f %f", */ /* R, */ /* t->f_mat[dep*t->ldscape_sz+j], */ /* ((occup[j]>0) ? (t->lbda) : (1.0)), */ /* t->tau); */ } /* printf("\n. %f ",p_branch[i]); */ } // Increase the number of branching nodes by one (you just added 2 new and removed 1 old) n_branching_nodes++; nd_idx += 2; } while(n_branching_nodes < n_otu); // Set the times at the tips For(i,2*tree->n_otu-1) if(tree->times->nd_t[i] < 0.0) tree->times->nd_t[i] = time; // Reverse time scale For(i,2*tree->n_otu-1) tree->times->nd_t[i] -= time; /* For(i,2*tree->n_otu-1) tree->times->nd_t[i] = FABS(tree->times->nd_t[i]); */ // Bubble sort to put all the tips at the top of the tree->a_nodes array do { swap = NO; For(i,2*tree->n_otu-2) { if(!tree->a_nodes[i+1]->v[1] && tree->a_nodes[i]->v[1]) { buff_nd = tree->a_nodes[i+1]; tree->a_nodes[i+1] = tree->a_nodes[i]; tree->a_nodes[i] = buff_nd; buff_t = tree->times->nd_t[i+1]; tree->times->nd_t[i+1] = tree->times->nd_t[i]; tree->times->nd_t[i] = buff_t; buff_l = t->idx_loc[i+1]; t->idx_loc[i+1] = t->idx_loc[i]; t->idx_loc[i] = buff_l; swap = YES; } } } while(swap == YES); // The rest below is just bookeeping... For(i,2*tree->n_otu-1) tree->a_nodes[i]->num = i; For(i,2*tree->n_otu-1) { if(i < tree->n_otu) tree->a_nodes[i]->tax = YES; else tree->a_nodes[i]->tax = NO; } /* printf("\n++++++++++++++++++\n"); */ /* For(i,2*tree->n_otu-1) */ /* { */ /* printf("\n. Node %3d [%p] anc:%3d v1:%3d v2:%3d time: %f", */ /* tree->a_nodes[i]->num, */ /* (void *)tree->a_nodes[i], */ /* tree->a_nodes[i]->v[0] ? tree->a_nodes[i]->v[0]->num : -1, */ /* tree->a_nodes[i]->v[1] ? tree->a_nodes[i]->v[1]->num : -1, */ /* tree->a_nodes[i]->v[2] ? tree->a_nodes[i]->v[2]->num : -1, */ /* tree->times->nd_t[i]); */ /* } */ for(i=0;in_otu;i++) { if(!tree->a_nodes[i]->name) tree->a_nodes[i]->name = (char *)mCalloc(T_MAX_NAME,sizeof(char)); strcpy(tree->a_nodes[i]->name,"x"); sprintf(tree->a_nodes[i]->name+1,"%d",i); } tree->n_root->v[1]->v[0] = tree->n_root->v[2]; tree->n_root->v[2]->v[0] = tree->n_root->v[1]; tree->num_curr_branch_available = 0; Connect_Edges_To_Nodes_Recur(tree->a_nodes[0],tree->a_nodes[0]->v[0],tree); tree->e_root = tree->n_root->v[1]->b[0]; For(i,2*tree->n_otu-3) { tree->a_edges[i]->l->v = FABS(tree->times->nd_t[tree->a_edges[i]->left->num] - tree->times->nd_t[tree->a_edges[i]->rght->num]); } tree->e_root->l->v = FABS(tree->times->nd_t[tree->n_root->v[1]->num] - tree->times->nd_t[tree->n_root->num]) + FABS(tree->times->nd_t[tree->n_root->v[2]->num] - tree->times->nd_t[tree->n_root->num]); tree->n_root->l[1] = FABS(tree->times->nd_t[tree->n_root->v[1]->num] - tree->times->nd_t[tree->n_root->num]); tree->n_root->l[2] = FABS(tree->times->nd_t[tree->n_root->v[2]->num] - tree->times->nd_t[tree->n_root->num]); tree->n_root_pos = FABS(tree->times->nd_t[tree->n_root->v[2]->num] - tree->times->nd_t[tree->n_root->num]) / tree->e_root->l->v; /* printf("\n. %s ",Write_Tree(tree)); */ DR_Draw_Tree("essai.ps",tree); /* for(i=0;in_otu;i++) */ /* printf("\n. %4s %4d [%5.2f %5.2f]",tree->a_nodes[i]->name, */ /* t->idx_loc[i], */ /* t->ldscape[t->idx_loc[i]*t->n_dim+0], */ /* t->ldscape[t->idx_loc[i]*t->n_dim+1]); */ Update_Ancestors(tree->n_root,tree->n_root->v[2],tree->n_root->b[2],tree); Update_Ancestors(tree->n_root,tree->n_root->v[1],tree->n_root->b[1],tree); Free(branching_nodes); Free(p_branch); Free(p_mig); return(tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Simualte n coordinates (in 2D) void GEO_Simulate_Coordinates(int n, t_geo *t) { int i; phydbl width; width = 5.; for(i=0;ildscape[i*t->n_dim+0] = -width/2. + Uni()*width; */ /* t->ldscape[i*t->n_dim+1] = width/2.; */ t->coord_loc[i]->lonlat[0] = -width/2. + Uni()*width; t->coord_loc[i]->lonlat[1] = width/2.; } /* t->ldscape[0*t->n_dim+0] = 0.0; */ /* t->ldscape[0*t->n_dim+1] = 0.0; */ /* t->ldscape[1*t->n_dim+0] = 0.1; */ /* t->ldscape[1*t->n_dim+1] = 0.1; */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void GEO_Optimize_Sigma(t_geo *t, t_tree *tree) { Generic_Brent_Lk(&(t->sigma), t->min_sigma, t->max_sigma, 1.E-5, 1000, NO, GEO_Wrap_Lk,NULL,tree,NULL,NO); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void GEO_Optimize_Lambda(t_geo *t, t_tree *tree) { Generic_Brent_Lk(&(t->lbda), t->min_lbda, t->max_lbda, 1.E-5, 1000, NO, GEO_Wrap_Lk,NULL,tree,NULL,NO); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void GEO_Optimize_Tau(t_geo *t, t_tree *tree) { Generic_Brent_Lk(&(t->tau), t->min_tau, t->max_tau, 1.E-5, 1000, NO, GEO_Wrap_Lk,NULL,tree,NULL,NO); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl GEO_Wrap_Lk(t_edge *b, t_tree *tree, supert_tree *stree) { return GEO_Lk(tree->geo,tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void GEO_Init_Geo_Struct(t_geo *t) { t->c_lnL = UNLIKELY; t->sigma = 1.0; t->min_sigma = 1.E-3; t->max_sigma = 1.E+3; t->sigma_thresh = t->max_sigma; t->lbda = 1.0; t->min_lbda = 1.E-3; t->max_lbda = 1.E+3; t->tau = 1.0; t->min_tau = 1.E-3; t->max_tau = 1.E+3; t->dum = 1.0; t->min_dum = 1.E-3; t->max_dum = 1.E+3; t->n_dim = 2; t->ldscape_sz = 1; t->update_fmat = YES; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void GEO_Randomize_Locations(t_node *n, t_geo *t, t_tree *tree) { if(n->tax == YES) return; else { t_node *v1, *v2; int i; phydbl *probs; // vector of probability of picking each location phydbl sum; phydbl u; probs = (phydbl *)mCalloc(t->ldscape_sz,sizeof(phydbl)); v1 = v2 = NULL; for(i=0;i<3;i++) { if(n->v[i] != n->anc && n->b[i] != tree->e_root) { if(!v1) v1 = n->v[i]; else v2 = n->v[i]; } } if(v1->tax && v2->tax) { Free(probs); return; } else if(v1->tax && !v2->tax && t->idx_loc[v1->num] != t->idx_loc[n->num]) { t->idx_loc[v2->num] = t->idx_loc[n->num]; } else if(v2->tax && !v1->tax && t->idx_loc[v2->num] != t->idx_loc[n->num]) { t->idx_loc[v1->num] = t->idx_loc[n->num]; } else if(v1->tax && !v2->tax && t->idx_loc[v1->num] == t->idx_loc[n->num]) { sum = 0.0; for(i=0;ildscape_sz;i++) sum += t->idx_loc_beneath[v2->num * t->ldscape_sz + i]; for(i=0;ildscape_sz;i++) probs[i] = t->idx_loc_beneath[v2->num * t->ldscape_sz + i]/sum; t->idx_loc[v2->num] = Sample_i_With_Proba_pi(probs,t->ldscape_sz); } else if(v2->tax && !v1->tax && t->idx_loc[v2->num] == t->idx_loc[n->num]) { sum = 0.0; for(i=0;ildscape_sz;i++) sum += t->idx_loc_beneath[v1->num * t->ldscape_sz + i]; for(i=0;ildscape_sz;i++) probs[i] = t->idx_loc_beneath[v1->num * t->ldscape_sz + i]/sum; t->idx_loc[v1->num] = Sample_i_With_Proba_pi(probs,t->ldscape_sz); } else { int n_v1, n_v2; phydbl p; n_v1 = t->idx_loc_beneath[v1->num * t->ldscape_sz + t->idx_loc[n->num]]; n_v2 = t->idx_loc_beneath[v2->num * t->ldscape_sz + t->idx_loc[n->num]]; if(n_v1 + n_v2 < 1) { PhyML_Printf("\n== Err in file %s at line %d\n",__FILE__,__LINE__); Exit("\n"); } p = n_v1 / (n_v1 + n_v2); u = Uni(); if(u < p) { sum = 0.0; for(i=0;ildscape_sz;i++) sum += t->idx_loc_beneath[v2->num * t->ldscape_sz + i]; for(i=0;ildscape_sz;i++) probs[i] = t->idx_loc_beneath[v2->num * t->ldscape_sz + i]/sum; t->idx_loc[v2->num] = Sample_i_With_Proba_pi(probs,t->ldscape_sz); t->idx_loc[v1->num] = t->idx_loc[n->num]; } else { if(t->idx_loc_beneath[v2->num * t->ldscape_sz + t->idx_loc[n->num]] > 0) { sum = 0.0; for(i=0;ildscape_sz;i++) sum += t->idx_loc_beneath[v1->num * t->ldscape_sz + i]; for(i=0;ildscape_sz;i++) probs[i] = t->idx_loc_beneath[v1->num * t->ldscape_sz + i]/sum; t->idx_loc[v1->num] = Sample_i_With_Proba_pi(probs,t->ldscape_sz); t->idx_loc[v2->num] = t->idx_loc[n->num]; } else { sum = 0.0; for(i=0;ildscape_sz;i++) sum += t->idx_loc_beneath[v2->num * t->ldscape_sz + i]; for(i=0;ildscape_sz;i++) probs[i] = t->idx_loc_beneath[v2->num * t->ldscape_sz + i]/sum; t->idx_loc[v2->num] = Sample_i_With_Proba_pi(probs,t->ldscape_sz); t->idx_loc[v1->num] = t->idx_loc[n->num]; } } if(t->idx_loc[v1->num] != t->idx_loc[n->num] && t->idx_loc[v2->num] != t->idx_loc[n->num]) { PhyML_Printf("\n. %d %d %d",t->idx_loc[v1->num],t->idx_loc[v2->num],t->idx_loc[n->num]); PhyML_Printf("\n== Err in file %s at line %d\n",__FILE__,__LINE__); Exit("\n"); } if(t->idx_loc_beneath[v1->num * t->ldscape_sz + t->idx_loc[v1->num]] < 1) { PhyML_Printf("\n. %d %d %d",t->idx_loc[v1->num],t->idx_loc[v2->num],t->idx_loc[n->num]); PhyML_Printf("\n== Err in file %s at line %d\n",__FILE__,__LINE__); Exit("\n"); } if(t->idx_loc_beneath[v2->num * t->ldscape_sz + t->idx_loc[v2->num]] < 1) { PhyML_Printf("\n. %d %d %d",t->idx_loc[v1->num],t->idx_loc[v2->num],t->idx_loc[n->num]); PhyML_Printf("\n== Err in file %s at line %d\n",__FILE__,__LINE__); Exit("\n"); } } Free(probs); for(i=0;i<3;i++) if(n->v[i] != n->anc && n->b[i] != tree->e_root) GEO_Randomize_Locations(n->v[i],t,tree); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void GEO_Get_Locations_Beneath(t_geo *t, t_tree *tree) { int i; GEO_Get_Locations_Beneath_Post(tree->n_root,tree->n_root->v[1],t,tree); GEO_Get_Locations_Beneath_Post(tree->n_root,tree->n_root->v[2],t,tree); for(i=0;ildscape_sz;i++) { t->idx_loc_beneath[tree->n_root->num*t->ldscape_sz+i] = t->idx_loc_beneath[tree->n_root->v[1]->num*t->ldscape_sz+i] + t->idx_loc_beneath[tree->n_root->v[2]->num*t->ldscape_sz+i]; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void GEO_Get_Locations_Beneath_Post(t_node *a, t_node *d, t_geo *t, t_tree *tree) { if(d->tax) { t->idx_loc_beneath[d->num*t->ldscape_sz+t->idx_loc[d->num]] = 1; return; } else { int i; t_node *v1, *v2; for(i=0;i<3;i++) { if(d->v[i] != a && d->b[i] != tree->e_root) { GEO_Get_Locations_Beneath_Post(d,d->v[i],t,tree); } } v1 = v2 = NULL; for(i=0;i<3;i++) { if(d->v[i] != a && d->b[i] != tree->e_root) { if(!v1) v1 = d->v[i]; else v2 = d->v[i]; } } for(i=0;ildscape_sz;i++) { t->idx_loc_beneath[ d->num*t->ldscape_sz+i] = t->idx_loc_beneath[v1->num*t->ldscape_sz+i] + t->idx_loc_beneath[v2->num*t->ldscape_sz+i] ; } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void GEO_Get_Sigma_Max(t_geo *t) { int i,j; phydbl mean_dist,inv_mean_dist; phydbl sigma_a, sigma_b, sigma_c; phydbl overlap_a, overlap_b, overlap_c; phydbl d_intersect; phydbl overlap_target; phydbl eps; int n_iter,n_iter_max; eps = 1.E-6; overlap_target = 0.95; n_iter_max = 100; mean_dist = -1.; inv_mean_dist = -1.; for(i=0;ildscape_sz-1;i++) { for(j=i+1;jldscape_sz;j++) { /* dist = POW(t->ldscape[i*t->n_dim+0] - t->ldscape[j*t->n_dim+0],1); */ /* if(dist > mean_dist) mean_dist = dist; */ /* dist = POW(t->ldscape[i*t->n_dim+1] - t->ldscape[j*t->n_dim+1],1); */ /* if(dist > mean_dist) mean_dist = dist; */ /* mean_dist += FABS(t->ldscape[i*t->n_dim+0] - t->ldscape[j*t->n_dim+0]); */ /* mean_dist += FABS(t->ldscape[i*t->n_dim+1] - t->ldscape[j*t->n_dim+1]); */ mean_dist += FABS(t->coord_loc[i]->lonlat[0] - t->coord_loc[j]->lonlat[0]); mean_dist += FABS(t->coord_loc[i]->lonlat[1] - t->coord_loc[j]->lonlat[1]); } } mean_dist /= t->ldscape_sz*(t->ldscape_sz-1)/2.; inv_mean_dist = 1./mean_dist; PhyML_Printf("\n. Mean distance between locations: %f",mean_dist); sigma_a = t->min_sigma; sigma_b = 1.0; sigma_c = t->max_sigma; /* sigma_a = t->min_sigma; sigma_b = 1.0; sigma_c = 10.; */ n_iter = 0; do { d_intersect = Inverse_Truncated_Normal(inv_mean_dist,0.0,sigma_a,0.0,mean_dist); overlap_a = (Pnorm(mean_dist,0.0,sigma_a) - Pnorm(d_intersect,0.0,sigma_a))/ (Pnorm(mean_dist,0.0,sigma_a) - Pnorm(0.0,0.0,sigma_a)) + d_intersect / mean_dist; /* printf("\n. inter: %f %f [%f]",d_intersect,mean_dist,d_intersect / mean_dist); */ d_intersect = Inverse_Truncated_Normal(inv_mean_dist,0.0,sigma_b,0.0,mean_dist); overlap_b = (Pnorm(mean_dist,0.0,sigma_b) - Pnorm(d_intersect,0.0,sigma_b))/ (Pnorm(mean_dist,0.0,sigma_b) - Pnorm(0.0,0.0,sigma_b)) + d_intersect / mean_dist; /* printf("\n. inter: %f %f [%f]",d_intersect,mean_dist,d_intersect / mean_dist); */ d_intersect = Inverse_Truncated_Normal(inv_mean_dist,0.0,sigma_c,0.0,mean_dist); overlap_c = (Pnorm(mean_dist,0.0,sigma_c) - Pnorm(d_intersect,0.0,sigma_c))/ (Pnorm(mean_dist,0.0,sigma_c) - Pnorm(0.0,0.0,sigma_c)) + d_intersect / mean_dist; /* printf("\n. inter: %f %f [%f]",d_intersect,mean_dist,d_intersect / mean_dist); */ /* printf("\n. sigma_a:%f overlap_a:%f sigma_b:%f overlap_b:%f sigma_c:%f overlap_c:%f", */ /* sigma_a,overlap_a, */ /* sigma_b,overlap_b, */ /* sigma_c,overlap_c); */ if(overlap_target > overlap_a && overlap_target < overlap_b) { sigma_c = sigma_b; sigma_b = sigma_a + (sigma_c - sigma_a)/2.; } else if(overlap_target > overlap_b && overlap_target < overlap_c) { sigma_a = sigma_b; sigma_b = sigma_a + (sigma_c - sigma_a)/2.; } else if(overlap_target < overlap_a) { sigma_a /= 2.; } else if(overlap_target > overlap_c) { sigma_c *= 2.; } n_iter++; } while(sigma_c - sigma_a > eps && n_iter < n_iter_max); /* if(sigma_c - sigma_a > eps) */ /* { */ /* PhyML_Printf("\n== Error detected in getting maximum value of sigma."); */ /* PhyML_Printf("\n== Err in file %s at line %d\n",__FILE__,__LINE__); */ /* Exit("\n"); */ /* } */ /* else */ /* { */ /* PhyML_Printf("\n== Threshold for sigma: %f",sigma_b); */ /* } */ t->sigma_thresh = sigma_b; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void MCMC_Geo_Updown_Tau_Lbda(t_tree *tree) { phydbl K,mult,u,alpha,ratio; phydbl cur_lnL,new_lnL; phydbl cur_tau,new_tau; phydbl cur_lbda,new_lbda; K = tree->mcmc->tune_move[tree->mcmc->num_move_geo_updown_tau_lbda]; cur_lnL = tree->geo->c_lnL; new_lnL = tree->geo->c_lnL; cur_tau = tree->geo->tau; new_tau = tree->geo->tau; cur_lbda = tree->geo->lbda; new_lbda = tree->geo->lbda; u = Uni(); mult = exp(K*(u-0.5)); /* Multiply tau by K */ new_tau = cur_tau * K; /* Divide lbda by same amount */ new_lbda = cur_lbda / K; if( new_lbda < tree->geo->min_lbda || new_lbda > tree->geo->max_lbda || new_tau < tree->geo->min_tau || new_tau > tree->geo->max_tau ) { tree->mcmc->run_move[tree->mcmc->num_move_geo_updown_tau_lbda]++; return; } tree->geo->tau = new_tau; tree->geo->lbda = new_lbda; if(tree->eval_alnL) new_lnL = GEO_Lk(tree->geo,tree); ratio = 0.0; /* Proposal ratio: 2n-2=> number of multiplications, 1=>number of divisions */ ratio += 0.0*log(mult); /* (1-1)*log(mult); */ /* Likelihood density ratio */ ratio += (new_lnL - cur_lnL); /* printf("\n. new_tau: %f new_lbda:%f cur_lnL:%f new_lnL:%f",new_tau,new_lbda,cur_lnL,new_lnL); */ ratio = exp(ratio); alpha = MIN(1.,ratio); u = Uni(); if(u > alpha) /* Reject */ { tree->geo->tau = cur_tau; tree->geo->lbda = cur_lbda; tree->geo->c_lnL = cur_lnL; } else { tree->mcmc->acc_move[tree->mcmc->num_move_geo_updown_tau_lbda]++; } tree->mcmc->run_move[tree->mcmc->num_move_geo_updown_tau_lbda]++; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void MCMC_Geo_Updown_Lbda_Sigma(t_tree *tree) { phydbl K,mult,u,alpha,ratio; phydbl cur_lnL,new_lnL; phydbl cur_lbda,new_lbda; phydbl cur_sigma,new_sigma; K = tree->mcmc->tune_move[tree->mcmc->num_move_geo_updown_lbda_sigma]; cur_lnL = tree->geo->c_lnL; new_lnL = tree->geo->c_lnL; cur_lbda = tree->geo->lbda; new_lbda = tree->geo->lbda; cur_sigma = tree->geo->sigma; new_sigma = tree->geo->sigma; u = Uni(); mult = exp(K*(u-0.5)); /* Multiply lbda by K */ new_lbda = cur_lbda * K; /* Divide sigma by same amount */ new_sigma = cur_sigma / K; if( new_sigma < tree->geo->min_sigma || new_sigma > tree->geo->max_sigma || new_lbda < tree->geo->min_lbda || new_lbda > tree->geo->max_lbda ) { tree->mcmc->run_move[tree->mcmc->num_move_geo_updown_lbda_sigma]++; return; } tree->geo->lbda = new_lbda; tree->geo->sigma = new_sigma; if(tree->eval_alnL) new_lnL = GEO_Lk(tree->geo,tree); ratio = 0.0; /* Proposal ratio: 2n-2=> number of multiplications, 1=>number of divisions */ ratio += 0.0*log(mult); /* (1-1)*log(mult); */ /* Likelihood density ratio */ ratio += (new_lnL - cur_lnL); /* printf("\n. new_lbda: %f new_sigma:%f cur_lnL:%f new_lnL:%f",new_lbda,new_sigma,cur_lnL,new_lnL); */ ratio = exp(ratio); alpha = MIN(1.,ratio); u = Uni(); if(u > alpha) /* Reject */ { tree->geo->lbda = cur_lbda; tree->geo->sigma = cur_sigma; tree->geo->c_lnL = cur_lnL; } else { tree->mcmc->acc_move[tree->mcmc->num_move_geo_updown_lbda_sigma]++; } tree->mcmc->run_move[tree->mcmc->num_move_geo_updown_lbda_sigma]++; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void GEO_Read_In_Landscape(char *file_name, t_geo *t, phydbl **ldscape, int **loc_hash, t_tree *tree) { FILE *fp; char *s; phydbl longitude, lattitude; int tax,loc; PhyML_Printf("\n"); PhyML_Printf("\n. Reading landscape file '%s'.\n",file_name); s = (char *)mCalloc(T_MAX_LINE,sizeof(char)); (*ldscape) = (phydbl *)mCalloc(10*t->n_dim,sizeof(phydbl)); (*loc_hash) = (int *)mCalloc(tree->n_otu,sizeof(int)); fp = Openfile(file_name,0); tax = loc = -1; t->ldscape_sz = 0; do { if(fscanf(fp,"%s",s) == EOF) break; if(strlen(s) > 0) for(tax=0;taxn_otu;tax++) if(!strcmp(tree->a_nodes[tax]->name,s)) break; if(tax == tree->n_otu) { PhyML_Printf("\n== Could not find a taxon with name '%s' in the tree provided.",s); /* PhyML_Printf("\n== Err. in file %s at line %d\n",__FILE__,__LINE__); */ /* Exit("\n"); */ continue; } /* sscanf(line+pos,"%lf %lf",&longitude,&lattitude); */ if(fscanf(fp,"%lf",&longitude) == EOF) break; if(fscanf(fp,"%lf",&lattitude) == EOF) break; /* printf("\n. s: %s %f %f",s,longitude,lattitude); */ for(loc=0;locldscape_sz;loc++) { if(FABS(longitude-(*ldscape)[loc*t->n_dim+0]) < 1.E-10 && FABS(lattitude-(*ldscape)[loc*t->n_dim+1]) < 1.E-10) { break; } } if(loc == t->ldscape_sz) { t->ldscape_sz++; (*ldscape)[(t->ldscape_sz-1)*t->n_dim+0] = longitude; (*ldscape)[(t->ldscape_sz-1)*t->n_dim+1] = lattitude; if(!(t->ldscape_sz%10)) { (*ldscape) = (phydbl *)mRealloc((*ldscape),(t->ldscape_sz+10)*t->n_dim,sizeof(phydbl)); } } (*loc_hash)[tax] = loc; } while(1); for(tax=0;taxn_otu;tax++) PhyML_Printf("\n. Taxon %30s, longitude: %12f, lattitude: %12f [%4d]", tree->a_nodes[tax]->name, (*ldscape)[(*loc_hash)[tax]*t->n_dim+0], (*ldscape)[(*loc_hash)[tax]*t->n_dim+1], (*loc_hash)[tax]); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phyml-3.3.20220408/src/geo.h000066400000000000000000000034011422404706600151250ustar00rootroot00000000000000/* PHYML : a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences Copyright (C) Stephane Guindon. Oct 2003 onward All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include #ifndef GEO_H #define GEO_H #include "utilities.h" int GEO_Main(int argc, char **argv); void Free_Geo(t_geo *t); void GEO_Update_Fmat(t_geo *t); void GEO_Update_Sorted_Nd(t_geo *t,t_tree *tree); void GEO_Update_Occup(t_geo *t,t_tree *tree); void GEO_Update_Rmat(t_node *n,t_geo *t,t_tree *tree); phydbl GEO_Lk(t_geo *t,t_tree *tree); void GEO_Init_Tloc_Tips(t_geo *t,t_tree *tree); phydbl GEO_Total_Migration_Rate(t_node *n,t_geo *t); int GEO_Get_Arrival_Location(t_node *n,t_geo *t,t_tree *tree); void GEO_Simulate_Coordinates(int n, t_geo *t); t_tree *GEO_Simulate(t_geo *t, int n_otu); void GEO_Optimize_Sigma(t_geo *t, t_tree *tree); phydbl GEO_Wrap_Lk(t_edge *b, t_tree *tree, supert_tree *stree); void GEO_Optimize_Lambda(t_geo *t, t_tree *tree); void GEO_Init_Geo_Struct(t_geo *t); void GEO_Optimize_Tau(t_geo *t, t_tree *tree); void GEO_Get_Locations_Beneath(t_geo *t, t_tree *tree); void GEO_Get_Locations_Beneath_Post(t_node *a, t_node *d, t_geo *t, t_tree *tree); void GEO_Randomize_Locations_Pre(t_node *n, t_geo *t, t_tree *tree); void GEO_Randomize_Locations(t_node *n, t_geo *t, t_tree *tree); void GEO_Get_Sigma_Max(t_geo *t); void MCMC_Geo_Updown_Tau_Lbda(t_tree *tree); void MCMC_Geo_Updown_Lbda_Sigma(t_tree *tree); int GEO_Simulate_Estimate(int argc, char **argv); void GEO_Read_In_Landscape(char *file_name, t_geo *t, phydbl **ldscape, int **loc_hash, t_tree *tree); int GEO_Estimate(int argc, char **argv); phydbl *GEO_MCMC(t_tree *tree); #endif phyml-3.3.20220408/src/help.c000066400000000000000000000370721422404706600153110ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include "help.h" /* int T_MAX_FILE; */ /* phydbl SMALL; */ /* phydbl UNLIKELY; */ ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Usage() { char *BOLD=(char *)mCalloc(10,sizeof(char)); char *FLAT=(char *)mCalloc(10,sizeof(char)); char *LINE=(char *)mCalloc(10,sizeof(char)); char *cha; cha = getenv("OS"); if(cha!=NULL) { strcpy(BOLD, ""); strcpy(FLAT, ""); strcpy(LINE, ""); } else { strcpy(BOLD, "\033[00;01m"); strcpy(FLAT, "\033[00;00m"); strcpy(LINE, "\033[00;04m"); } #ifdef PHYML PhyML_Printf("%sNAME\n" "%s\t- PhyML %s - \n\n" "%s\t\''A simple, fast, and accurate algorithm to estimate\n" "%s\tlarge phylogenies by maximum likelihood\''\n\n" "%s\tStephane Guindon and Olivier Gascuel,\n" "%s\tSystematic Biology 52(5):696-704, 2003.\n\n" "%s\tPlease cite this paper if you use this software in your publications.\n",BOLD,FLAT,VERSION,FLAT,FLAT,FLAT,FLAT,FLAT); #endif #ifdef PHYTIME PhyML_Printf("%sNAME\n" "%s\t- PhyTime %s - \n\n" "%s\t'Bayesian estimation of divergence times from large sequence alignments.'\n" "%s\tStephane Guindon,\n" "%s\tMolecular Biology and Evolution 27(8):1768-81, 2010.\n\n" "%s\tPlease cite this paper if you use this software in your publications.\n",BOLD,FLAT,VERSION,FLAT,FLAT,FLAT,FLAT,FLAT); #endif #ifdef PHYML PhyML_Printf("%s\nSYNOPSIS:\n\n" "%s\tphyml %s[command args]\n",BOLD,BOLD,BOLD); #endif #ifdef PHYTIME PhyML_Printf("%s\nSYNOPSIS:\n\n" "%s\tphytime %s[command args]\n",BOLD,BOLD,BOLD); #endif #ifdef PHYML PhyML_Printf("%s\n\tAll the options below are optional (except '%s-i%s' if you want to use the command-line interface).\n\n",FLAT,BOLD,FLAT); #endif #ifdef PHYTIME PhyML_Printf("%s\n\tAll the options below are optional except '%s-i%s','%s-u%s' and '%s--calibration%s'.\n\n",FLAT,BOLD,FLAT,BOLD,FLAT,BOLD,FLAT); #endif PhyML_Printf("%s\nCommand options:\n%s",BOLD,FLAT); PhyML_Printf("\n\t%s-i (or --input) %sseq_file_name%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\t%sseq_file_name%s is the name of the nucleotide or amino-acid sequence file in PHYLIP format.\n",LINE,FLAT); PhyML_Printf("\n"); PhyML_Printf("%s\n\t-d (or --datatype) ""%sdata_type%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\t%sdata_type%s is 'nt' for nucleotide (default), 'aa' for amino-acid sequences, or 'generic',\n",LINE,FLAT); PhyML_Printf("\t\t(use NEXUS file format and the 'symbols' parameter here).\n"); PhyML_Printf("\n"); PhyML_Printf("%s\n\t-q (or --sequential)\n",BOLD); PhyML_Printf("%s\t\tChanges interleaved format (default) to sequential format.\n",FLAT); PhyML_Printf("\n"); #ifndef PHYTIME PhyML_Printf("%s\n\t-n (or --multiple) ""%snb_data_sets%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\t%snb_data_sets%s is an integer corresponding to the number of data sets to analyse.\n",LINE,FLAT); PhyML_Printf("\n"); #endif #ifndef PHYTIME PhyML_Printf("%s\n\t-p (or --pars)%s\n",BOLD,FLAT); PhyML_Printf("%s\t\tUse a minimum parsimony starting tree. This option is taken into account when the '-u' option\n",FLAT); PhyML_Printf("%s\t\tis absent and when tree topology modifications are to be done.\n",FLAT); PhyML_Printf("\n"); #endif #ifndef PHYTIME PhyML_Printf("%s\n\t-b (or --bootstrap) %sint%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\t%sint%s > 0: %sint%s is the number of bootstrap replicates.\n",LINE,FLAT,LINE,FLAT); PhyML_Printf("\t\t%sint%s = 0: neither approximate likelihood ratio test nor bootstrap values are computed.\n",LINE,FLAT); PhyML_Printf("\t\t%sint%s = -1: approximate likelihood ratio test returning aLRT statistics.\n",LINE,FLAT); PhyML_Printf("\t\t%sint%s = -2: approximate likelihood ratio test returning Chi2-based parametric branch supports.\n",LINE,FLAT); /* PhyML_Printf("\t\t%sint%s = -3 : minimum of Chi2-based parametric and SH-like branch supports.\n",LINE,FLAT); */ PhyML_Printf("\t\t%sint%s = -4: SH-like branch supports alone.\n",LINE,FLAT); PhyML_Printf("\t\t%sint%s = -5: (default) approximate Bayes branch supports.\n",LINE,FLAT); PhyML_Printf("\n"); PhyML_Printf("%s\n\t--tbe%s\n",BOLD,FLAT); PhyML_Printf("\t\tComputes TBE instead of FBP (standard) bootstrap support\n"); PhyML_Printf("\t\tHas no effect with -b <= 0\n"); #endif PhyML_Printf("%s\n\t-m (or --model) %smodel%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\tmodel%s : substitution model name.\n",FLAT); PhyML_Printf("\t\t%s- %sNucleotide%s-based models : %sHKY85%s (default) | %sJC69%s | %sK80%s | %sF81%s | %sF84%s \n", FLAT, LINE,FLAT, LINE,FLAT, LINE,FLAT, LINE,FLAT, LINE,FLAT, LINE,FLAT); PhyML_Printf("\t\t %sTN93%s | %sGTR%s | %scustom (*)%s\n", LINE,FLAT, LINE,FLAT, LINE,FLAT); PhyML_Printf("\t\t(*) : for the custom option, a string of six digits identifies the model. For instance, 000000\n"); PhyML_Printf("\t\t corresponds to F81 (or JC69 provided the distribution of nucleotide frequencies is uniform).\n"); PhyML_Printf("\t\t 012345 corresponds to GTR. This option can be used for encoding any model that is a nested within GTR.\n"); PhyML_Printf("\n"); PhyML_Printf("\t\t%s- %sAmino-acid%s based models : %sLG%s (default) | %sWAG%s | %sJTT%s | %sMtREV%s | %sDayhoff%s | %sDCMut%s \n",FLAT, LINE,FLAT, LINE,FLAT, LINE,FLAT, LINE,FLAT, LINE,FLAT, LINE,FLAT, LINE,FLAT); PhyML_Printf("\t\t %sRtREV%s | %sCpREV%s | %sVT%s | %sAB%s | %sBlosum62%s | %sMtMam%s | %sMtArt%s\n", LINE,FLAT, LINE,FLAT, LINE,FLAT, LINE,FLAT, LINE,FLAT, LINE,FLAT, LINE,FLAT); PhyML_Printf("\t\t %sHIVw%s | %sHIVb%s | %scustom%s\n", LINE,FLAT, LINE,FLAT, LINE,FLAT); PhyML_Printf("\n"); PhyML_Printf("%s\n\t--aa_rate_file %sfilename%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\t%sfilename%s is the name of the file that provides the amino acid substitution rate matrix in PAML format.\n",LINE,FLAT); PhyML_Printf("\t\tIt is compulsory to use this option when analysing amino acid sequences with the `custom' model.\n"); PhyML_Printf("\n"); #ifndef PHYTIME PhyML_Printf("%s\n\t-f %se%s, %sm%s, %so%s or %sfA,fC,fG,fT%s\n",BOLD,LINE,BOLD,LINE,BOLD,LINE,FLAT); PhyML_Printf("\t\t%se%s : the character frequencies are determined by counting the number of amino-acids \n",LINE,FLAT); PhyML_Printf("\t\t or nucleotides from the sequence alignment. \n"); PhyML_Printf("\n"); PhyML_Printf("\t\t%sm%s : the character frequencies are determined as follows : \n",LINE,FLAT); PhyML_Printf("%s\t\t- %sNucleotide%s sequences: the equilibrium base frequencies are optimized using maximum likelihood.\n",FLAT,LINE,FLAT); PhyML_Printf("%s\t\t- %sAmino-acid%s sequences: the equilibrium amino-acid frequencies are estimated using\n" "\t\t the frequencies defined by the substitution model.\n",FLAT,LINE,FLAT); PhyML_Printf("\n"); PhyML_Printf("\t\t%so%s : the character frequencies (amino-acids or nucleotides) are optimized using maximum likelihood \n",LINE,FLAT); PhyML_Printf("\n"); PhyML_Printf("\t\t%sfA,fC,fG,fT%s : only valid for nucleotide-based models. fA, fC, fG and fT are floating numbers that \n",LINE,FLAT); PhyML_Printf("\t\t correspond to the frequencies of A, C, G and T respectively (WARNING: do not use any blank space between\n"); PhyML_Printf("\t\t your values of nucleotide frequencies, only commas!)\n"); PhyML_Printf("\n"); #endif #ifdef PHYTIME PhyML_Printf("%s\n\t--calibration %sfilename%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\t%sfilename%s is the name of the calibration file that provides a priori defined boundaries for node ages.\n",LINE,FLAT); PhyML_Printf("\t\tPlease read the manual for more information about the format of this file.\n"); PhyML_Printf("\n"); #endif PhyML_Printf("%s\n\t-t (or --ts/tv) %sts/tv_ratio%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\tts/tv_ratio%s : transition/transversion ratio. DNA sequences only.\n",FLAT); PhyML_Printf("\t\tCan be a fixed positive value (ex:4.0) or %se%s to get the maximum likelihood estimate.\n",LINE,FLAT); PhyML_Printf("\n"); PhyML_Printf("%s\n\t-v (or --pinv) %sprop_invar%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\tprop_invar%s : proportion of invariable sites.\n",FLAT); PhyML_Printf("\t\tCan be a fixed value in the [0,1] range or %se%s to get the maximum likelihood estimate.\n",LINE,FLAT); PhyML_Printf("\n"); PhyML_Printf("%s\n\t-c (or --nclasses) %snb_subst_cat%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\tnb_subst_cat%s : number of relative substitution rate categories. Default : %snb_subst_cat%s=4.\n", FLAT,LINE,FLAT); PhyML_Printf("\t\tMust be a positive integer.\n"); PhyML_Printf("\n"); PhyML_Printf("%s\n\t--freerates (or --free_rates or --freerate or --free_rate)\n",BOLD); PhyML_Printf("\t\t%s FreeRate model of substitution rate variation across sites.\n",FLAT); PhyML_Printf("\n"); PhyML_Printf("%s\n\t-a (or --alpha) %sgamma%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\tgamma%s : distribution of the gamma distribution shape parameter.\n",FLAT); PhyML_Printf("\t\tCan be a fixed positive value or %se%s to get the maximum likelihood estimate.\n",LINE,FLAT); PhyML_Printf("\n"); #ifndef PHYTIME PhyML_Printf("%s\n\t-s (or --search) %smove%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\t Deprecated option.\n"); PhyML_Printf("\t\tTree topology search operation option.\n"); PhyML_Printf("\t\tCan be either %sNNI%s (default, fast) or %sSPR%s (a bit slower than NNI) or %sBEST%s (best of NNI and SPR search).\n",LINE,FLAT,LINE,FLAT,LINE,FLAT); PhyML_Printf("\n"); #endif PhyML_Printf("%s\n\t-u (or --inputtree) %suser_tree_file%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\tuser_tree_file%s : starting tree filename. The tree must be in Newick format.\n",FLAT); PhyML_Printf("\n"); #ifndef PHYTIME PhyML_Printf("%s\n\t-o %sparams%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\tThis option focuses on specific parameter optimisation.\n"); PhyML_Printf("\t\t%sparams%s=tlr : tree topology (t), branch length (l) and rate parameters (r) are optimised.\n",LINE,FLAT); PhyML_Printf("\t\t%sparams%s=tl : tree topology and branch length are optimised.\n",LINE,FLAT); PhyML_Printf("\t\t%sparams%s=lr : branch length and rate parameters are optimised.\n",LINE,FLAT); PhyML_Printf("\t\t%sparams%s=l : branch length are optimised.\n",LINE,FLAT); PhyML_Printf("\t\t%sparams%s=r : rate parameters are optimised.\n",LINE,FLAT); PhyML_Printf("\t\t%sparams%s=n : no parameter is optimised.\n",LINE,FLAT); PhyML_Printf("\n"); #endif #ifndef PHYTIME PhyML_Printf("%s\n\t--rand_start%s\n",BOLD,FLAT); PhyML_Printf("\t\tThis option sets the initial tree to random.\n"); PhyML_Printf("\t\tIt is only valid if SPR searches are to be performed.\n"); PhyML_Printf("\n"); #endif #ifndef PHYTIME PhyML_Printf("%s\n\t--n_rand_starts %snum%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\tnum%s is the number of initial random trees to be used.\n",FLAT); PhyML_Printf("\t\tIt is only valid if SPR searches are to be performed.\n"); PhyML_Printf("\n"); #endif PhyML_Printf("%s\n\t--r_seed %snum%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\tnum%s is the seed used to initiate the random number generator.\n",FLAT); PhyML_Printf("\t\tMust be an integer.\n"); PhyML_Printf("\n"); #ifndef PHYTIME PhyML_Printf("%s\n\t--print_site_lnl%s\n",BOLD,FLAT); PhyML_Printf("\t\t%sPrint the likelihood for each site in file *_phyml_lk.txt.\n",FLAT); PhyML_Printf("\n"); #endif #ifndef PHYTIME PhyML_Printf("%s\n\t--print_trace%s\n",BOLD,FLAT); PhyML_Printf("\t\t%sPrint each phylogeny explored during the tree search process\n",FLAT); PhyML_Printf("\t\t%sin file *_phyml_trace.txt.\n",FLAT); PhyML_Printf("\n"); #endif PhyML_Printf("%s\n\t--run_id %sID_string%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\t%sAppend the string %sID_string%s at the end of each PhyML output file.\n",FLAT,LINE,FLAT); PhyML_Printf("\t\t%sThis option may be useful when running simulations involving PhyML.\n",FLAT); PhyML_Printf("\n"); PhyML_Printf("%s\n\t--quiet%s\n",BOLD,FLAT); PhyML_Printf("\t\t%sNo interactive question (for running in batch mode) and quiet output.\n",FLAT); PhyML_Printf("\n"); PhyML_Printf("%s\n\t--no_memory_check%s\n",BOLD,FLAT); PhyML_Printf("\t\t%sNo interactive question for memory usage (for running in batch mode). Normal output otherwise.\n",FLAT); PhyML_Printf("\n"); #ifndef PHYTIME PhyML_Printf("%s\n\t--leave_duplicates%s\n",BOLD,FLAT); PhyML_Printf("\t\t%sPhyML removes duplicate sequences by default. Use this option to leave them in.\n",FLAT); PhyML_Printf("\n"); #endif #ifndef PHYTIME PhyML_Printf("%s\n\t--alias_subpatt%s\n",BOLD,FLAT); PhyML_Printf("\t\t%sSite aliasing is generalized at the subtree level. Sometimes lead to faster calculations.\n",FLAT); PhyML_Printf("\t\t%sSee Kosakovsky Pond SL, Muse SV, Sytematic Biology (2004) for an example.\n",FLAT); PhyML_Printf("\n"); #endif #ifndef PHYTIME PhyML_Printf("%s\n\t--boot_progress_display %snum%s (default=20)\n",BOLD,LINE,FLAT); PhyML_Printf("\t\t%snum%s is the frequency at which the bootstrap progress bar will be updated.\n",LINE,FLAT); PhyML_Printf("\t\tMust be an integer.\n"); PhyML_Printf("\n"); #endif #ifdef PHYTIME PhyML_Printf("%s\n\t--chain_len %snum%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\t%snum%s is the number of generations or runs of the Markov Chain Monte Carlo. Set to 1E+6 by default. \n",LINE,FLAT); PhyML_Printf("\t\tMust be an integer.\n"); PhyML_Printf("\n"); #endif /* #ifdef PHYTIME */ /* PhyML_Printf("%s\n\t--burnin %snum%s\n",BOLD,LINE,FLAT); */ /* PhyML_Printf("\t\t%snum%s is the number of generations of runs of the Markov Chain Monte Carlo during the 'burnin' period.\n",LINE,FLAT); */ /* PhyML_Printf("\t\t%sSet to 1E+5 by default. Must be an integer. \n",FLAT); */ /* PhyML_Printf("\n"); */ /* #endif */ #ifdef PHYTIME PhyML_Printf("%s\n\t--sample_freq %snum%s\n",BOLD,LINE,FLAT); PhyML_Printf("\t\tThe chain is sampled every %snum%s generations. Set to 1E+3 by default. \n",LINE,FLAT); PhyML_Printf("\t\tMust be an integer.\n"); PhyML_Printf("\n"); #endif #ifdef PHYTIME PhyML_Printf("%s\n\t--no_sequences%s\n",BOLD,FLAT); PhyML_Printf("\t\tUse this option to run the sampler without sequence data.\n"); PhyML_Printf("\n"); #endif #ifdef PHYTIME PhyML_Printf("%s\n\t--fastlk%s\n",BOLD,FLAT); PhyML_Printf("\t\tUse the multivariate normal approximation to the likelihood and speed up calculations\n"); PhyML_Printf("\n"); #endif #ifdef PHYML PhyML_Printf("%sPHYLIP-LIKE INTERFACE\n""%s\n\tYou can also use PhyML with no argument, in this case change the value of\n",BOLD,FLAT); PhyML_Printf("%s\ta parameter by typing its corresponding character as shown on screen.\n\n",FLAT); #endif #ifdef PHYML PhyML_Printf("%sEXAMPLES\n\n" "%s\tDNA interleaved sequence file, default parameters : ""%s ./phyml -i seqs1" "%s\n\tAA interleaved sequence file, default parameters : ""%s ./phyml -i seqs2 -d aa" "%s\n\tAA sequential sequence file, with customization : ""%s ./phyml -i seqs3 -q -d aa -m JTT -c 4 -a e%s\n",BOLD,FLAT,BOLD,FLAT,BOLD,FLAT,BOLD,FLAT); #endif Exit(""); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phyml-3.3.20220408/src/help.h000066400000000000000000000007351422404706600153120ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include #ifndef OPTIONS_H #define OPTIONS_H #include "utilities.h" #include "cl.h" #include "models.h" #include "free.h" #include "interface.h" void Usage(); #endif phyml-3.3.20220408/src/init.c000066400000000000000000005615111422404706600153240ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include "init.h" #ifdef BEAGLE #include "beagle_utils.h" #endif void Init_Eigen_Struct(eigen *this) { this->next = NULL; this->prev = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Scalar_Dbl(scalar_dbl *p) { p->v = -1.; p->onoff = ON; p->next = NULL; p->prev = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Scalar_Int(scalar_int *p) { p->v = -1.; p->next = NULL; p->prev = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Vect_Dbl(int len, vect_dbl *p) { p->len = len; p->next = NULL; p->prev = NULL; p->v = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Vect_Int(int len, vect_int *p) { p->len = len; p->next = NULL; p->prev = NULL; p->v = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_String(t_string *ts) { ts->len = -1.; ts->next = NULL; ts->prev = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Efrq(phydbl *b_frq, t_efrq *f) { f->next = NULL; f->prev = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Tree(t_tree *tree, int n_otu) { tree->n_otu = n_otu; tree->mat = NULL; tree->n_root = NULL; tree->e_root = NULL; tree->ps_tree = NULL; tree->short_l = NULL; tree->mutmap = NULL; tree->next = NULL; tree->prev = NULL; tree->next = NULL; tree->prev = NULL; tree->mixt_tree = NULL; tree->geo = NULL; tree->xml_root = NULL; tree->aux_tree = NULL; tree->verbose = VL3; tree->edge_list = NULL; tree->node_list = NULL; tree->is_mixt_tree = NO; tree->tree_num = 0; tree->depth_curr_path = 0; tree->has_bip = NO; tree->n_moves = 0; tree->bl_from_node_stamps = 0; tree->lock_topo = NO; tree->ps_page_number = 0; tree->init_lnL = UNLIKELY; tree->best_lnL = UNLIKELY; tree->old_lnL = UNLIKELY; tree->c_lnL = UNLIKELY; tree->p_lnL = UNLIKELY; tree->sum_min_sum_scale = .0; tree->n_swap = 0; tree->best_pars = 1E+5; tree->n_pattern = -1; tree->n_root_pos = -1.; tree->print_labels = YES; tree->write_br_lens = YES; tree->num_curr_branch_available = 0; tree->tip_order_score = .0; tree->write_tax_names = YES; tree->update_alias_subpatt = NO; tree->bl_ndigits = 8; tree->n_short_l = 100; tree->norm_scale = 0.0; tree->br_len_recorded = NO; tree->apply_lk_scaling = YES; tree->dp = 0; tree->ignore_root = YES; tree->ignore_mixt_info = NO; tree->annealing_temp = 0.; tree->both_sides = NO; tree->json_num = 0; tree->update_eigen_lr = NO; tree->use_eigen_lr = NO; tree->eval_alnL = YES; tree->eval_rlnL = YES; tree->eval_glnL = YES; tree->scaling_method = NO; tree->perform_spr_right_away = YES; tree->tip_root = 0; tree->n_edges_traversed = 0; tree->fully_nni_opt = NO; tree->n_tot_bl_opt = 0; tree->numerical_warning = NO; #ifdef BEAGLE tree->b_inst = UNINITIALIZED; #endif } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Edge_Light(t_edge *b, int num) { b->num = num; b->bip_score = 0; b->tdist_score = .0; b->dist_btw_edges = .0; b->topo_dist_btw_edges = 0; b->has_zero_br_len = NO; b->n_jumps = 0; b->l_var->v = -1.; b->does_exist = YES; b->l->v = -1.; b->bin_cod_num = -1.; b->l->onoff = ON; b->next = NULL; b->prev = NULL; b->next_mixt = NULL; b->prev_mixt = NULL; b->p_lk_left = NULL; b->p_lk_rght = NULL; b->p_lk_loc_left = NULL; b->p_lk_loc_rght = NULL; b->Pij_rr = NULL; b->label = NULL; b->pars_l = NULL; b->pars_r = NULL; b->ui_l = NULL; b->ui_r = NULL; b->p_pars_l = NULL; b->p_pars_r = NULL; b->n_diff_states_l = NULL; b->n_diff_states_r = NULL; b->update_partial_lk_left = YES; b->update_partial_lk_rght = YES; #ifdef BEAGLE b->p_lk_left_idx = num; b->p_lk_rght_idx = UNINITIALIZED; //Will be initialized later when the total number of branches is known (i.e. in Make_Tree_From_Scratch()) b->Pij_rr_idx = num; b->p_lk_tip_idx = UNINITIALIZED; //Will be initialized later only if this branch is connected to a tip #endif } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Node_Light(t_node *n, int num) { n->num = num; n->tax = -1; n->dist_to_root = .0; n->common = 1; n->ext_node = NULL; n->name = NULL; n->ori_name = NULL; n->c_seq = NULL; n->c_seq_anc = NULL; n->y_rank = 0.; n->y_rank_ori = 0.; n->y_rank_max = 0.; n->y_rank_min = 0.; n->anc = NULL; n->b_anc = NULL; n->rank = 0; n->match_node = NULL; n->id_rank = 0; n->next = NULL; n->prev = NULL; n->n_cal = 0; n->ldsk = NULL; n->rk_next = NULL; n->rk_prev = NULL; n->label = NULL; /* n->next = NULL; */ /* n->prev = NULL; */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_NNI(t_nni *a_nni) { a_nni->left = NULL; a_nni->rght = NULL; a_nni->b = NULL; a_nni->init_l = NULL; a_nni->init_v = NULL; a_nni->init_lk = .0; a_nni->score = +1.0; a_nni->best_l = NULL; a_nni->best_v = NULL; a_nni->swap_node_v1 = NULL; a_nni->swap_node_v2 = NULL; a_nni->swap_node_v3 = NULL; a_nni->swap_node_v4 = NULL; a_nni->lk0 = UNLIKELY; a_nni->lk1 = UNLIKELY; a_nni->lk2 = UNLIKELY; a_nni->l0 = NULL; a_nni->l1 = NULL; a_nni->l2 = NULL; a_nni->v0 = NULL; a_nni->v1 = NULL; a_nni->v2 = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Nexus_Format(nexcom **com) { /*****************************/ strcpy(com[0]->name,"dimensions"); com[0]->nparm = 2; com[0]->nxt_token_t = NEXUS_PARM; com[0]->cur_token_t = NEXUS_COM; com[0]->parm[0] = Make_Nexus_Parm(); strcpy(com[0]->parm[0]->name,"ntax"); com[0]->parm[0]->fp = Read_Nexus_Dimensions; com[0]->parm[0]->com = com[0]; com[0]->parm[0]->nxt_token_t = NEXUS_EQUAL; com[0]->parm[0]->cur_token_t = NEXUS_PARM; com[0]->parm[1] = Make_Nexus_Parm(); strcpy(com[0]->parm[1]->name,"nchar"); com[0]->parm[1]->fp = Read_Nexus_Dimensions; com[0]->parm[1]->com = com[0]; com[0]->parm[1]->nxt_token_t = NEXUS_EQUAL; com[0]->parm[1]->cur_token_t = NEXUS_PARM; /*****************************/ strcpy(com[1]->name,"format"); com[1]->nparm = 11; com[1]->nxt_token_t = NEXUS_PARM; com[1]->cur_token_t = NEXUS_COM; com[1]->parm[0] = Make_Nexus_Parm(); strcpy(com[1]->parm[0]->name,"datatype"); com[1]->parm[0]->fp = Read_Nexus_Format; com[1]->parm[0]->com = com[1]; com[1]->parm[0]->nxt_token_t = NEXUS_EQUAL; com[1]->parm[0]->cur_token_t = NEXUS_PARM; com[1]->parm[1] = Make_Nexus_Parm(); strcpy(com[1]->parm[1]->name,"respectcase"); com[1]->parm[1]->fp = Read_Nexus_Format; com[1]->parm[1]->com = com[1]; com[1]->parm[1]->nxt_token_t = NEXUS_PARM; com[1]->parm[1]->cur_token_t = NEXUS_VALUE; com[1]->parm[2] = Make_Nexus_Parm(); strcpy(com[1]->parm[2]->name,"missing"); com[1]->parm[2]->fp = Read_Nexus_Format; com[1]->parm[2]->com = com[1]; com[1]->parm[2]->nxt_token_t = NEXUS_EQUAL; com[1]->parm[2]->cur_token_t = NEXUS_PARM; com[1]->parm[3] = Make_Nexus_Parm(); strcpy(com[1]->parm[3]->name,"gap"); com[1]->parm[3]->fp = Read_Nexus_Format; com[1]->parm[3]->com = com[1]; com[1]->parm[3]->nxt_token_t = NEXUS_EQUAL; com[1]->parm[3]->cur_token_t = NEXUS_PARM; com[1]->parm[4] = Make_Nexus_Parm(); strcpy(com[1]->parm[4]->name,"symbols"); com[1]->parm[4]->fp = Read_Nexus_Format; com[1]->parm[4]->com = com[1]; com[1]->parm[4]->nxt_token_t = NEXUS_EQUAL; com[1]->parm[4]->cur_token_t = NEXUS_PARM; com[1]->parm[5] = Make_Nexus_Parm(); strcpy(com[1]->parm[5]->name,"equate"); com[1]->parm[5]->fp = Read_Nexus_Format; com[1]->parm[5]->com = com[1]; com[1]->parm[5]->nxt_token_t = NEXUS_EQUAL; com[1]->parm[5]->cur_token_t = NEXUS_PARM; com[1]->parm[6] = Make_Nexus_Parm(); strcpy(com[1]->parm[6]->name,"matchchar"); com[1]->parm[6]->fp = Read_Nexus_Format; com[1]->parm[6]->com = com[1]; com[1]->parm[6]->nxt_token_t = NEXUS_EQUAL; com[1]->parm[6]->cur_token_t = NEXUS_PARM; com[1]->parm[7] = Make_Nexus_Parm(); strcpy(com[1]->parm[7]->name,"transpose"); com[1]->parm[7]->fp = Read_Nexus_Format; com[1]->parm[7]->com = com[1]; com[1]->parm[7]->nxt_token_t = NEXUS_PARM; com[1]->parm[7]->cur_token_t = NEXUS_VALUE; com[1]->parm[8] = Make_Nexus_Parm(); strcpy(com[1]->parm[8]->name,"interleave"); com[1]->parm[8]->fp = Read_Nexus_Format; com[1]->parm[8]->com = com[1]; com[1]->parm[8]->nxt_token_t = NEXUS_PARM; com[1]->parm[8]->cur_token_t = NEXUS_VALUE; com[1]->parm[9] = Make_Nexus_Parm(); strcpy(com[1]->parm[9]->name,"items"); com[1]->parm[9]->fp = Read_Nexus_Format; com[1]->parm[9]->com = com[1]; com[1]->parm[9]->nxt_token_t = NEXUS_EQUAL; com[1]->parm[9]->cur_token_t = NEXUS_PARM; com[1]->parm[10] = Make_Nexus_Parm(); strcpy(com[1]->parm[10]->name,"statesformat"); com[1]->parm[10]->fp = Read_Nexus_Format; com[1]->parm[10]->com = com[1]; com[1]->parm[10]->nxt_token_t = NEXUS_EQUAL; com[1]->parm[10]->cur_token_t = NEXUS_PARM; /*****************************/ strcpy(com[2]->name,"eliminate"); com[2]->nparm = 0; com[2]->nxt_token_t = NEXUS_VALUE; com[2]->cur_token_t = NEXUS_COM; /*****************************/ strcpy(com[3]->name,"taxlabels"); com[3]->nparm = 0; com[3]->nxt_token_t = -1; com[3]->cur_token_t = -1; /*****************************/ strcpy(com[4]->name,"charstatelabels"); com[4]->nparm = 0; com[4]->nxt_token_t = -1; com[4]->cur_token_t = -1; /*****************************/ strcpy(com[5]->name,"charlabels"); com[5]->nparm = 0; com[5]->nxt_token_t = -1; com[5]->cur_token_t = -1; /*****************************/ strcpy(com[6]->name,"statelabels"); com[6]->nparm = 0; com[6]->nxt_token_t = -1; com[6]->cur_token_t = -1; /*****************************/ strcpy(com[7]->name,"matrix"); com[7]->nparm = 1; com[7]->nxt_token_t = NEXUS_COM; com[7]->cur_token_t = NEXUS_VALUE; /* This will allow us to skip directly to the matrix reading function */ com[7]->parm[0] = Make_Nexus_Parm(); strcpy(com[7]->parm[0]->name,"matrix"); com[7]->parm[0]->fp = Read_Nexus_Matrix; com[7]->parm[0]->com = com[7]; com[7]->parm[0]->nxt_token_t = NEXUS_COM; com[7]->parm[0]->cur_token_t = -1; /*****************************/ strcpy(com[8]->name,"begin"); com[8]->nparm = 3; com[8]->nxt_token_t = NEXUS_PARM; com[8]->cur_token_t = NEXUS_COM; com[8]->parm[0] = Make_Nexus_Parm(); strcpy(com[8]->parm[0]->name,"data"); com[8]->parm[0]->fp = Read_Nexus_Begin; com[8]->parm[0]->com = com[8]; com[8]->parm[0]->nxt_token_t = NEXUS_COM; com[8]->parm[0]->cur_token_t = NEXUS_PARM; com[8]->parm[1] = Make_Nexus_Parm(); strcpy(com[8]->parm[1]->name,"trees"); com[8]->parm[1]->fp = Read_Nexus_Begin; com[8]->parm[1]->com = com[8]; com[8]->parm[1]->nxt_token_t = NEXUS_COM; com[8]->parm[1]->cur_token_t = NEXUS_PARM; com[8]->parm[2] = Make_Nexus_Parm(); strcpy(com[8]->parm[2]->name,"taxa"); com[8]->parm[2]->fp = Read_Nexus_Taxa; com[8]->parm[2]->com = com[8]; com[8]->parm[2]->nxt_token_t = NEXUS_COM; com[8]->parm[2]->cur_token_t = NEXUS_VALUE; /*****************************/ strcpy(com[9]->name,"end"); com[9]->nparm = 0; com[9]->nxt_token_t = -1; com[9]->cur_token_t = -1; /*****************************/ strcpy(com[10]->name,"translate"); com[10]->nparm = 1; com[10]->nxt_token_t = NEXUS_COM; com[10]->cur_token_t = NEXUS_VALUE; com[10]->parm[0] = Make_Nexus_Parm(); strcpy(com[10]->parm[0]->name,"translate"); com[10]->parm[0]->fp = Read_Nexus_Translate; com[10]->parm[0]->com = com[10]; com[10]->parm[0]->nxt_token_t = NEXUS_COM; com[10]->parm[0]->cur_token_t = -1; /*****************************/ strcpy(com[11]->name,"tree"); com[11]->nparm = 1; com[11]->nxt_token_t = NEXUS_COM; com[11]->cur_token_t = NEXUS_VALUE; com[11]->parm[0] = Make_Nexus_Parm(); strcpy(com[11]->parm[0]->name,"tree"); com[11]->parm[0]->fp = Read_Nexus_Tree; com[11]->parm[0]->com = com[11]; com[11]->parm[0]->nxt_token_t = -1; com[11]->parm[0]->cur_token_t = -1; /*****************************/ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Mat(matrix *mat, calign *data) { int i; mat->n_otu = data->n_otu; mat->r = mat->n_otu; mat->curr_int = mat->n_otu; mat->method = 1; for(i=0;in_otu;i++) { strcpy(mat->name[i],data->c_seq[i]->name); mat->on_off[i] = 1; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Set_Defaults_Input(option* io) { io->fp_in_align = NULL; io->fp_in_tree = NULL; io->fp_in_constraint_tree = NULL; io->fp_out_tree = NULL; io->fp_out_trees = NULL; io->fp_out_boot_tree = NULL; io->fp_out_boot_stats = NULL; io->fp_out_stats = NULL; io->fp_out_ancestral_seq = NULL; io->fp_out_ancestral_tree = NULL; io->fp_in_coord = NULL; io->fp_out_trace = NULL; io->fp_weight_file = NULL; io->fp_out_json_trace = NULL; io->fp_out_lk = NULL; io->fp_out_trace = NULL; io->long_tax_names = NULL; io->short_tax_names = NULL; io->lon = NULL; io->lat = NULL; io->z_scores = NULL; io->cstr_tree = NULL; io->next = NULL; io->prev = NULL; io->tree = NULL; io->mod = NULL; strcpy(io->nt_or_cd,"nucleotides"); io->tbe_bootstrap = NO; io->n_data_sets = 1; io->interleaved = 1; io->in_tree = 0; io->out_tree_file_open_mode = 1; io->out_stats_file_open_mode = 1; io->init_len = -1; io->n_otu = -1; io->n_data_set_asked = -1; io->print_boot_trees = 1; io->n_part = 1; io->ratio_test = ABAYES; io->multigene = 0; io->config_multigene = 0; io->curr_interface = 0; io->r_seed = -1; io->collapse_boot = 0; io->random_boot_seq_order = YES; io->print_trace = NO; io->print_json_trace = NO; io->print_site_lnl = NO; io->m4_model = NO; io->rm_ambigu = NO; io->append_run_ID = NO; io->quiet = NO; io->datatype = NT; io->colalias = YES; io->data_file_format = PHYLIP; io->tree_file_format = PHYLIP; io->boot_prog_every = 20; io->mem_question = YES; io->do_alias_subpatt = NO; io->lk_approx = EXACT; io->codpos = -1; io->mutmap = NO; io->state_len = 1; io->ancestral = NO; io->use_xml = NO; io->has_io_weights = NO; io->do_boot = NO; io->do_alrt = YES; io->do_tbe = NO; io->print_node_num = NO; io->print_support_val = NO; io->n_boot_replicates = 0; io->print_mat_and_exit = NO; #ifdef BEAGLE io->beagle_resource = 0; #endif io->precision = 0; MCMC_Init_MCMC_Struct(NULL,io,io->mcmc); RATES_Init_Rate_Struct(io->rates,NULL,-1); io->rates->model_id = LOGNORMAL; TIMES_Init_Time_Struct(io->times,NULL,-1); io->times->model_id = COALESCENT; io->times->coalescent_model_id = STRICTCOALESCENT; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Rmat(t_rmat *rmat) { rmat->n_diff_rr = 1; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Set_Defaults_Model(t_mod *mod) { Set_Defaults_Ras(mod->ras); strcpy(mod->modelname->s,"HKY85"); strcpy(mod->custom_mod_string->s,"000000"); mod->next = NULL; mod->prev = NULL; mod->next_mixt = NULL; mod->prev_mixt = NULL; mod->r_mat = NULL; mod->e_frq = NULL; mod->whichmodel = HKY85; mod->n_mixt_classes = 0; mod->mod_num = 0; mod->update_eigen = NO; mod->is_mixt_mod = NO; mod->kappa->v = 4.0; mod->lambda->v = 1.0; mod->l_var_sigma = 1.E-2; mod->e_frq_weight->v = 1.0; mod->r_mat_weight->v = 1.0; mod->ns = 4; mod->use_m4mod = NO; mod->ras->gamma_median = NO; mod->m4mod = NULL; /* mod->r_mat->n_diff_rr = 0; */ /* mod->r_mat->rr = NULL; */ /* mod->r_mat->rr_val = NULL; */ /* mod->r_mat->n_rr_per_cat = NULL; */ mod->io = NULL; mod->log_l = NO; mod->gamma_mgf_bl = NO; mod->br_len_mult->v = 1.0; #if !(defined PHYTIME || defined PHYREX) mod->l_min = 1.E-8; mod->l_max = 100.0; #else mod->l_min = 1.E-8; mod->l_max = 1.E+3; #endif mod->l_var_min = mod->l_min; mod->l_var_max = mod->l_max; mod->br_len_mult->v = 1.0; mod->br_len_mult_unscaled->v = 1.0; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Set_Defaults_Ras(t_ras *ras) { ras->n_catg = 4; ras->normalise_rr = YES; ras->pinvar->v = 0.0; ras->alpha->v = 1.0; ras->invar = NO; ras->free_mixt_rates = NO; ras->parent_class_number = 0; ras->init_r_proba = YES; ras->init_rr = YES; ras->sort_rate_classes = NO; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Set_Defaults_Optimiz(t_opt *s_opt) { s_opt->last_opt = YES; s_opt->opt_subst_param = YES; s_opt->opt_alpha = YES; s_opt->opt_kappa = YES; s_opt->opt_bl = YES; s_opt->opt_lambda = NO; s_opt->opt_pinvar = NO; s_opt->opt_cov_delta = NO; s_opt->opt_cov_alpha = NO; s_opt->opt_cov_free_rates = NO; s_opt->opt_rr = NO; s_opt->init_lk = UNLIKELY; s_opt->n_it_max = 1000; s_opt->opt_topo = YES; s_opt->topo_search = NNI_MOVE; s_opt->random_input_tree = 0; s_opt->n_rand_starts = 5; s_opt->brent_it_max = BRENT_IT_MAX; s_opt->steph_spr = YES; s_opt->opt_br_len_mult = NO; s_opt->min_n_triple_moves = 20; s_opt->max_rank_triple_move = 0; s_opt->n_improvements = 0; s_opt->max_spr_depth = 0; s_opt->opt_clock_r = YES; s_opt->min_diff_lk_local = 1.E-03; s_opt->min_diff_lk_global = 1.E-03; s_opt->min_diff_lk_move = 1.E-03; s_opt->p_moves_to_examine = 0.15; s_opt->fast_nni = NO; s_opt->greedy = NO; s_opt->general_pars = NO; s_opt->tree_size_mult = 1; s_opt->opt_five_branch = YES; s_opt->nni_br_len_opt = YES; s_opt->pars_thresh = 5; s_opt->hybrid_thresh = NO; s_opt->quickdirty = NO; s_opt->spr_pars = YES; s_opt->spr_lnL = NO; s_opt->min_depth_path = 0; s_opt->eval_list_regraft = NO; s_opt->max_depth_path = 2000; s_opt->deepest_path = 2000; s_opt->max_delta_lnL_spr = 2000000.; s_opt->max_delta_lnL_spr_current = 0.0; s_opt->worst_lnL_spr = BIG; s_opt->br_len_in_spr = 10; s_opt->opt_free_mixt_rates = YES; s_opt->constrained_br_len = NO; s_opt->opt_gamma_br_len = NO; s_opt->first_opt_free_mixt_rates = YES; s_opt->wim_n_rgrft = -1; s_opt->wim_n_globl = -1; s_opt->wim_max_dist = -1; s_opt->wim_n_optim = -1; s_opt->wim_n_best = -1; s_opt->wim_inside_opt = 0; s_opt->opt_rmat_weight = NO; s_opt->opt_efrq_weight = NO; s_opt->skip_tree_traversal = NO; s_opt->serial_free_rates = YES; s_opt->curr_opt_free_rates = NO; s_opt->state_freq = EMPIRICAL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void XML_Init_Attribute(xml_attr *attr) { } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void XML_Init_Node(xml_node *parent, xml_node *new_node, char *name) { if(name) strcpy(new_node->name,name); new_node->parent = parent ? parent : NULL; new_node->next = NULL; new_node->prev = NULL; new_node->child = NULL; new_node->ds->obj = NULL; new_node->ds->next = NULL; if(parent) { if(!parent->child) { parent->child = new_node; } else { xml_node *node = parent->child; while(node->next) node = node->next; node->next = new_node; new_node->prev = node; } } new_node->attr = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void RATES_Init_Rate_Struct(t_rate *rates, t_rate *existing_rates, int n_otu) { int i; if(existing_rates && existing_rates->model_id != -1) { rates->model_id = existing_rates->model_id; } else { rates->model_id = NONE; } rates->met_within_gibbs = NO; rates->c_lnL = UNLIKELY; rates->p_lnL = UNLIKELY; rates->c_lnP = UNLIKELY; rates->p_lnP = UNLIKELY; rates->adjust_rates = 0; rates->use_rates = 1; rates->lexp = 1.E-3; rates->norm_fact = 1.0; rates->inflate_var = 1.0; rates->br_r_recorded = NO; /* Important to start from high subst. rate to ``reveal'' time structure */ /* Low rate, and thus old root, makes serially sampled data look like */ /* tips all have the same age. */ rates->clock_r = 1.E-1; rates->min_clock = 1.E-8; rates->max_clock = 1.E+2; rates->clock_r_has_prior = NO; rates->clock_r_prior_mean = 1.0E-1; rates->clock_r_prior_var = 1.0E-8; rates->max_rate = 1.E+1; rates->min_rate = 1.E-1; /* rates->max_rate = 5.; */ /* rates->min_rate = 0.2; */ rates->nu = 1.0; rates->min_nu = 0.0; rates->max_nu = 1.0E+3; rates->autocor_rate_prior = 1.0; rates->min_dt = 0.0; rates->step_rate = 1.E-4; rates->approx = 1; rates->bl_from_rt = NO; rates->update_mean_l = NO; rates->update_cov_l = NO; rates->p_max = 0.01; if(n_otu > 0) { for(i=0;i<(2*n_otu-2)*(2*n_otu-2);++i) rates->cov_l[i] = 0.0; for(i=0;i<2*n_otu-2;++i) { rates->mean_r[i] = 1.0; rates->mean_l[i] = 0.0; rates->cur_l[i] = 0.01; } for(i=0;i<2*n_otu-1;++i) { rates->nd_r[i] = 1.0; rates->br_r[i] = 1.0; rates->br_do_updt[i] = YES; } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void TIMES_Init_Time_Struct(t_time *times, t_time *existing_times, int n_otu) { if(existing_times && existing_times->model_id != -1) { times->model_id = existing_times->model_id; } else { times->model_id = COALESCENT; times->coalescent_model_id = STRICTCOALESCENT; } times->scaled_pop_size = 1.E+2; times->scaled_pop_size_min = 1.E-1; times->scaled_pop_size_max = 1.E+3; times->neff_growth = 1E-6; times->neff_growth_min = -5.; times->neff_growth_max = +5.; times->c_lnL = UNLIKELY; times->p_lnL = UNLIKELY; times->c_lnP = UNLIKELY; times->p_lnP = UNLIKELY; times->c_lnL_jps = UNLIKELY; times->nd_t_recorded = NO; times->birth_rate = 1.E-1; times->birth_rate_min = 1.E-6; times->birth_rate_max = 1.E+0; times->birth_rate_pivot = 1.E-1; times->death_rate = 1.E-1; times->death_rate_min = 1.E-6; times->death_rate_min = 1.E+0; times->death_rate_pivot = 1.E-1; if(n_otu > 0) { for(int i=0;i<2*n_otu-2;++i) { times->n_jps[i] = -1; times->t_jps[i] = -1; } for(int i=0;i<2*n_otu-1;++i) { times->mean_t[i] = 0.0; times->nd_t[i] = 0.0; times->true_t[i] = 0.0; if(i < n_otu) { times->t_has_prior[i] = YES; times->t_prior_max[i] = 0.0; times->t_prior_min[i] = 0.0; } else { times->t_has_prior[i] = NO; times->t_prior_max[i] = 0.0; times->t_prior_min[i] = -BIG; } times->has_survived[i] = NO; times->t_rank[i] = i; } } times->update_time_norm_const = NO; times->is_asynchronous = NO; times->augmented_coalescent = NO; times->neff_prior_mean = 1.0; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_One_Spr(t_spr *a_spr) { a_spr->lnL = UNLIKELY; a_spr->pars = 1E+5; a_spr->depth_path = 0; a_spr->dist = 0; a_spr->init_target_l = NULL; a_spr->init_target_v = NULL; a_spr->l0 = NULL; a_spr->l1 = NULL; a_spr->l2 = NULL; a_spr->v0 = NULL; a_spr->v1 = NULL; a_spr->v2 = NULL; a_spr->n_link = NULL; a_spr->n_opp_to_link = NULL; a_spr->b_opp_to_link = NULL; a_spr->b_target = NULL; a_spr->b_init_target = NULL; a_spr->next = NULL; a_spr->prev = NULL; a_spr->next = NULL; a_spr->prev = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Target_Tip(t_clad *clade, t_tree *tree) { int i,j; for(i=0;in_tax;++i) { for(j=0;jn_otu;++j) { if(!strcmp(tree->a_nodes[j]->name,clade->tax_list[i])) { clade->tip_list[i] = tree->a_nodes[j]; break; } } assert(j != tree->n_otu); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Model(calign *data, t_mod *mod, option *io) { int i,j; phydbl sum,aux; phydbl *dr, *di, *space; assert(data); assert(mod); assert(io); #ifdef BEAGLE mod->b_inst = UNINITIALIZED; //prevents calling an uninitialized BEAGLE instance (for ex: prevents Update_Eigen(), Update_RAS(), from calling BEAGLE) mod->optimizing_topology = false; #endif mod->ns = io->mod->ns; if(io->datatype == GENERIC) mod->whichmodel = JC69; dr = (phydbl *)mCalloc( mod->ns,sizeof(phydbl)); di = (phydbl *)mCalloc( mod->ns,sizeof(phydbl)); space = (phydbl *)mCalloc(2*mod->ns,sizeof(phydbl)); if(mod->log_l == YES) { mod->l_min = log(mod->l_min); mod->l_max = log(mod->l_max); } // Init unscaled relative rate frequencies if(mod->ras->init_r_proba == YES) { /* for(i=0;iras->n_catg;i++) mod->ras->gamma_r_proba->v[i] = (phydbl)1./mod->ras->n_catg; */ /* for(i=0;iras->n_catg;i++) mod->ras->gamma_r_proba_unscaled->v[i] = (phydbl)(i+1); */ for(i=0;iras->n_catg;i++) mod->ras->gamma_r_proba_unscaled->v[i] = 1.0; } else { mod->ras->gamma_r_proba_unscaled->v[mod->ras->n_catg-1] = 1.0; for(i=0;iras->n_catg;i++) { sum = 0.0; for(j=0;jras->gamma_r_proba->v[j]; mod->ras->gamma_r_proba_unscaled->v[i] = sum * mod->ras->gamma_r_proba_unscaled->v[mod->ras->n_catg-1]; } } // Init unscaled relative rates if(mod->ras->init_rr == YES) { if(mod->ras->n_catg > 1) { /* for(i=0;iras->n_catg;i++) mod->ras->gamma_rr->v[i] = (phydbl)i; */ /* for(i=0;iras->n_catg;i++) mod->ras->gamma_rr_unscaled->v[i] = (phydbl)i; */ for(i=0;iras->n_catg;i++) mod->ras->gamma_rr_unscaled->v[i] = 1.0 + (phydbl)i/10.; } else { mod->ras->gamma_rr->v[0] = 1.0; mod->ras->gamma_rr_unscaled->v[0] = 1.0; } } else { for(i=0;iras->n_catg;i++) mod->ras->gamma_rr_unscaled->v[i] = mod->ras->gamma_rr->v[i]; } if(io->datatype == NT) { /* Set the substitution parameters to their default values if they are not fixed by the user */ if(mod->s_opt->opt_kappa == YES) { mod->kappa->v = 4.0; mod->lambda->v = 1.0; } if(mod->whichmodel == CUSTOM) { for(i=0;i<6;i++) { mod->r_mat->rr_val->v[i] = log(1.0); /* mod->r_mat->rr_val->v[i] = exp(1.0); */ mod->r_mat->rr->v[i] = 1.0; } /* Condition below is true if custom model corresponds to TN93 or K80 */ if(mod->r_mat->rr_num->v[AC] == mod->r_mat->rr_num->v[AT] && mod->r_mat->rr_num->v[AT] == mod->r_mat->rr_num->v[CG] && mod->r_mat->rr_num->v[CG] == mod->r_mat->rr_num->v[GT] && mod->r_mat->rr_num->v[AG] != mod->r_mat->rr_num->v[AC] && mod->r_mat->rr_num->v[CT] != mod->r_mat->rr_num->v[AC]) { for(i=1;ir_mat->n_diff_rr;i++) { mod->r_mat->rr_val->v[i] = log(2.0); /* mod->r_mat->rr_val->v[i] = exp(2.0); */ mod->r_mat->rr->v[i] = 2.0; } } else if(mod->r_mat->n_diff_rr == 6) /* Custom <-> GTR model */ { mod->r_mat->rr_val->v[AG] = log(2.0); mod->r_mat->rr_val->v[CT] = log(2.0); /* mod->r_mat->rr_val->v[AG] = exp(2.0); */ /* mod->r_mat->rr_val->v[CT] = exp(2.0); */ mod->r_mat->rr->v[AG] = 2.0; mod->r_mat->rr->v[CT] = 2.0; } } else if(mod->whichmodel == GTR) { if(mod->s_opt->opt_rr == YES) { for(i=0;i<6;i++) mod->r_mat->rr_val->v[i] = log(1.0); /* for(i=0;i<6;i++) mod->r_mat->rr_val->v[i] = exp(1.0); */ for(i=0;i<6;i++) mod->r_mat->rr->v[i] = 1.0; mod->r_mat->rr_val->v[AG] = log(2.0); mod->r_mat->rr_val->v[CT] = log(2.0); /* mod->r_mat->rr_val->v[AG] = exp(2.0); */ /* mod->r_mat->rr_val->v[CT] = exp(2.0); */ mod->r_mat->rr->v[AG] = 2.0; mod->r_mat->rr->v[CT] = 2.0; } } } if(mod->s_opt->opt_alpha) mod->ras->alpha->v = 1.0; if(mod->s_opt->opt_pinvar) mod->ras->pinvar->v = 0.2; if(io->datatype == NT) /* Nucleotides */ { /* init for nucleotides */ for(i=0;ins;i++) mod->e_frq->pi->v[i] = 1./(phydbl)mod->ns; if(mod->whichmodel == JC69) { mod->e_frq->pi->v[0] = mod->e_frq->pi->v[1] = mod->e_frq->pi->v[2] = mod->e_frq->pi->v[3] = .25; mod->kappa->v = 1.; mod->s_opt->state_freq = MODEL; mod->s_opt->opt_kappa = NO; mod->s_opt->opt_lambda = NO; } if(mod->whichmodel == K80) { mod->e_frq->pi->v[0] = mod->e_frq->pi->v[1] = mod->e_frq->pi->v[2] = mod->e_frq->pi->v[3] = .25; mod->s_opt->state_freq = MODEL; mod->s_opt->opt_lambda = NO; } if(mod->whichmodel == F81) { if(mod->s_opt->state_freq == EMPIRICAL || mod->s_opt->state_freq == ML) Init_Efrqs_Using_Observed_Freqs(mod->e_frq,data->obs_state_frq,mod->ns); else if(mod->s_opt->state_freq == USER) for(i=0;i<4;i++) mod->e_frq->pi->v[i] = mod->e_frq->user_b_freq->v[i]; mod->kappa->v = 1.; } if(mod->whichmodel == F84) { if(mod->s_opt->state_freq == EMPIRICAL || mod->s_opt->state_freq == ML) Init_Efrqs_Using_Observed_Freqs(mod->e_frq,data->obs_state_frq,mod->ns); else if(mod->s_opt->state_freq == USER) for(i=0;i<4;i++) mod->e_frq->pi->v[i] = mod->e_frq->user_b_freq->v[i]; else { PhyML_Fprintf(stderr,"\n. The F84 model is not compatible with the '-f m' option (use '-f e' or '-f o' instead. See the documentation).\n"); assert(false); } aux = ((mod->e_frq->pi->v[0]+mod->e_frq->pi->v[2])-(mod->e_frq->pi->v[1]+mod->e_frq->pi->v[3]))/(2.*mod->kappa->v); mod->lambda->v = ((mod->e_frq->pi->v[1]+mod->e_frq->pi->v[3]) + aux)/((mod->e_frq->pi->v[0]+mod->e_frq->pi->v[2]) - aux); } if(mod->whichmodel == TN93) { if(mod->s_opt->state_freq == EMPIRICAL || mod->s_opt->state_freq == ML) Init_Efrqs_Using_Observed_Freqs(mod->e_frq,data->obs_state_frq,mod->ns); else if(mod->s_opt->state_freq == USER) for(i=0;i<4;i++) mod->e_frq->pi->v[i] = mod->e_frq->user_b_freq->v[i]; else { PhyML_Fprintf(stderr,"\n. The TN93 model is not compatible with the '-f m' option (use '-f e' or '-f o' instead. See the documentation).\n"); assert(false); } if(io->mod->s_opt->opt_kappa) io->mod->s_opt->opt_lambda = YES; } if(mod->whichmodel == HKY85) { if(mod->s_opt->state_freq == EMPIRICAL || mod->s_opt->state_freq == ML) Init_Efrqs_Using_Observed_Freqs(mod->e_frq,data->obs_state_frq,mod->ns); else if(mod->s_opt->state_freq == USER) for(i=0;i<4;i++) mod->e_frq->pi->v[i] = mod->e_frq->user_b_freq->v[i]; else { PhyML_Fprintf(stderr,"\n. The HKY85 model is not compatible with the '-f m' option (use '-f e' or '-f o' instead. See the documentation).\n"); assert(false); } } if(mod->whichmodel == GTR) { if(mod->s_opt->state_freq == EMPIRICAL || mod->s_opt->state_freq == ML) Init_Efrqs_Using_Observed_Freqs(mod->e_frq,data->obs_state_frq,mod->ns); else if(mod->s_opt->state_freq == USER) for(i=0;i<4;i++) mod->e_frq->pi->v[i] = mod->e_frq->user_b_freq->v[i]; else { PhyML_Fprintf(stderr,"\n. The GTR model is not compatible with the '-f m' option (use '-f e' or '-f o' instead. See the documentation).\n"); assert(false); } mod->kappa->v = 1.; mod->custom_mod_string->s[0] = '0'; mod->custom_mod_string->s[1] = '1'; mod->custom_mod_string->s[2] = '2'; mod->custom_mod_string->s[3] = '3'; mod->custom_mod_string->s[4] = '4'; mod->custom_mod_string->s[5] = '5'; Translate_Custom_Mod_String(mod); } if(mod->whichmodel == CUSTOM) { if(mod->s_opt->state_freq == EMPIRICAL) Init_Efrqs_Using_Observed_Freqs(mod->e_frq,data->obs_state_frq,mod->ns); else for(i=0;i<4;i++) mod->e_frq->pi->v[i] = mod->e_frq->user_b_freq->v[i]; mod->kappa->v = 1.; } for(i=0;ins;i++) mod->e_frq->pi_unscaled->v[i] = log(mod->e_frq->pi->v[i]); } else if(mod->io->datatype == AA) { /* init for amino-acids */ /* see comments of PMat_Empirical for details */ phydbl *qmat,*rr,*f; qmat = mod->r_mat->qmat->v; rr = mod->r_mat->rr_val->v; f = mod->e_frq->pi->v; for(i=0;ins*mod->ns;++i) qmat[i] = .0; for(i=0;ins;++i) f[i] = .0; switch(mod->whichmodel) { case DAYHOFF : { Init_Qmat_Dayhoff(qmat,f); break; } case JTT : { Init_Qmat_JTT(qmat,f); break; } case MTREV : { Init_Qmat_MtREV(qmat,f); break; } case LG : { Init_Qmat_LG(qmat,f); break; } case WAG : { Init_Qmat_WAG(qmat,f); break; } case DCMUT : { Init_Qmat_DCMut(qmat,f); break; } case RTREV : { Init_Qmat_RtREV(qmat,f); break; } case CPREV : { Init_Qmat_CpREV(qmat,f); break; } case VT : { Init_Qmat_VT(qmat,f); break; } case BLOSUM62 : { Init_Qmat_Blosum62(qmat,f); break; } case MTMAM : { Init_Qmat_MtMam(qmat,f); break; } case MTART : { Init_Qmat_MtArt(qmat,f); break; } case HIVW : { Init_Qmat_HIVw(qmat,f); break; } case HIVB : { Init_Qmat_HIVb(qmat,f); break; } case AB : { Init_Qmat_AB(qmat,f); break; } case CUSTOMAA : { mod->fp_aa_rate_mat = Openfile(mod->aa_rate_mat_file->s,READ); Read_Qmat(qmat,f,mod->fp_aa_rate_mat); fclose(mod->fp_aa_rate_mat); break; } case FLU : { Init_Qmat_FLU(qmat,f); break; } default : { Init_Qmat_LG(qmat,f); break; } } for(i=0;ins;i++) for(j=i+1;jns;j++) rr[MIN(i,j) * mod->ns + MAX(i,j) - (MIN(i,j)+1+(int)POW(MIN(i,j)+1,2))/2] = qmat[i*mod->ns+j]; if(mod->s_opt->state_freq == EMPIRICAL) Init_Efrqs_Using_Observed_Freqs(mod->e_frq,data->obs_state_frq,mod->ns); // Adjust equilibrium state frequencies if some of them are too close to zero // in order to avoid numerical precision issues. int iter = 0; do { for(i=0;ins;i++) if(mod->e_frq->pi->v[i] < E_FRQ_MIN) mod->e_frq->pi->v[i] = E_FRQ_MIN; sum = 0.0; for(i=0;ins;i++) sum += fabs(mod->e_frq->pi->v[i]); for(i=0;ins;i++) mod->e_frq->pi->v[i] /= sum; iter++; } while(iter < 10); for(i=0;ins;i++) mod->e_frq->pi_unscaled->v[i] = log(mod->e_frq->pi->v[i]); mod->mr->v = Update_Qmat_Generic(rr,f,mod->ns,qmat); } else if(mod->io->datatype == GENERIC) { /* Uniform state frequencies */ for(i=0;ins;i++) mod->e_frq->pi->v[i] = 1./(phydbl)mod->ns; mod->kappa->v = 1; mod->s_opt->state_freq = MODEL; mod->s_opt->opt_kappa = NO; mod->s_opt->opt_lambda = NO; for(i=0;ins*(mod->ns-1)/2;i++) mod->r_mat->rr_val->v[i] = log(1.0); /* for(i=0;ins*(mod->ns-1)/2;i++) mod->r_mat->rr_val->v[i] = exp(1.0); */ for(i=0;ins*(mod->ns-1)/2;i++) mod->r_mat->rr->v[i] = 1.0; } else { PhyML_Fprintf(stderr,"\n. Datatype not recognized.\n"); PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d\n",__FILE__,__LINE__); Warn_And_Exit(""); } Init_Eigen_Struct(mod->eigen); mod->update_eigen = YES; Update_Eigen(mod); if(mod->is_mixt_mod == YES) MIXT_Init_Model(mod); free(dr); free(di); free(space); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Efrqs_Using_Observed_Freqs(t_efrq *f, phydbl *o, int ns) { int i; phydbl eps,sum; assert(f); assert(o); // To avoid numerical prevision issues eps = 1.E-50; for(i=0;ipi->v[i] = MAX(o[i],eps); sum = 0.0; for(i=0;ipi->v[i]; for(i=0;ipi->v[i] /= sum;; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int Init_Qmat_Dayhoff(phydbl *daa, phydbl *pi) { /* Dayhoff's model data * Dayhoff, M.O., Schwartz, R.M., Orcutt, B.C. (1978) * "A model of evolutionary change in proteins." * Dayhoff, M.O.(ed.) Atlas of Protein Sequence Structur., Vol5, Suppl3. * National Biomedical Research Foundation, Washington DC, pp.345-352. */ int i,j,naa; naa = 20; /* PhyML_Printf("\n\n. REMINDER : THIS IS NOT DAYHOFF !!!\n\n"); */ /* daa[1*20 + 0] = 0.538903; */ /* daa[2*20 + 0] = 0.412504; */ /* daa[2*20 + 1] = 0.736081; */ /* daa[3*20 + 0] = 0.586915; */ /* daa[3*20 + 1] = 0.108051; */ /* daa[3*20 + 2] = 5.446642; */ /* daa[4*20 + 0] = 2.189718; */ /* daa[4*20 + 1] = 0.830604; */ /* daa[4*20 + 2] = 0.573426; */ /* daa[4*20 + 3] = 0.077565; */ /* daa[5*20 + 0] = 1.08213; */ /* daa[5*20 + 1] = 2.950693; */ /* daa[5*20 + 2] = 1.739514; */ /* daa[5*20 + 3] = 0.559035; */ /* daa[5*20 + 4] = 0.111314; */ /* daa[6*20 + 0] = 1.386865; */ /* daa[6*20 + 1] = 0.358434; */ /* daa[6*20 + 2] = 0.541447; */ /* daa[6*20 + 3] = 5.406871; */ /* daa[6*20 + 4] = 0.003738; */ /* daa[6*20 + 5] = 4.124043; */ /* daa[7*20 + 0] = 2.085747; */ /* daa[7*20 + 1] = 0.453132; */ /* daa[7*20 + 2] = 1.815844; */ /* daa[7*20 + 3] = 1.08647; */ /* daa[7*20 + 4] = 0.526418; */ /* daa[7*20 + 5] = 0.347693; */ /* daa[7*20 + 6] = 0.438476; */ /* daa[8*20 + 0] = 0.407898; */ /* daa[8*20 + 1] = 2.689322; */ /* daa[8*20 + 2] = 5.386808; */ /* daa[8*20 + 3] = 0.884563; */ /* daa[8*20 + 4] = 0.658583; */ /* daa[8*20 + 5] = 4.588358; */ /* daa[8*20 + 6] = 0.386218; */ /* daa[8*20 + 7] = 0.368668; */ /* daa[9*20 + 0] = 0.10177; */ /* daa[9*20 + 1] = 0.104875; */ /* daa[9*20 + 2] = 0.16239; */ /* daa[9*20 + 3] = 0.011698; */ /* daa[9*20 + 4] = 0.253282; */ /* daa[9*20 + 5] = 0.083872; */ /* daa[9*20 + 6] = 0.041767; */ /* daa[9*20 + 7] = 0.009778; */ /* daa[9*20 + 8] = 0.1042; */ /* daa[10*20 + 0] = 0.248202; */ /* daa[10*20 + 1] = 0.375742; */ /* daa[10*20 + 2] = 0.093863; */ /* daa[10*20 + 3] = 0.019241; */ /* daa[10*20 + 4] = 0.572688; */ /* daa[10*20 + 5] = 0.703538; */ /* daa[10*20 + 6] = 0.071961; */ /* daa[10*20 + 7] = 0.03006; */ /* daa[10*20 + 8] = 0.418222; */ /* daa[10*20 + 9] = 3.702051; */ /* daa[11*20 + 0] = 0.652019; */ /* daa[11*20 + 1] = 5.940478; */ /* daa[11*20 + 2] = 2.352253; */ /* daa[11*20 + 3] = 0.231001; */ /* daa[11*20 + 4] = 0.027995; */ /* daa[11*20 + 5] = 3.646743; */ /* daa[11*20 + 6] = 1.507981; */ /* daa[11*20 + 7] = 0.331175; */ /* daa[11*20 + 8] = 0.698362; */ /* daa[11*20 + 9] = 0.140326; */ /* daa[11*20 + 10] = 0.171396; */ /* daa[12*20 + 0] = 0.694226; */ /* daa[12*20 + 1] = 0.419899; */ /* daa[12*20 + 2] = 0.326927; */ /* daa[12*20 + 3] = 0.039488; */ /* daa[12*20 + 4] = 0.844827; */ /* daa[12*20 + 5] = 1.394214; */ /* daa[12*20 + 6] = 0.133235; */ /* daa[12*20 + 7] = 0.085075; */ /* daa[12*20 + 8] = 0.347092; */ /* daa[12*20 + 9] = 4.051255; */ /* daa[12*20 + 10] = 6.650794; */ /* daa[12*20 + 11] = 0.617549; */ /* daa[13*20 + 0] = 0.155206; */ /* daa[13*20 + 1] = 0.057971; */ /* daa[13*20 + 2] = 0.09816; */ /* daa[13*20 + 3] = 0.020441; */ /* daa[13*20 + 4] = 0.904305; */ /* daa[13*20 + 5] = 0.052719; */ /* daa[13*20 + 6] = 0.0219; */ /* daa[13*20 + 7] = 0.046668; */ /* daa[13*20 + 8] = 0.890005; */ /* daa[13*20 + 9] = 0.844963; */ /* daa[13*20 + 10] = 2.348881; */ /* daa[13*20 + 11] = 0.028372; */ /* daa[13*20 + 12] = 1.671635; */ /* daa[14*20 + 0] = 1.433475; */ /* daa[14*20 + 1] = 0.328393; */ /* daa[14*20 + 2] = 0.173181; */ /* daa[14*20 + 3] = 0.431874; */ /* daa[14*20 + 4] = 0.09902; */ /* daa[14*20 + 5] = 0.592324; */ /* daa[14*20 + 6] = 0.488352; */ /* daa[14*20 + 7] = 0.23865; */ /* daa[14*20 + 8] = 0.462856; */ /* daa[14*20 + 9] = 0.057048; */ /* daa[14*20 + 10] = 0.233532; */ /* daa[14*20 + 11] = 0.387808; */ /* daa[14*20 + 12] = 0.096377; */ /* daa[14*20 + 13] = 0.079912; */ /* daa[15*20 + 0] = 4.887126; */ /* daa[15*20 + 1] = 0.883923; */ /* daa[15*20 + 2] = 4.627163; */ /* daa[15*20 + 3] = 1.122164; */ /* daa[15*20 + 4] = 3.186667; */ /* daa[15*20 + 5] = 1.085947; */ /* daa[15*20 + 6] = 0.569339; */ /* daa[15*20 + 7] = 1.993432; */ /* daa[15*20 + 8] = 0.867972; */ /* daa[15*20 + 9] = 0.070512; */ /* daa[15*20 + 10] = 0.163009; */ /* daa[15*20 + 11] = 0.718913; */ /* daa[15*20 + 12] = 0.301103; */ /* daa[15*20 + 13] = 0.32579; */ /* daa[15*20 + 14] = 1.449582; */ /* daa[16*20 + 0] = 2.030538; */ /* daa[16*20 + 1] = 0.639463; */ /* daa[16*20 + 2] = 2.076294; */ /* daa[16*20 + 3] = 0.377239; */ /* daa[16*20 + 4] = 1.42848; */ /* daa[16*20 + 5] = 0.979403; */ /* daa[16*20 + 6] = 0.647562; */ /* daa[16*20 + 7] = 0.145556; */ /* daa[16*20 + 8] = 0.493329; */ /* daa[16*20 + 9] = 0.973405; */ /* daa[16*20 + 10] = 0.271824; */ /* daa[16*20 + 11] = 1.20033; */ /* daa[16*20 + 12] = 1.659187; */ /* daa[16*20 + 13] = 0.1217; */ /* daa[16*20 + 14] = 0.571399; */ /* daa[16*20 + 15] = 6.641034; */ /* daa[17*20 + 0] = 0.131405; */ /* daa[17*20 + 1] = 0.552911; */ /* daa[17*20 + 2] = 0.079985; */ /* daa[17*20 + 3] = 0.060514; */ /* daa[17*20 + 4] = 0.633662; */ /* daa[17*20 + 5] = 0.21823; */ /* daa[17*20 + 6] = 0.074988; */ /* daa[17*20 + 7] = 0.169114; */ /* daa[17*20 + 8] = 0.847725; */ /* daa[17*20 + 9] = 0.10627; */ /* daa[17*20 + 10] = 0.622044; */ /* daa[17*20 + 11] = 0.060755; */ /* daa[17*20 + 12] = 0.719575; */ /* daa[17*20 + 13] = 3.14824; */ /* daa[17*20 + 14] = 0.077123; */ /* daa[17*20 + 15] = 0.276716; */ /* daa[17*20 + 16] = 0.148883; */ /* daa[18*20 + 0] = 0.165179; */ /* daa[18*20 + 1] = 0.224883; */ /* daa[18*20 + 2] = 0.528334; */ /* daa[18*20 + 3] = 0.121252; */ /* daa[18*20 + 4] = 1.174118; */ /* daa[18*20 + 5] = 0.177062; */ /* daa[18*20 + 6] = 0.074715; */ /* daa[18*20 + 7] = 0.042356; */ /* daa[18*20 + 8] = 5.911187; */ /* daa[18*20 + 9] = 0.192481; */ /* daa[18*20 + 10] = 0.321454; */ /* daa[18*20 + 11] = 0.090556; */ /* daa[18*20 + 12] = 0.406415; */ /* daa[18*20 + 13] = 10.908861; */ /* daa[18*20 + 14] = 0.070752; */ /* daa[18*20 + 15] = 0.328483; */ /* daa[18*20 + 16] = 0.181539; */ /* daa[18*20 + 17] = 3.823886; */ /* daa[19*20 + 0] = 1.78517; */ /* daa[19*20 + 1] = 0.166975; */ /* daa[19*20 + 2] = 0.106482; */ /* daa[19*20 + 3] = 0.041707; */ /* daa[19*20 + 4] = 1.876812; */ /* daa[19*20 + 5] = 0.22421; */ /* daa[19*20 + 6] = 0.247356; */ /* daa[19*20 + 7] = 0.06688; */ /* daa[19*20 + 8] = 0.1436; */ /* daa[19*20 + 9] = 9.60184; */ /* daa[19*20 + 10] = 1.599119; */ /* daa[19*20 + 11] = 0.17319; */ /* daa[19*20 + 12] = 1.645134; */ /* daa[19*20 + 13] = 0.438571; */ /* daa[19*20 + 14] = 0.252486; */ /* daa[19*20 + 15] = 0.105536; */ /* daa[19*20 + 16] = 1.789097; */ /* daa[19*20 + 17] = 0.147951; */ /* daa[19*20 + 18] = 0.200571; */ /* for (i=0; i 2664 (67% of the original 3933 positions) The species included in the analysis were: Harpiosquilla harpax [NCBI_TaxID 287944] Ixodes uriae [NCBI_TaxID 59655] Heptathela hangzhouensis [NCBI_TaxID 216259] Triops longicaudatus [NCBI_TaxID 58777] Gryllotalpa orientalis [NCBI_TaxID 213494] lepidopsocid RS-2001 [NCBI_TaxID 159971] Locusta migratoria [NCBI_TaxID 7004] Drosophila yakuba [NCBI_TaxID 7245] Ostrinia furnacalis [NCBI_TaxID 93504] Megabalanus volcano [NCBI_TaxID 266495] Periplaneta fuliginosa [NCBI_TaxID 36977] Thermobia domestica [NCBI_TaxID 89055] Aleurochiton aceris [NCBI_TaxID 266942] Schizaphis graminum [NCBI_TaxID 13262] Pteronarcys princeps [NCBI_TaxID 285953] Aleurodicus dugesii [NCBI_TaxID 30099] Pollicipes polymerus [NCBI_TaxID 36137] Gomphiocephalus hodgsoni [NCBI_TaxID 221270] Habronattus oregonensis [NCBI_TaxID 130930] Speleonectes tulumensis [NCBI_TaxID 84346] Hutchinsoniella macracantha [NCBI_TaxID 84335] Haemaphysalis flava [NCBI_TaxID 181088] Scutigera coleoptrata [NCBI_TaxID 29022] Vargula hilgendorfii [NCBI_TaxID 6674] Tricholepidion gertschi [NCBI_TaxID 89825] Varroa destructor [NCBI_TaxID 109461] Bombyx mandarina [NCBI_TaxID 7092] Thyropygus sp. [NCBI_TaxID 174155] Tribolium castaneum [NCBI_TaxID 7070] Pagurus longicarpus [NCBI_TaxID 111067] Limulus polyphemus [NCBI_TaxID 6850] Tetrodontophora bielanensis [NCBI_TaxID 48717] Penaeus monodon [NCBI_TaxID 6687] Daphnia pulex [NCBI_TaxID 6669] Apis mellifera [NCBI_TaxID 7469] Anopheles gambiae [NCBI_TaxID 7165] The topology used for inferring the model was: (((Daph_pulex,Trio_longi),((((((Aleu_aceri,Aleu_duges),Schi_grami),lepi_RS_20), ((((Ostr_furna,Bomb_manda),(Dros_yakub,Anop_gambi)),Apis_melli),Trib_casta)), ((Gryl_orien,Locu_migra),(Pter_princ,Peri_fulig))),(Tric_gerts,Ther_domes)), (Scut_coleo,Thyr_sp),Varg_hilge,Hutc_macra,((((Ixod_uriae,Haem_flava),Varr_destr), (Habr_orego,Hept_hangz)),Limu_polyp),(Poll_polym,Mega_volca),(Gomp_hodgs,Tetr_biela), ((Pagu_longi,Pena_monod),Harp_harpa),Spel_tulum)); Note this is not the ML topology but the consensus one (based on morphological data, phylogenetic reconstruction using nuclear genes, etc). Where relationships are not clear, a polytomy was introduced (it contains quite a lot of polytomies!). The model was estimated using (the great and helpful) Ziheng Yang's Paml software package. A four-categorized gamma distribution was used to account for heterogeneity (alpha was estimated to be 0.47821). Sites with ambiguity data were taken into account. If you would like the data related to this matrix, please contact fabascal@uvigo.es. Federico Abascal (c)2005. */ int i,j,naa; naa = 20; daa[1*20+ 0] = 0.2; daa[2*20+ 0] = 0.2; daa[2*20+ 1] = 0.2; daa[3*20+ 0] = 0.6; daa[3*20+ 1] = 4.3; daa[3*20+ 2] = 500.2; daa[4*20+ 0] = 253.5; daa[4*20+ 1] = 35.5; daa[4*20+ 2] = 98.2; daa[4*20+ 3] = 10.6; daa[5*20+ 0] = 0.2; daa[5*20+ 1] = 154.0; daa[5*20+ 2] = 261.8; daa[5*20+ 3] = 0.2; daa[5*20+ 4] = 0.2; daa[6*20+ 0] = 0.2; daa[6*20+ 1] = 0.2; daa[6*20+ 2] = 183.0; daa[6*20+ 3] = 861.8; daa[6*20+ 4] = 0.2; daa[6*20+ 5] = 261.6; daa[7*20+ 0] = 199.8; daa[7*20+ 1] = 0.2; daa[7*20+ 2] = 120.5; daa[7*20+ 3] = 12.5; daa[7*20+ 4] = 80.5; daa[7*20+ 5] = 2.6; daa[7*20+ 6] = 43.9; daa[8*20+ 0] = 0.2; daa[8*20+ 1] = 41.3; daa[8*20+ 2] = 179.5; daa[8*20+ 3] = 0.2; daa[8*20+ 4] = 12.4; daa[8*20+ 5] = 313.5; daa[8*20+ 6] = 15.2; daa[8*20+ 7] = 0.2; daa[9*20+ 0] = 25.7; daa[9*20+ 1] = 1.8; daa[9*20+ 2] = 21.3; daa[9*20+ 3] = 6.6; daa[9*20+ 4] = 63.0; daa[9*20+ 5] = 10.5; daa[9*20+ 6] = 6.8; daa[9*20+ 7] = 2.7; daa[9*20+ 8] = 0.2; daa[10*20+ 0] = 3.7; daa[10*20+ 1] = 1.8; daa[10*20+ 2] = 12.6; daa[10*20+ 3] = 1.2; daa[10*20+ 4] = 78.7; daa[10*20+ 5] = 16.3; daa[10*20+ 6] = 1.7; daa[10*20+ 7] = 1.4; daa[10*20+ 8] = 5.5; daa[10*20+ 9] = 514.5; daa[11*20+ 0] = 0.2; daa[11*20+ 1] = 208.6; daa[11*20+ 2] = 467.3; daa[11*20+ 3] = 1.7; daa[11*20+ 4] = 0.2; daa[11*20+ 5] = 349.3; daa[11*20+ 6] = 106.3; daa[11*20+ 7] = 0.2; daa[11*20+ 8] = 0.2; daa[11*20+ 9] = 3.5; daa[11*20+ 10] = 3.8; daa[12*20+ 0] = 120.6; daa[12*20+ 1] = 5.2; daa[12*20+ 2] = 78.8; daa[12*20+ 3] = 0.2; daa[12*20+ 4] = 312.3; daa[12*20+ 5] = 67.3; daa[12*20+ 6] = 0.2; daa[12*20+ 7] = 55.7; daa[12*20+ 8] = 0.2; daa[12*20+ 9] = 514.8; daa[12*20+ 10] = 885.5; daa[12*20+ 11] = 105.6; daa[13*20+ 0] = 13.1; daa[13*20+ 1] = 4.7; daa[13*20+ 2] = 19.7; daa[13*20+ 3] = 0.2; daa[13*20+ 4] = 184.1; daa[13*20+ 5] = 0.2; daa[13*20+ 6] = 0.2; daa[13*20+ 7] = 0.8; daa[13*20+ 8] = 13.8; daa[13*20+ 9] = 117.9; daa[13*20+ 10] = 262.6; daa[13*20+ 11] = 10.7; daa[13*20+ 12] = 321.6; daa[14*20+ 0] = 49.3; daa[14*20+ 1] = 0.2; daa[14*20+ 2] = 16.5; daa[14*20+ 3] = 0.2; daa[14*20+ 4] = 0.2; daa[14*20+ 5] = 39.3; daa[14*20+ 6] = 7.9; daa[14*20+ 7] = 0.2; daa[14*20+ 8] = 0.8; daa[14*20+ 9] = 0.2; daa[14*20+ 10] = 12.2; daa[14*20+ 11] = 16.8; daa[14*20+ 12] = 5.3; daa[14*20+ 13] = 14.6; daa[15*20+ 0] = 673.0; daa[15*20+ 1] = 2.7; daa[15*20+ 2] = 398.4; daa[15*20+ 3] = 44.4; daa[15*20+ 4] = 664.2; daa[15*20+ 5] = 52.4; daa[15*20+ 6] = 31.5; daa[15*20+ 7] = 226.0; daa[15*20+ 8] = 10.6; daa[15*20+ 9] = 7.2; daa[15*20+ 10] = 8.2; daa[15*20+ 11] = 144.2; daa[15*20+ 12] = 111.7; daa[15*20+ 13] = 36.1; daa[15*20+ 14] = 86.5; daa[16*20+ 0] = 243.9; daa[16*20+ 1] = 0.2; daa[16*20+ 2] = 165.9; daa[16*20+ 3] = 0.2; daa[16*20+ 4] = 182.8; daa[16*20+ 5] = 43.7; daa[16*20+ 6] = 43.4; daa[16*20+ 7] = 0.2; daa[16*20+ 8] = 18.6; daa[16*20+ 9] = 203.7; daa[16*20+ 10] = 47.8; daa[16*20+ 11] = 69.5; daa[16*20+ 12] = 288.6; daa[16*20+ 13] = 13.5; daa[16*20+ 14] = 46.8; daa[16*20+ 15] = 660.4; daa[17*20+ 0] = 0.2; daa[17*20+ 1] = 0.2; daa[17*20+ 2] = 7.7; daa[17*20+ 3] = 0.2; daa[17*20+ 4] = 21.6; daa[17*20+ 5] = 6.7; daa[17*20+ 6] = 11.0; daa[17*20+ 7] = 1.9; daa[17*20+ 8] = 0.2; daa[17*20+ 9] = 0.2; daa[17*20+ 10] = 21.1; daa[17*20+ 11] = 16.0; daa[17*20+ 12] = 70.7; daa[17*20+ 13] = 53.7; daa[17*20+ 14] = 0.2; daa[17*20+ 15] = 2.4; daa[17*20+ 16] = 0.2; daa[18*20+ 0] = 1.2; daa[18*20+ 1] = 3.9; daa[18*20+ 2] = 251.2; daa[18*20+ 3] = 0.2; daa[18*20+ 4] = 72.0; daa[18*20+ 5] = 86.7; daa[18*20+ 6] = 7.7; daa[18*20+ 7] = 8.6; daa[18*20+ 8] = 191.4; daa[18*20+ 9] = 12.3; daa[18*20+ 10] = 19.8; daa[18*20+ 11] = 117.1; daa[18*20+ 12] = 70.9; daa[18*20+ 13] = 791.6; daa[18*20+ 14] = 18.4; daa[18*20+ 15] = 30.5; daa[18*20+ 16] = 46.0; daa[18*20+ 17] = 37.7; daa[19*20+ 0] = 339.9; daa[19*20+ 1] = 0.2; daa[19*20+ 2] = 22.6; daa[19*20+ 3] = 0.2; daa[19*20+ 4] = 350.4; daa[19*20+ 5] = 0.2; daa[19*20+ 6] = 13.6; daa[19*20+ 7] = 2.6; daa[19*20+ 8] = 0.2; daa[19*20+ 9] = 1854.5; daa[19*20+ 10] = 84.7; daa[19*20+ 11] = 26.1; daa[19*20+ 12] = 281.3; daa[19*20+ 13] = 51.9; daa[19*20+ 14] = 31.7; daa[19*20+ 15] = 60.6; daa[19*20+ 16] = 544.1; daa[19*20+ 17] = 0.2; daa[19*20+ 18] = 1.6; /* MtArt.old: esta es la MtArt que hice con 26 secuencias (2 outgroups) con una topologia incorrecta daa[1*20+ 0] = 0.2; daa[2*20+ 0] = 0.2; daa[2*20+ 1] = 8.0; daa[3*20+ 0] = 0.2; daa[3*20+ 1] = 0.2; daa[3*20+ 2] = 441.7; daa[4*20+ 0] = 287.9; daa[4*20+ 1] = 48.4; daa[4*20+ 2] = 82.4; daa[4*20+ 3] = 0.2; daa[5*20+ 0] = 0.2; daa[5*20+ 1] = 149.9; daa[5*20+ 2] = 278.6; daa[5*20+ 3] = 0.2; daa[5*20+ 4] = 21.7; daa[6*20+ 0] = 6.6; daa[6*20+ 1] = 0.2; daa[6*20+ 2] = 213.9; daa[6*20+ 3] = 760.8; daa[6*20+ 4] = 0.2; daa[6*20+ 5] = 292.9; daa[7*20+ 0] = 228.2; daa[7*20+ 1] = 0.2; daa[7*20+ 2] = 97.1; daa[7*20+ 3] = 10.4; daa[7*20+ 4] = 98.4; daa[7*20+ 5] = 4.0; daa[7*20+ 6] = 48.7; daa[8*20+ 0] = 0.2; daa[8*20+ 1] = 56.7; daa[8*20+ 2] = 156.4; daa[8*20+ 3] = 24.5; daa[8*20+ 4] = 15.5; daa[8*20+ 5] = 328.6; daa[8*20+ 6] = 7.0; daa[8*20+ 7] = 8.4; daa[9*20+ 0] = 26.4; daa[9*20+ 1] = 1.6; daa[9*20+ 2] = 40.1; daa[9*20+ 3] = 0.2; daa[9*20+ 4] = 22.1; daa[9*20+ 5] = 13.8; daa[9*20+ 6] = 0.2; daa[9*20+ 7] = 3.6; daa[9*20+ 8] = 0.2; daa[10*20+ 0] = 3.4; daa[10*20+ 1] = 0.6; daa[10*20+ 2] = 13.8; daa[10*20+ 3] = 0.7; daa[10*20+ 4] = 76.9; daa[10*20+ 5] = 12.1; daa[10*20+ 6] = 5.4; daa[10*20+ 7] = 2.5; daa[10*20+ 8] = 2.9; daa[10*20+ 9] = 542.6; daa[11*20+ 0] = 0.2; daa[11*20+ 1] = 240.2; daa[11*20+ 2] = 602.8; daa[11*20+ 3] = 35.5; daa[11*20+ 4] = 0.2; daa[11*20+ 5] = 357.6; daa[11*20+ 6] = 62.6; daa[11*20+ 7] = 0.2; daa[11*20+ 8] = 3.3; daa[11*20+ 9] = 0.2; daa[11*20+ 10] = 17.5; daa[12*20+ 0] = 119.0; daa[12*20+ 1] = 0.2; daa[12*20+ 2] = 91.4; daa[12*20+ 3] = 6.4; daa[12*20+ 4] = 332.3; daa[12*20+ 5] = 65.4; daa[12*20+ 6] = 0.2; daa[12*20+ 7] = 60.4; daa[12*20+ 8] = 2.4; daa[12*20+ 9] = 492.5; daa[12*20+ 10] = 815.8; daa[12*20+ 11] = 67.3; daa[13*20+ 0] = 8.2; daa[13*20+ 1] = 6.4; daa[13*20+ 2] = 31.5; daa[13*20+ 3] = 3.4; daa[13*20+ 4] = 174.4; daa[13*20+ 5] = 5.7; daa[13*20+ 6] = 5.7; daa[13*20+ 7] = 2.1; daa[13*20+ 8] = 11.0; daa[13*20+ 9] = 94.4; daa[13*20+ 10] = 243.3; daa[13*20+ 11] = 12.3; daa[13*20+ 12] = 357.8; daa[14*20+ 0] = 62.5; daa[14*20+ 1] = 0.4; daa[14*20+ 2] = 17.5; daa[14*20+ 3] = 0.2; daa[14*20+ 4] = 0.2; daa[14*20+ 5] = 48.6; daa[14*20+ 6] = 17.7; daa[14*20+ 7] = 2.7; daa[14*20+ 8] = 0.2; daa[14*20+ 9] = 0.2; daa[14*20+ 10] = 11.2; daa[14*20+ 11] = 21.7; daa[14*20+ 12] = 5.2; daa[14*20+ 13] = 12.6; daa[15*20+ 0] = 659.0; daa[15*20+ 1] = 5.2; daa[15*20+ 2] = 469.8; daa[15*20+ 3] = 52.3; daa[15*20+ 4] = 570.7; daa[15*20+ 5] = 47.8; daa[15*20+ 6] = 37.3; daa[15*20+ 7] = 227.8; daa[15*20+ 8] = 12.7; daa[15*20+ 9] = 12.3; daa[15*20+ 10] = 7.4; daa[15*20+ 11] = 189.0; daa[15*20+ 12] = 155.3; daa[15*20+ 13] = 43.8; daa[15*20+ 14] = 103.4; daa[16*20+ 0] = 276.4; daa[16*20+ 1] = 1.6; daa[16*20+ 2] = 175.6; daa[16*20+ 3] = 0.2; daa[16*20+ 4] = 96.2; daa[16*20+ 5] = 71.4; daa[16*20+ 6] = 37.4; daa[16*20+ 7] = 0.2; daa[16*20+ 8] = 14.2; daa[16*20+ 9] = 212.5; daa[16*20+ 10] = 38.5; daa[16*20+ 11] = 97.4; daa[16*20+ 12] = 254.7; daa[16*20+ 13] = 2.1; daa[16*20+ 14] = 41.6; daa[16*20+ 15] = 670.6; daa[17*20+ 0] = 6.2; daa[17*20+ 1] = 0.2; daa[17*20+ 2] = 0.2; daa[17*20+ 3] = 5.6; daa[17*20+ 4] = 0.2; daa[17*20+ 5] = 0.2; daa[17*20+ 6] = 3.1; daa[17*20+ 7] = 0.4; daa[17*20+ 8] = 0.2; daa[17*20+ 9] = 15.2; daa[17*20+ 10] = 11.5; daa[17*20+ 11] = 32.6; daa[17*20+ 12] = 82.4; daa[17*20+ 13] = 81.9; daa[17*20+ 14] = 0.2; daa[17*20+ 15] = 9.7; daa[17*20+ 16] = 0.2; daa[18*20+ 0] = 1.6; daa[18*20+ 1] = 7.7; daa[18*20+ 2] = 242.5; daa[18*20+ 3] = 0.2; daa[18*20+ 4] = 88.0; daa[18*20+ 5] = 93.1; daa[18*20+ 6] = 0.2; daa[18*20+ 7] = 6.0; daa[18*20+ 8] = 113.7; daa[18*20+ 9] = 22.1; daa[18*20+ 10] = 17.2; daa[18*20+ 11] = 138.5; daa[18*20+ 12] = 37.6; daa[18*20+ 13] = 770.2; daa[18*20+ 14] = 5.3; daa[18*20+ 15] = 25.0; daa[18*20+ 16] = 55.5; daa[18*20+ 17] = 69.3; daa[19*20+ 0] = 307.8; daa[19*20+ 1] = 2.2; daa[19*20+ 2] = 6.9; daa[19*20+ 3] = 0.2; daa[19*20+ 4] = 405.7; daa[19*20+ 5] = 0.8; daa[19*20+ 6] = 20.2; daa[19*20+ 7] = 5.7; daa[19*20+ 8] = 0.2; daa[19*20+ 9] = 1687.9; daa[19*20+ 10] = 49.4; daa[19*20+ 11] = 23.4; daa[19*20+ 12] = 329.9; daa[19*20+ 13] = 86.3; daa[19*20+ 14] = 27.3; daa[19*20+ 15] = 95.0; daa[19*20+ 16] = 443.0; daa[19*20+ 17] = 2.4; daa[19*20+ 18] = 0.2; 3*/ for (i=0; idim = n_dim; Random_String(t->id,3); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void PHYREX_Init_Disk_Event(t_dsk *t, int n_dim, t_phyrex_mod *mmod) { t->prev = NULL; t->next = NULL; t->img = NULL; t->mmod = NULL; t->age_fixed = NO; t->cum_glnL = 0.0; t->cum_tlnL = 0.0; Random_String(t->id,3); GEO_Init_Coord(t->centr,n_dim); if(mmod != NULL) t->mmod = mmod; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void PHYREX_Init_Migrep_Mod(t_phyrex_mod *t, int n_dim, phydbl min_lat, phydbl min_lon, phydbl max_lat, phydbl max_lon) { assert(n_dim == 2); if(t->model_id == -1) t->model_id = SLFV_GAUSSIAN; t->n_dim = n_dim; if(t->sampling_scheme == -1) t->sampling_scheme = SPATIAL_SAMPLING_DETECTION; if(t->use_locations == -1) t->use_locations = YES; t->lim_up->lonlat[0] = max_lat; t->lim_up->lonlat[1] = max_lon; t->lim_do->lonlat[0] = min_lat; t->lim_do->lonlat[1] = min_lon; t->min_rad = 0.0; t->max_rad = 1.0*((max_lat-min_lat)+(max_lon-min_lon)); t->rad = 0.01*((max_lat-min_lat)+(max_lon-min_lon)); t->prior_param_rad = 1./(0.1*((max_lat-min_lat)+(max_lon-min_lon))); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void PHYREX_Set_Default_Migrep_Mod(int n_otu, t_phyrex_mod *t) { for(int i=0;i<2*n_otu-1;++i) t->sigsq_scale[i] = 1.0; t->sigsq_scale_min = 0.1; t->sigsq_scale_max = 10.; t->rrw_norm_fact = 1.0; t->model_id = -1; t->use_locations = -1; t->sampling_scheme = -1; t->safe_phyrex = YES; t->lim_up->lonlat[0] = 100.; t->lim_up->lonlat[1] = 100.; t->lim_do->lonlat[0] = 0.0; t->lim_do->lonlat[1] = 0.0; t->lbda = 1.E-0; t->min_lbda = 1.E-6; t->max_lbda = 1.E+4; t->prior_param_lbda = 1.0; t->mu = 0.800; t->min_mu = 0.000; t->max_mu = 1.000; t->prior_param_mu = 1.000; t->min_rad = 0.0; t->max_rad = 100.; t->rad = 4.; t->prior_param_rad = 1.; t->update_rad = NO; t->min_sigsq = 1.E-1; t->max_sigsq = 1.E+3; t->prior_param_sigsq = 10.0; assert(t->n_dim > 0); for(int i=0;in_dim;++i) t->sigsq[i] = t->min_sigsq + (t->max_sigsq-t->min_sigsq)/20.; t->nu = 1.0E-0; t->c_lnL = UNLIKELY; t->p_lnL = UNLIKELY; t->c_lnP = UNLIKELY; t->p_lnP = UNLIKELY; t->c_ln_prior_rad = UNLIKELY; t->c_ln_prior_lbda = UNLIKELY; t->c_ln_prior_mu = UNLIKELY; t->soft_bound_area = 0.1; t->max_num_of_intervals = 10000000; t->disp_prior_mean = 1.0; t->integrateAncestralLocations = YES; t->rrw_param_val = 2.0; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void PHYREX_Init_Lindisk_Node(t_ldsk *t, t_dsk *disk, int n_dim) { t->disk = disk; /* disk->ldsk = t; */ t->prev = NULL; t->next = NULL; t->img = NULL; t->nd = NULL; t->is_hit = NO; t->n_next = 0; t->rr = 1.0; t->sigsq = 1.0; GEO_Init_Coord(t->coord, n_dim); GEO_Init_Coord(t->cpy_coord,n_dim); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void MCMC_Init_MCMC_Struct(char *filename, option *io, t_mcmc *mcmc) { int pid; assert(mcmc); mcmc->io = io; mcmc->is = NO; mcmc->run = 0; mcmc->sample_interval = 1E+3; mcmc->chain_len = 1E+7; mcmc->chain_len_burnin = 1E+4; mcmc->randomize = YES; mcmc->norm_freq = 1E+3; mcmc->max_tune = 1.E+6; mcmc->min_tune = 1.E-6; mcmc->print_every = 2; mcmc->is_burnin = NO; mcmc->nd_t_digits = 4; mcmc->always_yes = NO; mcmc->max_lag = 1000; mcmc->sample_num = 0; if(filename) { char *s; s = (char *)mCalloc(T_MAX_NAME,sizeof(char)); strcpy(mcmc->out_filename,filename); pid = getpid(); sprintf(mcmc->out_filename+strlen(mcmc->out_filename),".%d",pid); strcpy(s,mcmc->io->in_align_file); strcat(s,"_"); strcat(s,mcmc->out_filename); strcat(s,".stats"); mcmc->out_fp_stats = fopen(s,"w"); strcpy(s,mcmc->io->in_align_file); strcat(s,"_"); strcat(s,mcmc->out_filename); strcat(s,".trees"); mcmc->out_fp_trees = fopen(s,"w"); strcpy(s,mcmc->io->in_align_file); strcat(s,"_"); strcat(s,mcmc->out_filename); strcat(s,".constree"); mcmc->out_fp_constree = fopen(s,"w"); /* strcpy(s,tree->mcmc->out_filename); */ /* strcat(s,".means"); */ /* tree->mcmc->out_fp_means = fopen(s,"w"); */ /* strcpy(s,tree->mcmc->out_filename); */ /* strcat(s,".lasts"); */ /* tree->mcmc->out_fp_last = fopen(s,"w"); */ Free(s); } else { mcmc->out_fp_stats = stderr; mcmc->out_fp_trees = stderr; /* tree->mcmc->out_fp_means = stderr; */ /* tree->mcmc->out_fp_last = stderr; */ } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Calibration(t_cal *cal) { cal->next = NULL; cal->prev = NULL; cal->lower = -1.; cal->upper = -1.; cal->is_primary = FALSE; cal->alpha_proba_list = NULL; cal->clade_list = NULL; cal->clade_list_size = 0; cal->id = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_All_Calibration(t_tree *tree) { int i; assert(tree->rates && tree->times->a_cal); For(i,2*tree->n_otu-1) Init_Calibration(tree->times->a_cal[i]); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Sarea(t_sarea *s) { assert(s); s->n_poly = 0; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Calign(int n_otu, int crunch_len, int init_len, calign *this) { this->obs_pinvar = .0; this->n_otu = n_otu; this->clean_len = -1; this->crunch_len = crunch_len; this->init_len = init_len; this->format = 0; this->io_wght = NULL; for(int i=0; ic_seq[i]); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Cseq(align *this) { this->is_duplicate = NO; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Linked_List(t_ll *list) { list->head = list; list->tail = list; list->next = NULL; list->prev = NULL; list->v = NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_NNI_Score(phydbl val, t_edge *b, t_tree *tree) { b->nni->score = val; if(tree->is_mixt_tree == YES) MIXT_Init_NNI_Score(val,b,tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// #ifdef M4 void M4_Init_Model(m4 *m4mod, calign *data, t_mod *mod) { int i,j,ct; phydbl fq; if(mod->io->datatype == NT) m4mod->n_o = 4; else if(mod->io->datatype == AA) m4mod->n_o = 20; else { PhyML_Fprintf(stderr,"\n. Not implemented yet."); PhyML_Fprintf(stderr,"\n. Err in file %s at line %d\n",__FILE__,__LINE__); Warn_And_Exit(""); } mod->ns = m4mod->n_o * m4mod->n_h; for(i=0;in_o;i++) m4mod->o_fq[i] = mod->e_frq->pi->v[i]; /*! At that stage, the mod->pi vector as been initialized under a standard non covarion type of model. Use these frequencies as they have been set according to the nucleotide substitution model chosen (e.g., 1/4 for JC69). !*/ for(i=0;i<(int)(m4mod->n_h);++i) m4mod->multipl[i] = 1.; ct = 0; for(i=0;in_o-1;i++) { for(j=i+1;jn_o;j++) { m4mod->o_rr[ct] = MAX(mod->r_mat->qmat->v[i*m4mod->n_o+j],1.E-5); ct++; } } for(i=0;i<(int)(m4mod->n_h*(m4mod->n_h-1)/2);++i) m4mod->h_rr[i] = 1.; fq = (phydbl)(1./m4mod->n_h); if(mod->s_opt->opt_cov_delta) m4mod->delta = 1.0; if(mod->s_opt->opt_cov_alpha) m4mod->alpha = 1.0; for(i=0;in_h;i++) m4mod->h_fq[i] = fq; for(i=0;in_h;i++) m4mod->h_fq_unscaled[i] = 1.0; for(i=0;in_h;i++) m4mod->multipl[i] = (phydbl)i; for(i=0;in_h;i++) m4mod->multipl_unscaled[i] = (phydbl)i; Set_Update_Eigen(YES,mod); M4_Update_Qmat(m4mod,mod); } #endif ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Initialise x values using coordinates at the tip nodes along // one dimension (with index dim_idx) void RW_Init_Contrasts(int dim_idx, t_tree *tree) { for(int i=0;in_otu;++i) tree->ctrst->x[i] = tree->a_nodes[i]->ldsk->coord->lonlat[dim_idx]; for(int i=0;i<2*tree->n_otu-1;++i) tree->ctrst->tprime[i] = tree->times->nd_t[i]; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phyml-3.3.20220408/src/init.h000066400000000000000000000060571422404706600153300ustar00rootroot00000000000000/* PHYML : a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences Copyright (C) Stephane Guindon. Oct 2003 onward All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include #ifndef INIT_H #define INIT_H #include "utilities.h" void Init_Cseq(align *this); void Init_Eigen_Struct(eigen *this); void Init_Scalar_Dbl(scalar_dbl *p); void Init_Scalar_Int(scalar_int *p); void Init_Vect_Dbl(int len,vect_dbl *p); void Init_Vect_Int(int len,vect_int *p); void Init_Tree(t_tree *tree,int n_otu); void Init_Edge_Light(t_edge *b,int num); void Init_Node_Light(t_node *n,int num); void Init_NNI(t_nni *a_nni); void Init_Nexus_Format(nexcom **com); void Init_Mat(matrix *mat,calign *data); void Set_Defaults_Input(option *io); void Set_Defaults_Model(t_mod *mod); void Set_Defaults_Optimiz(t_opt *s_opt); void XML_Init_Node(xml_node *prev,xml_node *new_node,char *name); void Init_One_Spr(t_spr *a_spr); void Init_Model(calign *data,t_mod *mod,option *io); int Init_Qmat_Dayhoff(phydbl *daa,phydbl *pi); int Init_Qmat_DCMut(phydbl *daa,phydbl *pi); int Init_Qmat_MtArt(phydbl *daa,phydbl *pi); int Init_Qmat_HIVb(phydbl *daa,phydbl *pi); int Init_Qmat_HIVw(phydbl *daa,phydbl *pi); int Init_Qmat_JTT(phydbl *daa,phydbl *pi); int Init_Qmat_MtREV(phydbl *daa,phydbl *pi); int Init_Qmat_LG(phydbl *daa,phydbl *pi); int Init_Qmat_WAG(phydbl *daa,phydbl *pi); int Init_Qmat_RtREV(phydbl *daa,phydbl *pi); int Init_Qmat_CpREV(phydbl *daa,phydbl *pi); int Init_Qmat_VT(phydbl *daa,phydbl *pi); int Init_Qmat_Blosum62(phydbl *daa,phydbl *pi); int Init_Qmat_MtMam(phydbl *daa,phydbl *pi); int Init_Qmat_AB(phydbl *daa, phydbl *pi); void XML_Init_Attribute(xml_attr *attr); void Init_String(t_string *ts); void Init_Efrq(phydbl *b_frq, t_efrq *f); void RATES_Init_Rate_Struct(t_rate *rates, t_rate *existing_rates, int n_otu); void TIMES_Init_Time_Struct(t_time *times, t_time *existing_time, int n_otu); void Init_Rmat(t_rmat *rmat); void Init_MGF_Bl(t_tree *tree); int Init_Qmat_FLU(phydbl *daa, phydbl *pi); void Set_Defaults_Ras(t_ras *ras); void GEO_Init_Coord(t_geo_coord *t, int n_dim); void PHYREX_Init_Disk_Event(t_dsk *t, int n_dim, t_phyrex_mod *mod); void PHYREX_Init_Lindisk_Node(t_ldsk *t, t_dsk *devt, int n_dim); void PHYREX_Init_Migrep_Mod(t_phyrex_mod *t, int n_dim, phydbl min_lat, phydbl min_lon, phydbl max_lat, phydbl max_lon); void MCMC_Init_MCMC_Struct(char *filename, option *io, t_mcmc *mcmc); void Init_Calibration(t_cal *cal); void Init_All_Calibration(t_tree *tree); void Init_Sarea(t_sarea *s); void Init_Efrqs_Using_Observed_Freqs(t_efrq *f, phydbl *o, int ns); void Init_Calign(int n_otu, int crunch_len, int init_len, calign *this); void Init_Linked_List(t_ll *list); void Init_Target_Tip(t_clad *clade, t_tree *tree); void Init_NNI_Score(phydbl val, t_edge *b, t_tree *tree); void M4_Init_Model(m4 *m4mod, calign *data, t_mod *mod); void RW_Init_Contrasts(int dim_idx, t_tree *tree); void PHYREX_Set_Default_Migrep_Mod(int n_otu, t_phyrex_mod *t); #endif phyml-3.3.20220408/src/interface.c000066400000000000000000001447631422404706600163270ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include "interface.h" #include "mg.h" void Launch_Interface(option *io) { Launch_Interface_Input(io); io->ready_to_go = 0; do { switch(io->curr_interface) { case INTERFACE_DATA_TYPE : { Launch_Interface_Data_Type(io); break; } case INTERFACE_MULTIGENE : { Launch_Interface_Multigene(io); break; } case INTERFACE_MODEL : { Launch_Interface_Model(io); break; } case INTERFACE_TOPO_SEARCH : { Launch_Interface_Topo_Search(io); break; } case INTERFACE_BRANCH_SUPPORT : { Launch_Interface_Branch_Support(io); break; } default : { PhyML_Printf("\n== Err in file %s at line %d\n\n",__FILE__,__LINE__); Exit(""); break; } } }while(!io->ready_to_go); if(io->in_tree == 2) { PhyML_Printf("\n. Enter the name of the input tree file > "); Getstring_Stdin(io->in_tree_file); io->fp_in_tree = Openfile(io->in_tree_file,0); } if((io->mod->whichmodel == CUSTOMAA) && (io->datatype == AA)) { char *filename; filename = (char *)mCalloc(T_MAX_NAME,sizeof(char)); fflush(NULL); PhyML_Printf("\n"); PhyML_Printf("\n. Enter the rate matrix file name > "); fflush(NULL); Getstring_Stdin(filename); io->mod->fp_aa_rate_mat = Openfile(filename,0); strcpy(io->mod->aa_rate_mat_file->s,filename); PhyML_Printf("\n"); Free(filename); fflush(NULL); } if((io->mod->s_opt->n_rand_starts) && (io->mod->s_opt->topo_search == NNI_MOVE) && (io->mod->s_opt->random_input_tree)) { Warn_And_Exit("\n. The random starting tree option is only compatible with SPR based search options.\n"); } if ((io->datatype == NT) && (io->mod->whichmodel > 10)) { char choix; PhyML_Printf("\n== Err: model incompatible with the data type. Please use JC69, K80, F81, HKY, F84, TN93 or GTR\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Warn_And_Exit("\n"); } else if ((io->datatype == AA) && (io->mod->whichmodel < 11)) { char choix; PhyML_Printf("\n== Err: model incompatible with the data type. Please use LG, Dayhoff, JTT, MtREV, WAG, DCMut, RtREV, CpREV, VT, Blosum62, MtMam, MtArt, HIVw, HIVb or AB.\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } if(io->m4_model == YES) { #ifdef M4 io->mod->ns *= io->mod->m4mod->n_h; io->mod->use_m4mod = 1; M4_Make_Complete(io->mod->m4mod->n_h, io->mod->m4mod->n_o, io->mod->m4mod); #endif } else { io->mod->s_opt->opt_cov_delta = 0; io->mod->s_opt->opt_cov_alpha = 0; io->mod->s_opt->opt_cov_free_rates = 0; } if((io->mod->s_opt->opt_cov_free_rates) && (io->mod->s_opt->opt_cov_alpha)) { io->mod->s_opt->opt_cov_free_rates = 0; io->mod->m4mod->use_cov_alpha = 0; io->mod->m4mod->use_cov_free = 1; } if(io->print_site_lnl) { strcpy(io->out_lk_file,io->in_align_file); strcat(io->out_lk_file, "_phyml_lk.txt"); if(io->append_run_ID) { strcat(io->out_lk_file,"_"); strcat(io->out_lk_file,io->run_id_string); } io->fp_out_lk = Openfile(io->out_lk_file,1); } if(io->print_trace) { strcpy(io->out_trace_file,io->in_align_file); strcat(io->out_trace_file,"_phyml_trace.txt"); if(io->append_run_ID) { strcat(io->out_trace_file,"_"); strcat(io->out_trace_file,io->run_id_string); } io->fp_out_trace = Openfile(io->out_trace_file,1); } if(io->mod->s_opt->random_input_tree) { strcpy(io->out_trees_file,io->in_align_file); strcat(io->out_trees_file,"_phyml_trees.txt"); if(io->append_run_ID) { strcat(io->out_trees_file,"_"); strcat(io->out_trees_file,io->run_id_string); } io->fp_out_trees = Openfile(io->out_trees_file,1); } if((io->print_boot_trees) && (io->n_boot_replicates > 0)) { strcpy(io->out_boot_tree_file,io->in_align_file); strcat(io->out_boot_tree_file,"_phyml_boot_trees.txt"); if(io->append_run_ID) { strcat(io->out_boot_tree_file,"_"); strcat(io->out_boot_tree_file,io->run_id_string); } io->fp_out_boot_tree = Openfile(io->out_boot_tree_file,1); strcpy(io->out_boot_stats_file,io->in_align_file); strcat(io->out_boot_stats_file,"_phyml_boot_stats.txt"); if(io->append_run_ID) { strcat(io->out_boot_stats_file,"_"); strcat(io->out_boot_stats_file,io->run_id_string); } io->fp_out_boot_stats = Openfile(io->out_boot_stats_file,1); } if(io->append_run_ID) { strcat(io->out_tree_file,"_"); strcat(io->out_stats_file,"_"); strcat(io->out_tree_file,io->run_id_string); strcat(io->out_stats_file,io->run_id_string); } if(io->mod->ras->n_catg == 1) io->mod->s_opt->opt_alpha = 0; if(io->mod->whichmodel != K80 && io->mod->whichmodel != HKY85 && io->mod->whichmodel != F84 && io->mod->whichmodel != TN93) { io->mod->s_opt->opt_kappa = 0; } io->fp_out_tree = Openfile(io->out_tree_file,1); io->fp_out_stats = Openfile(io->out_stats_file,1); // VINCENT: do not necessarily optimize relative rate parameters, they could be set by user if(io->mod->whichmodel == GTR) { if (io->mod->s_opt->opt_subst_param) io->mod->s_opt->opt_rr = YES; else io->mod->s_opt->opt_rr = NO; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Clear() { #ifdef WIN32 system("cls"); #elif UNIX PhyML_Printf("\033[2J\033[H"); #endif } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Launch_Interface_Input(option *io) { char choix; int n_trial; Clear(); Print_Banner(stdout); #ifdef EVOLVE char *n_data_sets; PhyML_Printf("\n\n"); PhyML_Printf("\n. Enter the tree file name > "); fflush(NULL); Getstring_Stdin(io->in_tree_file); io->fp_in_tree = Openfile(io->in_tree_file,0); PhyML_Printf("\n"); PhyML_Printf("\n. Enter the reference sequence file name > "); fflush(NULL); Getstring_Stdin(io->in_align_file); io->fp_in_align = Openfile(io->in_align_file,0); PhyML_Printf("\n"); PhyML_Printf("\n. Number of data sets > "); n_data_sets = (char *)mCalloc(10000,sizeof(char)); Getstring_Stdin(n_data_sets); n_trial = 0; while((!atoi(n_data_sets)) || (atoi(n_data_sets) < 0)) { if(++n_trial > 10) Exit("\n== Err : the number of sets must be a positive integer"); PhyML_Printf("\n. The number of sets must be a positive integer"); PhyML_Printf("\n. Enter a new value > "); Getstring_Stdin(n_data_sets); } io->n_data_set_asked = atoi(n_data_sets); Free(n_data_sets); #elif OPTIMIZ PhyML_Printf("\n. Enter the tree file name > "); fflush(NULL); Getstring_Stdin(io->in_tree_file); io->fp_in_tree = Openfile(io->in_tree_file,0); PhyML_Printf("\n"); PhyML_Printf("\n. Enter the reference sequence file name > "); fflush(NULL); Getstring_Stdin(io->in_align_file); io->fp_in_align = Openfile(io->in_align_file,0); PhyML_Printf("\n"); #elif defined(PHYML) || defined(PART) || defined(PHYML_INSERT) PhyML_Printf("\n. Enter the sequence file name > "); fflush(NULL); Getstring_Stdin(io->in_align_file); io->fp_in_align = Openfile(io->in_align_file,0); #endif #if defined(PHYML) || defined(PART) || defined(PHYML_INSERT) strcpy(io->out_stats_file,io->in_align_file); strcat(io->out_stats_file,"_phyml_stats.txt"); strcpy(io->out_tree_file,io->in_align_file); strcat(io->out_tree_file,"_phyml_tree.txt"); strcpy(io->out_lk_file,io->in_align_file); strcat(io->out_lk_file,"_phyml_lk.txt"); #endif #ifdef EVOLVE if(Filexists("evolve_out.txt")); #elif OPTIMIZ if(Filexists("optimiz_out.txt")) #elif defined(PHYML) || defined(PART) || defined(PHYML_INSERT) if(Filexists(io->out_stats_file)) #endif { PhyML_Printf("\n"); #ifdef EVOLVE PhyML_Printf("\n. A file 'evolve_out' already exists"); #elif OPTIMIZ PhyML_Printf("\n. A file 'optimiz_out' already exists"); #elif defined(PHYML) || defined(PART) || defined(PHYML_INSERT) PhyML_Printf("\n. A file '%s' already exists",io->out_stats_file); #endif PhyML_Printf("\n. Do you want to Replace it or Append to it ? "); n_trial = 0; do { PhyML_Printf("\n. Please type R or A > "); if(!scanf("%c",&choix)) Exit("\n"); if(choix == '\n') choix = 'r'; else getchar(); if(++n_trial>10) Exit("\n"); Uppercase(&choix); } while((choix != 'R') && (choix != 'A')); if(choix == 'R') io->out_stats_file_open_mode = 1; else io->out_stats_file_open_mode = 2; } /* io->fp_out_stats = Openfile(io->out_stats_file,io->out_stats_file_open_mode); */ #ifdef EVOLVE if(Filexists("evolve_seq.txt")) #elif OPTIMIZ if(Filexists("optimiz_tree.txt")) #elif defined(PHYML) || defined(PART) || defined(PHYML_INSERT) if(Filexists(io->out_tree_file)) #endif { PhyML_Printf("\n"); #ifdef EVOLVE PhyML_Printf("\n. A file 'evolve_seq' already exists"); #elif OPTIMIZ PhyML_Printf("\n. A file 'optimiz_tree' already exists"); #elif defined(PHYML) || defined(PART) || defined(PHYML_INSERT) PhyML_Printf("\n. A file '%s' already exists",io->out_tree_file); #endif PhyML_Printf("\n. Do you want to Replace it or Append to it ? "); n_trial = 0; do { PhyML_Printf("\n. Please type R or A > "); if(!scanf("%c",&choix)) Exit("\n"); if(choix == '\n') choix = 'r'; else getchar(); Uppercase(&choix); if(++n_trial>10) Exit("\n"); } while((choix != 'R') && (choix != 'A')); if(choix == 'R') io->out_tree_file_open_mode = 1; else io->out_tree_file_open_mode = 2; } /* io->fp_out_tree = Openfile(io->out_tree_file,io->out_tree_file_open_mode); */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Launch_Interface_Data_Type(option *io) { char choix; char *s; char *buff; Clear(); Print_Banner(stdout); if(io->config_multigene) Print_Data_Set_Number(io,stdout); s = (char *)mCalloc(100,sizeof(char)); buff = (char *)mCalloc(100,sizeof(char)); PhyML_Printf("\n\n"); PhyML_Printf(" ................... \n"); PhyML_Printf(" Menu : Input Data \n"); PhyML_Printf(" ......................... \n"); PhyML_Printf("\n\n"); PhyML_Printf(" [+] " ".................................... Next sub-menu\n"); PhyML_Printf(" [-] " "................................ Previous sub-menu\n"); PhyML_Printf(" [Y] " ".............................. Launch the analysis\n"); PhyML_Printf("\n"); if(io->datatype == NT) strcpy(s,"DNA"); else if(io->datatype == AA) strcpy(s,"AA"); else strcpy(s,"Generic"); PhyML_Printf(" [D] " "....................... Data type (DNA/AA/Generic) " " %-15s \n",s); PhyML_Printf(" [I] " "...... Input sequences interleaved (or sequential) " " %-15s \n", (io->interleaved)?("interleaved"):("sequential")); strcpy(s,""); sprintf(s," (%d sets)",io->n_data_sets); strcpy(buff,(io->n_data_sets > 1)?("yes"):("no")); buff=strcat(buff,(io->n_data_sets > 1)?(s):("\0")); PhyML_Printf(" [M] " "....................... Analyze multiple data sets " " %-15s \n",buff); if(!io->append_run_ID) strcpy(s,"none"); else strcpy(s,io->run_id_string); PhyML_Printf(" [R] " "........................................... Run ID " " %-15s \n",s); PhyML_Printf("\n\n. Are these settings correct ? " "(type '+', '-', 'Y' or other letter for one to change) "); if(!scanf("%c",&choix)) Exit("\n"); if(choix != '\n') getchar(); /* \n */ fflush(NULL); Uppercase(&choix); switch(choix) { /* case '\n': */ /* { */ /* io->curr_interface++; */ /* break; */ /* } */ case 'R' : { io->append_run_ID = (io->append_run_ID)?(0):(1); PhyML_Printf("\n. Enter a run ID (any string of characters) > "); Getstring_Stdin(io->run_id_string); break; } case 'M' : { char *c; int n_trial; PhyML_Printf("\n. How many data sets > "); c = (char *)mCalloc(100,sizeof(char)); Getstring_Stdin(c); n_trial = 0; while((!atoi(c)) || (atoi(c) < 0)) { if(++n_trial > 10) Exit("\n== Err : The number of data sets must be a positive integer"); PhyML_Printf("\n. The number of data sets must be a positive integer"); PhyML_Printf("\n. Enter a new value > "); Getstring_Stdin(c); } io->n_data_sets = atoi(c); #ifdef PART if(io->n_data_sets > 1) { io->multigene = 1; } #endif if((io->do_boot || io->do_tbe) && (io->n_data_sets > 1)) { PhyML_Printf("\n. Bootstrap option is not allowed with multiple data sets !\n"); PhyML_Printf("\n. Type any key to exit."); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } Free(c); break; } case 'I' : { if(io->interleaved) io->interleaved = 0; else io->interleaved = 1; break; } case 'D' : { if(io->datatype == NT) { io->datatype = AA; io->mod->ns = 20; io->mod->s_opt->opt_kappa = 0; io->mod->whichmodel = LG; strcpy(io->mod->modelname->s,"LG"); } else if(io->datatype == AA) { io->datatype = GENERIC; io->mod->whichmodel = JC69; strcpy(io->mod->modelname->s,"JC69"); strcpy(io->nt_or_cd,"natural numbers"); } else if(io->datatype == GENERIC) { io->datatype = NT; io->mod->ns = 4; io->mod->whichmodel = HKY85; strcpy(io->mod->modelname->s,"HKY85"); strcpy(io->nt_or_cd,"nucleotides"); } break; } case '-' : { io->curr_interface = (io->config_multigene)?(INTERFACE_MODEL):(INTERFACE_BRANCH_SUPPORT); break; } case '+' : { io->curr_interface = (io->multigene)?(INTERFACE_MULTIGENE):(INTERFACE_MODEL); break; } case 'Y' : { io->ready_to_go = 1; break; } default : { break; } } Free(s); Free(buff); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Launch_Interface_Model(option *io) { char choix; char *s; s = (char *)mCalloc(100,sizeof(char)); Clear(); Print_Banner(stdout); if(io->config_multigene) Print_Data_Set_Number(io,stdout); PhyML_Printf("\n\n"); PhyML_Printf(" ........................... \n"); PhyML_Printf(" Menu : Substitution Model \n"); PhyML_Printf(" ................................. \n"); PhyML_Printf("\n\n"); PhyML_Printf(" [+] " ".................................... Next sub-menu\n"); PhyML_Printf(" [-] " "................................ Previous sub-menu\n"); PhyML_Printf(" [Y] " ".............................. Launch the analysis\n"); PhyML_Printf("\n"); if (io->datatype == NT) { if(!strcmp(io->nt_or_cd,"nucleotides")) { PhyML_Printf(" [M] " "................. Model of nucleotide substitution " " %-15s \n", io->mod->modelname->s); if((io->mod->whichmodel == F81) || (io->mod->whichmodel == HKY85) || (io->mod->whichmodel == F84) || (io->mod->whichmodel == TN93) || (io->mod->whichmodel == GTR) || (io->mod->whichmodel == CUSTOM)) { PhyML_Printf(" [F] " "................. Optimise equilibrium frequencies " " %-15s \n", (io->mod->s_opt->state_freq == ML)?("yes"):("no")); } if(io->mod->whichmodel == CUSTOM) { if(io->mod->s_opt->state_freq == EMPIRICAL) { PhyML_Printf(" [E] " "......... Equilibrium frequencies " " %-15s \n", "empirical"); } else if(io->mod->s_opt->state_freq == USER) { PhyML_Printf(" [E] " "......... Equilibrium frequencies " " %-15s \n", "user-defined"); } else if(io->mod->s_opt->state_freq == ML) { PhyML_Printf(" [E] " "......... Equilibrium frequencies " " %-15s \n", "optimized"); } PhyML_Printf(" [K] " "............................. Current custom model " " %-15s \n", io->mod->custom_mod_string->s); PhyML_Printf(" [O] " "................ Optimise relative rate parameters " " %-15s \n",(io->mod->s_opt->opt_rr)?("yes"):("no")); } } } else if(io->datatype == AA) { PhyML_Printf(" [M] " "................ Model of amino-acids substitution " " %-15s \n", io->mod->modelname->s); if(io->mod->s_opt->state_freq == EMPIRICAL) PhyML_Printf(" [F] " ". Amino acid frequencies " " %-15s \n", "empirical"); else if(io->mod->s_opt->state_freq == MODEL) PhyML_Printf(" [F] " ". Amino acid frequencies " " %-15s \n", "model"); else if(io->mod->s_opt->state_freq == ML) PhyML_Printf(" [F] " ". Amino acid frequencies " " %-15s \n", "optimized"); } else if(io->datatype == GENERIC) { PhyML_Printf(" [M] " "................. Model of nucleotide substitution " " %-15s \n", io->mod->modelname->s); } if ((io->datatype == NT) && ((io->mod->whichmodel == K80) || (io->mod->whichmodel == HKY85)|| (io->mod->whichmodel == F84) || (io->mod->whichmodel == TN93))) { strcpy(s,(io->mod->s_opt->opt_kappa)?("estimated"):("fixed")); if(io->mod->s_opt->opt_kappa) strcat(s,""); else strcat(s," (ts/tv = "); /* (io->mod->s_opt->opt_kappa)?((char *)strcat(s,"")):((char *)sprintf(s+(int)strlen((char *)s),"%3.2f)",io->mod->kappa->v)); */ if(io->mod->s_opt->opt_kappa) { strcat((char *)s,""); } else { sprintf((char *)(s+(int)strlen(s)),"%3.2f)",io->mod->kappa->v); } PhyML_Printf(" [T] " ".................... Ts/tv ratio (fixed/estimated) " " %-15s \n",s); } strcpy(s,io->mod->s_opt->opt_pinvar?"estimated":"fixed"); strcat(s,io->mod->s_opt->opt_pinvar?"":" (p-invar = "); if(io->mod->s_opt->opt_pinvar) { strcat(s,""); } else { sprintf((char *)(s+strlen((char *)s)),"%3.2f)",io->mod->ras->pinvar->v); } PhyML_Printf(" [V] " ". Proportion of invariable sites (fixed/estimated)" " %-15s \n",s); PhyML_Printf(" [R] " "....... One category of substitution rate (yes/no) " " %-15s \n", (io->mod->ras->n_catg > 1)?("no"):("yes")); if(io->mod->ras->n_catg > 1) { PhyML_Printf(" [C] " "........... Number of substitution rate categories " " %-15d \n", io->mod->ras->n_catg); } if(io->mod->ras->n_catg > 1) { PhyML_Printf(" [G] " "............. Gamma distributed rates across sites " " %-15s \n",(io->mod->ras->free_mixt_rates)?("no"):("yes")); } if((io->mod->ras->n_catg > 1) && (io->mod->ras->free_mixt_rates == NO)) { strcpy(s,(io->mod->s_opt->opt_alpha)?("estimated"):("fixed")); strcat(s,io->mod->s_opt->opt_alpha?"":" (alpha = "); if(io->mod->s_opt->opt_alpha) { strcat(s, ""); } else { sprintf(s+strlen(s),"%3.2f)",io->mod->ras->alpha->v); } PhyML_Printf(" [A] " "... Gamma distribution parameter (fixed/estimated) " " %-15s \n",s); /* strcpy(s,(io->mod->ras->gamma_median)?("median"):("mean")); */ /* PhyML_Printf(" [G] " */ /* ".........'Middle' of each rate class (mean/median) " */ /* " %-15s \n",s); */ } PhyML_Printf("\n\n. Are these settings correct ? " "(type '+', '-', 'Y' or other letter for one to change) "); if(!scanf("%c",&choix)) Exit("\n"); if(choix != '\n') getchar(); /* \n */ Uppercase(&choix); switch(choix) { /* case '\n': */ /* { */ /* io->curr_interface++; */ /* break; */ /* } */ case 'G' : { io->mod->ras->free_mixt_rates = (io->mod->ras->free_mixt_rates)?(NO):(YES); break; } case 'O' : { io->mod->s_opt->opt_rr = (io->mod->s_opt->opt_rr)?(0):(1); break; } case 'K' : { int i,j; char **rr_param,*rr; t_mod *mod; int n_trial; if(io->mod->whichmodel == CUSTOM) { rr_param = (char **)mCalloc(6,sizeof(char *)); for(i=0;i<6;i++) rr_param[i] = (char *)mCalloc(10,sizeof(char)); rr = (char *)mCalloc(50,sizeof(char)); mod = io->mod; mod->s_opt->opt_rr = 1; n_trial = 0; do { PhyML_Printf("\n. Enter a new custom model > "); Getstring_Stdin(io->mod->custom_mod_string->s); if(strlen(io->mod->custom_mod_string->s) == 6) { for(i=0;i<6;i++) { while(!isdigit((int)io->mod->custom_mod_string->s[i])) { if(++n_trial > 10) Exit("\n== Err : this string is not valid !\n"); PhyML_Printf("\n. This string is not valid\n"); PhyML_Printf("\n. Enter a new model > "); Getstring_Stdin(io->mod->custom_mod_string->s); } } if(i == 6) break; } else { PhyML_Printf("\n. The string should be of length 6\n"); n_trial++; } }while(n_trial < 10); if(n_trial == 10) Exit(""); if(!mod->r_mat) { mod->r_mat = (t_rmat *)Make_Rmat(mod->ns); Init_Rmat(mod->r_mat); Make_Custom_Model(mod); Translate_Custom_Mod_String(io->mod); } strcpy(rr_param[0],"A<->C"); strcpy(rr_param[1],"A<->G"); strcpy(rr_param[2],"A<->T"); strcpy(rr_param[3],"C<->G"); strcpy(rr_param[4],"C<->T"); strcpy(rr_param[5],"G<->T"); PhyML_Printf("\n. Set the relative rate values\n"); for(i=0;ir_mat->n_diff_rr;i++) { sprintf(rr,"\n. ["); for(j=0;j<6;j++) { if(mod->r_mat->rr_num->v[j] == i) { sprintf(rr+strlen(rr),"%s = ",rr_param[j]); } } sprintf(rr+strlen(rr)-3,"]"); PhyML_Printf("%s",rr); PhyML_Printf(" (current=%.2f) > ",mod->r_mat->rr_val->v[i]); Getstring_Stdin(rr); if(rr[0] != '\0') { n_trial = 0; while((atof(rr) < .0)) { if(++n_trial > 10) Exit("\n== Err : the value of this parameter must be a positive number\n"); PhyML_Printf("\n. The value of this parameter must be a positive number\n"); PhyML_Printf("\n. Enter a new value > "); Getstring_Stdin(rr); } io->mod->r_mat->rr_val->v[i] = (phydbl)atof(rr); } } for(i=0;i<6;i++) Free(rr_param[i]); Free(rr_param); Free(rr); } break; } case 'E' : { int i; if(io->mod->whichmodel == CUSTOM) { if(io->mod->s_opt->state_freq == EMPIRICAL) io->mod->s_opt->state_freq = ML; else if(io->mod->s_opt->state_freq == ML) io->mod->s_opt->state_freq = USER; else if(io->mod->s_opt->state_freq == USER) io->mod->s_opt->state_freq = MODEL; else if(io->mod->s_opt->state_freq == MODEL) io->mod->s_opt->state_freq = EMPIRICAL; if(io->mod->s_opt->state_freq == USER) { char **bases; char *bs; phydbl sum; int n_trial; bases = (char **)mCalloc(4,sizeof(char *)); for(i=0;i<4;i++) bases[i] = (char *)mCalloc(50,sizeof(char)); bs = (char *)mCalloc(100,sizeof(char)); strcpy(bases[0],". f(A)> "); strcpy(bases[1],". f(C)> "); strcpy(bases[2],". f(G)> "); strcpy(bases[3],". f(T)> "); PhyML_Printf("\n. Set nucleotide frequencies \n"); sum = .0; for(i=0;i<4;i++) { PhyML_Printf("%s",bases[i]); Getstring_Stdin(bs); n_trial = 0; while((atof(bs) < .0001) || (bs[0] == '\0')) { if(++n_trial > 10) Exit("\n== Err : the value of this parameter must be a positive number\n"); PhyML_Printf("\n. The value of this parameter must be a positive number\n"); PhyML_Printf("\n. Enter a new value > "); Getstring_Stdin(bs); } io->mod->e_frq->user_b_freq->v[i] = (phydbl)atof(bs); sum += io->mod->e_frq->user_b_freq->v[i]; } for(i=0;i<4;i++) io->mod->e_frq->user_b_freq->v[i] /= sum; if(sum > 1.0 || sum < 1.0) { PhyML_Printf("\n. The nucleotide frequencies have to be normalised in order to sum to 1.0.\n"); PhyML_Printf("\n. The frequencies are now : f(A)=%f, f(C)=%f, f(G)=%f, f(T)=%f.\n", io->mod->e_frq->user_b_freq->v[0], io->mod->e_frq->user_b_freq->v[1], io->mod->e_frq->user_b_freq->v[2], io->mod->e_frq->user_b_freq->v[3]); PhyML_Printf("\n. Enter any key to continue.\n"); if(!scanf("%c",bs)) Exit("\n"); } for(i=0;i<4;i++) Free(bases[i]); Free(bases); Free(bs); } else { Warn_And_Exit("\n. 'E' is not a valid option with these model settings.\n"); } } break; } case 'A' : { char answer; int n_trial; answer = 0; switch(io->mod->s_opt->opt_alpha) { case 0 : { PhyML_Printf("\n. Optimise alpha ? [Y/n] "); if(!scanf("%c",&answer)) Exit("\n"); if(answer == '\n') answer = 'Y'; else getchar(); break; } case 1 : { PhyML_Printf("\n. Optimise alpha ? [N/y] "); if(!scanf("%c",&answer)) Exit("\n"); if(answer == '\n') answer = 'N'; else getchar(); break; } default : Exit("\n"); } n_trial = 0; while((answer != 'Y') && (answer != 'y') && (answer != 'N') && (answer != 'n')) { if(++n_trial > 10) Exit("\n== Err : wrong answers !"); PhyML_Printf("\n. Optimise alpha ? [N/y] "); if(!scanf("%c",&answer)) Exit("\n"); if(answer == '\n') answer = 'N'; else getchar(); } switch(answer) { case 'Y' : case 'y' : { io->mod->s_opt->opt_alpha = 1; io->mod->s_opt->opt_subst_param = 1; break; } case 'N' : case 'n' : { char *a; a = (char *)mCalloc(100,sizeof(char)); io->mod->ras->alpha->v = 10.0; io->mod->s_opt->opt_alpha = 0; PhyML_Printf("\n. Enter your value of alpha > "); Getstring_Stdin(a); n_trial = 0; while(atof(a) < 1.E-10) { if(++n_trial > 10) Exit("\n== Err : alpha must be > 1.E-10\n"); PhyML_Printf("\n. Alpha must be 1.E-10\n"); PhyML_Printf("\n. Enter a new value > "); Getstring_Stdin(a); } io->mod->ras->alpha->v = (phydbl)atof(a); Free(a); io->mod->s_opt->opt_alpha = 0; break; } } break; } case 'C' : { char *c; int n_trial; PhyML_Printf("\n. Enter your number of categories > "); c = (char *)mCalloc(100,sizeof(char)); Getstring_Stdin(c); n_trial = 0; while((!atoi(c)) || (atoi(c) < 0)) { if(++n_trial > 10) Exit("\n== Err : the number of categories must be a positive integer\n"); PhyML_Printf("\n. The number of categories must be a positive integer\n"); PhyML_Printf("\n. Enter a new value > "); Getstring_Stdin(c); } io->mod->ras->n_catg = atoi(c); Free(c); break; } case 'R' : { io->mod->ras->n_catg = (io->mod->ras->n_catg == 1)?(4):(1); break; } case 'V' : { char answer; int n_trial; answer = 0; switch(io->mod->s_opt->opt_pinvar) { case 0 : { PhyML_Printf("\n. Optimise p-invar ? [Y/n] "); answer = 0; if(!scanf("%c", &answer)) Exit("\n"); if(answer == '\n') answer = 'Y'; else getchar(); break; } case 1 : { PhyML_Printf("\n. Optimise p-invar ? [N/y] "); if(!scanf("%c", &answer)) Exit("\n"); if(answer == '\n') answer = 'N'; else getchar(); break; } default : Exit("\n"); } n_trial = 0; while((answer != 'Y') && (answer != 'y') && (answer != 'N') && (answer != 'n')) { if(++n_trial > 10) Exit("\n== Err : wrong answers !"); PhyML_Printf("\n. Optimise p-invar ? [N/y] "); if(!scanf("%c", &answer)) Exit("\n"); if(answer == '\n') answer = 'N'; else getchar(); } switch(answer) { case 'Y' : case 'y' : { io->mod->s_opt->opt_subst_param = 1; io->mod->s_opt->opt_pinvar = 1; io->mod->ras->pinvar->v = 0.2; io->mod->ras->invar = 1; break; } case 'N' : case 'n' : { char *p; p = (char *)mCalloc(100,sizeof(char)); PhyML_Printf("\n. Enter your value of p-invar > "); Getstring_Stdin(p); n_trial = 0; while((atof(p) < 0.0) || (atof(p) > 1.0)) { if(++n_trial > 10) Exit("\n== Err : the proportion of invariable sites must be a positive number between 0.0 and 1.0"); PhyML_Printf("\n. The proportion must be a positive number between 0.0 and 1.0\n"); PhyML_Printf("\n. Enter a new value > "); Getstring_Stdin(p); } io->mod->ras->pinvar->v = (phydbl)atof(p); if(io->mod->ras->pinvar->v > 0.0+SMALL) io->mod->ras->invar = 1; else io->mod->ras->invar = 0; Free(p); io->mod->s_opt->opt_pinvar = 0; break; } } break; } case 'T' : { char answer; int n_trial; answer = 0; if((io->datatype == AA) || (io->mod->whichmodel == JC69)|| (io->mod->whichmodel == F81) || (io->mod->whichmodel == GTR) || (io->mod->whichmodel == CUSTOM)) { PhyML_Printf("\n. 'K' is not a valid choice for this model\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } switch(io->mod->s_opt->opt_kappa) { case 0 : { PhyML_Printf("\n. Optimise ts/tv ratio ? [Y/n] "); if(!scanf("%c", &answer)) Exit("\n"); if(answer == '\n') answer = 'Y'; else getchar(); break; } case 1 : { PhyML_Printf("\n. Optimise ts/tv ratio ? [N/y] "); if(!scanf("%c", &answer)) Exit("\n"); if(answer == '\n') answer = 'N'; else getchar(); break; } default : Exit("\n"); } n_trial = 0; while((answer != 'Y') && (answer != 'y') && (answer != 'N') && (answer != 'n')) { if(++n_trial > 10) Exit("\n== Err : wrong answers !"); PhyML_Printf("\n. Optimise ts/tv ratio ? [N/y] "); if(!scanf("%c", &answer)) Exit("\n"); if(answer == '\n') answer = 'N'; else getchar(); } switch(answer) { case 'Y' : case 'y' : { io->mod->kappa->v = 4.0; io->mod->s_opt->opt_subst_param = 1; io->mod->s_opt->opt_kappa = 1; if(io->mod->whichmodel == TN93) io->mod->s_opt->opt_lambda = 1; break; } case 'N' : case 'n' : { char *t; t = (char *)mCalloc(100,sizeof(char)); io->mod->s_opt->opt_kappa = 0; PhyML_Printf("\n. Enter your value of the ts/tv ratio > "); Getstring_Stdin(t); n_trial = 0; while(atof(t) < .0) { if(++n_trial > 10) Exit("\n== Err : the ts/tv ratio must be a positive number\n"); PhyML_Printf("\n. The ratio must be a positive number"); PhyML_Printf("\n. Enter a new value > "); Getstring_Stdin(t); } io->mod->kappa->v = (phydbl)atof(t); io->mod->s_opt->opt_kappa = 0; io->mod->s_opt->opt_lambda = 0; Free(t); break; } } break; } case 'F' : { if((io->mod->whichmodel == JC69) || (io->mod->whichmodel == K80)) { Warn_And_Exit("\n. 'F' is not a valid choice with these model settings.\n"); } if(io->mod->s_opt->state_freq == EMPIRICAL) io->mod->s_opt->state_freq = ML; else if(io->mod->s_opt->state_freq == ML) io->mod->s_opt->state_freq = USER; else if(io->mod->s_opt->state_freq == USER) io->mod->s_opt->state_freq = MODEL; else if(io->mod->s_opt->state_freq == MODEL) io->mod->s_opt->state_freq = EMPIRICAL; break; } case 'M' : { if(io->datatype == NT) { if(!strcmp(io->nt_or_cd,"nucleotides")) { if(io->mod->whichmodel == JC69) { io->mod->whichmodel = K80; } else if(io->mod->whichmodel == K80) { io->mod->whichmodel = F81; } else if(io->mod->whichmodel == F81) { io->mod->whichmodel = HKY85; } else if(io->mod->whichmodel == HKY85) { io->mod->whichmodel = F84; } else if(io->mod->whichmodel == F84) { io->mod->whichmodel = TN93; } else if(io->mod->whichmodel == TN93) { io->mod->whichmodel = GTR; } else if(io->mod->whichmodel == GTR) { io->mod->whichmodel = CUSTOM; } else if(io->mod->whichmodel == CUSTOM) { io->mod->whichmodel = JC69; } } } else if(io->datatype == AA) { if(io->mod->whichmodel == LG) { io->mod->whichmodel = WAG; } else if(io->mod->whichmodel == WAG) { io->mod->whichmodel = DAYHOFF; } else if(io->mod->whichmodel == DAYHOFF) { io->mod->whichmodel = JTT; } else if(io->mod->whichmodel == JTT) { io->mod->whichmodel = BLOSUM62; } else if(io->mod->whichmodel == BLOSUM62) { io->mod->whichmodel = MTREV; } else if(io->mod->whichmodel == MTREV) { io->mod->whichmodel = RTREV; } else if(io->mod->whichmodel == RTREV) { io->mod->whichmodel = CPREV; } else if(io->mod->whichmodel == CPREV) { io->mod->whichmodel = DCMUT; } else if(io->mod->whichmodel == DCMUT) { io->mod->whichmodel = VT; } else if(io->mod->whichmodel == VT) { io->mod->whichmodel = MTMAM; } else if(io->mod->whichmodel == MTMAM) { io->mod->whichmodel = MTART; } else if(io->mod->whichmodel == MTART) { io->mod->whichmodel = HIVW; } else if(io->mod->whichmodel == HIVW) { io->mod->whichmodel = HIVB; } else if(io->mod->whichmodel == HIVB) { io->mod->whichmodel = AB; } else if(io->mod->whichmodel == AB) { io->mod->whichmodel = CUSTOMAA; } else if(io->mod->whichmodel == CUSTOMAA) { io->mod->whichmodel = LG; } } else if(io->datatype == GENERIC) { io->mod->whichmodel = JC69; } Set_Model_Name(io->mod); break; } case '-' : { io->curr_interface = INTERFACE_DATA_TYPE; break; } case '+' : { io->curr_interface = (io->config_multigene)?(INTERFACE_DATA_TYPE):(INTERFACE_TOPO_SEARCH); break; } case 'Y' : { io->ready_to_go = 1; break; } default : { break; } } if(io->mod->s_opt->opt_alpha || io->mod->s_opt->opt_kappa || io->mod->s_opt->opt_lambda || io->mod->s_opt->opt_pinvar || io->mod->s_opt->opt_rr) io->mod->s_opt->opt_subst_param = 1; else io->mod->s_opt->opt_subst_param = 0; Free(s); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Launch_Interface_Topo_Search(option *io) { char choix; char *s; s = (char *)mCalloc(100,sizeof(char)); Clear(); Print_Banner(stdout); PhyML_Printf("\n\n"); PhyML_Printf(" ....................... \n"); PhyML_Printf(" Menu : Tree Searching \n"); PhyML_Printf(" ............................. \n"); PhyML_Printf("\n\n"); PhyML_Printf(" [+] " ".................................... Next sub-menu\n"); PhyML_Printf(" [-] " "................................ Previous sub-menu\n"); PhyML_Printf(" [Y] " ".............................. Launch the analysis\n"); PhyML_Printf("\n"); PhyML_Printf(" [O] " "........................... Optimise tree topology " " %-15s \n", (io->mod->s_opt->opt_topo)?("yes"):("no")); if(io->mod->s_opt->opt_topo) { switch(io->in_tree) { case 0: { strcpy(s,"BioNJ"); break; } case 1: { strcpy(s,"parsimony"); break; } case 2: { strcpy(s,"user tree"); break; } } PhyML_Printf(" [U] " "........ Starting tree (BioNJ/parsimony/user tree) " " %-15s \n",s); } else { switch(io->in_tree) { case 0: { strcpy(s,"BioNJ"); break; } case 2: { strcpy(s,"user tree"); break; } } PhyML_Printf(" [U] " "..................... Input tree (BioNJ/user tree) " " %-15s \n",s); } if(io->mod->s_opt->opt_topo) { char *s; s = (char *)mCalloc(T_MAX_OPTION,sizeof(char)); io->mod->s_opt->topo_search = SPR_MOVE; if(io->mod->s_opt->topo_search == NNI_MOVE) { /* strcpy(s,"NNI moves (fast, approximate)\0"); */ strcpy(s,"SPR moves\0"); } else if(io->mod->s_opt->topo_search == SPR_MOVE) { strcpy(s,"SPR moves\0"); } else if(io->mod->s_opt->topo_search == BEST_OF_NNI_AND_SPR) { strcpy(s,"SPR moves\0"); /* strcpy(s,"Best of NNI and SPR \0"); */ } PhyML_Printf(" [S] " ".................. Tree topology search operations " " %-15s \n",s); Free(s); if(io->mod->s_opt->topo_search != NNI_MOVE) { PhyML_Printf(" [R] " "........................ Add random starting trees " " %-15s \n", (io->mod->s_opt->random_input_tree)?("yes"):("no")); if(io->mod->s_opt->random_input_tree) { PhyML_Printf(" [N] " ".................. Number of random starting trees " " %-15d \n",io->mod->s_opt->n_rand_starts); } } } else { PhyML_Printf(" [L] " ".......................... Optimise branch lengths " " %-15s \n", (io->mod->s_opt->opt_bl)?("yes"):("no")); } PhyML_Printf("\n\n. Are these settings correct ? " "(type '+', '-', 'Y' or other letter for one to change) "); if(!scanf("%c",&choix)) Exit("\n"); if(choix != '\n') getchar(); /* \n */ Free(s); Uppercase(&choix); switch(choix) { case '-' : { io->curr_interface = INTERFACE_MODEL; break; } case '+' : { io->curr_interface = INTERFACE_BRANCH_SUPPORT; break; } case 'Y' : { io->ready_to_go = 1; break; } case 'U' : { io->in_tree++; if(!io->mod->s_opt->opt_topo) { if(io->in_tree == 1) io->in_tree = 2; } if(io->in_tree == 3) io->in_tree = 0; break; } case 'N' : { char *n; int n_trial; PhyML_Printf("\n. Enter your number of starting trees > "); n = (char *)mCalloc(100,sizeof(char)); Getstring_Stdin(n); n_trial = 0; while(atoi(n) < 1) { if(++n_trial > 10) Exit("\n== Err : the number of starting trees must be a positive integer\n"); PhyML_Printf("\n. The number of starting trees must be a positive integer\n"); PhyML_Printf("\n. Enter a new value > "); Getstring_Stdin(n); } io->mod->s_opt->n_rand_starts = atoi(n); io->n_trees = 1; Free(n); break; } case 'O' : { io->mod->s_opt->opt_topo = (io->mod->s_opt->opt_topo)?(0):(1); break; } case 'L' : { if(!io->mod->s_opt->opt_topo) { io->mod->s_opt->opt_bl = (io->mod->s_opt->opt_bl)?(0):(1); } break; } case 'S' : { if(io->mod->s_opt->topo_search == NNI_MOVE) { io->mod->s_opt->topo_search = SPR_MOVE; io->mod->s_opt->n_rand_starts = 1; io->mod->s_opt->random_input_tree = 0; io->mod->s_opt->greedy = 0; } else if(io->mod->s_opt->topo_search == SPR_MOVE) { io->mod->s_opt->topo_search = BEST_OF_NNI_AND_SPR; io->mod->s_opt->n_rand_starts = 1; io->mod->s_opt->random_input_tree = 0; io->mod->s_opt->greedy = 0; } else if(io->mod->s_opt->topo_search == BEST_OF_NNI_AND_SPR) { io->mod->s_opt->topo_search = NNI_MOVE; io->mod->s_opt->n_rand_starts = 1; io->mod->s_opt->random_input_tree = 0; io->mod->s_opt->greedy = 0; } break; } case 'R' : { io->mod->s_opt->random_input_tree = (io->mod->s_opt->random_input_tree)?(0):(1); if(io->mod->s_opt->random_input_tree) { if(io->fp_in_tree) fclose(io->fp_in_tree); /* io->in_tree = 0; */ io->n_trees = 1; io->mod->s_opt->n_rand_starts = 5; strcpy(io->out_trees_file,io->in_align_file); strcat(io->out_trees_file,"_phyml_trees.txt"); } break; } default : { break; } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Launch_Interface_Branch_Support(option *io) { char choix; char *s; s = (char *)mCalloc(100,sizeof(char)); Clear(); Print_Banner(stdout); PhyML_Printf("\n\n"); PhyML_Printf(" ....................... \n"); PhyML_Printf(" Menu : Branch Support \n"); PhyML_Printf(" ............................. \n"); PhyML_Printf("\n\n"); PhyML_Printf(" [+] " ".................................... Next sub-menu\n"); PhyML_Printf(" [-] " "................................ Previous sub-menu\n"); PhyML_Printf(" [Y] " ".............................. Launch the analysis\n"); PhyML_Printf("\n"); strcpy(s,(io->do_boot || io->do_tbe)?("yes"):("no")); if(io->n_boot_replicates > 0) sprintf(s+strlen(s)," (%d replicate%s%s)", io->n_boot_replicates, (io->n_boot_replicates>1)?("s"):(""), (io->do_tbe)?(", TBE"):("")); /* PhyML_Printf(" [+] " */ PhyML_Printf(" [B] " "................ Non parametric bootstrap analysis " " %-15s \n",s); if(io->ratio_test == 0) { strcpy(s,"no"); } else if(io->ratio_test == 1) { strcpy(s,"yes / aLRT statistics"); } else if(io->ratio_test == 2) { strcpy(s,"yes / Chi2-based supports"); } else if(io->ratio_test == 3) { strcpy(s,"yes / min of SH-like & Chi2-based supports"); } else if(io->ratio_test == 4) { strcpy(s,"yes / SH-like supports"); } else if(io->ratio_test == 5) { strcpy(s,"yes / aBayes supports"); } /* PhyML_Printf(" [+] " */ PhyML_Printf(" [A] " "................ Approximate likelihood ratio test " " %-15s \n",s); PhyML_Printf("\n. Are these settings correct ? " "(type '+', '-', 'Y' or other letter for one to change) "); if(!scanf("%c",&choix)) Exit("\n"); if(choix != '\n') getchar(); /* \n */ Uppercase(&choix); Free(s); switch(choix) { /* case '\n': */ /* { */ /* io->curr_interface++; */ /* break; */ /* } */ case 'B' : { if(io->n_boot_replicates > 0) io->n_boot_replicates = 0; else { char *r; char answer; int n_trial; answer = 0; io->ratio_test = 0; if(io->n_data_sets > 1) { PhyML_Printf("\n. Bootstrap option is not allowed with multiple data sets.\n"); PhyML_Printf("\n. Type any key to exit.\n"); if(!scanf("%c",&choix)) Exit("\n"); Exit("\n"); } PhyML_Printf("\n. Number of replicates > "); r = (char *)mCalloc(T_MAX_OPTION,sizeof(char)); Getstring_Stdin(r); n_trial = 0; while((!atoi(r)) || (atoi(r) < 0)) { if(++n_trial > 10) Exit("\n== Err : the number of replicates must be a positive integer\n"); PhyML_Printf("\n. The number of replicates must be a positive integer"); PhyML_Printf("\n. Enter a new value > "); Getstring_Stdin(r); } io->n_boot_replicates = atoi(r); PhyML_Printf("\n. Print bootstrap trees (and statistics) ? (%s) > ", (io->print_boot_trees)?("Y/n"):("y/N")); if(!scanf("%c",&answer)) Exit("\n"); if(answer == '\n') answer = (io->print_boot_trees)?('Y'):('N'); else getchar(); switch(answer) { case 'Y' : case 'y' : { io->print_boot_trees = 1; strcpy(io->out_boot_tree_file,io->in_align_file); strcat(io->out_boot_tree_file,"_phyml_boot_trees.txt"); io->fp_out_boot_tree = Openfile(io->out_boot_tree_file,1); strcpy(io->out_boot_stats_file,io->in_align_file); strcat(io->out_boot_stats_file,"_phyml_boot_stats.txt"); io->fp_out_boot_stats = Openfile(io->out_boot_stats_file,1); break; } case 'N' : case 'n' : { io->print_boot_trees = 0; io->fp_out_boot_tree = NULL; io->fp_out_boot_stats = NULL; break; } } PhyML_Printf("\n. Compute TBE instead of FBP ? (%s) > ", (io->do_tbe)?("Y/n"):("y/N")); if(!scanf("%c",&answer)) Exit("\n"); if(answer == '\n') answer = (io->do_tbe)?('Y'):('N'); else getchar(); switch(answer) { case 'Y' : case 'y' : { io->do_tbe = YES; io->do_boot = NO; break; } case 'N' : case 'n' : { io->do_tbe = NO; io->do_boot = YES; break; } } Free(r); } break; } case 'A' : { io->do_boot = NO; io->do_tbe = NO; io->do_alrt = YES; io->n_boot_replicates = 0; switch(io->ratio_test) { case 0 : { io->ratio_test = 1; break; } case 1 : { io->ratio_test = 2; break; } case 2 : { /* io->ratio_test = 3; */ io->ratio_test = 4; break; } case 3 : { io->ratio_test = 4; break; } case 4 : { io->ratio_test = 5; break; } case 5 : { io->ratio_test = 0; break; } } break; } case '-' : { io->curr_interface = INTERFACE_TOPO_SEARCH; break; } case '+' : { io->curr_interface = INTERFACE_DATA_TYPE; break; } case 'Y' : { io->ready_to_go = 1; break; } default : break; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Launch_Interface_Multigene(option *io) { #ifdef PART if((io->n_data_sets > 1) && (io->multigene)) { int set, n_trial; char choix; io->n_part = io->n_data_sets; io->st = (supert_tree *)PART_Make_Supert_tree_Light(io); io->st->n_part = io->n_data_sets; for(set=0;setn_part;set++) { io->st->optionlist[set] = Make_Input(); Set_Defaults_Input(io->st->optionlist[set]); Set_Defaults_Model(io->st->optionlist[set]->mod); Set_Defaults_Optimiz(io->st->optionlist[set]->mod->s_opt); io->st->optionlist[set]->curr_gt = set; PhyML_Printf("\n. Enter the sequence file name [data set %2d] > ",set+1); fflush(NULL); Getstring_Stdin(io->st->optionlist[set]->in_align_file); io->st->optionlist[set]->fp_in_align = Openfile(io->st->optionlist[set]->in_align_file,0); } PhyML_Printf("\n. Do you want to use your own initial tree ? [N/y] "); if(!scanf("%c", &choix)) Exit("\n"); if(choix != '\n') getchar(); n_trial = 0; while((choix != 'Y') && (choix != 'y') && (choix != 'N') && (choix != 'n')) { if(++n_trial > 10) Exit("\n== Err : wrong answers !"); PhyML_Printf("\n. Do you want to use your own initial tree ? [N/y] ? "); if(!scanf("%c", &choix)) Exit("\n"); if(choix == '\n') choix = 'N'; else getchar(); } switch(choix) { case '\n' : break; case 'Y' : case 'y' : { io->in_tree = 2; PhyML_Printf("\n. Enter the name of the tree file > "); Getstring_Stdin(io->in_tree_file); io->fp_in_tree = Openfile(io->in_tree_file,0); break; } case 'N' : case 'n' : { io->in_tree = 0; break; } default : break; } io->curr_gt = 0; do { io->st->optionlist[io->curr_gt]->config_multigene = 1; do { switch(io->st->optionlist[io->curr_gt]->curr_interface) { case INTERFACE_DATA_TYPE : { Launch_Interface_Data_Type(io->st->optionlist[io->curr_gt]); break; } case INTERFACE_MODEL : { Launch_Interface_Model(io->st->optionlist[io->curr_gt]); break; } default : { PhyML_Printf("\n== Err in file %s at line %d\n\n",__FILE__,__LINE__); Exit(""); break; } } }while(!io->st->optionlist[io->curr_gt]->ready_to_go); io->curr_gt++; }while(io->curr_gt < io->n_part); } io->ready_to_go = 1; PART_Fill_Model_Partitions_Table(io->st); #endif } phyml-3.3.20220408/src/interface.h000066400000000000000000000014451422404706600163210ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include #ifndef INTERFACE_H #define INTERFACE_H #include "utilities.h" #include "help.h" #include "models.h" #include "free.h" #include "help.h" void Launch_Interface(option *input); void Clear(); void Launch_Interface_Input(option *input); void Launch_Interface_Data_Type(option *input); void Launch_Interface_Model(option *input); void Launch_Interface_Topo_Search(option *input); void Launch_Interface_Branch_Support(option *input); void Launch_Interface_Multigene(option *input); #endif phyml-3.3.20220408/src/invitee.c000066400000000000000000002766561422404706600160410ustar00rootroot00000000000000#include "spr.h" #include "utilities.h" #include "lk.h" #include "optimiz.h" #include "bionj.h" #include "models.h" #include "free.h" #include "help.h" #include "simu.h" #include "eigen.h" #include "pars.h" #include "alrt.h" #include "mixt.h" #include "invitee.h" #ifdef MPI #include "mpi_boot.h" #endif #include ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void PhyTime_XML(char *xml_file) { FILE *f; char **clade, *clade_name, **mon_list; phydbl low, up; int i, j, n_taxa, clade_size, node_num, n_mon; //rnd_num xml_node *n_r, *n_t, *n_m, *n_cur; t_cal *last_calib; align **data, **c_seq; option *io; calign *cdata; t_opt *s_opt; t_mod *mod; time_t t_beg,t_end; int r_seed; char *most_likely_tree; int user_lk_approx; t_tree *tree; t_node **a_nodes; //*node; m4 *m4mod; srand(time(NULL)); i = 0; j = 0; last_calib = NULL; mod = NULL; most_likely_tree = NULL; n_taxa = 0; node_num = -1; //file can/cannot be open: if ((f =(FILE *)fopen(xml_file, "r")) == NULL) { PhyML_Printf("\n== File '%s' can not be opened...\n",xml_file); Exit("\n"); } n_r = XML_Load_File(f); //memory allocation for model parameters: io = (option *)Make_Input(); mod = (t_mod *)Make_Model_Basic(); s_opt = (t_opt *)Make_Optimiz(); m4mod = (m4 *)M4_Make_Light(); Set_Defaults_Input(io); Set_Defaults_Model(mod); Set_Defaults_Optimiz(s_opt); io -> mod = mod; mod = io -> mod; mod -> s_opt = s_opt; clade_size = -1; //////////////////////////////////////////////////////////////////////////// //////////////////////reading tree topology://////////////////////////////// //looking for a node n_t = XML_Search_Node_Name("topology", YES, n_r); //setting tree: /* tree = (t_tree *)mCalloc(1,sizeof(t_tree)); */ n_cur = XML_Search_Node_Name("instance", YES, n_t); if(n_cur != NULL) { if(XML_Search_Attribute(n_cur, "user.tree") != NULL) { strcpy(io -> out_tree_file, XML_Search_Attribute(n_cur, "user.tree") -> value); io -> fp_out_tree = Openfile(io -> out_tree_file, 1); io -> tree = Read_Tree_File_Phylip(io -> fp_in_tree); } else { PhyML_Printf("\n== No input tree was not found. \n"); PhyML_Printf("\n== Please specify either a tree file name or enter the whole tree directly in the XML file (in Newick format). \n"); Exit("\n"); } } else io -> tree = Read_Tree(&n_t -> value); io -> n_otu = io -> tree -> n_otu; tree = io -> tree; //setting initial values to n_calib: For(i, 2 * tree -> n_otu - 2) { tree -> a_nodes[i] -> n_calib = 0; //PhyML_Printf("\n. '%d' \n", tree -> a_nodes[i] -> n_calib); } //////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////// //memory for nodes: a_nodes = (t_node **)mCalloc(2 * io -> n_otu - 1,sizeof(t_node *)); For(i, 2 * io -> n_otu - 2) a_nodes[i] = (t_node *)mCalloc(1,sizeof(t_node)); //setting a model: tree -> rates = RATES_Make_Rate_Struct(io -> n_otu); RATES_Init_Rate_Struct(tree -> rates, io -> rates, tree -> n_otu); tree -> times = TIMES_Make_Time_Struct(io -> n_otu); TIMES_Init_Time_Struct(tree -> times, io -> times, tree -> n_otu); //reading seed: if(XML_Search_Attribute(n_r, "seed")) io -> r_seed = String_To_Dbl(XML_Search_Attribute(n_r, "seed") -> value); //TO DO: check that the tree has a root Update_Ancestors(io -> tree -> n_root, io -> tree -> n_root -> v[2], io -> tree -> n_root -> b[2], io -> tree); Update_Ancestors(io -> tree -> n_root, io -> tree -> n_root -> v[1], io -> tree -> n_root -> b[1], io -> tree); //////////////////////////////////////////////////////////////////////////// //////////////////////memory allocation for temp parameters///////////////// //memory for monitor flag: io -> mcmc -> monitor = (int *)mCalloc(2 * io -> n_otu - 1,sizeof(int)); //memory for sequences: n_cur = XML_Search_Node_Name("alignment", YES, n_r); data = (align **)mCalloc(io -> n_otu,sizeof(align *)); For(i, io -> n_otu) { data[i] = (align *)mCalloc(1,sizeof(align)); data[i] -> name = (char *)mCalloc(T_MAX_NAME,sizeof(char)); if(n_cur -> child -> value != NULL) data[i] -> state = (char *)mCalloc(strlen(n_cur -> child -> value) + 1,sizeof(char)); else data[i] -> state = (char *)mCalloc(T_MAX_SEQ,sizeof(char)); } io -> data = data; //tree -> data = data; //memory for clade: clade_name = (char *)mCalloc(T_MAX_NAME,sizeof(char)); //memory for list of clades to be monitored mon_list = (char **)mCalloc(T_MAX_FILE,sizeof(char *)); For(i, T_MAX_FILE) { mon_list[i] = (char *)mCalloc(T_MAX_NAME,sizeof(char)); } //////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////// //reading monitor node: i = 0; n_m = XML_Search_Node_Name("monitor", YES, n_r); if(n_m != NULL) { do { strcpy(mon_list[i], n_m -> child -> attr -> value); i++; if(n_m -> child) n_m -> child = n_m -> child -> next; else break; } while(n_m -> child); n_mon = i; } else { n_mon = 0; PhyML_Printf("\n. There is no clade to be monitored. \n"); } //////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////// //chekcing for calibration node (upper or lower bound) to exist: n_cur = XML_Search_Node_Name("calibration", YES, n_r); if(n_cur == NULL) { PhyML_Printf("\n== There is no calibration information provided. \n"); PhyML_Printf("\n== Please check your data. \n"); Exit("\n"); } else { if(XML_Search_Node_Name("upper", NO, n_cur -> child) == NULL && XML_Search_Node_Name("lower", NO, n_cur -> child) == NULL) { PhyML_Printf("\n== There is no calibration information provided. \n"); PhyML_Printf("\n== Please check your data. \n"); Exit("\n"); } } //////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////// n_r = n_r -> child; tree -> rates -> tot_num_cal = 0; do { if(!strcmp(n_r -> name, "alignment"))//looking for a node . { if(!n_r -> attr -> value) { PhyML_Printf("\n== Sequence type (nt / aa) must be provided. \n"); PhyML_Printf("\n== Please, include this information as an attribute of component <%s>. \n", n_r -> name); Exit("\n"); } char *s; s = To_Upper_String(n_r -> attr -> value); /* if(!strcmp(To_Upper_String(n_r -> attr -> value), "NT")) */ if(!strcmp(s, "NT")) { io -> datatype = 0; io -> mod -> ns = 4; } /* if(!strcmp(To_Upper_String(n_r -> attr -> value), "AA")) */ if(!strcmp(s, "AA")) { io -> datatype = 1; io -> mod -> ns = 20; } free(s); n_cur = XML_Search_Node_Name("instance", YES, n_r); if(n_cur != NULL) { if(XML_Search_Attribute(n_cur, "user.alignment") != NULL) { strcpy(io -> in_align_file, XML_Search_Attribute(n_cur, "user.alignment") -> value); io -> fp_in_align = Openfile(io -> in_align_file, 1); Detect_Align_File_Format(io); io -> data = Get_Seq(io); } } else { char *s; i = 0; s = To_Upper_String(n_r -> child -> value); do { strcpy(io -> in_align_file, "invitee"); strcpy(io -> data[i] -> name, n_r -> child -> attr -> value); /* strcpy(io -> data[i] -> state, To_Upper_String(n_r -> child -> value)); */ strcpy(io -> data[i] -> state, s); i++; if(n_r -> child -> next) n_r -> child = n_r -> child -> next; else break; } while(n_r -> child); n_taxa = i; free(s); } //checking if a sequences of the same lengths: i = 1; For(i, n_taxa) if(strlen(io -> data[0] -> state) != strlen(io -> data[i] -> state)) { PhyML_Printf("\n== All the sequences should be of the same length. Please check your data...\n"); Exit("\n"); break; } //checking sequence names: i = 0; For(i, n_taxa) Check_Sequence_Name(io -> data[i] -> name); //check if a number of tips is equal to a number of taxa: if(n_taxa != io -> n_otu) { PhyML_Printf("\n== The number of taxa is not the same as a number of tips. Check your data...\n"); Exit("\n"); } //deleting '-', etc. from sequences: io -> data[0] -> len = strlen(io -> data[0] -> state); Post_Process_Data(io); n_r = n_r -> next; } else if(!strcmp(n_r -> name, "calibration"))//looking for a node . { tree -> rates -> tot_num_cal++; if (tree -> rates -> calib == NULL) tree -> rates -> calib = Make_Calib(tree -> n_otu); if(last_calib) { last_calib -> next = tree -> rates -> calib; tree -> rates -> calib -> prev = last_calib; } last_calib = tree -> rates -> calib; low = -BIG; up = BIG; n_cur = XML_Search_Node_Name("lower", YES, n_r); if(n_cur != NULL) low = String_To_Dbl(n_cur -> value); n_cur = XML_Search_Node_Name("upper", YES, n_r); if(n_cur != NULL) up = String_To_Dbl(n_cur -> value); do { if(!strcmp("appliesto", n_r -> child -> name)) { //case of internal node: strcpy(clade_name, n_r -> child -> attr -> value);//reached clade names n_r -> child -> attr -> value if(!strcmp("root", clade_name)) { node_num = io -> tree -> n_root -> num; } else if(strcmp("NO_CLADE", clade_name) && strcmp("root", clade_name)) { xml_node *n_clade, *nd2; nd2 = n_r -> parent; n_clade = XML_Search_Node_Generic("clade", "id", clade_name, YES, nd2); if(n_clade) //found clade with a given name { i = 0; xml_node *nd; nd = n_clade -> child; clade = XML_Read_Clade(nd, tree); clade_size = XML_Number_Of_Taxa_In_Clade(nd); node_num = Find_Clade(clade, clade_size, io -> tree); } else { PhyML_Printf("\n== The calibration information for clade [%s] was not found.", clade_name); PhyML_Printf("\n== Err. in file %s at line %d\n",__FILE__,__LINE__); Exit("\n"); } } if(strcmp("NO_CLADE", clade_name)) { For(j, n_mon) { if(!strcmp(clade_name, mon_list[j])) io -> mcmc -> monitor[node_num] = YES; } } if(strcmp("NO_CLADE", clade_name)) { tree -> rates -> calib -> proba[node_num] = String_To_Dbl(n_r -> child -> attr -> next -> value); if(!n_r -> child -> attr -> next && n_r -> child -> next == NULL) tree -> rates -> calib -> proba[node_num] = 1.; if(!n_r -> child -> attr -> next && n_r -> child -> next) { PhyML_Printf("\n== You either need to provide information about the probability with which calibration "); PhyML_Printf("\n== applies to a node or you need to apply calibration only to one node."); PhyML_Printf("\n. Err. in file %s at line %d\n",__FILE__,__LINE__); Exit("\n"); } tree -> rates -> calib -> all_applies_to[tree -> rates -> calib -> n_all_applies_to] -> num = node_num; } else tree -> rates -> calib -> proba[2 * tree -> n_otu - 1] = String_To_Dbl(n_r -> child -> attr -> next -> value); tree -> rates -> calib -> n_all_applies_to++; tree -> rates -> calib -> lower = low; tree -> rates -> calib -> upper = up; PhyML_Printf("\n. Lower bound: %f.", low); PhyML_Printf("\n. Upper bound: %f.", up); ///////////////////////////////////////////////////////////////////////////////////////////////////// PhyML_Printf("\n. ......................................................................."); PhyML_Printf("\n"); if(strcmp(clade_name, "NO_CLADE")) PhyML_Printf("\n. Clade name: [%s]", clade_name); else { PhyML_Printf("\n. Calibration with:"); PhyML_Printf("\n. Lower bound set to: %15f time units.", low); PhyML_Printf("\n. Upper bound set to: %15f time units.", up); PhyML_Printf("\n. does not apply to any node with probability [%f]", String_To_Dbl(n_r -> child -> attr -> next -> value)); PhyML_Printf("\n. ......................................................................."); } if(strcmp(clade_name, "root") && strcmp(clade_name, "NO_CLADE")) { For(i, clade_size) PhyML_Printf("\n. Taxon name: [%s]", clade[i]); } if(strcmp(clade_name, "NO_CLADE")) { PhyML_Printf("\n. Node number to which calibration applies to is [%d] with probability [%f]", node_num, String_To_Dbl(n_r -> child -> attr -> next -> value)); PhyML_Printf("\n. Lower bound set to: %15f time units.", low); PhyML_Printf("\n. Upper bound set to: %15f time units.", up); PhyML_Printf("\n. ......................................................................."); } ///////////////////////////////////////////////////////////////////////////////////////////////////// if(n_r -> child -> next) n_r -> child = n_r -> child -> next; else break; } else if(n_r -> child -> next) n_r -> child = n_r -> child -> next; else break; } while(n_r -> child); tree -> rates -> calib = tree -> rates -> calib -> next; n_r = n_r -> next; } else if(!strcmp(n_r -> name, "ratematrices"))//initializing rate matrix: { if(n_r -> child) { Make_Ratematrice_From_XML_Node(n_r -> child, io, mod); n_r = n_r -> next; } else n_r = n_r -> next; } else if(!strcmp(n_r -> name, "equfreqs"))//initializing frequencies: { if(n_r -> child) { Make_Efrq_From_XML_Node(n_r -> child , io, mod); n_r = n_r -> next; } else n_r = n_r -> next; } else if(!strcmp(n_r -> name, "siterates"))//initializing site rates: { if(n_r -> child) { Make_RAS_From_XML_Node(n_r, io -> mod); n_r = n_r -> next; } else n_r = n_r -> next; } else if (n_r -> next) n_r = n_r -> next; else break; } while(1); tree -> rates -> calib = last_calib; while(tree -> rates -> calib -> prev) tree -> rates -> calib = tree -> rates -> calib -> prev; //////////////////////////////////////////////////////////////////////////////////////////////// //Check for the sum of probabilities for one calibration add up to one do { phydbl p = 0.0; for(i = tree -> n_otu; i < 2 * tree -> n_otu; i++) { p = p + tree -> rates -> calib -> proba[i]; /* PhyML_Printf("\n. # applies to %d \n", tree -> rates -> calib -> n_all_applies_to); */ /* PhyML_Printf("\n. %f \n", tree -> rates -> calib -> proba[i]); */ } if(!Are_Equal(p, 1.0, 1.E-10)) { PhyML_Printf("\n. ......................................................................."); PhyML_Printf("\n. WARNING! The sum of the probabilities for the calibration with:"); PhyML_Printf("\n. Lower bound set to: %15f time units.", tree -> rates -> calib -> lower); PhyML_Printf("\n. Upper bound set to: %15f time units.", tree -> rates -> calib -> upper); PhyML_Printf("\n. is not equal to 1."); PhyML_Printf("\n. The probability that this calibration does not apply to any node is set to [%f].", 1.0 - p); PhyML_Printf("\n. ......................................................................."); tree -> rates -> calib -> proba[2 * tree -> n_otu - 1] = 1.0 - p; tree -> rates -> calib -> n_all_applies_to++; /* PhyML_Printf("\n==You need to provide proper probabilities for the calibration. \n"); */ /* PhyML_Printf("\n. Err. in file %s at line %d\n",__FILE__,__LINE__); */ /* Exit("\n"); */ } if(tree -> rates -> calib -> next) tree -> rates -> calib = tree -> rates -> calib -> next; else break; } while(tree -> rates -> calib); while(tree -> rates -> calib -> prev) tree -> rates -> calib = tree -> rates -> calib -> prev; //////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////// //clear memory: free(clade_name); For(i, tree -> n_otu) { free(clade[i]); } free(clade); For(i, T_MAX_FILE) { free(mon_list[i]); } free(mon_list); /* do */ /* { */ /* printf("\n %f %f \n", tree -> rates -> calib -> lower, tree -> rates -> calib -> upper); */ /* printf("\n numb applies to: %d \n", tree -> rates -> calib -> n_all_applies_to); */ /* For(i, tree -> rates -> calib -> n_all_applies_to) printf("\n Node number %d \n", tree -> rates -> calib -> all_applies_to[i] -> num); */ /* if(tree -> rates -> calib -> next) tree -> rates -> calib = tree -> rates -> calib -> next; */ /* else break; */ /* } */ /* while(tree -> rates -> calib); */ /* while(tree -> rates -> calib -> prev) tree -> rates -> calib = tree -> rates -> calib -> prev; */ /* Exit("\n"); */ //////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////// //START analysis: r_seed = (io -> r_seed < 0)?(time(NULL)):(io -> r_seed); srand(r_seed); rand(); PhyML_Printf("\n. Seed: %d\n", r_seed); PhyML_Printf("\n. Pid: %d\n",getpid()); PhyML_Printf("\n. Compressing sequences...\n"); data = io -> data; data[0] -> len = strlen(data[0] -> state); //////////////////////////////////////////////////////////////////////////// //memory for compressed sequences: cdata = (calign *)mCalloc(1,sizeof(calign)); c_seq = (align **)mCalloc(io -> n_otu,sizeof(align *)); For(i, io -> n_otu) { c_seq[i] = (align *)mCalloc(1,sizeof(align)); c_seq[i] -> name = (char *)mCalloc(T_MAX_NAME,sizeof(char)); c_seq[i] -> state = (char *)mCalloc(data[0] -> len + 1,sizeof(char)); } cdata -> c_seq = c_seq; //////////////////////////////////////////////////////////////////////////// cdata = Compact_Data(data, io); Free_Seq(io -> data, cdata -> n_otu); io -> mod -> io = io; Check_Ambiguities(cdata, io -> mod -> io -> datatype, io -> state_len); Make_Model_Complete(mod); Init_Model(cdata, mod, io); if(io -> mod -> use_m4mod) M4_Init_Model(mod -> m4mod, cdata, mod); time(&(t_beg)); RATES_Fill_Lca_Table(tree); tree -> mod = mod; tree -> io = io; tree -> data = cdata; tree -> n_pattern = tree -> data -> crunch_len / tree -> io -> state_len; Set_Both_Sides(YES, tree); Make_Tree_For_Pars(tree); Make_Tree_For_Lk(tree); Make_Spr(tree); //calculate the probabilities of each combination of calibrations: TIMES_Calib_Partial_Proba(tree); int cal_numb = 0; do { if(!Are_Equal(tree -> rates -> times_partial_proba[cal_numb], 0.0, 1.E-10)) break; else cal_numb += 1; } while(1); PhyML_Printf("\n. Calibration number chosen %d.\n", cal_numb); Set_Current_Calibration(cal_numb, tree); tree -> rates -> numb_calib_chosen[cal_numb]++; int tot_num_comb; tot_num_comb = Number_Of_Comb(tree -> rates -> calib); PhyML_Printf("\n. The number of calibration combinations that is going to be considered is %d.\n", tot_num_comb); /* For(i, tot_num_comb) Set_Current_Calibration(i, tree); */ /* Exit("\n"); */ //set initial value for Hastings ratio for conditional jump: /* tree -> rates -> c_lnL_Hastings_ratio = 0.0; */ TIMES_Set_All_Node_Priors(tree); tree -> rates -> cur_comb_numb = cal_numb; tree -> rates -> log_K_cur = K_Constant_Prior_Times_Log(tree); TIMES_Get_Number_Of_Time_Slices(tree); TIMES_Label_Edges_With_Calibration_Intervals(tree); tree -> write_br_lens = NO; PhyML_Printf("\n. Input tree with calibration information ('almost' compatible with MCMCtree).\n"); char *t; t = Write_Tree(tree, YES); PhyML_Printf("\n. %s \n", t); free(t); tree -> write_br_lens = YES; // Work with log of branch lengths? if(tree -> mod -> log_l == YES) Log_Br_Len(tree); if(io -> mcmc -> use_data == YES) { // Force the exact likelihood score user_lk_approx = tree -> io -> lk_approx; tree -> io -> lk_approx = EXACT; /* MLE for branch lengths */ /* printf("\n. %s",Write_Tree(tree,NO)); */ /* printf("\n. alpha %f",tree->mod->ras->alpha->v); */ /* printf("\n. %f %f %f %f",tree->mod->e_frq->pi->v[0],tree->mod->e_frq->pi->v[1],tree->mod->e_frq->pi->v[2],tree->mod->e_frq->pi->v[3]); */ /* Lk(NULL,tree); */ /* printf("\n. %f",tree->c_lnL); */ /* Exit("\n"); */ Round_Optimize(tree, tree -> data, ROUND_MAX); // Set vector of mean branch lengths for the Normal approximation of the likelihood RATES_Set_Mean_L(tree); // Estimate the matrix of covariance for the Normal approximation of the likelihood PhyML_Printf("\n"); PhyML_Printf("\n. Computing Hessian..."); tree -> rates -> bl_from_rt = 0; Free(tree -> rates -> cov_l); tree -> rates -> cov_l = Hessian_Seo(tree); // tree->rates->cov_l = Hessian_Log(tree); For(i, (2 * tree -> n_otu - 3) * (2 * tree -> n_otu - 3)) tree -> rates -> cov_l[i] *= -1.0; if(!Iter_Matinv(tree -> rates -> cov_l, 2 * tree -> n_otu - 3, 2 * tree -> n_otu - 3, YES)) Exit("\n"); tree -> rates -> covdet = Matrix_Det(tree -> rates -> cov_l, 2 * tree -> n_otu - 3, YES); For(i,(2 * tree -> n_otu - 3) * (2 * tree -> n_otu - 3)) tree -> rates -> invcov[i] = tree -> rates -> cov_l[i]; if(!Iter_Matinv(tree -> rates -> invcov, 2 * tree -> n_otu - 3, 2 * tree -> n_otu - 3, YES)) Exit("\n"); tree -> rates -> grad_l = Gradient(tree); // Pre-calculation of conditional variances to speed up calculations RATES_Bl_To_Ml(tree); RATES_Get_Conditional_Variances(tree); RATES_Get_All_Reg_Coeff(tree); RATES_Get_Trip_Conditional_Variances(tree); RATES_Get_All_Trip_Reg_Coeff(tree); Lk(NULL, tree); PhyML_Printf("\n"); PhyML_Printf("\n. p(data|model) [exact ] ~ %.2f",tree -> c_lnL); tree -> io -> lk_approx = NORMAL; For(i,2 * tree -> n_otu - 3) tree -> rates -> u_cur_l[i] = tree -> rates -> mean_l[i] ; tree -> c_lnL = Lk(NULL,tree); PhyML_Printf("\n. p(data|model) [approx] ~ %.2f",tree -> c_lnL); tree -> io -> lk_approx = user_lk_approx; } tree -> rates -> model = io -> rates -> model; PhyML_Printf("\n. Selected model '%s' \n", RATES_Get_Model_Name(io -> rates -> model)); if(tree -> rates -> model == GUINDON) tree -> mod -> gamma_mgf_bl = YES; tree -> rates -> bl_from_rt = YES; if(tree -> io -> cstr_tree) Find_Surviving_Edges_In_Small_Tree(tree, tree -> io -> cstr_tree); time(&t_beg); tree -> mcmc = MCMC_Make_MCMC_Struct(); MCMC_Copy_MCMC_Struct(tree -> io -> mcmc, tree -> mcmc, "phytime"); tree -> mod -> m4mod = m4mod; MCMC_Complete_MCMC(tree -> mcmc, tree); tree -> mcmc -> is_burnin = NO; MCMC(tree); PhyML_Printf("\n"); for(i=0;i<6;i++) printf(" %d ", tree -> rates -> numb_calib_chosen[i]); PhyML_Printf("\n"); MCMC_Close_MCMC(tree -> mcmc); MCMC_Free_MCMC(tree -> mcmc); PhyML_Printf("\n"); Free_Calib(tree -> rates -> calib); Add_Root(tree->a_edges[0],tree); Free_Tree_Pars(tree); Free_Tree_Lk(tree); Free_Tree(tree); Free_Calign(cdata); Free_Model(mod); if(io -> fp_in_align) fclose(io -> fp_in_align); if(io -> fp_in_tree) fclose(io -> fp_in_tree); if(io -> fp_out_lk) fclose(io -> fp_out_lk); if(io -> fp_out_tree) fclose(io -> fp_out_tree); if(io -> fp_out_trees) fclose(io -> fp_out_trees); if(io -> fp_out_stats) fclose(io -> fp_out_stats); fclose(f); Free(most_likely_tree); Free_Input(io); time(&t_end); Print_Time_Info(t_beg,t_end); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //Calculate the prior probability for node times taking into account the //probailitis with which each calibration applies to the particular node. phydbl TIMES_Calib_Cond_Prob(t_tree *tree) { phydbl times_lk, *Yule_val, *times_partial_proba, times_tot_proba, c, constant, ln_t; int i, tot_num_comb; t_cal *calib; times_tot_proba = 0.0; calib = tree -> rates -> calib; times_partial_proba = tree -> rates -> times_partial_proba; tot_num_comb = Number_Of_Comb(calib); Yule_val = (phydbl *)mCalloc(tot_num_comb, sizeof(phydbl)); For(i, tot_num_comb) { Set_Current_Calibration(i, tree); int result; result = TRUE; TIMES_Set_All_Node_Priors_S(&result, tree); times_lk = TIMES_Lk_Yule_Order_Root_Cond(tree); constant = 0.0; if(tot_num_comb > 1 && times_lk > -INFINITY && result != FALSE && tree->rates->update_time_norm_const == YES) Yule_val[i] = constant + times_lk; while(calib -> prev) calib = calib -> prev; } /* Exit("\n"); */ c = .0; times_tot_proba = 0.0; For(i, tot_num_comb) { times_tot_proba += times_partial_proba[i] * exp(Yule_val[i] + c); } ln_t = -c + log(times_tot_proba); free(Yule_val); return(ln_t); } //////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////// //Function calculates the normalizing constant K of the joint distribution Yule_Order. //Use the fact that density Yule_Order can be used streight forward only in case of the complete //overlap of the calibration intervals for all of the nodes or in case of no overlap. phydbl K_Constant_Prior_Times_Log(t_tree *tree) { int i, j, k, f, n_otu, *indic, *n_slice, *slice_numbers; phydbl buf, chop_bound, *t_prior_min, *t_prior_max, *t_slice, *t_slice_min, *t_slice_max, *g_i_node; t_prior_min = tree -> rates -> t_prior_min; t_prior_max = tree -> rates -> t_prior_max; n_otu = tree -> n_otu; t_slice = (phydbl *)mCalloc(2 * (n_otu - 1), sizeof(phydbl)); //the vector of the union of lower and upper bounds, lined up in incresing order. t_slice_min = (phydbl *)mCalloc(2 * n_otu - 3, sizeof(phydbl)); //vector of the lower bounds of the sliced intervals. t_slice_max = (phydbl *)mCalloc(2 * n_otu - 3, sizeof(phydbl)); //vector of the upper bounds of the sliced intervals. indic = (int *)mCalloc((n_otu - 1) * (2 * n_otu - 3), sizeof(int)); //vector of the indicators, columns - node numbers (i + n_otu), rows - the number of the sliced interval. slice_numbers = (int *)mCalloc((n_otu - 1) * (2 * n_otu - 3), sizeof(int )); //vecor of the slice intervals numbers, columns node numbers (i + n_otu), rows - the number of the sliced interval. n_slice = (int *)mCalloc(n_otu - 1, sizeof(int)); //vector of the numbers of sliced intervals that apply to one node with number (i + n_otu). g_i_node = (phydbl *)mCalloc((n_otu - 1) * (2 * n_otu - 3), sizeof(phydbl)); //vector of the values of the function g evaluated for each node. i = 0; /* K = 0; */ j = n_otu; //////////////////////////////////////////////////////////////////////////// //Put prior bounds in one vector t_slice. Excluding tips. For(i, n_otu - 1) { t_slice[i] = t_prior_min[j]; j++; } j = n_otu; for(i = n_otu - 1; i < 2 * n_otu - 3; i++) { t_slice[i] = t_prior_max[j]; j++; } if(tree -> rates -> nd_t[tree -> n_root -> num] > t_prior_min[tree -> n_root -> num]) chop_bound = MIN(tree -> rates -> nd_t[tree -> n_root -> num], t_prior_max[tree -> n_root -> num]); else chop_bound = t_prior_min[tree -> n_root -> num]; t_slice[2 * n_otu - 3] = chop_bound; //////////////////////////////////////////////////////////////////////////// //Get slices in increasing order. Excluding tips. do { f = NO; For(j, 2 * n_otu - 3) { if(t_slice[j] > t_slice[j + 1]) { buf = t_slice[j]; t_slice[j] = t_slice[j + 1]; t_slice[j + 1] = buf; f = YES; } } } while(f); for(j = 1; j < 2 * n_otu - 2; j++) t_slice[j] = MAX(chop_bound, t_slice[j]); //////////////////////////////////////////////////////////////////////////// //Get the intervals with respect to slices. Total number of t_slice_min(max) - 2 * n_otu - 3. Excluding tips. i = 0; For(j, 2 * n_otu - 3) { t_slice_min[j] = t_slice[i]; t_slice_max[j] = t_slice[i + 1]; i++; } //////////////////////////////////////////////////////////////////////////// //Getting indicators for the node number [i + n_otu] to have slice. i = i + n_otu is the node number on the tree and j is the slice number, total //number of intervals is 2 * n_otu - 3. Excluding tips. For(i, n_otu - 1) { For(j, 2 * n_otu - 3) { if(Are_Equal(t_prior_min[i + n_otu], t_slice_min[j], 1.E-10) && t_prior_max[i + n_otu] > t_slice_max[j] && t_prior_min[i + n_otu] < t_slice_max[j] && !Are_Equal(t_slice_max[j], t_slice_min[j], 1.E-10)) indic[i * (2 * n_otu - 3) + j] = 1; else if(Are_Equal(t_prior_max[i + n_otu], t_slice_max[j], 1.E-10) && t_prior_min[i + n_otu] < t_slice_min[j] && t_prior_max[i + n_otu] > t_slice_min[j] && !Are_Equal(t_slice_max[j], t_slice_min[j], 1.E-10)) indic[i * (2 * n_otu - 3) + j] = 1; else if(t_prior_min[i + n_otu] < t_slice_min[j] && t_prior_max[i + n_otu] > t_slice_max[j] && !Are_Equal(t_slice_max[j], t_slice_min[j], 1.E-10)) indic[i * (2 * n_otu - 3) + j] = 1; else if(Are_Equal(t_prior_min[i + n_otu], t_slice_min[j], 1.E-10) && Are_Equal(t_prior_max[i + n_otu], t_slice_max[j], 1.E-10)) indic[i * (2 * n_otu - 3) + j] = 1; } } For(i, n_otu - 2) { indic[i * (2 * n_otu - 3)] = 0; } for(j = 1; j < 2 * n_otu - 3; j++) { indic[(n_otu - 2) * (2 * n_otu - 3) + j] = 0; } /* printf("\n"); */ /* printf(" ", j); */ /* For(j, 2 * n_otu - 3) printf(". '%d' ", j); */ /* printf("\n"); */ /* For(i, n_otu - 1) */ /* { */ /* printf(" ['%d'] ", i + n_otu); */ /* For(j, 2 * n_otu - 3) */ /* { */ /* printf(". '%d' ", indic[i * (2 * n_otu - 3) + j]); */ /* } */ /* printf("\n"); */ /* } */ //////////////////////////////////////////////////////////////////////////// //Running through all of the combinations of slices int *cur_slices, *cur_slices_shr, *cur_slices_cpy, *slices_start_node, shr_num_slices; phydbl *t_cur_slice_min, *t_cur_slice_max; phydbl tot_num_comb; phydbl log_g_i, lmbd; lmbd = tree -> rates -> birth_rate; tot_num_comb = 1; t_cur_slice_min = (phydbl *)mCalloc(n_otu - 1, sizeof(phydbl)); t_cur_slice_max = (phydbl *)mCalloc(n_otu - 1, sizeof(phydbl)); cur_slices = (int *)mCalloc(n_otu - 1, sizeof(int)); //the vector of the current slices with repetition. cur_slices_cpy = (int *)mCalloc(n_otu - 1, sizeof(int)); slices_start_node = (int *)mCalloc(n_otu - 1, sizeof(int)); cur_slices_shr = (int *)mCalloc(n_otu - 1, sizeof(int)); //the vector of the current slices without repetition. //////////////////////////////////////////////////////////////////////////// //Get the number of slices that can be applied for each node and the vectors of slice numbers for each node. For(i, n_otu - 1) { k = 0; For(j, 2 * n_otu - 3) { if(indic[i * (2 * n_otu - 3) + j] == 1) { ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// g_i_node[i * (2 * n_otu - 3) + j] = (exp(lmbd * t_slice_max[j]) - exp(lmbd * t_slice_min[j])) / (exp(lmbd * t_prior_max[i + n_otu]) - exp(lmbd * t_prior_min[i + n_otu])); ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// slice_numbers[i * (2 * n_otu - 3) + k] = j; n_slice[i]++; k++; } } } /* printf("\n"); */ /* For(i, n_otu - 1) */ /* { */ /* printf(" ['%d'] ", i + n_otu); */ /* For(j, 2 * n_otu - 3) */ /* { */ /* printf(". '%f' ", g_i_node[i * (2 * n_otu - 3) + j]); */ /* } */ /* printf("\n"); */ /* } */ /* printf("\n"); */ /* For(i, n_otu - 1) */ /* { */ /* printf(" ['%d'] ", i + n_otu); */ /* For(j, n_slice[i]) */ /* { */ /* printf(". '%d' ", slice_numbers[i * (2 * n_otu - 3) + j]); */ /* } */ /* printf("\n"); */ /* } */ For(i, n_otu - 1) { tot_num_comb = tot_num_comb * n_slice[i]; } int r, numb_unsuc, max_size, comb_numb, *combinations, *max_combination, numb_approx; /* **combinations; */ phydbl K_total; phydbl *t_slice_min_f, *t_slice_max_f, *K_val_approx; int *root_nodes, *dif; phydbl K_total_cur, max_K_val, scl_const; max_size = 1000000; combinations = (int *)mCalloc(max_size*(n_otu-1), sizeof(int)); max_combination = (int *)mCalloc((n_otu-1), sizeof(int)); t_slice_min_f = (phydbl *)mCalloc(n_otu - 1, sizeof(phydbl)); t_slice_max_f = (phydbl *)mCalloc(n_otu - 1, sizeof(phydbl)); K_val_approx = (phydbl *)mCalloc(max_size, sizeof(phydbl)); root_nodes = (int *)mCalloc(n_otu - 1, sizeof(int)); dif = (int *)mCalloc(n_otu - 1, sizeof(int)); numb_approx = 0; max_K_val = 0; scl_const = 0.0; /* lmbd = 4.0; */ numb_unsuc = 0; ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// ////////////////////////////////CALCULATING THE MAXIMUM VALUE OF m_i * g_i FOR ONE COMBINATION OF SLICES://////////////////////////////////////////////////////////////// comb_numb = 0; K_total = 0.0; do { /* printf("\n ____________________________________________________________________________________________________ \n"); */ int x = 0, f = FALSE; For(i, n_otu - 1) { r = rand()%(n_slice[i]); t_slice_min_f[i] = t_slice_min[slice_numbers[i * (2 * n_otu - 3) + r]]; t_slice_max_f[i] = t_slice_max[slice_numbers[i * (2 * n_otu - 3) + r]]; cur_slices[i] = slice_numbers[i * (2 * n_otu - 3) + r]; } For(i, n_otu - 1) { cur_slices_cpy[i] = cur_slices[i]; slices_start_node[i] = cur_slices[i]; } For(i, n_otu - 1) { for(j = i + 1; j < n_otu - 1; j++) { if(cur_slices[i] == cur_slices[j]) cur_slices[j] = -1; } } //For(i, n_otu - 1) printf(" Slice number'%d' \n", cur_slices[i]); shr_num_slices = 0; For(i, n_otu -1) { if(cur_slices[i] >= 0) { cur_slices_shr[shr_num_slices] = cur_slices[i]; shr_num_slices++; } } int result_1; int c = 0; int num_elem; result_1 = TRUE; Check_Time_Slices(tree -> n_root, tree -> n_root -> v[1], &result_1, t_slice_min_f, t_slice_max_f, tree); Check_Time_Slices(tree -> n_root, tree -> n_root -> v[2], &result_1, t_slice_min_f, t_slice_max_f, tree); /* printf("\n. [START] Result '%d' \n", result_1); */ K_total_cur = 0.0; if(result_1 != TRUE) K_total_cur = 0.0; else { int n_1, n_2; num_elem = 0; x = 0; f = FALSE; if(comb_numb > 0) { For(i, comb_numb) { x = 0; For(j, n_otu - 1) { dif[j] = combinations[i*(n_otu-1) + j] - cur_slices_cpy[j]; if(dif[j] == 0) x++; } if(x == n_otu - 1) f = TRUE; } } if(!f) { For(i, n_otu - 1) combinations[comb_numb*(n_otu - 1) + i] = cur_slices_cpy[i]; /* printf("\n"); */ /* printf(" [1][CUR SLICES PROPOSED] "); */ /* printf(" [COMB NUMBER] = [%d] --- ", comb_numb); */ /* For(i, n_otu - 1) printf(". [%d] .", combinations[comb_numb*(n_otu - 1) + i]); */ /* printf("\n"); */ For(i, shr_num_slices) { Search_Root_Node_In_Slice(tree -> n_root, tree -> n_root -> v[1], root_nodes, &num_elem, t_slice_min[cur_slices_shr[i]], t_slice_max[cur_slices_shr[i]], t_slice_min_f, t_slice_max_f, tree); Search_Root_Node_In_Slice(tree -> n_root, tree -> n_root -> v[2], root_nodes, &num_elem, t_slice_min[cur_slices_shr[i]], t_slice_max[cur_slices_shr[i]], t_slice_min_f, t_slice_max_f, tree); } /* for(i = 0; i < n_otu - 2; i++) printf("\n. [START] Node [%d] Slice_min [%f] Slice_max [%f] \n", i + n_otu, t_slice_min_f[i], t_slice_max_f[i]); */ For(j, num_elem) { n_1 = 0; n_2 = 0; Number_Of_Nodes_In_Slice(tree -> a_nodes[root_nodes[j] + n_otu], tree -> a_nodes[root_nodes[j] + n_otu] -> v[1], &n_1, t_slice_min_f, t_slice_max_f, tree); Number_Of_Nodes_In_Slice(tree -> a_nodes[root_nodes[j] + n_otu], tree -> a_nodes[root_nodes[j] + n_otu] -> v[2], &n_2, t_slice_min_f, t_slice_max_f, tree); /* printf("\n. n_1 [%d] n_2 [%d]\n", n_1, n_2); */ K_total_cur = K_total_cur + log(1) - log(n_1 + n_2 + 1) - LnGamma(n_1 + 1) - LnGamma(n_2 + 1); /* printf("\n. K_total_cur [%f] \n", K_total_cur); */ /* printf("\n. [START] log(m_i) [%f] \n", log(1) - log(n_1 + n_2 + 1) - LnGamma(n_1 + 1) - LnGamma(n_2 + 1)); */ } /* printf("\n. [START] log(m_i) [%f] \n", K_total_cur); */ /* printf("\n. K_total_cur [%f] \n", K_total_cur); */ log_g_i = 0.0; For(i, n_otu - 2) log_g_i += log_g_i(lmbd, t_slice_max_f[i], t_slice_min_f[i], t_prior_max[i + n_otu], MAX(t_prior_min[i + n_otu],tree->times->nd_t[tree->n_root->num])); /* printf("\n. [START] log(g_i) [%f] \n", log_g_i); */ K_total_cur = exp(K_total_cur + log_g_i + scl_const); if(K_total_cur > max_K_val) { For(i, n_otu - 1) max_combination[i] = cur_slices_cpy[i]; max_K_val = K_total_cur; } K_val_approx[numb_approx] = K_total_cur; numb_approx++; /* printf("\n. [APPROX] Approximated constant after start (K_total_cur) = [%f] \n", (K_total_cur)); */ /* printf("\n. [START] m_i * g_i [%f] \n", K_total_cur); */ /* printf("\n. [START] sum(m_i * g_i) = m_i * g_i [%f] \n", K_total_cur); */ /* printf("\n. K of the tree for starting combination of slices [%f] \n", K_total_cur); */ comb_numb++; if(comb_numb > max_size) { combinations = (int *)mRealloc(combinations, max_size*(n_otu - 1) + (n_otu - 1),sizeof(char)); max_size = max_size + 1; } c++; } } ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// /* printf("\n. ____________________________________________________________________________________________ \n"); */ phydbl K_part, K_max; int m, n, count; K_max = K_total_cur; /* printf("\n. K_total [%f] \n", K_total); */ do { r = rand()%(n_otu - 1); count = 0; For(m, r) { /* printf("\n. NODE NUMBER ['%d'] \n", m + n_otu); */ For(n, 2 * n_otu - 3) { K_part = 0.0; if(g_i_node[m * (2 * n_otu - 3) + n] > 0) { t_slice_min_f[m] = t_slice_min[n]; t_slice_max_f[m] = t_slice_max[n]; cur_slices_cpy[m] = n; For(i, n_otu - 1) cur_slices[i] = cur_slices_cpy[i]; For(i, n_otu - 1) { for(j = i + 1; j < n_otu - 1; j++) { if(cur_slices[i] == cur_slices[j]) cur_slices[j] = -1; } } shr_num_slices = 0; For(i, n_otu -1) { if(cur_slices[i] >= 0) { cur_slices_shr[shr_num_slices] = cur_slices[i]; shr_num_slices++; } } result_1 = TRUE; Check_Time_Slices(tree -> n_root, tree -> n_root -> v[1], &result_1, t_slice_min_f, t_slice_max_f, tree); Check_Time_Slices(tree -> n_root, tree -> n_root -> v[2], &result_1, t_slice_min_f, t_slice_max_f, tree); if(result_1 != TRUE) K_part = 0.0; else { int n_1, n_2; x = 0; f = FALSE; num_elem = 0; if(comb_numb > 0) { For(i, comb_numb) { x = 0; For(j, n_otu - 1) { dif[j] = combinations[i*(n_otu-1) + j] - cur_slices_cpy[j]; if(dif[j] == 0) x++; } if(x == n_otu - 1) f = TRUE; } } if(!f) { For(i, n_otu - 1) combinations[comb_numb*(n_otu-1) + i] = cur_slices_cpy[i]; /* printf("\n"); */ /* printf(" [2][CUR SLICES PROPOSED] "); */ /* printf(" [COMB NUMBER] = [%d] --- ", comb_numb); */ /* For(i, n_otu - 1) printf(". [%d] .", combinations[comb_numb*(n_otu-1) + i]); */ /* printf("\n"); */ For(i, shr_num_slices) { Search_Root_Node_In_Slice(tree -> n_root, tree -> n_root -> v[1], root_nodes, &num_elem, t_slice_min[cur_slices_shr[i]], t_slice_max[cur_slices_shr[i]], t_slice_min_f, t_slice_max_f, tree); Search_Root_Node_In_Slice(tree -> n_root, tree -> n_root -> v[2], root_nodes, &num_elem, t_slice_min[cur_slices_shr[i]], t_slice_max[cur_slices_shr[i]], t_slice_min_f, t_slice_max_f, tree); } For(j, num_elem) { n_1 = 0; n_2 = 0; Number_Of_Nodes_In_Slice(tree -> a_nodes[root_nodes[j] + n_otu], tree -> a_nodes[root_nodes[j] + n_otu] -> v[1], &n_1, t_slice_min_f, t_slice_max_f, tree); Number_Of_Nodes_In_Slice(tree -> a_nodes[root_nodes[j] + n_otu], tree -> a_nodes[root_nodes[j] + n_otu] -> v[2], &n_2, t_slice_min_f, t_slice_max_f, tree); /* printf("\n. n_1 [%d] n_2 [%d]\n", n_1, n_2); */ K_part = K_part + log(1) - log(n_1 + n_2 + 1) - LnGamma(n_1 + 1) - LnGamma(n_2 + 1); /* printf("\n. [CONT] log(m_i) [%f] \n", log(1) - log(n_1 + n_2 + 1) - LnGamma(n_1 + 1) - LnGamma(n_2 + 1)); */ } /* printf("\n. [CONT] log(m_i) [%f] \n", K_part); */ /* printf("\n. K_part [%f] \n", K_part); */ if(K_part > max_K_val) { For(i, n_otu - 1) max_combination[i] = cur_slices_cpy[i]; max_K_val = K_part; } log_g_i = 0.0; For(i, n_otu - 2) log_g_i += log_g_i(lmbd, t_slice_max_f[i], t_slice_min_f[i], t_prior_max[i + n_otu], MAX(t_prior_min[i + n_otu],tree->times->nd_t[tree->n_root->num])); /* printf("\n. [START] log(g_i) [%f] \n", log_g_i); */ K_part = exp(K_part + log_g_i + scl_const); if(K_part > max_K_val) { For(i, n_otu - 1) max_combination[i] = cur_slices_cpy[i]; max_K_val = K_part; } /* printf("\n. [START] m_i * g_i [%f] \n", K_part); */ K_val_approx[numb_approx] = K_part; numb_approx++; K_total_cur = K_total_cur + K_part; /* printf("\n. [APPROX] Approximated constant after one run (K_part) = [%f] \n", (K_part)); */ /* printf("\n. K_max [%f] K_part [%f] \n", K_max, K_part); */ /* printf("\n. [CONT] sum(m_i * g_i) [%f] \n", K_total_cur); */ if(K_max < K_part) { K_max = K_part; /* For(i, n_otu - 1) slices_start_node[i] = cur_slices_cpy[i]; */ count++; } comb_numb++; if(comb_numb > max_size) { combinations = (int *)mRealloc(combinations, max_size*(n_otu - 1) + (n_otu - 1),sizeof(char)); max_size = max_size + 1; } c++; } } } } } for(m = r; m < n_otu - 1; m++) { For(n, 2 * n_otu - 3) { K_part = 0.0; if(g_i_node[m * (2 * n_otu - 3) + n] > 0) { t_slice_min_f[m] = t_slice_min[n]; t_slice_max_f[m] = t_slice_max[n]; cur_slices_cpy[m] = n; For(i, n_otu - 1) cur_slices[i] = cur_slices_cpy[i]; For(i, n_otu - 1) { for(j = i + 1; j < n_otu - 1; j++) { if(cur_slices[i] == cur_slices[j]) cur_slices[j] = -1; } } shr_num_slices = 0; For(i, n_otu -1) { if(cur_slices[i] >= 0) { cur_slices_shr[shr_num_slices] = cur_slices[i]; shr_num_slices++; } } result_1 = TRUE; Check_Time_Slices(tree -> n_root, tree -> n_root -> v[1], &result_1, t_slice_min_f, t_slice_max_f, tree); Check_Time_Slices(tree -> n_root, tree -> n_root -> v[2], &result_1, t_slice_min_f, t_slice_max_f, tree); if(result_1 != TRUE) K_part = 0.0; else { int n_1, n_2; x = 0; f = FALSE; num_elem = 0; if(comb_numb > 0) { For(i, comb_numb) { x = 0; For(j, n_otu - 1) { dif[j] = combinations[i*(n_otu-1) + j] - cur_slices_cpy[j]; /* dif[j] = combinations[i][j] - cur_slices_cpy[j]; */ if(dif[j] == 0) x++; /* printf(". [%d] ", dif[j]); */ } if(x == n_otu - 1) f = TRUE; } } if(!f) { For(i, n_otu - 1) combinations[comb_numb*(n_otu-1) + i] = cur_slices_cpy[i]; /* printf("\n"); */ /* printf(" [2][CUR SLICES PROPOSED] "); */ /* printf(" [COMB NUMBER] = [%d] --- ", comb_numb); */ /* For(i, n_otu - 1) printf(". [%d] .", combinations[comb_numb*(n_otu-1) + i]); */ /* printf("\n"); */ For(i, shr_num_slices) { Search_Root_Node_In_Slice(tree -> n_root, tree -> n_root -> v[1], root_nodes, &num_elem, t_slice_min[cur_slices_shr[i]], t_slice_max[cur_slices_shr[i]], t_slice_min_f, t_slice_max_f, tree); Search_Root_Node_In_Slice(tree -> n_root, tree -> n_root -> v[2], root_nodes, &num_elem, t_slice_min[cur_slices_shr[i]], t_slice_max[cur_slices_shr[i]], t_slice_min_f, t_slice_max_f, tree); } For(j, num_elem) { n_1 = 0; n_2 = 0; Number_Of_Nodes_In_Slice(tree -> a_nodes[root_nodes[j] + n_otu], tree -> a_nodes[root_nodes[j] + n_otu] -> v[1], &n_1, t_slice_min_f, t_slice_max_f, tree); Number_Of_Nodes_In_Slice(tree -> a_nodes[root_nodes[j] + n_otu], tree -> a_nodes[root_nodes[j] + n_otu] -> v[2], &n_2, t_slice_min_f, t_slice_max_f, tree); /* printf("\n. n_1 [%d] n_2 [%d]\n", n_1, n_2); */ K_part = K_part + log(1) - log(n_1 + n_2 + 1) - LnGamma(n_1 + 1) - LnGamma(n_2 + 1); /* printf("\n. [CONT] log(m_i) [%f] \n", log(1) - log(n_1 + n_2 + 1) - LnGamma(n_1 + 1) - LnGamma(n_2 + 1)); */ } /* printf("\n. [CONT] log(m_i) [%f] \n", K_part); */ /* printf("\n. K_part [%f] \n", K_part); */ log_g_i = 0.0; For(i, n_otu - 2) log_g_i += log_g_i(lmbd, t_slice_max_f[i], t_slice_min_f[i], t_prior_max[i + n_otu], MAX(t_prior_min[i + n_otu],tree->times->nd_t[tree->n_root->num])); /* printf("\n. [START] log(g_i) [%f] \n", log_g_i); */ K_part = exp(K_part + log_g_i + scl_const); if(K_part > max_K_val) { For(i, n_otu - 1) max_combination[i] = cur_slices_cpy[i]; max_K_val = K_part; } /* printf("\n. [START] m_i * g_i [%f] \n", K_part); */ K_val_approx[numb_approx] = K_part; numb_approx++; K_total_cur = K_total_cur + K_part; /* printf("\n. [APPROX] Approximated constant after one run (K_part) = [%f] \n", (K_part)); */ /* printf("\n. K_max [%f] K_part [%f] \n", K_max, K_part); */ /* printf("\n. [CONT] sum(m_i * g_i) [%f] \n", K_total_cur); */ if(K_max < K_part) { K_max = K_part; /* For(i, n_otu - 1) slices_start_node[i] = cur_slices_cpy[i]; */ count++; } comb_numb++; if(comb_numb > max_size) { combinations = (int *)mRealloc(combinations, max_size*(n_otu - 1) + (n_otu - 1),sizeof(char)); max_size = max_size + 1; } c++; } } } } } if(Are_Equal(count, 0, 1.E-10)) break; } while(1); ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// For(m, n_otu - 1) { For(i, n_otu - 1) cur_slices_cpy[i] = max_combination[i]; For(i, n_otu - 1) { t_slice_min_f[i] = t_slice_min[cur_slices_cpy[i]]; t_slice_max_f[i] = t_slice_max[cur_slices_cpy[i]]; } For(n, 2 * n_otu - 3) { K_part = 0.0; if(g_i_node[m * (2 * n_otu - 3) + n] > 0) { t_slice_min_f[m] = t_slice_min[n]; t_slice_max_f[m] = t_slice_max[n]; cur_slices_cpy[m] = n; For(i, n_otu - 1) cur_slices[i] = cur_slices_cpy[i]; For(i, n_otu - 1) { for(j = i + 1; j < n_otu - 1; j++) { if(cur_slices[i] == cur_slices[j]) cur_slices[j] = -1; } } shr_num_slices = 0; For(i, n_otu -1) { if(cur_slices[i] >= 0) { cur_slices_shr[shr_num_slices] = cur_slices[i]; shr_num_slices++; } } result_1 = TRUE; Check_Time_Slices(tree -> n_root, tree -> n_root -> v[1], &result_1, t_slice_min_f, t_slice_max_f, tree); Check_Time_Slices(tree -> n_root, tree -> n_root -> v[2], &result_1, t_slice_min_f, t_slice_max_f, tree); if(result_1 != TRUE) K_part = 0.0; else { int n_1, n_2; x = 0; f = FALSE; num_elem = 0; if(comb_numb > 0) { For(i, comb_numb) { x = 0; For(j, n_otu - 1) { dif[j] = combinations[i*(n_otu-1) + j] - cur_slices_cpy[j]; /* dif[j] = combinations[i][j] - cur_slices_cpy[j]; */ if(dif[j] == 0) x++; /* printf(". [%d] ", dif[j]); */ } if(x == n_otu - 1) f = TRUE; } } if(!f) { For(i, n_otu - 1) combinations[comb_numb*(n_otu-1) + i] = cur_slices_cpy[i]; /* printf("\n"); */ /* printf(" [2][CUR SLICES PROPOSED] "); */ /* printf(" [COMB NUMBER] = [%d] --- ", comb_numb); */ /* For(i, n_otu - 1) printf(". [%d] .", combinations[comb_numb*(n_otu-1) + i]); */ /* printf("\n"); */ For(i, shr_num_slices) { Search_Root_Node_In_Slice(tree -> n_root, tree -> n_root -> v[1], root_nodes, &num_elem, t_slice_min[cur_slices_shr[i]], t_slice_max[cur_slices_shr[i]], t_slice_min_f, t_slice_max_f, tree); Search_Root_Node_In_Slice(tree -> n_root, tree -> n_root -> v[2], root_nodes, &num_elem, t_slice_min[cur_slices_shr[i]], t_slice_max[cur_slices_shr[i]], t_slice_min_f, t_slice_max_f, tree); } For(j, num_elem) { n_1 = 0; n_2 = 0; Number_Of_Nodes_In_Slice(tree -> a_nodes[root_nodes[j] + n_otu], tree -> a_nodes[root_nodes[j] + n_otu] -> v[1], &n_1, t_slice_min_f, t_slice_max_f, tree); Number_Of_Nodes_In_Slice(tree -> a_nodes[root_nodes[j] + n_otu], tree -> a_nodes[root_nodes[j] + n_otu] -> v[2], &n_2, t_slice_min_f, t_slice_max_f, tree); /* printf("\n. n_1 [%d] n_2 [%d]\n", n_1, n_2); */ K_part = K_part + log(1) - log(n_1 + n_2 + 1) - LnGamma(n_1 + 1) - LnGamma(n_2 + 1); /* printf("\n. [CONT] log(m_i) [%f] \n", log(1) - log(n_1 + n_2 + 1) - LnGamma(n_1 + 1) - LnGamma(n_2 + 1)); */ } /* printf("\n. [CONT] log(m_i) [%f] \n", K_part); */ /* printf("\n. K_part [%f] \n", K_part); */ log_g_i = 0.0; For(i, n_otu - 2) log_g_i += log_g_i(lmbd, t_slice_max_f[i], t_slice_min_f[i], t_prior_max[i + n_otu], MAX(t_prior_min[i + n_otu],tree->times->nd_t[tree->n_root->num])); /* printf("\n. [START] log(g_i) [%f] \n", log_g_i); */ K_part = exp(K_part + log_g_i + scl_const); if(K_part > max_K_val) { For(i, n_otu - 1) max_combination[i] = cur_slices_cpy[i]; max_K_val = K_part; } /* printf("\n. [START] m_i * g_i [%f] \n", K_part); */ K_val_approx[numb_approx] = K_part; numb_approx++; K_total_cur = K_total_cur + K_part; comb_numb++; if(comb_numb > max_size) { combinations = (int *)mRealloc(combinations, max_size*(n_otu - 1) + (n_otu - 1),sizeof(char)); max_size = max_size + 1; } c++; } } } } } ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// K_total = K_total + K_total_cur; /* printf("\n. [APPROX TOTAL] Approximated constant (K_total) = [%f] \n", (K_total)); */ /* printf("\n%G %d", K_total, c); */ if(Are_Equal(c, 0, 1.E-10)) numb_unsuc++; else numb_unsuc = 0; } while(numb_unsuc < 100); /* printf("\n"); */ /* printf("\n. [APPROX TOTAL] log(K_total) = [%f] \n", log(K_total)); */ /* printf("\n. [APPROX TOTAL] log(Constant) = scl_const - log(K_total) = [%f] \n", scl_const - log(K_total)); */ /* printf("\n. [APPROX TOTAL] Approximated constant 1 / (K_total) = [%f] \n", 1 / (K_total)); */ /* printf("\n ____________________________________________________________________________________________________ \n"); */ /* printf("\n. [APPROX TOTAL] Numb_approx = [%d] \n", numb_approx); */ /* printf("\n"); */ /* } */ ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// free(combinations); free(max_combination); free(dif); free(t_cur_slice_min); free(t_cur_slice_max); free(cur_slices); free(cur_slices_cpy); free(slices_start_node); free(cur_slices_shr); free(t_slice); free(t_slice_min); free(t_slice_max); free(t_slice_min_f); free(t_slice_max_f); free(indic); free(slice_numbers); free(root_nodes); free(n_slice); free(g_i_node); free(K_val_approx); return(-log(K_total)); } //////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////// int Number_Of_Comb_Slices(int m, int num_elem, int *n_slice) { int i, num_comb; i = 0; num_comb = 1; for(i = m; i < num_elem; i++) num_comb = num_comb * n_slice[i]; return(num_comb); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //Check the combination of the time slices to be set correctly. void Check_Time_Slices(t_node *a, t_node *d, int *result, phydbl *t_cur_slice_min, phydbl *t_cur_slice_max, t_tree *tree) { int n_otu; n_otu = tree -> n_otu; d -> anc = a; if(d -> tax) return; else { if(t_cur_slice_max[d -> num - n_otu] < t_cur_slice_max[a -> num - n_otu]) { *result = FALSE; } int i; for(i=0;i<3;i++) if((d -> v[i] != d -> anc) && (d -> b[i] != tree -> e_root)) Check_Time_Slices(d, d -> v[i], result, t_cur_slice_min, t_cur_slice_max, tree); } } ////////////////////////////////////////////////////////////// ///////////////////////////////////////////////////////////// //Getting the number of nodes on both sides from the node d_start, that are in the slice of that node. void Number_Of_Nodes_In_Slice(t_node *d_start, t_node *d, int *n, phydbl *t_cur_slice_min, phydbl *t_cur_slice_max, t_tree *tree) { int n_otu; n_otu = tree -> n_otu; if(d -> tax) return; else { if(Are_Equal(t_cur_slice_max[d_start -> num - n_otu], t_cur_slice_max[d -> num - n_otu], 1.E-10) && Are_Equal(t_cur_slice_min[d_start -> num - n_otu], t_cur_slice_min[d -> num - n_otu], 1.E-10)) { (*n)++; int i; for(i=0;i<3;i++) if((d -> v[i] != d -> anc) && (d -> b[i] != tree -> e_root)) Number_Of_Nodes_In_Slice(d_start, d -> v[i], n, t_cur_slice_min, t_cur_slice_max, tree); } } } ////////////////////////////////////////////////////////////// ///////////////////////////////////////////////////////////// //Returnig the root node in the given slice. void Search_Root_Node_In_Slice(t_node *d_start, t_node *d, int *root_nodes, int *num_elem, phydbl t_slice_min, phydbl t_slice_max, phydbl *t_cur_slice_min, phydbl *t_cur_slice_max, t_tree *tree) { int j, n_otu, f; j = 0; f = FALSE; n_otu = tree -> n_otu; if(Are_Equal(t_cur_slice_max[d_start -> num - n_otu], t_slice_max, 1.E-10) && Are_Equal(t_cur_slice_min[d_start -> num - n_otu], t_slice_min, 1.E-10)) { For(j, *num_elem) if(d_start -> num - n_otu == (root_nodes[j])) f = TRUE; if(f != TRUE) { (root_nodes[(*num_elem)]) = d_start -> num - n_otu; (*num_elem)++; return; } } else { d -> anc = d_start; if(d -> tax) return; else { if(Are_Equal(t_cur_slice_max[d -> num - n_otu], t_slice_max, 1.E-10) && Are_Equal(t_cur_slice_min[d -> num - n_otu], t_slice_min, 1.E-10)) { (root_nodes[*num_elem]) = d -> num - n_otu; (*num_elem)++; return; } int i; for(i=0;i<3;i++) if((d -> v[i] != d -> anc) && (d -> b[i] != tree -> e_root)) Search_Root_Node_In_Slice(d, d -> v[i], root_nodes, num_elem, t_slice_min, t_slice_max, t_cur_slice_min, t_cur_slice_max, tree); } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int Factorial(int base) { if(base == 0) return(1); if(base == 1) return(1); return(base * Factorial(base-1)); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //Calculate the vector of the norm.constants for prior on node times. //The length of the vector is the total number of combinations of calibrations. phydbl *Norm_Constant_Prior_Times(t_tree *tree) { phydbl *log_K_val; int i, tot_num_comb; t_cal *calib; calib = tree -> rates -> calib; tot_num_comb = Number_Of_Comb(calib); log_K_val = (phydbl *)mCalloc(tot_num_comb, sizeof(phydbl)); For(i, tot_num_comb) { Set_Current_Calibration(i, tree); int result = TRUE; TIMES_Set_All_Node_Priors_S(&result, tree); if(result == TRUE) log_K_val[i] = K_Constant_Prior_Times_Log(tree); while(calib -> prev) calib = calib -> prev; } return(log_K_val); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //Sets a vector of the partial probabilities for each combination of calibrations void TIMES_Calib_Partial_Proba(t_tree *tree) { phydbl *times_partial_proba, proba, *t_prior_min, *t_prior_max; int i, j, k, tot_num_comb; t_cal *calib; short int *t_has_prior; proba = 0.0; times_partial_proba = tree -> rates -> times_partial_proba; calib = tree -> rates -> calib; t_prior_min = tree -> rates -> t_prior_min; t_prior_max = tree -> rates -> t_prior_max; t_has_prior = tree -> rates -> t_has_prior; tot_num_comb = Number_Of_Comb(calib); For(i, tot_num_comb) { times_partial_proba[i] = 1.0; for(j = tree -> n_otu; j < 2 * tree -> n_otu - 1; j++) { t_prior_min[j] = -BIG; t_prior_max[j] = BIG; t_has_prior[j] = NO; } do { k = (i % Number_Of_Comb(calib)) / Number_Of_Comb(calib -> next); if(calib -> all_applies_to[k] -> num) { t_prior_min[calib -> all_applies_to[k] -> num] = MAX(t_prior_min[calib -> all_applies_to[k] -> num], calib -> lower); t_prior_max[calib -> all_applies_to[k] -> num] = MIN(t_prior_max[calib -> all_applies_to[k] -> num], calib -> upper); t_has_prior[calib -> all_applies_to[k] -> num] = YES; proba = calib -> proba[calib -> all_applies_to[k] -> num]; times_partial_proba[i] *= proba; } else { proba = calib -> proba[2 * tree -> n_otu - 1]; times_partial_proba[i] *= proba; } if(calib -> next) calib = calib -> next; else break; } while(calib); int result; result = TRUE; TIMES_Set_All_Node_Priors_S(&result, tree); if(result != TRUE) times_partial_proba[i] = 0; /* printf("\n. [4] Partial Proba [%f] \n", times_partial_proba[i]); */ while(calib -> prev) calib = calib -> prev; } phydbl sum_proba; sum_proba = 0.0; For(i, tot_num_comb) sum_proba += times_partial_proba[i]; if(!Are_Equal(sum_proba, 1.0, 1.E-10)) { For(i, tot_num_comb) times_partial_proba[i] = times_partial_proba[i] / sum_proba; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //Function checks if the randomized node times are within the //upper and lower time limits, taken into account the times of //the ancestor and descendent. void Check_Node_Time(t_node *a, t_node *d, int *result, t_tree *tree) { phydbl t_low, t_up; phydbl *t_prior_min, *t_prior_max, *nd_t; t_prior_min = tree -> rates -> t_prior_min; t_prior_max = tree -> rates -> t_prior_max; nd_t = tree -> rates -> nd_t; if((a == tree -> n_root) && ((nd_t[a -> num] > MIN(t_prior_max[a -> num], MIN(nd_t[a -> v[1] -> num], nd_t[a -> v[2] -> num]))) || (nd_t[a -> num] < t_prior_min[a -> num]))) { *result = FALSE; return; } if(d -> tax) return; else { t_low = MAX(t_prior_min[d -> num], nd_t[d -> anc -> num]); t_up = MIN(t_prior_max[d -> num], MIN(nd_t[d -> v[1] -> num], nd_t[d -> v[2] -> num])); /* printf("\n. CHECK: %d t:%f u:%f d:%f",d->num,nd_t[d->num],t_up,t_low); */ if(nd_t[d -> num] < t_low || nd_t[d -> num] > t_up) { *result = FALSE; return; } int i; for(i=0;i<3;i++) if((d -> v[i] != d -> anc) && (d -> b[i] != tree -> e_root)) Check_Node_Time(d, d -> v[i], result, tree); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //Function calculates the TOTAL number of calibration combinations, //given the number of nodes to which each calibartion applies to. int Number_Of_Comb(t_cal *calib) { int num_comb; if(!calib) return(1); num_comb = 1; do { num_comb *= calib -> n_all_applies_to; if(calib -> next) calib = calib -> next; else break; } while(calib); return(num_comb); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //Function calculates the TOTAL number of calibration combinations, //given the number of nodes to which each calibartion applies to. int Number_Of_Calib(t_cal *calib) { int num_calib; num_calib = 0; do { num_calib++; if(calib -> next) calib = calib -> next; else break; } while(calib); return(num_calib); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Function sets current calibration in the following way: // Suppose we have a vector of calibrations C=(C1, C2, C3), each calibration // applies to a set of nodes. We can reach each node number through the indexes (corresponds // to the number the information was read). C1={0,1,2}, C2={0,1}, C3={0}; // The total number of combinations is 3*2*1=6. The first combination with row number 0 // will be {0,0,0}, the second row will be {0,1,0} and so on. Calling the node numbers with // the above indexes will return current calibration. Also sets the vector of probabilities // for current calibration combination. void Set_Current_Calibration(int row, t_tree *tree) { t_cal *calib; phydbl *t_prior_min, *t_prior_max; short int *t_has_prior; int k, i, j, *curr_nd_for_cal; calib = tree -> rates -> calib; t_prior_min = tree -> rates -> t_prior_min; t_prior_max = tree -> rates -> t_prior_max; t_has_prior = tree -> rates -> t_has_prior; curr_nd_for_cal = tree -> rates -> curr_nd_for_cal; /* printf("\n COMBINATION NUMBER [%d] \n", row); */ for(j = tree -> n_otu; j < 2 * tree -> n_otu - 1; j++) { t_prior_min[j] = -BIG; t_prior_max[j] = BIG; t_has_prior[j] = NO; } k = -1; i = 0; do { k = (row % Number_Of_Comb(calib)) / Number_Of_Comb(calib -> next); if(calib -> all_applies_to[k] -> num) { /* printf("\n"); */ /* printf(" %f %f %f %f ", calib -> lower, calib -> upper, t_prior_min[calib -> all_applies_to[k] -> num], t_prior_max[calib -> all_applies_to[k] -> num]); */ /* printf("\n"); */ /* printf("\n"); */ /* printf(" Node number [%d] ", calib -> all_applies_to[k] -> num); */ /* printf("\n"); */ t_prior_min[calib -> all_applies_to[k] -> num] = MAX(t_prior_min[calib -> all_applies_to[k] -> num], calib -> lower); /* printf("\n Prior min [%f] \n", t_prior_min[calib -> all_applies_to[k] -> num]); */ t_prior_max[calib -> all_applies_to[k] -> num] = MIN(t_prior_max[calib -> all_applies_to[k] -> num], calib -> upper); /* printf("\n Prior max [%f] \n", t_prior_max[calib -> all_applies_to[k] -> num]); */ t_has_prior[calib -> all_applies_to[k] -> num] = YES; curr_nd_for_cal[i] = calib -> all_applies_to[k] -> num; i++; } if(calib->next) calib = calib->next; else break; } while(calib); while(calib -> prev) calib = calib -> prev; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //Randomly choose a combination of calibrations drawing an index of calibration combination, //used function Set_Cur_Calibration. void Random_Calibration(t_tree *tree) { int rnd, num_comb; t_cal *calib; calib = tree -> rates -> calib; num_comb = Number_Of_Comb(calib); srand(time(NULL)); rnd = rand()%(num_comb); Set_Current_Calibration(rnd, tree); TIMES_Set_All_Node_Priors(tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //Variable curr_nd_for_cal is a vector of node numbers, the length of that vector is a number of calibrations. //Function randomly updates that vector by randomly changing one node and setting times limits with respect //to a new vector. int RND_Calibration_And_Node_Number(t_tree *tree) { int i, j, tot_num_cal, cal_num, node_ind, node_num, *curr_nd_for_cal; phydbl *t_prior_min, *t_prior_max; //*times_partial_proba; short int *t_has_prior; t_cal *cal; tot_num_cal = tree -> rates -> tot_num_cal; t_prior_min = tree -> rates -> t_prior_min; t_prior_max = tree -> rates -> t_prior_max; t_has_prior = tree -> rates -> t_has_prior; curr_nd_for_cal = tree -> rates -> curr_nd_for_cal; cal = tree -> rates -> calib; cal_num = rand()%(tot_num_cal - 1); i = 0; while (i != cal_num) { cal = cal -> next; i++; } node_ind = rand()%(cal -> n_all_applies_to); node_num = cal -> all_applies_to[node_ind] -> num; curr_nd_for_cal[cal_num] = node_num; for(j = tree -> n_otu; j < 2 * tree -> n_otu - 1; j++) { t_prior_min[j] = -BIG; t_prior_max[j] = BIG; t_has_prior[j] = NO; } while(cal -> prev) cal = cal -> prev; i = 0; do { t_prior_min[curr_nd_for_cal[i]] = cal -> lower; t_prior_max[curr_nd_for_cal[i]] = cal -> upper; t_has_prior[curr_nd_for_cal[i]] = YES; i++; if(cal->next) cal = cal -> next; else break; } while(cal); while(cal -> prev) cal = cal -> prev; TIMES_Set_All_Node_Priors(tree); return(node_num); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //Return the value uniformly distributed between two values. phydbl Randomize_One_Node_Time(phydbl min, phydbl max) { phydbl u; u = Uni(); u *= (max - min); u += min; return(u); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //Calculates the Hastings ratio for the analysis. Used in case of //calibration conditional jump. NOT THE RIGHT ONE TO USE! void Lk_Hastings_Ratio_Times(t_node *a, t_node *d, phydbl *tot_prob, t_tree *tree) { phydbl t_low, t_up; phydbl *t_prior_min, *t_prior_max, *nd_t; t_prior_min = tree -> rates -> t_prior_min; t_prior_max = tree -> rates -> t_prior_max; nd_t = tree -> rates -> nd_t; if(d -> tax) return; else { t_low = MAX(t_prior_min[d -> num], nd_t[d -> anc -> num]); t_up = MIN(t_prior_max[d -> num], MIN(nd_t[d -> v[1] -> num], nd_t[d -> v[2] -> num])); (*tot_prob) += log(1) - log(t_up - t_low); int i; for(i=0;i<3;i++) if((d -> v[i] != d -> anc) && (d -> b[i] != tree -> e_root)) { Lk_Hastings_Ratio_Times(d, d -> v[i], tot_prob, tree); } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //Updates nodes which are below a randomized node in case if new proposed time //for that node is below the current value. void Update_Descendent_Cond_Jump(t_node *a, t_node *d, phydbl *L_Hast_ratio, t_tree *tree) { int result = TRUE; phydbl t_low, t_up; phydbl *t_prior_min, *t_prior_max, *nd_t; t_prior_min = tree -> rates -> t_prior_min; t_prior_max = tree -> rates -> t_prior_max; nd_t = tree -> rates -> nd_t; Check_Node_Time(tree -> n_root, tree -> n_root -> v[1], &result, tree); Check_Node_Time(tree -> n_root, tree -> n_root -> v[2], &result, tree); if(d -> tax) return; else { if(result != TRUE) { int i; t_low = MAX(nd_t[a -> num], t_prior_min[d -> num]); if(t_low < MIN(nd_t[d -> v[1] -> num], nd_t[d -> v[2] -> num])) t_up = MIN(t_prior_max[d -> num], MIN(nd_t[d -> v[1] -> num], nd_t[d -> v[2] -> num])); else t_up = t_prior_max[d -> num]; nd_t[d -> num] = Randomize_One_Node_Time(t_low, t_up); (*L_Hast_ratio) += log(1) - log(t_up - t_low); for(i=0;i<3;i++) if((d -> v[i] != d -> anc) && (d -> b[i] != tree -> e_root)) Update_Descendent_Cond_Jump(d, d -> v[i], L_Hast_ratio, tree); } else return; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //Updates nodes which are above a randomized node in case if new proposed time //for that node is above the current value. void Update_Ancestor_Cond_Jump(t_node *d, phydbl *L_Hast_ratio, t_tree *tree) { int result = TRUE; phydbl t_low, t_up; phydbl *t_prior_min, *t_prior_max, *nd_t; t_prior_min = tree -> rates -> t_prior_min; t_prior_max = tree -> rates -> t_prior_max; nd_t = tree -> rates -> nd_t; Check_Node_Time(tree -> n_root, tree -> n_root -> v[1], &result, tree); Check_Node_Time(tree -> n_root, tree -> n_root -> v[2], &result, tree); if(result != TRUE) { if(d == tree -> n_root) { t_low = t_prior_min[d -> num]; t_up = MIN(t_prior_max[d -> num], MIN(nd_t[d -> v[1] -> num], nd_t[d -> v[2] -> num])); nd_t[d -> num] = Randomize_One_Node_Time(t_low, t_up); (*L_Hast_ratio) += log(1) - log(t_up - t_low); return; } else { t_up = MIN(t_prior_max[d -> num], MIN(nd_t[d -> v[1] -> num], nd_t[d -> v[2] -> num])); if(nd_t[d -> anc -> num] > t_up) t_low = t_prior_min[d -> num]; else t_low = MAX(t_prior_min[d -> num], nd_t[d -> anc -> num]); nd_t[d -> num] = Randomize_One_Node_Time(t_low, t_up); (*L_Hast_ratio) += log(1) - log(t_up - t_low); Update_Ancestor_Cond_Jump(d -> anc, L_Hast_ratio, tree); } } else return; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //when made a calibration conditional jump, updates node times //with respect to the new calibration which was made with respect //to the randomly chosen node, the root is fixed. Updates only those nodes //that are not within new intervals. Traverse up and down. void Update_Times_RND_Node_Ancestor_Descendant(int rnd_node, phydbl *L_Hast_ratio, t_tree *tree) { int i; phydbl *t_prior_min, *t_prior_max, *nd_t; phydbl new_time_rnd_node = 0.0; t_prior_min = tree -> rates -> t_prior_min; t_prior_max = tree -> rates -> t_prior_max; nd_t = tree -> rates -> nd_t; new_time_rnd_node = Randomize_One_Node_Time(t_prior_min[rnd_node], t_prior_max[rnd_node]); nd_t[rnd_node] = new_time_rnd_node; Update_Ancestor_Cond_Jump(tree -> a_nodes[rnd_node] -> anc, L_Hast_ratio, tree); for(i=0;i<3;i++) if((tree -> a_nodes[rnd_node] -> v[i] != tree -> a_nodes[rnd_node] -> anc) && (tree -> a_nodes[rnd_node] -> b[i] != tree -> e_root)) Update_Descendent_Cond_Jump(tree -> a_nodes[rnd_node], tree -> a_nodes[rnd_node] -> v[i], L_Hast_ratio, tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// //when made a calibration conditional jump, updates node times //with respect to the new calibration which was made with respect //to the randomly chosen node, starting from the root down to the tips. //Updates only those nodes that are not within new intervals. void Update_Times_Down_Tree(t_node *a, t_node *d, phydbl *L_Hastings_ratio, t_tree *tree) { int i; phydbl *t_prior_min, *t_prior_max, *nd_t, t_low, t_up; t_prior_min = tree -> rates -> t_prior_min; t_prior_max = tree -> rates -> t_prior_max; nd_t = tree -> rates -> nd_t; t_low = MAX(t_prior_min[d -> num], nd_t[a -> num]); t_up = t_prior_max[d -> num]; //printf("\n. [1] Node number: [%d] \n", d -> num); if(d -> tax) return; else { if(nd_t[d -> num] > t_up || nd_t[d -> num] < t_low) { //printf("\n. [2] Node number: [%d] \n", d -> num); //(*L_Hastings_ratio) += (log(1) - log(t_up - t_low)); (*L_Hastings_ratio) += (- log(t_up - t_low)); nd_t[d -> num] = Randomize_One_Node_Time(t_low, t_up); /* t_prior_min[d -> num] = t_low; */ /* t_prior_max[d -> num] = t_up; */ } for(i=0;i<3;i++) if((d -> v[i] != d -> anc) && (d -> b[i] != tree -> e_root)) Update_Times_Down_Tree(d, d -> v[i], L_Hastings_ratio, tree); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// xml_node *XML_Search_Node_Attribute_Value_Clade(char *attr_name, char *value, int skip, xml_node *node) { xml_node *match; //printf("\n. Node name [%s] Attr name [%s] Attr value [%s] \n", node -> name, attr_name, value); if(!node) { PhyML_Printf("\n== node: %p attr: %p",node,node?node->attr:NULL); PhyML_Printf("\n== Err. in file %s at line %d\n",__FILE__,__LINE__); Exit("\n"); } match = NULL; if(skip == NO && node -> attr) { xml_attr *attr; attr = node -> attr; do { if(!strcmp(attr -> name, attr_name) && !strcmp(attr -> value, value)) { match = node; break; } attr = attr->next; if(!attr) break; } while(1); } if(match) return(match); if(node -> next && !match) { match = XML_Search_Node_Attribute_Value_Clade(attr_name, value, NO, node -> next); return match; } return NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void TIMES_Set_All_Node_Priors_S(int *result, t_tree *tree) { int i; phydbl min_prior; /* Set all t_prior_max values */ TIMES_Set_All_Node_Priors_Bottom_Up_S(tree->n_root,tree->n_root->v[2], result, tree); TIMES_Set_All_Node_Priors_Bottom_Up_S(tree->n_root,tree->n_root->v[1], result, tree); tree->times->t_prior_max[tree->n_root->num] = MIN(tree->times->t_prior_max[tree->n_root->num], MIN(tree->times->t_prior_max[tree->n_root->v[2]->num], tree->times->t_prior_max[tree->n_root->v[1]->num])); /* Set all t_prior_min values */ if(!tree->times->t_has_prior[tree->n_root->num]) { min_prior = 1.E+10; For(i,2*tree->n_otu-2) { if(tree->times->t_has_prior[i]) { if(tree->times->t_prior_min[i] < min_prior) min_prior = tree->times->t_prior_min[i]; } } tree->times->t_prior_min[tree->n_root->num] = 2.0 * min_prior; } if(tree->times->t_prior_min[tree->n_root->num] > 0.0) { *result = FALSE; } TIMES_Set_All_Node_Priors_Top_Down_S(tree->n_root,tree->n_root->v[2], result, tree); TIMES_Set_All_Node_Priors_Top_Down_S(tree->n_root,tree->n_root->v[1], result, tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void TIMES_Set_All_Node_Priors_Bottom_Up_S(t_node *a, t_node *d, int *result, t_tree *tree) { int i; phydbl t_sup; if(d->tax) return; else { t_node *v1, *v2; /* the two sons of d */ for(i=0;i<3;i++) { if((d->v[i] != a) && (d->b[i] != tree->e_root)) { TIMES_Set_All_Node_Priors_Bottom_Up_S(d,d->v[i], result, tree); } } v1 = v2 = NULL; for(i=0;i<3;i++) if((d->v[i] != a) && (d->b[i] != tree->e_root)) { if(!v1) v1 = d->v[i]; else v2 = d->v[i]; } if(tree->times->t_has_prior[d->num] == YES) { t_sup = MIN(tree->times->t_prior_max[d->num], MIN(tree->times->t_prior_max[v1->num], tree->times->t_prior_max[v2->num])); tree->times->t_prior_max[d->num] = t_sup; if(tree->times->t_prior_max[d->num] < tree->times->t_prior_min[d->num]) { *result = FALSE; } } else { tree->times->t_prior_max[d->num] = MIN(tree->times->t_prior_max[v1->num], tree->times->t_prior_max[v2->num]); } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void TIMES_Set_All_Node_Priors_Top_Down_S(t_node *a, t_node *d, int *result, t_tree *tree) { if(d->tax) return; else { int i; if(tree->times->t_has_prior[d->num] == YES) { tree->times->t_prior_min[d->num] = MAX(tree->times->t_prior_min[d->num],tree->times->t_prior_min[a->num]); if(tree->times->t_prior_max[d->num] < tree->times->t_prior_min[d->num]) { *result = FALSE; } } else { tree->times->t_prior_min[d->num] = tree->times->t_prior_min[a->num]; } for(i=0;i<3;i++) { if((d->v[i] != a) && (d->b[i] != tree->e_root)) { TIMES_Set_All_Node_Priors_Top_Down_S(d,d->v[i], result, tree); } } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl log_g_i(phydbl lmbd, phydbl t_slice_max, phydbl t_slice_min, phydbl t_prior_max, phydbl t_prior_min) { phydbl result = 0.0; if(lmbd * t_prior_min < -10.0) { phydbl K; K = -10.0 - lmbd * t_prior_min; /* printf("\n. K = [%f] \n", K); */ if(lmbd * t_prior_max + K > 700.0) { PhyML_Printf("\n. Please scale your calibration intervals. \n"); PhyML_Printf("\n. Cannot calculate exp(-lmbd * t_prior_max). \n"); PhyML_Printf("\n. Err. in file %s at line %d\n\n",__FILE__,__LINE__); Warn_And_Exit("\n"); } else { result = log(exp(lmbd * t_slice_max + K) - exp(lmbd * t_slice_min + K)) - log(exp(lmbd * t_prior_max + K) - exp(lmbd * t_prior_min + K)); } } else { result = log(exp(lmbd * t_slice_max) - exp(lmbd * t_slice_min)) - log(exp(lmbd * t_prior_max) - exp(lmbd * t_prior_min)); } return(result); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int CombineInt(int int1, int int2) { int mem; mem = 500 * 2; char cResult[mem]; sprintf(cResult, "%d%d", int1, int2); return atoi(cResult); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Jump_Calibration_Move_Pre(t_node *a, t_node *d, phydbl old_ta, phydbl *log_hastings_ratio, t_tree *tree) { int i; phydbl *t_prior_min_new, *t_prior_max_new, t_low_new, t_up_new; phydbl *t_prior_min_old, *t_prior_max_old, t_low_old, t_up_old; phydbl *nd_t, old_t; phydbl eps = DBL_MIN; int move_anyway; t_prior_min_new = tree -> rates -> t_prior_min; t_prior_max_new = tree -> rates -> t_prior_max; t_prior_min_old = tree -> rates -> t_prior_min_ori; t_prior_max_old = tree -> rates -> t_prior_max_ori; nd_t = tree -> rates -> nd_t; t_low_new = MAX(t_prior_min_new[d -> num], nd_t[a -> num]); t_up_new = t_prior_max_new[d -> num]; t_low_old = MAX(t_prior_min_old[d -> num], old_ta); t_up_old = t_prior_max_old[d -> num]; old_t = nd_t[d -> num]; /* move_anyway = NO; */ /* if(Uni() < .5) move_anyway = YES; */ move_anyway = YES; if(d -> tax) return; else { if((nd_t[d -> num] > t_up_new || nd_t[d -> num] < t_low_new) || (move_anyway == YES)) /* Hastings ratio */ { (*log_hastings_ratio) += log(t_up_new - t_low_new + eps); } if((nd_t[d -> num] > t_up_new || nd_t[d -> num] < t_low_new) || (move_anyway == YES)) /* Do the jump */ { nd_t[d -> num] = Uni()*(t_up_new - t_low_new) + t_low_new; } if((nd_t[d -> num] > t_up_old || nd_t[d -> num] < t_low_old) || (move_anyway == YES)) /* Hastings ratio */ { (*log_hastings_ratio) -= log(t_up_old - t_low_old + eps); } for(i=0;i<3;i++) if((d -> v[i] != d -> anc) && (d -> b[i] != tree -> e_root)) Jump_Calibration_Move_Pre(d, d -> v[i], old_t, log_hastings_ratio, tree); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Multiple_Time_Proposal_Density(t_node *a, t_node *d, phydbl *time_proposal_density, t_tree *tree) { int i; phydbl *t_prior_min, *t_prior_max, *nd_t, t_low, t_up; t_prior_min = tree -> rates -> t_prior_min; t_prior_max = tree -> rates -> t_prior_max; nd_t = tree -> rates -> nd_t; //printf("\n. [1] Node number: [%d] \n", d -> num); if(d -> tax) return; else { t_low = MAX(t_prior_min[d -> num], nd_t[a -> num]); t_up = t_prior_max[d -> num]; /* t_up = MIN(t_prior_max[d -> num], MIN(nd_t[d -> v[1] -> num], nd_t[d -> v[2] -> num])); */ /* printf("\n. Low [%f] Up [%f] \n", t_low, t_up); */ (*time_proposal_density) += (- log(t_up - t_low)); for(i=0;i<3;i++) if((d -> v[i] != d -> anc) && (d -> b[i] != tree -> e_root)) Multiple_Time_Proposal_Density(d, d -> v[i], time_proposal_density, tree); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phyml-3.3.20220408/src/invitee.h000066400000000000000000000050241422404706600160210ustar00rootroot00000000000000#ifndef INVITEE_H #define INVITEE_H #include "utilities.h" int My_Function(int argc, char **argv); int My_main(int argc, char **argv); void PhyTime_XML(char *xml_file); phydbl TIMES_Calib_Cond_Prob(t_tree *tree); int Number_Of_Comb(t_cal *calib); int Number_Of_Calib(t_cal *calib); void Check_Node_Time(t_node *a, t_node *d, int *result, t_tree *tree); void Set_Current_Calibration(int row, t_tree *tree); void Random_Calibration(t_tree *tree); int RND_Calibration_And_Node_Number(t_tree *tree); phydbl Randomize_One_Node_Time(phydbl min, phydbl max); void Lk_Hastings_Ratio_Times(t_node *a, t_node *d, phydbl *tot_prob, t_tree *tree); void Update_Descendent_Cond_Jump(t_node *a, t_node *d, phydbl *L_Hast_ratio, t_tree *tree); void Update_Ancestor_Cond_Jump(t_node *d, phydbl *L_Hast_ratio, t_tree *tree); void Update_Times_RND_Node_Ancestor_Descendant(int rnd_node, phydbl *L_Hast_ratio, t_tree *tree); void Update_Times_Down_Tree(t_node *a, t_node *d, phydbl *L_Hastings_ratio, t_tree *tree); phydbl K_Constant_Prior_Times_Log(t_tree *tree); int Number_Of_Comb_Slices(int m, int num_elem, int *n_slice); void Check_Time_Slices(t_node *a, t_node *d, int *result, phydbl *t_cur_slice_min, phydbl *t_cur_slice_max, t_tree *tree); void Number_Of_Nodes_In_Slice(t_node *d_start, t_node *d, int *n, phydbl *t_cur_slice_min, phydbl *t_cur_slice_max, t_tree *tree); void Search_Root_Node_In_Slice(t_node *d_start, t_node *d, int *root_nodes, int *num_elem, phydbl t_slice_min, phydbl t_slice_max, phydbl *t_cur_slice_min, phydbl *t_cur_slice_max, t_tree *tree); int Factorial(int base); phydbl *Norm_Constant_Prior_Times(t_tree *tree); void TIMES_Calib_Partial_Proba(t_tree *tree); xml_node *XML_Search_Node_Attribute_Value_Clade(char *attr_name, char *value, int skip, xml_node *node); char **XML_Read_Clade(xml_node *n_clade, t_tree *tree); int XML_Number_Of_Taxa_In_Clade(xml_node *n_clade); void TIMES_Set_All_Node_Priors_S(int *result, t_tree *tree); void TIMES_Set_All_Node_Priors_Bottom_Up_S(t_node *a, t_node *d, int *result, t_tree *tree); void TIMES_Set_All_Node_Priors_Top_Down_S(t_node *a, t_node *d, int *result, t_tree *tree); phydbl LOG_g_i(phydbl lmbd, phydbl t_slice_max, phydbl t_slice_min, phydbl t_prior_max, phydbl t_prior_min); int CombineInt(int int1, int int2); void Update_Current_Times_Down_Tree(t_node *a, t_node *d, t_tree *tree); void Multiple_Time_Proposal_Density(t_node *a, t_node *d, phydbl *time_proposal_density, t_tree *tree); void Jump_Calibration_Move_Pre(t_node *a, t_node *d, phydbl old_ta, phydbl *log_hastings_ratio, t_tree *tree); #endif phyml-3.3.20220408/src/io.c000066400000000000000000006537131422404706600147760ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include "io.h" #include "assert.h" #ifdef BEAGLE #include "libhmsbeagle/beagle.h" #endif /* Tree parser function. We need to pass a pointer to the string of characters since this string might be freed and then re-allocated by that function (i.e., its address in memory might change) */ t_tree *Read_Tree(char **s_tree) { char **subs; int i,n_ext,n_int,n_otu; t_tree *tree; int degree,len; t_node *root_node; n_int = n_ext = 0; n_otu=0; for(i=0;i<(int)strlen((*s_tree));++i) { if((*s_tree)[i] == '[') do ++i; while((*s_tree)[i] != ']'); // Skip labels if((*s_tree)[i] == ',') n_otu++; } n_otu+=1; tree = Make_Tree_From_Scratch(n_otu,NULL); subs = Sub_Trees((*s_tree),°ree); Clean_Multifurcation(subs,degree,3); if(degree == 2) { /* Unroot_Tree(subs); */ /* degree = 3; */ /* root_node = tree->a_nodes[n_otu]; */ root_node = tree->a_nodes[2*n_otu-2]; root_node->num = 2*n_otu-2; tree->n_root = root_node; n_int -= 1; } else { root_node = tree->a_nodes[n_otu]; root_node->num = n_otu; tree->n_root = NULL; } if(degree > 3) /* Multifurcation at the root. Need to re-assemble the subtrees since Clean_Multifurcation added sets of parenthesis and the corresponding NULL edges */ { degree = 3; Free((*s_tree)); len = 0; for(i=0;itax = 0; tree->has_branch_lengths = 0; tree->num_curr_branch_available = tree->n_otu; for(i=0;in_root) { if(tree->n_root->v[1]->tax == NO && tree->n_root->v[2]->tax == NO) { tree->e_root = tree->a_edges[tree->num_curr_branch_available]; tree->n_root->b[1] = tree->a_edges[tree->num_curr_branch_available+1]; tree->n_root->b[2] = tree->a_edges[tree->num_curr_branch_available+2]; } else { for(i=0;in_otu;++i) if(tree->a_edges[i]->left == NULL && tree->a_edges[i]->rght == NULL) break; assert(i != tree->n_otu); tree->e_root = tree->a_edges[i]; tree->n_root->b[1] = tree->a_edges[tree->num_curr_branch_available]; tree->n_root->b[2] = tree->a_edges[tree->num_curr_branch_available+1]; } tree->n_root->v[2]->v[0] = tree->n_root->v[1]; tree->n_root->v[1]->v[0] = tree->n_root->v[2]; // Reading edge lengths subs = Sub_Trees((*s_tree),°ree); Read_Branch_Length(subs[0],(*s_tree),tree->n_root->b[1],tree); Read_Branch_Length(subs[1],(*s_tree),tree->n_root->b[2],tree); Free(subs); Connect_One_Edge_To_Two_Nodes(tree->n_root->v[2], tree->n_root->v[1], tree->e_root, tree); tree->e_root->l->v = tree->n_root->b[2]->l->v + tree->n_root->b[1]->l->v; if(tree->e_root->l->v > 0.0) tree->n_root_pos = tree->n_root->b[2]->l->v / tree->e_root->l->v; else tree->n_root_pos = .5; Update_Ancestors(tree->n_root,tree->n_root->v[2],tree->n_root->b[2],tree); Update_Ancestors(tree->n_root,tree->n_root->v[1],tree->n_root->b[1],tree); } return tree; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* 'a' in t_node a stands for ancestor. 'd' stands for descendant */ void R_rtree(char *s_tree_a, char *s_tree_d, t_node *a, t_tree *tree, int *n_int, int *n_ext) { int i; t_node *d; int n_otu = tree->n_otu; if(strstr(s_tree_a," ")) { PhyML_Fprintf(stderr,"\n. [%s]",s_tree_a); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } // Internal edge if(s_tree_d[0] == '(') { char **subs; int degree; t_edge *b; (*n_int)+=1; if((*n_int + n_otu) == (2*n_otu-1)) { PhyML_Fprintf(stderr,"\n. The number of internal nodes in the tree exceeds the number of taxa minus one."); PhyML_Fprintf(stderr,"\n. There probably is a formating problem in the input tree."); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } d = tree->a_nodes[n_otu+*n_int]; d->num = n_otu + *n_int; d->tax = 0; b = tree->a_edges[tree->num_curr_branch_available]; Read_Branch_Support(s_tree_d,s_tree_a,b,tree); Read_Branch_Length(s_tree_d,s_tree_a,b,tree); Read_Node_Label(s_tree_d,s_tree_a,d); if(tree->n_root && a == tree->n_root) { if(!a->v[1]) a->v[1] = d; else a->v[2] = d; } else { for(i=0;i<3;i++) { if(!a->v[i]) { a->v[i] = d; break; } } } d->v[0]=a; if(!(tree->n_root && a == tree->n_root)) Connect_One_Edge_To_Two_Nodes(a,d,tree->a_edges[tree->num_curr_branch_available],tree); subs=Sub_Trees(s_tree_d,°ree); if(degree < 2) { PhyML_Fprintf(stderr,"\n. A problem was detected in the following subtree:"); PhyML_Fprintf(stderr,"\n. %s",s_tree_d); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } if(degree > 2) { Clean_Multifurcation(subs,degree,2); Free(s_tree_d); s_tree_d = (char *)mCalloc(strlen(subs[0])+strlen(subs[1])+5,sizeof(char)); i = 0; while(subs[i] != NULL) Free(subs[i++]); Free(subs); subs=Sub_Trees(s_tree_d,°ree); } R_rtree(s_tree_d,subs[0],d,tree,n_int,n_ext); R_rtree(s_tree_d,subs[1],d,tree,n_int,n_ext); i = 2; while(subs[i] != NULL) Free(subs[i++]); Free(subs); } // External edge else { int i; d = tree->a_nodes[*n_ext]; d->tax = 1; Read_Node_Name(d,s_tree_d,tree->a_edges[*n_ext],tree); Read_Branch_Length(s_tree_d,s_tree_a,tree->a_edges[*n_ext],tree); Read_Node_Label(s_tree_d,s_tree_a,d); if(tree->n_root && a == tree->n_root) { if(!a->v[1]) a->v[1] = d; else a->v[2] = d; } else { for(i=0;i<3;i++) { if(!a->v[i]) { a->v[i]=d; break; } } } d->v[0]=a; if(!(tree->n_root && a == tree->n_root)) { Connect_One_Edge_To_Two_Nodes(a,d,tree->a_edges[*n_ext],tree); } d->num=*n_ext; (*n_ext)+=1; } Free(s_tree_d); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Read_Node_Name(t_node *d, char *s_tree_d, t_edge *b, t_tree *tree) { d->name = (char *)mCalloc(strlen(s_tree_d)+1,sizeof(char )); strcpy(d->name,s_tree_d); d->ori_name = d->name; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Read_Branch_Support(char *s_d, char *s_a, t_edge *b, t_tree *tree) { char *sub_tp; char *p; if(s_d[0] != '(') return; // b is an external edge -> no edge support on it sub_tp = (char *)mCalloc(10+strlen(s_d)+1,sizeof(char)); sub_tp[0] = '('; sub_tp[1] = '\0'; strcat(sub_tp,s_d); p = strstr(s_a,sub_tp); if(!p) { sub_tp[0] = ','; sub_tp[1] = '\0'; strcat(sub_tp,s_d); p = strstr(s_a,sub_tp); } if(p) { b->support_val = atof((char *)p+(int)strlen(sub_tp)); p = p + strlen(sub_tp); if(p[0] == '[') { while(p[0] != ']') p++; p++; } // Skip label b->support_val = atof((char *)p); /* PhyML_Printf("\n. READ SUPPORT for s_d: %s b: %f",s_d,b->support_val); */ } Free(sub_tp); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Read_Branch_Length(char *s_d, char *s_a, t_edge *b, t_tree *tree) { char *sub_tp; char *p; sub_tp = (char *)mCalloc(10+strlen(s_d)+1,sizeof(char)); sub_tp[0] = '('; sub_tp[1] = '\0'; strcat(sub_tp,s_d); p = strstr(s_a,sub_tp); if(!p) { sub_tp[0] = ','; sub_tp[1] = '\0'; strcat(sub_tp,s_d); p = strstr(s_a,sub_tp); } if(p) { p = p + strlen(sub_tp); while(p[0] != ':' && p[0] != '\0') p++; if(p[0] == ':') { p++; b->l->v = atof((char *)p); /* PhyML_Printf("\n. READ LENGTH for s_d: %s b: %f",s_d,b->l->v); */ tree->has_branch_lengths = YES; b->does_exist = YES; } else { b->l->v = -1.; } } else { b->l->v = -1.; } Free(sub_tp); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Read_Node_Label(char *s_d, char *s_a, t_node *n) { char *sub_tp,*s_lab; char *p; sub_tp = (char *)mCalloc(3+(int)strlen(s_d)+1,sizeof(char)); s_lab = (char *)mCalloc(strlen(s_a)+1,sizeof(char)); sub_tp[0] = '('; sub_tp[1] = '\0'; strcat(sub_tp,s_d); strcat(sub_tp,"[\0"); p = strstr(s_a,sub_tp); if(!p) { sub_tp[0] = ','; sub_tp[1] = '\0'; strcat(sub_tp,s_d); strcat(sub_tp,"[\0"); p = strstr(s_a,sub_tp); } if(p) { p = p+strlen(sub_tp)-1; assert(p[0]=='['); s_lab[0]='['; if(sscanf(p,"[%[^]]]",s_lab+1) != 1) { PhyML_Fprintf(stderr,"\n. Label is in wrong format. A proper label should"); PhyML_Fprintf(stderr,"\n. look as follows: \"[xxx={yyy},xxxx={yy},...]\""); assert(FALSE); } s_lab[strlen(s_lab)]=']'; s_lab[strlen(s_lab)]='\0'; /* PhyML_Printf("\n. READ LABEL for s_d: %s b: %s",s_d,s_lab); */ n->label = Read_Labels(s_lab); } Free(sub_tp); Free(s_lab); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Clean_Multifurcation(char **subtrees, int current_deg, int end_deg) { if(current_deg <= end_deg) return; else { char *s_tmp; int i; /* s_tmp = (char *)mCalloc(T_MAX_LINE,sizeof(char)); */ s_tmp = (char *)mCalloc(10+ (int)strlen(subtrees[0])+1+ (int)strlen(subtrees[1])+1, sizeof(char)); strcat(s_tmp,"(\0"); strcat(s_tmp,subtrees[0]); strcat(s_tmp,",\0"); strcat(s_tmp,subtrees[1]); strcat(s_tmp,")#NULL\0"); /* Add the label 'NULL' to identify a non-existing edge */ Free(subtrees[0]); subtrees[0] = s_tmp; for(i=1;in_otu;++i) PhyML_Fprintf(fp,"\t%3d\t%s,\n",i+1,tree->a_nodes[i]->name); PhyML_Fprintf(fp,"\tUTREE PAUP_1=\n"); PhyML_Fprintf(fp,"%s\n",s_tree); PhyML_Fprintf(fp,"ENDBLOCK;"); } Free(s_tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// char *Write_Tree(t_tree *tree) { char *s; int i,available; #ifndef MPI int init_len; init_len = 3*(int)T_MAX_NAME; s=(char *)mCalloc(init_len,sizeof(char)); available = init_len; #elif defined MPI s=(char *)mCalloc(T_MAX_LINE,sizeof(char)); #endif i = -1; s[0]='('; if(tree->n_root == NULL) { i = 0; while((!tree->a_nodes[tree->n_otu+i]->v[0]) || (!tree->a_nodes[tree->n_otu+i]->v[1]) || (!tree->a_nodes[tree->n_otu+i]->v[2])) i++; R_wtree(tree->a_nodes[tree->n_otu+i],tree->a_nodes[tree->n_otu+i]->v[0],tree->a_nodes[tree->n_otu+i]->b[0],&available,&s,tree); R_wtree(tree->a_nodes[tree->n_otu+i],tree->a_nodes[tree->n_otu+i]->v[1],tree->a_nodes[tree->n_otu+i]->b[1],&available,&s,tree); R_wtree(tree->a_nodes[tree->n_otu+i],tree->a_nodes[tree->n_otu+i]->v[2],tree->a_nodes[tree->n_otu+i]->b[2],&available,&s,tree); } else { R_wtree(tree->n_root,tree->n_root->v[1],tree->n_root->b[1],&available,&s,tree); R_wtree(tree->n_root,tree->n_root->v[2],tree->n_root->b[2],&available,&s,tree); } s[(int)strlen(s)-1]=')'; if(tree->n_root != NULL) if(tree->print_labels == YES) Print_Labels(NULL,s+(int)strlen(s),tree->n_root->label); if(tree->io && tree->io->print_node_num == YES) { if(!tree->n_root) sprintf(s+(int)strlen(s),"%d",tree->a_nodes[tree->n_otu+i]->num); else sprintf(s+(int)strlen(s),"%d",tree->n_root->num); } s[(int)strlen(s)]=';'; return s; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void R_wtree(t_node *pere, t_node *fils, t_edge *b, int *available, char **s_tree, t_tree *tree) { int i,p; char *format; int last_len; phydbl mean_len; format = (char *)mCalloc(100,sizeof(char)); sprintf(format,"%%.%df",tree->bl_ndigits); p = -1; if(fils->tax == YES) // Tip node { last_len = (int)strlen(*s_tree); if(OUTPUT_TREE_FORMAT == NEWICK) { if(tree->write_tax_names == YES) { if(tree->io && tree->io->long_tax_names) { strcat(*s_tree,tree->io->long_tax_names[fils->num]); } else { if(fils->name && strlen(fils->name) > 0) strcat(*s_tree,fils->name); else sprintf(*s_tree+(int)strlen(*s_tree),"%d",fils->num+1); } } else if(tree->write_tax_names == NO) { sprintf(*s_tree+(int)strlen(*s_tree),"%d",fils->num+1); } } else if(OUTPUT_TREE_FORMAT == NEXUS) { sprintf(*s_tree+(int)strlen(*s_tree),"%d",fils->num+1); } else { PhyML_Printf("\n. Unknown tree format."); PhyML_Printf("\n. Err. in file %s at line %d\n",__FILE__,__LINE__); PhyML_Printf("\n. s=%s\n",*s_tree); } if((fils->b) && (fils->b[0]) && (tree->write_br_lens == YES)) { if(tree->print_labels == YES) Print_Labels(NULL,*s_tree+(int)strlen(*s_tree),fils->label); (*s_tree)[(int)strlen(*s_tree)] = ':'; if(tree->print_labels == YES) Print_Labels(NULL,*s_tree+(int)strlen(*s_tree),b->label); if(tree->is_mixt_tree == NO) mean_len = b->l->v; else mean_len = MIXT_Get_Mean_Edge_Len(b,tree); sprintf(*s_tree+(int)strlen(*s_tree),format,MAX(0.0,mean_len)); } (*s_tree)[(int)strlen(*s_tree)] = ','; #ifndef MPI (*available) -= ((int)strlen(*s_tree) - last_len); /* printf("\n0 Available = %d [%d %d]",(*available),(int)strlen(*s_tree),last_len); */ /* printf("\n0 %s [%d,%d]",*s_tree,(int)(int)strlen(*s_tree),*available); */ if(*available < 0) { PhyML_Fprintf(stderr,"\n. s=%s\n",*s_tree); PhyML_Fprintf(stderr,"\n. len=%d\n",(int)strlen(*s_tree)); PhyML_Fprintf(stderr,"\n. The sequence names in your input file might be too long."); PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d\n",__FILE__,__LINE__); Warn_And_Exit(""); } if(*available < (int)T_MAX_NAME) { (*s_tree) = (char *)mRealloc(*s_tree,(int)strlen(*s_tree)+3*(int)T_MAX_NAME,sizeof(char)); for(i=0;i<3*(int)T_MAX_NAME;++i) (*s_tree)[(int)strlen(*s_tree)+i] = '\0'; (*available) = 3*(int)T_MAX_NAME; } #endif } else // Internal node { (*s_tree)[(int)strlen(*s_tree)]='('; #ifndef MPI (*available) -= 1; if(*available < (int)T_MAX_NAME) { (*s_tree) = (char *)mRealloc(*s_tree,(int)strlen(*s_tree)+3*(int)T_MAX_NAME,sizeof(char)); for(i=0;i<3*(int)T_MAX_NAME;++i) (*s_tree)[(int)strlen(*s_tree)+i] = '\0'; (*available) = 3*(int)T_MAX_NAME; } #endif for(i=0;i<3;i++) { if((fils->v[i] != pere) && (fils->b[i] != tree->e_root)) R_wtree(fils,fils->v[i],fils->b[i],available,s_tree,tree); else p=i; } if(p < 0) { PhyML_Fprintf(stderr,"\n. pere: %d fils=%d root=%d root->v[2]=%d root->v[1]=%d",pere->num,fils->num,tree->n_root->num,tree->n_root->v[2]->num,tree->n_root->v[1]->num); PhyML_Fprintf(stderr,"\n. fils=%d root=%d root->v[2]=%d root->v[1]=%d",fils->num,tree->n_root->num,tree->n_root->v[2]->num,tree->n_root->v[1]->num); PhyML_Fprintf(stderr,"\n. tree->e_root=%d fils->b[0]=%d fils->b[1]=%d fils->b[2]=%d",tree->e_root->num,fils->b[0]->num,fils->b[1]->num,fils->b[2]->num); assert(false); } last_len = (int)strlen(*s_tree); (*s_tree)[last_len-1] = ')'; if((fils->b) && (tree->write_br_lens == YES)) { if(tree->print_labels == YES) Print_Labels(NULL,*s_tree+(int)strlen(*s_tree),fils->label); if(tree->io && tree->io->print_support_val == YES) { if(tree->io->do_boot == YES) { sprintf(*s_tree+(int)strlen(*s_tree),"%.0f",fils->b[p]->support_val); } else { sprintf(*s_tree+(int)strlen(*s_tree),"%f",fils->b[p]->support_val); } } if(tree->io && tree->io->print_node_num == YES) { sprintf(*s_tree+(int)strlen(*s_tree),"%d",fils->num); } fflush(NULL); (*s_tree)[(int)strlen(*s_tree)] = ':'; if(tree->print_labels == YES) Print_Labels(NULL,*s_tree+(int)strlen(*s_tree),b->label); if(tree->is_mixt_tree == NO) mean_len = b->l->v; else mean_len = MIXT_Get_Mean_Edge_Len(b,tree); sprintf(*s_tree+(int)strlen(*s_tree),format,MAX(0.0,mean_len)); } (*s_tree)[(int)strlen(*s_tree)] = ','; #ifndef MPI (*available) -= ((int)strlen(*s_tree) - last_len); if(*available < 0) { PhyML_Fprintf(stderr,"\n. available = %d",*available); PhyML_Fprintf(stderr,"\n. Err in file %s at line %d\n",__FILE__,__LINE__); Warn_And_Exit(""); } if(*available < (int)T_MAX_NAME) { (*s_tree) = (char *)mRealloc(*s_tree,(int)strlen(*s_tree)+3*(int)T_MAX_NAME,sizeof(char)); for(i=0;i<3*(int)T_MAX_NAME;++i) (*s_tree)[(int)strlen(*s_tree)+i] = '\0'; (*available) = 3*(int)T_MAX_NAME; } #endif } Free(format); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Detect_Align_File_Format(option *io) { int c; fpos_t curr_pos; fgetpos(io->fp_in_align,&curr_pos); errno = 0; while((c=fgetc(io->fp_in_align)) != EOF) { if(errno) io->data_file_format = PHYLIP; else if(c == '#') { char s[10],t[6]="NEXUS"; if(!fgets(s,6,io->fp_in_align)) { PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d\n",__FILE__,__LINE__); Exit("\n"); } if(!strcmp(t,s)) { fsetpos(io->fp_in_align,&curr_pos); io->data_file_format = NEXUS; return; } } } fsetpos(io->fp_in_align,&curr_pos); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Detect_Tree_File_Format(option *io) { int c; fpos_t curr_pos; fgetpos(io->fp_in_tree,&curr_pos); errno = 0; while((c=fgetc(io->fp_in_tree)) != EOF) { if(errno) { io->tree_file_format = PHYLIP; PhyML_Printf("\n. Detected PHYLIP tree file format."); } else if(c == '#') { char s[10],t[6]="NEXUS"; if(!fgets(s,6,io->fp_in_tree)) { PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d\n",__FILE__,__LINE__); Warn_And_Exit(""); } if(!strcmp(t,s)) { fsetpos(io->fp_in_tree,&curr_pos); io->tree_file_format = NEXUS; PhyML_Printf("\n. Detected NEXUS tree file format."); return; } } } fsetpos(io->fp_in_tree,&curr_pos); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// align **Get_Seq(option *io) { io->data = NULL; if(!io->fp_in_align) { PhyML_Fprintf(stderr,"\n. Filehandle to '%s' seems to be closed.",io->in_align_file); Exit("\n"); } Detect_Align_File_Format(io); switch(io->data_file_format) { case PHYLIP: { io->data = Get_Seq_Phylip(io); break; } case NEXUS: { io->nex_com_list = Make_Nexus_Com(); Init_Nexus_Format(io->nex_com_list); Get_Nexus_Data(io->fp_in_align,io); Free_Nexus(io); break; } default: { PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d (function '%s')\n",__FILE__,__LINE__,__FUNCTION__); Exit("\n"); break; } } if(!io->data) { PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d (function '%s')\n",__FILE__,__LINE__,__FUNCTION__); Exit("\n"); } else { Post_Process_Data(io); } if(io->n_otu < 3) { PhyML_Fprintf(stderr,"\n. PhyML needs at least three sequences to perform an analysis."); assert(FALSE); } return io->data; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Post_Process_Data(option *io) { int i,j,swap; align *data_buff; for(i=0;idata[0]->len;++i) { for(j=0;jn_otu;++j) { if((io->data[j]->state[i] == '*') || (io->data[j]->state[i] == '?') || (io->data[j]->state[i] == '-')) io->data[j]->state[i] = 'X'; if((io->datatype == NT) && (io->data[j]->state[i] == 'N')) io->data[j]->state[i] = 'X'; if(io->data[j]->state[i] == 'U') io->data[j]->state[i] = 'T'; } } for(i=0;in_otu;++i) io->data[i]->len = io->data[0]->len; /* Sequences are ordered alphabetically */ data_buff = NULL; swap = TRUE; /* swap = FALSE; */ while(swap == TRUE) { swap = FALSE; for(i=0;in_otu-1;i++) { for(j=i+1;jn_otu;j++) { if(strcmp(io->data[i]->name,io->data[j]->name) < 0) { swap = TRUE; data_buff = io->data[i]; io->data[i] = io->data[j]; io->data[j] = data_buff; } } } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* align **Get_Seq_Nexus(option *io) */ /* { */ /* char *s,*ori_s; */ /* char *token; */ /* int in_comment; */ /* nexcom *curr_com; */ /* nexparm *curr_parm; */ /* int nxt_token_t,cur_token_t; */ /* s = (char *)mCalloc(T_MAX_LINE,sizeof(char)); */ /* token = (char *)mCalloc(T_MAX_TOKEN,sizeof(char)); */ /* ori_s = s; */ /* in_comment = NO; */ /* curr_com = NULL; */ /* curr_parm = NULL; */ /* nxt_token_t = NEXUS_COM; */ /* cur_token_t = -1; */ /* while(fgets(s,T_MAX_LINE,io->fp_in_align)) */ /* { */ /* do */ /* { */ /* Get_Token(&s,token); */ /* /\* PhyML_Printf("\n. Token: '%s' next_token=%d cur_token=%d",token,nxt_token_t,cur_token_t); *\/ */ /* if(token[0] == '\0') break; */ /* if(token[0] == ';') */ /* { */ /* curr_com = NULL; */ /* curr_parm = NULL; */ /* nxt_token_t = NEXUS_COM; */ /* cur_token_t = -1; */ /* break; /\* End of command *\/ */ /* } */ /* if(nxt_token_t == NEXUS_EQUAL) */ /* { */ /* cur_token_t = NEXUS_VALUE; */ /* nxt_token_t = NEXUS_PARM; */ /* continue; */ /* } */ /* if((nxt_token_t == NEXUS_COM) && (cur_token_t != NEXUS_VALUE)) */ /* { */ /* Find_Nexus_Com(token,&curr_com,&curr_parm,io->nex_com_list); */ /* if(curr_com) */ /* { */ /* nxt_token_t = curr_com->nxt_token_t; */ /* cur_token_t = curr_com->cur_token_t; */ /* } */ /* if(cur_token_t != NEXUS_VALUE) continue; */ /* } */ /* if((nxt_token_t == NEXUS_PARM) && (cur_token_t != NEXUS_VALUE)) */ /* { */ /* Find_Nexus_Parm(token,&curr_parm,curr_com); */ /* if(curr_parm) */ /* { */ /* nxt_token_t = curr_parm->nxt_token_t; */ /* cur_token_t = curr_parm->cur_token_t; */ /* } */ /* if(cur_token_t != NEXUS_VALUE) continue; */ /* } */ /* if(cur_token_t == NEXUS_VALUE) */ /* { */ /* if((curr_parm->fp)(token,curr_parm,io)) /\* Read in parameter value *\/ */ /* { */ /* nxt_token_t = NEXUS_PARM; */ /* cur_token_t = -1; */ /* } */ /* } */ /* } */ /* while(strlen(token) > 0); */ /* } */ /* Free(ori_s); */ /* Free(token); */ /* return io->data; */ /* } */ /* /\*********************************************************\/ */ void Get_Nexus_Data(FILE *fp, option *io) { char *token; nexcom *curr_com; nexparm *curr_parm; int nxt_token_t,cur_token_t; token = (char *)mCalloc(T_MAX_TOKEN,sizeof(char)); curr_com = NULL; curr_parm = NULL; nxt_token_t = NEXUS_COM; cur_token_t = -1; do { if(!Get_Token(fp,token)) break; /* PhyML_Printf("\n+ Token: '%s' next_token=%d cur_token=%d",token,nxt_token_t,cur_token_t); */ if(token[0] == ';') { curr_com = NULL; curr_parm = NULL; nxt_token_t = NEXUS_COM; cur_token_t = -1; } if(nxt_token_t == NEXUS_EQUAL) { cur_token_t = NEXUS_VALUE; nxt_token_t = NEXUS_PARM; continue; } if((nxt_token_t == NEXUS_COM) && (cur_token_t != NEXUS_VALUE)) { Find_Nexus_Com(token,&curr_com,&curr_parm,io->nex_com_list); if(curr_com) { nxt_token_t = curr_com->nxt_token_t; cur_token_t = curr_com->cur_token_t; } if(cur_token_t != NEXUS_VALUE) continue; } if((nxt_token_t == NEXUS_PARM) && (cur_token_t != NEXUS_VALUE)) { Find_Nexus_Parm(token,&curr_parm,curr_com); if(curr_parm) { nxt_token_t = curr_parm->nxt_token_t; cur_token_t = curr_parm->cur_token_t; } if(cur_token_t != NEXUS_VALUE) continue; } if(cur_token_t == NEXUS_VALUE) { if((curr_parm->fp)(token,curr_parm,io)) /* Read in parameter value */ { nxt_token_t = NEXUS_PARM; cur_token_t = -1; } } } while(strlen(token) > 0); Free(token); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int Get_Token(FILE *fp, char *token) { char c; c = ' '; while(c == ' ' || c == '\t' || c == '\n' || c == '\r') { c = fgetc(fp); if(c == EOF) return 0; } if(c == '"') { do { *token = c; token++; c = fgetc(fp); if(c == EOF) return 0; } while(c != '"'); *token = c; /* c = fgetc(fp); */ if(c == EOF) return 0; *(token+1) = '\0'; return 1; } if(c == '[') { Skip_Comment(fp); c = fgetc(fp); *token = c; token++; if(c == EOF) return 0; return 1; } if(c == '#') { *token = c; token++; } else if(c == ';') { *token = c; token++; } else if(c == ',') { *token = c; token++; } else if(c == '.') { *token = c; token++; } else if(c == '=') { *token = c; token++; } else if(c == '(') { *token = c; token++; } else if(c == ')') { *token = c; token++; } else if(c == '{') { *token = c; token++; } else if(c == '}') { *token = c; token++; } else if(c == '?') { *token = c; token++; } else if(c == '-') { *token = c; token++; } else { while(isgraph(c) && c != ';' && c != '-' && c != ',' && c != '=') { *(token++) = c; c = fgetc(fp); if(c == EOF) return 0; } fseek(fp,-1*sizeof(char),SEEK_CUR); } *token = '\0'; return 1; } /* void Get_Token(char *line, char *token) */ /* { */ /* while(**line == ' ' || **line == '\t') (*line)++; */ /* if(**line == '"') */ /* { */ /* do { *token = **line; (*line)++; token++; } while(**line != '"'); */ /* *token = **line; */ /* (*line)++; */ /* *(token+1) = '\0'; */ /* return; */ /* } */ /* if(**line == '[') */ /* { */ /* int in_comment; */ /* in_comment = 1; */ /* do */ /* { */ /* (*line)++; */ /* if(**line == '[') */ /* { */ /* in_comment++; */ /* } */ /* else if(**line == ']') in_comment--; */ /* } */ /* while(in_comment); */ /* (*line)++; */ /* return; */ /* } */ /* if(**line == '#') {*token = **line; (*line)++; token++; } */ /* else if(**line == ';') {*token = **line; (*line)++; token++; } */ /* else if(**line == ',') {*token = **line; (*line)++; token++; } */ /* else if(**line == '.') {*token = **line; (*line)++; token++; } */ /* else if(**line == '=') {*token = **line; (*line)++; token++; } */ /* else if(**line == '(') {*token = **line; (*line)++; token++; } */ /* else if(**line == ')') {*token = **line; (*line)++; token++; } */ /* else if(**line == '{') {*token = **line; (*line)++; token++; } */ /* else if(**line == '}') {*token = **line; (*line)++; token++; } */ /* else if(**line == '?') {*token = **line; (*line)++; token++; } */ /* else if(**line == '-') {*token = **line; (*line)++; token++; } */ /* else */ /* { */ /* while(isgraph(**line) && **line != ';' && **line != '=' && **line != ',') */ /* { */ /* *(token++) = **line; */ /* (*line)++; */ /* } */ /* } */ /* *token = '\0'; */ /* } */ ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// align **Get_Seq_Phylip(option *io) { Read_Ntax_Len_Phylip(io->fp_in_align,&io->n_otu,&io->init_len); if(io->n_otu > N_MAX_OTU) { PhyML_Fprintf(stderr,"\n. The number of taxa should not exceed %d",N_MAX_OTU); assert(FALSE); } if(io->interleaved == YES) io->data = Read_Seq_Interleaved(io); else io->data = Read_Seq_Sequential(io); return io->data; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Read_Ntax_Len_Phylip(FILE *fp ,int *n_otu, int *n_tax) { char *line; int readok; line = (char *)mCalloc(T_MAX_LINE,sizeof(char)); readok = 0; do { if(fscanf(fp,"%s",line) == EOF) { Free(line); PhyML_Fprintf(stderr,"\n. PhyML can't read in this alignment."); PhyML_Fprintf(stderr,"\n. Could it be that sequence file is empty?"); PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d\n",__FILE__,__LINE__); Exit("\n"); } else { if(strcmp(line,"\n") && strcmp(line,"\r") && strcmp(line,"\t")) { sscanf(line,"%d",n_otu); if(*n_otu <= 0) Warn_And_Exit("\n. The number of taxa cannot be negative.\n"); if(!fscanf(fp,"%s",line)) Exit("\n"); sscanf(line,"%d",n_tax); if(*n_tax <= 0) Warn_And_Exit("\n. The sequence length cannot be negative.\n"); else readok = 1; } } }while(!readok); Free(line); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// align **Read_Seq_Sequential(option *io) { int i; char *line; align **data; /* char c; */ char *format; format = (char *)mCalloc(T_MAX_NAME,sizeof(char)); line = (char *)mCalloc(T_MAX_LINE,sizeof(char)); data = (align **)mCalloc(io->n_otu,sizeof(align *)); /* while((c=fgetc(in))!='\n'); */ /* while(((c=fgetc(io->fp_in_align))!='\n') && (c != ' ') && (c != '\r') && (c != '\t')); */ sprintf(format, "%%%ds", T_MAX_NAME); for(i=0;in_otu;i++) { data[i] = (align *)mCalloc(1,sizeof(align)); data[i]->name = (char *)mCalloc(T_MAX_NAME,sizeof(char)); data[i]->state = (char *)mCalloc(io->init_len*io->state_len+1,sizeof(char)); data[i]->is_ambigu = NULL; data[i]->len = 0; if(!fscanf(io->fp_in_align,format,data[i]->name)) Exit("\n"); Check_Sequence_Name(data[i]->name); while(data[i]->len < io->init_len * io->state_len) assert(Read_One_Line_Seq(&data,i,io->fp_in_align)); if(data[i]->len != io->init_len * io->state_len) { PhyML_Fprintf(stderr,"\n. Err. Problem with species %s's sequence (check the format).\n",data[i]->name); PhyML_Fprintf(stderr,"\n. Observed sequence length: %d, expected length: %d\n",data[i]->len, io->init_len * io->state_len); Warn_And_Exit(""); } } for(i=0;in_otu;i++) data[i]->state[data[i]->len] = '\0'; Restrict_To_Coding_Position(data,io); Free(format); Free(line); return data; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// align **Read_Seq_Interleaved(option *io) { int i,end,num_block; char *line; align **data; /* char c; */ char *format; fpos_t curr_pos; line = (char *)mCalloc(T_MAX_LINE,sizeof(char)); format = (char *)mCalloc(T_MAX_NAME, sizeof(char)); data = (align **)mCalloc(io->n_otu,sizeof(align *)); /* while(((c=fgetc(io->fp_in_align))!='\n') && (c != ' ') && (c != '\r') && (c != '\t')); */ sprintf(format, "%%%ds", T_MAX_NAME); end = 0; for(i=0;in_otu;i++) { data[i] = (align *)mCalloc(1,sizeof(align)); data[i]->name = (char *)mCalloc(T_MAX_NAME,sizeof(char)); data[i]->state = (char *)mCalloc(io->init_len*io->state_len+1,sizeof(char)); data[i]->len = 0; data[i]->is_ambigu = NULL; if(!fscanf(io->fp_in_align,format,data[i]->name)) Exit("\n"); Check_Sequence_Name(data[i]->name); if(!Read_One_Line_Seq(&data,i,io->fp_in_align)) { end = 1; if((i != io->n_otu) && (i != io->n_otu-1)) { PhyML_Fprintf(stderr,"\n. Err.: problem with species %s's sequence.\n",data[i]->name); PhyML_Fprintf(stderr,"\n. Observed sequence length: %d, expected length: %d\n",data[i]->len, io->init_len * io->state_len); Exit(""); } break; } } if(data[0]->len == io->init_len * io->state_len) end = 1; /* if(end) printf("\n. finished yet '%c'\n",fgetc(io->fp_in_align)); */ if(!end) { end = 0; num_block = 1; do { num_block++; /* interblock */ if(!fgets(line,T_MAX_LINE,io->fp_in_align)) break; if(line[0] != 13 && line[0] != 10) { PhyML_Fprintf(stderr,"\n. Err.: one or more missing sequences in block %d.\n",num_block-1); Exit(""); } for(i=0;in_otu;i++) if(data[i]->len != io->init_len * io->state_len) break; if(i == io->n_otu) break; for(i=0;in_otu;i++) { /* Skip the taxon name, if any, in this interleaved block */ fgetpos(io->fp_in_align,&curr_pos); if(!fscanf(io->fp_in_align,format,line)) { PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d\n",__FILE__,__LINE__); Warn_And_Exit(""); } if(line && strcmp(line,data[i]->name)) fsetpos(io->fp_in_align,&curr_pos); if(data[i]->len > io->init_len * io->state_len) { PhyML_Fprintf(stderr,"\n. Observed sequence length=%d expected length=%d.\n",data[i]->len,io->init_len * io->state_len); PhyML_Fprintf(stderr,"\n. Err.: Problem with species %s's sequence.\n",data[i]->name); Exit(""); } else if(!Read_One_Line_Seq(&data,i,io->fp_in_align)) { end = 1; if((i != io->n_otu) && (i != io->n_otu-1)) { PhyML_Fprintf(stderr,"\n. Err.: Problem with species %s's sequence.\n",data[i]->name); PhyML_Fprintf(stderr,"\n. Observed sequence length: %d, expected length: %d.\n",data[i]->len, io->init_len * io->state_len); Exit(""); } break; } } }while(!end); } for(i=0;in_otu;i++) data[i]->state[data[i]->len] = '\0'; for(i=0;in_otu;i++) { if(data[i]->len != io->init_len * io->state_len) { PhyML_Fprintf(stderr,"\n. Check sequence '%s' length (expected length: %d, observed length: %d) [OTU %d].\n",data[i]->name,io->init_len,data[i]->len,i+1); Exit(""); } } Restrict_To_Coding_Position(data,io); Free(format); Free(line); return data; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int Read_One_Line_Seq(align ***data, int num_otu, FILE *in) { char c = ' '; int nchar = 0; while(1) { /* if((c == EOF) || (c == '\n') || (c == '\r')) break; */ if((c == 13) || (c == 10)) { /* PhyML_Printf("[%d %d]\n",c,nchar); fflush(NULL); */ if(!nchar) { c=(char)fgetc(in); continue; } else { /* PhyML_Printf("break\n"); */ break; } } else if(c == EOF) { /* PhyML_Printf("EOL\n"); */ break; } else if((c == ' ') || (c == '\t') || (c == 32)) { /* PhyML_Printf("[%d]",c); */ c=(char)fgetc(in); continue; } nchar++; Uppercase(&c); if(c == '.') { c = (*data)[0]->state[(*data)[num_otu]->len]; if(!num_otu) Warn_And_Exit("\n. Err: Symbol \".\" should not appear in the first sequence\n"); } (*data)[num_otu]->state[(*data)[num_otu]->len]=c; (*data)[num_otu]->len++; c = (char)fgetc(in); /* PhyML_Printf("[%c %d]",c,c); fflush(NULL); */ if(c == ';') break; } /* printf("\n. Exit nchar: %d [%d]\n",nchar,c==EOF); */ if(c == EOF) return 0; else return 1; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// scalar_dbl *Read_Io_Weights(option *io) { scalar_dbl *w,*ori,*prev = NULL; double val; assert(io->weight_file); io->fp_weight_file = Openfile(io->weight_file,READ); w = (scalar_dbl *)mCalloc(1,sizeof(scalar_dbl)); ori = w; do { if(fscanf(io->fp_weight_file,"%lf,",&val) == EOF) break; w->v = (phydbl)val; w->next = (scalar_dbl *)mCalloc(1,sizeof(scalar_dbl)); prev = w; w = w->next; } while(1); /* Remove the last allocated and empty element of the list */ if(prev !=NULL && prev->next != NULL) { Free(prev->next); prev->next=NULL; } fclose(io->fp_weight_file); return(ori); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// char *Return_Tree_String_Phylip(FILE *fp_input_tree) { char *line; int i; char c; int open,maxopen; if(fp_input_tree == NULL) { PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d\n",__FILE__,__LINE__); Warn_And_Exit(""); } do { c=fgetc(fp_input_tree); } while((c != '(') && (c != EOF)); if(c==EOF) return NULL; line = (char *)mCalloc(1,sizeof(char)); open = 1; maxopen = open; i=0; for(;;) { if((c == ' ') || (c == '\n')) { c=fgetc(fp_input_tree); if(c == EOF || c == ';') break; else continue; } /* if(c == '[') */ /* { */ /* Skip_Comment(fp_input_tree); */ /* c = fgetc(fp_input_tree); */ /* if(c == EOF || c == ';') break; */ /* } */ line = (char *)mRealloc(line,i+2,sizeof(char)); line[i]=c; i++; c=fgetc(fp_input_tree); if(c==EOF || c==';') break; if(c=='(') open++; if(c==')') open--; if(open>maxopen) maxopen = open; } line[i] = '\0'; /* if(maxopen == 1) return NULL; */ return line; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// t_tree *Read_Tree_File_Phylip(FILE *fp_input_tree) { char *line; t_tree *tree; line = Return_Tree_String_Phylip(fp_input_tree); tree = Read_Tree(&line); Free(line); return tree; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Site(calign *cdata, int num, int n_otu, char *sep, int stepsize, FILE *fp) { int i,j; PhyML_Fprintf(fp,"\n"); for(i=0;ic_seq[i]->name); for(j=0;jc_seq[i]->state[num+j]); PhyML_Fprintf(fp,"%s",sep); } PhyML_Fprintf(fp,"%s",sep); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Site_Lk(t_tree *tree, FILE *fp) { int site; int catg; char *s; phydbl postmean,sum; assert(fp); rewind(fp); if(tree->is_mixt_tree == YES) { MIXT_Print_Site_Lk(tree,fp); return; } assert(tree->io->print_site_lnl == YES); if(!tree->io->print_trace) { s = (char *)mCalloc(T_MAX_LINE,sizeof(char)); PhyML_Fprintf(fp,"Note : P(D|M) is the probability of site D given the model M (i.e., the site likelihood)\n"); if(tree->mod->ras->n_catg > 1 || tree->mod->ras->invar) { PhyML_Fprintf(fp,"P*(D|M,rr[x]) is the scaled probability of site D given the model M and the relative rate\n"); PhyML_Fprintf(fp,"of evolution rr[x], where x is the class of rate to be considered.\n"); PhyML_Fprintf(fp,"The actual conditional probability is given by P*(D|M,rr[x])/2^F, where\n"); PhyML_Fprintf(fp,"F is the scaling factor (see column 'Scaler').\n"); PhyML_Fprintf(fp,"For invariant sites, P(D|M,rr[0]=0) is the actual conditional probability\n"); PhyML_Fprintf(fp,"(i.e., it is not scaled).\n"); } PhyML_Fprintf(fp,"\n\n"); sprintf(s,"Site"); PhyML_Fprintf(fp, "%-12s",s); sprintf(s,"P(D|M)"); PhyML_Fprintf(fp,"%-15s",s); sprintf(s,"Scaler"); PhyML_Fprintf(fp,"%-7s",s); sprintf(s,"Pattern"); PhyML_Fprintf(fp, "%-9s",s); if(tree->mod->ras->n_catg > 1) { for(catg=0;catgmod->ras->n_catg;catg++) { sprintf(s,"P*(D|M,rr[%d]=%5.4f)",catg+1,tree->mod->ras->gamma_rr->v[catg]); PhyML_Fprintf(fp,"%-23s",s); } sprintf(s,"Posterior mean"); PhyML_Fprintf(fp,"%-22s",s); } if(tree->mod->ras->invar) { sprintf(s,"P(D|M,rr[0]=0)"); PhyML_Fprintf(fp,"%-16s",s); } sprintf(s,"NDistinctStates"); PhyML_Fprintf(fp,"%-16s",s); PhyML_Fprintf(fp,"\n"); Init_Ui_Tips(tree); for(site=0;sitedata->init_len;site++) { PhyML_Fprintf(fp,"%-12d",site+1); PhyML_Fprintf(fp,"%-15g",tree->cur_site_lk[tree->data->sitepatt[site]]); PhyML_Fprintf(fp,"%-7d",tree->fact_sum_scale[tree->data->sitepatt[site]]); PhyML_Fprintf(fp,"%-9d",tree->data->sitepatt[site]); if(tree->mod->ras->n_catg > 1) { for(catg=0;catgmod->ras->n_catg;catg++) { PhyML_Fprintf(fp,"%-23g",tree->unscaled_site_lk_cat[tree->data->sitepatt[site]*tree->mod->ras->n_catg + catg]); } postmean = .0; for(catg=0;catgmod->ras->n_catg;catg++) postmean += tree->mod->ras->gamma_rr->v[catg] * tree->unscaled_site_lk_cat[tree->data->sitepatt[site]*tree->mod->ras->n_catg + catg] * tree->mod->ras->gamma_r_proba->v[catg]; sum = .0; for(catg=0;catgmod->ras->n_catg;catg++) { sum += tree->unscaled_site_lk_cat[tree->data->sitepatt[site]*tree->mod->ras->n_catg + catg] * tree->mod->ras->gamma_r_proba->v[catg]; } postmean /= sum; PhyML_Fprintf(fp,"%-22g",postmean); } if(tree->mod->ras->invar) { if((phydbl)tree->data->invar[tree->data->sitepatt[site]] > -0.5) PhyML_Fprintf(fp,"%-16g",tree->mod->e_frq->pi->v[tree->data->invar[tree->data->sitepatt[site]]]); else PhyML_Fprintf(fp,"%-16g",0.0); } PhyML_Fprintf(fp,"%-16d",Number_Of_Diff_States_One_Site(tree->data->sitepatt[site],tree)); PhyML_Fprintf(fp,"\n"); } Free(s); } else { for(site=0;sitedata->init_len;site++) PhyML_Fprintf(fp,"%.2f\t",log(tree->cur_site_lk[tree->data->sitepatt[site]])); PhyML_Fprintf(fp,"\n"); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Seq(FILE *fp, align **data, int n_otu) { int i,j; PhyML_Fprintf(fp,"%d\t%d\n",n_otu,data[0]->len); for(i=0;iname); /* for(j=0;j<20;j++) */ /* { */ /* if(j<(int)strlen(data[i]->name)) */ /* fputc(data[i]->name[j],fp); */ /* else fputc(' ',fp); */ /* } */ /* PhyML_Printf("%10d ",i); */ For(j,data[i]->len) { PhyML_Fprintf(fp,"%c",data[i]->state[j]); } PhyML_Fprintf(fp,"\n"); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_CSeq(FILE *fp, int compressed, calign *cdata, t_tree *tree) { int i,j; int n_otu; n_otu = cdata->n_otu; if(cdata->format == PHYLIP) { PhyML_Fprintf(fp,"%d\t%d\n",n_otu,cdata->init_len); } else if(cdata->format == NEXUS) { PhyML_Fprintf(fp,"#NEXUS\n"); PhyML_Fprintf(fp,"begin data\n"); PhyML_Fprintf(fp,"dimensions ntax=%d nchar=%d;\n",n_otu,cdata->init_len); PhyML_Fprintf(fp,"format sequential datatype=dna;\n"); PhyML_Fprintf(fp,"matrix\n"); } else { PhyML_Fprintf(fp,"This sample file is to be processed by IBDSim (http://www1.montpellier.inra.fr/CBGP/software/ibdsim/)"); } if(cdata->format == PHYLIP || cdata->format == NEXUS) { for(i=0;ic_seq[i]->name)) fputc(cdata->c_seq[i]->name[j],fp); else fputc(' ',fp); } if(compressed == YES) /* Print out compressed sequences */ PhyML_Fprintf(fp,"%s",cdata->c_seq[i]->state); else /* Print out uncompressed sequences */ { for(j=0;jinit_len;j++) { PhyML_Fprintf(fp,"%c",cdata->c_seq[i]->state[cdata->sitepatt[j]]); } } PhyML_Fprintf(fp,"\n"); } PhyML_Fprintf(fp,"\n"); if(cdata->format == NEXUS) { PhyML_Fprintf(fp,";\n"); PhyML_Fprintf(fp,"END;\n"); } } else if(cdata->format == IBDSIM) { for(i=0;iinit_len;i++) PhyML_Fprintf(fp,"\nlocus %6d",i); for(i=0;ia_nodes[i]->coord->lonlat[0], tree->a_nodes[i]->coord->lonlat[1]); for(j=0;jinit_len;j++) { switch(tree->a_nodes[i]->c_seq->state[j]) { case 'A' : { PhyML_Fprintf(fp,"001 "); break; } case 'C' : { PhyML_Fprintf(fp,"002 "); break; } case 'G' : { PhyML_Fprintf(fp,"003 "); break; } case 'T' : { PhyML_Fprintf(fp,"004 "); break; } } } } } /* PhyML_Printf("\t"); */ /* for(j=0;jcrunch_len;j++) */ /* PhyML_Printf("%.0f ",cdata->wght[j]); */ /* PhyML_Printf("\n"); */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_CSeq_Select(FILE *fp, int compressed, calign *cdata, t_tree *tree) { int i,j; int n_otu; phydbl eps; int slice = 14; eps = 1.E-6; n_otu = 0; for(i=0;in_otu;i++) if(tree->times->nd_t[i] < tree->times->time_slice_lims[slice] + eps) n_otu++; PhyML_Fprintf(fp,"%d\t%d\n",n_otu,cdata->init_len); n_otu = cdata->n_otu; for(i=0;itimes->nd_t[i] < tree->times->time_slice_lims[slice] + eps) { for(j=0;j<50;j++) { if(j<(int)strlen(cdata->c_seq[i]->name)) fputc(cdata->c_seq[i]->name[j],fp); else fputc(' ',fp); } if(compressed == YES) /* Print out compressed sequences */ PhyML_Fprintf(fp,"%s",cdata->c_seq[i]->state); else /* Print out uncompressed sequences */ { for(j=0;jinit_len;j++) { PhyML_Fprintf(fp,"%c",cdata->c_seq[i]->state[cdata->sitepatt[j]]); } } PhyML_Fprintf(fp,"\n"); } } if(cdata->format == 1) { PhyML_Fprintf(fp,";\n"); PhyML_Fprintf(fp,"END;\n"); } /* PhyML_Printf("\t"); */ /* for(j=0;jcrunch_len;j++) */ /* PhyML_Printf("%.0f ",cdata->wght[j]); */ /* PhyML_Printf("\n"); */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Dist(matrix *mat) { int i,j; for(i=0;in_otu;i++) { PhyML_Printf("%s ",mat->name[i]); for(j=0;jn_otu;j++) PhyML_Printf("%9.6f ",mat->dist[i][j]); PhyML_Printf("\n"); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Node(t_node *a, t_node *d, t_tree *tree) { int i; int dir; dir = -1; for(i=0;i<3;i++) if(a->v[i] == d) {dir = i; break;} PhyML_Printf("Node nums: %3d %3d (dir:%3d) (a->anc:%3d) (d->anc:%3d) ta:%8.4f td:%8.4f t_min:%6.2f t_max:%6.2f", a->num,d->num,dir,a->anc?a->anc->num:(-1), d->anc?d->anc->num:(-1), tree->rates?tree->times->nd_t[a->num]:-1., tree->rates?tree->times->nd_t[d->num]:-1., tree->rates?tree->times->t_prior_min[a->num]:-1., tree->rates?tree->times->t_prior_max[a->num]:-1.); PhyML_Printf(" names = '%10s' '%10s' ; ",a->name,d->name); for(i=0;i<3;i++) if(a->v[i] == d) { if(a->b[i]) { PhyML_Printf("Branch num = %3d%c (%3d %3d) %f", a->b[i]->num,a->b[i]==tree->e_root?'*':' ',a->b[i]->left->num, a->b[i]->rght->num,a->b[i]->l->v); if(a->b[i]->left->tax) PhyML_Printf(" WARNING LEFT->TAX!"); break; } } PhyML_Printf("\n"); if(d->tax) return; else for(i=0;i<3;i++) if(d->v[i] != a && d->b[i] != tree->e_root) Print_Node(d,d->v[i],tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Node_Brief(t_node *a, t_node *d, t_tree *tree, FILE *fp) { int i; int dir; dir = -1; for(i=0;i<3;i++) if(a->v[i] == d) {dir = i; break;} PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"Node nums: %3d %3d (dir:%3d)", a->num,d->num,dir); PhyML_Fprintf(fp,"\tnames = '%10s' '%10s' ; ",a->name,d->name); for(i=0;i<3;i++) if(a->v[i] == d) { if(a->b[i]) { PhyML_Fprintf(fp,"Branch num = %3d%c (%3d %3d) length:%10f", a->b[i]->num,a->b[i]==tree->e_root?'*':' ',a->b[i]->left->num, a->b[i]->rght->num,a->b[i]->l->v); if(a->b[i]->left->tax) PhyML_Printf(" WARNING LEFT->TAX!"); break; } } if(d->tax) return; else for(i=0;i<3;i++) if(d->v[i] != a && d->b[i] != tree->e_root) Print_Node_Brief(d,d->v[i],tree,fp); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Model(t_mod *mod) { int i,j,k; PhyML_Printf("\n. name=%s",mod->modelname->s); PhyML_Printf("\n. string=%s",mod->custom_mod_string); PhyML_Printf("\n. mod_num=%d",mod->mod_num); PhyML_Printf("\n. ns=%d",mod->ns); PhyML_Printf("\n. n_catg=%d",mod->ras->n_catg); PhyML_Printf("\n. kappa=%f",mod->kappa->v); PhyML_Printf("\n. alpha=%f",mod->ras->alpha->v); PhyML_Printf("\n. lambda=%f",mod->lambda->v); PhyML_Printf("\n. pinvar=%f",mod->ras->pinvar->v); PhyML_Printf("\n. br_len_mult=%f",mod->br_len_mult->v); PhyML_Printf("\n. whichmodel=%d",mod->whichmodel); PhyML_Printf("\n. update_eigen=%d",mod->update_eigen); PhyML_Printf("\n. bootstrap=%d",mod->io->n_boot_replicates); PhyML_Printf("\n. n_diff_rr=%d",mod->r_mat->n_diff_rr); PhyML_Printf("\n. invar=%d",mod->ras->invar); PhyML_Printf("\n. use_m4mod=%d",mod->use_m4mod); PhyML_Printf("\n. gamma_median=%d",mod->ras->gamma_median); PhyML_Printf("\n. state_len=%d",mod->io->state_len); PhyML_Printf("\n. log_l=%d",mod->log_l); PhyML_Printf("\n. l_min=%f",mod->l_min); PhyML_Printf("\n. l_max=%f",mod->l_max); PhyML_Printf("\n. free_mixt_rates=%d",mod->ras->free_mixt_rates); PhyML_Printf("\n. gamma_mgf_bl=%d",mod->gamma_mgf_bl); PhyML_Printf("\n. Pi\n"); for(i=0;ins;i++) PhyML_Printf(" %f ",mod->e_frq->pi->v[i]); PhyML_Printf("\n"); for(i=0;ins;i++) PhyML_Printf(" %f ",mod->e_frq->pi_unscaled->v[i]); PhyML_Printf("\n. Rates\n"); for(i=0;iras->n_catg;i++) PhyML_Printf(" %f ",mod->ras->gamma_r_proba->v[i]); PhyML_Printf("\n"); for(i=0;iras->n_catg;i++) PhyML_Printf(" %f ",mod->ras->gamma_r_proba_unscaled->v[i]); PhyML_Printf("\n"); for(i=0;iras->n_catg;i++) PhyML_Printf(" %f ",mod->ras->gamma_rr->v[i]); PhyML_Printf("\n"); for(i=0;iras->n_catg;i++) PhyML_Printf(" %f ",mod->ras->gamma_rr_unscaled->v[i]); PhyML_Printf("\n. Qmat \n"); if(mod->whichmodel == CUSTOM) { fflush(NULL); for(i=0;i<6;i++) {PhyML_Printf(" %12f ",mod->r_mat->rr->v[i]); fflush(NULL);} for(i=0;i<6;i++) {PhyML_Printf(" %12f ",mod->r_mat->rr_val->v[i]); fflush(NULL);} for(i=0;i<6;i++) {PhyML_Printf(" %12d ",mod->r_mat->rr_num->v[i]); fflush(NULL);} for(i=0;i<6;i++) {PhyML_Printf(" %12d ",mod->r_mat->n_rr_per_cat->v[i]); fflush(NULL);} } for(i=0;ins;i++) { PhyML_Printf(" "); for(j=0;j<4;j++) PhyML_Printf("%8.5f ",mod->r_mat->qmat->v[i*4+j]); PhyML_Printf("\n"); } PhyML_Printf("\n. Freqs"); PhyML_Printf("\n"); for(i=0;ins;i++) PhyML_Printf(" %12f ",mod->e_frq->user_b_freq->v[i]); PhyML_Printf("\n"); for(i=0;ins;i++) PhyML_Printf(" %12f ",mod->e_frq->pi->v[i]); PhyML_Printf("\n"); for(i=0;ins;i++) PhyML_Printf(" %12f ",mod->e_frq->pi_unscaled->v[i]); PhyML_Printf("\n. Eigen\n"); For(i,2*mod->ns) PhyML_Printf(" %f ",mod->eigen->space[i]); /* PhyML_Printf("\n"); */ /* For(i,2*mod->ns) PhyML_Printf(" %f ",mod->eigen->space_int[i]); */ PhyML_Printf("\n"); for(i=0;ins;i++) PhyML_Printf(" %f ",mod->eigen->e_val[i]); PhyML_Printf("\n"); for(i=0;ins;i++) PhyML_Printf(" %f ",mod->eigen->e_val_im[i]); PhyML_Printf("\n"); For(i,mod->ns*mod->ns) PhyML_Printf(" %f ",mod->eigen->r_e_vect[i]); PhyML_Printf("\n"); For(i,mod->ns*mod->ns) PhyML_Printf(" %f ",mod->eigen->r_e_vect_im[i]); PhyML_Printf("\n"); For(i,mod->ns*mod->ns) PhyML_Printf(" %f ",mod->eigen->l_e_vect[i]); PhyML_Printf("\n"); For(i,mod->ns*mod->ns) PhyML_Printf(" %f ",mod->eigen->q[i]); PhyML_Printf("\n"); PhyML_Printf("\n. Pij"); for(k=0;kras->n_catg;k++) { PMat(0.01*mod->ras->gamma_rr->v[k],mod,mod->ns*mod->ns*k,mod->Pij_rr->v,NULL); PhyML_Printf("\n. l=%f\n",0.01*mod->ras->gamma_rr->v[k]); for(i=0;ins;i++) { PhyML_Printf(" "); for(j=0;jns;j++) PhyML_Printf("%8.5f ",mod->Pij_rr->v[k*mod->ns*mod->ns+i*mod->ns+j]); PhyML_Printf("\n"); } } PhyML_Printf("\n"); fflush(NULL); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Mat(matrix *mat) { int i,j; PhyML_Printf("\n\n"); PhyML_Printf("%d",mat->n_otu); PhyML_Printf("\n"); for(i=0;in_otu;i++) { for(j=0;j<13;j++) { if(j>=(int)strlen(mat->name[i])) putchar(' '); else putchar(mat->name[i][j]); } for(j=0;jn_otu;j++) { char s[2]="-"; if(mat->dist[i][j] < .0) PhyML_Printf("%12s",s); else PhyML_Printf("%12f",mat->dist[i][j]); } PhyML_Printf("\n"); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// FILE *Openfile(char *filename, int mode) { FILE *fp; char *s; int open_test=0; s = filename; fp = NULL; switch(mode) { case READ : { while(!(fp = (FILE *)fopen(s,"r")) && ++open_test<10) { PhyML_Printf("\n. Can't open file '%s', enter a new name : ",s); Getstring_Stdin(s); } break; } case WRITE : { fp = (FILE *)fopen(s,"w"); break; } case APPEND : { fp = (FILE *)fopen(s,"a"); break; } case READWRITE : { fp = (FILE *)fopen(s,"w+"); break; } default : break; } /* Free(s); */ return fp; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Fp_Out(FILE *fp_out, time_t t_beg, time_t t_end, t_tree *tree, option *io, int n_data_set, int num_tree, int add_citation, int precision) { char *s; char format[8]; div_t hour,min; int i, j; if (precision > 0) snprintf(format,8,"%%.%huf",(unsigned short)precision); if(n_data_set == 1) { rewind(fp_out); Print_Banner_Small(fp_out); } PhyML_Fprintf(fp_out,"\n. Sequence filename: \t\t\t%s", Basename(io->in_align_file)); PhyML_Fprintf(fp_out,"\n. Data set: \t\t\t\t#%d",n_data_set); if(io->mod->s_opt->random_input_tree) PhyML_Fprintf(fp_out,"\n. Random init tree: \t\t\t#%d",num_tree+1); else if(io->n_trees > 1) PhyML_Fprintf(fp_out,"\n. Starting tree number: \t\t#%d",num_tree+1); /* if(io->mod->s_opt->opt_topo) */ /* PhyML_Fprintf(fp_out,"\n. Tree topology search: \t\tSPRs"); */ /* else */ /* PhyML_Fprintf(fp_out,"\n. Tree topology: \t\t\tfixed"); */ /* was after Sequence file ; moved here FLT */ s = (char *)mCalloc(T_MAX_LINE,sizeof(char)); if(io->in_tree == 2) { strcat(strcat(strcat(s,"user tree ("),io->in_tree_file),")"); } else { if(!io->mod->s_opt->random_input_tree) { if(io->in_tree == 0) strcat(s,"BioNJ"); if(io->in_tree == 1) strcat(s,"parsimony"); } else strcat(s,"random tree"); } PhyML_Fprintf(fp_out,"\n. Initial tree: \t\t\t%s",s); Free(s); if(tree->io->datatype == NT) { PhyML_Fprintf(fp_out,"\n. Model of nucleotides substitution: \t%s",tree->mod->modelname->s); if(io->mod->whichmodel == CUSTOM) PhyML_Fprintf(fp_out," (%s)",io->mod->custom_mod_string); } else if(tree->io->datatype == AA) { PhyML_Fprintf(fp_out,"\n. Model of amino acids substitution: \t%s",tree->mod->modelname->s); if(io->mod->whichmodel == CUSTOMAA) PhyML_Fprintf(fp_out," (%s)",tree->mod->aa_rate_mat_file->s); } else { fprintf(fp_out,"\n. Substitution model: \t\t\t%s",tree->mod->modelname->s); } PhyML_Fprintf(fp_out,"\n. Number of taxa: \t\t\t%d",tree->n_otu);/*added FLT*/ PhyML_Fprintf(fp_out,"\n. Log-likelihood: \t\t\t%.5f",tree->c_lnL);/*was last ; moved here FLT*/ Unconstraint_Lk(tree); PhyML_Fprintf(fp_out,"\n. Unconstrained log-likelihood: \t%.5f",tree->unconstraint_lk); Composite_Lk(tree); PhyML_Fprintf(fp_out,"\n. Composite log-likelihood: \t\t%.5f",tree->composite_lk); PhyML_Fprintf(fp_out,"\n. Parsimony: \t\t\t\t%d",tree->c_pars); PhyML_Fprintf(fp_out,"\n. Tree size: \t\t\t\t%.5f",Get_Tree_Size(tree)); /* if(tree->mod->ras->n_catg > 1 && tree->mod->ras->free_mixt_rates == NO) */ if(tree->mod->ras->free_mixt_rates == NO) { PhyML_Fprintf(fp_out,"\n. Discrete gamma model: \t\t%s","Yes"); PhyML_Fprintf(fp_out,"\n - Number of classes: \t\t\t%d",tree->mod->ras->n_catg); PhyML_Fprintf(fp_out,"\n - Gamma shape parameter: \t\t%.3f",tree->mod->ras->alpha->v); for(i=0;imod->ras->n_catg;i++) { PhyML_Fprintf(fp_out,"\n - Relative rate in class %d: \t\t%.5f [freq=%4f] \t\t",i+1,tree->mod->ras->gamma_rr->v[i],tree->mod->ras->gamma_r_proba->v[i]); } } else if(tree->mod->ras->free_mixt_rates == YES) { int *rk; rk = Ranks(tree->mod->ras->gamma_rr->v,tree->mod->ras->n_catg); PhyML_Fprintf(fp_out,"\n. FreeRate model: \t\t\t%s","Yes"); PhyML_Fprintf(fp_out,"\n - Number of classes: \t\t\t%d",tree->mod->ras->n_catg); for(i=0;imod->ras->n_catg;i++) { PhyML_Fprintf(fp_out,"\n - Relative rate in class %d: \t\t%.5f [freq=%4f] \t\t",i+1,tree->mod->ras->gamma_rr->v[rk[i]],tree->mod->ras->gamma_r_proba->v[rk[i]]); } Free(rk); } if(tree->mod->ras->invar) PhyML_Fprintf(fp_out,"\n. Proportion of invariant: \t\t%.3f",tree->mod->ras->pinvar->v); if(tree->mod->gamma_mgf_bl == YES) PhyML_Fprintf(fp_out,"\n. Variance of branch lengths: \t\t%f",tree->mod->l_var_sigma); /*was before Discrete gamma model ; moved here FLT*/ if((tree->mod->whichmodel == K80) || (tree->mod->whichmodel == HKY85) || (tree->mod->whichmodel == F84)) { PhyML_Fprintf(fp_out,"\n. Transition/transversion ratio: \t"); if (precision > 0) PhyML_Fprintf(fp_out,format,tree->mod->kappa->v); else PhyML_Fprintf(fp_out,"%f",tree->mod->kappa->v); } else if(tree->mod->whichmodel == TN93) { PhyML_Fprintf(fp_out,"\n. Transition/transversion ratio for purines: \t\t"); if (precision > 0) PhyML_Fprintf(fp_out,format,tree->mod->kappa->v*2.*tree->mod->lambda->v/(1.+tree->mod->lambda->v)); else PhyML_Fprintf(fp_out,"%f",tree->mod->kappa->v*2.*tree->mod->lambda->v/(1.+tree->mod->lambda->v)); PhyML_Fprintf(fp_out,"\n. Transition/transversion ratio for pyrimidines: \t"); if (precision > 0) PhyML_Fprintf(fp_out,format,tree->mod->kappa->v*2./(1.+tree->mod->lambda->v)); else PhyML_Fprintf(fp_out,"%f",tree->mod->kappa->v*2./(1.+tree->mod->lambda->v)); } if(tree->io->datatype == NT) { PhyML_Fprintf(fp_out,"\n. Nucleotides frequencies:"); if (precision > 0) { PhyML_Fprintf(fp_out,"\n - f(A)= "); PhyML_Fprintf(fp_out,format,tree->mod->e_frq->pi->v[0]); PhyML_Fprintf(fp_out,"\n - f(C)= "); PhyML_Fprintf(fp_out,format,tree->mod->e_frq->pi->v[1]); PhyML_Fprintf(fp_out,"\n - f(G)= "); PhyML_Fprintf(fp_out,format,tree->mod->e_frq->pi->v[2]); PhyML_Fprintf(fp_out,"\n - f(T)= "); PhyML_Fprintf(fp_out,format,tree->mod->e_frq->pi->v[3]); } else { PhyML_Fprintf(fp_out,"\n - f(A)= %8.5f",tree->mod->e_frq->pi->v[0]); PhyML_Fprintf(fp_out,"\n - f(C)= %8.5f",tree->mod->e_frq->pi->v[1]); PhyML_Fprintf(fp_out,"\n - f(G)= %8.5f",tree->mod->e_frq->pi->v[2]); PhyML_Fprintf(fp_out,"\n - f(T)= %8.5f",tree->mod->e_frq->pi->v[3]); } } /*****************************************/ if((tree->mod->whichmodel == GTR) || (tree->mod->whichmodel == CUSTOM)) { Update_Qmat_GTR(tree->mod->r_mat->rr->v, tree->mod->r_mat->rr_val->v, tree->mod->r_mat->rr_num->v, tree->mod->e_frq->pi->v, tree->mod->r_mat->qmat->v, tree->mod->s_opt->opt_rr); PhyML_Fprintf(fp_out,"\n"); PhyML_Fprintf(fp_out,". GTR relative rate parameters :"); if (precision > 0) { PhyML_Fprintf(fp_out,"\n A <-> C "); if (tree->mod->r_mat->rr->v[0] < 10) PhyML_Fprintf(fp_out," "); PhyML_Fprintf(fp_out,format, tree->mod->r_mat->rr->v[0]); PhyML_Fprintf(fp_out,"\n A <-> G "); if (tree->mod->r_mat->rr->v[1] < 10) PhyML_Fprintf(fp_out," "); PhyML_Fprintf(fp_out,format, tree->mod->r_mat->rr->v[1]); PhyML_Fprintf(fp_out,"\n A <-> T "); if (tree->mod->r_mat->rr->v[2] < 10) PhyML_Fprintf(fp_out," "); PhyML_Fprintf(fp_out,format, tree->mod->r_mat->rr->v[2]); PhyML_Fprintf(fp_out,"\n C <-> G "); if (tree->mod->r_mat->rr->v[3] < 10) PhyML_Fprintf(fp_out," "); PhyML_Fprintf(fp_out,format, tree->mod->r_mat->rr->v[3]); PhyML_Fprintf(fp_out,"\n C <-> T "); if (tree->mod->r_mat->rr->v[4] < 10) PhyML_Fprintf(fp_out," "); PhyML_Fprintf(fp_out,format, tree->mod->r_mat->rr->v[4]); PhyML_Fprintf(fp_out,"\n G <-> T "); if (tree->mod->r_mat->rr->v[5] < 10) PhyML_Fprintf(fp_out," "); PhyML_Fprintf(fp_out,format, tree->mod->r_mat->rr->v[5]); } else { PhyML_Fprintf(fp_out,"\n A <-> C %8.5f", tree->mod->r_mat->rr->v[0]); PhyML_Fprintf(fp_out,"\n A <-> G %8.5f", tree->mod->r_mat->rr->v[1]); PhyML_Fprintf(fp_out,"\n A <-> T %8.5f", tree->mod->r_mat->rr->v[2]); PhyML_Fprintf(fp_out,"\n C <-> G %8.5f", tree->mod->r_mat->rr->v[3]); PhyML_Fprintf(fp_out,"\n C <-> T %8.5f", tree->mod->r_mat->rr->v[4]); PhyML_Fprintf(fp_out,"\n G <-> T %8.5f", tree->mod->r_mat->rr->v[5]); } PhyML_Fprintf(fp_out,"\n. Instantaneous rate matrix : "); if (precision > 0) { PhyML_Fprintf(fp_out,"\n [A"); for(i=0;imod->r_mat->qmat->v[i*4+j]); } PhyML_Fprintf(fp_out,"\n"); } } else { PhyML_Fprintf(fp_out,"\n [A---------C---------G---------T------]\n"); for(i=0;i<4;i++) { PhyML_Fprintf(fp_out," "); for(j=0;j<4;j++) PhyML_Fprintf(fp_out,"%8.5f ",tree->mod->r_mat->qmat->v[i*4+j]); PhyML_Fprintf(fp_out,"\n"); } } //PhyML_Fprintf(fp_out,"\n"); } /*****************************************/ if(io->ratio_test == 1) { PhyML_Fprintf(fp_out,". aLRT statistics to test branches"); } else if(io->ratio_test == 2) { PhyML_Fprintf(fp_out,". aLRT branch supports (cubic approximation, mixture of Chi2s distribution)"); } PhyML_Fprintf(fp_out,"\n"); PhyML_Fprintf(fp_out,"\n. Run ID:\t\t\t\t%s", (io->append_run_ID) ? (io->run_id_string): ("none")); PhyML_Fprintf(fp_out,"\n. Random seed:\t\t\t\t%d", io->r_seed); PhyML_Fprintf(fp_out,"\n. Subtree patterns aliasing:\t\t%s",io->do_alias_subpatt?"yes":"no"); PhyML_Fprintf(fp_out,"\n. Version:\t\t\t\t%s", VERSION); hour = div(t_end-t_beg,3600); min = div(t_end-t_beg,60 ); min.quot -= hour.quot*60; PhyML_Fprintf(fp_out,"\n. Time used:\t\t\t\t%dh%dm%ds (%d seconds)", hour.quot,min.quot,(int)(t_end-t_beg)%60,(int)(t_end-t_beg)); if(add_citation == YES) { PhyML_Fprintf(fp_out,"\n\n"); PhyML_Fprintf(fp_out," oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo\n"); PhyML_Fprintf(fp_out," Suggested citations:\n"); PhyML_Fprintf(fp_out," S. Guindon, JF. Dufayard, V. Lefort, M. Anisimova, W. Hordijk, O. Gascuel\n"); PhyML_Fprintf(fp_out," \"New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.\"\n"); PhyML_Fprintf(fp_out," Systematic Biology. 2010. 59(3):307-321.\n"); PhyML_Fprintf(fp_out,"\n"); PhyML_Fprintf(fp_out," S. Guindon & O. Gascuel\n"); PhyML_Fprintf(fp_out," \"A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood\"\n"); PhyML_Fprintf(fp_out," Systematic Biology. 2003. 52(5):696-704.\n"); PhyML_Fprintf(fp_out," oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo\n"); } else { PhyML_Fprintf(fp_out,"\n\n"); PhyML_Fprintf(fp_out," oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo\n"); PhyML_Fprintf(fp_out,"\n"); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /*FLT wrote this function*/ void Print_Fp_Out_Lines(FILE *fp_out, time_t t_beg, time_t t_end, t_tree *tree, option *io, int n_data_set) { char *s; /*div_t hour,min;*/ if (n_data_set==1) { PhyML_Fprintf(fp_out,". Sequence file : [%s]\n\n", Basename(io->in_align_file)); if((tree->io->datatype == NT) || (tree->io->datatype == AA)) { (tree->io->datatype == NT)? (PhyML_Fprintf(fp_out,". Model of nucleotides substitution : %s\n\n",io->mod->modelname->s)): (PhyML_Fprintf(fp_out,". Model of amino acids substitution : %s\n\n",io->mod->modelname->s)); } s = (char *)mCalloc(100,sizeof(char)); switch(io->in_tree) { case 0: { strcpy(s,"BioNJ"); break; } case 1: { strcpy(s,"parsimony"); break; } case 2: { strcpy(s,"user tree ("); strcat(s,io->in_tree_file); strcat(s,")"); break; } } PhyML_Fprintf(fp_out,". Initial tree : [%s]\n\n",s); Free(s); PhyML_Fprintf(fp_out,"\n"); /*headline 1*/ PhyML_Fprintf(fp_out, ". Data\t"); PhyML_Fprintf(fp_out,"Nb of \t"); PhyML_Fprintf(fp_out,"Likelihood\t"); PhyML_Fprintf(fp_out, "Discrete \t"); if(tree->mod->ras->n_catg > 1) PhyML_Fprintf(fp_out, "Number of \tGamma shape\t"); PhyML_Fprintf(fp_out,"Proportion of\t"); if(tree->mod->whichmodel <= 6) PhyML_Fprintf(fp_out,"Transition/ \t"); PhyML_Fprintf(fp_out,"Nucleotides frequencies \t"); if((tree->mod->whichmodel == GTR) || (tree->mod->whichmodel == CUSTOM)) PhyML_Fprintf(fp_out,"Instantaneous rate matrix \t"); /* PhyML_Fprintf(fp_out,"Time\t");*/ PhyML_Fprintf(fp_out, "\n"); /*headline 2*/ PhyML_Fprintf(fp_out, " set\t"); PhyML_Fprintf(fp_out,"taxa\t"); PhyML_Fprintf(fp_out,"loglk \t"); PhyML_Fprintf(fp_out, "gamma model\t"); if(tree->mod->ras->n_catg > 1) PhyML_Fprintf(fp_out, "categories\tparameter \t"); PhyML_Fprintf(fp_out,"invariant \t"); if(tree->mod->whichmodel <= 6) PhyML_Fprintf(fp_out,"transversion\t"); PhyML_Fprintf(fp_out,"f(A) f(C) f(G) f(T) \t"); if((tree->mod->whichmodel == GTR) || (tree->mod->whichmodel == CUSTOM)) PhyML_Fprintf(fp_out,"[A---------C---------G---------T------]\t"); /* PhyML_PhyML_Fprintf(fp_out,"used\t");*/ PhyML_Fprintf(fp_out, "\n"); /*headline 3*/ if(tree->mod->whichmodel == TN93) { PhyML_Fprintf(fp_out," \t \t \t \t"); if(tree->mod->ras->n_catg > 1) PhyML_Fprintf(fp_out," \t \t"); PhyML_Fprintf(fp_out," \t"); PhyML_Fprintf(fp_out,"purines pyrimid.\t"); PhyML_Fprintf(fp_out, "\n"); } PhyML_Fprintf(fp_out, "\n"); } /*line items*/ PhyML_Fprintf(fp_out," #%d\t",n_data_set); PhyML_Fprintf(fp_out,"%d \t",tree->n_otu); PhyML_Fprintf(fp_out,"%.5f\t",tree->c_lnL); PhyML_Fprintf(fp_out,"%s \t", (tree->mod->ras->n_catg>1)?("Yes"):("No ")); if(tree->mod->ras->n_catg > 1) { PhyML_Fprintf(fp_out,"%d \t",tree->mod->ras->n_catg); PhyML_Fprintf(fp_out,"%.3f \t",tree->mod->ras->alpha->v); } /*if(tree->mod->ras->invar)*/ PhyML_Fprintf(fp_out,"%.3f \t",tree->mod->ras->pinvar->v); if(tree->mod->whichmodel <= 5) { PhyML_Fprintf(fp_out,"%.3f \t",tree->mod->kappa->v); } else if(tree->mod->whichmodel == TN93) { PhyML_Fprintf(fp_out,"%.3f ", tree->mod->kappa->v*2.*tree->mod->lambda->v/(1.+tree->mod->lambda->v)); PhyML_Fprintf(fp_out,"%.3f\t", tree->mod->kappa->v*2./(1.+tree->mod->lambda->v)); } if(tree->io->datatype == NT) { PhyML_Fprintf(fp_out,"%8.5f ",tree->mod->e_frq->pi->v[0]); PhyML_Fprintf(fp_out,"%8.5f ",tree->mod->e_frq->pi->v[1]); PhyML_Fprintf(fp_out,"%8.5f ",tree->mod->e_frq->pi->v[2]); PhyML_Fprintf(fp_out,"%8.5f\t",tree->mod->e_frq->pi->v[3]); } /* hour = div(t_end-t_beg,3600); min = div(t_end-t_beg,60 ); min.quot -= hour.quot*60; PhyML_Fprintf(fp_out,"%dh%dm%ds\t", hour.quot,min.quot,(int)(t_end-t_beg)%60); if(t_end-t_beg > 60) PhyML_Fprintf(fp_out,". -> %d seconds\t",(int)(t_end-t_beg)); */ /*****************************************/ if((tree->mod->whichmodel == GTR) || (tree->mod->whichmodel == CUSTOM)) { int i,j; for(i=0;i<4;i++) { if (i!=0) { /*format*/ PhyML_Fprintf(fp_out," \t \t \t \t"); if(tree->mod->ras->n_catg > 1) PhyML_Fprintf(fp_out," \t \t"); PhyML_Fprintf(fp_out," \t \t"); } for(j=0;j<4;j++) PhyML_Fprintf(fp_out,"%8.5f ",tree->mod->r_mat->qmat->v[i*4+j]); if (i<3) PhyML_Fprintf(fp_out,"\n"); } } /*****************************************/ PhyML_Fprintf(fp_out, "\n\n"); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Freq(t_tree *tree) { switch(tree->io->datatype) { case NT: { PhyML_Printf("A : %f\n",tree->mod->e_frq->pi->v[0]); PhyML_Printf("C : %f\n",tree->mod->e_frq->pi->v[1]); PhyML_Printf("G : %f\n",tree->mod->e_frq->pi->v[2]); PhyML_Printf("T : %f\n",tree->mod->e_frq->pi->v[3]); break; } case AA: { PhyML_Printf("A : %f\n",tree->mod->e_frq->pi->v[0]); PhyML_Printf("R : %f\n",tree->mod->e_frq->pi->v[1]); PhyML_Printf("N : %f\n",tree->mod->e_frq->pi->v[2]); PhyML_Printf("D : %f\n",tree->mod->e_frq->pi->v[3]); PhyML_Printf("C : %f\n",tree->mod->e_frq->pi->v[4]); PhyML_Printf("Q : %f\n",tree->mod->e_frq->pi->v[5]); PhyML_Printf("E : %f\n",tree->mod->e_frq->pi->v[6]); PhyML_Printf("G : %f\n",tree->mod->e_frq->pi->v[7]); PhyML_Printf("H : %f\n",tree->mod->e_frq->pi->v[8]); PhyML_Printf("I : %f\n",tree->mod->e_frq->pi->v[9]); PhyML_Printf("L : %f\n",tree->mod->e_frq->pi->v[10]); PhyML_Printf("K : %f\n",tree->mod->e_frq->pi->v[11]); PhyML_Printf("M : %f\n",tree->mod->e_frq->pi->v[12]); PhyML_Printf("F : %f\n",tree->mod->e_frq->pi->v[13]); PhyML_Printf("P : %f\n",tree->mod->e_frq->pi->v[14]); PhyML_Printf("S : %f\n",tree->mod->e_frq->pi->v[15]); PhyML_Printf("T : %f\n",tree->mod->e_frq->pi->v[16]); PhyML_Printf("W : %f\n",tree->mod->e_frq->pi->v[17]); PhyML_Printf("Y : %f\n",tree->mod->e_frq->pi->v[18]); PhyML_Printf("V : %f\n",tree->mod->e_frq->pi->v[19]); PhyML_Printf("N : %f\n",tree->mod->e_frq->pi->v[20]); break; } default : {break;} } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Settings(option *io) { int answer; char *s; s = (char *)mCalloc(100,sizeof(char)); PhyML_Printf("\n\n\n"); PhyML_Printf("\n\n"); PhyML_Printf("\n ////////////////////////////////////.\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"); PhyML_Printf("\n \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\.//////////////////////////////////////////\n"); PhyML_Printf("\n . Sequence filename:\t\t\t\t %s", Basename(io->in_align_file)); if(io->datatype == NT) strcpy(s,"dna"); else if(io->datatype == AA) strcpy(s,"aa"); else strcpy(s,"generic"); PhyML_Printf("\n . Data type:\t\t\t\t\t %s",s); PhyML_Printf("\n . Alphabet size:\t\t\t\t %d",io->mod->ns); PhyML_Printf("\n . Sequence format:\t\t\t\t %s", io->interleaved ? "interleaved": "sequential"); PhyML_Printf("\n . Number of data sets:\t\t\t\t %d", io->n_data_sets); PhyML_Printf("\n . Nb of bootstrapped data sets:\t\t\t %d", io->n_boot_replicates); if (io->n_boot_replicates > 0) PhyML_Printf("\n . Compute approximate likelihood ratio test:\t no"); else { if(io->ratio_test == 1) PhyML_Printf("\n . Compute approximate likelihood ratio test:\t yes (aLRT statistics)"); else if(io->ratio_test == 2) PhyML_Printf("\n . Compute approximate likelihood ratio test:\t yes (Chi2-based parametric branch supports)"); else if(io->ratio_test == 3) PhyML_Printf("\n . Compute approximate likelihood ratio test:\t yes (Minimum of SH-like and Chi2-based branch supports)"); else if(io->ratio_test == 4) PhyML_Printf("\n . Compute approximate likelihood ratio test:\t yes (SH-like branch supports)"); else if(io->ratio_test == 5) PhyML_Printf("\n . Compute approximate likelihood ratio test:\t yes (aBayes branch supports)"); } PhyML_Printf("\n . Model name:\t\t\t\t\t %s", io->mod->modelname->s); if(io->datatype == AA && io->mod->whichmodel == CUSTOMAA) PhyML_Printf(" (%s)",io->mod->aa_rate_mat_file->s); if (io->datatype == NT) { if ((io->mod->whichmodel == K80) || (io->mod->whichmodel == HKY85)|| (io->mod->whichmodel == F84) || (io->mod->whichmodel == TN93)) { if(io->mod->s_opt && io->mod->s_opt->opt_kappa) PhyML_Printf("\n . Ts/tv ratio:\t\t\t\t\t estimated"); else PhyML_Printf("\n . Ts/tv ratio:\t\t\t\t\t %f", io->mod->kappa->v); } } if(io->mod->s_opt && io->mod->s_opt->opt_pinvar) PhyML_Printf("\n . Proportion of invariable sites:\t\t estimated"); else PhyML_Printf("\n . Proportion of invariable sites:\t\t %f", io->mod->ras->pinvar->v); if(io->mod->ras->free_mixt_rates == NO) PhyML_Printf("\n . RAS model:\t\t\t\t\t discrete Gamma"); else PhyML_Printf("\n . RAS model:\t\t\t\t\t FreeRate"); PhyML_Printf("\n . Number of subst. rate catgs:\t\t\t %d", io->mod->ras->n_catg); if(io->mod->ras->n_catg > 1) { if(io->mod->ras->free_mixt_rates == NO) { if(io->mod->s_opt && io->mod->s_opt->opt_alpha) PhyML_Printf("\n . Gamma distribution parameter:\t\t\t estimated"); else PhyML_Printf("\n . Gamma distribution parameter:\t\t\t %f", io->mod->ras->alpha->v); PhyML_Printf("\n . 'Middle' of each rate class:\t\t\t %s",(io->mod->ras->gamma_median)?("median"):("mean")); } } if(io->datatype == AA) { if(io->mod->s_opt->state_freq == ML) PhyML_Printf("\n . Amino-acid equilibrium frequencies:\t\t optimized"); else if(io->mod->s_opt->state_freq == EMPIRICAL) PhyML_Printf("\n . Amino-acid equilibrium frequencies:\t\t empirical"); else if(io->mod->s_opt->state_freq == USER) PhyML_Printf("\n . Amino-acid equilibrium frequencies:\t\t user-defined"); else if(io->mod->s_opt->state_freq == MODEL) PhyML_Printf("\n . Amino-acid equilibrium frequencies:\t\t model-defined"); } else if(io->datatype == NT) { if((io->mod->whichmodel != JC69) && (io->mod->whichmodel != K80) && (io->mod->whichmodel != F81)) { if(io->mod->s_opt->state_freq == ML) PhyML_Printf("\n . Nucleotide equilibrium frequencies:\t\t optimized"); else if(io->mod->s_opt->state_freq == EMPIRICAL) PhyML_Printf("\n . Nucleotide equilibrium frequencies:\t\t empirical"); else if(io->mod->s_opt->state_freq == USER) PhyML_Printf("\n . Nucleotide equilibrium frequencies:\t\t user-defined"); else if(io->mod->s_opt->state_freq == MODEL) PhyML_Printf("\n . Nucleotide equilibrium frequencies:\t\t model-defined"); } } PhyML_Printf("\n . Optimise tree topology:\t\t\t %s", (io->mod->s_opt && io->mod->s_opt->opt_topo) ? "yes": "no"); switch(io->in_tree) { case 0: { strcpy(s,"BioNJ"); break; } case 1: { strcpy(s,"parsimony"); break; } case 2: { strcpy(s,"user tree ("); strcat(s,Basename(io->in_tree_file)); strcat(s,")"); break; } } if(io->mod->s_opt && io->mod->s_opt->opt_topo) { /* if(io->mod->s_opt->topo_search == NNI_MOVE) PhyML_Printf("\n . Tree topology search:\t\t\t\t NNIs"); */ /* else if(io->mod->s_opt->topo_search == SPR_MOVE) PhyML_Printf("\n . Tree topology search:\t\t\t\t SPRs"); */ /* else if(io->mod->s_opt->topo_search == BEST_OF_NNI_AND_SPR) PhyML_Printf("\n . Tree topology search:\t\t\t\t Best of NNIs and SPRs"); */ PhyML_Printf("\n . Starting tree:\t\t\t\t %s",s); PhyML_Printf("\n . Add random input tree:\t\t\t %s", (io->mod->s_opt->random_input_tree) ? "yes": "no"); if(io->mod->s_opt->random_input_tree) PhyML_Printf("\n . Number of random starting trees:\t\t %d", io->mod->s_opt->n_rand_starts); } else if(io->mod->s_opt && !io->mod->s_opt->random_input_tree) PhyML_Printf("\n . Evaluated tree:\t\t\t\t \"%s\"",s); PhyML_Printf("\n . Optimise branch lengths:\t\t\t %s", (io->mod->s_opt && io->mod->s_opt->opt_bl) ? "yes": "no"); PhyML_Printf("\n . Minimum length of an edge:\t\t\t %g",io->mod->l_min); answer = 0; if(io->mod->s_opt && (io->mod->s_opt->opt_alpha || io->mod->s_opt->opt_kappa || io->mod->s_opt->opt_lambda || io->mod->s_opt->opt_pinvar || io->mod->s_opt->opt_rr)) answer = 1; PhyML_Printf("\n . Optimise substitution model parameters:\t %s", (answer) ? "yes": "no"); PhyML_Printf("\n . Run ID:\t\t\t\t\t %s", (io->append_run_ID) ? (io->run_id_string): ("none")); PhyML_Printf("\n . Random seed:\t\t\t\t\t %d", io->r_seed); PhyML_Printf("\n . Subtree patterns aliasing:\t\t\t %s",io->do_alias_subpatt?"yes":"no"); PhyML_Printf("\n . Version:\t\t\t\t\t %s", VERSION); PhyML_Printf("\n . Byte alignment:\t\t\t\t %d",BYTE_ALIGN); PhyML_Printf("\n . AVX enabled:\t\t\t\t\t %s", #if defined(__AVX__) "yes" #else "no" #endif ); PhyML_Printf("\n . SSE enabled:\t\t\t\t\t %s", #if defined(__SSE3__) "yes" #else "no" #endif ); /* PhyML_Printf("\n\n \u205C \u205C \u205C \u205C \u205C \u205C \u205C \u205C \u205C \u205C \u205C \u205C \n"); */ PhyML_Printf("\n"); PhyML_Printf("\n ////////////////////////////////////.\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"); PhyML_Printf("\n \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\.//////////////////////////////////////////\n"); PhyML_Printf("\n\n"); fflush(NULL); Free(s); } void Print_Banner(FILE *fp) { PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp," oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo\n"); PhyML_Fprintf(fp," \n"); PhyML_Fprintf(fp," --- PhyML %s --- \n",VERSION); PhyML_Fprintf(fp," \n"); PhyML_Fprintf(fp," A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood \n"); PhyML_Fprintf(fp," Stephane Guindon & Olivier Gascuel \n"); PhyML_Fprintf(fp," \n"); PhyML_Fprintf(fp," http://www.atgc-montpellier.fr/phyml \n"); PhyML_Fprintf(fp," \n"); PhyML_Fprintf(fp," Copyright CNRS - Universite Montpellier \n"); PhyML_Fprintf(fp," \n"); PhyML_Fprintf(fp," oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo\n"); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Banner_Small(FILE *fp) { PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp," oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo\n"); PhyML_Fprintf(fp," --- PhyML %s --- \n",VERSION); PhyML_Fprintf(fp," http://www.atgc-montpellier.fr/phyml \n"); PhyML_Fprintf(fp," Copyright CNRS - Universite Montpellier \n"); PhyML_Fprintf(fp," oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo\n"); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Data_Set_Number(option *io, FILE *fp) { PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp," \n"); PhyML_Fprintf(fp," [ Data set number %3d ] \n",io->curr_gt+1); PhyML_Fprintf(fp," \n"); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Lk(t_tree *tree, char *string) { t_tree *loc_tree; loc_tree = tree; /*! Rewind back to the first mixt_tree */ while(loc_tree->prev) loc_tree = loc_tree->prev; time(&(loc_tree->t_current)); PhyML_Printf("\n. (%5d sec) [%15.4f] %s", (int)(loc_tree->t_current-loc_tree->t_beg),Get_Lk(tree), string); #ifndef QUIET fflush(NULL); #endif } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_List(t_ll *list) { t_ll *ll; ll = list->head; do { t_node *n; n = (t_node *)ll->v; PhyML_Printf("\n. list elem: %p next: %p prev: %p [%d] %p %p",(void *)ll,(void *)ll->next,(void *)ll->prev,n->num,(void *)ll->head,(void *)ll->tail); ll = ll->next; } while(ll != NULL); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Pars(t_tree *tree) { time(&(tree->t_current)); PhyML_Printf("\n. (%5d sec) [%5d]",(int)(tree->t_current-tree->t_beg),tree->c_pars); #ifndef QUIET fflush(NULL); #endif } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Lk_And_Pars(t_tree *tree) { time(&(tree->t_current)); PhyML_Printf("\n. (%5d sec) [%15.4f] [%5d]", (int)(tree->t_current-tree->t_beg), tree->c_lnL,tree->c_pars); #ifndef QUIET fflush(NULL); #endif } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Read_Qmat(phydbl *daa, phydbl *pi, FILE *fp) { int i,j; phydbl sum; double val; assert(fp); rewind(fp); for(i=1;i<20;i++) { for(j=0;j<19;j++) { /* if(!fscanf(fp,"%lf",&(daa[i*20+j]))) Exit("\n"); */ if(!fscanf(fp,"%lf",&val)) { PhyML_Fprintf(stderr,"\n. Rate matrix file does not appear to have a proper format. Please refer to the documentation."); Exit("\n"); } daa[i*20+j] = (phydbl)val; daa[j*20+i] = daa[i*20+j]; if(j == i-1) break; } } for(i=0;i<20;i++) { if(!fscanf(fp,"%lf",&val)) Exit("\n"); pi[i] = (phydbl)val; } sum = .0; for(i=0;i<20;i++) sum += pi[i]; if(FABS(sum - 1.) > 1.E-06) { PhyML_Printf("\n. Sum of amino-acid frequencies: %f",sum); PhyML_Printf("\n. Scaling amino-acid frequencies...\n"); for(i=0;i<20;i++) pi[i] /= sum; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Qmat_AA(phydbl *daa, phydbl *pi) { int i,j,cpt; cpt = 0; for(i=0;i<20;i++) { for(j=0;ja_nodes[0], tree->a_nodes[0]->v[0], tree->a_nodes[0]->b[0], fp, tree); /* mean_div_left = .0; */ /* for(k=0;ka_edges[j]->div_post_pred_left[k]; */ /* } */ /* mean_div_rght = .0; */ /* for(k=0;ka_edges[j]->div_post_pred_rght[k]; */ /* mean_div_left /= (phydbl)tree->data->init_len; */ /* mean_div_rght /= (phydbl)tree->data->init_len; */ /* PhyML_Fprintf(fp,"%4d 0 %f\n",j,mean_div_left); */ /* PhyML_Fprintf(fp,"%4d 1 %f\n",j,mean_div_rght); */ /* mean_div_left = .0; */ /* for(k=0;ka_edges[j]->div_post_pred_left[k]; */ /* mean_div_rght = .0; */ /* for(k=0;ka_edges[j]->div_post_pred_rght[k]; */ /* } */ /* if((mean_div_left != tree->data->init_len) || (mean_div_rght != tree->data->init_len)) */ /* { */ /* PhyML_Printf("\n. mean_div_left = %f mean_div_rght = %f init_len = %d", */ /* mean_div_left,mean_div_rght,tree->data->init_len); */ /* PhyML_Printf("\n. Err in file %s at line %d\n",__FILE__,__LINE__); */ /* Warn_And_Exit(""); */ /* } */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Diversity_Pre(t_node *a, t_node *d, t_edge *b, FILE *fp, t_tree *tree) { int k,ns; ns = -1; if(d->tax) return; else { if(tree->io->datatype == NT) ns = 4; else if(tree->io->datatype == AA) ns = 20; if(d == b->left) for(k=0;knum,k,b->div_post_pred_left[k]); else for(k=0;knum,k,b->div_post_pred_rght[k]); for(k=0;k<3;k++) if(d->v[k] != a) Print_Diversity_Pre(d,d->v[k],d->b[k],fp,tree); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Tip_Partials(t_tree* tree, t_node* d) { if(!d->tax) { fprintf(stdout,"Node %d is not a Taxa\n",d->num);fflush(stdout); return; } assert(d->b[0]->rght == d); assert(d->b[0]->rght->tax); fprintf(stdout,"Taxa/Node %d\n",d->num); int site, i; for(site=0;siten_pattern;++site) { fprintf(stdout,"[%d: ",site); for(i=0;imod->ns;++i) { int tip_partial = d->b[0]->p_lk_tip_r[site*tree->mod->ns + i]; fprintf(stdout,"%d",tip_partial);fflush(stdout); } fprintf(stdout,"] ");fflush(stdout); } fprintf(stdout,"\n");fflush(stdout); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Edge_PMats(t_tree* tree, t_edge* b) { phydbl *Pij; #ifdef BEAGLE Pij = (phydbl*)malloc(tree->mod->ns * tree->mod->ns * tree->mod->ras->n_catg * sizeof(phydbl)); if (NULL==Pij) Warn_And_Exit(__PRETTY_FUNCTION__); int ret = beagleGetTransitionMatrix(tree->b_inst, b->Pij_rr_idx, Pij); if(ret<0){ fprintf(stderr, "beagleGetTransitionMatrix() on instance %i failed:%i\n\n",tree->b_inst,ret); Free(Pij); Exit(""); } #else Pij = b->Pij_rr; #endif fprintf(stdout,"\nflattened P-Matrices (for each rate category) state*state*num_rates[%d*%d*%d] for branch num:%i\n",tree->mod->ns,tree->mod->ns,tree->mod->ras->n_catg, b->num); int i; for(i=0;imod->ns * tree->mod->ns * tree->mod->ras->n_catg;++i){ fprintf(stdout,"%f,",Pij[i]); fflush(stdout); } fprintf(stdout,"\n"); fflush(stdout); #ifdef BEAGLE Free(Pij); #endif } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_All_Edge_PMats(t_tree* tree) { int i; for(i=0;i < 2*tree->n_otu-3;++i) Print_Edge_PMats(tree, tree->a_edges[i]); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Edge_Likelihoods(t_tree* tree, t_edge* b, bool scientific/*Print in scientific notation?*/) { int catg, site, j; char* fmt = scientific ? "[%d,%d,%d]%e ":"[%d,%d,%d]%f "; //rate category, site, state, likelihood phydbl* lk_left = b->p_lk_left; phydbl* lk_right = b->p_lk_rght; fprintf(stdout,"\n");fflush(stdout); if(NULL!=lk_left)//not a tip? { fprintf(stdout,"Partial Likelihoods on LEFT subtree of Branch %d [rate,site,state]:\n",b->num); for(catg=0;catgmod->ras->n_catg;++catg) for(site=0;siten_pattern;++site) for(j=0;jmod->ns;++j) #ifdef BEAGLE fprintf(stdout,fmt,catg,site,j,lk_left[catg*tree->n_pattern*tree->mod->ns + site*tree->mod->ns + j]); #else fprintf(stdout,fmt,catg,site,j,lk_left[catg*tree->mod->ns + site*tree->mod->ras->n_catg*tree->mod->ns + j]); #endif fflush(stdout); } else //is a tip { fprintf(stdout,"Likelihoods on LEFT tip of Branch %d [site,state]:\n",b->num); for(site=0;siten_pattern;++site) for(j=0;jmod->ns;++j) fprintf(stdout,"[%d,%d]%.1f ",site,j,b->p_lk_tip_l[site*tree->mod->ns + j]); fflush(stdout); } fprintf(stdout,"\n");fflush(stdout); if(NULL!=lk_right)//not a tip? { fprintf(stdout,"Partial Likelihoods on RIGHT subtree of Branch %d [rate,site,state]:\n",b->num); for(catg=0;catgmod->ras->n_catg;++catg) for(site=0;siten_pattern;++site) for(j=0;jmod->ns;++j) #ifdef BEAGLE fprintf(stdout,fmt,catg,site,j,lk_right[catg*tree->n_pattern*tree->mod->ns + site*tree->mod->ns + j]); #else fprintf(stdout,fmt,catg,site,j,lk_right[catg*tree->mod->ns + site*tree->mod->ras->n_catg*tree->mod->ns + j]); #endif fflush(stdout); } else //is a tip { fprintf(stdout,"Likelihoods on RIGHT tip of Branch %d [site,state]:\n",b->num); for(site=0;siten_pattern;++site) for(j=0;jmod->ns;++j) fprintf(stdout,"[%d,%d]%.1f ",site,j,b->p_lk_tip_r[site*tree->mod->ns + j]); fflush(stdout); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_All_Edge_Likelihoods(t_tree* tree) { int i; for(i=0;i < 2*tree->n_otu-3; ++i) Print_Edge_Likelihoods(tree, tree->a_edges[i],false); fflush(NULL); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Dump_Arr_S(short int* arr, int num) { int i; if(NULL==arr) { PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d (function '%s')\n",__FILE__,__LINE__,__FUNCTION__); Exit("\n. Trying to print NULL array"); return; } fprintf(stdout,"["); for(i=0;in_otu); for(i=0; i<2*tree->n_otu-1; ++i) { if(tree->a_edges[i]) PhyML_Fprintf(stdout,"\n. Edge %p %3d, Length: %f LeftNode %3d [%s], RightNode %3d [%s]", tree->a_edges[i], tree->a_edges[i]->num, tree->a_edges[i]->l->v, tree->a_edges[i]->left ? tree->a_edges[i]->left->num : -1, tree->a_edges[i]->left ? tree->a_edges[i]->left->tax ? tree->a_edges[i]->left->name : "" : "", tree->a_edges[i]->rght ? tree->a_edges[i]->rght->num : -1, tree->a_edges[i]->left ? tree->a_edges[i]->rght->tax ? tree->a_edges[i]->rght->name : "" : ""); else PhyML_Fprintf(stdout,"\n. NULL"); } for(i=0; i<2*tree->n_otu-1; ++i) { if(tree->a_nodes[i]) PhyML_Fprintf(stdout,"\n. Node %p %3d v0: %3d v1: %3d v2: %3d b0: %3d b1: %3d b2: %3d", tree->a_nodes[i], tree->a_nodes[i]->num, tree->a_nodes[i]->v[0] ? tree->a_nodes[i]->v[0]->num : -1, tree->a_nodes[i]->v[1] ? tree->a_nodes[i]->v[1]->num : -1, tree->a_nodes[i]->v[2] ? tree->a_nodes[i]->v[2]->num : -1, tree->a_nodes[i]->v[0] ? tree->a_nodes[i]->b[0]->num : -1, tree->a_nodes[i]->v[1] ? tree->a_nodes[i]->b[1]->num : -1, tree->a_nodes[i]->v[2] ? tree->a_nodes[i]->b[2]->num : -1); else PhyML_Fprintf(stdout,"\n. NULL"); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// t_tree *Read_User_Tree(calign *cdata, t_mod *mod, option *io) { t_tree *tree; PhyML_Printf("\n. Reading tree..."); fflush(NULL); if(io->n_trees == 1) rewind(io->fp_in_tree); tree = Read_Tree_File_Phylip(io->fp_in_tree); /* fclose(io->fp_in_tree); */ /* io->fp_in_tree = NULL; */ if(tree == NULL) Exit("\n. Input tree not found..."); /* Add branch lengths if necessary */ if(tree->has_branch_lengths == NO) Add_BioNJ_Branch_Lengths(tree,cdata,mod,NULL); return tree; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Time_Info(time_t t_beg, time_t t_end) { div_t hour,min; hour = div(t_end-t_beg,3600); min = div(t_end-t_beg,60 ); min.quot -= hour.quot*60; PhyML_Printf("\n\n. Time used %dh%dm%ds\n", hour.quot,min.quot,(int)(t_end-t_beg)%60); PhyML_Printf("\noooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo\n"); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Time_Info_Brief(time_t t_beg, time_t t_end) { div_t hour,min; hour = div(t_end-t_beg,3600); min = div(t_end-t_beg,60 ); min.quot -= hour.quot*60; PhyML_Printf("\n. Time used %dh%dm%ds\n", hour.quot,min.quot,(int)(t_end-t_beg)%60); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void PhyML_Printf(char *format, ...) { va_list ptr; #ifdef MPI if(Global_myRank == 0) { va_start (ptr, format); vprintf (format, ptr); va_end(ptr); } #else va_start (ptr, format); vprintf (format, ptr); va_end(ptr); #endif fflush (NULL); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void PhyML_Fprintf(FILE *fp, char *format, ...) { va_list ptr; #ifdef MPI if(Global_myRank == 0) { va_start (ptr, format); vfprintf (fp,format, ptr); va_end(ptr); } #else va_start (ptr, format); vfprintf (fp,format, ptr); va_end(ptr); #endif fflush (NULL); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int PhyML_Fscanf(FILE *fp, char *format, ...) { va_list ptr; int rv; rv = -1; #ifdef MPI if(Global_myRank == 0) { va_start (ptr, format); rv = vfscanf(fp,format,ptr); if(!rv) Generic_Exit(__FILE__,__LINE__,__FUNCTION__); va_end(ptr); } #else va_start (ptr, format); rv = vfscanf(fp,format,ptr); if(!rv) Generic_Exit(__FILE__,__LINE__,__FUNCTION__); va_end(ptr); #endif return(rv); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Read_Clade_Priors(char *file_name, t_tree *tree) { FILE *fp; char *s,*line; int n_clade_priors; int clade_size; char **clade_list; int i,pos; phydbl prior_low,prior_up; int node_num; PhyML_Printf("\n"); PhyML_Printf("\n. Reading prior on node ages.\n"); line = (char *)mCalloc(T_MAX_LINE,sizeof(char)); s = (char *)mCalloc(T_MAX_LINE,sizeof(char)); clade_list = (char **)mCalloc(tree->n_otu,sizeof(char *)); for(i=0;in_otu;i++) clade_list[i] = (char *)mCalloc(T_MAX_NAME,sizeof(char)); fp = Openfile(file_name,0); n_clade_priors = 0; do { if(!fgets(line,T_MAX_LINE,fp)) break; clade_size = 0; pos = 0; do { i = 0; while(line[pos] == ' ') pos++; while((line[pos] != ' ') && (line[pos] != '\n') && line[pos] != '#') { s[i] = line[pos]; i++; pos++; } s[i] = '\0'; /* PhyML_Printf("\n. s = %s\n",s); */ if(line[pos] == '\n' || line[pos] == '#') break; pos++; if(strcmp(s,"|")) { strcpy(clade_list[clade_size],s); clade_size++; } else break; } while(1); if(line[pos] != '#' && line[pos] != '\n') { phydbl val1, val2; /* sscanf(line+pos,"%lf %lf",&prior_up,&prior_low); */ sscanf(line+pos,"%lf %lf",&val1,&val2); prior_up = (phydbl)val1; prior_low = (phydbl)val2; node_num = -1; if(!strcmp("@root@",clade_list[0])) node_num = tree->n_root->num; else node_num = Find_Clade(clade_list, clade_size, tree); n_clade_priors++; if(node_num < 0) { PhyML_Printf("\n"); PhyML_Printf("\n"); PhyML_Printf("\n. ................................................................."); PhyML_Printf("\n. WARNING: could not find any clade in the tree referred to with the following taxon names:"); for(i=0;itimes->t_has_prior[node_num] = YES; tree->times->t_prior_min[node_num] = -MAX(prior_low,prior_up); tree->times->t_prior_max[node_num] = -MIN(prior_low,prior_up); if(fabs(prior_low - prior_up) < 1.E-6 && tree->a_nodes[node_num]->tax == YES) tree->times->nd_t[node_num] = prior_low; PhyML_Printf("\n"); PhyML_Printf("\n. [%3d]..................................................................",n_clade_priors); PhyML_Printf("\n. Node %4d matches the clade referred to with the following taxon names:",node_num); for(i=0;i 1 ? "clades":"clade"); if(!n_clade_priors) { PhyML_Fprintf(stderr,"\n. PhyTime could not find any prior on node age."); PhyML_Fprintf(stderr,"\n. This is likely due to a problem in the calibration "); PhyML_Fprintf(stderr,"\n. file format. Make sure, for instance, that there is "); PhyML_Fprintf(stderr,"\n. a blank character between the end of the last name"); PhyML_Fprintf(stderr,"\n. of each clade and the character `|'. Otherwise, "); PhyML_Fprintf(stderr,"\n. please refer to the example file.\n"); PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d (function '%s')\n",__FILE__,__LINE__,__FUNCTION__); Warn_And_Exit(""); } for(i=0;in_otu;i++) Free(clade_list[i]); Free(clade_list); Free(line); Free(s); fclose(fp); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// option *Get_Input(int argc, char **argv) { option *io; t_mod *mod; t_opt *s_opt; int rv; rv = 1; io = (option *)Make_Input(); mod = (t_mod *)Make_Model_Basic(); s_opt = (t_opt *)Make_Optimiz(); Set_Defaults_Input(io); Set_Defaults_Model(mod); Set_Defaults_Optimiz(s_opt); io->mod = mod; mod->io = io; mod->s_opt = s_opt; #ifdef MPI rv = Read_Command_Line(io,argc,argv); #elif (defined PHYTIME || defined INVITEE || defined PHYREX || defined TEST) rv = Read_Command_Line(io,argc,argv); #else switch (argc) { case 1: { Launch_Interface(io); break; } default: { rv = Read_Command_Line(io,argc,argv); } } #endif if(rv) return io; else return NULL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int Set_Whichmodel(int select) { int wm; wm = -1; switch(select) { case 1: { wm = JC69; break; } case 2: { wm = K80; break; } case 3: { wm = F81; break; } case 4: { wm = HKY85; break; } case 5: { wm = F84; break; } case 6: { wm = TN93; break; } case 7: { wm = GTR; break; } case 8: { wm = CUSTOM; break; } case 11: { wm = WAG; break; } case 12: { wm = DAYHOFF; break; } case 13: { wm = JTT; break; } case 14: { wm = BLOSUM62; break; } case 15: { wm = MTREV; break; } case 16: { wm = RTREV; break; } case 17: { wm = CPREV; break; } case 18: { wm = DCMUT; break; } case 19: { wm = VT; break; } case 20: { wm = MTMAM; break; } case 21: { wm = MTART; break; } case 22: { wm = HIVW; break; } case 23: { wm = HIVB; break; } case 24: { wm = FLU; break; } case 25: { wm = CUSTOMAA; break; } case 26: { wm = LG; break; } case 27: { wm = AB; break; } default: { PhyML_Fprintf(stderr,"\n. Model number %d is unknown. Please use a valid model name",select); Exit("\n"); break; } } return wm; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Print_Data_Structure(int final, FILE *fp, t_tree *mixt_tree) { int n_partition_elem; char *s; t_tree *tree,*cpy_mixt_tree; int c,cc,cc_efrq,cc_rmat,cc_lens; char *param; int *link_efrq,*link_rmat,*link_lens; phydbl r_mat_weight_sum, e_frq_weight_sum; PhyML_Fprintf(fp,"\n. Starting tree: %s", mixt_tree->io->in_tree == 2?mixt_tree->io->in_tree_file:"BioNJ"); cpy_mixt_tree = mixt_tree; n_partition_elem = 1; tree = mixt_tree; do { tree = tree->next_mixt; if(!tree) break; n_partition_elem++; } while(1); s = (char *)mCalloc(2,sizeof(char)); s[0] = ' '; s[1] = '\0'; tree = mixt_tree; do { s = (char *)mRealloc(s,(int)(strlen(s)+strlen(tree->io->in_align_file)+2+2),sizeof(char)); strcat(s,tree->io->in_align_file); strcat(s,", "); tree = tree->next_mixt; if(!tree) break; } while(1); s[(int)strlen(s)-2]=' '; s[(int)strlen(s)-1]='\0'; if(final == NO) PhyML_Fprintf(fp,"\n\n. Processing %d data %s (%s)",n_partition_elem,n_partition_elem>1?"sets":"set",s); if(final == YES) PhyML_Fprintf(fp,"\n\n. Processed %d data %s (%s)",n_partition_elem,n_partition_elem>1?"sets":"set",s); Free(s); if(final == YES) PhyML_Fprintf(fp,"\n\n. Final log-likelihood: %f",mixt_tree->c_lnL); r_mat_weight_sum = MIXT_Get_Sum_Chained_Scalar_Dbl(mixt_tree->next->mod->r_mat_weight); e_frq_weight_sum = MIXT_Get_Sum_Chained_Scalar_Dbl(mixt_tree->next->mod->e_frq_weight); do { int class = 0; PhyML_Fprintf(fp,"\n\n"); PhyML_Fprintf(fp,"\n _______________________________________________________________________ "); PhyML_Fprintf(fp,"\n| |"); PhyML_Fprintf(fp,"\n| %40s (partition element %2d) |",mixt_tree->io->in_align_file,mixt_tree->dp); PhyML_Fprintf(fp,"\n|_______________________________________________________________________|"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n. Number of rate classes:\t\t%20d",mixt_tree->mod->ras->n_catg+(mixt_tree->mod->ras->invar ?1:0)); if(mixt_tree->mod->ras->n_catg > 1) { PhyML_Fprintf(fp,"\n. Model of rate variation:\t\t%20s", mixt_tree->mod->ras->free_mixt_rates?"FreeRates": mixt_tree->mod->ras->invar?"Gamma+Inv":"Gamma"); if(mixt_tree->mod->ras->free_mixt_rates == NO) { PhyML_Fprintf(fp,"\n. Gamma shape parameter value:\t\t%20.2f",mixt_tree->mod->ras->alpha->v); PhyML_Fprintf(fp,"\n Optimise: \t\t\t\t%20s",mixt_tree->mod->s_opt->opt_alpha==YES?"yes":"no"); } if(mixt_tree->mod->ras->invar == YES) { PhyML_Fprintf(fp,"\n. Proportion of invariable sites:\t%20.2f",mixt_tree->mod->ras->pinvar->v); PhyML_Fprintf(fp,"\n Optimise: \t\t\t\t%20s",mixt_tree->mod->s_opt->opt_pinvar==YES?"yes":"no"); } } PhyML_Fprintf(fp,"\n. Relative average rate:\t\t%20f",mixt_tree->mod->br_len_mult->v); tree = mixt_tree; do { if(tree->is_mixt_tree) tree = tree->next; PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n. Mixture class %d",class+1); if(mixt_tree->mod->ras->n_catg > 1) { if(tree->mod->ras->invar == NO) { PhyML_Fprintf(fp,"\n Relative substitution rate:\t%20f",mixt_tree->mod->ras->gamma_rr->v[tree->mod->ras->parent_class_number]); PhyML_Fprintf(fp,"\n Rel. rate freq. (> 0 rates):\t%20f",mixt_tree->mod->ras->gamma_r_proba->v[tree->mod->ras->parent_class_number]); PhyML_Fprintf(fp,"\n Rate class number:\t\t%20d",tree->mod->ras->parent_class_number); } else { PhyML_Fprintf(fp,"\n Relative substitution rate:\t%20f",0.0); PhyML_Fprintf(fp,"\n Relative rate freq.:\t\t%20f",mixt_tree->mod->ras->pinvar->v); } } PhyML_Fprintf(fp,"\n Substitution model:\t\t%20s",tree->mod->modelname->s); if(tree->mod->whichmodel == CUSTOM) PhyML_Fprintf(fp,"\n Substitution model code:\t%20s",tree->mod->custom_mod_string->s); if(tree->mod->whichmodel == CUSTOMAA) PhyML_Fprintf(fp,"\n Rate matrix file name:\t%20s",tree->mod->aa_rate_mat_file->s); if(tree->mod->whichmodel == K80 || tree->mod->whichmodel == HKY85 || tree->mod->whichmodel == TN93) { PhyML_Fprintf(fp,"\n Value of the ts/tv ratio:\t%20f",tree->mod->kappa->v); PhyML_Fprintf(fp,"\n Optimise ts/tv ratio:\t%20s",tree->mod->s_opt->opt_kappa?"yes":"no"); } else if(tree->mod->whichmodel == GTR || tree->mod->whichmodel == CUSTOM) { PhyML_Fprintf(fp,"\n Optimise subst. rates:\t%20s",tree->mod->s_opt->opt_rr?"yes":"no"); if(final == YES) { PhyML_Fprintf(fp,"\n Subst. rate A<->C:\t\t%20.2f",tree->mod->r_mat->rr->v[0]); PhyML_Fprintf(fp,"\n Subst. rate A<->G:\t\t%20.2f",tree->mod->r_mat->rr->v[1]); PhyML_Fprintf(fp,"\n Subst. rate A<->T:\t\t%20.2f",tree->mod->r_mat->rr->v[2]); PhyML_Fprintf(fp,"\n Subst. rate C<->G:\t\t%20.2f",tree->mod->r_mat->rr->v[3]); PhyML_Fprintf(fp,"\n Subst. rate C<->T:\t\t%20.2f",tree->mod->r_mat->rr->v[4]); PhyML_Fprintf(fp,"\n Subst. rate G<->T:\t\t%20.2f",tree->mod->r_mat->rr->v[5]); } } PhyML_Fprintf(fp,"\n Rate matrix weight:\t\t%20f",tree->mod->r_mat_weight->v / r_mat_weight_sum); if(tree->io->datatype == NT && tree->mod->whichmodel != JC69 && tree->mod->whichmodel != K80) { PhyML_Fprintf(fp,"\n Optimise nucleotide freq.:\t%20s",tree->mod->s_opt->state_freq == ML ?"yes":"no"); if(final == YES) { PhyML_Fprintf(fp,"\n Freq(A):\t\t\t%20.2f",tree->mod->e_frq->pi->v[0]); PhyML_Fprintf(fp,"\n Freq(C):\t\t\t%20.2f",tree->mod->e_frq->pi->v[1]); PhyML_Fprintf(fp,"\n Freq(G):\t\t\t%20.2f",tree->mod->e_frq->pi->v[2]); PhyML_Fprintf(fp,"\n Freq(T):\t\t\t%20.2f",tree->mod->e_frq->pi->v[3]); } } else if(tree->io->datatype == AA) { char *s; s = (char *)mCalloc(50,sizeof(char)); if(tree->mod->s_opt->state_freq == EMPIRICAL) { strcpy(s,"Empirical"); } else if(tree->mod->s_opt->state_freq == MODEL) { strcpy(s,"Model"); } else if(tree->mod->s_opt->state_freq == ML) { strcpy(s,"Optimized"); } PhyML_Fprintf(fp,"\n Amino-acid freq.:\t\t%20s",s); Free(s); } PhyML_Fprintf(fp,"\n Equ. freq. weight:\t\t%20f",tree->mod->e_frq_weight->v / e_frq_weight_sum); class++; tree = tree->next; if(tree && tree->is_mixt_tree == YES) break; } while(tree); mixt_tree = mixt_tree->next_mixt; if(!mixt_tree) break; } while(1); tree = cpy_mixt_tree; c = 0; do { if(tree->is_mixt_tree) tree = tree->next; c++; tree = tree->next; } while(tree); link_efrq = (int *)mCalloc(c,sizeof(int)); link_lens = (int *)mCalloc(c,sizeof(int)); link_rmat = (int *)mCalloc(c,sizeof(int)); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n _______________________________________________________________________ "); PhyML_Fprintf(fp,"\n| |"); PhyML_Fprintf(fp,"\n| Model summary table |"); PhyML_Fprintf(fp,"\n|_______________________________________________________________________|"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); /* PhyML_Fprintf(fp," "); */ PhyML_Fprintf(fp," ------------------"); tree = cpy_mixt_tree; c = 0; do { if(tree->is_mixt_tree) tree = tree->next; PhyML_Fprintf(fp,"---"); tree = tree->next; } while(tree); param = (char *)mCalloc(30,sizeof(char)); PhyML_Fprintf(fp,"\n"); strcpy(param,"Partition element "); PhyML_Fprintf(fp," %18s",param); tree = cpy_mixt_tree; c = 0; do { if(tree->is_mixt_tree) tree = tree->next; PhyML_Fprintf(fp,"%2d ",tree->mixt_tree->dp); tree = tree->next; } while(tree); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp," ------------------"); tree = cpy_mixt_tree; c = 0; do { if(tree->is_mixt_tree) tree = tree->next; PhyML_Fprintf(fp,"---"); tree = tree->next; } while(tree); tree = cpy_mixt_tree; c = 0; do { if(tree->is_mixt_tree) tree = tree->next; link_rmat[c] = -1; link_lens[c] = -1; link_efrq[c] = -1; tree = tree->next; c++; } while(tree); mixt_tree = cpy_mixt_tree; cc_efrq = 97; cc_rmat = 97; cc_lens = 97; cc = 0; do { if(mixt_tree->is_mixt_tree) mixt_tree = mixt_tree->next; if(link_efrq[cc] < 0) { link_efrq[cc] = cc_efrq; tree = mixt_tree->next; c = cc+1; if(tree) { do { if(tree->is_mixt_tree) tree = tree->next; if(mixt_tree->mod->e_frq == tree->mod->e_frq) link_efrq[c] = cc_efrq; tree = tree->next; c++; } while(tree); } cc_efrq++; } if(link_lens[cc] < 0) { link_lens[cc] = cc_lens; tree = mixt_tree->next; c = cc+1; if(tree) { do { if(tree->is_mixt_tree) tree = tree->next; if(mixt_tree->a_edges[0]->l == tree->a_edges[0]->l) link_lens[c] = cc_lens; tree = tree->next; c++; } while(tree); } cc_lens++; } if(link_rmat[cc] < 0) { link_rmat[cc] = cc_rmat; tree = mixt_tree->next; c = cc+1; if(tree) { do { if(tree->is_mixt_tree) tree = tree->next; if(mixt_tree->mod->r_mat == tree->mod->r_mat && mixt_tree->mod->whichmodel == tree->mod->whichmodel && !strcmp(mixt_tree->mod->custom_mod_string->s, tree->mod->custom_mod_string->s) && !strcmp(mixt_tree->mod->aa_rate_mat_file->s, tree->mod->aa_rate_mat_file->s)) link_rmat[c] = cc_rmat; tree = tree->next; c++; } while(tree); } cc_rmat++; } cc++; mixt_tree = mixt_tree->next; } while(mixt_tree); PhyML_Fprintf(fp,"\n"); strcpy(param,"State frequencies "); PhyML_Fprintf(fp," %18s",param); tree = cpy_mixt_tree; c = 0; do { if(tree->is_mixt_tree) tree = tree->next; PhyML_Fprintf(fp,"%2c ",link_efrq[c]); tree = tree->next; c++; } while(tree); PhyML_Fprintf(fp,"\n"); strcpy(param,"Branch lengths "); PhyML_Fprintf(fp," %18s",param); tree = cpy_mixt_tree; c = 0; do { if(tree->is_mixt_tree) tree = tree->next; PhyML_Fprintf(fp,"%2c ",link_lens[c]); tree = tree->next; c++; } while(tree); PhyML_Fprintf(fp,"\n"); strcpy(param,"Rate matrix "); PhyML_Fprintf(fp," %18s",param); tree = cpy_mixt_tree; c = 0; do { if(tree->is_mixt_tree) tree = tree->next; PhyML_Fprintf(fp,"%2c ",link_rmat[c]); tree = tree->next; c++; } while(tree); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp," ------------------"); tree = cpy_mixt_tree; c = 0; do { if(tree->is_mixt_tree) tree = tree->next; PhyML_Fprintf(fp,"---"); tree = tree->next; } while(tree); PhyML_Fprintf(fp,"\n"); if(final == YES) { tree = cpy_mixt_tree; c = 0; do { PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n. Tree estimated from data partition %d",c++); Br_Len_Involving_Invar(tree->next); Rescale_Br_Len_Multiplier_Tree(tree->next); s = Write_Tree(tree->next); /*! tree->next is not a mixt_tree so edge lengths are not averaged over when writing the tree out. */ PhyML_Fprintf(fp,"\n %s",s); Br_Len_Not_Involving_Invar(tree->next); Unscale_Br_Len_Multiplier_Tree(tree->next); Free(s); tree = tree->next_mixt; } while(tree); } Free(param); Free(link_efrq); Free(link_rmat); Free(link_lens); mixt_tree = cpy_mixt_tree; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// option *PhyML_XML(char *xml_filename) { char *most_likely_tree; phydbl best_lnL; int num_rand_tree; t_tree *mixt_tree,*tree; option *io,*dum; xml_node *xml_root; mixt_tree = XML_Process_Base(xml_filename); io = mixt_tree->io; most_likely_tree = NULL; xml_root = mixt_tree->xml_root; best_lnL = UNLIKELY; dum = NULL; dum = (option *)mCalloc(1,sizeof(option)); dum->use_xml = YES; for(num_rand_tree=0;num_rand_treemod->s_opt->n_rand_starts;num_rand_tree++) { MIXT_Check_Model_Validity(mixt_tree); MIXT_Chain_Models(mixt_tree); Init_Model(mixt_tree->mod->io->cdata,mixt_tree->mod,mixt_tree->mod->io); Set_Model_Parameters(mixt_tree->mod); Print_Data_Structure(NO,stdout,mixt_tree); tree = MIXT_Starting_Tree(mixt_tree); Copy_Tree(tree,mixt_tree); Free_Tree(tree); if(mixt_tree->io->mod->s_opt->random_input_tree) { PhyML_Printf("\n\n. [%3d/%3d]",num_rand_tree+1,mixt_tree->io->mod->s_opt->n_rand_starts); Random_Tree(mixt_tree); } Copy_Tree(mixt_tree,mixt_tree->next); Connect_CSeqs_To_Nodes(mixt_tree->mod->io->cdata,mixt_tree->mod->io,mixt_tree); Init_T_Beg(mixt_tree); Make_Tree_For_Pars(mixt_tree); Make_Tree_For_Lk(mixt_tree); Make_Spr(mixt_tree); MIXT_Chain_All(mixt_tree); MIXT_Check_Edge_Lens_In_All_Elem(mixt_tree); MIXT_Turn_Branches_OnOff_In_All_Elem(ON,mixt_tree); MIXT_Check_Invar_Struct_In_Each_Partition_Elem(mixt_tree); MIXT_Check_RAS_Struct_In_Each_Partition_Elem(mixt_tree); Br_Len_Not_Involving_Invar(mixt_tree); Unscale_Br_Len_Multiplier_Tree(mixt_tree); Set_Both_Sides(YES,mixt_tree); Set_Update_Eigen(YES,mixt_tree->mod); Lk(NULL,mixt_tree); Set_Update_Eigen(NO,mixt_tree->mod); if(mixt_tree->mod->s_opt->opt_topo) { Global_Spr_Search(mixt_tree); } else { Round_Optimize(mixt_tree,ROUND_MAX); } PhyML_Printf("\n\n. Log-likelihood = %f",mixt_tree->c_lnL); if((num_rand_tree == io->mod->s_opt->n_rand_starts-1) && (io->mod->s_opt->random_input_tree)) { num_rand_tree--; io->mod->s_opt->random_input_tree = NO; } Br_Len_Involving_Invar(mixt_tree); Rescale_Br_Len_Multiplier_Tree(mixt_tree); /*! Print the tree estimated using the current random (or BioNJ) starting tree */ if(io->mod->s_opt->n_rand_starts > 1) { Print_Tree(io->fp_out_trees,mixt_tree); fflush(NULL); } if(mixt_tree->c_lnL > best_lnL) { best_lnL = mixt_tree->c_lnL; if(most_likely_tree) Free(most_likely_tree); if(io->ratio_test) aLRT(mixt_tree); most_likely_tree = Write_Tree(mixt_tree); mixt_tree->lock_topo = NO; } tree = mixt_tree; do { if(tree->io->print_site_lnl == YES) Print_Site_Lk(tree,tree->io->fp_out_lk); tree = tree->next_mixt; } while(tree); MIXT_Init_T_End(mixt_tree); Print_Data_Structure(YES,mixt_tree->io->fp_out_stats,mixt_tree); Free_Best_Spr(mixt_tree); Free_Spr_List_One_Edge(mixt_tree); Free_Spr_List_All_Edge(mixt_tree); Free_Tree_Pars(mixt_tree); Free_Tree_Lk(mixt_tree); } /*! Print the most likely tree in the output file */ if(!mixt_tree->io->quiet) PhyML_Printf("\n\n. Printing the most likely tree in file '%s'...\n", Basename(mixt_tree->io->out_tree_file)); PhyML_Fprintf(mixt_tree->io->fp_out_tree,"%s\n",most_likely_tree); /*! Bootstrap analysis */ MIXT_Bootstrap(most_likely_tree,xml_root); while(io->prev != NULL) io = io->prev; Free(most_likely_tree); tree = mixt_tree; do { Free_Calign(tree->data); tree = tree->next_mixt; } while(tree); tree = mixt_tree; do { Free_Optimiz(tree->mod->s_opt); tree = tree->next; } while(tree); Free_Model_Complete(mixt_tree->mod); Free_Model_Basic(mixt_tree->mod); Free_Tree(mixt_tree); if(io->fp_out_trees) fclose(io->fp_out_trees); if(io->fp_out_tree) fclose(io->fp_out_tree); if(io->fp_out_stats) fclose(io->fp_out_stats); if(io->fp_out_json_trace) fclose(io->fp_out_json_trace); Free_Input(io); XML_Free_XML_Tree(xml_root); return(dum); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /*! Check that the same nodes in the different mixt_trees are connected to the same taxa */ void Check_Taxa_Sets(t_tree *mixt_tree) { t_tree *tree; int i; tree = mixt_tree; do { if(tree->next) { for(i=0;in_otu;i++) { if(strcmp(tree->a_nodes[i]->name,tree->next->a_nodes[i]->name)) { PhyML_Fprintf(stderr,"\n. There seems to be a problem in one (or more) of your"); PhyML_Fprintf(stderr,"\n. sequence alignments. PhyML could not match taxon"); PhyML_Fprintf(stderr,"\n. '%s' found in file '%s' with any of the taxa",tree->a_nodes[i]->name,tree->io->in_align_file); PhyML_Fprintf(stderr,"\n. listed in file '%s'.",tree->next->io->in_align_file); Exit("\n"); } } } tree = tree->next; } while(tree); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Make_Ratematrix_From_XML_Node(xml_node *instance, option *io, t_mod *mod) { char *model = NULL; int select; model = XML_Get_Attribute_Value(instance,"model"); if(model == NULL) { PhyML_Fprintf(stderr,"\n. Poorly formated XML file."); PhyML_Fprintf(stderr,"\n. Attribute 'model' is mandatory in a node."); Exit("\n"); } select = XML_Validate_Attr_Int(model,27, "xxxxx", //0 "JC69", //1 "K80", //2 "F81", //3 "HKY85", //4 "F84", //5 "TN93", //6 "GTR", //7 "CUSTOM", //8 "xxxxx", //9 "xxxxx", //10 "WAG", //11 "DAYHOFF", //12 "JTT", //13 "BLOSUM62", //14 "MTREV", //15 "RTREV", //16 "CPREV", //17 "DCMUT", //18 "VT", //19 "MTMAM", //20 "MTART", //21 "HIVW", //22 "HIVB", //23 "FLU", //24 "CUSTOMAA", //25 "LG", //26 "AB" ); //27 if(select < 9) { if(io->datatype != NT) { PhyML_Fprintf(stderr,"\n. Data type and selected model are incompatible"); Exit("\n"); } } else { if(io->datatype != AA) { PhyML_Fprintf(stderr,"\n. Data type and selected model are incompatible"); Exit("\n"); } } mod->r_mat = (t_rmat *)Make_Rmat(mod->ns); Init_Rmat(mod->r_mat); /*! Set model number & name */ mod->whichmodel = Set_Whichmodel(select); Set_Model_Name(mod); if(mod->whichmodel == K80 || mod->whichmodel == HKY85 || mod->whichmodel == TN93) { char *tstv,*opt_tstv; tstv = XML_Get_Attribute_Value(instance,"tstv"); if(tstv) { mod->s_opt->opt_kappa = NO; mod->kappa->v = String_To_Dbl(tstv); } else { mod->s_opt->opt_kappa = YES; } opt_tstv = XML_Get_Attribute_Value(instance,"optimise.tstv"); if(opt_tstv) { if(!strcmp(opt_tstv,"true") || !strcmp(opt_tstv,"yes")) { mod->s_opt->opt_kappa = YES; mod->s_opt->opt_subst_param = YES; } else { mod->s_opt->opt_kappa = NO; mod->s_opt->opt_subst_param = NO; } } } else { mod->s_opt->opt_kappa = NO; } if(mod->whichmodel == GTR || mod->whichmodel == CUSTOM) { char *opt_rr; opt_rr = XML_Get_Attribute_Value(instance,"optimise.rr"); if(opt_rr) { if(!strcmp(opt_rr,"yes") || !strcmp(opt_rr,"true")) { mod->s_opt->opt_rr = YES; mod->s_opt->opt_subst_param = YES; } else { mod->s_opt->opt_rr = NO; mod->s_opt->opt_subst_param = NO; } } } if(mod->whichmodel == GTR || mod->whichmodel == CUSTOM) { xml_node *rr; char *val; phydbl v; rr = XML_Search_Node_Name("rr",YES,instance); if(rr != NULL) { mod->r_mat = (t_rmat *)Make_Rmat(mod->ns); Init_Rmat(mod->r_mat); Make_Custom_Model(mod); // A<->C val = XML_Get_Attribute_Value(rr,"AC"); if(val == NULL) { PhyML_Printf("\n. Please specify the relative rate of substitution between A and T"); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } else { v = strtod(val,NULL); if(v != 0 && v > 0.0) { mod->r_mat->rr->v[0] = v; mod->r_mat->rr_val->v[0] = log(v); } else { PhyML_Printf("\n. Invalid relative rate parameter value: '%s'.\n", val); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } } // A<->G val = XML_Get_Attribute_Value(rr,"AG"); if(val == NULL) { PhyML_Printf("\n. Please specify the relative rate of substitution between A and T"); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } else { v = strtod(val,NULL); if(v != 0 && v > 0.0) { mod->r_mat->rr->v[1] = v; mod->r_mat->rr_val->v[1] = log(v); } else { PhyML_Printf("\n. Invalid relative rate parameter value: '%s'.\n", val); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } } // A<->T val = XML_Get_Attribute_Value(rr,"AT"); if(val == NULL) { PhyML_Printf("\n. Please specify the relative rate of substitution between A and T"); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } else { v = strtod(val,NULL); if(v != 0 && v > 0.0) { mod->r_mat->rr->v[2] = v; mod->r_mat->rr_val->v[2] = log(v); } else { PhyML_Printf("\n. Invalid relative rate parameter value: '%s'.\n", val); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } } // C<->G val = XML_Get_Attribute_Value(rr,"CG"); if(val == NULL) { PhyML_Printf("\n. Please specify the relative rate of substitution between A and T"); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } else { v = strtod(val,NULL); if(v != 0 && v > 0.0) { mod->r_mat->rr->v[3] = v; mod->r_mat->rr_val->v[3] = log(v); } else { PhyML_Printf("\n. Invalid relative rate parameter value: '%s'.\n", val); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } } // C<->T val = XML_Get_Attribute_Value(rr,"CT"); if(val == NULL) { PhyML_Printf("\n. Please specify the relative rate of substitution between A and T"); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } else { v = strtod(val,NULL); if(v != 0 && v > 0.0) { mod->r_mat->rr->v[4] = v; mod->r_mat->rr_val->v[4] = log(v); } else { PhyML_Printf("\n. Invalid relative rate parameter value: '%s'.\n", val); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } } // G<->T val = XML_Get_Attribute_Value(rr,"GT"); if(val != NULL) { v = strtod(val,NULL); if(v != 0 && v > 0.0) { mod->r_mat->rr->v[5] = v; mod->r_mat->rr_val->v[5] = log(v); } else { PhyML_Printf("\n. Invalid relative rate parameter value: '%s'.\n", val); Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } } } } /*! Custom model for nucleotide sequences. Read the corresponding code. */ if(mod->whichmodel == CUSTOM) { char *model_code = NULL; model_code = XML_Get_Attribute_Value(instance,"model.code"); if(!model_code) { PhyML_Fprintf(stderr,"\n. No valid 'model.code' attribute could be found.\n"); PhyML_Fprintf(stderr,"\n. Please fix your XML file.\n"); Exit("\n"); } else { strcpy(mod->custom_mod_string->s,model_code); } } /*! Custom model for amino-acids. Read in the rate matrix file */ if(mod->whichmodel == CUSTOMAA) { char *r_mat_file; r_mat_file = XML_Get_Attribute_Value(instance,"ratematrix.file"); if(!r_mat_file) { PhyML_Fprintf(stderr,"\n. No valid 'ratematrix.file' attribute could be found."); PhyML_Fprintf(stderr,"\n. Please fix your XML file.\n"); Exit("\n"); } else { strcpy(mod->aa_rate_mat_file->s,r_mat_file); } /* Free(r_mat_file); */ } char *buff; buff = XML_Get_Attribute_Value(instance->parent,"optimise.weights"); if(buff && (!strcmp(buff,"yes") || !strcmp(buff,"true"))) { mod->s_opt->opt_rmat_weight = YES; } else { mod->s_opt->opt_rmat_weight = NO; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Make_Efrq_From_XML_Node(xml_node *instance, option *io, t_mod *mod) { char *buff; mod->e_frq = (t_efrq *)Make_Efrq(mod->ns); Init_Efrq(NULL,mod->e_frq); buff = XML_Get_Attribute_Value(instance,"freqs"); if(buff) { if(!strcmp(buff,"empirical")) { mod->s_opt->state_freq = EMPIRICAL; } if(!strcmp(buff,"model")) { mod->s_opt->state_freq = MODEL; } else if(!strcmp(buff,"optimized")) { mod->s_opt->state_freq = ML; } else if(!strcmp(buff,"user")) { mod->s_opt->state_freq = ML; } } buff = XML_Get_Attribute_Value(instance,"base.freqs"); if(buff) { if(io->datatype == AA) { PhyML_Fprintf(stderr,"\n. Option 'base.freqs' is not allowed with amino-acid data."); Exit("\n"); } else { phydbl A,C,G,T; if(mod->s_opt->state_freq != USER) { PhyML_Fprintf(stderr,"\n. Please set 'freqs=user'."); Exit("\n"); } sscanf(buff,"%lf,%lf,%lf,%lf",&A,&C,&G,&T); mod->e_frq->user_b_freq->v[0] = (phydbl)A; mod->e_frq->user_b_freq->v[1] = (phydbl)C; mod->e_frq->user_b_freq->v[2] = (phydbl)G; mod->e_frq->user_b_freq->v[3] = (phydbl)T; mod->s_opt->state_freq = USER; } } buff = XML_Get_Attribute_Value(instance->parent,"optimise.weights"); if(buff && (!strcmp(buff,"yes") || !strcmp(buff,"true"))) { mod->s_opt->opt_efrq_weight = YES; } else { mod->s_opt->opt_efrq_weight = NO; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Make_Topology_From_XML_Node(xml_node *instance, option *io, t_mod *mod) { // Starting tree char *init_tree; init_tree = XML_Get_Attribute_Value(instance,"init.tree"); if(!init_tree) { PhyML_Fprintf(stderr,"\n. Attribute 'init.tree=bionj|user|random' is mandatory"); PhyML_Fprintf(stderr,"\n. Please amend your XML file accordingly."); Exit("\n"); } if(!strcmp(init_tree,"user") || !strcmp(init_tree,"User")) { char *starting_tree = NULL; starting_tree = XML_Get_Attribute_Value(instance,"file.name"); if(!Filexists(starting_tree)) { PhyML_Fprintf(stderr,"\n. The tree file '%s' could not be found.",starting_tree); Exit("\n"); } else { strcpy(io->in_tree_file,starting_tree); io->in_tree = 2; io->fp_in_tree = Openfile(io->in_tree_file,0); } } else if(!strcmp(init_tree,"random") || !strcmp(init_tree,"Random")) { char *n_rand_starts = NULL; io->mod->s_opt->random_input_tree = YES; n_rand_starts = XML_Get_Attribute_Value(instance,"n.rand.starts"); if(n_rand_starts) { mod->s_opt->n_rand_starts = atoi(n_rand_starts); if(mod->s_opt->n_rand_starts < 1) Exit("\n. Number of random starting trees must be > 0.\n\n"); } strcpy(io->out_trees_file,io->in_align_file); strcat(io->out_trees_file,"_phyml_rand_trees"); if(io->append_run_ID) { strcat(io->out_trees_file,"_"); strcat(io->out_trees_file,io->run_id_string); } strcat(io->out_trees_file,".txt"); io->fp_out_trees = Openfile(io->out_trees_file,1); } else if(!strcmp(init_tree,"parsimony") || !strcmp(init_tree,"Parsimony")) { io->in_tree = 1; } // Estimate tree topology char *optimise = NULL; optimise = XML_Get_Attribute_Value(instance,"optimise.tree"); if(optimise) { int select; select = XML_Validate_Attr_Int(optimise,6, "true","yes","y", "false","no","n"); if(select > 2) io->mod->s_opt->opt_topo = NO; else { char *search; int select; search = XML_Get_Attribute_Value(instance,"search"); if(search == NULL) { io->mod->s_opt->topo_search = SPR_MOVE; io->mod->s_opt->opt_topo = YES; } else { select = XML_Validate_Attr_Int(search,4,"spr","nni","best","none"); switch(select) { case 0: { io->mod->s_opt->topo_search = SPR_MOVE; io->mod->s_opt->opt_topo = YES; break; } case 1: { io->mod->s_opt->topo_search = NNI_MOVE; io->mod->s_opt->opt_topo = YES; break; } case 2: { io->mod->s_opt->topo_search = BEST_OF_NNI_AND_SPR; io->mod->s_opt->opt_topo = YES; break; } case 3: { io->mod->s_opt->opt_topo = NO; break; } default: { PhyML_Fprintf(stderr,"\n. Topology search option '%s' is not valid.",search); Exit("\n"); break; } } } } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Make_RAS_From_XML_Node(xml_node *parent, t_mod *mod) { xml_node *w; char *family; int select; family = NULL; mod->ras->n_catg = 0; XML_Check_Siterates_Node(parent); w = XML_Search_Node_Name("weights",YES,parent); if(w) { family = XML_Get_Attribute_Value(w,"family"); if(family == NULL) select = -1; else select = XML_Validate_Attr_Int(family,3,"gamma","gamma+inv","freerates"); switch(select) { case 0:// Gamma model { char *alpha,*alpha_opt; mod->s_opt->opt_pinvar = NO; mod->ras->invar = NO; alpha = XML_Get_Attribute_Value(w,"alpha"); if(alpha) { if(!strcmp(alpha,"estimate") || !strcmp(alpha,"estimated") || !strcmp(alpha,"optimise") || !strcmp(alpha,"optimised")) { mod->s_opt->opt_alpha = YES; } else { mod->s_opt->opt_alpha = NO; mod->ras->alpha->v = String_To_Dbl(alpha); } } alpha_opt = XML_Get_Attribute_Value(w,"optimise.alpha"); if(alpha_opt) { if(!strcmp(alpha_opt,"yes") || !strcmp(alpha_opt,"true")) { mod->s_opt->opt_alpha = YES; } else { mod->s_opt->opt_alpha = NO; } } mod->ras->n_catg = XML_Get_Number_Of_Classes_Siterates(parent); Make_RAS_Complete(mod->ras); break; } case 1: // Gamma+Inv model { char *alpha,*pinv,*alpha_opt,*pinv_opt; mod->ras->invar = YES; mod->s_opt->opt_pinvar = YES; alpha = XML_Get_Attribute_Value(w,"alpha"); if(alpha) { if(!strcmp(alpha,"estimate") || !strcmp(alpha,"estimated") || !strcmp(alpha,"optimise") || !strcmp(alpha,"optimised")) { mod->s_opt->opt_alpha = YES; } else { mod->s_opt->opt_alpha = NO; mod->ras->alpha->v = String_To_Dbl(alpha);; } } alpha_opt = XML_Get_Attribute_Value(w,"optimise.alpha"); if(alpha_opt) { if(!strcmp(alpha_opt,"yes") || !strcmp(alpha_opt,"true")) { mod->s_opt->opt_alpha = YES; } else { mod->s_opt->opt_alpha = NO; } } pinv = XML_Get_Attribute_Value(w,"pinv"); if(pinv) { if(!strcmp(pinv,"estimate") || !strcmp(pinv,"estimated") || !strcmp(pinv,"optimise") || !strcmp(pinv,"optimised")) { mod->s_opt->opt_pinvar = YES; } else { mod->s_opt->opt_pinvar = NO; mod->ras->pinvar->v = String_To_Dbl(pinv);; } } pinv_opt = XML_Get_Attribute_Value(w,"optimise.pinv"); if(pinv_opt) { if(!strcmp(pinv_opt,"yes") || !strcmp(pinv_opt,"true")) { mod->s_opt->opt_pinvar = YES; } else { mod->s_opt->opt_pinvar = NO; } } mod->ras->n_catg = XML_Get_Number_Of_Classes_Siterates(parent); break; } case 2: // FreeRate model { char *opt_freerates; mod->ras->free_mixt_rates = YES; mod->s_opt->opt_free_mixt_rates = YES; opt_freerates = XML_Get_Attribute_Value(w,"optimise.freerates"); if(opt_freerates) { if(!strcmp(opt_freerates,"yes") || !strcmp(opt_freerates,"true")) { mod->s_opt->opt_free_mixt_rates = YES; } else { mod->s_opt->opt_free_mixt_rates = NO; } } mod->ras->n_catg = XML_Get_Number_Of_Classes_Siterates(parent); break; } default: { if(family != NULL) PhyML_Printf("\n. family: %s",family); else { PhyML_Printf("\n. Please specify a model family (\"gamma\", \"gamma+inv\" or \"freerate\")"); PhyML_Printf("\n. for every 'weights' element in every 'siterate' element. Note that " ); PhyML_Printf("\n. a \"gamma\" or a \"freerate\" model with a single rate class amounts"); PhyML_Printf("\n. to no variation of rates across sites, if this is the model you'd"); PhyML_Printf("\n. like to implement..."); } PhyML_Printf("\n. Err. in file %s at line %d\n",__FILE__,__LINE__); Exit("\n"); } } } int nc = XML_Get_Number_Of_Classes_Siterates(parent); if(w && nc != mod->ras->n_catg) { PhyML_Printf("\n. component '%s'. The number of classes ",parent->id); PhyML_Printf("\n. specified in the component should be "); PhyML_Printf("\n. the same as that of nodes. Please fix"); PhyML_Printf("\n. your XML file accordingly."); Exit("\n"); } if(!w) mod->ras->n_catg = nc; Make_RAS_Complete(mod->ras); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void Generic_Exit(const char *file, int line, const char *function) { PhyML_Fprintf(stderr,"\n. Err. in file '%s' (line %d)",file,line); if(function != NULL) PhyML_Printf(", function '%s'",function); # if defined(PHYTIME) PhyML_Fprintf(stderr,"\n. PhyTime finished prematurely."); # elif defined(PHYREX) PhyML_Fprintf(stderr,"\n. PhyREX finished prematurely."); # elif defined(PHYML) PhyML_Fprintf(stderr,"\n. PhyML finished prematurely."); #else PhyML_Fprintf(stderr,"\n. The execution finished prematurely."); #endif Exit("\n"); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void JSON_Write_Object(json_o *obj, FILE *where) { json_kv *kv; assert(obj); assert(where); kv = obj->kv; assert(kv); PhyML_Fprintf(where,"{"); do { PhyML_Fprintf(where,"\"%s\":",kv->key); if(kv->value != NULL) PhyML_Fprintf(where,"\"%s\"",kv->value); else if(kv->array != NULL) JSON_Write_Array(kv->array,where); else if(kv->object != NULL) JSON_Write_Object(kv->object,where); kv = kv->next; if(kv) PhyML_Fprintf(where,","); } while(kv); PhyML_Fprintf(where,"}"); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void JSON_Write_Array(json_a *array, FILE *where) { json_o *o; assert(where); assert(array); o = array->object; assert(o); PhyML_Fprintf(where,"["); do { JSON_Write_Object(o,where); o = o->next; if(o) PhyML_Fprintf(where,","); } while(o); PhyML_Fprintf(where,"]\n"); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void JSON_Write_All(json_a *array, FILE *where) { JSON_Write_Array(array,where); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ json_o *JSON_Tree_To_Object(t_tree *mixt_tree) { t_tree *tree; json_kv *state,*kv; json_o *ret,*o; json_a *a; int string_len; char *s; time_t t_end; string_len = 20; ret = (json_o *)mCalloc(1,sizeof(json_o)); ret->next = NULL; state = (json_kv *)mCalloc(1,sizeof(json_kv)); ret->kv = state; state->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(state->key,"state"); state->value = NULL; state->array = NULL; state->object = NULL; state->next = NULL; state->array = (json_a *)mCalloc(1,sizeof(json_a)); a = state->array; a->object = (json_o *)mCalloc(1,sizeof(json_o)); o = a->object; // State num o->kv = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = o->kv; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"type"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"t_num"); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"id"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"state_num"); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"value"); kv->value = (char *)mCalloc(string_len,sizeof(char)); sprintf(kv->value,"%d",mixt_tree->json_num); mixt_tree->json_num++; kv->next = NULL; // Time o->next = (json_o *)mCalloc(1,sizeof(json_o)); o = o->next; o->kv = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = o->kv; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"type"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"t_time"); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"id"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"time"); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"value"); kv->value = (char *)mCalloc(string_len,sizeof(char)); time(&t_end); sprintf(kv->value,"%d",(int)(t_end-mixt_tree->t_beg)); kv->next = NULL; // Tree o->next = (json_o *)mCalloc(1,sizeof(json_o)); o = o->next; o->kv = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = o->kv; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"type"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"t_tree"); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"id"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"tree"); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"value"); s = Write_Tree(mixt_tree); kv->value = (char *)mCalloc((int)strlen(s)+1,sizeof(char)); sprintf(kv->value,"%s",s); Free(s); kv->next = NULL; // Likelihood o->next = (json_o *)mCalloc(1,sizeof(json_o)); o = o->next; o->kv = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = o->kv; kv->value = NULL; kv->array = NULL; kv->object = NULL; o->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"type"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"t_param"); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"id"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"likelihood"); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"value"); kv->value = (char *)mCalloc(string_len,sizeof(char)); sprintf(kv->value,"%G",mixt_tree->c_lnL); kv->next = NULL; // TsTv { int n_tstv,i; scalar_dbl **tstv; n_tstv = 0; tstv = NULL; tree = mixt_tree; do { if(tree->is_mixt_tree == YES) tree = tree->next; for(i=0;imod->kappa == tstv[i]) break; if(i == n_tstv) { if(!tstv) tstv = (scalar_dbl **)mCalloc(1,sizeof(scalar_dbl *)); else tstv = (scalar_dbl **)mRealloc(tstv,n_tstv+1,sizeof(scalar_dbl *)); tstv[n_tstv] = tree->mod->kappa; n_tstv++; if(tree->mod->s_opt->opt_kappa == YES) { o->next = (json_o *)mCalloc(1,sizeof(json_o)); o = o->next; o->kv = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = o->kv; kv->value = NULL; kv->array = NULL; kv->object = NULL; o->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"type"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"t_param"); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"id"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"tstv"); sprintf(kv->value+strlen(kv->value),"%d",n_tstv); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"value"); kv->value = (char *)mCalloc(string_len,sizeof(char)); sprintf(kv->value,"%G",tree->mod->kappa->v); kv->next = NULL; } } tree = tree->next; } while(tree); if(tstv) Free(tstv); } // Alpha { int n_alpha,i; scalar_dbl **alpha; n_alpha = 0; alpha = NULL; tree = mixt_tree; do { for(i=0;imod->ras->alpha == alpha[i]) break; if(i == n_alpha) { if(!alpha) alpha = (scalar_dbl **)mCalloc(1,sizeof(scalar_dbl *)); else alpha = (scalar_dbl **)mRealloc(alpha,n_alpha+1,sizeof(scalar_dbl *)); alpha[n_alpha] = tree->mod->ras->alpha; n_alpha++; if(tree->mod->s_opt->opt_alpha == YES) { o->next = (json_o *)mCalloc(1,sizeof(json_o)); o = o->next; o->kv = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = o->kv; kv->value = NULL; kv->array = NULL; kv->object = NULL; o->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"type"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"t_param"); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"id"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"alpha"); sprintf(kv->value+strlen(kv->value),"%d",n_alpha); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"value"); kv->value = (char *)mCalloc(string_len,sizeof(char)); sprintf(kv->value,"%G",tree->mod->ras->alpha->v); kv->next = NULL; } } tree = tree->next_mixt; } while(tree); if(alpha) Free(alpha); } // Tree size { tree = mixt_tree; int tree_num = 1; do { o->next = (json_o *)mCalloc(1,sizeof(json_o)); o = o->next; o->kv = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = o->kv; kv->value = NULL; kv->array = NULL; kv->object = NULL; o->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"type"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"t_param"); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"id"); kv->value = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->value,"tree_size"); sprintf(kv->value+strlen(kv->value),"%d",tree_num); kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"value"); kv->value = (char *)mCalloc(string_len,sizeof(char)); sprintf(kv->value,"%G",Get_Tree_Size(tree)); kv->next = NULL; tree = tree->next_mixt; } while(tree); } return(ret); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ json_o *JSON_Tree_To_Object_Light(t_tree *mixt_tree) { t_tree *tree; json_kv *state,*kv; json_o *ret,*o; int string_len; char *s; time_t t_end; string_len = 20; ret = (json_o *)mCalloc(1,sizeof(json_o)); ret->next = NULL; state = (json_kv *)mCalloc(1,sizeof(json_kv)); ret->kv = state; state->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(state->key,"state"); state->value = NULL; state->array = NULL; state->object = NULL; state->next = NULL; state->object = (json_o *)mCalloc(1,sizeof(json_o)); o = state->object; o->kv = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = o->kv; // State num kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"state_num"); kv->value = (char *)mCalloc(string_len,sizeof(char)); sprintf(kv->value,"%d",mixt_tree->json_num); // Time kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"time"); kv->value = (char *)mCalloc(string_len,sizeof(char)); time(&t_end); sprintf(kv->value,"%d",(int)(t_end-mixt_tree->t_beg)); // Tree kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; kv->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"tree"); s = Write_Tree(mixt_tree); kv->value = (char *)mCalloc((int)strlen(s)+1,sizeof(char)); sprintf(kv->value,"%s",s); Free(s); // Likelihood kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; o->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"likelihood"); kv->value = (char *)mCalloc(string_len,sizeof(char)); sprintf(kv->value,"%G",mixt_tree->c_lnL); // TsTv { int n_tstv,i; scalar_dbl **tstv; n_tstv = 0; tstv = NULL; tree = mixt_tree; do { if(tree->is_mixt_tree == YES) tree = tree->next; for(i=0;imod->kappa == tstv[i]) break; if(i == n_tstv) { if(!tstv) tstv = (scalar_dbl **)mCalloc(1,sizeof(scalar_dbl *)); else tstv = (scalar_dbl **)mRealloc(tstv,n_tstv+1,sizeof(scalar_dbl *)); tstv[n_tstv] = tree->mod->kappa; n_tstv++; if(tree->mod->s_opt->opt_kappa == YES) { kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; o->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"tstv"); sprintf(kv->key+strlen(kv->key),"%d",n_tstv); kv->value = (char *)mCalloc(string_len,sizeof(char)); sprintf(kv->value,"%G",tree->mod->kappa->v); } } tree = tree->next; } while(tree); if(tstv) Free(tstv); } // Alpha { int n_alpha,i; scalar_dbl **alpha; n_alpha = 0; alpha = NULL; tree = mixt_tree; do { for(i=0;imod->ras->alpha == alpha[i]) break; if(i == n_alpha) { if(!alpha) alpha = (scalar_dbl **)mCalloc(1,sizeof(scalar_dbl *)); else alpha = (scalar_dbl **)mRealloc(alpha,n_alpha+1,sizeof(scalar_dbl *)); alpha[n_alpha] = tree->mod->ras->alpha; n_alpha++; if(tree->mod->s_opt->opt_alpha == YES) { kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; o->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"alpha"); sprintf(kv->key+strlen(kv->key),"%d",n_alpha); kv->value = (char *)mCalloc(string_len,sizeof(char)); sprintf(kv->value,"%G",tree->mod->ras->alpha->v); } } tree = tree->next_mixt; } while(tree); if(alpha) Free(alpha); } // Tree size { tree = mixt_tree; int tree_num = 1; do { kv->next = (json_kv *)mCalloc(1,sizeof(json_kv)); kv = kv->next; kv->value = NULL; kv->array = NULL; kv->object = NULL; o->next = NULL; kv->key = (char *)mCalloc(string_len,sizeof(char)); strcpy(kv->key,"tree_size"); sprintf(kv->key+strlen(kv->key),"%d",tree_num); kv->value = (char *)mCalloc(string_len,sizeof(char)); sprintf(kv->value,"%G",Get_Tree_Size(tree)); tree = tree->next_mixt; } while(tree); } kv->next = NULL; return(ret); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void JSON_Tree_Io(t_tree *tree, FILE *where) { // Append json_o *o; fpos_t pos; char c; fgetpos(where,&pos); rewind(where); c = fgetc(where); if(c != '[') { PhyML_Fprintf(where,"["); } else { fsetpos(where,&pos); fseek(where,-1,SEEK_CUR); PhyML_Fprintf(where,","); } PhyML_Fprintf(where,"\n"); /* o = JSON_Tree_To_Object(tree); */ o = JSON_Tree_To_Object_Light(tree); JSON_Write_Object(o,where); JSON_Free_Object(o); PhyML_Fprintf(where,"]"); fflush(where); // Print out latest tree + info /* json_o *o; */ /* rewind(where); */ /* /\* PhyML_Fprintf(where,"["); *\/ */ /* o = JSON_Tree_To_Object(tree); */ /* JSON_Write_Object(o,where); */ /* JSON_Free_Object(o); */ /* /\* PhyML_Fprintf(where,"]"); *\/ */ /* fflush(where); */ } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void Print_Lk_Given_Edge_Recurr(t_node *a, t_node *d, t_edge *b, t_tree *tree) { PhyML_Printf("\n___ Edge %3d (left=%3d rght=%3d) lnL=%f", b->num, b->left->num, b->rght->num, Lk(b,tree)); if(d->tax) return; else { int i; for(i=0;i<3;i++) if(d->v[i] != a) Print_Lk_Given_Edge_Recurr(d,d->v[i],d->b[i],tree); } } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void Collect_Edge_Support_Values(t_tree *tree) { int i; for(i=0;i<2*tree->n_otu-3;++i) { if(tree->io->do_boot == YES) { tree->a_edges[i]->support_val = tree->a_edges[i]->bip_score; } else if(tree->io->do_tbe == YES) { int pminus1=MIN(tree->a_edges[i]->left->bip_size[tree->a_edges[i]->l_r], tree->a_edges[i]->rght->bip_size[tree->a_edges[i]->r_l])-1; tree->a_edges[i]->support_val = 1-((tree->a_edges[i]->tdist_score)/(tree->io->n_boot_replicates*1.0))/pminus1; } else if(tree->io->do_alrt == YES) { tree->a_edges[i]->support_val = tree->a_edges[i]->ratio_test; } } } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ #if defined (PHYREX) void PHYREX_Output_Tree_Structure(FILE *fp, t_tree *tree) { char *s; s = PHYREX_Print_Tree_Structure(tree); PhyML_Fprintf(fp,"%s",s); Free(s); } #endif /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ #if defined (PHYREX) char *PHYREX_Print_Tree_Structure(t_tree *tree) { t_dsk *disk; char *s,*buff; FILE *fp; fpos_t pos; fp = tmpfile(); assert(fp); buff = (char *)mCalloc(T_MAX_LINE,sizeof(char)); disk = tree->young_disk; while(disk->prev != NULL) disk = disk->prev; assert(disk->ldsk); assert(disk->ldsk->n_next > 0); fgetpos(fp,&pos); PhyML_Fprintf(fp,"begin\n"); fsetpos(fp,&pos); if(fgets(buff,T_MAX_LINE,fp) == NULL) Generic_Exit(__FILE__,__LINE__,__FUNCTION__); s = (char *)mCalloc((int)strlen(buff)+1,sizeof(char)); sprintf(s+strlen(s),"%s",buff); fgetpos(fp,&pos); PhyML_Fprintf(fp,"%d %d\n",tree->n_otu,tree->mmod->n_dim); fsetpos(fp,&pos); if(fgets(buff,T_MAX_LINE,fp) == NULL) Generic_Exit(__FILE__,__LINE__,__FUNCTION__); s = (char *)mRealloc(s,(int)(strlen(s)+strlen(buff)+1),sizeof(char)); sprintf(s+strlen(s),"%s",buff); do { fgetpos(fp,&pos); PhyML_Fprintf(fp,"%s ",disk->id); PhyML_Fprintf(fp,"%f ",disk->time); PhyML_Fprintf(fp,"%f %f ",disk->centr->lonlat[0],disk->centr->lonlat[1]); if(disk->ldsk != NULL) { s = (char *)mRealloc(s,(int)strlen(s)+9,sizeof(char)); PhyML_Fprintf(fp,"*%s ",disk->ldsk->coord->id); PhyML_Fprintf(fp,"%f %f ",disk->ldsk->coord->lonlat[0],disk->ldsk->coord->lonlat[1]); } for(int i=0;in_ldsk_a;++i) { PhyML_Fprintf(fp,"%s ",disk->ldsk_a[i]->coord->id); } PhyML_Fprintf(fp,"\n"); fsetpos(fp,&pos); if(fgets(buff,T_MAX_LINE,fp) == NULL) Generic_Exit(__FILE__,__LINE__,__FUNCTION__); s = (char *)mRealloc(s,(int)(strlen(s)+strlen(buff)+1),sizeof(char)); sprintf(s+strlen(s),"%s",buff); disk = disk->next; } while(disk); fgetpos(fp,&pos); PhyML_Fprintf(fp,"end"); fsetpos(fp,&pos); if(fgets(buff,T_MAX_LINE,fp) == NULL) Generic_Exit(__FILE__,__LINE__,__FUNCTION__); s = (char *)mRealloc(s,(int)(strlen(s)+strlen(buff)+1),sizeof(char)); sprintf(s+strlen(s),"%s",buff); fclose(fp); Free(buff); return(s); } #endif /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ #if defined (PHYREX) void PHYREX_Check_Point(FILE *fp, t_tree *tree) { xml_node *n; char *s; if(XML_Search_Node_Attribute_Value("add","true",NO,tree->xml_root) == NULL) { XML_Add_Attribute(tree->xml_root,"add","true"); } n = XML_Search_Node_Name("slfv",YES,tree->xml_root); if(n == NULL) { n = XML_Add_Node(tree->xml_root,"slfv"); XML_Set_Node_Id(n,"SLFV1"); } s = PHYREX_Print_Tree_Structure(tree); XML_Set_Node_Value(n,s); XML_Update_XML_Struct_Given_Model_Params(tree); /* XML_Update_XML_Struct_Given_MCMC_Params(tree); */ XML_Write_XML_Graph(fp,tree->xml_root); } #endif /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ #if defined (PHYREX) void PHYREX_Input_Tree_Structure(FILE *fp) { char *tk,*s,delim[4]="\n\r "; int n_otu,n_dim,idx; t_dsk *disk; t_ldsk *new_ldsk,*root_ldsk,*ldsk; s = (char *)mCalloc(T_MAX_LINE,sizeof(char)); do { if(fgets(s,T_MAX_LINE,fp) == NULL) Generic_Exit(__FILE__,__LINE__,__FUNCTION__); } while(strstr(s,"begin") == NULL); if(fgets(s,T_MAX_LINE,fp) == NULL) Generic_Exit(__FILE__,__LINE__,__FUNCTION__); tk = strtok(s,delim); n_otu = strtod(tk,NULL); tk = strtok(NULL,delim); n_dim = strtod(tk,NULL); root_ldsk = NULL; new_ldsk = NULL; while(fgets(s,T_MAX_LINE,fp) != NULL) { tk = strtok(s,delim); if(strcmp(tk,"end") && tk != NULL) { disk = PHYREX_Make_Disk_Event(n_dim,n_otu); strcpy(disk->id,tk); tk = strtok(NULL,delim); disk->time = strtold(tk,NULL); tk = strtok(NULL,delim); disk->centr->lonlat[0] = strtold(tk,NULL); tk = strtok(NULL,delim); disk->centr->lonlat[1] = strtold(tk,NULL); tk = strtok(NULL,delim); if(tk[0] == '*') // disk has ldsk on it { if(root_ldsk == NULL) { disk->ldsk = PHYREX_Make_Lindisk_Node(n_dim); disk->ldsk->disk = disk; root_ldsk = disk->ldsk; } else { disk->ldsk = PHYREX_Find_Ldsk_From_Id(tk+1,root_ldsk); assert(disk->ldsk != NULL); } strcpy(disk->ldsk->coord->id,tk+1); tk = strtok(NULL,delim); disk->ldsk->coord->lonlat[0] = strtold(tk,NULL); tk = strtok(NULL,delim); disk->ldsk->coord->lonlat[1] = strtold(tk,NULL); do { tk = strtok(NULL,delim); if(tk == NULL) break; new_ldsk = PHYREX_Make_Lindisk_Node(n_dim); strcpy(new_ldsk->coord->id,tk); PHYREX_Make_Lindisk_Next(disk->ldsk); disk->ldsk->next[disk->ldsk->n_next-1] = new_ldsk; disk->ldsk_a[disk->ldsk->n_next-1] = new_ldsk; new_ldsk->prev = disk->ldsk; } while(tk); } else { idx = 0; do { tk = strtok(NULL,delim); if(tk == NULL) break; ldsk = PHYREX_Find_Ldsk_From_Id(tk,root_ldsk); assert(ldsk); disk->ldsk_a[idx] = ldsk; idx++; } while(tk); } } } Free(s); } #endif /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void Output_Scalar_Dbl(scalar_dbl *t, char *sep, FILE *fp) { scalar_dbl *l; l = t; do { PhyML_Fprintf(fp,"%g%s",l->v,sep); l = l->next; } while(l); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ // s should look like that: "xxx={yyy},xxxx={yy},..." t_label *Read_Labels(char *s) { t_label *lab,*init_lab; char *s_cpy,*s_big,*s_small; char *label; char *key,*val; if(s[0] != '[' || s[(int)strlen(s)-1] != ']') { PhyML_Fprintf(stderr,"\n. Label is in wrong format. A proper label should"); PhyML_Fprintf(stderr,"\n. look as follows: \"[xxx={yyy},xxxx={yy},...]\""); assert(FALSE); } lab = Make_Label(); init_lab = lab; s_cpy = (char *)mCalloc((int)strlen(s)-1,sizeof(char)); strncpy(s_cpy,s+1,(strlen(s)-2)*sizeof(char)); s_cpy[strlen(s)-2]='\0'; label = strtok_r(s_cpy,",",&s_big); while(label != NULL) { key=strtok_r(label,"=",&s_small); val=strtok_r(NULL,"=",&s_small); Free(lab->key); lab->key = (char *)mCalloc(strlen(key)+1,sizeof(char)); strcpy(lab->key,key); Free(lab->val); lab->val = (char *)mCalloc(strlen(val)+1,sizeof(char)); strcpy(lab->val,val); label = strtok_r(NULL,",",&s_big); if(label != NULL) { lab->sep=','; lab->next = Make_Label(); lab = lab->next; } } lab = init_lab; return(lab); } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ void Print_Labels(FILE *fp_where, char *s_where, t_label *label) { if(label == NULL) return; else { t_label *lab; lab = label; if(fp_where != NULL) { PhyML_Fprintf(fp_where,"["); } else { sprintf(s_where+(int)strlen(s_where),"["); } while(lab) { if(fp_where != NULL) { PhyML_Fprintf(fp_where,"%s=%s",lab->key,lab->val); if(lab->next != NULL) PhyML_Fprintf(fp_where,","); } else { sprintf(s_where+(int)strlen(s_where),"%s=%s",lab->key,lab->val); if(lab->next != NULL) sprintf(s_where+(int)strlen(s_where),","); } lab = lab->next; } if(fp_where != NULL) { PhyML_Fprintf(fp_where,"]"); } else { sprintf(s_where+(int)strlen(s_where),"]"); } } } /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ #if (defined PHYREX) void PHYREX_Print_MultiTypeTree_Config_File(int n_sites, char *filename, t_tree *tree) { int i, j, n_demes; char *s,**deme_names; FILE *fp; fp = Openfile(filename,WRITE); assert(fp); deme_names = (char **)mCalloc(n_sites,sizeof(char *)); n_demes = 0; for(i=0;in_otu;i++) { s = strrchr(tree->a_nodes[i]->ldsk->coord->id,'_'); for(j=0;j"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); for(i=0;in_otu;i++) { PhyML_Fprintf(fp,"\n", tree->a_nodes[i]->ldsk->coord->id, /* tree->a_nodes[i]->coord->id, */ tree->a_nodes[i]->name, tree->a_nodes[i]->c_seq->state); } PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\nbeast.math.distributions.Uniform"); PhyML_Fprintf(fp,"\nbeast.math.distributions.Exponential"); PhyML_Fprintf(fp,"\nbeast.math.distributions.LogNormalDistributionModel"); PhyML_Fprintf(fp,"\nbeast.math.distributions.Normal"); PhyML_Fprintf(fp,"\nbeast.math.distributions.Beta"); PhyML_Fprintf(fp,"\nbeast.math.distributions.Gamma"); PhyML_Fprintf(fp,"\nbeast.math.distributions.LaplaceDistribution"); PhyML_Fprintf(fp,"\nbeast.math.distributions.Prior"); PhyML_Fprintf(fp,"\nbeast.math.distributions.InverseGamma"); PhyML_Fprintf(fp,"\nbeast.math.distributions.OneOnX"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); s = (char *)mCalloc(T_MAX_LINE,sizeof(char)); For(i,n_demes*(n_demes-1)) strcat(s,"1.0 "); PhyML_Fprintf(fp,"\n%s",n_demes*(n_demes-1),s); Free(s); s = (char *)mCalloc(T_MAX_LINE,sizeof(char)); for(i=0;i%s",n_demes,s); Free(s); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\nn_otu;i++) { s = strrchr(tree->a_nodes[i]->ldsk->coord->id,'_'); PhyML_Fprintf(fp,"%s=%s", tree->a_nodes[i]->ldsk->coord->id, s+1); if(i < tree->n_otu-1) PhyML_Fprintf(fp,","); else PhyML_Fprintf(fp,"\">"); } PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n2.0"); s = (char *)mCalloc(T_MAX_LINE,sizeof(char)); for(i=0;i%s",n_demes,s); Free(s); s = (char *)mCalloc(T_MAX_LINE,sizeof(char)); For(i,n_demes*(n_demes-1)) strcat(s,"1.0 "); PhyML_Fprintf(fp,"\n%s",n_demes*(n_demes-1),s); Free(s); PhyML_Fprintf(fp,"\n0.25"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n1.0"); PhyML_Fprintf(fp,"\n1.25"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n1.0"); PhyML_Fprintf(fp,"\n1.25"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n1.0"); PhyML_Fprintf(fp,"\n1.25"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n1.0"); PhyML_Fprintf(fp,"\n1.0"); PhyML_Fprintf(fp,"\n0.0"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n%G",1.0); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); /* PhyML_Fprintf(fp,"\n"); */ /* PhyML_Fprintf(fp,"\n"); */ /* PhyML_Fprintf(fp,"\n"); */ PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); PhyML_Fprintf(fp,"\n"); for(i=0;iio->fp_out_stats; if(tree->mcmc->run == 0) { if(XML_Search_Node_Attribute_Value("add","true",NO,tree->xml_root) == NULL) { PhyML_Fprintf(fp_stats,"\n# before rand glnL: %f alnL: %f",tree->mmod->c_lnL,tree->c_lnL); PhyML_Fprintf(fp_stats,"\n# ninter: %d",PHYREX_Total_Number_Of_Intervals(tree)); PhyML_Fprintf(fp_stats,"\n# ncoal: %d",PHYREX_Total_Number_Of_Coal_Disks(tree)); PhyML_Fprintf(fp_stats,"\n# nhits: %d",PHYREX_Total_Number_Of_Hit_Disks(tree)); PhyML_Fprintf(fp_stats,"\n# true lbda: %f",tree->mmod->lbda); PhyML_Fprintf(fp_stats,"\n# true mu: %f",tree->mmod->mu); PhyML_Fprintf(fp_stats,"\n# true rad: %f",SLFV_Update_Radius(tree)); PhyML_Fprintf(fp_stats,"\n# true sigsq: %f",tree->mmod->sigsq[0]); PhyML_Fprintf(fp_stats,"\n# true neigh. size: %f",SLFV_Neighborhood_Size(tree)); PhyML_Fprintf(fp_stats,"\n# fst-based estimate of neighborhood size: %f",SLFV_Neighborhood_Size_Regression(tree)); PhyML_Fprintf(fp_stats,"\n# nucleotide diversity: %f",Nucleotide_Diversity(tree->data)); PhyML_Fprintf(fp_stats,"\n# length of a generation: %G time units",SLFV_Generation_Length(tree)); PhyML_Fprintf(fp_stats,"\n# clock rate: %G subst. per time unit",tree->rates->clock_r); PhyML_Fprintf(fp_stats,"\n# after rand glnL: %f alnL: %f",tree->mmod->c_lnL,tree->c_lnL); PhyML_Fprintf(fp_stats,"\n# ninter: %d",PHYREX_Total_Number_Of_Intervals(tree)); PhyML_Fprintf(fp_stats,"\n# ncoal: %d",PHYREX_Total_Number_Of_Coal_Disks(tree)); PhyML_Fprintf(fp_stats,"\n# nhits: %d",PHYREX_Total_Number_Of_Hit_Disks(tree)); PhyML_Fprintf(fp_stats,"\n# start lbda: %f",tree->mmod->lbda); PhyML_Fprintf(fp_stats,"\n# start mu: %f",tree->mmod->mu); PhyML_Fprintf(fp_stats,"\n# start rad: %f",tree->mmod->rad); PhyML_Fprintf(fp_stats,"\n# dist. in tree: "); for(int i=0;in_otu-1;++i) for(int j=i+1;jn_otu;++j) PhyML_Fprintf(fp_stats,"%G ",PHYREX_Dist_Between_Two_Ldsk(tree->a_nodes[i]->ldsk,tree->a_nodes[j]->ldsk,tree)); PhyML_Fprintf(fp_stats,"\n# dist. in space: "); for(int i=0;in_otu-1;++i) for(int j=i+1;jn_otu;++j) PhyML_Fprintf(fp_stats,"%G ",Euclidean_Dist(tree->a_nodes[i]->ldsk->coord,tree->a_nodes[j]->ldsk->coord)); PhyML_Fprintf(fp_stats,"\n"); PhyML_Fprintf(fp_stats,"%s\t","sample"); PhyML_Fprintf(fp_stats,"%s\t","lnPost"); PhyML_Fprintf(fp_stats,"%s\t","lnAlgn"); PhyML_Fprintf(fp_stats,"%s\t","lnSpac"); PhyML_Fprintf(fp_stats,"%s\t","lnRate"); /* PhyML_Fprintf(fp_stats,"%s\t","lnRateAutoCor"); */ /* PhyML_Fprintf(fp_stats,"%s\t","lnRateClock"); */ /* PhyML_Fprintf(fp_stats,"%s\t","lnRateRoot1"); */ /* PhyML_Fprintf(fp_stats,"%s\t","lnRateRoot2"); */ /* PhyML_Fprintf(fp_stats,"%s\t","lnRater1"); */ /* PhyML_Fprintf(fp_stats,"%s\t","lnRater2"); */ PhyML_Fprintf(fp_stats,"%s\t","lnTime"); PhyML_Fprintf(fp_stats,"%s\t","substRate"); for(int i=0;immod->n_dim;++i) PhyML_Fprintf(fp_stats,"%s%s\t","sigSq",(i==0)?("Lon"):((i==1)?("Lat"):("xx"))); PhyML_Fprintf(fp_stats,"%s\t","nEff"); PhyML_Fprintf(fp_stats,"%s\t","growth"); PhyML_Fprintf(fp_stats,"%s\t","dispDist"); PhyML_Fprintf(fp_stats,"%s\t","dispDistAlt"); PhyML_Fprintf(fp_stats,"%s\t","rootTime"); PhyML_Fprintf(fp_stats,"%s\t","rootLon"); PhyML_Fprintf(fp_stats,"%s\t","rootLat"); /* for(int i=0;imod->kappa);++i) PhyML_Fprintf(fp_stats,"tstv%d\t",i); */ /* if(tree->mod->ras->free_mixt_rates == NO) PhyML_Fprintf(fp_stats,"alpha\t"); */ /* else */ /* { */ /* for(int i=0;imod->ras->n_catg;++i) PhyML_Fprintf(fp_stats,"p(%d)\t",i+1); */ /* for(int i=0;imod->ras->n_catg;++i) PhyML_Fprintf(fp_stats,"rr(%d)\t",i+1); */ /* } */ PhyML_Fprintf(fp_stats,"%s\t","nu"); PhyML_Fprintf(fp_stats,"%s\t","rrNormFact"); PhyML_Fprintf(fp_stats,"%s\t","rrwNormFact"); /* PhyML_Fprintf(fp_stats,"%s\t","treeLen"); */ /* PhyML_Fprintf(fp_stats,"%s\t","speed"); */ /* PhyML_Fprintf(fp_stats,"%s\t","accSPR"); */ /* PhyML_Fprintf(fp_stats,"%s\t","accSPRsLide"); */ /* PhyML_Fprintf(fp_stats,"%s\t","accNarrowExchange"); */ /* PhyML_Fprintf(fp_stats,"%s\t","accWideExchange"); */ /* PhyML_Fprintf(fp_stats,"%s\t","accLdskGivenDisk"); */ /* PhyML_Fprintf(fp_stats,"%s\t","accMoveRootTime"); */ /* PhyML_Fprintf(fp_stats,"%s\t","accLdskTipToRoot"); */ /* PhyML_Fprintf(fp_stats,"%s\t","tuneScale"); */ /* PhyML_Fprintf(fp_stats,"%s\t","tuneLdskGivenDisk"); */ /* PhyML_Fprintf(fp_stats,"%s\t","tuneLdskTipToRoot"); */ /* PhyML_Fprintf(fp_stats,"%s\t","tuneRootTime"); */ /* for(int i=0;i<1;++i) */ /* { */ /* PhyML_Fprintf(fp_stats,"aux%dLocLnL\t",i); */ /* PhyML_Fprintf(fp_stats,"aux%dTimeLnL\t",i); */ /* } */ /* PhyML_Fprintf(fp_stats,"aux0RootTime\t"); */ /* PhyML_Fprintf(fp_stats,"%s\t","auxaccSPR"); */ /* PhyML_Fprintf(fp_stats,"%s\t","auxaccSPRsLide"); */ /* PhyML_Fprintf(fp_stats,"%s\t","auxaccNarrowExchange"); */ /* PhyML_Fprintf(fp_stats,"%s\t","auxaccWideExchange"); */ /* PhyML_Fprintf(fp_stats,"%s\t","auxaccLdskGivenDisk"); */ /* PhyML_Fprintf(fp_stats,"%s\t","auxaccMoveRootTime"); */ /* PhyML_Fprintf(fp_stats,"%s\t","auxaccLdskTipToRoot"); */ /* PhyML_Fprintf(fp_stats,"%s\t","auxtuneScale"); */ /* PhyML_Fprintf(fp_stats,"%s\t","auxtuneLdskGivenDisk"); */ /* PhyML_Fprintf(fp_stats,"%s\t","auxtuneLdskTipToRoot"); */ /* PhyML_Fprintf(fp_stats,"%s\t","auxtuneRootTime"); */ /* for(int i=0;i<2*tree->n_otu-1;++i) PhyML_Fprintf(fp_stats,"sigSq%d\t",i); */ /* for(int i=0;i<2*tree->n_otu-1;++i) PhyML_Fprintf(fp_stats,"auxsigSq%d\t",i); */ PhyML_Fprintf(fp_stats,"sigSqSon1\t"); PhyML_Fprintf(fp_stats,"sigSqSon2\t"); PhyML_Fprintf(fp_stats,"rrSon1\t"); PhyML_Fprintf(fp_stats,"rrSon2\t"); } } if(!(tree->mcmc->run%tree->mcmc->sample_interval) && tree->mcmc->sample_interval > 0) { /* Lk(NULL,tree); */ /* TIMES_Lk(tree); */ /* RATES_Lk(tree); */ /* LOCATION_Lk(tree); */ time(&(tree->mcmc->time_end)); PhyML_Fprintf(fp_stats,"\n"); PhyML_Fprintf(fp_stats,"%6d\t",tree->mcmc->run); PhyML_Fprintf(fp_stats,"%.2f\t",PHYREX_Get_Posterior(tree)); PhyML_Fprintf(fp_stats,"%.2f\t",tree->c_lnL); PhyML_Fprintf(fp_stats,"%.2f\t",tree->mmod->c_lnL); PhyML_Fprintf(fp_stats,"%.2f\t",tree->rates->c_lnL); /* PhyML_Fprintf(fp_stats,"%f\t",RATES_Autocor_Prior(tree)); */ /* PhyML_Fprintf(fp_stats,"%f\t",RATES_Clock_R_Prior(tree)); */ /* PhyML_Fprintf(fp_stats,"%f\t",RATES_Lk_Core(-1.,tree->rates->br_r[tree->n_root->v[1]->num],-1,-1,-1,-1,-1,tree->times->nd_t[tree->n_root->v[1]->num] - tree->times->nd_t[tree->n_root->num],tree)); */ /* PhyML_Fprintf(fp_stats,"%f\t",RATES_Lk_Core(-1.,tree->rates->br_r[tree->n_root->v[2]->num],-1,-1,-1,-1,-1,tree->times->nd_t[tree->n_root->v[2]->num] - tree->times->nd_t[tree->n_root->num],tree)); */ /* PhyML_Fprintf(fp_stats,"%f\t",tree->times->nd_t[tree->n_root->v[1]->num]); */ /* PhyML_Fprintf(fp_stats,"%f\t",tree->times->nd_t[tree->n_root->v[2]->num]); */ PhyML_Fprintf(fp_stats,"%.2f\t",tree->times->c_lnL); PhyML_Fprintf(fp_stats,"%g\t",tree->rates->clock_r); for(int i=0;immod->n_dim;++i) PhyML_Fprintf(fp_stats,"%g\t",tree->mmod->sigsq[i]); PhyML_Fprintf(fp_stats,"%g\t",tree->times->scaled_pop_size); PhyML_Fprintf(fp_stats,"%g\t",tree->times->neff_growth); PhyML_Fprintf(fp_stats,"%g\t",PHYREX_Realized_Dispersal_Dist(tree)); PhyML_Fprintf(fp_stats,"%g\t",PHYREX_Realized_Dispersal_Dist_Alt(tree)); PhyML_Fprintf(fp_stats,"%.2f\t",tree->n_root->ldsk->disk->time); if(RRW_Is_Rw(tree->mmod) == YES && tree->mmod->integrateAncestralLocations == YES) RRW_Sample_Node_Location(tree->n_root->ldsk,tree); PhyML_Fprintf(fp_stats,"%g\t",tree->n_root->ldsk->coord->lonlat[0]); PhyML_Fprintf(fp_stats,"%g\t",tree->n_root->ldsk->coord->lonlat[1]); /* Output_Scalar_Dbl(tree->mod->kappa,"\t",fp_stats); */ /* if(tree->mod->ras->free_mixt_rates == NO) PhyML_Fprintf(fp_stats,"%g\t",tree->mod->ras->alpha->v); */ /* else */ /* { */ /* for(int i=0;imod->ras->n_catg;++i) PhyML_Fprintf(fp_stats,"%g\t",tree->mod->ras->gamma_r_proba->v[i]); */ /* for(int i=0;imod->ras->n_catg;++i) PhyML_Fprintf(fp_stats,"%g\t",tree->mod->ras->gamma_rr->v[i]); */ /* } */ PhyML_Fprintf(fp_stats,"%g\t",tree->rates->nu); PhyML_Fprintf(fp_stats,"%g\t",tree->rates->norm_fact); PhyML_Fprintf(fp_stats,"%g\t",tree->mmod->rrw_norm_fact); /* PhyML_Fprintf(fp_stats,"%g\t",Tree_Length(tree)); */ /* PhyML_Fprintf(fp_stats,"%g\t",difftime(tree->mcmc->time_end,tree->mcmc->time_beg)/(phydbl)tree->mcmc->sample_interval); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->mcmc->acc_rate[tree->mcmc->num_move_phyrex_spr]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->mcmc->acc_rate[tree->mcmc->num_move_phyrex_spr_slide]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->mcmc->acc_rate[tree->mcmc->num_move_phyrex_narrow_exchange]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->mcmc->acc_rate[tree->mcmc->num_move_phyrex_wide_exchange]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->mcmc->acc_rate[tree->mcmc->num_move_phyrex_ldsk_given_disk]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->mcmc->acc_rate[tree->mcmc->num_move_root_time]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->mcmc->acc_rate[tree->mcmc->num_move_phyrex_ldsk_tip_to_root]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->mcmc->tune_move[tree->mcmc->num_move_phyrex_scale_times]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->mcmc->tune_move[tree->mcmc->num_move_phyrex_ldsk_given_disk]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->mcmc->tune_move[tree->mcmc->num_move_phyrex_ldsk_tip_to_root]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->mcmc->tune_move[tree->mcmc->num_move_root_time]); */ /* for(int i=0;i<1;++i) */ /* { */ /* PhyML_Fprintf(fp_stats,"%.2f\t",tree->aux_tree[i]->mmod->c_lnL); */ /* PhyML_Fprintf(fp_stats,"%.2f\t",tree->aux_tree[i]->times->c_lnL); */ /* } */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->aux_tree[0]->times->nd_t[tree->aux_tree[0]->n_root->num]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->aux_tree[0]->mcmc->acc_rate[tree->aux_tree[0]->mcmc->num_move_phyrex_spr]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->aux_tree[0]->mcmc->acc_rate[tree->aux_tree[0]->mcmc->num_move_phyrex_spr_slide]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->aux_tree[0]->mcmc->acc_rate[tree->aux_tree[0]->mcmc->num_move_phyrex_narrow_exchange]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->aux_tree[0]->mcmc->acc_rate[tree->aux_tree[0]->mcmc->num_move_phyrex_wide_exchange]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->aux_tree[0]->mcmc->acc_rate[tree->aux_tree[0]->mcmc->num_move_phyrex_ldsk_given_disk]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->aux_tree[0]->mcmc->acc_rate[tree->aux_tree[0]->mcmc->num_move_root_time]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->aux_tree[0]->mcmc->acc_rate[tree->aux_tree[0]->mcmc->num_move_phyrex_ldsk_tip_to_root]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->aux_tree[0]->mcmc->tune_move[tree->aux_tree[0]->mcmc->num_move_phyrex_scale_times]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->aux_tree[0]->mcmc->tune_move[tree->aux_tree[0]->mcmc->num_move_phyrex_ldsk_given_disk]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->aux_tree[0]->mcmc->tune_move[tree->aux_tree[0]->mcmc->num_move_phyrex_ldsk_tip_to_root]); */ /* PhyML_Fprintf(fp_stats,"%g\t",tree->aux_tree[0]->mcmc->tune_move[tree->aux_tree[0]->mcmc->num_move_root_time]); */ /* for(int i=0;i<2*tree->n_otu-1;++i) PhyML_Fprintf(fp_stats,"%g\t",tree->mmod->sigsq_scale[i]); */ /* for(int i=0;i<2*tree->n_otu-1;++i) PhyML_Fprintf(fp_stats,"%g\t",tree->aux_tree[0]->mmod->sigsq_scale[i]); */ PhyML_Fprintf(fp_stats,"%f\t",tree->mmod->sigsq_scale[tree->n_root->v[1]->num]); PhyML_Fprintf(fp_stats,"%f\t",tree->mmod->sigsq_scale[tree->n_root->v[2]->num]); PhyML_Fprintf(fp_stats,"%f\t",tree->rates->br_r[tree->n_root->v[1]->num]); PhyML_Fprintf(fp_stats,"%f\t",tree->rates->br_r[tree->n_root->v[2]->num]); fflush(NULL); time(&(tree->mcmc->time_beg)); tree->mcmc->sample_num++; } } #endif /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ #if (defined PHYREX) void PHYREX_Print_MCMC_Tree(t_tree *tree) { FILE *fp_tree; fp_tree = tree->io->fp_out_tree; if(tree->mcmc->run == 0) { PhyML_Fprintf(fp_tree,"#NEXUS"); PhyML_Fprintf(fp_tree,"\n\nBegin taxa;"); PhyML_Fprintf(fp_tree,"\n\tDimensions ntax=%d;",tree->n_otu); PhyML_Fprintf(fp_tree,"\n\tTaxlabels"); for(int i=0;in_otu;++i) { PhyML_Fprintf(fp_tree,"\n\t\t'%s'",tree->a_nodes[i]->name); } PhyML_Fprintf(fp_tree,"\n\t;"); PhyML_Fprintf(fp_tree,"\n\tend;"); PhyML_Fprintf(fp_tree,"\n\n"); PhyML_Fprintf(fp_tree,"\nBegin trees;"); PhyML_Fprintf(fp_tree,"\n\tTranslate"); for(int i=0;in_otu;++i) { PhyML_Fprintf(fp_tree,"\n\t%d '%s'",i+1,tree->a_nodes[i]->name); if(in_otu-1) PhyML_Fprintf(fp_tree,","); } PhyML_Fprintf(fp_tree,"\n;"); } if(!(tree->mcmc->run%tree->mcmc->sample_interval) && tree->mcmc->sample_interval > 0) { Record_Br_Len(tree); TIMES_Time_To_Bl(tree); tree->bl_ndigits = 3; /* tree->bl_ndigits = 7; */ tree->write_tax_names = NO; PHYREX_Label_Nodes_With_Locations(tree); PHYREX_Label_Edges(tree); char *s = Write_Tree(tree); PhyML_Fprintf(fp_tree,"\ntree %d [&lnP=%f,precision={1.e-1,1e-02,1e-01}] = [&R] %s",tree->mcmc->run,tree->c_lnL,s); PhyML_Fprintf(fp_tree,"\nend;"); fseek(fp_tree,-5,SEEK_END); tree->write_tax_names = YES; Free(s); Restore_Br_Len(tree); fflush(NULL); } } #endif /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ #if (defined PHYREX) void PHYREX_Print_MCMC_Summary(t_tree *tree) { if(tree->mcmc->run == 0) PhyML_Fprintf(stdout,"\n. %23s\t%25s\t%15s\t%15s\t%15s\t%15s\t%13s\t%10s\t%19s", "run (aux.run)", "operator", "lnSpac", "lnTime", "lnAlgn", "lnPost", "rootTime", "substRate", "(rootLon;rootLat)"); if(!(tree->mcmc->run%tree->mcmc->print_every) && tree->mcmc->print_every > 0) { if(RRW_Is_Rw(tree->mmod) == YES && tree->mmod->integrateAncestralLocations == YES) RRW_Sample_Node_Location(tree->n_root->ldsk,tree); PhyML_Fprintf(stdout,"\n. %10d (%10d)\t%25s\t%15.2f\t%15.2f\t%15.2f\t%15.2f\t%13.1f\t%10f\t(%8.2f;%8.2f)", tree->mcmc->run, tree->aux_tree[0]->mcmc->run, tree->mcmc->move_idx > -1 ? tree->mcmc->move_name[tree->mcmc->move_idx] : "", tree->mmod->c_lnL, tree->times->c_lnL, tree->c_lnL, PHYREX_Get_Posterior(tree), tree->times->nd_t[tree->n_root->num], tree->rates->clock_r, tree->n_root->ldsk->coord->lonlat[0], tree->n_root->ldsk->coord->lonlat[1]); if(tree->numerical_warning == YES) PhyML_Fprintf(stdout," -- WARNING: numerical precision issue detected..."); } } #endif /*//////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////*/ phyml-3.3.20220408/src/io.h000066400000000000000000000131451422404706600147700ustar00rootroot00000000000000/* PHYML : a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences Copyright (C) Stephane Guindon. Oct 2003 onward All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include #ifndef IO_H #define IO_H #include "utilities.h" t_tree *Read_Tree(char **s_tree); void R_rtree(char *s_tree_a,char *s_tree_d,t_node *a,t_tree *tree,int *n_int,int *n_ext); void Read_Node_Label(char *s_d, char *s_a, t_node *n); void Read_Branch_Length(char *s_d, char *s_a, t_edge *b, t_tree *tree); void Read_Node_Name(t_node *d, char *s_tree_d, t_edge *b, t_tree *tree); void Clean_Multifurcation(char **subtrees,int current_deg,int end_deg); char **Sub_Trees(char *tree,int *degree); int Next_Par(char *s,int pos); void Print_List(t_ll *list); void Print_Tree(FILE *fp,t_tree *tree); char *Write_Tree(t_tree *tree); void R_wtree_Custom(t_node *pere,t_node *fils,int *available,char **s_tree,int *pos,t_tree *tree); void Detect_Align_File_Format(option *io); void Detect_Tree_File_Format(option *io); align **Get_Seq(option *io); void Get_Nexus_Data(FILE *fp,option *io); int Get_Token(FILE *fp,char *token); align **Get_Seq_Phylip(option *io); void Read_Ntax_Len_Phylip(FILE *fp,int *n_otu,int *n_tax); align **Read_Seq_Sequential(option *io); align **Read_Seq_Interleaved(option *io); int Read_One_Line_Seq(align ***data,int num_otu,FILE *in); char *Return_Tree_String_Phylip(FILE *fp_input_tree); t_tree *Read_Tree_File_Phylip(FILE *fp_input_tree); void Print_Site_Lk(t_tree *tree,FILE *fp); void Print_Seq(FILE *fp, align **data, int n_otu); void Print_CSeq(FILE *fp,int compressed,calign *cdata,t_tree *tree); void Print_CSeq_Select(FILE *fp,int compressed,calign *cdata,t_tree *tree); void Print_Dist(matrix *mat); void Print_Node(t_node *a,t_node *d,t_tree *tree); void Print_Model(t_mod *mod); void Print_Mat(matrix *mat); FILE *Openfile(char *filename,int mode); //void Print_Fp_Out(FILE *fp_out,time_t t_beg,time_t t_end,t_tree *tree,option *io,int n_data_set,int num_tree, int add_citation); void Print_Fp_Out(FILE *fp_out,time_t t_beg,time_t t_end,t_tree *tree,option *io,int n_data_set,int num_tree, int add_citation, int precision); void Print_Fp_Out_Lines(FILE *fp_out,time_t t_beg,time_t t_end,t_tree *tree,option *io,int n_data_set); void Print_Freq(t_tree *tree); void Print_Settings(option *io); void Print_Banner(FILE *fp); void Print_Banner_Small(FILE *fp); void Print_Data_Set_Number(option *io,FILE *fp); void Print_Lk(t_tree *tree,char *string); void Print_Pars(t_tree *tree); void Print_Lk_And_Pars(t_tree *tree); void Read_Qmat(phydbl *daa,phydbl *pi,FILE *fp); void Print_Qmat_AA(phydbl *daa,phydbl *pi); void Print_Square_Matrix_Generic(int n,phydbl *mat); void Print_Diversity(FILE *fp,t_tree *tree); void Print_Diversity_Pre(t_node *a,t_node *d,t_edge *b,FILE *fp,t_tree *tree); t_tree *Read_User_Tree(calign *cdata,t_mod *mod,option *io); void Print_Time_Info(time_t t_beg,time_t t_end); void Print_Time_Info_Brief(time_t t_beg,time_t t_end); void PhyML_Printf(char *format,...); void PhyML_Fprintf(FILE *fp,char *format,...); void Read_Clade_Priors(char *file_name,t_tree *tree); option *Get_Input(int argc,char **argv); void Print_Data_Structure(int final, FILE *fp, t_tree *root); int Set_Whichmodel(int select); void Print_Site(calign *cdata, int num, int n_otu, char *sep, int stepsize, FILE *fp); option *PhyML_XML(char *xml_filename); void Check_Taxa_Sets(t_tree *mixt_tree); void Make_Ratematrix_From_XML_Node(xml_node *instance, option *io, t_mod *mod); void Make_Efrq_From_XML_Node(xml_node *instance, option *io, t_mod *mod); void Make_Topology_From_XML_Node(xml_node *instance, option *io, t_mod *mod); void Make_RAS_From_XML_Node(xml_node *parent, t_mod *mod); void Post_Process_Data(option *io); int *Return_Int(int in); void Print_All_Edge_PMats(t_tree* tree); void Print_All_Edge_Likelihoods(t_tree* tree); void Print_Edge_Likelihoods(t_tree* tree, t_edge* b, bool scientific); void Print_Edge_PMats(t_tree* tree, t_edge* b); void Print_Tip_Partials(t_tree* tree, t_node* d); void Dump_Arr_D(phydbl* arr, int num); void Dump_Arr_S(short int* arr, int num); void Dump_Arr_I(int* arr, int num); void Print_Tree_Structure(t_tree* tree); void Print_Node_Brief(t_node *a, t_node *d, t_tree *tree, FILE *fp); void Generic_Exit(const char *file, int line, const char *function); void JSON_Write_Object(json_o *obj, FILE *where); void JSON_Write_Array(json_a *array, FILE *where); void JSON_Write_All(json_a *array, FILE *where); void JSON_Tree_Io(t_tree *tree, FILE *where); json_o *JSON_Tree_To_Object(t_tree *tree); json_o *JSON_Tree_To_Object_Light(t_tree *mixt_tree); scalar_dbl *Read_Io_Weights(option *io); void Print_Lk_Given_Edge_Recurr(t_node *a, t_node *d, t_edge *b, t_tree *tree); void R_wtree(t_node *pere, t_node *fils, t_edge *b, int *available, char **s_tree, t_tree *tree); void Read_Branch_Support(char *s_d, char *s_a, t_edge *b, t_tree *tree); void Collect_Edge_Support_Values(t_tree *tree); int PhyML_Fscanf(FILE *fp, char *format, ...); void Output_Scalar_Dbl(scalar_dbl *t, char *sep, FILE *fp); t_label *Read_Labels(char *s); void Print_Labels(FILE *fp_where, char *s_where, t_label *label); #ifdef PHYREX void PHYREX_Print_MultiTypeTree_Config_File(int n_sites, char *filename, t_tree *tree); void PHYREX_Print_MCMC_Tree(t_tree *tree); void PHYREX_Print_MCMC_Stats(t_tree *tree); void PHYREX_Print_MCMC_Summary(t_tree *tree); void PHYREX_Output_Tree_Structure(FILE *fp, t_tree *tree); void PHYREX_Input_Tree_Structure(FILE *fp); char *PHYREX_Print_Tree_Structure(t_tree *tree); void PHYREX_Check_Point(FILE *fp, t_tree *tree); #endif #endif phyml-3.3.20220408/src/lk.c000066400000000000000000003163031422404706600147640ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include "assert.h" #include "lk.h" #ifdef BEAGLE #include "beagle_utils.h" #endif #include ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Tips_At_One_Site_Nucleotides_Float(char state, int pos, phydbl *p_lk) { switch(state) { case 'A' : p_lk[pos+0]=1.; p_lk[pos+1]=p_lk[pos+2]=p_lk[pos+3]=.0; break; case 'C' : p_lk[pos+1]=1.; p_lk[pos+0]=p_lk[pos+2]=p_lk[pos+3]=.0; break; case 'G' : p_lk[pos+2]=1.; p_lk[pos+1]=p_lk[pos+0]=p_lk[pos+3]=.0; break; case 'T' : p_lk[pos+3]=1.; p_lk[pos+1]=p_lk[pos+2]=p_lk[pos+0]=.0; break; case 'U' : p_lk[pos+3]=1.; p_lk[pos+1]=p_lk[pos+2]=p_lk[pos+0]=.0; break; case 'M' : p_lk[pos+0]=p_lk[pos+1]=1.; p_lk[pos+2]=p_lk[pos+3]=.0; break; case 'R' : p_lk[pos+0]=p_lk[pos+2]=1.; p_lk[pos+1]=p_lk[pos+3]=.0; break; case 'W' : p_lk[pos+0]=p_lk[pos+3]=1.; p_lk[pos+1]=p_lk[pos+2]=.0; break; case 'S' : p_lk[pos+1]=p_lk[pos+2]=1.; p_lk[pos+0]=p_lk[pos+3]=.0; break; case 'Y' : p_lk[pos+1]=p_lk[pos+3]=1.; p_lk[pos+0]=p_lk[pos+2]=.0; break; case 'K' : p_lk[pos+2]=p_lk[pos+3]=1.; p_lk[pos+0]=p_lk[pos+1]=.0; break; case 'B' : p_lk[pos+1]=p_lk[pos+2]=p_lk[pos+3]=1.; p_lk[pos+0]=.0; break; case 'D' : p_lk[pos+0]=p_lk[pos+2]=p_lk[pos+3]=1.; p_lk[pos+1]=.0; break; case 'H' : p_lk[pos+0]=p_lk[pos+1]=p_lk[pos+3]=1.; p_lk[pos+2]=.0; break; case 'V' : p_lk[pos+0]=p_lk[pos+1]=p_lk[pos+2]=1.; p_lk[pos+3]=.0; break; case 'N' : case 'X' : case '?' : case 'O' : case '-' : p_lk[pos+0]=p_lk[pos+1]=p_lk[pos+2]=p_lk[pos+3]=1.;break; default : { PhyML_Fprintf(stderr,"\n. Unknown character state : '%c'.\n",state); Exit("\n. Init failed (data type supposed to be DNA)\n"); break; } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Tips_At_One_Site_Nucleotides_Int(char state, int pos, short int *p_pars) { switch(state) { case 'A' : p_pars[pos+0]=1; p_pars[pos+1]=p_pars[pos+2]=p_pars[pos+3]=0; break; case 'C' : p_pars[pos+1]=1; p_pars[pos+0]=p_pars[pos+2]=p_pars[pos+3]=0; break; case 'G' : p_pars[pos+2]=1; p_pars[pos+1]=p_pars[pos+0]=p_pars[pos+3]=0; break; case 'T' : p_pars[pos+3]=1; p_pars[pos+1]=p_pars[pos+2]=p_pars[pos+0]=0; break; case 'U' : p_pars[pos+3]=1; p_pars[pos+1]=p_pars[pos+2]=p_pars[pos+0]=0; break; case 'M' : p_pars[pos+0]=p_pars[pos+1]=1; p_pars[pos+2]=p_pars[pos+3]=0; break; case 'R' : p_pars[pos+0]=p_pars[pos+2]=1; p_pars[pos+1]=p_pars[pos+3]=0; break; case 'W' : p_pars[pos+0]=p_pars[pos+3]=1; p_pars[pos+1]=p_pars[pos+2]=0; break; case 'S' : p_pars[pos+1]=p_pars[pos+2]=1; p_pars[pos+0]=p_pars[pos+3]=0; break; case 'Y' : p_pars[pos+1]=p_pars[pos+3]=1; p_pars[pos+0]=p_pars[pos+2]=0; break; case 'K' : p_pars[pos+2]=p_pars[pos+3]=1; p_pars[pos+0]=p_pars[pos+1]=0; break; case 'B' : p_pars[pos+1]=p_pars[pos+2]=p_pars[pos+3]=1; p_pars[pos+0]=0; break; case 'D' : p_pars[pos+0]=p_pars[pos+2]=p_pars[pos+3]=1; p_pars[pos+1]=0; break; case 'H' : p_pars[pos+0]=p_pars[pos+1]=p_pars[pos+3]=1; p_pars[pos+2]=0; break; case 'V' : p_pars[pos+0]=p_pars[pos+1]=p_pars[pos+2]=1; p_pars[pos+3]=0; break; case 'N' : case 'X' : case '?' : case 'O' : case '-' : p_pars[pos+0]=p_pars[pos+1]=p_pars[pos+2]=p_pars[pos+3]=1;break; default : { PhyML_Fprintf(stderr,"\n. Unknown character state : '%c'.\n",state); Exit("\n. Init failed (data type supposed to be DNA)\n"); break; } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Tips_At_One_Site_AA_Float(char aa, int pos, phydbl *p_lk) { int i; for(i=0;i<20;i++) p_lk[pos+i] = .0; switch(aa){ case 'A' : p_lk[pos+0]= 1.; break;/* Alanine */ case 'R' : p_lk[pos+1]= 1.; break;/* Arginine */ case 'N' : p_lk[pos+2]= 1.; break;/* Asparagine */ case 'D' : p_lk[pos+3]= 1.; break;/* Aspartic acid */ case 'C' : p_lk[pos+4]= 1.; break;/* Cysteine */ case 'Q' : p_lk[pos+5]= 1.; break;/* Glutamine */ case 'E' : p_lk[pos+6]= 1.; break;/* Glutamic acid */ case 'G' : p_lk[pos+7]= 1.; break;/* Glycine */ case 'H' : p_lk[pos+8]= 1.; break;/* Histidine */ case 'I' : p_lk[pos+9]= 1.; break;/* Isoleucine */ case 'L' : p_lk[pos+10]=1.; break;/* Leucine */ case 'K' : p_lk[pos+11]=1.; break;/* Lysine */ case 'M' : p_lk[pos+12]=1.; break;/* Methionine */ case 'F' : p_lk[pos+13]=1.; break;/* Phenylalanin */ case 'P' : p_lk[pos+14]=1.; break;/* Proline */ case 'S' : p_lk[pos+15]=1.; break;/* Serine */ case 'T' : p_lk[pos+16]=1.; break;/* Threonine */ case 'W' : p_lk[pos+17]=1.; break;/* Tryptophan */ case 'Y' : p_lk[pos+18]=1.; break;/* Tyrosine */ case 'V' : p_lk[pos+19]=1.; break;/* Valine */ case 'B' : p_lk[pos+2]= 1.; break;/* Asparagine */ case 'Z' : p_lk[pos+5]= 1.; break;/* Glutamine */ case 'X' : case '?' : case '-' : for(i=0;i<20;i++) p_lk[pos+i] = 1.; break; default : { PhyML_Fprintf(stderr,"\n. Unknown character state : '%c'.\n",aa); Exit("\n. Init failed (data type supposed to be amino-acids)\n"); break; } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Tips_At_One_Site_AA_Int(char aa, int pos, short int *p_pars) { int i; for(i=0;i<20;i++) p_pars[pos+i] = .0; switch(aa){ case 'A' : p_pars[pos+0] = 1; break;/* Alanine */ case 'R' : p_pars[pos+1] = 1; break;/* Arginine */ case 'N' : p_pars[pos+2] = 1; break;/* Asparagine */ case 'D' : p_pars[pos+3] = 1; break;/* Aspartic acid */ case 'C' : p_pars[pos+4] = 1; break;/* Cysteine */ case 'Q' : p_pars[pos+5] = 1; break;/* Glutamine */ case 'E' : p_pars[pos+6] = 1; break;/* Glutamic acid */ case 'G' : p_pars[pos+7] = 1; break;/* Glycine */ case 'H' : p_pars[pos+8] = 1; break;/* Histidine */ case 'I' : p_pars[pos+9] = 1; break;/* Isoleucine */ case 'L' : p_pars[pos+10] = 1; break;/* Leucine */ case 'K' : p_pars[pos+11] = 1; break;/* Lysine */ case 'M' : p_pars[pos+12] = 1; break;/* Methionine */ case 'F' : p_pars[pos+13] = 1; break;/* Phenylalanin */ case 'P' : p_pars[pos+14] = 1; break;/* Proline */ case 'S' : p_pars[pos+15] = 1; break;/* Serine */ case 'T' : p_pars[pos+16] = 1; break;/* Threonine */ case 'W' : p_pars[pos+17] = 1; break;/* Tryptophan */ case 'Y' : p_pars[pos+18] = 1; break;/* Tyrosine */ case 'V' : p_pars[pos+19] = 1; break;/* Valine */ case 'B' : p_pars[pos+2] = 1; break;/* Asparagine */ case 'Z' : p_pars[pos+5] = 1; break;/* Glutamine */ case 'X' : case '?' : case '-' : for(i=0;i<20;i++) p_pars[pos+i] = 1; break; default : { PhyML_Fprintf(stderr,"\n. Unknown character state : '%c'.\n",aa); Exit("\n. Init failed (data type supposed to be amino-acids)\n"); break; } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Tips_At_One_Site_Generic_Float(char *state, int ns, int state_len, int pos, phydbl *p_lk) { int i; int state_int; for(i=0;i ns) { PhyML_Fprintf(stderr,"\n. %s %d cstate: %.2s istate: %d state_len: %d.\n",__FILE__,__LINE__,state,state_int,state_len); PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d (function '%s') \n",__FILE__,__LINE__,__FUNCTION__); Warn_And_Exit(""); } p_lk[pos+state_int] = 1.; /* PhyML_Printf("\n. %s %d cstate: %.2s istate: %d state_len: %d ns: %d pos: %d",__FILE__,__LINE__,state,state_int,state_len,ns,pos); */ } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Tips_At_One_Site_Generic_Int(char *state, int ns, int state_len, int pos, short int *p_pars) { int i; int state_int; for(i=0;i ns) { PhyML_Fprintf(stderr,"\n. %s %d cstate: %.2s istate: %d state_len: %d.\n",__FILE__,__LINE__,state,state_int,state_len); PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d (function '%s') \n",__FILE__,__LINE__,__FUNCTION__); Warn_And_Exit(""); } p_pars[pos+state_int] = 1; /* PhyML_Printf("\n* %s %d cstate: %.2s istate: %d state_len: %d ns: %d pos: %d",__FILE__,__LINE__,state,state_int,state_len,ns,pos); */ } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Get_All_Partial_Lk_Scale(t_tree *tree, t_edge *b_fcus, t_node *a, t_node *d) { Update_Partial_Lk(tree,b_fcus,d); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Post_Order_Lk(t_node *a, t_node *d, t_tree *tree) { int i,dir; dir = -1; if(d->tax) return; else { if(tree->is_mixt_tree) { MIXT_Post_Order_Lk(a,d,tree); return; } if(tree->n_root != NULL) { for(i=0;i<3;++i) { if(d->v[i] != a && d->b[i] != tree->e_root) Post_Order_Lk(d,d->v[i],tree); else dir = i; } } else { for(i=0;i<3;i++) { if(d->v[i] != a) Post_Order_Lk(d,d->v[i],tree); else dir = i; } } if(dir < 0) { PhyML_Printf("\n. a->num: %d d->num: %d d->v[0]->num: %d d->v[1]->num: %d d->v[2]->num: %d d->b[0]->num: %d d->b[1]->num: %d d->b[2]->num: %d root ? %d e_root ? %d\n", a?a->num:-1, d?d->num:1, d->v[0]?d->v[0]->num:-1, d->v[1]?d->v[1]->num:-1, d->v[2]?d->v[2]->num:-1, d->b[0]?d->b[0]->num:-1, d->b[1]?d->b[1]->num:-1, d->b[2]?d->b[2]->num:-1, tree->n_root?tree->n_root->num:-1, tree->e_root?tree->e_root->num:-1); assert(FALSE); } /* PhyML_Printf("\n. a:%d [%d] d:%d dir:%d [%p %p] [%p %p %p] [%p %p]", */ /* a->num, */ /* a == tree->n_root, */ /* d->num, */ /* dir, */ /* d->b[dir], */ /* tree->e_root, */ /* d->b[0],d->b[1],d->b[2], */ /* tree->n_root->b[1],tree->n_root->b[2]); */ if(tree->ignore_root == NO && d->b[dir] == tree->e_root) { if(d == tree->n_root->v[1]) Update_Partial_Lk(tree,tree->n_root->b[1],d); else Update_Partial_Lk(tree,tree->n_root->b[2],d); } else { Update_Partial_Lk(tree,d->b[dir],d); } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Pre_Order_Lk(t_node *a, t_node *d, t_tree *tree) { int i; if(d->tax) return; else { if(tree->is_mixt_tree) { MIXT_Pre_Order_Lk(a,d,tree); return; } if(tree->n_root) { for(i=0;i<3;++i) { if(d->v[i] != a && d->b[i] != tree->e_root) { Update_Partial_Lk(tree,d->b[i],d); Pre_Order_Lk(d,d->v[i],tree); } } } else { for(i=0;i<3;++i) { if(d->v[i] != a) { Update_Partial_Lk(tree,d->b[i],d); Pre_Order_Lk(d,d->v[i],tree); } } } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Updates all partial likelihood vectors. Depending on whether // both_sides = YES or NO, only 'up' or 'up'&'down' partials will // be updates void Update_All_Partial_Lk(t_tree *tree) { if(tree->n_root) { if(tree->ignore_root == NO) { Post_Order_Lk(tree->n_root,tree->n_root->v[1],tree); Post_Order_Lk(tree->n_root,tree->n_root->v[2],tree); Update_Partial_Lk(tree,tree->n_root->b[1],tree->n_root); Update_Partial_Lk(tree,tree->n_root->b[2],tree->n_root); if(tree->both_sides == YES) { Pre_Order_Lk(tree->n_root,tree->n_root->v[2],tree); Pre_Order_Lk(tree->n_root,tree->n_root->v[1],tree); } } else { Post_Order_Lk(tree->e_root->rght,tree->e_root->left,tree); Post_Order_Lk(tree->e_root->left,tree->e_root->rght,tree); if(tree->both_sides == YES) { Pre_Order_Lk(tree->e_root->rght,tree->e_root->left,tree); Pre_Order_Lk(tree->e_root->left,tree->e_root->rght,tree); } } } else { Post_Order_Lk(tree->a_nodes[tree->tip_root],tree->a_nodes[tree->tip_root]->v[0],tree); if(tree->both_sides == YES) Pre_Order_Lk(tree->a_nodes[tree->tip_root],tree->a_nodes[tree->tip_root]->v[0],tree); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Lk(t_edge *b, t_tree *tree) { unsigned int br,catg,state,ambiguity_check,site; phydbl len,*expl,*dot_prod,*p_lk_left,*p_lk_rght; const unsigned int ns = tree->mod->ns; const unsigned int ncatg = tree->mod->ras->n_catg; const unsigned int npatterns = tree->n_pattern; const unsigned int nsncatg = ns * ncatg; tree->numerical_warning = NO; /* if(tree->eval_alnL == NO) return UNLIKELY; */ if(b == NULL && tree->mod->s_opt->curr_opt_free_rates == YES) { tree->mod->s_opt->curr_opt_free_rates = NO; Optimize_Free_Rate_Weights(tree,YES,YES); tree->mod->s_opt->curr_opt_free_rates = YES; } if(tree->is_mixt_tree == YES) { #ifdef BEAGLE Warn_And_Exit(TODO_BEAGLE); #endif MIXT_Lk(b,tree); return tree->c_lnL; } tree->old_lnL = tree->c_lnL; if(tree->rates && tree->io && tree->io->lk_approx == NORMAL) { #ifdef BEAGLE Warn_And_Exit(TODO_BEAGLE); #endif tree->c_lnL = Lk_Normal_Approx(tree); return tree->c_lnL; } expl = tree->expl; dot_prod = tree->dot_prod; if(b == NULL) { Update_Boundaries(tree->mod); Update_RAS(tree->mod); Update_Efrq(tree->mod); Update_Eigen(tree->mod); } if(tree->mod->s_opt->skip_tree_traversal == NO) { if(!b) //Update PMat for all edges { for(br=0;br<2*tree->n_otu-3;++br) { Update_PMat_At_Given_Edge(tree->a_edges[br],tree); } if(tree->n_root && tree->ignore_root == NO) { Update_PMat_At_Given_Edge(tree->n_root->b[1],tree); Update_PMat_At_Given_Edge(tree->n_root->b[2],tree); } } else //Update PMat for a specific edge { if(tree->use_eigen_lr == NO) { if(tree->n_root && (b == tree->n_root->b[1] || b == tree->n_root->b[2]) && tree->ignore_root == YES) { Update_PMat_At_Given_Edge(tree->e_root,tree); } else { Update_PMat_At_Given_Edge(b,tree); } } } if(!b) { if(tree->n_root != NULL) { if(tree->ignore_root == NO) { Post_Order_Lk(tree->n_root,tree->n_root->v[1],tree); Post_Order_Lk(tree->n_root,tree->n_root->v[2],tree); Update_Partial_Lk(tree,tree->n_root->b[1],tree->n_root); Update_Partial_Lk(tree,tree->n_root->b[2],tree->n_root); if(tree->both_sides == YES) { Pre_Order_Lk(tree->n_root,tree->n_root->v[2],tree); Pre_Order_Lk(tree->n_root,tree->n_root->v[1],tree); } } else { Post_Order_Lk(tree->e_root->rght,tree->e_root->left,tree); Post_Order_Lk(tree->e_root->left,tree->e_root->rght,tree); if(tree->both_sides == YES) { Pre_Order_Lk(tree->e_root->rght,tree->e_root->left,tree); Pre_Order_Lk(tree->e_root->left,tree->e_root->rght,tree); } } } else { Post_Order_Lk(tree->a_nodes[tree->tip_root],tree->a_nodes[tree->tip_root]->v[0],tree); if(tree->both_sides == YES) Pre_Order_Lk(tree->a_nodes[tree->tip_root],tree->a_nodes[tree->tip_root]->v[0],tree); } } } if(!b) { if(tree->n_root) { if(tree->ignore_root == NO) b = (tree->n_root->v[1]->tax == NO)?(tree->n_root->b[2]):(tree->n_root->b[1]); else b = tree->e_root; } else b = tree->a_nodes[tree->tip_root]->b[0]; } tree->c_lnL = .0; tree->sum_min_sum_scale = .0; #ifdef BEAGLE calc_edgelks_beagle(b, tree); #else if(tree->update_eigen_lr == YES) Update_Eigen_Lr(b,tree); if(tree->use_eigen_lr == YES) { for(catg=0;catgl->v)*tree->mod->ras->gamma_rr->v[catg]; len *= tree->mod->br_len_mult->v; if(tree->mixt_tree != NULL) len *= tree->mixt_tree->mod->ras->gamma_rr->v[tree->mod->ras->parent_class_number]; if(len < tree->mod->l_min) len = tree->mod->l_min; else if(len > tree->mod->l_max) len = tree->mod->l_max; for(state=0;statemod->eigen->e_val[state],len); } } p_lk_left = b->p_lk_left; p_lk_rght = b->rght->tax ? b->p_lk_tip_r : b->p_lk_rght; for(site=0;sitecurr_site = site; if((b->rght->tax) && (tree->mod->s_opt->greedy == NO)) { ambiguity_check = b->rght->c_seq->is_ambigu[tree->curr_site]; if(ambiguity_check == NO) { state = b->rght->c_seq->d_state[tree->curr_site]; } } if(tree->mod->use_m4mod) ambiguity_check = YES; if(tree->use_eigen_lr == YES) { if(tree->data->wght[site] > SMALL) Lk_Core_Eigen_Lr(expl,dot_prod,b,tree); dot_prod += nsncatg; } else { if(tree->data->wght[site] > SMALL) Lk_Core(state,ambiguity_check,p_lk_left,p_lk_rght,b->Pij_rr,b->tPij_rr,b,tree); if(b->rght->tax == YES) { p_lk_left += nsncatg; p_lk_rght += ns; } else { p_lk_left += nsncatg; p_lk_rght += nsncatg; } } } #endif return tree->c_lnL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // First derivative of the log-likelihood with respect // to the length of edge b phydbl dLk(phydbl *l, t_edge *b, t_tree *tree) { unsigned int catg,state,site; phydbl len,rr; phydbl lk,dlk,dlnlk,lnlk; phydbl ev,expevlen; const unsigned int ns = tree->mod->ns; const unsigned int ncatg = tree->mod->ras->n_catg; const unsigned int npattern = tree->n_pattern; phydbl *dot_prod = tree->dot_prod; phydbl *expl = tree->expl; tree->numerical_warning = NO; assert(isnan(*l) == FALSE); if(*l < tree->mod->l_min) *l = tree->mod->l_min; else if(*l > tree->mod->l_max) *l = tree->mod->l_max; assert(b != NULL); if(tree->is_mixt_tree == YES) { #ifdef BEAGLE Warn_And_Exit(TODO_BEAGLE); #endif return MIXT_dLk(l,b,tree); } if(tree->update_eigen_lr == YES) Update_Eigen_Lr(b,tree); for(catg=0;catgmod->ras->gamma_rr->v[catg]; rr *= tree->mod->br_len_mult->v; len = (*l) * rr; if(isinf(len) || isnan(len)) { PhyML_Fprintf(stderr,"\n. len=%f rr=%f l=%f",len,rr,*l); assert(FALSE); } if(len < tree->mod->l_min) len = tree->mod->l_min; else if(len > tree->mod->l_max) len = tree->mod->l_max; // value of rr should be corrected too if any of these two conditions // is true. Leads to numerical precision issues though... for(state=0;statemod->eigen->e_val[state]; expevlen = exp(ev*len); expl[catg*2*ns + 2*state] = expevlen; expl[catg*2*ns + 2*state + 1] = expevlen*ev*rr; } } dlnlk = 0.0; lnlk = 0.0; for(site=0;sitedata->wght[site] > SMALL) { tree->curr_site = site; Lk_dLk_Core_Eigen_Lr(expl,dot_prod+site*ns*ncatg,b,&lk,&dlk,tree); assert(lk > .0); dlk /= lk; dlnlk += tree->data->wght[site] * dlk; lnlk += tree->data->wght[site] * (log(lk) - (phydbl)LOG2 * tree->fact_sum_scale[site]); } } tree->c_dlnL = dlnlk; tree->c_lnL = lnlk; return tree->c_lnL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* Core of the likelihood calculation. Assume that the partial likelihoods on both sides of t_edge *b are up-to-date. Calculate the log-likelihood at one site. Note: this function can be used to evaluate first or second derivative of the likelihood function with respect to the length of b, at a given site. Hence, be careful with the meaning of 'site_lk'. If 'derivative=TRUE', then site_lk is either the first or second derivative of the likelihood at that site, given the length of edge 'b'. */ phydbl Lk_Core(int state, int ambiguity_check, phydbl *p_lk_left, phydbl *p_lk_rght, phydbl *Pij_rr, phydbl *tPij_rr, t_edge *b, t_tree *tree) { phydbl site_lk,res,*pi,*site_lk_cat,log_site_lk; unsigned int catg; const unsigned int ns = tree->mod->ns; const unsigned int ncatg = tree->mod->ras->n_catg; const unsigned int site = tree->curr_site; const unsigned nsns = ns*ns; assert(tree->data->wght[site] > SMALL); pi = tree->mod->e_frq->pi->v; if(tree->mod->s_opt->skip_tree_traversal == NO) { for(catg=0;catgsite_lk_cat[catg] = AVX_Lk_Core_One_Class_No_Eigen_Lr(p_lk_left,p_lk_rght,Pij_rr,tPij_rr,pi,ns,ambiguity_check,state); #elif (defined(__SSE__) || defined(__SSE2__) || defined(__SSE3__)) tree->site_lk_cat[catg] = SSE_Lk_Core_One_Class_No_Eigen_Lr(p_lk_left,p_lk_rght,Pij_rr,tPij_rr,pi,ns,ambiguity_check,state); #else tree->site_lk_cat[catg] = Lk_Core_One_Class_No_Eigen_Lr(p_lk_left,p_lk_rght,Pij_rr,pi,ns,ambiguity_check,state); #endif } else { tree->site_lk_cat[catg] = Lk_Core_One_Class_No_Eigen_Lr(p_lk_left,p_lk_rght,Pij_rr,pi,ns, ambiguity_check, state); } Pij_rr += nsns; tPij_rr += nsns; if(b->left->tax == NO) p_lk_left += ns; if(b->rght->tax == NO) p_lk_rght += ns; } Pull_Scaling_Factors(site,b,tree); } site_lk = .0; site_lk_cat = tree->unscaled_site_lk_cat + site*ncatg; for(catg=0;catgmod->ras->gamma_r_proba->v[catg]; if(tree->mod->ras->invar == YES) { int num_prec_issue = NO; phydbl inv_site_lk = Invariant_Lk(tree->fact_sum_scale[site],site,&num_prec_issue,tree); switch(num_prec_issue) { case YES : { assert(isinf(inv_site_lk)); tree->fact_sum_scale[site] = 0; inv_site_lk = Invariant_Lk(0,site,&num_prec_issue,tree); site_lk = inv_site_lk * tree->mod->ras->pinvar->v; break; } case NO : { site_lk = site_lk * (1. - tree->mod->ras->pinvar->v) + inv_site_lk * tree->mod->ras->pinvar->v; break; } } } if(tree->apply_lk_scaling == YES) res = site_lk / pow(2,tree->fact_sum_scale[site]); else res = site_lk; if(site_lk < SMALL) { site_lk = SMALL; tree->numerical_warning = YES; } log_site_lk = log(site_lk) - (phydbl)LOG2 * tree->fact_sum_scale[site]; // log_site_lk = log(site_lk_scaled / 2^(left_subtree+right_subtree)) tree->c_lnL_sorted[site] = log_site_lk; tree->c_lnL += tree->data->wght[site] * log_site_lk; tree->cur_site_lk[site] = exp(log_site_lk); // note to self : add opt out option to avoid calculating this if not necessary /* printf("\n. clnL: %f",tree->c_lnL); */ return res; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Lk_Core_Eigen_Lr(phydbl *expl, phydbl *dot_prod, t_edge *b, t_tree *tree) { phydbl site_lk,res,*site_lk_cat,log_site_lk; unsigned int catg; int num_prec_issue; const unsigned int ns = tree->mod->ns; const unsigned int ncatg = tree->mod->ras->n_catg; const unsigned int site = tree->curr_site; assert(tree->data->wght[site] > SMALL); if(tree->mod->s_opt->skip_tree_traversal == NO) { for(catg=0;catgmod->io->datatype == NT || tree->mod->io->datatype == AA) { #if (defined(__AVX__) || defined(__AVX2__)) tree->site_lk_cat[catg] = AVX_Lk_Core_One_Class_Eigen_Lr(dot_prod,expl ? expl : NULL,ns); #elif (defined(__SSE__) || defined(__SSE2__) || defined(__SSE3__)) tree->site_lk_cat[catg] = SSE_Lk_Core_One_Class_Eigen_Lr(dot_prod,expl ? expl : NULL,ns); #else tree->site_lk_cat[catg] = Lk_Core_One_Class_Eigen_Lr(dot_prod,expl ? expl : NULL,ns); #endif } else { tree->site_lk_cat[catg] = Lk_Core_One_Class_Eigen_Lr(dot_prod,expl ? expl : NULL,ns); } dot_prod += ns; if(expl) expl += ns; } Pull_Scaling_Factors(site,b,tree); } site_lk_cat = tree->unscaled_site_lk_cat + site*ncatg; site_lk = .0; for(catg=0;catgmod->ras->gamma_r_proba->v[catg]; if(tree->mod->ras->invar == YES) { num_prec_issue = NO; phydbl inv_site_lk = Invariant_Lk(tree->fact_sum_scale[site],site,&num_prec_issue,tree); switch(num_prec_issue) { case YES : { assert(isinf(inv_site_lk)); tree->fact_sum_scale[site] = 0; inv_site_lk = Invariant_Lk(0,site,&num_prec_issue,tree); site_lk = inv_site_lk * tree->mod->ras->pinvar->v; break; } case NO : { site_lk = site_lk * (1. - tree->mod->ras->pinvar->v) + inv_site_lk * tree->mod->ras->pinvar->v; break; } } } if(site_lk < SMALL) { site_lk = SMALL; tree->numerical_warning = YES; } // likelihood (or 1st, 2nd derivative) not rescaled here. Valid only if all partial likelihoods // were scaled using the same factor, i.e., when scaling_method == SCALE_FAST. In this case, the // scaling factors will cancel out in dlk/lk and d2lk/lk res = site_lk; log_site_lk = log(site_lk) - (phydbl)LOG2 * tree->fact_sum_scale[site]; // log_site_lk = log(site_lk_scaled / 2^(left_subtree+right_subtree)) tree->c_lnL_sorted[site] = log_site_lk; tree->c_lnL += tree->data->wght[site] * log_site_lk; tree->cur_site_lk[site] = exp(log_site_lk); // note to self : add opt out option to avoid calculating this if not necessary return res; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Compute likelihood and first derivative of likelihood with respect to the length of edge b *unscaled* void Lk_dLk_Core_Eigen_Lr(phydbl *expl, phydbl *dot_prod, t_edge *b, phydbl *lk, phydbl *dlk, t_tree *tree) { phydbl core_lk,core_dlk; unsigned int catg; int num_prec_issue; const unsigned int ns = tree->mod->ns; const unsigned int ncatg = tree->mod->ras->n_catg; const unsigned int site = tree->curr_site; *lk = *dlk = 0.0; assert(tree->data->wght[site] > SMALL); if(tree->mod->s_opt->skip_tree_traversal == NO) { for(catg=0;catgmod->io->datatype == NT || tree->mod->io->datatype == AA) { #if (defined(__AVX__) || defined(__AVX2__)) AVX_Lk_dLk_Core_One_Class_Eigen_Lr(dot_prod, expl ? expl : NULL, ns,&core_lk,&core_dlk); #elif (defined(__SSE__) || defined(__SSE2__) || defined(__SSE3__)) SSE_Lk_dLk_Core_One_Class_Eigen_Lr(dot_prod, expl ? expl : NULL, ns,&core_lk,&core_dlk); #else Lk_dLk_Core_One_Class_Eigen_Lr(dot_prod, expl ? expl : NULL, ns,&core_lk,&core_dlk); #endif } else { Lk_dLk_Core_One_Class_Eigen_Lr(dot_prod, expl ? expl : NULL, ns,&core_lk,&core_dlk); } *lk += core_lk * tree->mod->ras->gamma_r_proba->v[catg]; *dlk += core_dlk * tree->mod->ras->gamma_r_proba->v[catg]; dot_prod += ns; if(expl) expl += 2*ns; } Pull_Scaling_Factors(site,b,tree); } if(tree->mod->ras->invar == YES) { num_prec_issue = NO; phydbl inv_site_lk = Invariant_Lk(tree->fact_sum_scale[site],site,&num_prec_issue,tree); switch(num_prec_issue) { case YES : { *lk = inv_site_lk * tree->mod->ras->pinvar->v; *dlk = 0.0; break; } case NO : { *lk = *lk * (1. - tree->mod->ras->pinvar->v) + inv_site_lk * tree->mod->ras->pinvar->v; *dlk = *dlk * (1. - tree->mod->ras->pinvar->v); break; } } } if(*lk < SMALL) { *lk = SMALL; tree->numerical_warning = YES; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Update_Eigen_Lr(t_edge *b, t_tree *tree) { unsigned int site,catg,i,j; phydbl *dot_prod,*r_e_vect,*l_e_vect,*p_lk_left,*p_lk_rght,*pi; phydbl left,rght; const unsigned int npattern = tree->n_pattern; const unsigned int ns = tree->mod->ns; const unsigned int ncatg = tree->mod->ras->n_catg; const unsigned int nsncatg = ns*ncatg; if(tree->is_mixt_tree == YES) { MIXT_Update_Eigen_Lr(b,tree); return; } if(tree->mod->ns == 4 || tree->mod->ns == 20) { #if (defined(__AVX__) || defined(__AVX2__)) AVX_Update_Eigen_Lr(b,tree); return; #elif (defined(__SSE__) || defined(__SSE2__) || defined(__SSE3__)) SSE_Update_Eigen_Lr(b,tree); return; #endif } assert(tree->update_eigen_lr == YES); dot_prod = tree->dot_prod; r_e_vect = tree->mod->eigen->r_e_vect; l_e_vect = tree->mod->eigen->l_e_vect; pi = tree->mod->e_frq->pi->v; if(b->left->tax == YES) p_lk_left = b->p_lk_tip_l; else p_lk_left = b->p_lk_left; if(b->rght->tax == YES) p_lk_rght = b->p_lk_tip_r; else p_lk_rght = b->p_lk_rght; for(site=0;sitedata->wght[site] > SMALL) { for(catg=0;catgleft->tax == NO) p_lk_left += ns; if(b->rght->tax == NO) p_lk_rght += ns; } if(b->left->tax == YES) p_lk_left += ns; if(b->rght->tax == YES) p_lk_rght += ns; } else { if(b->left->tax == YES) p_lk_left += ns; else p_lk_left += nsncatg; if(b->rght->tax == YES) p_lk_rght += ns; else p_lk_rght += nsncatg; dot_prod += nsncatg; } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Rate_Correction(int exponent, phydbl *site_lk_cat) { int piecewise_exponent; phydbl multiplier,dum; unsigned long long int one = 1; dum = *site_lk_cat; if(exponent >= 0) { /* Multiply by 2^exponent */ do { piecewise_exponent = MIN(exponent,63); multiplier = (phydbl)(one << piecewise_exponent); dum = dum * multiplier; exponent = exponent - piecewise_exponent; } while(exponent != 0); } else { /* Divide by 2^exponent */ do { piecewise_exponent = MAX(exponent,-63); multiplier = 1. / (phydbl)(one << -piecewise_exponent); dum = dum * multiplier; exponent = exponent - piecewise_exponent; } while(exponent != 0); } *site_lk_cat = dum; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Lk_Core_One_Class_Eigen_Lr(phydbl *dot_prod, phydbl *expl, int ns) { unsigned int l; phydbl lk = 0.0; if(expl != NULL) for(l=0;l .0) /* Only bother ascending into the subtrees if the likelihood of state k, at site "site*dim2" is > 0 */ { sum = .0; for(l=0;lmod->ras->invar == YES) { /* The site is invariant */ if(tree->data->invar[site] > -0.5) { inv_site_lk = tree->mod->e_frq->pi->v[tree->data->invar[site]]; /* printf("\n. inv_site_lk = %f [%c] [%d] invar: %d",inv_site_lk,tree->data->c_seq[0]->state[site],tree->data->invar[site],tree->data->invar[site]); */ if(tree->apply_lk_scaling == YES) { exponent = fact_sum_scale; do { piecewise_exponent = MIN(exponent,63); multiplier = (phydbl)((unsigned long long)(1) << piecewise_exponent); inv_site_lk *= multiplier; exponent -= piecewise_exponent; } while(exponent != 0); } /* Update the value of site_lk */ if(isinf(inv_site_lk)) // P(D|r=0) >> P(D|r>0) => assume P(D) = P(D|r=0)P(r=0) { int i; PhyML_Fprintf(stderr,"\n. fact_sum_scale: %d",fact_sum_scale); PhyML_Fprintf(stderr,"\n. pi: %f",tree->mod->e_frq->pi->v[tree->data->invar[site]]); for(i=0;imod->ns;i++) PhyML_Fprintf(stderr,"\n. pi %d: %f",i,tree->mod->e_frq->pi->v[i]); PhyML_Fprintf(stderr,"\n. Numerical precision issue alert."); PhyML_Fprintf(stderr,"\n. File %s at line %d (function '%s')\n",__FILE__,__LINE__,__FUNCTION__); (*num_prec_issue) = YES; } } } return inv_site_lk; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* Update partial likelihood on edge b on the side of b where node d lies. */ void Update_Partial_Lk(t_tree *tree, t_edge *b, t_node *d) { /* if(tree->eval_alnL == NO) return; */ if(b->left == d && b->update_partial_lk_left == NO) return; if(b->rght == d && b->update_partial_lk_rght == NO) return; if(tree->is_mixt_tree) { MIXT_Update_Partial_Lk(tree,b,d); return; } if((tree->io->do_alias_subpatt == YES) && (tree->update_alias_subpatt == YES)) Alias_One_Subpatt((d==b->left)?(b->rght):(b->left),d,tree); if(d->tax) return; #ifdef BEAGLE update_beagle_partials(tree, b, d); #else if(tree->mod->use_m4mod == NO) { if(tree->mod->ns == 4 || tree->mod->ns == 20) { #if (defined(__AVX__) || defined(__AVX2__)) AVX_Update_Partial_Lk(tree,b,d); #elif (defined(__SSE__) || defined(__SSE2__) || defined(__SSE3__)) SSE_Update_Partial_Lk(tree,b,d); #else Default_Update_Partial_Lk(tree,b,d); #endif } else { Update_Partial_Lk_Generic(tree,b,d); } } else { Update_Partial_Lk_Generic(tree,b,d); } #endif } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// #ifndef BEAGLE void Update_Partial_Lk_Generic(t_tree *tree, t_edge *b, t_node *d) { /* | |<- b | d / \ / \ / \ n_v1 n_v2 */ t_node *n_v1, *n_v2; phydbl p1_lk1,p2_lk2; phydbl *p_lk,*p_lk_v1,*p_lk_v2; phydbl *Pij1,*Pij2; phydbl *tPij1,*tPij2; int *sum_scale, *sum_scale_v1, *sum_scale_v2; int sum_scale_v1_val, sum_scale_v2_val; int i,j; int catg,site; int n_patterns; short int ambiguity_check_v1,ambiguity_check_v2; int state_v1,state_v2; phydbl smallest_p_lk,largest_p_lk; int *p_lk_loc; unsigned const int ncatg = tree->mod->ras->n_catg; unsigned const int ns = tree->mod->ns; unsigned const int ncatgns = ncatg * ns; unsigned const int nsns = ns * ns; if(tree->n_root && tree->ignore_root == YES && (d == tree->n_root->v[1] || d == tree->n_root->v[2]) && (b == tree->n_root->b[1] || b == tree->n_root->b[2])) { assert(FALSE); } state_v1 = state_v2 = -1; ambiguity_check_v1 = ambiguity_check_v2 = NO; sum_scale_v1_val = sum_scale_v2_val = 0; p1_lk1 = p2_lk2 = .0; if(d->tax) { PhyML_Fprintf(stderr,"\n. t_node %d is a leaf...",d->num); PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d (function '%s').\n",__FILE__,__LINE__,__FUNCTION__); Exit("\n"); } n_patterns = tree->n_pattern; n_v1 = n_v2 = NULL; p_lk = p_lk_v1 = p_lk_v2 = NULL; Pij1 = Pij2 = NULL; tPij1 = tPij2 = NULL; sum_scale_v1 = sum_scale_v2 = NULL; p_lk_loc = NULL; smallest_p_lk = BIG; Set_All_Partial_Lk(&n_v1,&n_v2, &p_lk,&sum_scale,&p_lk_loc, &Pij1,&tPij1,&p_lk_v1,&sum_scale_v1, &Pij2,&tPij2,&p_lk_v2,&sum_scale_v2, d,b,tree); /* For every site in the alignment */ for(site=0;sitedata->wght[site] > SMALL) { state_v1 = state_v2 = -1; ambiguity_check_v1 = ambiguity_check_v2 = NO; if(tree->mod->s_opt->greedy == NO) { /* n_v1 and n_v2 are tip nodes */ if(n_v1 && n_v1->tax) { /* Is the state at this tip ambiguous? */ ambiguity_check_v1 = n_v1->c_seq->is_ambigu[site]; /* if(ambiguity_check_v1 == NO) state_v1 = Get_State_From_Partial_Pars(n_v1->b[0]->p_lk_tip_r,site*ns,tree); */ if(ambiguity_check_v1 == NO) state_v1 = n_v1->c_seq->d_state[site]; } if(n_v2 && n_v2->tax) { /* Is the state at this tip ambiguous? */ ambiguity_check_v2 = n_v2->c_seq->is_ambigu[site]; /* ambiguity_check_v2 = tree->data->c_seq[n_v2->num]->is_ambigu[site]; */ /* if(ambiguity_check_v2 == NO) state_v2 = Get_State_From_Partial_Pars(n_v2->b[0]->p_lk_tip_r,site*ns,tree); */ if(ambiguity_check_v2 == NO) state_v2 = n_v2->c_seq->d_state[site]; } } if(tree->mod->use_m4mod) { ambiguity_check_v1 = YES; ambiguity_check_v2 = YES; } /* For all the rate classes */ for(catg=0;catgmod->ras->skip_rate_cat[catg] == YES) continue; smallest_p_lk = BIG; /* For all the states at node d */ for(i=0;imod->ns;i++) { p1_lk1 = .0; if(n_v1) { /* n_v1 is a tip */ if((n_v1->tax) && (!tree->mod->s_opt->greedy)) { if(ambiguity_check_v1 == NO) { /* For the (non-ambiguous) state at node n_v1 */ p1_lk1 = Pij1[catg*nsns+i*ns+state_v1]; } else { /* For all the states at node n_v1 */ for(j=0;jmod->ns;j++) { p1_lk1 += Pij1[catg*nsns+i*ns+j] * (phydbl)n_v1->b[0]->p_lk_tip_r[site*ns+j]; } } } /* n_v1 is an internal node */ else { /* For the states at node n_v1 */ for(j=0;jmod->ns;j++) { p1_lk1 += Pij1[catg*nsns+i*ns+j] * p_lk_v1[site*ncatgns+catg*ns+j]; } } } else { p1_lk1 = 1.0; } p2_lk2 = .0; /* We do exactly the same as for node n_v1 but for node n_v2 this time.*/ if(n_v2) { /* n_v2 is a tip */ if((n_v2->tax) && (!tree->mod->s_opt->greedy)) { if(ambiguity_check_v2 == NO) { /* For the (non-ambiguous) state at node n_v2 */ p2_lk2 = Pij2[catg*nsns+i*ns+state_v2]; } else { /* For all the states at node n_v2 */ for(j=0;jmod->ns;j++) { p2_lk2 += Pij2[catg*nsns+i*ns+j] * (phydbl)n_v2->b[0]->p_lk_tip_r[site*ns+j]; } } } /* n_v2 is an internal node */ else { /* For all the states at node n_v2 */ for(j=0;jmod->ns;j++) { p2_lk2 += Pij2[catg*nsns+i*ns+j] * p_lk_v2[site*ncatgns+catg*ns+j]; } } } else { p2_lk2 = 1.0; } p_lk[site*ncatgns+catg*ns+i] = p1_lk1 * p2_lk2; /* if(site == 0) PhyML_Printf("\n+ site: %d %G",site,p_lk[site*ncatgns+catg*ns+i]); */ } if(tree->scaling_method == SCALE_RATE_SPECIFIC) { smallest_p_lk = BIG; for(i=0;imod->augmented == NO && tree->apply_lk_scaling == YES && (n_v1->tax == NO || n_v2->tax == NO)) { int curr_scaler_pow; curr_scaler_pow = (int)(-500.*LOG2-log(smallest_p_lk))/LOG2; sum_scale[site*ncatg+catg] += curr_scaler_pow; for(i=0;iscaling_method == SCALE_FAST) { sum_scale_v1_val = (sum_scale_v1)?(sum_scale_v1[site]):(0); sum_scale_v2_val = (sum_scale_v2)?(sum_scale_v2[site]):(0); sum_scale[site] = sum_scale_v1_val + sum_scale_v2_val; sum_scale[site*ncatg+catg] = sum_scale_v1_val + sum_scale_v2_val; assert(sum_scale[site] < 1024); largest_p_lk = -BIG; for(i=0;i largest_p_lk) largest_p_lk = p_lk[site*ncatgns+i] ; if(largest_p_lk < INV_TWO_TO_THE_LARGE && tree->mod->augmented == NO && tree->apply_lk_scaling == YES) { for(i=0;imod->ras->n_catg; const unsigned int ns = tree->mod->ns; const unsigned int n_patterns = tree->n_pattern; if(tree->n_root && tree->ignore_root == YES && (d == tree->n_root->v[1] || d == tree->n_root->v[2]) && (b == tree->n_root->b[1] || b == tree->n_root->b[2])) { assert(FALSE); } if(d->tax) { PhyML_Fprintf(stderr,"\n. t_node %d is a leaf...",d->num); PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d (function '%s')\n",__FILE__,__LINE__,__FUNCTION__); Exit("\n"); } n_v1 = n_v2 = NULL; p_lk = p_lk_v1 = p_lk_v2 = NULL; Pij1 = Pij2 = NULL; tPij1 = tPij2 = NULL; p_lk_loc = NULL; sum_scale_v1 = NULL; sum_scale_v2 = NULL; Set_All_Partial_Lk(&n_v1,&n_v2, &p_lk,&sum_scale,&p_lk_loc, &Pij1,&tPij1,&p_lk_v1,&sum_scale_v1, &Pij2,&tPij2,&p_lk_v2,&sum_scale_v2, d,b,tree); Core_Default_Update_Partial_Lk(n_v1,n_v2, p_lk,p_lk_v1,p_lk_v2, Pij1,Pij2, sum_scale,sum_scale_v1,sum_scale_v2, ns,ncatg,n_patterns, tree->apply_lk_scaling, tree->data->wght); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Core_Default_Update_Partial_Lk(const t_node *n_v1, const t_node *n_v2, phydbl *plk0, const phydbl *plk1, const phydbl *plk2, const phydbl *Pij1, const phydbl *Pij2, int *sum_scale0, const int *sum_scale1, const int *sum_scale2, const int ns, const int ncatg, const int npatterns, const int apply_scaling, const phydbl *wght) { unsigned int i,site,ncatgns,catg,nsns; int state_v1,state_v2; int ambiguity_check_v1,ambiguity_check_v2; int sum_scale_v1_val, sum_scale_v2_val; phydbl largest_p_lk; const phydbl *init_Pij1, *init_Pij2; ncatgns = ncatg*ns; nsns = ns*ns; init_Pij1 = Pij1; init_Pij2 = Pij2; /* For every site in the alignment */ for(site=0;site SMALL) { state_v1 = state_v2 = -1; ambiguity_check_v1 = ambiguity_check_v2 = YES; /* n_v1 and n_v2 are tip nodes */ if(n_v1->tax) { /* Is the state at this tip ambiguous? */ ambiguity_check_v1 = n_v1->c_seq->is_ambigu[site]; if(ambiguity_check_v1 == NO) state_v1 = n_v1->c_seq->d_state[site]; } if(n_v2->tax) { /* Is the state at this tip ambiguous? */ ambiguity_check_v2 = n_v2->c_seq->is_ambigu[site]; if(ambiguity_check_v2 == NO) state_v2 = n_v2->c_seq->d_state[site]; } Pij1 = init_Pij1; Pij2 = init_Pij2; /* For all the rate classes */ for(catg=0;catgtax) ? 0 : ns; plk2 += (n_v2->tax) ? 0 : ns; plk0 += ns; } plk1 += (n_v1->tax) ? ns : 0; plk2 += (n_v2->tax) ? ns : 0; sum_scale_v1_val = (sum_scale1)?(sum_scale1[site]):(0); sum_scale_v2_val = (sum_scale2)?(sum_scale2[site]):(0); sum_scale0[site] = sum_scale_v1_val + sum_scale_v2_val; plk0 -= ncatgns; largest_p_lk = -BIG; for(i=0;i largest_p_lk) largest_p_lk = plk0[i]; if(largest_p_lk < INV_TWO_TO_THE_LARGE && apply_scaling == YES) { for(i=0;itax) ? ns : ncatgns; plk2 += (n_v2->tax) ? ns : ncatgns; } } } #endif ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Return_Abs_Lk(t_tree *tree) { Lk(NULL,tree); return FABS(tree->c_lnL); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// matrix *ML_Dist(calign *data, t_mod *mod) { int i,j,k,l; phydbl init; int n_catg; phydbl d_max,sum; matrix *mat; calign *twodata,*tmpdata; int state0, state1; phydbl *F,len; eigen *eigen_struct; tmpdata = (calign *)mCalloc(1,sizeof(calign)); tmpdata->c_seq = (align **)mCalloc(2,sizeof(align *)); tmpdata->obs_state_frq = (phydbl *)mCalloc(mod->ns,sizeof(phydbl)); tmpdata->ambigu = (short int *)mCalloc(data->crunch_len,sizeof(short int)); F = (phydbl *)mCalloc(mod->ns*mod->ns,sizeof(phydbl )); eigen_struct = (eigen *)Make_Eigen_Struct(mod->ns); Set_Update_Eigen(YES,mod); Update_Boundaries(mod); Update_Eigen(mod); tmpdata->n_otu = 2; tmpdata->crunch_len = data->crunch_len; tmpdata->init_len = data->init_len; mat = NULL; if(mod->io->datatype == NT) mat = (mod->whichmodel < 10)?(K80_dist(data,1E+6)):(JC69_Dist(data,mod)); else if(mod->io->datatype == AA) mat = JC69_Dist(data,mod); else if(mod->io->datatype == GENERIC) mat = JC69_Dist(data,mod); for(i=0;iras->n_catg;i++) /* Don't use the discrete gamma distribution */ { mod->ras->gamma_rr->v[i] = 1.0; mod->ras->gamma_r_proba->v[i] = 1.0; } n_catg = mod->ras->n_catg; mod->ras->n_catg = 1; for(j=0;jn_otu-1;j++) { tmpdata->c_seq[0] = data->c_seq[j]; tmpdata->c_seq[0]->name = data->c_seq[j]->name; tmpdata->wght = data->wght; for(k=j+1;kn_otu;k++) { tmpdata->c_seq[1] = data->c_seq[k]; tmpdata->c_seq[1]->name = data->c_seq[k]->name; twodata = Compact_Cdata(tmpdata,mod->io); Check_Ambiguities(twodata,mod->io->datatype,mod->io->state_len); Hide_Ambiguities(twodata); init = mat->dist[j][k]; if((init > DIST_MAX-SMALL) || (init < .0)) init = 0.1; d_max = init; for(i=0;ins*mod->ns;++i) F[i]=.0; len = 0.0; for(l=0;lc_seq[0]->len;++l) { state0 = Assign_State(twodata->c_seq[0]->state+l*mod->io->state_len,mod->io->datatype,mod->io->state_len); state1 = Assign_State(twodata->c_seq[1]->state+l*mod->io->state_len,mod->io->datatype,mod->io->state_len); if((state0 > -1) && (state1 > -1)) { F[mod->ns*state0+state1] += twodata->wght[l]; len += twodata->wght[l]; } } if(len > .0) { for(i=0;ins*mod->ns;++i) F[i] /= len; } sum = 0.; for(i=0;ins*mod->ns;++i) sum += F[i]; /* for(i=0;ins*mod->ns;++i) PhyML_Printf("\n. %g",F[i]); */ /* if(sum < .001) d_max = -1.; */ if(sum < .001) d_max = init; else if((sum > 1. - .001) && (sum < 1. + .001)) Opt_Dist_F(&(d_max),F,mod); else { PhyML_Fprintf(stderr,"\n\n. sum = %f",sum); PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d (function '%s') \n",__FILE__,__LINE__,__FUNCTION__); Exit(""); } if(d_max >= DIST_MAX) d_max = DIST_MAX; /* Do not correct for dist < BL_MIN, otherwise Fill_Missing_Dist * will not be called */ mat->dist[j][k] = d_max; mat->dist[k][j] = mat->dist[j][k]; Free_Calign(twodata); } } mod->ras->n_catg = n_catg; Free(tmpdata->ambigu); Free(tmpdata->obs_state_frq); Free(tmpdata->c_seq); free(tmpdata); Free_Eigen(eigen_struct); Free(F); return mat; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Lk_Given_Two_Seq(calign *data, int numseq1, int numseq2, phydbl dist, t_mod *mod, phydbl *loglk) { align *seq1,*seq2; phydbl site_lk,log_site_lk; int i,j,k,l; /* phydbl **p_lk_l,**p_lk_r; */ phydbl *p_lk_l,*p_lk_r; phydbl len; int dim1,dim2; dim1 = mod->ns; dim2 = mod->ns * mod->ns; DiscreteGamma(mod->ras->gamma_r_proba->v, mod->ras->gamma_rr->v, mod->ras->alpha->v, mod->ras->alpha->v,mod->ras->n_catg,mod->ras->gamma_median); seq1 = data->c_seq[numseq1]; seq2 = data->c_seq[numseq2]; p_lk_l = (phydbl *)mCalloc(data->c_seq[0]->len * mod->ns,sizeof(phydbl)); p_lk_r = (phydbl *)mCalloc(data->c_seq[0]->len * mod->ns,sizeof(phydbl)); for(i=0;iras->n_catg;i++) { len = dist*mod->ras->gamma_rr->v[i]; if(len < mod->l_min) len = mod->l_min; else if(len > mod->l_max) len = mod->l_max; PMat(len,mod,dim2*i,mod->Pij_rr->v,NULL); } if(mod->io->datatype == NT) { For(i,data->c_seq[0]->len) { Init_Tips_At_One_Site_Nucleotides_Float(seq1->state[i],i*mod->ns,p_lk_l); Init_Tips_At_One_Site_Nucleotides_Float(seq2->state[i],i*mod->ns,p_lk_r); } } else if(mod->io->datatype == AA) { For(i,data->c_seq[0]->len) { Init_Tips_At_One_Site_AA_Float(seq1->state[i],i*mod->ns,p_lk_l); Init_Tips_At_One_Site_AA_Float(seq2->state[i],i*mod->ns,p_lk_r); } } else { PhyML_Fprintf(stderr,"\n\n. Not implemented yet..."); PhyML_Fprintf(stderr,"\n. Err in file %s at line %d\n\n",__FILE__,__LINE__); Warn_And_Exit("\n"); } site_lk = .0; *loglk = 0; For(i,data->c_seq[0]->len) { if(data->wght[i] > 0.0) { site_lk = log_site_lk = .0; if(!data->ambigu[i]) { for(k=0;kns;k++) {if(p_lk_l[i*mod->ns+k] > .0001) break;} for(l=0;lns;l++) {if(p_lk_r[i*mod->ns+l] > .0001) break;} for(j=0;jras->n_catg;j++) { site_lk += mod->ras->gamma_r_proba->v[j] * mod->e_frq->pi->v[k] * p_lk_l[i*dim1+k] * mod->Pij_rr->v[j*dim2+k*dim1+l] * p_lk_r[i*dim1+l]; } } else { for(j=0;jras->n_catg;j++) { for(k=0;kns;k++) /*sort sum terms ? No global effect*/ { for(l=0;lns;l++) { site_lk += mod->ras->gamma_r_proba->v[j] * mod->e_frq->pi->v[k] * p_lk_l[i*dim1+k] * mod->Pij_rr->v[j*dim2+k*dim1+l] * p_lk_r[i*dim1+l]; } } } } if(site_lk <= .0) { PhyML_Fprintf(stderr,"\n\n. '%c' '%c'\n",seq1->state[i],seq2->state[i]); Exit("\n. Err: site lk <= 0\n"); } log_site_lk += (phydbl)log(site_lk); *loglk += data->wght[i] * log_site_lk;/* sort sum terms ? No global effect*/ } } /* For(i,data->c_seq[0]->len) */ /* { */ /* Free(p_lk_l[i]); */ /* Free(p_lk_r[i]); */ /* } */ Free(p_lk_l); Free(p_lk_r); return *loglk; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Multinomial log likelihood void Unconstraint_Lk(t_tree *tree) { int i; tree->unconstraint_lk = .0; for(i=0;idata->crunch_len;i++) tree->unconstraint_lk += tree->data->wght[i]*(phydbl)log(tree->data->wght[i]); tree->unconstraint_lk -= tree->data->init_len*(phydbl)log(tree->data->init_len); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Log-likelihood assuming a tree with edge of infinite lengths void Composite_Lk(t_tree *tree) { int i; tree->composite_lk = 0.0; for(i=0;imod->ns;++i) tree->composite_lk += tree->data->obs_state_frq[i]* tree->data->init_len* tree->n_otu* log(tree->mod->e_frq->pi->v[i]); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Partial_Lk_Tips_Double(t_tree *tree) { unsigned int curr_site,i,dim1; if(tree->is_mixt_tree == YES) return; dim1 = tree->mod->ns; for(i=0;in_otu;i++) { if(!tree->a_nodes[i]->c_seq || strcmp(tree->a_nodes[i]->c_seq->name,tree->a_nodes[i]->name)) { PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d (function '%s') \n",__FILE__,__LINE__,__FUNCTION__); Exit(""); } } for(curr_site=0;curr_sitedata->crunch_len;curr_site++) { for(i=0;in_otu;i++) { if (tree->io->datatype == NT) Init_Tips_At_One_Site_Nucleotides_Float(tree->a_nodes[i]->c_seq->state[curr_site], curr_site*dim1, tree->a_nodes[i]->b[0]->p_lk_tip_r); else if(tree->io->datatype == AA) Init_Tips_At_One_Site_AA_Float(tree->a_nodes[i]->c_seq->state[curr_site], curr_site*dim1, tree->a_nodes[i]->b[0]->p_lk_tip_r); else if(tree->io->datatype == GENERIC) Init_Tips_At_One_Site_Generic_Float(tree->a_nodes[i]->c_seq->state+curr_site*tree->mod->io->state_len, tree->mod->ns, tree->mod->io->state_len, curr_site*dim1, tree->a_nodes[i]->b[0]->p_lk_tip_r); } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Partial_Lk_Tips_Int(t_tree *tree) { int curr_site,i,dim1; if(tree->is_mixt_tree == YES) return; dim1 = tree->mod->ns; for(i=0;in_otu;i++) { if(!tree->a_nodes[i]->c_seq || strcmp(tree->a_nodes[i]->c_seq->name,tree->a_nodes[i]->name)) { PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d (function '%s') \n",__FILE__,__LINE__,__FUNCTION__); Exit(""); } } for(curr_site=0;curr_sitedata->crunch_len;curr_site++) { for(i=0;in_otu;i++) { /* printf("\n. site: %3d %c",curr_site,tree->a_nodes[i]->c_seq->state[curr_site]); */ /* printf("\n. init at %s %p",tree->a_nodes[i]->name,tree->a_nodes[i]->b[0]->p_lk_tip_r); fflush(NULL); */ if(tree->io->datatype == NT) { Init_Tips_At_One_Site_Nucleotides_Float(tree->a_nodes[i]->c_seq->state[curr_site], curr_site*dim1, tree->a_nodes[i]->b[0]->p_lk_tip_r); /* Init_Tips_At_One_Site_Nucleotides_Int(tree->data->c_seq[i]->state[curr_site], */ /* curr_site*dim1, */ /* tree->a_nodes[i]->b[0]->p_lk_tip_r); */ } else if(tree->io->datatype == AA) Init_Tips_At_One_Site_AA_Float(tree->a_nodes[i]->c_seq->state[curr_site], curr_site*dim1, tree->a_nodes[i]->b[0]->p_lk_tip_r); /* Init_Tips_At_One_Site_AA_Int(tree->data->c_seq[i]->state[curr_site], */ /* curr_site*dim1, */ /* tree->a_nodes[i]->b[0]->p_lk_tip_r); */ else if(tree->io->datatype == GENERIC) { Init_Tips_At_One_Site_Generic_Float(tree->a_nodes[i]->c_seq->state+curr_site*tree->mod->io->state_len, tree->mod->ns, tree->mod->io->state_len, curr_site*dim1, tree->a_nodes[i]->b[0]->p_lk_tip_r); /* Init_Tips_At_One_Site_Generic_Int(tree->data->c_seq[i]->state+curr_site*tree->mod->io->state_len, */ /* tree->mod->ns, */ /* tree->mod->io->state_len, */ /* curr_site*dim1, */ /* tree->a_nodes[i]->b[0]->p_lk_tip_r); */ } #ifdef BEAGLE //Recall that tip partials are stored on the branch leading //to the tip, rather than on the tip itself (hence `p_lk_tip_idx` //is a field of the branch (i.e. b[0]) rather than the node. //Secondly, the BEAGLE's partial buffers are laid out as //BEAGLE's partials buffer = [ tax1, tax2, ..., taxN, b1Left, b2Left, b3Left,...,bMLeft, b1Rght, b2Rght, b3Rght,...,bMRght] (N taxa, M branches) tree->a_nodes[i]->b[0]->p_lk_tip_idx = i; #endif } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Update_PMat_At_Given_Edge(t_edge *b_fcus, t_tree *tree) { int i; phydbl len; phydbl l_min, l_max; phydbl shape, scale, mean, var; assert(b_fcus); assert(tree); assert(tree->eval_alnL == YES); if(tree->is_mixt_tree == YES) { MIXT_Update_PMat_At_Given_Edge(b_fcus,tree); return; } if(b_fcus->Pij_rr == NULL) { PhyML_Printf("\n. b_fcus is e_root ? %d node left: %d node rght: %d left is root ? %d right is root ? %d [%p] [%d] [%d]", (b_fcus == tree->e_root) ? 1 : 0, b_fcus->left->num, b_fcus->rght->num, (b_fcus->left == tree->n_root) ? 1 : 0, (b_fcus->rght == tree->n_root) ? 1 : 0, tree->aux_tree, tree->eval_alnL, tree->is_mixt_tree); assert(false); } if(tree->mixt_tree != NULL) assert(tree->mod->ras->n_catg == 1); if(tree->io->mod->gamma_mgf_bl == YES) Set_Br_Len_Var(b_fcus,tree); l_min = tree->mod->l_min; l_max = tree->mod->l_max; len = -1.0; if(tree->mod->log_l == YES) b_fcus->l->v = exp(b_fcus->l->v); for(i=0;imod->ras->n_catg;i++) { if(tree->mod->ras->skip_rate_cat[i] == YES) continue; //Update the branch length if(b_fcus->has_zero_br_len == YES) { #ifdef BEAGLE Warn_And_Exit(TODO_BEAGLE); #endif len = -1.0; mean = -1.0; var = -1.0; } else { len = MAX(0.0,b_fcus->l->v)*tree->mod->ras->gamma_rr->v[i];//branch_len * rate len *= tree->mod->br_len_mult->v; if(tree->mixt_tree) len *= tree->mixt_tree->mod->ras->gamma_rr->v[tree->mod->ras->parent_class_number]; if(len < l_min) len = l_min; else if(len > l_max) len = l_max; mean = len; var = MAX(0.0,b_fcus->l_var->v) * POW(tree->mod->ras->gamma_rr->v[i]*tree->mod->br_len_mult->v,2); if(tree->mixt_tree) var *= POW(tree->mixt_tree->mod->ras->gamma_rr->v[tree->mod->ras->parent_class_number],2); /* var = 1.E-10; */ if(var > tree->mod->l_var_max) var = tree->mod->l_var_max; if(var < tree->mod->l_var_min) var = tree->mod->l_var_min; } //Update the transition prob. matrix if(tree->mod->gamma_mgf_bl == NO) { #ifdef BEAGLE assert(UNINITIALIZED != tree->mod->b_inst); #endif PMat(len,tree->mod,i*tree->mod->ns*tree->mod->ns,b_fcus->Pij_rr,b_fcus->tPij_rr); } else { #ifdef BEAGLE Warn_And_Exit(TODO_BEAGLE); #endif shape = mean*mean/var; scale = var/mean; PMat_MGF_Gamma(b_fcus->Pij_rr+tree->mod->ns*tree->mod->ns*i,shape,scale,1.0,tree->mod); } } #ifdef BEAGLE int whichmodel = tree->mod->whichmodel; //Only for some models we use Beagle to compute/update the P-matrices, for other models //we compute them in PhyML and explicitly set the P-matrices in BEAGLE if((tree->mod->io->datatype == AA || whichmodel==GTR || whichmodel==CUSTOM) && tree->mod->use_m4mod == NO) { if(b_fcus->has_zero_br_len == YES) Warn_And_Exit(TODO_BEAGLE); // update_beagle_eigen(tree->mod); update_beagle_ras(tree->mod); // len = MAX(0.0, b_fcus->l->v) * tree->mod->br_len_mult->v; int p_matrices[1] = b_fcus->Pij_rr_idx; double branch_lens[1] = len; int ret = beagleUpdateTransitionMatrices(tree->b_inst,0,p_matrices,NULL,NULL,branch_lens,1); if(ret<0) { PhyML_Fprintf(stderr, "beagleUpdateTransitionMatrices() on instance %i failed:%i\n\n",tree->b_inst,ret); Exit(""); } //Retrieve a "local" copy of the P-matrix ret = beagleGetTransitionMatrix(tree->b_inst, b_fcus->Pij_rr_idx, b_fcus->Pij_rr); if(ret<0) { PhyML_Fprintf(stderr, "beagleGetTransitionMatrix() on instance %i failed:%i\n\n",tree->b_inst,ret); Exit(""); } } else { int ret = beagleSetTransitionMatrix(tree->b_inst, b_fcus->Pij_rr_idx, b_fcus->Pij_rr, -1); if(ret<0) { PhyML_Fprintf(stderr, "beagleSetTransitionMatrix() on instance %i failed:%i\n\n",tree->b_inst,ret); Exit(""); } } #endif if(tree->mod->log_l == YES) b_fcus->l->v = log(b_fcus->l->v); // Print_Model(tree->mod); // Dump_Arr_D(tree->cur_site_lk, tree->n_pattern); // Print_Edge_PMats(tree, b_fcus); // Print_Edge_Likelihoods(tree,b_fcus,true); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Update_Partial_Lk_Along_A_Path(t_node **path, int path_length, t_tree *tree) { int i,j; for(i=0;iv[j] == path[i+1]) { if(path[i] == path[i]->b[j]->left) { Update_Partial_Lk(tree,path[i]->b[j],path[i]->b[j]->left); } else if(path[i] == path[i]->b[j]->rght) { Update_Partial_Lk(tree,path[i]->b[j],path[i]->b[j]->rght); } else { PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d. \n",__FILE__,__LINE__); assert(FALSE); } break; } #ifdef DEBUG if(j == 3) { PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d.\n",__FILE__,__LINE__); assert(FALSE); } #endif } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Lk_Dist(phydbl *F, phydbl dist, t_mod *mod) { int i,j,k; phydbl lnL,len; int dim1,dim2; phydbl pi, pijk; // Compute likelihood of the model made of the // first class of the mixture. /* if(mod->is_mixt_mod == YES) mod = mod->next; */ /* assert(mod); */ if(mod->log_l == YES) dist = exp(dist); for(k=0;kras->n_catg;k++) { len = dist*mod->ras->gamma_rr->v[k]; if(len < mod->l_min) len = mod->l_min; else if(len > mod->l_max) len = mod->l_max; PMat(len,mod,mod->ns*mod->ns*k,mod->Pij_rr->v,NULL); /* PhyML_Printf("\n. p: %g len: %g",mod->Pij_rr->v[0],len); */ } dim1 = mod->ns*mod->ns; dim2 = mod->ns; lnL = .0; pi = -1.; pijk = -1.; for(i=0;ins-1;i++) { pi = mod->e_frq->pi->v[i]; for(j=i+1;jns;j++) { for(k=0;kras->n_catg;k++) { pijk = mod->Pij_rr->v[dim1*k+dim2*i+j]; lnL += (F[dim1*k+dim2*i+j] + F[dim1*k+dim2*j+i]) * log(pi * pijk); /* PhyML_Printf("\n. pijk: %g lnL: %g",pijk,lnL); */ } } } for(i=0;ins;i++) { pi = mod->e_frq->pi->v[i]; for(k=0;kras->n_catg;k++) { pijk = mod->Pij_rr->v[dim1*k+dim2*i+i]; lnL += F[dim1*k+dim2*i+i]* log(pi * pijk); /* PhyML_Printf("\n. pijk: %g lnL: %g",pijk,lnL); */ } } return lnL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Update_Lk_At_Given_Edge(t_edge *b_fcus, t_tree *tree) { Update_Partial_Lk(tree,b_fcus,b_fcus->left); Update_Partial_Lk(tree,b_fcus,b_fcus->rght); tree->c_lnL = Lk(b_fcus,tree); return tree->c_lnL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Init_Partial_Lk_Loc(t_tree *tree) { int i,j; t_node *d; int *patt_id_d; if(tree->is_mixt_tree == YES) return; for(i=0;i<2*tree->n_otu-1;++i) { for(j=0;jn_pattern;j++) { tree->a_edges[i]->p_lk_loc_left[j] = j; tree->a_edges[i]->p_lk_loc_rght[j] = j; } } for(i=0;in_otu;i++) { d = tree->a_nodes[i]; patt_id_d = (d == d->b[0]->left)?(d->b[0]->patt_id_left):(d->b[0]->patt_id_rght); for(j=0;jn_pattern;j++) { assert(tree->a_nodes[d->num]->c_seq); patt_id_d[j] = (int)tree->a_nodes[d->num]->c_seq->state[j]; } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Lk_Normal_Approx(t_tree *tree) { phydbl lnL; int i; int dim; phydbl first_order; dim = 2*tree->n_otu-3; lnL = Dnorm_Multi_Given_InvCov_Det(tree->rates->u_cur_l, tree->rates->mean_l, tree->rates->invcov, tree->rates->covdet, 2*tree->n_otu-3,YES); first_order = 0.0; for(i=0;irates->u_cur_l[i] - tree->rates->mean_l[i]) * tree->rates->grad_l[i]; lnL += first_order; return(lnL); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Wrap_Part_Lk_At_Given_Edge(t_edge *b, t_tree *tree, supert_tree *stree) { return -1.0; /* return PART_Lk_At_Given_Edge(b,stree);; */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Wrap_Part_Lk(t_edge *b, t_tree *tree, supert_tree *stree) { return -1.0; /* return PART_Lk(stree); */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Wrap_Lk(t_edge *b, t_tree *tree, supert_tree *stree) { Lk(NULL,tree); return tree->c_lnL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// #if (defined GEO) phydbl Wrap_Geo_Lk(t_edge *b, t_tree *tree, supert_tree *stree) { TIPO_Lk(tree); return tree->geo_lnL; } #endif ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Wrap_Lk_At_Given_Edge(t_edge *b, t_tree *tree, supert_tree *stree) { Lk(b,tree); return tree->c_lnL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// #if(defined PHYREX || PHYTIME) phydbl Wrap_Lk_Rates(t_edge *b, t_tree *tree, supert_tree *stree) { RATES_Lk(tree); return tree->rates->c_lnL; } #endif ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// #if(defined PHYREX || PHYTIME) phydbl Wrap_Lk_Times(t_edge *b, t_tree *tree, supert_tree *stree) { TIMES_Lk(tree); return tree->times->c_lnL; } #endif ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Wrap_Lk_Linreg(t_edge *b, t_tree *tree, supert_tree *stree) { /* RATES_Lk_Linreg(tree); */ return -1.; /* return tree->rates->c_lnL_linreg; */ } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl Wrap_Diff_Lk_Norm_At_Given_Edge(t_edge *b, t_tree *tree, supert_tree *stree) { phydbl diff; diff = Diff_Lk_Norm_At_Given_Edge(b,tree); return(-diff); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// int Check_Lk_At_Given_Edge(int verbose, t_tree *tree) { int res; int i; phydbl *lk; lk = (phydbl *)mCalloc(2*tree->n_otu-3,sizeof(phydbl)); res = 0; for(i=0;i<2*tree->n_otu-3;++i) { lk[i] = Lk(tree->a_edges[i],tree); if(verbose == YES) PhyML_Printf("\n. Edge %3d %13G %f %13G", tree->a_edges[i]->num,tree->a_edges[i]->l->v,lk[i], tree->a_edges[i]->l_var->v); } if(tree->n_root && tree->ignore_root == NO) { Lk(tree->n_root->b[1],tree); if(verbose == YES) PhyML_Printf("\nx Edge %3d %13G %f %13G", tree->n_root->b[1]->num,tree->n_root->b[1]->l->v,tree->c_lnL, tree->n_root->b[1]->l_var->v ); Lk(tree->n_root->b[2],tree); if(verbose == YES) PhyML_Printf("\nx Edge %3d %13G %f %13G", tree->n_root->b[2]->num,tree->n_root->b[2]->l->v,tree->c_lnL, tree->n_root->b[2]->l_var->v ); } res=1; for(i=1;i<2*tree->n_otu-3;i++) { if(FABS(lk[i]-lk[i-1]) > 1.E-2) res=0; } Free(lk); return res; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Computes the value of fact_sum_scale, the part of the scaling factors // that is common to all classes of the mixture and scale the // likelihood for each mixture using the part of the scaling // factors that is class-specific void Pull_Scaling_Factors(int site, t_edge *b, t_tree *tree) { unsigned int catg; const unsigned int ncatg = tree->mod->ras->n_catg; if(tree->apply_lk_scaling == NO) { tree->fact_sum_scale[site] = 0; for(catg=0;catgunscaled_site_lk_cat[site*ncatg+catg] = tree->site_lk_cat[catg]; return; } else { switch(tree->scaling_method) { case SCALE_RATE_SPECIFIC : { int *sum_scale_left_cat,*sum_scale_rght_cat; int exponent; phydbl max_sum_scale,min_sum_scale; phydbl sum,tmp,dum; sum_scale_left_cat = b->sum_scale_left_cat; sum_scale_rght_cat = b->sum_scale_rght_cat; max_sum_scale = (phydbl)BIG; min_sum_scale = -(phydbl)BIG; for(catg=0;catgsum_scale_left)? (b->sum_scale_left[site*ncatg+catg]): (0.0); sum_scale_rght_cat[catg] = (b->sum_scale_rght)? (b->sum_scale_rght[site*ncatg+catg]): (0.0); sum = sum_scale_left_cat[catg] + sum_scale_rght_cat[catg]; if(sum < .0) { PhyML_Fprintf(stderr,"\n. tree: %s\n",Write_Tree(tree)); PhyML_Fprintf(stderr,"\n. b->num = %d sum = %G root ? %d",sum,b->num,b == tree->e_root); PhyML_Fprintf(stderr,"\n. Err. in file %s at line %d.\n",__FILE__,__LINE__); Exit("\n"); } dum = log(FABS(tree->site_lk_cat[catg])); tmp = sum + ((phydbl)LOGBIG - dum)/(phydbl)LOG2; if(tmp < max_sum_scale) max_sum_scale = tmp; /* min of the maxs */ tmp = sum + ((phydbl)LOGSMALL - dum)/(phydbl)LOG2; if(tmp > min_sum_scale) min_sum_scale = tmp; /* max of the mins */ assert(isnan(tmp) == NO); } if(min_sum_scale > max_sum_scale) { #ifdef SAFEMODE PhyML_Printf("\n. Numerical precision issue alert."); PhyML_Printf("\n. min_sum_scale = %G max_sum_scale = %G",min_sum_scale,max_sum_scale); #endif min_sum_scale = max_sum_scale; } tree->fact_sum_scale[site] = (int)((max_sum_scale + min_sum_scale) / 2); /* Apply scaling factors */ for(catg=0;catgfact_sum_scale[site]; Rate_Correction(exponent,tree->site_lk_cat + catg); } break; } case SCALE_FAST : { int sum_scale_left,sum_scale_rght; sum_scale_left = (b->sum_scale_left)? (b->sum_scale_left[site]): (0.0); sum_scale_rght = (b->sum_scale_rght)? (b->sum_scale_rght[site]): (0.0); tree->fact_sum_scale[site] = sum_scale_left + sum_scale_rght; break; } } for(catg=0;catgunscaled_site_lk_cat[site*ncatg+catg] = tree->site_lk_cat[catg]; } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// // Tree should be ready for likelihood analysis when calling // this function. void Stepwise_Add_Lk(t_tree *tree) { t_edge **residuals,**targets,*best_target; int *nd_idx,i,j,n_targets,*tg_idx,n_opt; residuals = (t_edge **)mCalloc(tree->n_otu-3,sizeof(t_edge *)); targets = (t_edge **)mCalloc(2*tree->n_otu-3,sizeof(t_edge *)); best_target = NULL; nd_idx = Permutate(tree->n_otu-3); // Remove all tips except that corresponding to a_nodes[0], // a_nodes[1] and a_nodes[2]. for(i=0;in_otu-3;i++) { Prune_Subtree(tree->a_nodes[i+3]->v[0], tree->a_nodes[i+3], NULL, residuals+i, tree); } // Initial targets n_targets = 3; for(i=0;ia_nodes[i]->b[0]; // Regraft each tip on the tree at most parsimonious position for(i=0;in_otu-3;i++) { Set_Both_Sides(YES,tree); Lk(NULL,tree); printf("\n. [%d/%d]",i,tree->n_otu-3); tree->best_lnL = UNLIKELY; best_target = NULL; tg_idx = Permutate(n_targets); for(j=0;ja_nodes[nd_idx[i]+3]->v[0], NULL, residuals[i], NULL, tree); Update_PMat_At_Given_Edge(targets[tg_idx[j]],tree); Update_PMat_At_Given_Edge(tree->a_nodes[nd_idx[i]+3]->b[0],tree); Update_Partial_Lk(tree,residuals[i],tree->a_nodes[nd_idx[i]+3]->v[0]); Lk(residuals[i],tree); if(tree->c_lnL > tree->best_lnL) { tree->best_lnL = tree->c_lnL; best_target = targets[tg_idx[j]]; } Prune_Subtree(tree->a_nodes[nd_idx[i]+3]->v[0], tree->a_nodes[nd_idx[i]+3], NULL, residuals+i, tree); } assert(best_target); Graft_Subtree(best_target, tree->a_nodes[nd_idx[i]+3]->v[0], NULL, residuals[i], NULL, tree); n_opt = 0; do Optimize_Br_Len_Serie (2,tree); while(n_opt++ < 3); targets[n_targets] = residuals[i]; targets[n_targets+1] = tree->a_nodes[nd_idx[i]+3]->b[0]; Free(tg_idx); n_targets+=2; } Round_Optimize(tree,5); PhyML_Fprintf(stderr,"\n. lk: %f",tree->c_lnL); Exit("\n"); Free(nd_idx); Free(residuals); Free(targets); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* | | |b | |d / \ / \ / \ / \ /v1 \v2 Set p_lk and sum_scale for subtrees with d, v1 and v2 as root, Pij for edges b, and the two edges connecting d to v1 and d to v2; Account for rooted trees. */ void Set_All_Partial_Lk(t_node **n_v1, t_node **n_v2, phydbl **p_lk, int **sum_scale, int **p_lk_loc, phydbl **Pij1, phydbl **tPij1, phydbl **p_lk_v1, int **sum_scale_v1, phydbl **Pij2, phydbl **tPij2, phydbl **p_lk_v2, int **sum_scale_v2, t_node *d, t_edge *b, t_tree *tree #ifdef BEAGLE , int *dest_p_idx, int *child1_p_idx, int* child2_p_idx, int* Pij1_idx, int* Pij2_idx #endif ) { unsigned int i; assert(tree->is_mixt_tree == NO); assert(d->tax == NO); if(tree->n_root == NULL || tree->ignore_root == YES) { /* Does d lie on the "left" or "right" of the branch? */ if(d == b->left) { *p_lk = b->p_lk_left; *sum_scale = b->sum_scale_left; #ifdef BEAGLE *dest_p_idx = b->p_lk_left_idx; #endif } else { *p_lk = b->p_lk_rght; *sum_scale = b->sum_scale_rght; #ifdef BEAGLE *dest_p_idx = b->p_lk_rght_idx; #endif } *n_v1 = *n_v2 = NULL; for(i=0;i<3;++i) { if(d->b[i] != b) { if(!(*n_v1)) { *n_v1 = d->v[i]; #ifdef BEAGLE Set_Partial_Lk_One_Side(Pij1,tPij1,p_lk_v1,sum_scale_v1,d,d->b[i],tree,child1_p_idx,Pij1_idx); #else Set_Partial_Lk_One_Side(Pij1,tPij1,p_lk_v1,sum_scale_v1,d,d->b[i],tree); #endif } else if(!(*n_v2)) { *n_v2 = d->v[i]; #ifdef BEAGLE Set_Partial_Lk_One_Side(Pij2,tPij2,p_lk_v2,sum_scale_v2,d,d->b[i],tree,child2_p_idx,Pij2_idx); #else Set_Partial_Lk_One_Side(Pij2,tPij2,p_lk_v2,sum_scale_v2,d,d->b[i],tree); #endif } else { PhyML_Printf("\n. Issue detected with node %d.\n",d->num); assert(FALSE); } } } } else { if(b == tree->e_root) { if(d == tree->n_root->v[1]) b = tree->n_root->b[1]; else if(d == tree->n_root->v[2]) b = tree->n_root->b[2]; else assert(FALSE); } if(d == tree->n_root) { if(b == tree->n_root->b[1]) { *p_lk = tree->n_root->b[1]->p_lk_left; *sum_scale = tree->n_root->b[1]->sum_scale_left; #ifdef BEAGLE *dest_p_idx = tree->n_root->b[1]->p_lk_left_idx; #endif } else { *p_lk = tree->n_root->b[2]->p_lk_left; *sum_scale = tree->n_root->b[2]->sum_scale_left; #ifdef BEAGLE *dest_p_idx = tree->n_root->b[2]->p_lk_left_idx; #endif } *n_v1 = NULL; *Pij1 = NULL; *tPij1 = NULL; *p_lk_v1 = NULL; *sum_scale_v1 = NULL; if(b == tree->n_root->b[1]) { *n_v2 = tree->n_root->v[2]; *Pij2 = tree->n_root->b[2]->Pij_rr; *tPij2 = tree->n_root->b[2]->tPij_rr; *p_lk_v2 = tree->n_root->b[2]->p_lk_rght; *sum_scale_v2 = tree->n_root->b[2]->sum_scale_rght; #ifdef BEAGLE *child2_p_idx = tree->n_root->b[2]->p_lk_rght_idx; *Pij2_idx = tree->n_root->b[2]->Pij_rr_idx; #endif } else if(b == tree->n_root->b[2]) { *n_v2 = tree->n_root->v[1]; *Pij2 = tree->n_root->b[1]->Pij_rr; *tPij2 = tree->n_root->b[1]->tPij_rr; *p_lk_v2 = tree->n_root->b[1]->p_lk_rght; *sum_scale_v2 = tree->n_root->b[1]->sum_scale_rght; #ifdef BEAGLE *child2_p_idx = tree->n_root->b[1]->p_lk_rght_idx; *Pij2_idx = tree->n_root->b[1]->Pij_rr_idx; #endif } else assert(FALSE); } else if(d == tree->n_root->v[1] || d == tree->n_root->v[2]) { if(b == tree->n_root->b[1] || b == tree->n_root->b[2]) { if(b == tree->n_root->b[1]) { *p_lk = tree->n_root->b[1]->p_lk_rght; *sum_scale = tree->n_root->b[1]->sum_scale_rght; #ifdef BEAGLE *dest_p_idx = tree->n_root->b[1]->p_lk_rght_idx; #endif } else { *p_lk = tree->n_root->b[2]->p_lk_rght; *sum_scale = tree->n_root->b[2]->sum_scale_rght; #ifdef BEAGLE *dest_p_idx = tree->n_root->b[2]->p_lk_rght_idx; #endif } *n_v1 = *n_v2 = NULL; for(i=0;i<3;++i) { if(d->b[i] != tree->e_root) { if(!(*n_v1)) { *n_v1 = d->v[i]; #ifdef BEAGLE Set_Partial_Lk_One_Side(Pij1,tPij1,p_lk_v1,sum_scale_v1,d,d->b[i],tree,child1_p_idx,Pij1_idx); #else Set_Partial_Lk_One_Side(Pij1,tPij1,p_lk_v1,sum_scale_v1,d,d->b[i],tree); #endif } else { *n_v2 = d->v[i]; #ifdef BEAGLE Set_Partial_Lk_One_Side(Pij2,tPij2,p_lk_v2,sum_scale_v2,d,d->b[i],tree,child2_p_idx,Pij2_idx); #else Set_Partial_Lk_One_Side(Pij2,tPij2,p_lk_v2,sum_scale_v2,d,d->b[i],tree); #endif } } } } else { if(d == b->left) { *p_lk = b->p_lk_left; *sum_scale = b->sum_scale_left; *p_lk_loc = b->p_lk_loc_left; #ifdef BEAGLE *dest_p_idx = b->p_lk_left_idx; #endif } else { *p_lk = b->p_lk_rght; *sum_scale = b->sum_scale_rght; *p_lk_loc = b->p_lk_loc_rght; #ifdef BEAGLE *dest_p_idx = b->p_lk_rght_idx; #endif } *n_v1 = tree->n_root; #ifdef BEAGLE Set_Partial_Lk_One_Side(Pij1,tPij1,p_lk_v1,sum_scale_v1,d, (d == tree->n_root->v[1])? (tree->n_root->b[1]): (tree->n_root->b[2]), tree,child1_p_idx,Pij1_idx); #else Set_Partial_Lk_One_Side(Pij1,tPij1,p_lk_v1,sum_scale_v1,d, (d == tree->n_root->v[1])? (tree->n_root->b[1]): (tree->n_root->b[2]), tree); #endif for(i=0;i<3;i++) { if(d->b[i] != tree->e_root && d->b[i] != b) { *n_v2 = d->v[i]; #ifdef BEAGLE Set_Partial_Lk_One_Side(Pij2,tPij2,p_lk_v2,sum_scale_v2,d,d->b[i],tree,child2_p_idx,Pij2_idx); #else Set_Partial_Lk_One_Side(Pij2,tPij2,p_lk_v2,sum_scale_v2,d,d->b[i],tree); #endif break; } } } } else { if(d == b->left) { *p_lk = b->p_lk_left; *sum_scale = b->sum_scale_left; *p_lk_loc = b->p_lk_loc_left; #ifdef BEAGLE *dest_p_idx = b->p_lk_left_idx; #endif } else { *p_lk = b->p_lk_rght; *sum_scale = b->sum_scale_rght; *p_lk_loc = b->p_lk_loc_rght; #ifdef BEAGLE *dest_p_idx = b->p_lk_rght_idx; #endif } *n_v1 = *n_v2 = NULL; for(i=0;i<3;i++) { if(d->b[i] != b) { if(!(*n_v1)) { *n_v1 = d->v[i]; #ifdef BEAGLE Set_Partial_Lk_One_Side(Pij1,tPij1,p_lk_v1,sum_scale_v1,d,d->b[i],tree,child1_p_idx,Pij1_idx); #else Set_Partial_Lk_One_Side(Pij1,tPij1,p_lk_v1,sum_scale_v1,d,d->b[i],tree); #endif } else { *n_v2 = d->v[i]; #ifdef BEAGLE Set_Partial_Lk_One_Side(Pij2,tPij2,p_lk_v2,sum_scale_v2,d,d->b[i],tree,child2_p_idx,Pij2_idx); #else Set_Partial_Lk_One_Side(Pij2,tPij2,p_lk_v2,sum_scale_v2,d,d->b[i],tree); #endif } } } } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* | | |d / \ / \ / \b / \ / \x (either n_v1 or n_v2) Returns p_lk and sum_scale for subtree with x as root, Pij for edge b */ void Set_Partial_Lk_One_Side(phydbl **Pij, phydbl **tPij, phydbl **p_lk, int **sum_scale, t_node *d, t_edge *b, t_tree *tree #ifdef BEAGLE , int* child_p_idx, int* Pij_idx #endif ) { if(Pij != NULL) { *Pij = b->Pij_rr; *tPij = b->tPij_rr; #ifdef BEAGLE *Pij_idx = b->Pij_rr_idx; #endif } if(d->tax == NO) { if(d == b->left) // if d is on the left of b, then d's neighbor is on the right { *p_lk = (b->rght->tax == YES) ? b->p_lk_tip_r : b->p_lk_rght; *sum_scale = b->sum_scale_rght; #ifdef BEAGLE *child_p_idx = b->rght->tax? b->p_lk_tip_idx: b->p_lk_rght_idx; #endif if(*p_lk == NULL) PhyML_Printf("\n. b:%d b->left:%d b->rght:%d d:%d", b->num, b->left->num, b->rght->num, d->num); assert(*p_lk); } else { *p_lk = b->p_lk_left; *sum_scale = b->sum_scale_left; #ifdef BEAGLE *child_p_idx = b->rght->tax? b->p_lk_tip_idx: b->p_lk_left_idx; #endif if(*p_lk == NULL) PhyML_Printf("\n. b:%d b->left:%d b->rght:%d d:%d", b->num, b->left->num, b->rght->num, d->num); assert(*p_lk); } } else { #ifdef BEAGLE Warn_And_Exit(TODO_BEAGLE); #endif *p_lk = NULL; *sum_scale = NULL; PhyML_Printf("\n. WARNING. p_lk set to NULL. d->num: %d b->num: %d",d->num,b->num); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Switch_Partial_Lk_Post(t_node *a, t_node *d, t_edge *b, short int yesno, t_tree *tree) { if(a == tree->n_root) assert(FALSE); if(d->tax == NO) { int i; for(i=0;i<3;++i) if(d->v[i] != a) Switch_Partial_Lk_Post(d,d->v[i],d->b[i],yesno,tree); } if(b->left == d) b->update_partial_lk_left = yesno; else if(b->rght == d) b->update_partial_lk_rght = yesno; else assert(FALSE); return; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Switch_Partial_Lk_Pre(t_node *a, t_node *d, t_edge *b, short int yesno, t_tree *tree) { if(a == tree->n_root) assert(FALSE); if(d->tax == YES) return; else { int i; for(i=0;i<3;++i) { if(d->v[i] != a) { if(d->b[i]->left == d) d->b[i]->update_partial_lk_left = yesno; else if(d->b[i]->rght == d) d->b[i]->update_partial_lk_rght = yesno; else assert(FALSE); } } for(i=0;i<3;++i) if(d->v[i] != a) Switch_Partial_Lk_Pre(d,d->v[i],d->b[i],yesno,tree); } return; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void Partial_Lk_Inin(const phydbl *Pij1, const phydbl *plk1, const phydbl *Pij2, const phydbl *plk2, const int ns, phydbl *plk0) { unsigned int i,j; for(i=0;i 1.0 || plk1[i] < 1.0 || plk2[i] > 1.0 || plk2[i] < 1.0) break; if(i != ns) { for(i=0;i #ifndef LK_H #define LK_H #include "utilities.h" #include "optimiz.h" #include "models.h" #include "free.h" #include "times.h" #include "mixt.h" #include "avx.h" #include "sse.h" void Update_All_Partial_Lk(t_tree *tree); void Init_Tips_At_One_Site_Nucleotides_Float(char state, int pos, phydbl *p_lk); void Init_Tips_At_One_Site_AA_Float(char aa, int pos, phydbl *p_lk); void Get_All_Partial_Lk(t_tree *tree,t_edge *b_fcus,t_node *a,t_node *d); void Get_All_Partial_Lk_Scale(t_tree *tree,t_edge *b_fcus,t_node *a,t_node *d); void Post_Order_Lk(t_node *pere, t_node *fils, t_tree *tree); void Pre_Order_Lk(t_node *pere, t_node *fils, t_tree *tree); phydbl Lk(t_edge *b, t_tree *tree); void Site_Lk(t_tree *tree); phydbl Return_Abs_Lk(t_tree *tree); matrix *ML_Dist(calign *data, t_mod *mod); phydbl Lk_Given_Two_Seq(calign *data, int numseq1, int numseq2, phydbl dist, t_mod *mod, phydbl *loglk); void Unconstraint_Lk(t_tree *tree); void Update_Partial_Lk(t_tree *tree,t_edge *b_fcus,t_node *n); void Update_Partial_Lk_Generic(t_tree *tree,t_edge *b_fcus,t_node *n); /* void Default_Update_Partial_Lk(t_tree *tree,t_edge *b_fcus,t_node *n); */ void Init_Partial_Lk_Tips_Double(t_tree *tree); void Init_Partial_Lk_Tips_Int(t_tree *tree); void Init_Partial_Lk_At_One_Node(t_node *a, t_tree *tree); void Update_PMat_At_Given_Edge(t_edge *b_fcus, t_tree *tree); void Sort_Sites_Based_On_Lk(t_tree *tree); void Get_Partial_Lk_Scale(t_tree *tree, t_edge *b_fcus, t_node *a, t_node *d); void Get_Partial_Lk(t_tree *tree, t_edge *b_fcus, t_node *a, t_node *d); void Init_Tips_At_One_Site_Nucleotides_Int(char state, int pos, short int *p_pars); void Init_Tips_At_One_Site_AA_Int(char aa, int pos, short int *p_pars); void Update_Partial_Lk_Along_A_Path(t_node **path, int path_length, t_tree *tree); phydbl Lk_Dist(phydbl *F, phydbl dist, t_mod *mod); phydbl Update_Lk_At_Given_Edge(t_edge *b_fcus, t_tree *tree); void Update_Partial_Lk_Greedy(t_tree *tree, t_edge *b_fcus, t_node *n); void Get_All_Partial_Lk_Scale_Greedy(t_tree *tree, t_edge *b_fcus, t_node *a, t_node *d); phydbl Lk_Triplet(t_node *a, t_node *d, t_tree *tree); phydbl *Post_Prob_Rates_At_Given_Edge(t_edge *b, phydbl *post_prob, t_tree *tree); phydbl Lk_With_MAP_Branch_Rates(t_tree *tree); void Init_Tips_At_One_Site_Generic_Int(char *state, int ns, int state_len, int pos, short int *p_pars); void Init_Tips_At_One_Site_Generic_Float(char *state, int ns, int state_len, int pos, phydbl *p_lk); void Backup_Partial_Lk(t_node *d, t_edge *b, t_tree *tree); void Restore_Partial_Lk(t_node *d, t_edge *b, t_tree *tree); void Backup_Partial_Scale(t_node *d, t_edge *b, t_tree *tree); void Restore_Partial_Scale(t_node *d, t_edge *b, t_tree *tree); void Init_Partial_Lk_Loc(t_tree *tree); phydbl Lk_Normal_Approx(t_tree *tree); phydbl Wrap_Lk(t_edge *b, t_tree *tree, supert_tree *stree); phydbl Wrap_Lk_At_Given_Edge(t_edge *b, t_tree *tree, supert_tree *stree); phydbl Wrap_Part_Lk_At_Given_Edge(t_edge *b, t_tree *tree, supert_tree *stree); phydbl Wrap_Part_Lk(t_edge *b, t_tree *tree, supert_tree *stree); phydbl Wrap_Geo_Lk(t_edge *b, t_tree *tree, supert_tree *stree); phydbl Wrap_Diff_Lk_Norm_At_Given_Edge(t_edge *b, t_tree *tree, supert_tree *stree); phydbl Wrap_Lk_Rates(t_edge *b, t_tree *tree, supert_tree *stree); phydbl Wrap_Lk_Linreg(t_edge *b, t_tree *tree, supert_tree *stree); phydbl Wrap_Lk_Times(t_edge *b, t_tree *tree, supert_tree *stree); phydbl Lk_LastFirst(t_tree *tree); int Check_Lk_At_Given_Edge(int verbose, t_tree *tree); void Stepwise_Add_Lk(t_tree *tree); void Update_Eigen_Lr(t_edge *b, t_tree *tree); phydbl dLk(phydbl *l, t_edge *b, t_tree *tree); phydbl Lk_Core(int state, int ambiguity_check,phydbl *p_lk_left, phydbl *p_lk_rght,phydbl *Pij_rr,phydbl *tPij_rr,t_edge *b,t_tree *tree); /* void Lk_dLk_Core_Eigen_Lr(phydbl *expl, phydbl *dot_prod, t_edge *b, phydbl *lk, phydbl *dlk, t_tree *tree); */ phydbl Lk_Core_Eigen_Lr(phydbl *expl, phydbl *dot_prod, t_edge *b, t_tree *tree); phydbl Invariant_Lk(int fact_sum_scale, int site, int *num_prec_issue, t_tree *tree); void Composite_Lk(t_tree *tree); int Sample_Ancestral_Seq_Core(t_node *a, t_node *d, t_edge *b, int r_cat, int site, t_tree *tree); void Default_Update_Partial_Lk(t_tree *tree, t_edge *b, t_node *d); void Lk_dLk_Core_Eigen_Lr(phydbl *expl, phydbl *dot_prod, t_edge *b, phydbl *lk, phydbl *dlk, t_tree *tree); void Lk_dLk_Core_One_Class_Eigen_Lr(phydbl *dot_prod, phydbl *expl, unsigned int ns, phydbl *lk, phydbl *dlk); phydbl Lk_Core_One_Class_Eigen_Lr(phydbl *dot_prod, phydbl *expl, int ns); void Core_Default_Update_Partial_Lk(const t_node *n_v1, const t_node *n_v2, phydbl *plk0, const phydbl *plk1, const phydbl *plk2, const phydbl *Pij1, const phydbl *Pij2, int *sum_scale0, const int *sum_scale1, const int *sum_scale2, const int ns, const int ncatg, const int npatterns, const int apply_scaling, const phydbl *wght); /* phydbl Lk_Core_One_Class_Eigen_Lr(phydbl *dot_prod, phydbl *expl, int ns); */ /* void Lk_dLk_Core_One_Class_Eigen_Lr(phydbl *dot_prod, phydbl *expl, unsigned int ns, phydbl *lk, phydbl *dlk); */ phydbl Lk_Core_One_Class_No_Eigen_Lr(phydbl *p_lk_left, phydbl *p_lk_rght, phydbl *Pij,phydbl *pi, int ns, int ambiguity_check, int state); void Pull_Scaling_Factors(int site,t_edge *b,t_tree *tree); void Set_All_Partial_Lk(t_node **n_v1, t_node **n_v2,phydbl **p_lk, int **sum_scale, int **p_lk_loc,phydbl **Pij1, phydbl **tPij1, phydbl **p_lk_v1, int **sum_scale_v1,phydbl **Pij2, phydbl **tPij2, phydbl **p_lk_v2, int **sum_scale_v2,t_node *d, t_edge *b, t_tree *tree #ifdef BEAGLE , int *dest_p_idx, int *child1_p_idx, int* child2_p_idx, int* Pij1_idx, int* Pij2_idx #endif ); void Set_Partial_Lk_One_Side(phydbl **Pij, phydbl **tPij, phydbl **p_lk, int **sum_scale, t_node *d, t_edge *b, t_tree *tree #ifdef BEAGLE , int* child_p_idx, int* Pij_idx #endif ); void Rate_Correction(int exponent, phydbl *site_lk_cat); void Switch_Partial_Lk_Pre(t_node *a, t_node *d, t_edge *b, short int yesno, t_tree *tree); void Switch_Partial_Lk_Post(t_node *a, t_node *d, t_edge *b, short int yesno, t_tree *tree); void Partial_Lk_Inin(const phydbl *Pij1, const phydbl *plk1, const phydbl *Pij2, const phydbl *plk2, const int ns, phydbl *plk0); void Partial_Lk_Exex(const phydbl *Pij1, const int state1, const phydbl *Pij2, const int state2, const int ns, phydbl *plk0); void Partial_Lk_Exin(const phydbl *Pij1, const int state1, const phydbl *Pij2, const phydbl *plk2, const int ns, phydbl *plk0); #endif phyml-3.3.20220408/src/location.c000066400000000000000000000152001422404706600161560ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ /* Routines that implement the spatial part of Etheridge and Barton's model of continuous-space coalescent and other models of migration/spatial dispersion. */ #include "location.h" ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// #ifdef PHYREX phydbl LOCATION_Lk(t_tree *tree) { phydbl lnL; lnL = UNLIKELY; if(tree->mmod->use_locations == NO) { tree->mmod->c_lnL = 0.0; return(tree->mmod->c_lnL); } switch(tree->mmod->model_id) { case SLFV_GAUSSIAN : { lnL = SLFV_Lk_Gaussian(tree); break; } case SLFV_UNIFORM : { PhyML_Fprintf(stderr,"\n. SLFV model with rectangle is not implemented. Sorry..."); assert(false); break; } case RRW_GAMMA : case RRW_LOGNORMAL : { if(tree->mmod->integrateAncestralLocations == NO) lnL = RRW_Lk(tree); else lnL = RRW_Independent_Contrasts(tree); break; } case RW : { lnL = RW_Lk(tree); break; } default : assert(false); } if(isinf(lnL) || isnan(lnL)) lnL = UNLIKELY; tree->mmod->c_lnL = lnL; return(tree->mmod->c_lnL); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl LOCATION_Prior(t_tree *tree) { phydbl lnP; lnP = 0.0; if(tree->mmod->use_locations == NO) { tree->mmod->c_lnL = 0.0; return(tree->mmod->c_lnL); } switch(tree->mmod->model_id) { case SLFV_GAUSSIAN : { lnP = UNLIKELY; break; } case SLFV_UNIFORM : { lnP = UNLIKELY; break; } case RRW_GAMMA : case RRW_LOGNORMAL : { lnP = RRW_Prior(tree); break; } case RW : { lnP = RW_Prior(tree); break; } default : assert(false); } if(isinf(lnP) || isnan(lnP)) lnP = UNLIKELY; tree->mmod->c_lnP = lnP; return(tree->mmod->c_lnP); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl LOCATION_Lk_Range(t_dsk *young, t_dsk *old, t_tree *tree) { if(tree->mmod->use_locations == NO) return(0.0); switch(tree->mmod->model_id) { case SLFV_GAUSSIAN : { return(SLFV_Lk_Gaussian_Range(young,old,tree)); break; } case SLFV_UNIFORM : { PhyML_Fprintf(stderr,"\n. SLFV model with rectangle is not implemented. Sorry..."); assert(false); break; } case RW : case RRW_GAMMA : case RRW_LOGNORMAL : { return(RRW_Lk_Range(young,old,tree)); break; } default : assert(FALSE); } return(-1.); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl LOCATION_Lk_Path(t_dsk *young, t_dsk *old, t_tree *tree) { if(tree->mmod->use_locations == NO) return(0.0); switch(tree->mmod->model_id) { case SLFV_GAUSSIAN : { return(SLFV_Lk_Gaussian_Range(young,old,tree)); break; } case SLFV_UNIFORM : { PhyML_Fprintf(stderr,"\n. SLFV model with rectangle is not implemented. Sorry..."); assert(false); break; } case RW : case RRW_GAMMA : case RRW_LOGNORMAL : { return(RRW_Forward_Lk_Path(old->ldsk,young->ldsk,tree)); break; } default : assert(FALSE); } return(-1.); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl LOCATION_Forward_Lk_Path(t_ldsk *a, t_ldsk *d, t_tree *tree) { phydbl lnL; lnL = UNLIKELY; switch(tree->mmod->model_id) { case SLFV_GAUSSIAN : { assert(false); break; } case SLFV_UNIFORM : { assert(false); break; } case RW : case RRW_GAMMA : case RRW_LOGNORMAL : { lnL = RRW_Forward_Lk_Path(a,d,tree); break; } default : assert(FALSE); } return lnL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl LOCATION_Lk_Core(t_dsk *disk, t_tree *tree) { phydbl lnL; switch(tree->mmod->model_id) { case SLFV_GAUSSIAN : { lnL = SLFV_Lk_Gaussian_Core(disk,tree); break; } case SLFV_UNIFORM : { PhyML_Fprintf(stderr,"\n. SLFV model with rectangle is not implemented. Sorry..."); assert(false); break; } case RW : case RRW_GAMMA : case RRW_LOGNORMAL : { lnL = RRW_Lk_Core(disk,tree); break; } default : assert(FALSE); } return lnL; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void LOCATION_Sample_Path(t_ldsk *young, t_ldsk *old, phydbl *sd, phydbl *global_hr, t_tree *tree) { switch(tree->mmod->model_id) { case SLFV_GAUSSIAN : { SLFV_Sample_Path(young,old,sd,global_hr,tree); break; } case SLFV_UNIFORM : { PhyML_Fprintf(stderr,"\n. SLFV model with rectangle is not implemented. Sorry..."); assert(false); break; } case RW : case RRW_GAMMA : case RRW_LOGNORMAL : { assert(false); break; } default : assert(FALSE); } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// char *LOCATION_Model_Id(t_phyrex_mod *mmod) { char *s; s = (char *)mCalloc(T_MAX_LINE,sizeof(char)); strcpy(s,"none"); switch(mmod->model_id) { case SLFV_GAUSSIAN : { strcpy(s,"spatial coalescent (Gaussian)"); break; } case SLFV_UNIFORM : { strcpy(s,"spatial coalescent (Rectangle)"); break; } case RW : { strcpy(s,"strict random walk"); break; } case RRW_GAMMA : { strcpy(s,"relaxed random walk (Gamma)"); break; } case RRW_LOGNORMAL : { strcpy(s,"relaxed random walk (Lognormal)"); break; } default : assert(FALSE); } return(s); } #endif phyml-3.3.20220408/src/location.h000066400000000000000000000015321422404706600161660ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ #include #ifndef LOCATION_H #define LOCATION_H #include "utilities.h" phydbl LOCATION_Lk(t_tree *tree); phydbl LOCATION_Prior(t_tree *tree); phydbl LOCATION_Lk_Range(t_dsk *young, t_dsk *old, t_tree *tree); phydbl LOCATION_Lk_Core(t_dsk *disk, t_tree *tree); void LOCATION_Sample_Path(t_ldsk *young, t_ldsk *old, phydbl *sd, phydbl *global_hr, t_tree *tree); phydbl LOCATION_Lk_Path(t_dsk *young, t_dsk *old, t_tree *tree); char *LOCATION_Model_Id(t_phyrex_mod *mmod); phydbl LOCATION_Forward_Lk_Path(t_ldsk *a, t_ldsk *d, t_tree *tree); #endif phyml-3.3.20220408/src/m4.c000066400000000000000000001401161422404706600146730ustar00rootroot00000000000000/* PhyML: a program that computes maximum likelihood phylogenies from DNA or AA homologous sequences. Copyright (C) Stephane Guindon. Oct 2003 onward. All parts of the source except where indicated are distributed under the GNU public licence. See http://www.opensource.org for details. */ /* Routines for Markov-Modulated Markov Models (M4) */ #include "m4.h" int M4_main(int argc, char **argv) { calign *cdata; option *io; t_tree *tree; int num_data_set; int num_tree,num_rand_tree; t_mod *mod; time_t t_beg,t_end; phydbl best_lnL; int r_seed; char *most_likely_tree=NULL; #ifdef MPI int rc; rc = MPI_Init(&argc,&argv); if (rc != MPI_SUCCESS) { PhyML_Printf("\n== Err. starting MPI program. Terminating.\n"); MPI_Abort(MPI_COMM_WORLD, rc); } MPI_Comm_size(MPI_COMM_WORLD,&Global_numTask); MPI_Comm_rank(MPI_COMM_WORLD,&Global_myRank); #endif #ifdef QUIET setvbuf(stdout,NULL,_IOFBF,2048); #endif tree = NULL; mod = NULL; best_lnL = UNLIKELY; io = (option *)Get_Input(argc,argv); r_seed = (io->r_seed < 0)?(time(NULL)):(io->r_seed); srand(r_seed); io->r_seed = r_seed; if(io->in_tree == 2) Test_Multiple_Data_Set_Format(io); else io->n_trees = 1; if((io->n_data_sets > 1) && (io->n_trees > 1)) { io->n_data_sets = MIN(io->n_trees,io->n_data_sets); io->n_trees = MIN(io->n_trees,io->n_data_sets); } for(num_data_set=0;num_data_setn_data_sets;num_data_set++) { best_lnL = UNLIKELY; Get_Seq(io); Make_Model_Complete(io->mod); Set_Model_Name(io->mod); Print_Settings(io); mod = io->mod; if(io->data) { if(io->n_data_sets > 1) PhyML_Printf("\n. Data set [#%d]\n",num_data_set+1); cdata = Compact_Data(io->data,io); Free_Seq(io->data,cdata->n_otu); if(cdata) Check_Ambiguities(cdata,io->datatype,io->state_len); else { PhyML_Printf("\n== Err. in file %s at line %d\n",__FILE__,__LINE__); Warn_And_Exit(""); } for(num_tree=(io->n_trees == 1)?(0):(num_data_set);num_tree < io->n_trees;num_tree++) { if(!io->mod->s_opt->random_input_tree) io->mod->s_opt->n_rand_starts = 1; for(num_rand_tree=0;num_rand_treemod->s_opt->n_rand_starts;num_rand_tree++) { if((io->mod->s_opt->random_input_tree) && (io->mod->s_opt->topo_search != NNI_MOVE)) if(!io->quiet) PhyML_Printf("\n. [Random start %3d/%3d]\n",num_rand_tree+1,io->mod->s_opt->n_rand_starts); Init_Model(cdata,mod,io); if(io->mod->use_m4mod) M4_Init_Model(mod->m4mod,cdata,mod); switch(io->in_tree) { case 0 : case 1 : { tree = Dist_And_BioNJ(cdata,mod,io); break; } case 2 : { tree = Read_User_Tree(cdata,mod,io); break; } } if(!tree) continue; time(&t_beg); time(&(tree->t_beg)); tree->mod = mod; tree->io = io; tree->data = cdata; tree->n_pattern = tree->data->crunch_len; Set_Both_Sides(YES,tree); if(mod->s_opt->random_input_tree) Random_Tree(tree); if((!num_data_set) && (!num_tree) && (!num_rand_tree)) Check_Memory_Amount(tree); Make_Tree_For_Pars(tree); Make_Tree_For_Lk(tree); Make_Spr(tree); if(io->do_alias_subpatt) { MIXT_Set_Alias_Subpatt(YES,tree); Lk(NULL,tree); MIXT_Set_Alias_Subpatt(NO,tree); } if(tree->mod->s_opt->opt_topo) { if(tree->mod->s_opt->topo_search == NNI_MOVE) Simu_Loop(tree); else if(tree->mod->s_opt->topo_search == SPR_MOVE) Global_Spr_Search(tree); else Best_Of_NNI_And_SPR(tree); } else { if(tree->mod->s_opt->opt_subst_param || tree->mod->s_opt->opt_bl) Round_Optimize(tree,ROUND_MAX); else Lk(NULL,tree); } Set_Both_Sides(YES,tree); Lk(NULL,tree); Pars(NULL,tree); Get_Tree_Size(tree); PhyML_Printf("\n. Log likelihood of the current tree: %f.\n",tree->c_lnL); Exit("\n"); /* */ M4_Compute_Proba_Hidden_States_On_Edges(tree); /* */ Get_Best_Root_Position(tree); /* Print the tree estimated using the current random (or BioNJ) starting tree */ if(io->mod->s_opt->n_rand_starts > 1) { Br_Len_Involving_Invar(tree); Print_Tree(io->fp_out_trees,tree); fflush(NULL); } /* Record the most likely tree in a string of characters */ if(tree->c_lnL > best_lnL) { best_lnL = tree->c_lnL; Br_Len_Involving_Invar(tree); if(most_likely_tree) Free(most_likely_tree); most_likely_tree = Write_Tree(tree); Get_Tree_Size(tree); } /* JF(tree); */ time(&t_end); Print_Fp_Out(io->fp_out_stats,t_beg,t_end,tree, io,num_data_set+1, (tree->mod->s_opt->n_rand_starts > 1)? (num_rand_tree):(num_tree),YES, io->precision); if(tree->io->print_site_lnl) Print_Site_Lk(tree,io->fp_out_lk); /* Start from BioNJ tree */ if((num_rand_tree == io->mod->s_opt->n_rand_starts-1) && (tree->mod->s_opt->random_input_tree)) { /* Do one more iteration in the loop, but don't randomize the tree */ num_rand_tree--; tree->mod->s_opt->random_input_tree = 0; } Free_Spr_List_One_Edge(tree); Free_One_Spr(tree->best_spr); if(tree->mat) Free_Mat(tree->mat); Free_Tree_Pars(tree); Free_Tree_Lk(tree); Free_Tree(tree); } /* Launch bootstrap analysis */ if(io->do_boot || io->do_tbe) { if(!io->quiet) PhyML_Printf("\n. Launch bootstrap analysis on the most likely tree...\n"); #ifdef MPI MPI_Bcast (most_likely_tree, strlen(most_likely_tree)+1, MPI_CHAR, 0, MPI_COMM_WORLD); if(!io->quiet) PhyML_Printf("\n. The bootstrap analysis will use %d CPUs.\n",Global_numTask); #endif most_likely_tree = Bootstrap_From_String(most_likely_tree,cdata,mod,io); } else if(io->ratio_test) { /* Launch aLRT */ if(!io->quiet) PhyML_Printf("\n. Compute aLRT branch supports on the most likely tree...\n"); most_likely_tree = aLRT_From_String(most_likely_tree,cdata,mod,io); } /* Print the most likely tree in the output file */ if(!io->quiet) PhyML_Printf("\n. Printing the most likely tree in file '%s'...\n", Basename(io->out_tree_file)); if(io->n_data_sets == 1) rewind(io->fp_out_tree); PhyML_Fprintf(io->fp_out_tree,"%s\n",most_likely_tree); if(io->n_trees > 1 && io->n_data_sets > 1) break; } Free_Calign(cdata); } else { PhyML_Printf("\n. No data was found.\n"); PhyML_Printf("\n. Err in file %s at line %d\n",__FILE__,__LINE__); Warn_And_Exit(""); } Free_Model_Complete(mod); } if(most_likely_tree) Free(most_likely_tree); if(mod->s_opt->n_rand_starts > 1) PhyML_Printf("\n. Best log likelihood: %f\n",best_lnL); Free_Optimiz(mod->s_opt); Free_Model_Basic(mod); if(io->fp_in_align) fclose(io->fp_in_align); if(io->fp_in_tree) fclose(io->fp_in_tree); if(io->fp_out_lk) fclose(io->fp_out_lk); if(io->fp_out_tree) fclose(io->fp_out_tree); if(io->fp_out_trees) fclose(io->fp_out_trees); if(io->fp_out_stats) fclose(io->fp_out_stats); Free_Input(io); time(&t_end); Print_Time_Info(t_beg,t_end); #ifdef MPI MPI_Finalize(); #endif return 0; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* Allocate memory */ m4 *M4_Make_Light() { m4 *m4mod; m4mod = (m4 *)mCalloc(1,sizeof(m4)); M4_Set_M4mod_Default(m4mod); return m4mod; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void M4_Set_M4mod_Default(m4 *m4mod) { m4mod->use_cov_alpha = 1; m4mod->use_cov_alpha = 0; m4mod->n_h = 3; m4mod->n_o = 4; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* Allocate memory */ void M4_Make_Complete(int n_h, int n_o, m4 *m4mod) { int i; m4mod->n_h = n_h; m4mod->n_o = n_o; m4mod->n_o = n_o; m4mod->o_rr = (phydbl *)mCalloc(n_o*n_o,sizeof(phydbl)); m4mod->o_fq = (phydbl *)mCalloc(n_o,sizeof(phydbl)); m4mod->o_mats = (phydbl **)mCalloc(n_h,sizeof(phydbl *)); for(i=0;io_mats[i] = (phydbl *)mCalloc(n_o*n_o,sizeof(phydbl)); m4mod->h_mat = (phydbl *)mCalloc(n_h*n_h,sizeof(phydbl)); m4mod->h_rr = (phydbl *)mCalloc(n_h*n_h,sizeof(phydbl)); m4mod->h_fq = (phydbl *)mCalloc(n_h,sizeof(phydbl)); m4mod->multipl = (phydbl *)mCalloc(n_h,sizeof(phydbl)); m4mod->multipl_unscaled = (phydbl *)mCalloc(n_h,sizeof(phydbl)); m4mod->h_fq_unscaled = (phydbl *)mCalloc(n_h,sizeof(phydbl)); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// /* Fill in the (big) rate matrix of the M4 t_mod */ void M4_Update_Qmat(m4 *m4mod, t_mod *mod) { int i,j; int n_s, n_o, n_h; phydbl mr, sum; /* The number of states in M4 models is the product of the number of hidden states (or classes) by the number of observable states */ n_s = mod->ns; n_o = m4mod->n_o; n_h = m4mod->n_h; /* Set the relative substitution rates */ if(mod->m4mod->use_cov_alpha) { DiscreteGamma(m4mod->h_fq,m4mod->multipl,m4mod->alpha,m4mod->alpha,m4mod->n_h,mod->ras->gamma_median); } else if(mod->m4mod->use_cov_free) { sum = .0; for(i=0;im4mod->n_h;i++) sum += FABS(mod->m4mod->h_fq_unscaled[i]); for(i=0;im4mod->n_h;i++) mod->m4mod->h_fq[i] = FABS(mod->m4mod->h_fq_unscaled[i])/sum; do { sum = .0; for(i=0;im4mod->n_h;i++) { if(mod->m4mod->h_fq[i] < 0.01) mod->m4mod->h_fq[i]=0.01; if(mod->m4mod->h_fq[i] > 0.99) mod->m4mod->h_fq[i]=0.99; sum += mod->m4mod->h_fq[i]; } for(i=0;im4mod->n_h;i++) mod->m4mod->h_fq[i]/=sum; } while((sum > 1.01) || (sum < 0.99)); /* Make sure the multipliers are centered on 1.0 */ sum = .0; for(i=0;im4mod->n_h;i++) sum += FABS(mod->m4mod->multipl_unscaled[i]) * mod->m4mod->h_fq[i]; for(i=0;im4mod->n_h;i++) mod->m4mod->multipl[i] = mod->m4mod->multipl_unscaled[i] / sum; /* printf("\n. WARNING\n"); */ /* mod->m4mod->h_fq[0] = 1./3; */ /* mod->m4mod->h_fq[1] = 1./3; */ /* mod->m4mod->h_fq[2] = 1./3; */ /* mod->m4mod->multipl[0] = 1.0; */ /* mod->m4mod->multipl[1] = 1.0; */ /* mod->m4mod->multipl[2] = 1.0; */ sum = 0; for(i=0;im4mod->n_h;i++) sum += mod->m4mod->multipl[i] * mod->m4mod->h_fq[i]; if(sum < 0.99 || sum > 1.01) { PhyML_Printf("\n. sum = %f",sum); PhyML_Printf("\n. Err in file %s at line %d\n\n",__FILE__,__LINE__); Warn_And_Exit("\n"); } /* PhyML_Printf("\n__ "); */ /* for(i=0;im4mod->n_h;i++) PhyML_Printf("\n.%f %f %f", */ /* mod->m4mod->h_fq[i], */ /* mod->m4mod->h_fq_unscaled[i], */ /* mod->m4mod->multipl[i]); */ } /* PhyML_Printf("\n."); */ /* PhyML_Printf("\n. M4 model parameters"); */ /* m4mod->delta=.0; */ /* PhyML_Printf("\n. Delta = %f",m4mod->delta); */ /* for(i=0;im4mod->n_h;i++) PhyML_Printf("\n. multipl %d = %f",i,m4mod->multipl[i]); */ /* for(i=0;im4mod->n_h;i++) PhyML_Printf("\n. fq %d = %f",i,m4mod->h_fq[i]); */ /* Set up the stationary frequency vector */ for(i=0;ie_frq->pi->v[i] = m4mod->o_fq[i%n_o] * m4mod->h_fq[i/n_o]; if(mod->whichmodel != CUSTOM && mod->whichmodel != GTR && mod->io->datatype == NT) { phydbl kappa1,kappa2; if((mod->whichmodel != F84) && (mod->whichmodel != TN93)) mod->lambda->v = 1.; else if(mod->whichmodel == F84) { mod->lambda->v = Get_Lambda_F84(mod->e_frq->pi->v,&(mod->kappa->v)); } kappa2 = mod->kappa->v*2./(1.+mod->lambda->v); kappa1 = kappa2 * mod->lambda->v; /* A <-> C */ m4mod->o_rr[0] = 1.0; /* A <-> G */ m4mod->o_rr[1] = kappa2; /* A <-> T */ m4mod->o_rr[2] = 1.0; /* C <-> G */ m4mod->o_rr[3] = 1.0; /* C <-> T */ m4mod->o_rr[4] = kappa1; } /* Fill in the matrices of nucleotide or amino-acid substitution rates here */ Update_Qmat_Generic(m4mod->o_rr, m4mod->o_fq, m4mod->n_o, m4mod->o_mats[0]); /* Print_Square_Matrix_Generic(n_o,m4mod->o_mats[0]); */ /* Multiply each of these matrices by a relative substitution rate */ for(i=1;in_h;i++) For(j,n_o*n_o) m4mod->o_mats[i][j] = m4mod->o_mats[0][j]*m4mod->multipl[i]; For(j,n_o*n_o) m4mod->o_mats[0][j] *= m4mod->multipl[0]; For(i,n_s*n_s) mod->r_mat->qmat->v[i] = .0; /* Diagonal blocks (i.e, nucleotide substitutions), symmetric */ for(i=0;ir_mat->qmat->v[i*n_s+j] = m4mod->o_mats[(int)(i/n_o)][(i%n_o)*n_o+j%n_o]; mod->r_mat->qmat->v[j*n_s+i] = mod->r_mat->qmat->v[i*n_s+j] * m4mod->o_fq[i%n_o] / m4mod->o_fq[j%n_o]; } } } /* Work out scaling factor such that the expected number of observed state substitution along a branch of length 1 is 1.*/ mr = .0; for(i=0;ir_mat->qmat->v[i*n_s+j]; mr += sum * m4mod->o_fq[i%n_o] * m4mod->h_fq[(int)(i/n_o)]; } /* Scale the diagonal blocks */ For(i,n_s*n_s) mod->r_mat->qmat->v[i] /= mr; /* We are done with the diagonal blocks. Let's fill the non-diagonal ones now. */ /* Fill the matrix of substitution rate across classes (switches) here */ Update_Qmat_Generic(m4mod->h_rr, m4mod->h_fq, m4mod->n_h, m4mod->h_mat); /* Print_Square_Matrix_Generic(m4mod->n_h,m4mod->h_mat); */ /* Multiply this matrix by the switching rate */ For(i,n_h*n_h) m4mod->h_mat[i] *= m4mod->delta; /* Fill the non diagonal blocks */ for(i=0;ir_mat->qmat->v[i*n_s+j] = m4mod->h_mat[(int)(i/n_o)*n_h+(int)(j/n_o)]; mod->r_mat->qmat->v[j*n_s+i] = mod->r_mat->qmat->v[i*n_s+j] * m4mod->h_fq[(int)(i/n_o)] / m4mod->h_fq[(int)(j/n_o)]; } } } } /* Note: class equilibrium frequencies are already built in the h_mat matrix. No need to 'add' these frequencies later on. */ /* We are done with the non diagonal blocks */ /* Diagonal cells */ for(i=0;ir_mat->qmat->v[i*n_s+j]; } mod->r_mat->qmat->v[i*n_s+i] = -sum; } For(i,n_s*n_s) mod->eigen->q[i] = mod->r_mat->qmat->v[i]; } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void M4_Init_Partial_Lk_Tips_Double(t_tree *tree) { int curr_site,i,j,k,l,dim1,dim2,dim3; dim1 = tree->mod->ras->n_catg * tree->mod->m4mod->n_h * tree->mod->m4mod->n_o; dim2 = tree->mod->m4mod->n_h * tree->mod->m4mod->n_o; dim3 = tree->mod->m4mod->n_o; Fors(curr_site,tree->data->crunch_len,tree->mod->io->state_len) { for(i=0;in_otu;i++) { for(j=1;jmod->m4mod->n_h;j++) { for(k=0;kmod->m4mod->n_o;k++) { tree->a_nodes[i]->b[0]->p_lk_rght[curr_site*dim1 + 0*dim2 + j*dim3+k] = tree->a_nodes[i]->b[0]->p_lk_rght[curr_site*dim1 + 0*dim2 + 0*dim3+k]; printf("\n() i=%d plk=%f", curr_site*dim1 + 0*dim2 + j*dim3+k, tree->a_nodes[i]->b[0]->p_lk_rght[curr_site*dim1 + 0*dim2 + j*dim3+k]); /* tree->a_nodes[i]->b[0]->p_lk_rght[curr_site][0][j*tree->mod->m4mod->n_o+k] = */ /* tree->a_nodes[i]->b[0]->p_lk_rght[curr_site][0][k]; */ } for(k=0;kmod->m4mod->n_o;k++) for(l=1;lmod->ras->n_catg;l++) tree->a_nodes[i]->b[0]->p_lk_rght[curr_site*dim1 + l*dim2 + j*dim3+k] = tree->a_nodes[i]->b[0]->p_lk_rght[curr_site*dim1 + 0*dim2 + j*dim3+k]; /* tree->a_nodes[i]->b[0]->p_lk_rght[curr_site][l][j*tree->mod->m4mod->n_o+k] = */ /* tree->a_nodes[i]->b[0]->p_lk_rght[curr_site][0][j*tree->mod->m4mod->n_o+k]; */ } } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void M4_Init_Partial_Lk_Tips_Int(t_tree *tree) { int curr_site,i,j,k,dim2,dim3; dim2 = tree->mod->m4mod->n_h * tree->mod->m4mod->n_o; dim3 = tree->mod->m4mod->n_o; Fors(curr_site,tree->data->crunch_len,tree->mod->io->state_len) { for(i=0;in_otu;i++) { for(j=1;jmod->m4mod->n_h;j++) { for(k=0;kmod->m4mod->n_o;k++) { tree->a_nodes[i]->b[0]->p_lk_tip_r[curr_site*dim2 + j*dim3+k] = tree->a_nodes[i]->b[0]->p_lk_tip_r[curr_site*dim2 + 0*dim3+k]; /* tree->a_nodes[i]->b[0]->p_lk_tip_r[curr_site][j*tree->mod->m4mod->n_o+k] = */ /* tree->a_nodes[i]->b[0]->p_lk_tip_r[curr_site][k]; */ } } } } } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// phydbl ****M4_Integral_Term_On_One_Edge(t_edge *b, t_tree *tree) { phydbl ****integral,*P1,*P2; int ns; int g,i,j,k,l; int step; ns = tree->mod->ns; P1 = (phydbl *)mCalloc(tree->mod->ras->n_catg*ns*ns,sizeof(phydbl)); P2 = (phydbl *)mCalloc(tree->mod->ras->n_catg*ns*ns,sizeof(phydbl)); integral = (phydbl ****)mCalloc(tree->mod->ras->n_catg,sizeof(phydbl ***)); for(g=0;gmod->ras->n_catg;g++) { integral[g] = (phydbl ***)mCalloc(ns,sizeof(phydbl **)); for(j=0;jmod->ras->n_catg;g++) { PMat(((phydbl)(i+0.5)/step)*b->l->v*tree->mod->ras->gamma_rr->v[g],tree->mod,g*ns*ns,P1,NULL); PMat(((phydbl)(step-i-0.5)/step)*b->l->v*tree->mod->ras->gamma_rr->v[g],tree->mod,g*ns*ns,P2,NULL); for(j=0;j