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parse_pangenome_matrix.pl
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parse_pangenome_matrix.pl
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#!/usr/bin/env perl
# 2017-23 Bruno Contreras-Moreira (1) and Pablo Vinuesa (2):
# 1: http://www.eead.csic.es/compbio (Estacion Experimental Aula Dei/CSIC/Fundacion ARAID, Spain)
# 2: http://www.ccg.unam.mx/~vinuesa (Center for Genomic Sciences, UNAM, Mexico)
# This script can be used to get user-defined subsets of pangenome matrices generated by compare_clusters.pl
# Takes 2 lists (A & B) with taxon names (as those used by get_homologues and compare_clusters)
# in order to analyze clusters present/absent in one with respect to the other.
# Can also be used to plot shell genome distribution (PDF, SVG and PNG, requires R http://www.r-project.org)
# Please edit %RGBCOLORS variable below if you wish to change the default colors of pangenome compartments
$|=1;
use strict;
use warnings;
use Getopt::Std;
use File::Basename;
use FindBin '$Bin';
use lib "$Bin/lib";
use lib "$Bin/lib/bioperl-1.5.2_102/";
use phyTools;
use marfil_homology;
use Bio::Graphics;
use Bio::SeqFeature::Generic;
my $CUTOFF = 100; # percentage of genomes to be used as cutoff for presence/absence
my $CUTOFFB = 100;
# globals used while producing R plots
my $VERBOSE = 0; # set to 1 to see R messages
my $YLIMRATIO = 1.2; # controls the length of (rounded) Y-axis marks with respect to max value in barplots
my %COLORS = ('cloud'=>'"red"','shell'=>'"orange"','soft_core'=>'"yellow"','core'=>'"white"'); # default plot colors
# optional RGB colors in 0-255 range
# guide to prepare friendly figures for colorblind people [http://jfly.iam.u-tokyo.ac.jp/color/index.html]
# uncomment and edit colors below to use RGB colors
my %RGBCOLORS = (
# 'cloud' =>'rgb(0,0,0,maxColorValue=255)',
# 'shell' =>'rgb(230,159,0,maxColorValue=255)',
# 'soft_core'=>'rgb(86,180,233,maxColorValue=255)',
# 'core' =>'rgb(0,158,115,maxColorValue=255)'
);
# globals used only with -p flag
my $DEFAULTGBKFEATURES = 'CDS,tRNA,rRNA'; # defines GenBank features to be parsed
my $MAKESVGGRAPH = 0; # default PNG, SVG is vectorial but requires module GD::SVG
my $PLOT_FULL_LABELS = 0; # if 0 shows cluster numbers, otherwise adds gene names to numbers
my $PLOTWIDTH = 1024;
my @FEATURES2CHECK = ('EXE_R');
check_installed_features(@FEATURES2CHECK);
my ($INP_matrix,$INP_absent,$INP_expansions,$INP_includeA,$INP_includeB,%opts) = ('',0,0,'','');
my ($INP_refgenome,$INP_list_taxa,$INP_plotshell,$INP_cutoff,$needAB,$Rparams) = ('',0,0,$CUTOFF,0,'');
my ($INP_nocloud,$INP_absentB,$INP_skip_singletons,$INP_shared_matrix,$INP_taxa_file) = (0,0,0,0,'');
my ($INP_cutoffB,$needB) = (0);
getopts('hagselxnp:m:I:A:B:P:S:R:', \%opts);
if(($opts{'h'})||(scalar(keys(%opts))==0))
{
print "\n[options]: \n";
print "-h \t this message\n";
print "-m \t input pangenome matrix .tab (required, made by compare_clusters.pl)\n"; #, ideally with -t 0)\n";
print "-s \t report cloud,shell,soft core and core clusters (optional, creates plot if R is installed)\n";
print "-l \t list taxa names present in clusters reported in -m matrix (optional, recommended before using -p option)\n";
print "-n \t do not compute cloud, matrix does not have cloud clusters (optional, requires -s)\n";
print "-x \t produce matrix of intersection pangenome clusters (optional, requires -s)\n";
print "-I \t use only taxon names included in file (optional, ignores -A,-g,-e)\n";
print "-A \t file with taxon names (.faa,.gbk,.nucl files) of group A (optional, example -A clade_list_pathogenic.txt)\n";
print "-B \t file with taxon names (.faa,.gbk,.nucl files) of group B (optional, example -B clade_list_symbiotic.txt)\n";
print "-a \t find genes/clusters which are absent in B (optional, requires -B)\n";
print "-g \t find genes/clusters present in A which are absent in B (optional, requires -A & -B)\n";
print "-e \t find gene family expansions in A with respect to B (optional, requires -A & -B)\n";
print "-S \t skip clusters with occupancy <S (optional, requires -x/-a/-g, example -S 2)\n";
print "-P \t % of -A & -B genomes that must comply presence/absence (optional, default=$CUTOFF, requires -g)\n";
print "-R \t % of -B genomes that must comply presence/absence (optional, requires -g & -P, if set -P applies to -A only)\n";
if(eval{ require GD } ) # show only if GD is available
{
print "-p \t plot pangenes on the genome map of this group A taxon ".
"(optional, example -p 'Escherichia coli K12',\n".
" \t ".
" requires -g, -A & -B, only works with clusters\n".
" \t ".
" derived from input files in GenBank format)\n";
}
exit(-1);
}
if(defined($opts{'m'})){
$INP_matrix = $opts{'m'};
if($INP_matrix =~ /.tr.tab/){
die "\n# $0 : cannot use a transposed -m matrix, exit\n";
}
}
else{ die "\n# $0 : need -m parameter, exit\n"; }
if(defined($opts{'l'})){ $INP_list_taxa = 1 }
if(defined($opts{'s'}))
{
$INP_plotshell = 1;
if(defined($opts{'n'}))
{
$INP_nocloud = 1;
}
if(defined($opts{'x'}))
{
$INP_shared_matrix = 1;
if($opts{'S'} && $opts{'S'} > 0)
{
$INP_skip_singletons = $opts{'S'};
}
}
}
if(defined($opts{'I'}))
{
$INP_taxa_file = $opts{'I'};
if(defined($opts{'a'}))
{
$INP_absentB = 1;
$needB = 1;
}
if(defined($opts{'B'}))
{
$INP_includeB = $opts{'B'};
}
elsif($needB){ die "\n# $0 : need -B parameter, exit\n"; }
if($INP_absentB && $opts{'S'} && $opts{'S'} > 0)
{
$INP_skip_singletons = $opts{'S'};
}
}
else
{
if(defined($opts{'a'}))
{
$INP_absentB = 1;
$needB = 1;
}
elsif(defined($opts{'g'}))
{
$INP_absent = 1;
$needAB = 1;
if(defined($opts{'P'}) && $opts{'P'} > 0 && $opts{'P'} <= 100)
{
$CUTOFF = $opts{'P'};
$INP_cutoff = $CUTOFF;
if(defined($opts{'R'}) && $opts{'R'} > 0 && $opts{'R'} <= 100)
{
$CUTOFFB = $opts{'R'};
$INP_cutoffB = $CUTOFFB;
}
else
{
$CUTOFFB = $CUTOFF;
}
}
}
elsif(defined($opts{'e'}))
{
$INP_expansions = 1;
$needAB = 1;
}
if(defined($opts{'p'}))
{
$INP_refgenome = $opts{'p'};
$needAB = 1;
}
if(defined($opts{'A'}))
{
$INP_includeA = $opts{'A'};
}
elsif($needAB){ die "\n# $0 : need -A parameter, exit\n"; }
if(defined($opts{'B'}))
{
$INP_includeB = $opts{'B'};
}
elsif($needAB || $needB){ die "\n# $0 : need -B parameter, exit\n"; }
if(($INP_absentB ||$INP_absent) && $opts{'S'} && $opts{'S'} > 0)
{
$INP_skip_singletons = $opts{'S'};
}
}
printf("\n# %s -m %s -I %s -A %s -B %s -a %d -g %d -e %d -p %s -s %d -n %d -l %d -x %d -P %d -R %d -S %d\n\n",
$0,$INP_matrix,$INP_taxa_file,$INP_includeA,$INP_includeB,$INP_absentB,$INP_absent,
$INP_expansions,$INP_refgenome,$INP_plotshell,$INP_nocloud,$INP_list_taxa,$INP_shared_matrix,
$INP_cutoff,$INP_cutoffB,$INP_skip_singletons);
if(!$needB && !$needAB && !$INP_plotshell && !$INP_list_taxa)
{
die "# $0: error, need either -s or -l option\n";
}
$CUTOFF /= 100; # use 0-1 float from now on
$CUTOFFB /= 100;
#################################### MAIN PROGRAM ################################################
my (%cluster_names,%pangemat,%included_taxa,$col,$cluster_dir);
my (%included_input_filesA,%included_input_filesB);
my ($n_of_clusters,$n_of_includedA,$n_of_includedB) = (0,0,0);
my ($real_clusters, $chrcolumns) = (0,0);
my ($outfile_root,$outpanfileA,$outexpanfileA,$taxon);
my ($shell_input,$shell_estimates,$shell_output_svg,$shell_circle_svg,);
my ($shell_output_png,$shell_output_pdf,$shell_circle_png,$shell_circle_pdf);
my ($cloudlistfile,$shelllistfile,$softcorelistfile,$corelistfile,$intersection_file);
my (@pansetA,@pansetB,@expA,@expB,@shell);
$outfile_root = $INP_refgenome; $outfile_root =~ s/\W+//g;
$outfile_root = (split(/\.tab/,$INP_matrix))[0] . '_' . $outfile_root;
if($INP_taxa_file)
{
$outfile_root .= '_'.basename($INP_taxa_file);
}
$shell_input = $outfile_root . '_shell_input.txt';
$shell_estimates = $outfile_root . '_shell_estimates.tab';
$shell_output_png = $outfile_root . '_shell.png';
$shell_output_pdf = $outfile_root . '_shell.pdf';
$shell_output_svg = $outfile_root . '_shell.svg';
$shell_circle_png = $outfile_root . '_shell_circle.png';
$shell_circle_pdf = $outfile_root . '_shell_circle.pdf';
$shell_circle_svg = $outfile_root . '_shell_circle.svg';
$cloudlistfile = $outfile_root . '_cloud_list.txt';
$shelllistfile = $outfile_root . '_shell_list.txt';
$softcorelistfile = $outfile_root . '_softcore_list.txt';
$corelistfile = $outfile_root . '_core_list.txt';
$outpanfileA = $outfile_root . '_pangenes_list.txt';
$outexpanfileA = $outfile_root . '_expansions_list.txt';
$intersection_file= $outfile_root . '_intersection.tab';
if($INP_skip_singletons)
{
$intersection_file= $outfile_root . "_intersection_min_occup$INP_skip_singletons.tab";
$outpanfileA = $outfile_root . '_pangenes_min_occup'.$INP_skip_singletons.'_list.txt';
}
## 0) read include file if required
if($INP_taxa_file)
{
open(INCLUDE_FILE,"<",$INP_taxa_file) || die "# EXIT : cannot read $INP_taxa_file\n";
while(<INCLUDE_FILE>)
{
$included_taxa{(split)[0]} = 1;
}
close(INCLUDE_FILE);
}
## 1) parse pangenome matrix
open(MAT,$INP_matrix) || die "# EXIT : cannot read $INP_matrix\n";
while(<MAT>)
{
next if(/^#/ || /^$/);
chomp;
my @data = split(/\t/,$_);
next if($data[0] =~ /^reference/ || $data[0] =~ /^redundant/); # nr matrices
#source:path/to/clusters/t101964_thrB.fna/t101965_thrC.fna/t...
#source:path/to/clusters/tchr:../t101964_thrB.fna/t101965_thrC.fna/t...
if($data[0] =~ /^source:(\S+)/) #source:path/to/clusters/t101964_thrB.fna/t101965_thrC.fna/t...
{
$cluster_dir = $1;
if($INP_list_taxa)
{
# print full list of taxa names present in clusters reported in pangenome matrix
my ($t,%cluster,%taxa,%alltaxa);
foreach $col (1 .. $#data)
{
%cluster = read_FASTA_sequence( $cluster_dir.'/'.$data[$col] );
%taxa = find_taxa_FASTA_headers(\%cluster);
foreach $t (keys(%taxa)){ $alltaxa{$t}++; }
}
print "# list of taxa present in clusters (to be used with -p option):\n";
foreach $t (sort keys(%alltaxa))
{
print "$t $alltaxa{$t}\n";
}
exit;
}
else
{
foreach $col (1 .. $#data){ $cluster_names{$col} = $data[$col] }
$n_of_clusters = $#data;
}
}
elsif($data[0] =~ /^non-redundant/) #non-redundant 24_Brdisv1ABR21043430m.faa+1 297_Brdisv1ABR21026756m.faa
{
if($INP_list_taxa)
{
print "# EXIT: cannot list taxa present in clusters of non-redundant matrix\n";
exit;
}
my $cl_name;
foreach $col (1 .. $#data)
{
$cl_name = $data[$col];
$cl_name = (split(/\+\d/,$cl_name))[0];
$cluster_names{$col} = $cl_name;
$n_of_clusters = $col;
}
}
else
{
#_Escherichia_coli_ETEC_H10407_uid42749.gbk 1 1 ...
#Bd3-1_r.1.cds.fna.nucl 1 2 ...
# skip taxon if not in include file, only if requested
next if($INP_taxa_file && !$included_taxa{$data[0]});
$chrcolumns = 0;
foreach $col (1 .. $#data)
{
$pangemat{$data[0]}[$col] = $data[$col];
if($data[$col] eq 'NA')
{
$chrcolumns++;
}
else
{
$shell[$col]++ if($data[$col] > 0)
}
}
}
}
close(MAT);
$real_clusters = $n_of_clusters - $chrcolumns;
print "# matrix contains $real_clusters clusters and ".scalar(keys(%pangemat))." taxa\n\n";
if($needB)
{
## 2.0) parse include_file B
open(INCL,$INP_includeB) || die "# EXIT : cannot read $INP_includeB\n";
while(<INCL>)
{
next if(/^#/ || /^$/);
$taxon = (split)[0];
$included_input_filesB{$taxon} = 1;
if(!$pangemat{$taxon})
{
die "# cannot match $taxon in $INP_matrix (included in $INP_includeB)\n";
}
}
close(INCL);
$n_of_includedB = scalar(keys(%included_input_filesB));
print "# taxa included in group B = $n_of_includedB\n\n";
}
elsif($needAB)
{
## 2.1) parse include_file A
open(INCL,$INP_includeA) || die "# EXIT : cannot read $INP_includeA\n";
while(<INCL>)
{
next if(/^#/ || /^$/);
$taxon = (split)[0];
$included_input_filesA{$taxon} = 1;
if(!$pangemat{$taxon})
{
die "# cannot match $taxon in $INP_matrix (included in $INP_includeA)\n";
}
}
close(INCL);
$n_of_includedA = scalar(keys(%included_input_filesA));
print "# taxa included in group A = $n_of_includedA\n\n";
## 2.2) parse include_file B
open(INCL,$INP_includeB) || die "# EXIT : cannot read $INP_includeB\n";
while(<INCL>)
{
next if(/^#/ || /^$/);
$taxon = (split)[0];
$included_input_filesB{$taxon} = 1;
if(!$pangemat{$taxon})
{
die "# cannot match $taxon in $INP_matrix (included in $INP_includeB)\n";
}
}
close(INCL);
$n_of_includedB = scalar(keys(%included_input_filesB));
print "# taxa included in group B = $n_of_includedB\n\n";
}
## 3) perform requested operations
if($INP_absentB)
{
print "\n# finding genes which are absent in B ...\n";
foreach $col (1 .. $n_of_clusters)
{
next if(!$shell[$col]);
my ($presentA,$absentA,$absentB,$presentB) = (0,0,0,0);
foreach $taxon (keys(%pangemat))
{
if($pangemat{$taxon}[$col])
{
if($included_input_filesB{$taxon}){ $presentB++ }
else{ $presentA++ }
}
else
{
if($included_input_filesB{$taxon}){ $absentB++ }
else{ $absentA++ }
}
}
if($presentA > 0 && $absentB == $n_of_includedB)
{
# pan gene in set A
next if($INP_skip_singletons && $presentA < $INP_skip_singletons);
push(@pansetA,$cluster_names{$col});
}
}
print "# file with genes absent in B (".scalar(@pansetA)."): $outpanfileA\n";
open(OUTLIST,">$outpanfileA") || die "# $0 : cannot create $outpanfileA\n";
printf OUTLIST ("# %s -m %s -A %s -B %s -g %d -e %d -p %s -P %d -S %d\n",
$0,$INP_matrix,$INP_includeA,$INP_includeB,$INP_absent,$INP_expansions,
$INP_refgenome,$INP_cutoff,$INP_skip_singletons);
if($INP_skip_singletons)
{
print OUTLIST "# genes absent in B with occupancy>=$INP_skip_singletons (".scalar(@pansetA)."):\n";
}
else{ print OUTLIST "# genes absent in B (".scalar(@pansetA)."):\n"; }
foreach $col (@pansetA){ print OUTLIST "$col\n" }
close(OUTLIST);
}
elsif($INP_absent)
{
print "\n# finding genes present in A which are absent in B ...\n";
foreach $col (1 .. $n_of_clusters)
{
my ($presentA,$absentA,$absentB,$presentB) = (0,0,0,0);
foreach $taxon (keys(%pangemat))
{
if($pangemat{$taxon}[$col])
{
if($included_input_filesA{$taxon}){ $presentA++ }
elsif($included_input_filesB{$taxon}){ $presentB++ }
}
else
{
if($included_input_filesA{$taxon}){ $absentA++ }
elsif($included_input_filesB{$taxon}){ $absentB++ }
}
}
if($presentA >= ($n_of_includedA*$CUTOFF) && $absentB >= ($n_of_includedB*$CUTOFFB))
{
# pan gene in set A
#print "$cluster_names{$col} present only in ($n_of_includedA) taxa in set A\n";
next if($INP_skip_singletons && $presentA < $INP_skip_singletons);
push(@pansetA,$cluster_names{$col});
}
elsif($presentB >= ($n_of_includedB*$CUTOFFB) && $absentA >= ($n_of_includedA*$CUTOFF))
{
# pan gene in set B
#print "$cluster_names{$col} present only in ($n_of_includedB) taxa in set B\n";
#push(@pansetB,$cluster_names{$col});
}
}
print "# file with genes present in set A and absent in B (".scalar(@pansetA)."): $outpanfileA\n";
open(OUTLIST,">$outpanfileA") || die "# $0 : cannot create $outpanfileA\n";
printf OUTLIST ("# %s -m %s -A %s -B %s -g %d -e %d -p %s -P %d -S %d\n",
$0,$INP_matrix,$INP_includeA,$INP_includeB,$INP_absent,$INP_expansions,
$INP_refgenome,$INP_cutoff,$INP_skip_singletons);
if($INP_skip_singletons)
{
print OUTLIST "# genes present in set A and absent in B with occupancy>=$INP_skip_singletons (".scalar(@pansetA)."):\n";
}
else{ print OUTLIST "# genes present in set A and absent in B (".scalar(@pansetA)."):\n"; }
foreach $col (@pansetA){ print OUTLIST "$col\n" }
close(OUTLIST);
# commented out Apr2012: user should reverse -A,-B options to get this
# print "# genes present in set B and absent in A (".scalar(@pansetB)."):\n";
# foreach $col (@pansetB){ print "$col\n" }
}
elsif($INP_expansions)
{
print "\n# finding gene family expansions in group A ...\n";
foreach $col (1 .. $n_of_clusters)
{
my ($presentA,$presentB,@sizeA,@sizeB) = (0,0);
my ($minA,$maxA,$minB,$maxB);
foreach $taxon (keys(%pangemat))
{
if($pangemat{$taxon}[$col])
{
if($included_input_filesA{$taxon})
{
$presentA++;
push(@sizeA,$pangemat{$taxon}[$col]);
}
elsif($included_input_filesB{$taxon})
{
$presentB++;
push(@sizeB,$pangemat{$taxon}[$col]);
}
}
}
if($presentA == $n_of_includedA && $presentB == $n_of_includedB)
{
@sizeA = sort {$a<=>$b} @sizeA;
$minA = $sizeA[0];
$maxA = $sizeA[$#sizeA];
@sizeB = sort {$a<=>$b} @sizeB;
$minB = $sizeB[0];
$maxB = $sizeB[$#sizeB];
# exapansions in set A: all A taxa must have sizeA > sizeB in all B taxa
if($minA > $maxB)
{
#print "$cluster_names{$col} expanded in set A\n";
push(@expA,$cluster_names{$col});
}
elsif($minB > $maxA)
{
#print "$cluster_names{$col} expanded in set B\n";
#push(@expB,$cluster_names{$col});
}
}
}
print "# file with genes expanded in set A (".scalar(@expA)."): $outexpanfileA\n";
open(OUTEXPANLIST,">$outexpanfileA") || die "# $0 : cannot create $outexpanfileA\n";
printf OUTEXPANLIST ("# %s -m %s -A %s -B %s -g %d -e %d -p %s -P %d -S %d\n",
$0,$INP_matrix,$INP_includeA,$INP_includeB,$INP_absent,$INP_expansions,
$INP_refgenome,$INP_cutoff,$INP_skip_singletons);
print OUTEXPANLIST "# genes expanded in set A (".scalar(@expA)."):\n";
foreach $col (@expA){ print OUTEXPANLIST "$col\n" }
close(OUTEXPANLIST);
# commented out Apr2012: user should reverse -A,-B options to get this
#print "# genes expanded in set B (".scalar(@expB)."):\n";
#foreach $col (@expB){ print "$col\n" }
}
# plot genomic map
if($INP_refgenome)
{
eval{ import GD; };
my ($start,$end,$strand,$gi,$feat,$pancluster,$source,$included,%contig);
print "\n# plotting pangenes in genomic context...\n";
# read $INP_refgenome GenBank file if format is ok | OLD VERSION
# my $ref_sources_features = extract_features_from_genbank($INP_refgenome,0,$DEFAULTGBKFEATURES);
# find reference genes contained in @pansetA clusters
my $taxonOK = 0;
foreach $pancluster (@pansetA)
{
my $fasta_ref = read_FASTA_file_array( $cluster_dir.'/'.$pancluster );
foreach my $seq ( 0 .. $#{$fasta_ref} )
{
# escape special chars such as [taxon name]
if($fasta_ref->[$seq][NAME] =~ /\Q$INP_refgenome\E/)
{
$taxonOK++;
#|NC_002528(640681):579687-580265:-1
#|rho61_2(1942384):5601-6167:-1 prokka style
$feat = (split(/\|/,$fasta_ref->[$seq][NAME]))[6]; # was 5 in previous versions
if($feat && $feat =~ /(\S+)?\((\d+)\):(\d+)-(\d+):(-*1)/)
{
$contig{$1}{'size'} = $2;
push(@{$contig{$1}{'clusters'}},[$3,$4,$5,$pancluster]);#print "# $1 $2 $3 $4 $5\n";
}
}
}
}
if(!$taxonOK)
{
die "# $0: error, cannot find reference genome '$INP_refgenome' in pangenomic clusters, please check spelling\n";
}
elsif(!keys(%contig))
{
die "# $0: error, cannot find genome coordinates in pangenomic clusters, cannot make plot\n";
}
# plot genome, contig by contig (source)
foreach $source (sort keys(%contig))
{
my $mapfile = $outfile_root . "_$source.png";
if($MAKESVGGRAPH)
{
$mapfile = $outfile_root . "_$source.svg"; # for full resolution images
}
print "# chromosome/contig= $source size= $contig{$source}{'size'} outfile= $mapfile\n";
my $panel;
if($MAKESVGGRAPH)
{
$panel = Bio::Graphics::Panel->new(
-image_class=>'GD::SVG',
-length => $contig{$source}{'size'}, -width => $PLOTWIDTH,
-pad_left => 20,-pad_right => 20,
);
}
else
{
$panel = Bio::Graphics::Panel->new(
-length => $contig{$source}{'size'}, -width => $PLOTWIDTH,
-pad_left => 20,-pad_right => 20,
);
}
my $full_length = Bio::SeqFeature::Generic->new( -start => 1, -end => $contig{$source}{'size'} );
$panel->add_track($full_length,
-glyph => 'arrow', -fgcolor => 'black',
-tick => 2,-double => 1
);
my $track = $panel->add_track( -glyph => 'generic', -strand_arrow => 1, -label => 1 );
#push(@{$contig{$1}{'clusters'}},[$3,$4,$5,$pancluster]);
foreach $feat (sort {$a->[0]<=>$b->[0]} @{$contig{$source}{'clusters'}})
{
($start,$end,$strand,$pancluster) = @$feat;
if(!$PLOT_FULL_LABELS){ $pancluster = (split(/_/,$pancluster))[0] }
my $feature = Bio::SeqFeature::Generic->new(
-display_name => $pancluster,
-start => $start,
-end => $end,
-strand => $strand
);
$track->add_feature($feature);
}
open(GRAPH,">$mapfile");
if($MAKESVGGRAPH){ print GRAPH $panel->svg() }
else
{
binmode GRAPH;
print GRAPH $panel->png();
}
close(GRAPH);
}
}
# plot shell genome
if($INP_plotshell)
{
unlink($shell_output_png,$shell_output_pdf,$shell_output_svg);
my @taxa = sort keys(%pangemat);
my @occup_class = qw( cloud shell soft_core core );
my $n_of_taxa = scalar(@taxa);
my ($cloudpos,$cloudmax,$cluster,$s,$class,%stats,%total,%taxa_total) = (-1,-1);
my $softcorepos = int($SOFTCOREFRACTION*$n_of_taxa);
if($n_of_taxa < 5)
{
die "# EXIT : need at least 5 taxa to perform -s analysis\n";
}
# calculate shell sums, fill gaps and get final number of clusters
$n_of_clusters = 0;
foreach $cluster (1 .. $#shell)
{
next if(!$shell[$cluster]);
$stats{$shell[$cluster]}++;
$n_of_clusters++;
}
foreach $s ( 1 .. $n_of_taxa ){ if(!$stats{$s}){ $stats{$s} = 0 } }
open(SINP,">$shell_input") || die "# EXIT : cannot create $shell_input\n";
foreach $s (sort {$a<=>$b} keys(%stats))
{
print SINP "$stats{$s}\n";
if($INP_nocloud == 0 && $stats{$s}>$cloudmax && $s+1 < $softcorepos)
{
$cloudmax = $stats{$s};
$cloudpos = $s + 1 ; # arbitrarily take max plus next column in plot
}
}
close(SINP);
if($INP_nocloud)
{
$cloudpos = 0;
}
# print lists of genomic compartments
open(CLOUDF,">$cloudlistfile") || die "# EXIT : cannot create $cloudlistfile\n";
open(SHELLF,">$shelllistfile") || die "# EXIT : cannot create $shelllistfile\n";
open(SCOREF,">$softcorelistfile") || die "# EXIT : cannot create $softcorelistfile\n";
open(COREF,">$corelistfile") || die "# EXIT : cannot create $corelistfile\n";
foreach $cluster (1 .. $#shell)
{
next if(!$shell[$cluster]);
if($shell[$cluster] <= $cloudpos)
{
print CLOUDF "$cluster_names{$cluster}\n";
$total{'cloud'}++;
# record which taxa contribute this sequence
foreach $taxon (@taxa){ if($pangemat{$taxon}[$cluster] > 0){ $taxa_total{'cloud'}{$taxon}++ } }
}
elsif($shell[$cluster] >= $softcorepos)
{
print SCOREF "$cluster_names{$cluster}\n";
$total{'soft_core'}++;
foreach $taxon (@taxa){ if($pangemat{$taxon}[$cluster] > 0){ $taxa_total{'soft_core'}{$taxon}++ } }
if($shell[$cluster] == $n_of_taxa)
{
print COREF "$cluster_names{$cluster}\n";
$total{'core'}++;
foreach $taxon (@taxa){ if($pangemat{$taxon}[$cluster] > 0){ $taxa_total{'core'}{$taxon}++ } }
}
}
else
{
print SHELLF "$cluster_names{$cluster}\n";
$total{'shell'}++;
foreach $taxon (@taxa){ if($pangemat{$taxon}[$cluster] > 0){ $taxa_total{'shell'}{$taxon}++ } }
}
}
close(CLOUDF);
close(CLOUDF);
close(SCOREF);
close(COREF);
if(!$total{'cloud'}){ $total{'cloud'} = 0 }
if(!$total{'shell'}){ $total{'shell'} = 0 }
if(!$total{'soft_core'}){ $total{'soft_core'} = 0 }
if(!$total{'core'}){ $total{'core'} = 0 }
print "# cloud size: $total{'cloud'} list: $cloudlistfile\n";
print "# shell size: $total{'shell'} list: $shelllistfile\n";
print "# soft core size: $total{'soft_core'} list: $softcorelistfile\n";
print "# core size: $total{'core'} (included in soft core) list: $corelistfile\n";
# create graphs and fit mix models if possible
if(feature_is_installed('R'))
{
# set colors
my %colors;
if(keys(%RGBCOLORS))
{
%colors = %RGBCOLORS;
print "\n# using RGB colors, defined in \%RGBCOLORS\n";
}
else
{
%colors = %COLORS;
print "\n# using default colors, defined in \%COLORS\n";
}
# calculate circle radii and sort colors accordingly
my (@radius,@color,$max);
for $s (sort {$total{$b}<=>$total{$a}} keys(%total))
{
push(@color,$colors{$s});
if(!defined($max)){ $max = $total{$s} }
push(@radius,sqrt($total{$s}/$max));
} #foreach my $ii (0 .. 3){ printf("%f %f\n",$radius[$ii],3.14159*($radius[$ii]**2)) }
if(!$VERBOSE){ $Rparams = '-q 2>&1 > /dev/null' }
print "\n# globals controlling R plots: \$YLIMRATIO=$YLIMRATIO\n";
open(RSHELL,"|R --no-save $Rparams ") || die "# cannot call R: $!\n";
print RSHELL<<EOR;
shell = read.table("$shell_input",header=F);
colors = c(
rep($colors{'cloud'},each=$cloudpos),
rep($colors{'shell'},each=$softcorepos-$cloudpos-1),
rep($colors{'soft_core'},each=$n_of_taxa-$softcorepos),
$colors{'core'}
);
pdf(file="$shell_output_pdf");
bars = barplot(shell\$V1,xlab='number of genomes in clusters (occupancy)',ylab='number of gene clusters',
main='',names.arg=1:$n_of_taxa,col=colors,ylim=c(0,$YLIMRATIO*max(shell\$V1)));
mtext(sprintf("total clusters = %s",format($n_of_clusters,big.mark=",",scientific=FALSE)),side=3,cex=0.8,line=0.5);
#text(x=bars[$n_of_taxa],y=$stats{$n_of_taxa}/2,labels=sprintf("%d",$stats{$n_of_taxa}),cex=0.8); # core size
legend('top', c('cloud','shell','soft core','core'), cex=1.0,
fill=c($colors{'cloud'},$colors{'shell'},$colors{'soft_core'},$colors{'core'}) );
dev.off()
png(file="$shell_output_png");
bars = barplot(shell\$V1,xlab='number of genomes in clusters (occupancy)',ylab='number of gene clusters',
main='',names.arg=1:$n_of_taxa,col=colors,ylim=c(0,$YLIMRATIO*max(shell\$V1)));
mtext(sprintf("total clusters = %s",format($n_of_clusters,big.mark=",",scientific=FALSE)),side=3,cex=0.8,line=0.5);
#text(x=bars[$n_of_taxa],y=$stats{$n_of_taxa}/2,labels=sprintf("%d",$stats{$n_of_taxa}),cex=0.8);
legend('top', c('cloud','shell','soft core','core'), cex=1.0,
fill=c($colors{'cloud'},$colors{'shell'},$colors{'soft_core'},$colors{'core'}) );
dev.off()
svg(file="$shell_output_svg");
bars = barplot(shell\$V1,xlab='number of genomes in clusters (occupancy)',ylab='number of gene clusters',
main='',names.arg=1:$n_of_taxa,col=colors,ylim=c(0,$YLIMRATIO*max(shell\$V1)));
mtext(sprintf("total clusters = %s",format($n_of_clusters,big.mark=",",scientific=FALSE)),side=3,cex=0.8,line=0.5);
#text(x=bars[$n_of_taxa],y=$stats{$n_of_taxa}/2,labels=sprintf("%d",$stats{$n_of_taxa}),cex=0.8);
legend('top', c('cloud','shell','soft core','core'), cex=1.0,
fill=c($colors{'cloud'},$colors{'shell'},$colors{'soft_core'},$colors{'core'}) );
dev.off()
## now make circle plots
circle <- function(x, y, r, ...)
{
ang <- seq(0, 2*pi, length = 100)
xx <- x + r * cos(ang)
yy <- y + r * sin(ang)
polygon(xx, yy, ...)
}
pdf(file="$shell_circle_pdf");
par(mar=c(0,0,0,0));
plot(-3,-3,ylim=c(0,3), xlim=c(0,3),axes=F);
circle(x=1.5,y=1.5,r=$radius[0],col=$color[0], border=NA);
circle(x=1.5,y=1.5,r=$radius[1],col=$color[1], border=NA);
circle(x=1.5,y=1.5,r=$radius[2],col=$color[2], border=NA);
circle(x=1.5,y=1.5,r=$radius[3],col=$color[3], border=NA);
text(1.5,0.25,sprintf("total gene clusters = %s taxa = %d",
format($n_of_clusters,big.mark=",",scientific=FALSE),$n_of_taxa),cex=0.8);
legend('topright', c(
"cloud ($total{'cloud'} , genomes<=$cloudpos)",
"shell ($total{'shell'})",
"soft core ($total{'soft_core'} , genomes>=$softcorepos)",
"core ($total{'core'} , genomes=$n_of_taxa)"),
cex=1.0, fill=c($colors{'cloud'},$colors{'shell'},$colors{'soft_core'},$colors{'core'}
) );
dev.off()
png(file="$shell_circle_png");
par(mar=c(0,0,0,0));
plot(-3,-3,ylim=c(0,3), xlim=c(0,3),axes=F);
circle(x=1.5,y=1.5,r=$radius[0],col=$color[0], border=NA);
circle(x=1.5,y=1.5,r=$radius[1],col=$color[1], border=NA);
circle(x=1.5,y=1.5,r=$radius[2],col=$color[2], border=NA);
circle(x=1.5,y=1.5,r=$radius[3],col=$color[3], border=NA);
text(1.5,0.25,sprintf("total gene clusters = %s taxa = %d",
format($n_of_clusters,big.mark=",",scientific=FALSE),$n_of_taxa),cex=0.8);
legend('topright', c(
"cloud ($total{'cloud'} , genomes<=$cloudpos)",
"shell ($total{'shell'})",
"soft core ($total{'soft_core'} , genomes>=$softcorepos)",
"core ($total{'core'} , genomes=$n_of_taxa)"),
cex=1.0, fill=c($colors{'cloud'},$colors{'shell'},$colors{'soft_core'},$colors{'core'}
));
dev.off()
svg(file="$shell_circle_svg");
par(mar=c(0,0,0,0));
plot(-3,-3,ylim=c(0,3), xlim=c(0,3),axes=F);
circle(x=1.5,y=1.5,r=$radius[0],col=$color[0], border=NA);
circle(x=1.5,y=1.5,r=$radius[1],col=$color[1], border=NA);
circle(x=1.5,y=1.5,r=$radius[2],col=$color[2], border=NA);
circle(x=1.5,y=1.5,r=$radius[3],col=$color[3], border=NA);
text(1.5,0.25,sprintf("total gene clusters = %s taxa = %d",
format($n_of_clusters,big.mark=",",scientific=FALSE),$n_of_taxa),cex=0.8);
legend('topright', c(
"cloud ($total{'cloud'} , genomes<=$cloudpos)",
"shell ($total{'shell'})",
"soft core ($total{'soft_core'} , genomes>=$softcorepos)",
"core ($total{'core'} , genomes=$n_of_taxa)"),
cex=1.0, fill=c($colors{'cloud'},$colors{'shell'},$colors{'soft_core'},$colors{'core'}
));
dev.off()
negTruncLogLike <- function( p, y, core.p )
{
# The negative zero-truncated log-likelihood function to be minimized
# by binomix.
# Originally by Lars Snipen
# Biostatistics, Norwegian University of Life Sciences.
np <- length( p )/2
p.det <- c( core.p, p[(np+1):length(p)] )
p.mix <- c( 1-sum( p[1:np] ), p[1:np] )
G <- length( y )
K <- length( p.mix )
n <- sum( y )
theta_0 <- choose( G, 0 ) * sum( p.mix * (1-p.det)^G )
L <- -n* log( 1 - theta_0 )
for( g in 1:G )
{
theta_g <- choose( G, g ) * sum( p.mix * p.det^g * (1-p.det)^(G-g) )
L <- L + y[g] * log( theta_g )
}
return( -L )
}
binomix <- function( y, ncomp=(2:5), core.detect.prob=1.0 )
{
# Function that estimates pan- and core-genome size using the zero-truncated
# binomial mixture model.
#
# y must be the number of genes found in 1,...,G genomes. ncomp must be a
# vector specifying the possible number of components (2 or more) to consider.
# core.detect.prob is the fixed detection probability of the core component. Usually
# core genes have detection probability 1.0, i.e. always detected, but due to
# inaccuracies in all computations, some core genes may not be detected in some
# genomes, hence the detection probability could be set fractionally less than 1.0.
#
# A matrix of results is returned, together with a list of mixture models, one
# for each possible number of components specified in ncomp. The result matrix
# has one row for each number of components, and four columns. The columns are
# Core.size, Pan.size, BIC and LogLikelihood.
#
# Originally by Lars Snipen
# Biostatistics, Norwegian University of Life Sciences.
# http://www.biomedcentral.com/content/pdf/1471-2164-10-385.pdf
# BMC Genomics 2009,10:385 doi:10.1186/1471-2164-10-385
n <- sum( y )
G <- length( y )
res.tab <- matrix( NA, nrow=length(ncomp)+1, ncol=4 )
colnames( res.tab ) <- c( "Core.size", "Pan.size", "BIC", "LogLikelihood" )
rownames( res.tab ) <- c( paste( ncomp, "components" ), "Sample" )
mix.list <- list( length(ncomp) )
ctr <- list( maxit=300, reltol=1e-6 )
for( i in 1:length( ncomp ) )
{
nc <- ncomp[i]
np <- nc - 1
pmix0 <- rep( 1, np )/nc # flat mixture proportions
pdet0 <- (1:np)/(np+1) # "all" possible detection probabilities
p.initial <- c(pmix0,pdet0)
A <- rbind( c(rep(1,np),rep(0,np)), c(rep(-1,np),rep(0,np)), diag(np+np), -1*diag(np+np) )
b <- c(0,-1,rep(0,np+np),rep(-1,np+np))
est <- constrOptim( theta=p.initial, f=negTruncLogLike, grad=NULL,
method="Nelder-Mead", control=ctr, ui=A, ci=b, y=y, core.p=core.detect.prob )
res.tab[i,4] <- -1*est\$value # the log-likelihood
res.tab[i,3] <- -2*res.tab[i,4] + log(n)*(np+nc) # the BIC-criterion
p.mix <- c( 1 - sum( est\$par[1:np] ), est\$par[1:np] )
p.det <- c( core.detect.prob, est\$par[(np+1):length( est\$par )] )
theta_0 <- choose( G, 0 ) * sum( p.mix * (1-p.det)^G )
y_0 <- n * theta_0/(1-theta_0)
res.tab[i,2] <- n + round( y_0 )
ixx <- which( p.det >= core.detect.prob )
res.tab[i,1] <- round( res.tab[i,2] * sum( p.mix[ixx] ) )
mix.list[[i]] <- list( Mixing.prop=p.mix, Detect.prob=p.det )
}
res.tab[(length(ncomp)+1),2] <- n
res.tab[(length(ncomp)+1),1] <- y[length(y)]
return( list( Result.matrix=res.tab, Mix.list=mix.list ) )
}
maxcomp <- min($n_of_taxa,10)
estimates <- binomix( shell\$V1 , ncomp=2:maxcomp )
write.table(estimates\$Result.matrix,quote=F,file="$shell_estimates")
q()
EOR
close(RSHELL);
print "\n# shell bar plots: $shell_output_png , $shell_output_pdf , $shell_output_svg\n";
print "# shell circle plots: $shell_circle_png , $shell_circle_pdf , $shell_circle_svg\n\n";
print "# pan-genome size estimates (Snipen mixture model PMID:19691844): $shell_estimates\n";
open(MIXTMOD,$shell_estimates);
while(<MIXTMOD>)
{
print;
}
close(MIXTMOD);