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package Bio::Tools::PSort::Module::Bayesian;
# 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 2 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, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
# OVERVIEW
# PSORT-B is described in Gardy, J.L. et al (2003). PSORT-B:
# improving protein subcellular localization prediction for
# Gram-negative bacteria. Nuc Acids Res 31(13):3613-17. Please
# cite this publication if you use PSORT-B in your research.
# The standalone version of PSORT-B is distributed under the GNU
# General Public Licence (Gnu GPL) (see the LICENSE file included
# in the download) by the Brinkman Laboratory, Simon Fraser
# University, Burnaby, B.C., Canada.
# This standalone version of PSORT-B has initially been developed
# for the Linux environment.
# This document describes the installation of the PSORT-B version
# 1.1.4 command line program and the PSORT-B server packages. For
# most purposes, following the installation instructions for the
# command line version will be sufficient.
# For further information, please contact psort-mail@sfu.ca.
use Bio::Tools::PSort::Module::OutputI;
use Bio::Tools::PSort::Report::Result;
use Data::Dumper;
use vars qw(@ISA);
@ISA = qw(Bio::Tools::PSort::Module::OutputI);
use strict;
sub new {
my ($class, @args) = @_;
my $self = $class->SUPER::new(@args);
my ($mfile, $prior) = $self->_rearrange([qw(MODEL PRIOR)], @args);
$self->throw("Bayesian model file not found: $mfile") if(! -e $mfile);
# Load the Bayesian model produced by the training program.
open(MFILE, $mfile) || $self->throw("error: open $mfile: $!");
my $model = join('', <MFILE>);
close(MFILE) || $self->throw("error: close $model: $!");
$self->{model} = eval($model);
$self->throw("Error loading Bayesian model: $@") if($@);
# Store any prior probabilities provided to us. (If none provided,
# each localization will be assumed to have an equal chance of
# appearing.)
if(defined($prior)) {
$self->throw("Prior probabilties must be a hash reference")
if(ref($prior) ne "HASH");
$self->{prior} = $prior;
} else {
# If we weren't provided a list of prior probabilities, figure out all the
# localizations we're going to be making predictions for.
for my $anal (keys(%{$self->{model}})) {
for my $loc (keys(%{$self->{model}->{$anal}})) {
$self->{prior}->{$loc} = 1;
}
}
}
return $self;
}
sub run {
my ($self, $seq, %res) = @_;
my (%preds, @res);
# Initialize to the prior probabilities of getting each localization.
%preds = %{$self->{prior}};
# This deals with SCL-BLASTe and 100% protein matches
if($res{'SCL-BLASTe-'} || $res{'SCL-BLASTe+'} || $res{'SCL-BLASTe_a'}) {
my $res;
if($res{'SCL-BLASTe-'}) {
($res) = @{$res{'SCL-BLASTe-'}};
} elsif($res{'SCL-BLASTe+'}) {
($res) = @{$res{'SCL-BLASTe+'}};
} else {
($res) = @{$res{'SCL-BLASTe_a'}};
}
my @locs = $res->localization;
if(!($locs[0] eq 'Unknown')) {
my %scores = ();
$scores{$_} = 1
for(@locs);
push(@res, new Bio::Tools::PSort::Report::Result(-loc => $_, -score => ($scores{$_}?1:0)))
for(keys(%preds));
return sort({$b->score <=> $a->score} @res);
}
}
for my $anal (keys(%res)) {
my (@res) = @{$res{$anal}};
# Ensure that the results from each analysis are the correct object type.
for my $r (@res) {
$self->throw("Not a Bio::Tools::PSort::Report::Result object")
if((! ref($r)) && (! $r->isa("Bio::Tools::PSort::Report::Result")));
}
for my $res (@res) {
# Get the prediction for the analysis.
my @locs = $res->localization;
for my $pred (@locs) {
next if($pred eq "Unknown");
# Update the probabilities for each location.
$preds{$_} *= $self->{model}->{$anal}->{$_}->{$pred}
for(keys(%preds));
}
}
}
# Scale the scores so they all sum to 1.0
my ($tot, $scale);
$tot += $_ for(values(%preds));
if($tot) {
$scale = 1/$tot;
$preds{$_} *= $scale for(keys(%preds));
}
# Sort the scores so the highest comes first.
push(@res, new Bio::Tools::PSort::Report::Result(-loc => $_, -score => $preds{$_}))
for(keys(%preds));
return sort({$b->score <=> $a->score} @res);
}
1;
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