Bio::Tools::Signalp ExtendedSignalp
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Summary
Bio::Tools::Signalp::ExtendedSignalp - enhanced parser for Signalp output
Package variables
Privates (from "my" definitions)
$FACTS = { 'maxC' => 1, 'maxS' => 1, 'maxY' => 1, 'meanS' => 1, 'D' => 1, }
Included modules
Bio::SeqFeature::Generic
Data::Dumper
Inherit
Bio::Tools::AnalysisResult Bio::Tools::Signalp
Synopsis
 use Bio::Tools::Signalp::ExtendedSignalp;
my $params = [qw(maxC maxY maxS meanS D)];
my $parser = new Bio::Tools::Signalp::ExtendedSignalp(
-fh => $filehandle
-factors => $params
);
$parser->factors($params); while( my $sp_feat = $parser->next_feature ) { #do something #eg push @sp_feat, $sp_feat; }
Description
# Please direct questions and support issues to bioperl-l@bioperl.org
Parser module for Signalp.
Based on the EnsEMBL module Bio::EnsEMBL::Pipeline::Runnable::Protein::Signalp
originally written by Marc Sohrmann (ms2 a sanger.ac.uk) Written in BioPipe by
Balamurugan Kumarasamy (savikalpa a fugu-sg.org) Cared for by the Fugu
Informatics team (fuguteam@fugu-sg.org)
You may distribute this module under the same terms as perl itself
Compared to the original SignalP, this method allow the user to filter results
out based on maxC maxY maxS meanS and D factor cutoff for the Neural Network (NN)
method only. The HMM method does not give any filters with 'YES' or 'NO' as result.
The user must be aware that the filters can only by applied on NN method.
Also, to ensure the compatibility with original Signalp parsing module, the user
must know that by default, if filters are empty, max Y and mean S filters are
automatically used to filter results.
If the used gives a list, then the parser will only report protein having 'YES'
for each factor.
This module supports parsing for full, summary and short output form signalp.
Actually, full and summary are equivalent in terms of filtering results.
Methods
newDescriptionCode
next_featureDescriptionCode
_filterokDescriptionCode
factorsDescriptionCode
_parsedDescriptionCode
_parseDescriptionCode
_parse_summary_formatDescriptionCode
_parse_nn_resultDescriptionCode
_parse_hmm_resultDescriptionCode
_parse_short_formatDescriptionCode
create_featureDescriptionCode
seqnameDescriptionCode
Methods description
newcode    nextTop
 Title   : new
Usage : my $obj = new Bio::Tools::Signalp::ExtendedSignalp();
Function: Builds a new Bio::Tools::Signalp::ExtendedSignalp object
Returns : Bio::Tools::Signalp::ExtendedSignalp
Args : -fh/-file => $val, # for initing input, see Bio::Root::IO
next_featurecodeprevnextTop
 Title   : next_feature
Usage : my $feat = $signalp->next_feature
Function: Get the next result feature from parser data
Returns : Bio::SeqFeature::Generic
Args : none
_filterokcodeprevnextTop
 Title   : _filterok
Usage : my $feat = $signalp->_filterok
Function: Check if the factors required by the user are all ok.
Returns : 1/0
Args : hash reference
factorscodeprevnextTop
 Title   : factors
Usage : my $feat = $signalp->factors
Function: Get/Set the filters required from the user
Returns : hash
Args : array reference
_parsedcodeprevnextTop
 Title   : _parsed
Usage : obj->_parsed()
Function: Get/Set if the result is parsed or not
Returns : 1/0 scalar
Args : On set 1
_parsecodeprevnextTop
 Title   : _parse
Usage : obj->_parse
Function: Parse the SignalP result
Returns :
Args :
_parse_summary_formatcodeprevnextTop
 Title   : _parse_summary_format
Usage : $self->_parse_summary_format
Function: Method to parse summary/full format from signalp output
It automatically fills filtered features.
Returns :
Args :
_parse_nn_resultcodeprevnextTop
 Title   : _parse_nn_result
Usage : obj->_parse_nn_result
Function: Parses the Neuronal Network (NN) part of the result
Returns : Hash reference
Args :
_parse_hmm_resultcodeprevnextTop
 Title   : _parse_hmm_result
Usage : obj->_parse_hmm_result
Function: Parses the Hiden Markov Model (HMM) part of the result
Returns : Hash reference
Args :
_parse_short_formatcodeprevnextTop
 Title   : _parse_short_format
Usage : $self->_parse_short_format
Function: Method to parse short format from signalp output
It automatically fills filtered features.
Returns :
Args :
create_featurecodeprevnextTop
 Title   : create_feature
Usage : obj->create_feature(\%feature)
Function: Internal(not to be used directly)
Returns :
Args :
seqnamecodeprevnextTop
 Title   : seqname
Usage : obj->seqname($name)
Function: Internal(not to be used directly)
Returns :
Args :
Methods code
newdescriptionprevnextTop
sub new {
      my($class,@args) = @_;

      my $self = $class->SUPER::new(@args);
      $self->_initialize_io(@args);

      my $factors = $self->_rearrange([qw(FACTORS)], @args);
      #To behave like the parent module (Bio::Tools::Signalp) we default factors to these two factors
if($factors && scalar(@$factors)){ $factors = $factors; } else{ $factors = [qw(maxY meanS)]; } $factors && $self->factors($factors); return $self;
}
next_featuredescriptionprevnextTop
sub next_feature {
    my ($self) = @_;

    if(!$self->_parsed()){
	$self->_parse();
    }

    return shift @{$self->{_features}} || undef;
}
_filterokdescriptionprevnextTop
sub _filterok {
    my($self, $hash) = @_;

    #We hope everything will be fine ;)
my $bool = 1; #If the user did not give any filter, we keep eveything
return $bool unless keys %{$self->{_factors}}; #If only one of the factors parsed is equal to NO based on the user factors cutoff
#Then the filter is not ok.
foreach my $fact (keys %{$self->factors()}){ if(exists($hash->{$fact}) && $hash->{$fact} =~ /^N/){ $bool = 0; } } return $bool;
}
factorsdescriptionprevnextTop
sub factors {
    my($self, $array) = @_;

    if($array){
	$self->{_factors} = { };
	foreach my $f (@$array){
	    if(exists($FACTS->{$f})){
		$self->{_factors}->{$f} = 1;
	    }
	    else{
		$self->throw("[$f] incorrect factor. Supported:\n- ".join("\n- ", keys %$FACTS)."\n");
	    }
	}
    }

    return $self->{_factors};
}
_parseddescriptionprevnextTop
sub _parsed {
    my($self, $parsed) = @_;

    if(defined($parsed)){
	$self->{_parsed} = $parsed;
    }

    return $self->{_parsed};
}
_parsedescriptionprevnextTop
sub _parse {
    my($self) = @_;

    #Let's read the file...
while (my $line = $self->_readline()) { chomp $line; #We want to be sure to catch the first non empty line to be ablte to determine
#which format we are working with...
next unless ($line =~ /^>(\S+)|^# SignalP-[NHM]+ \S+ predictions/); if($line =~ /^>(\S+)/){ $self->_pushback($line); $self->_parse_summary_format(); last; } elsif($line =~ /^# SignalP-[NHM]+ \S+ predictions/){ $self->_pushback($line); $self->_parse_short_format(); last; } else{ $self->throw("Unable to determine the format type."); } } return;
}
_parse_summary_formatdescriptionprevnextTop
sub _parse_summary_format {
    my($self) = @_;

    my $feature = undef;
    my $ok = 0;

    while(my $line = $self->_readline()){

	if($line =~ /^SignalP-NN result:/){
	    $self->_pushback($line);
	    $feature = $self->_parse_nn_result($feature);
	}
	if($line =~ /^SignalP-HMM result:/){
	    $self->_pushback($line);
	    $feature = $self->_parse_hmm_result($feature);
	}

	if($line =~ /^---------/ && $feature){
	    my $new_feature = $self->create_feature($feature);
	    push @{$self->{_features}}, $new_feature if $new_feature;
	    $feature = undef;
	}
    }

    return;
}
_parse_nn_resultdescriptionprevnextTop
sub _parse_nn_result {
    my($self, $feature) = @_;

    my $ok   = 0;
    my %facts;

    #SignalP-NN result:
#>MGG_11635.5 length = 100
## Measure Position Value Cutoff signal peptide?
# max. C 37 0.087 0.32 NO
# max. Y 37 0.042 0.33 NO
# max. S 3 0.062 0.87 NO
# mean S 1-36 0.024 0.48 NO
# D 1-36 0.033 0.43 NO
while(my $line = $self->_readline()){ chomp $line; if($line =~ /^SignalP-NN result:/){ $ok = 1; next; } $self->throw("Wrong line for parsing NN results.") unless $ok; if ($line=~/^\>(\S+)\s+length/) { $self->seqname($1); %facts = (); next; } elsif($line =~ /max\.\s+C\s+(\S+)\s+\S+\s+\S+\s+(\S+)/) { $feature->{maxCprob} = $1; $facts{maxC} = $2; next; } elsif ($line =~ /max\.\s+Y\s+(\S+)\s+\S+\s+\S+\s+(\S+)/) { $feature->{maxYprob} = $1; $facts{maxY} = $2; next; } elsif($line =~ /max\.\s+S\s+(\S+)\s+\S+\s+\S+\s+(\S+)/) { $feature->{maxSprob} = $1; $facts{maxS} = $2; next; } elsif ($line=~/mean\s+S\s+(\S+)\s+\S+\s+\S+\s+(\S+)/) { $feature->{meanSprob} = $1; $facts{meanS} = $2; next; } elsif ($line=~/\s+D\s+(\S+)\s+\S+\s+\S+\s+(\S+)/) { $feature->{Dprob} = $1; $facts{D} = $2; next; } #If we don't have this line it means that all the factors cutoff are equal to 'NO'
elsif ($line =~ /Most likely cleavage site between pos\.\s+(\d+)/) { #if($self->_filterok(\%facts)){
#$feature->{name} = $self->seqname();
#$feature->{start} = 1;
$feature->{end} = $1 + 1; #To be consistent with end given in short format
#}
#return $feature;
} elsif($line =~ /^\s*$/){ last; } } if($self->_filterok(\%facts)){ $feature->{name} = $self->seqname(); $feature->{start} = 1; $feature->{nnPrediction} = 'signal-peptide'; } return $feature;
}
_parse_hmm_resultdescriptionprevnextTop
sub _parse_hmm_result {
    my ($self, $feature_hash) = @_;

    my $ok = 0;

    #SignalP-HMM result:
#>MGG_11635.5
#Prediction: Non-secretory protein
#Signal peptide probability: 0.000
#Signal anchor probability: 0.000
#Max cleavage site probability: 0.000 between pos. -1 and 0
while(my $line = $self->_readline()){ chomp $line; next if $line =~ /^\s*$/o; if($line =~ /^SignalP-HMM result:/){ $ok = 1; next; } $self->throw("Wrong line for parsing HMM result.") unless $ok; if($line =~ /^>(\S+)/){ #In case we already seen a name with NN results
$feature_hash->{name} = $1 unless $self->seqname(); } elsif($line =~ /Prediction: (.+)$/){ $feature_hash->{hmmPrediction} = $1; } elsif($line =~ /Signal peptide probability: ([0-9\.]+)/){ $feature_hash->{peptideProb} = $1; } elsif($line =~ /Signal anchor probability: ([0-9\.]+)/){ $feature_hash->{anchorProb} = $1; } elsif($line =~ /Max cleavage site probability: (\S+) between pos. \S+ and (\S+)/){ $feature_hash->{cleavageSiteProb} = $1; #Strange case, if we don't have an end value in NN result (no nn method launched)
#We try anyway to get an end value, unless this value is lower than 1 which is
#the start
$feature_hash->{end} = $2 if($2 > 1 && !$feature_hash->{end}); $feature_hash->{start} = 1 unless $feature_hash->{start}; last; } } return $feature_hash;
}
_parse_short_formatdescriptionprevnextTop
sub _parse_short_format {
                                my($self) = @_;

    my $ok = 0;
    my $method = undef;
    $self->{_oformat} = 'short';

    #Output example
# SignalP-NN euk predictions # SignalP-HMM euk predictions
# name Cmax pos ? Ymax pos ? Smax pos ? Smean ? D ? # name ! Cmax pos ? Sprob ?
#Q5A8M1_CANAL 0.085 27 N 0.190 35 N 0.936 27 Y 0.418 N 0.304 N Q5A8M1_CANAL Q 0.001 35 N 0.002 N
#O74127_YARLI 0.121 21 N 0.284 21 N 0.953 11 Y 0.826 Y 0.555 Y O74127_YARLI S 0.485 23 N 0.668 Y
#Q5VJ86_9PEZI 0.355 24 Y 0.375 24 Y 0.798 12 N 0.447 N 0.411 N Q5VJ86_9PEZI Q 0.180 23 N 0.339 N
#Q5A8U5_CANAL 0.085 27 N 0.190 35 N 0.936 27 Y 0.418 N 0.304 N Q5A8U5_CANAL Q 0.001 35 N 0.002 N
while(my $line = $self->_readline()){ chomp $line; next if $line =~ /^\s*$|^# name/; if($line =~ /^#/){ $method = $line =~ /SignalP-NN .+ SignalP-HMM/ ? 'both' : $line =~ /SignalP-NN/ ? 'nn' : 'hmm'; next; } #$self->throw("It looks like the format is not 'short' format.") unless($ok);
my @data = split(/\s+/, $line); $self->seqname($data[0]); my $factors = { }; my $feature = { }; #NN results gives more fields than HMM
if($method eq 'both' || $method eq 'nn'){ $feature->{maxCprob} = $data[1]; $factors->{maxC} = $data[3]; $feature->{maxYprob} = $data[4]; $factors->{maxY} = $data[6]; $feature->{maxSprob} = $data[7]; $factors->{maxS} = $data[9]; $feature->{meanSprob}= $data[10]; $factors->{meanS} = $data[11]; $feature->{Dprob} = $data[12]; $factors->{D} = $data[13]; #It looks like the max Y position is reported as the most likely cleavage position
$feature->{end} = $data[5]; $feature->{nnPrediction} = 'signal-peptide'; if($method eq 'both'){ $feature->{hmmPrediction} = $data[15] eq 'Q' ? 'Non-secretory protein' : 'Signal peptide'; $feature->{cleavageSiteProb} = $data[16]; $feature->{peptideProb} = $data[19]; } } elsif($method eq 'hmm'){ #In short output anchor probability is not given
$feature->{hmmPrediction} = $data[1] eq 'Q' ? 'Non-secretory protein' : 'Signal peptide'; $feature->{cleavageSiteProb} = $data[2]; $feature->{peptideProb} = $data[5]; #It looks like the max cleavage probability position is given by the Cmax proability
$feature->{end} = $data[3]; } #Unfortunately, we cannot parse the filters for hmm method.
if($self->_filterok($factors)){ $feature->{name} = $self->seqname(); $feature->{start} = 1; $feature->{source} = 'Signalp'; $feature->{primary} = 'signal_peptide'; $feature->{program} = 'Signalp'; $feature->{logic_name} = 'signal_peptide'; my $new_feat = $self->create_feature($feature); push @{$self->{_features}}, $new_feat if $new_feat; } } return;
}
create_featuredescriptionprevnextTop
sub create_feature {
    my ($self, $feat) = @_;

    #If we don't have neither start nor end, we return.
unless($feat->{name} && $feat->{start} && $feat->{end}){ return; } # create feature object
my $feature = Bio::SeqFeature::Generic->new( -seq_id => $feat->{name}, -start => $feat->{start}, -end => $feat->{end}, -score => defined($feat->{peptideProb}) ? $feat->{peptideProb} : '', -source => 'Signalp', -primary => 'signal_peptide', -logic_name => 'signal_peptide', ); $feature->add_tag_value('peptideProb', $feat->{peptideProb}); $feature->add_tag_value('anchorProb', $feat->{anchorProb}); $feature->add_tag_value('evalue',$feat->{anchorProb}); $feature->add_tag_value('percent_id','NULL'); $feature->add_tag_value("hid",$feat->{primary}); $feature->add_tag_value('signalpPrediction', $feat->{hmmPrediction}); $feature->add_tag_value('cleavageSiteProb', $feat->{cleavageSiteProb}) if($feat->{cleavageSiteProb}); $feature->add_tag_value('nnPrediction', $feat->{nnPrediction}) if($feat->{nnPrediction}); $feature->add_tag_value('maxCprob', $feat->{maxCprob}) if(defined($feat->{maxCprob})); $feature->add_tag_value('maxSprob', $feat->{maxSprob}) if(defined($feat->{maxSprob})); $feature->add_tag_value('maxYprob', $feat->{maxYprob}) if(defined($feat->{maxYprob})); $feature->add_tag_value('meanSprob', $feat->{meanSprob}) if(defined($feat->{meanSprob})); $feature->add_tag_value('Dprob', $feat->{Dprob}) if(defined($feat->{Dprob})); return $feature;
}
seqnamedescriptionprevnextTop
sub seqname {
    my ($self,$seqname)=@_;

    if (defined($seqname)){
        $self->{'seqname'} = $seqname;
    }

    return $self->{'seqname'};

}


1;
}
General documentation
FEEDBACKTop
Mailing ListsTop
User feedback is an integral part of the evolution of this and other
Bioperl modules. Send your comments and suggestions preferably to
the Bioperl mailing list. Your participation is much appreciated.
  bioperl-l@bioperl.org                  - General discussion
http://bioperl.org/wiki/Mailing_lists - About the mailing lists
Support Top
Please direct usage questions or support issues to the mailing list:
bioperl-l@bioperl.org
rather than to the module maintainer directly. Many experienced and
reponsive experts will be able look at the problem and quickly
address it. Please include a thorough description of the problem
with code and data examples if at all possible.
Reporting BugsTop
Report bugs to the Bioperl bug tracking system to help us keep track
of the bugs and their resolution. Bug reports can be submitted via
the web:
  https://redmine.open-bio.org/projects/bioperl/
AUTHORTop
 Based on the Bio::Tools::Signalp module
Emmanuel Quevillon <emmanuel.quevillon@versailles.inra.fr>
APPENDIXTop
 The rest of the documentation details each of the object methods.
Internal methods are usually preceded with a _