comparison variant_effect_predictor/Bio/Tools/Sigcleave.pm @ 0:2bc9b66ada89 draft default tip

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#-----------------------------------------------------------------------------
# PACKAGE : Bio::Tools::Sigcleave
# AUTHOR  : Chris Dagdigian, [e-mail]
# CREATED : Jan 28 1999
# REVISION: $Id: Sigcleave.pm,v 1.17 2002/10/22 07:45:22 lapp Exp $
#
# Copyright (c) 1997-9 bioperl, Chris Dagdigian and others. All Rights Reserved.
#           This module is free software; you can redistribute it and/or 
#           modify it under the same terms as Perl itself.
#
# _History_
#
# Object framework ripped from Steve Chervits's SeqPattern.pm
# 
# Core EGCG Sigcleave emulation from perl code developed by
# Danh Nguyen & Kamalakar Gulukota which itself was based 
# loosely on Colgrove's signal.c program.
#
# The overall idea is to replicate the output of the sigcleave
# program which was distributed with the EGCG extension to the GCG sequence
# analysis package. There is also an accessor method for just getting at
# the raw results.
#
#-----------------------------------------------------------------------------

=head1 NAME

Bio::Tools::Sigcleave - Bioperl object for sigcleave analysis

=head1 SYNOPSIS

=head2 Object Creation

  use Bio::Tools::Sigcleave ();

  # to keep the module backwar compatible, you can pass it a sequence string, but
  # there recommended say is to pass it a Seq object

  # this works
  $seq = "MVLLLILSVLLLKEDVRGSAQSSERRVVAHMPGDIIIGALFSVHHQPTVDKVHERKCGAVREQYGI";
  $sig = new Bio::Tools::Sigcleave(-seq  => $seq,
                                                -type => 'protein',
                                                -threshold=>'3.5',
                                                );
  # but you do:
  $seqobj = Bio::PrimarySeq->new(-seq => $seq);

  $sig = new Bio::Tools::Sigcleave(-seq  => $seqobj,
                                                -threshold=>'3.5',
                                                );


  # now you can detect procaryotic signal sequences as well as eucaryotic
  $sig->matrix('eucaryotic'); # or 'procaryotic'


=head2 Object Methods & Accessors

  # you can use this method to fine tune the threshod before printing out the results
  $sig->result_count:

  %raw_results      = $sig->signals;
  $formatted_output = $sig->pretty_print;

=head1 DESCRIPTION

"Sigcleave" was a program distributed as part of the free EGCG add-on
to earlier versions of the GCG Sequence Analysis package. A new
implementation of the algorithm is now part of EMBOSS package.

From the EGCG documentation:

  SigCleave uses the von Heijne method to locate signal sequences, and
  to identify the cleavage site. The method is 95% accurate in
  resolving signal sequences from non-signal sequences with a cutoff
  score of 3.5, and 75-80% accurate in identifying the cleavage
  site. The program reports all hits above a minimum value.

The EGCG Sigcleave program was written by Peter Rice (E-mail:
[e-mail] Post: Informatics Division, The Sanger Centre,
Wellcome Trust Genome Campus, Hinxton, Cambs, CB10 1SA, UK).

Since EGCG is no longer distributed for the latest versions of GCG,
this code was developed to emulate the output of the original program
as much as possible for those who lost access to sigcleave when
upgrading to newer versions of GCG.

There are 2 accessor methods for this object. "signals" will return a
perl associative array containing the sigcleave scores keyed by amino
acid position.  "pretty_print" returns a formatted string similar to
the output of the original sigcleave utility.

In both cases, the "threshold" setting controls the score reporting
level. If no value for threshold is passed in by the user, the code
defaults to a reporting value of 3.5.

In this implemntation the accessor will never return any
score/position pair which does not meet the threshold limit. This is
the slightly different from the behaviour of the 8.1 EGCG sigcleave
program which will report the highest of the under-threshold results
if nothing else is found.


Example of pretty_print output:

	SIGCLEAVE of sigtest from: 1 to 146

	Report scores over 3.5
	Maximum score 4.9 at residue 131

	 Sequence:  FVILAAMSIQGSA-NLQTQWKSTASLALET
        	    | (signal)    | (mature peptide)
          	118            131

	 Other entries above 3.5

	Maximum score 3.7 at residue 112

	 Sequence:  CSRQLFGWLFCKV-HPGAIVFVILAAMSIQGSANLQTQWKSTASLALET
         	   | (signal)    | (mature peptide)
           	99            112


=head1 FEEDBACK

When updating and maintaining a module, it helps to know that people
are actually using it. Let us know if you find a bug, think this code
is useful or have any improvements/features to suggest.

=head2 Reporting Bugs

Report bugs to the Bioperl bug tracking system to help us keep track
the bugs and their resolution. Bug reports can be submitted via email
or the web:

    [e-mail]
    http://bugzilla.bioperl.org/

=head1 AUTHOR

Chris Dagdigian, [e-mail]  & others

=head1 CONTRIBUTORS

Heikki Lehvaslaiho, [e-mail]

=head1 VERSION

Bio::Tools::Sigcleave.pm, $Id: Sigcleave.pm,v 1.17 2002/10/22 07:45:22 lapp Exp $

=head1 COPYRIGHT

Copyright (c) 1999 Chris Dagdigian & others. All Rights Reserved.
This module is free software; you can redistribute it and/or modify it
under the same terms as Perl itself.

=head1 REFERENCES / SEE ALSO

von Heijne G. (1986) "A new method for predicting signal sequences
cleavage sites."  Nucleic Acids Res. 14, 4683-4690.

von Heijne G. (1987) in "Sequence Analysis in Molecular Biology:
Treasure Trove or Trivial Pursuit" (Acad. Press, (1987), 113-117).


=head1 APPENDIX

The following documentation describes the various functions
contained in this module. Some functions are for internal 
use and are not meant to be called by the user; they are 
preceded by an underscore ("_").


=cut

#
##
###
#### END of main POD documentation.
###
##
#


package Bio::Tools::Sigcleave;

use Bio::Root::Root;
use Bio::PrimarySeq;

@ISA = qw(Bio::Root::Root);
use strict;
use vars qw ($ID $VERSION %WeightTable_euc  %WeightTable_pro );
$ID  = 'Bio::Tools::Sigcleave';
$VERSION = 0.02;


  %WeightTable_euc = (
#Sample: 161 aligned sequences
# R     -13 -12 -11 -10  -9  -8  -7  -6  -5  -4  -3  -2  -1  +1  +2 Expect
 'A' => [16, 13, 14, 15, 20, 18, 18, 17, 25, 15, 47,  6, 80, 18,  6, 14.5],
 'C' => [ 3,  6,  9,  7,  9, 14,  6,  8,  5,  6, 19,  3,  9,  8,  3,  4.5],
 'D' => [ 0,  0,  0,  0,  0,  0,  0,  0,  5,  3,  0,  5,  0, 10, 11,  8.9],
 'E' => [ 0,  0,  0,  1,  0,  0,  0,  0,  3,  7,  0,  7,  0, 13, 14, 10.0],
 'F' => [13,  9, 11, 11,  6,  7, 18, 13,  4,  5,  0, 13,  0,  6,  4,  5.6],
 'G' => [ 4,  4,  3,  6,  3, 13,  3,  2, 19, 34,  5,  7, 39, 10,  7, 12.1],
 'H' => [ 0,  0,  0,  0,  0,  1,  1,  0,  5,  0,  0,  6,  0,  4,  2,  3.4],
 'I' => [15, 15,  8,  6, 11,  5,  4,  8,  5,  1, 10,  5,  0,  8,  7,  7.4],
 'K' => [ 0,  0,  0,  1,  0,  0,  1,  0,  0,  4,  0,  2,  0, 11,  9, 11.3],
 'L' => [71, 68, 72, 79, 78, 45, 64, 49, 10, 23,  8, 20,  1,  8,  4, 12.1],
 'M' => [ 0,  3,  7,  4,  1,  6,  2,  2,  0,  0,  0,  1,  0,  1,  2,  2.7],
 'N' => [ 0,  1,  0,  1,  1,  0,  0,  0,  3,  3,  0, 10,  0,  4,  7,  7.1],
 'P' => [ 2,  0,  2,  0,  0,  4,  1,  8, 20, 14,  0,  1,  3,  0, 22,  7.4],
 'Q' => [ 0,  0,  0,  1,  0,  6,  1,  0, 10,  8,  0, 18,  3, 19, 10,  6.3],
 'R' => [ 2,  0,  0,  0,  0,  1,  0,  0,  7,  4,  0, 15,  0, 12,  9,  7.6],
 'S' => [ 9,  3,  8,  6, 13, 10, 15, 16, 26, 11, 23, 17, 20, 15, 10, 11.4],
 'T' => [ 2, 10,  5,  4,  5, 13,  7,  7, 12,  6, 17,  8,  6,  3, 10,  9.7],
 'V' => [20, 25, 15, 18, 13, 15, 11, 27,  0, 12, 32,  3,  0,  8, 17, 11.1],
 'W' => [ 4,  3,  3,  1,  1,  2,  6,  3,  1,  3,  0,  9,  0,  2,  0,  1.8],
 'Y' => [ 0,  1,  4,  0,  0,  1,  3,  1,  1,  2,  0,  5,  0,  1,  7,  5.6]
);

  %WeightTable_pro = (
#Sample: 36 aligned sequences
#  R    -13 -12 -11 -10  -9  -8  -7  -6  -5  -4  -3  -2  -1  +1  +2 Expect
  'A' => [0,  8,  8,  9,  6,  7,  5,  6,  7,  7, 24,  2, 31, 18,  4,  3.2],
  'C' => [1,  0,  0,  1,  1,  0,  0,  1,  1,  0,  0,  0,  0,  0,  0,  1.0],
  'D' => [0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  2,  8,  2.0],
  'E' => [0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  4,  8,  2.2],
  'F' => [2,  4,  3,  4,  1,  1,  8,  0,  4,  1,  0,  7,  0,  1,  0,  1.3],
  'G' => [4,  2,  2,  2,  3,  5,  2,  4,  2,  2,  0,  2,  2,  1,  0,  2.7],
  'H' => [0,  0,  1,  0,  0,  0,  0,  1,  1,  0,  0,  7,  0,  1,  0,  0.8],
  'I' => [3,  1,  5,  1,  5,  0,  1,  3,  0,  0,  0,  0,  0,  0,  2,  1.7],
  'K' => [0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  2,  0,  3,  0,  2.5],
  'L' => [8, 11,  9,  8,  9, 13,  1,  0,  2,  2,  1,  2,  0,  0,  1,  2.7],
  'M' => [0,  2,  1,  1,  3,  2,  3,  0,  1,  2,  0,  4,  0,  0,  1,  0.6],
  'N' => [0,  0,  0,  0,  0,  0,  0,  1,  1,  1,  0,  3,  0,  1,  4,  1.6],
  'P' => [0,  1,  1,  1,  1,  1,  2,  3,  5,  2,  0,  0,  0,  0,  5,  1.7],
  'Q' => [0,  0,  0,  0,  0,  0,  0,  0,  2,  2,  0,  3,  0,  0,  1,  1.4],
  'R' => [0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  1.7],
  'S' => [1,  0,  1,  4,  4,  1,  5, 15,  5,  8,  5,  2,  2,  0,  0,  2.6],
  'T' => [2,  0,  4,  2,  2,  2,  2,  2,  5,  1,  3,  0,  1,  1,  2,  2.2],
  'V' => [5,  7,  1,  3,  1,  4,  7,  0,  0,  4,  3,  0,  0,  2,  0,  2.5],
  'W' => [0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  0.4],
  'Y' => [0,  0,  0,  0,  0,  0,  0,  0,  0,  3,  0,  1,  0,  0,  0,  1.3]
);


##
## Now we calculate the _real_ values for the weight tables
##
##
## yeah yeah yeah there is lots of math here that gets repeated
## every single time a sigcleave object gets created. This is
## a quick hack to make sure that we get the scores as accurate as
## possible. Need all those significant digits....
##
## suggestions for speedup aproaches welcome
##


foreach my $i (keys %WeightTable_euc) {
    my $expected = $WeightTable_euc{$i}[15];
    if ($expected > 0) {
	for (my $j=0; $j<16; $j++) {
	    if ($WeightTable_euc{$i}[$j] == 0) {
		$WeightTable_euc{$i}[$j] = 1; 
		if ($j == 10 || $j == 12) {
		    $WeightTable_euc{$i}[$j] = 1.e-10;
		}
	    }
	    $WeightTable_euc{$i}[$j] = log($WeightTable_euc{$i}[$j]/$expected);
	}
    }
}


foreach my $i (keys %WeightTable_pro) {
    my $expected = $WeightTable_pro{$i}[15];
    if ($expected > 0) {
	for (my $j=0; $j<16; $j++) {
	    if ($WeightTable_pro{$i}[$j] == 0) {
		$WeightTable_pro{$i}[$j] = 1; 
		if ($j == 10 || $j == 12) {
		    $WeightTable_pro{$i}[$j] = 1.e-10;
		}
	    }
	    $WeightTable_pro{$i}[$j] = log($WeightTable_pro{$i}[$j]/$expected);
	}
    }
}



#####################################################################################
##                                 CONSTRUCTOR                                     ##
#####################################################################################


sub new {
    my ($class, @args) = @_;

    my $self = $class->SUPER::new(@args);
    #my $self = Bio::Seq->new(@args);

    my ($seq, $threshold, $matrix) = $self->_rearrange([qw(SEQ THRESHOLD MATRIX)],@args);

    defined $threshold && $self->threshold($threshold);
    $matrix && $self->matrix($matrix);
    $seq && $self->seq($seq);

    return $self;
}



=head1 threshold

 Title     : threshold
 Usage     : $value = $self->threshold
 Purpose   : Read/write method sigcleave score reporting threshold.
 Returns   : float.
 Argument  : new value, float
 Throws    : on non-number argument
 Comments  : defaults to 3.5
 See Also   : n/a

=cut

#----------------
sub threshold {
#----------------
    my ($self, $value) = @_;
    if( defined $value) {
	$self->throw("I need a number, not [$value]")
	    if  $value !~ /^[+-]?[\d\.]+$/;
	$self->{'_threshold'} = $value;
    }
    return $self->{'_threshold'} || 3.5 ;
}

=head1 matrix

 Title     : matrix
 Usage     : $value = $self->matrix('procaryotic')
 Purpose   : Read/write method sigcleave matrix.
 Returns   : float.
 Argument  : new value: 'eucaryotic' or 'procaryotic'
 Throws    : on non-number argument
 Comments  : defaults to 3.5
 See Also   : n/a

=cut

#----------------
sub matrix {
#----------------
    my ($self, $value) = @_;
    if( defined $value) {
	$self->throw("I need 'eucaryotic' or 'procaryotic', not [$value]")
	    unless  $value eq 'eucaryotic' or $value eq 'procaryotic';
	$self->{'_matrix'} = $value;
    }
    return $self->{'_matrix'} || 'eucaryotic' ;

}

=head1 seq

 Title     : seq
 Usage     : $value = $self->seq('procaryotic')
 Purpose   : Read/write method sigcleave seq.
 Returns   : float.
 Argument  : new value: 'eucaryotic' or 'procaryotic'
 Throws    : on non-number argument
 Comments  : defaults to 3.5
 See Also   : n/a

=cut

#----------------
sub seq {
#----------------
    my ($self, $value) = @_;
    if( defined $value) {
	if ($value->isa('Bio::PrimarySeqI')) {
	    $self->{'_seq'} = $value;
	} else {
	    $self->{'_seq'} = Bio::PrimarySeq->new(-seq=>$value, 
						   -alphabet=>'protein');
	}
    }
    return $self->{'_seq'};
}



=head1 _Analyze

 Title     : _Analyze
 Usage     : N/A This is an internal method. Not meant to be called from outside
           : the package
           :
 Purpose   : calculates sigcleave score and amino acid position for the
           : given protein sequence. The score reporting threshold can
           : be adjusted by passing in the "threshold" parameter during
           : object construction. If no threshold is passed in, the code
           : defaults to reporting any scores equal to or above 3.5
           :
 Returns   : nothing. results are added to the object
 Argument  : none.
 Throws    : nothing.
 Comments  : nothing.
See Also   : n/a

=cut

#----------------
sub _Analyze {
#----------------
    my($self) = @_;

    my %signals;
    my @hitWeight = ();
    my @hitsort   = ();
    my @hitpos    = ();
    my $maxSite   = "";
    my $seqPos    = "";
    my $istart    = "";
    my $iend      = "";
    my $icol      = "";
    my $i         = "";
    my $weight    = "";
    my $k         = 0;
    my $c         = 0;
    my $seqBegin  = 0;
    my $pVal      = -13;
    my $nVal      = 2;
    my $nHits     = 0;
    my $seqEnd    = $self->seq->length;
    my $pep       = $self->seq->seq;
    my $minWeight = $self->threshold;
    my $matrix    = $self->matrix;

    ## The weight table is keyed by UPPERCASE letters so we uppercase
    ## the pep string because we don't want to alter the actual object
    ## sequence.

    $pep =~ tr/a-z/A-Z/;

    for ($seqPos = $seqBegin; $seqPos < $seqEnd; $seqPos++) {
	$istart = (0 > $seqPos + $pVal)? 0 : $seqPos + $pVal;
	$iend = ($seqPos + $nVal - 1 < $seqEnd)? $seqPos + $nVal - 1 : $seqEnd;
	$icol= $iend - $istart + 1;
	$weight = 0.00;
	for ($k=0; $k<$icol; $k++) {
	    $c = substr($pep, $istart + $k, 1);

	    ## CD: The if(defined) stuff was put in here because Sigcleave.pm
	    ## CD: kept getting warnings about undefined vals during 'make test' ...
	    if ($matrix eq 'eucaryotic') {
		$weight += $WeightTable_euc{$c}[$k] if defined $WeightTable_euc{$c}[$k];
	    } else {
		$weight += $WeightTable_pro{$c}[$k] if defined $WeightTable_pro{$c}[$k];
	    }
	}
	$signals{$seqPos+1} = sprintf ("%.1f", $weight)	if $weight >= $minWeight;
    }

    $self->{"_signal_scores"} = { %signals };
}


=head1 signals

 Title     : signals
 Usage     : %sigcleave_results = $sig->signals;
           :
 Purpose   : Accessor method for sigcleave results
           : 
 Returns   : Associative array. The key value represents the amino acid position
           : and the value represents the score. Only scores that
           : are greater than or equal to the THRESHOLD value are reported.
           : 
 Argument  : none.
 Throws    : none.
 Comments  : none.
See Also   : THRESHOLD

=cut

#----------------
sub signals {
#----------------
    my $self = shift;
    my %results;
    my $position;

    # do the calculations
    $self->_Analyze;

    foreach $position ( sort keys %{ $self->{'_signal_scores'} } ) {
	$results{$position} = $self->{'_signal_scores'}{$position};
    }
    return %results;
}


=head1 result_count

 Title     : result_count
 Usage     : $count = $sig->result_count;
           :
 Purpose   : Accessor method for sigcleave results
           : 
 Returns   : Integer, number of results above the threshold
           : 
 Argument  : none.
 Throws    : none.
 Comments  : none.

See Also   : THRESHOLD

=cut

#----------------
sub result_count {
#----------------
    my $self = shift;
    $self->_Analyze;
    return keys %{ $self->{'_signal_scores'} };
}


=head1 pretty_print

 Title     : pretty_print
 Usage     : $output = $sig->pretty_print;
           : print $sig->pretty_print;
           :
 Purpose   : Emulates the output of the EGCG Sigcleave
           : utility.
           : 
 Returns   : A formatted string.
 Argument  : none.
 Throws    : none.
 Comments  : none.
See Also   : n/a

=cut

#----------------
sub pretty_print {
#----------------
    my $self = shift;
    my $pos;
    my $output;
    my $cnt = 1;
    my %results  = $self->signals;
    my @hits     = keys %results;
    my $hitcount = $#hits; $hitcount++;
    my $thresh   = $self->threshold;
    my $seqlen   = $self->seq->length || 0;
    my $name     = $self->seq->id || 'NONAME';
    my $pep      = $self->seq->seq;
    $pep      =~ tr/a-z/A-Z/;

    $output = "SIGCLEAVE of $name from: 1 to $seqlen\n\n";

    if ($hitcount > 0) {
	$output .= "Report scores over $thresh\n";
	foreach $pos ((sort { $results{$b} cmp $results{$a} } keys %results)) {
	    my $start = $pos - 15;
	    $start = 1 if $start < 1;
	    my $sig = substr($pep,$start -1,$pos-$start );

	    $output .= sprintf ("Maximum score %1.1f at residue %3d\n",$results{$pos},$pos);
	    $output .= "\n";
	    $output .= " Sequence:  ";
	    $output .= $sig;
	    $output .= "-" x (15- length($sig));
	    $output .= "-";
	    $output .= substr($pep,$pos-1,50);
	    $output .= "\n";
	    $output .= " " x 12;
	    $output .= "| \(signal\)      | \(mature peptide\)\n";
	    $output .= sprintf("          %3d             %3d\n\n",$start,$pos);

	    if (($hitcount > 1) && ($cnt == 1)) {
    		$output .= " Other entries above $thresh\n\n";
	    }
	    $cnt++;
 	}
    }
    $output;
}


1;
__END__


#########################################################################
#  End of class 
#########################################################################