#
# BioPerl module for Bio::Align::DNAStatistics
#
# Please direct questions and support issues to <bioperl-l@bioperl.org>
#
# Cared for by Jason Stajich <jason-AT-bioperl.org>
#
# Copyright Jason Stajich
#
# You may distribute this module under the same terms as perl itself
# POD documentation - main docs before the code
=head1 NAME
Bio::Align::DNAStatistics - Calculate some statistics for a DNA alignment
=head1 SYNOPSIS
use Bio::AlignIO;
use Bio::Align::DNAStatistics;
my $stats = Bio::Align::DNAStatistics->new();
my $alignin = Bio::AlignIO->new(-format => 'emboss',
-file => 't/data/insulin.water');
my $aln = $alignin->next_aln;
my $jcmatrix = $stats->distance(-align => $aln,
-method => 'Jukes-Cantor');
print $jcmatrix->print_matrix;
## and for measurements of synonymous /nonsynonymous substitutions ##
my $in = Bio::AlignIO->new(-format => 'fasta',
-file => 't/data/nei_gojobori_test.aln');
my $alnobj = $in->next_aln;
my ($seq1id,$seq2id) = map { $_->display_id } $alnobj->each_seq;
my $results = $stats->calc_KaKs_pair($alnobj, $seq1id, $seq2id);
print "comparing ".$results->[0]{'Seq1'}." and ".$results->[0]{'Seq2'}."\n";
for (sort keys %{$results->[0]} ){
next if /Seq/;
printf("%-9s %.4f \n",$_ , $results->[0]{$_});
}
my $results2 = $stats->calc_all_KaKs_pairs($alnobj);
for my $an (@$results2){
print "comparing ". $an->{'Seq1'}." and ". $an->{'Seq2'}. " \n";
for (sort keys %$an ){
next if /Seq/;
printf("%-9s %.4f \n",$_ , $an->{$_});
}
print "\n\n";
}
my $result3 = $stats->calc_average_KaKs($alnobj, 1000);
for (sort keys %$result3 ){
next if /Seq/;
printf("%-9s %.4f \n",$_ , $result3->{$_});
}
=head1 DESCRIPTION
This object contains routines for calculating various statistics and
distances for DNA alignments. The routines are not well tested and do
contain errors at this point. Work is underway to correct them, but
do not expect this code to give you the right answer currently! Use
dnadist/distmat in the PHLYIP or EMBOSS packages to calculate the
distances.
Several different distance method calculations are supported. Listed
in brackets are the pattern which will match
=over 3
=item *
JukesCantor [jc|jukes|jukescantor|jukes-cantor]
=item *
Uncorrected [jcuncor|uncorrected]
=item *
F81 [f81|felsenstein]
=item *
Kimura [k2|k2p|k80|kimura]
=item *
Tamura [t92|tamura|tamura92]
=item *
F84 [f84|felsenstein84]
=item *
TajimaNei [tajimanei|tajima\-nei]
=item *
JinNei [jinnei|jin\-nei] (not implemented)
=back
There are also three methods to calculate the ratio of synonymous to
non-synonymous mutations. All are implementations of the Nei-Gojobori
evolutionary pathway method and use the Jukes-Cantor method of
nucleotide substitution. This method works well so long as the
nucleotide frequencies are roughly equal and there is no significant
transition/transversion bias. In order to use these methods there are
several pre-requisites for the alignment.
=over 3
=item 1
DNA alignment must be based on protein alignment. Use the subroutine
L<Bio::Align::Utilities/aa_to_dna_aln> to achieve this.
=item 2
Therefore alignment gaps must be in multiples of 3 (representing an aa
deletion/insertion) and at present must be indicated by a '-' symbol.
=item 3
Alignment must be solely of coding region and be in reading frame 0 to
achieve meaningful results
=item 4
Alignment must therefore be a multiple of 3 nucleotides long.
=item 5
All sequences must be the same length (including gaps). This should be
the case anyway if the sequences have been automatically aligned using
a program like Clustal.
=item 6
Only the standard codon alphabet is supported at present.
=back
calc_KaKs_pair() calculates a number of statistics for a named pair of
sequences in the alignment.
calc_all_KaKs_pairs() calculates these statistics for all pairwise
comparisons in an MSA. The statistics returned are:
=over 3
=item *
S_d - Number of synonymous mutations between the 2 sequences.
=item *
N_d - Number of non-synonymous mutations between the 2 sequences.
=item *
S - Mean number of synonymous sites in both sequences.
=item *
N - mean number of synonymous sites in both sequences.
=item *
P_s - proportion of synonymous differences in both sequences given by
P_s = S_d/S.
=item *
P_n - proportion of non-synonymous differences in both sequences given
by P_n = S_n/S.
=item *
D_s - estimation of synonymous mutations per synonymous site (by
Jukes-Cantor).
=item *
D_n - estimation of non-synonymous mutations per non-synonymous site (by
Jukes-Cantor).
=item *
D_n_var - estimation of variance of D_n .
=item *
D_s_var - estimation of variance of S_n.
=item *
z_value - calculation of z value.Positive value indicates D_n E<gt> D_s,
negative value indicates D_s E<gt> D_n.
=back
The statistics returned by calc_average_KaKs are:
=over 3
=item *
D_s - Average number of synonymous mutations/synonymous site.
=item *
D_n - Average number of non-synonymous mutations/non-synonymous site.
=item *
D_s_var - Estimated variance of Ds from bootstrapped alignments.
=item *
D_n_var - Estimated variance of Dn from bootstrapped alignments.
=item *
z_score - calculation of z value. Positive value indicates D_n E<gt>D_s,
negative values vice versa.
=back
The design of the code is based around the explanation of the
Nei-Gojobori algorithm in the excellent book "Molecular Evolution and
Phylogenetics" by Nei and Kumar, published by Oxford University
Press. The methods have been tested using the worked example 4.1 in
the book, and reproduce those results. If people like having this sort
of analysis in BioPerl other methods for estimating Ds and Dn can be
provided later.
Much of the DNA distance code is based on implementations in EMBOSS
(Rice et al, www.emboss.org) [distmat.c] and PHYLIP (J. Felsenstein et
al) [dnadist.c]. Insight also gained from Eddy, Durbin, Krogh, &
Mitchison.
=head1 REFERENCES
=over 3
=item *
D_JukesCantor
"Phylogenetic Inference", Swoffrod, Olsen, Waddell and Hillis, in
Mol. Systematics, 2nd ed, 1996, Ch 11. Derived from "Evolution of
Protein Molecules", Jukes & Cantor, in Mammalian Prot. Metab., III,
1969, pp. 21-132.
=item *
D_Tamura
K Tamura, Mol. Biol. Evol. 1992, 9, 678.
=item *
D_Kimura
M Kimura, J. Mol. Evol., 1980, 16, 111.
=item *
JinNei
Jin and Nei, Mol. Biol. Evol. 82, 7, 1990.
=item *
D_TajimaNei
Tajima and Nei, Mol. Biol. Evol. 1984, 1, 269.
=back
=head1 FEEDBACK
=head2 Mailing Lists
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
=head2 Support
Please direct usage questions or support issues to the mailing list:
I<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.
=head2 Reporting Bugs
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/
=head1 AUTHOR - Jason Stajich
Email jason-AT-bioperl.org
=head1 CONTRIBUTORS
Richard Adams, richard.adams@ed.ac.uk
=head1 APPENDIX
The rest of the documentation details each of the object methods.
Internal methods are usually preceded with a _
=cut
# Let the code begin...
package Bio::Align::DNAStatistics;
use vars qw(%DNAChanges @Nucleotides %NucleotideIndexes
$GapChars $SeqCount $DefaultGapPenalty %DistanceMethods
$CODONS %synchanges $synsites $Precision $GCChhars);
use strict;
use Bio::Align::PairwiseStatistics;
use Bio::Matrix::PhylipDist;
use Bio::Tools::IUPAC;
BEGIN {
$GapChars = '[\.\-]';
$GCChhars = '[GCS]';
@Nucleotides = qw(A G T C);
$SeqCount = 2;
$Precision = 5;
# these values come from EMBOSS distmat implementation
%NucleotideIndexes = ( 'A' => 0,
'T' => 1,
'C' => 2,
'G' => 3,
'AT' => 0,
'AC' => 1,
'AG' => 2,
'CT' => 3,
'GT' => 4,
'CG' => 5,
# these are wrong now
# 'S' => [ 1, 3],
# 'W' => [ 0, 4],
# 'Y' => [ 2, 3],
# 'R' => [ 0, 1],
# 'M' => [ 0, 3],
# 'K' => [ 1, 2],
# 'B' => [ 1, 2, 3],
# 'H' => [ 0, 2, 3],
# 'V' => [ 0, 1, 3],
# 'D' => [ 0, 1, 2],
);
$DefaultGapPenalty = 0;
# could put ambiguities here?
%DNAChanges = ( 'Transversions' => { 'A' => [ 'T', 'C'],
'T' => [ 'A', 'G'],
'C' => [ 'A', 'G'],
'G' => [ 'C', 'T'],
},
'Transitions' => { 'A' => [ 'G' ],
'G' => [ 'A' ],
'C' => [ 'T' ],
'T' => [ 'C' ],
},
);
%DistanceMethods = ( 'jc|jukes|jukescantor|jukes\-cantor' => 'JukesCantor',
'jcuncor|uncorrected' => 'Uncorrected',
'f81|felsenstein81' => 'F81',
'k2|k2p|k80|kimura' => 'Kimura',
't92|tamura|tamura92' => 'Tamura',
'f84|felsenstein84' => 'F84',
'tajimanei|tajima\-nei' => 'TajimaNei',
'jinnei|jin\-nei' => 'JinNei');
}
use base qw(Bio::Root::Root Bio::Align::StatisticsI);
## generate look up hashes for Nei_Gojobori methods##
$CODONS = get_codons();
my @t = split '', "FFLLSSSSYY**CC*WLLLLPPPPHHQQRRRRIIIMTTTTNNKKSSRRVVVVAAAADDEEGGGG";
#create look up hash of number of possible synonymous mutations per codon
$synsites = get_syn_sites();
#create reference look up hash of single basechanges in codons
%synchanges = get_syn_changes();
=head2 new
Title : new
Usage : my $obj = Bio::Align::DNAStatistics->new();
Function: Builds a new Bio::Align::DNAStatistics object
Returns : Bio::Align::DNAStatistics
Args : none
=cut
sub new {
my ($class,@args) = @_;
my $self = $class->SUPER::new(@args);
$self->pairwise_stats( Bio::Align::PairwiseStatistics->new());
return $self;
}
=head2 distance
Title : distance
Usage : my $distance_mat = $stats->distance(-align => $aln,
-method => $method);
Function: Calculates a distance matrix for all pairwise distances of
sequences in an alignment.
Returns : L<Bio::Matrix::PhylipDist> object
Args : -align => Bio::Align::AlignI object
-method => String specifying specific distance method
(implementing class may assume a default)
See also: L<Bio::Matrix::PhylipDist>
=cut
sub distance{
my ($self,@args) = @_;
my ($aln,$method) = $self->_rearrange([qw(ALIGN METHOD)],@args);
if( ! defined $aln || ! ref ($aln) || ! $aln->isa('Bio::Align::AlignI') ) {
$self->throw("Must supply a valid Bio::Align::AlignI for the -align parameter in distance");
}
$method ||= 'JukesCantor';
foreach my $m ( keys %DistanceMethods ) {
if(defined $m && $method =~ /$m/i ) {
my $mtd = "D_$DistanceMethods{$m}";
return $self->$mtd($aln);
}
}
$self->warn("Unrecognized distance method $method must be one of [".
join(',',$self->available_distance_methods())."]");
return;
}
=head2 available_distance_methods
Title : available_distance_methods
Usage : my @methods = $stats->available_distance_methods();
Function: Enumerates the possible distance methods
Returns : Array of strings
Args : none
=cut
sub available_distance_methods{
my ($self,@args) = @_;
return values %DistanceMethods;
}
=head2 D - distance methods
=cut
=head2 D_JukesCantor
Title : D_JukesCantor
Usage : my $d = $stat->D_JukesCantor($aln)
Function: Calculates D (pairwise distance) between 2 sequences in an
alignment using the Jukes-Cantor 1 parameter model.
Returns : L<Bio::Matrix::PhylipDist>
Args : L<Bio::Align::AlignI> of DNA sequences
double - gap penalty
=cut
sub D_JukesCantor{
my ($self,$aln,$gappenalty) = @_;
return 0 unless $self->_check_arg($aln);
$gappenalty = $DefaultGapPenalty unless defined $gappenalty;
# ambiguities ignored at this point
my (@seqs,@names,@values,%dist);
my $seqct = 0;
foreach my $seq ( $aln->each_seq) {
push @names, $seq->display_id;
push @seqs, uc $seq->seq();
$seqct++;
}
my $precisionstr = "%.$Precision"."f";
for(my $i = 0; $i < $seqct-1; $i++ ) {
# (diagonals) distance is 0 for same sequence
$dist{$names[$i]}->{$names[$i]} = [$i,$i];
$values[$i][$i] = sprintf($precisionstr,0);
for( my $j = $i+1; $j < $seqct; $j++ ) {
my ($matrix,$pfreq,$gaps) = $self->_build_nt_matrix($seqs[$i],
$seqs[$j]);
# just want diagonals
my $m = ( $matrix->[0]->[0] + $matrix->[1]->[1] +
$matrix->[2]->[2] + $matrix->[3]->[3] );
my $D = 1 - ( $m / ($aln->length - $gaps + ( $gaps * $gappenalty)));
my $d = (- 3 / 4) * log ( 1 - (4 * $D/ 3));
# fwd and rev lookup
$dist{$names[$i]}->{$names[$j]} = [$i,$j];
$dist{$names[$j]}->{$names[$i]} = [$i,$j];
$values[$j][$i] = $values[$i][$j] = sprintf($precisionstr,$d);
# (diagonals) distance is 0 for same sequence
$dist{$names[$j]}->{$names[$j]} = [$j,$j];
$values[$j][$j] = sprintf($precisionstr,0);
}
}
return Bio::Matrix::PhylipDist->new(-program => 'bioperl_DNAstats',
-matrix => \%dist,
-names => \@names,
-values => \@values);
}
=head2 D_F81
Title : D_F81
Usage : my $d = $stat->D_F81($aln)
Function: Calculates D (pairwise distance) between 2 sequences in an
alignment using the Felsenstein 1981 distance model.
Relaxes the assumption of equal base frequencies that is
in JC.
Returns : L<Bio::Matrix::PhylipDist>
Args : L<Bio::Align::AlignI> of DNA sequences
=cut
sub D_F81{
my ($self,$aln,$gappenalty) = @_;
return 0 unless $self->_check_arg($aln);
$gappenalty = $DefaultGapPenalty unless defined $gappenalty;
# ambiguities ignored at this point
my (@seqs,@names,@values,%dist);
my $seqct = 0;
foreach my $seq ( $aln->each_seq) {
push @names, $seq->display_id;;
push @seqs, uc $seq->seq();
$seqct++;
}
my $precisionstr = "%.$Precision"."f";
for(my $i = 0; $i < $seqct-1; $i++ ) {
# (diagonals) distance is 0 for same sequence
$dist{$names[$i]}->{$names[$i]} = [$i,$i];
$values[$i][$i] = sprintf($precisionstr,0);
for( my $j = $i+1; $j < $seqct; $j++ ) {
my ($matrix,$pfreq,$gaps) = $self->_build_nt_matrix($seqs[$i],
$seqs[$j]);
# just want diagonals
my $m = ( $matrix->[0]->[0] + $matrix->[1]->[1] +
$matrix->[2]->[2] + $matrix->[3]->[3] );
my $D = 1 - ( $m / ($aln->length - $gaps + ( $gaps * $gappenalty)));
my $d = (- 3 / 4) * log ( 1 - (4 * $D/ 3));
# fwd and rev lookup
$dist{$names[$i]}->{$names[$j]} = [$i,$j];
$dist{$names[$j]}->{$names[$i]} = [$i,$j];
$values[$j][$i] = $values[$i][$j] = sprintf($precisionstr,$d);
# (diagonals) distance is 0 for same sequence
$dist{$names[$j]}->{$names[$j]} = [$j,$j];
$values[$j][$j] = sprintf($precisionstr,0);
}
}
return Bio::Matrix::PhylipDist->new(-program => 'bioperl_DNAstats',
-matrix => \%dist,
-names => \@names,
-values => \@values);
}
=head2 D_Uncorrected
Title : D_Uncorrected
Usage : my $d = $stats->D_Uncorrected($aln)
Function: Calculate a distance D, no correction for multiple substitutions
is used. In rare cases where sequences may not overlap, 'NA' is
substituted for the distance.
Returns : L<Bio::Matrix::PhylipDist>
Args : L<Bio::Align::AlignI> (DNA Alignment)
[optional] gap penalty
=cut
sub D_Uncorrected {
my ($self,$aln,$gappenalty) = @_;
$gappenalty = $DefaultGapPenalty unless defined $gappenalty;
return 0 unless $self->_check_arg($aln);
# ambiguities ignored at this point
my (@seqs,@names,@values,%dist);
my $seqct = 0;
foreach my $seq ( $aln->each_seq) {
push @names, $seq->display_id;
push @seqs, uc $seq->seq();
$seqct++;
}
my $precisionstr = "%.$Precision"."f";
my $len = $aln->length;
for( my $i = 0; $i < $seqct-1; $i++ ) {
# (diagonals) distance is 0 for same sequence
$dist{$names[$i]}->{$names[$i]} = [$i,$i];
$values[$i][$i] = sprintf($precisionstr,0);
for( my $j = $i+1; $j < $seqct; $j++ ) {
my ($matrix,$pfreq,$gaps) = $self->_build_nt_matrix($seqs[$i],
$seqs[$j]);
my $m = ( $matrix->[0]->[0] +
$matrix->[1]->[1] +
$matrix->[2]->[2] +
$matrix->[3]->[3] );
my $denom = ( $len - $gaps + ( $gaps * $gappenalty));
$self->warn("No distance calculated between $names[$i] and $names[$j], inserting -1")
unless $denom;
my $D = $denom ? 1 - ( $m / $denom) : -1;
# fwd and rev lookup
$dist{$names[$i]}->{$names[$j]} = [$i,$j];
$dist{$names[$j]}->{$names[$i]} = [$i,$j];
$values[$j][$i] = $values[$i][$j] = $denom ? sprintf($precisionstr,$D)
: sprintf("%-*s", $Precision + 2, $D);
# (diagonals) distance is 0 for same sequence
$dist{$names[$j]}->{$names[$j]} = [$j,$j];
$values[$j][$j] = sprintf($precisionstr,0);
}
}
return Bio::Matrix::PhylipDist->new(-program => 'bioperl_DNAstats',
-matrix => \%dist,
-names => \@names,
-values => \@values);
}
# M Kimura, J. Mol. Evol., 1980, 16, 111.
=head2 D_Kimura
Title : D_Kimura
Usage : my $d = $stat->D_Kimura($aln)
Function: Calculates D (pairwise distance) between all pairs of sequences
in an alignment using the Kimura 2 parameter model.
Returns : L<Bio::Matrix::PhylipDist>
Args : L<Bio::Align::AlignI> of DNA sequences
=cut
sub D_Kimura {
my ($self,$aln) = @_;
return 0 unless $self->_check_arg($aln);
# ambiguities ignored at this point
my (@names,@values,%dist);
my $seqct = 0;
foreach my $seq ( $aln->each_seq) {
push @names, $seq->display_id;
$seqct++;
}
my $precisionstr = "%.$Precision"."f";
for( my $i = 0; $i < $seqct-1; $i++ ) {
# (diagonals) distance is 0 for same sequence
$dist{$names[$i]}->{$names[$i]} = [$i,$i];
$values[$i][$i] = sprintf($precisionstr,0);
for( my $j = $i+1; $j < $seqct; $j++ ) {
my $pairwise = $aln->select_noncont($i+1,$j+1);
my $L = $self->pairwise_stats->number_of_comparable_bases($pairwise);
unless( $L ) {
$L = 1;
}
my $P = $self->transitions($pairwise) / $L;
my $Q = $self->transversions($pairwise) / $L;
my $K = 0;
my $denom = ( 1 - (2 * $P) - $Q);
if( $denom == 0 ) {
$self->throw("cannot find distance for ",$i+1,
",",$j+1," $P, $Q\n");
}
my $a = 1 / ( 1 - (2 * $P) - $Q);
my $b = 1 / ( 1 - 2 * $Q );
if( $a < 0 || $b < 0 ) {
$K = -1;
} else{
$K = (1/2) * log ( $a ) + (1/4) * log($b);
}
# fwd and rev lookup
$dist{$names[$i]}->{$names[$j]} = [$i,$j];
$dist{$names[$j]}->{$names[$i]} = [$i,$j];
$values[$j][$i] = $values[$i][$j] = sprintf($precisionstr,$K);
# (diagonals) distance is 0 for same sequence
$dist{$names[$j]}->{$names[$j]} = [$j,$j];
$values[$j][$j] = sprintf($precisionstr,0);
}
}
return Bio::Matrix::PhylipDist->new(-program => 'bioperl_DNAstats',
-matrix => \%dist,
-names => \@names,
-values => \@values);
}
=head2 D_Kimura_variance
Title : D_Kimura
Usage : my $d = $stat->D_Kimura_variance($aln)
Function: Calculates D (pairwise distance) between all pairs of sequences
in an alignment using the Kimura 2 parameter model.
Returns : array of 2 L<Bio::Matrix::PhylipDist>,
the first is the Kimura distance and the second is
a matrix of variance V(K)
Args : L<Bio::Align::AlignI> of DNA sequences
=cut
sub D_Kimura_variance {
my ($self,$aln) = @_;
return 0 unless $self->_check_arg($aln);
# ambiguities ignored at this point
my (@names,@values,%dist,@var);
my $seqct = 0;
foreach my $seq ( $aln->each_seq) {
push @names, $seq->display_id;
$seqct++;
}
my $precisionstr = "%.$Precision"."f";
for( my $i = 0; $i < $seqct-1; $i++ ) {
# (diagonals) distance is 0 for same sequence
$dist{$names[$i]}->{$names[$i]} = [$i,$i];
$values[$i][$i] = sprintf($precisionstr,0);
for( my $j = $i+1; $j < $seqct; $j++ ) {
my $pairwise = $aln->select_noncont($i+1,$j+1);
my $L = $self->pairwise_stats->number_of_comparable_bases($pairwise);
unless( $L ) {
$L = 1;
}
my $P = $self->transitions($pairwise) / $L;
my $Q = $self->transversions($pairwise) / $L;
my ($a,$b,$K,$var_k);
my $a_denom = ( 1 - (2 * $P) - $Q);
my $b_denom = 1 - 2 * $Q;
unless( $a_denom > 0 && $b_denom > 0 ) {
$a = 1;
$b = 1;
$K = -1;
$var_k = -1;
} else {
$a = 1 / $a_denom;
$b = 1 / $b_denom;
$K = (1/2) * log ( $a ) + (1/4) * log($b);
# from Wu and Li 1985 which in turn is from Kimura 1980
my $c = ( $a - $b ) / 2;
my $d = ( $a + $b ) / 2;
$var_k = ( $a**2 * $P + $d**2 * $Q - ( $a * $P + $d * $Q)**2 ) / $L;
}
# fwd and rev lookup
$dist{$names[$i]}->{$names[$j]} = [$i,$j];
$dist{$names[$j]}->{$names[$i]} = [$i,$j];
$values[$j][$i] = $values[$i][$j] = sprintf($precisionstr,$K);
# (diagonals) distance is 0 for same sequence
$dist{$names[$j]}->{$names[$j]} = [$j,$j];
$values[$j]->[$j] = sprintf($precisionstr,0);
$var[$j]->[$i] = $var[$i]->[$j] = sprintf($precisionstr,$var_k);
$var[$j]->[$j] = $values[$j]->[$j];
}
}
return ( Bio::Matrix::PhylipDist->new(-program => 'bioperl_DNAstats',
-matrix => \%dist,
-names => \@names,
-values => \@values),
Bio::Matrix::PhylipDist->new(-program => 'bioperl_DNAstats',
-matrix => \%dist,
-names => \@names,
-values => \@var)
);
}
# K Tamura, Mol. Biol. Evol. 1992, 9, 678.
=head2 D_Tamura
Title : D_Tamura
Usage : Calculates D (pairwise distance) between 2 sequences in an
alignment using Tamura 1992 distance model.
Returns : L<Bio::Matrix::PhylipDist>
Args : L<Bio::Align::AlignI> of DNA sequences
=cut
sub D_Tamura {
my ($self,$aln) = @_;
return 0 unless $self->_check_arg($aln);
# ambiguities ignored at this point
my (@seqs,@names,@values,%dist,$i,$j);
my $seqct = 0;
my $length = $aln->length;
foreach my $seq ( $aln->each_seq) {
push @names, $seq->display_id;;
push @seqs, uc $seq->seq();
$seqct++;
}
my $precisionstr = "%.$Precision"."f";
my (@gap,@gc,@trans,@tranv,@score);
$i = 0;
for my $t1 ( @seqs ) {
$j = 0;
for my $t2 ( @seqs ) {
$gap[$i][$j] = 0;
for( my $k = 0; $k < $length; $k++ ) {
my ($c1,$c2) = ( substr($seqs[$i],$k,1),
substr($seqs[$j],$k,1) );
if( $c1 =~ /^$GapChars$/ ||
$c2 =~ /^$GapChars$/ ) {
$gap[$i][$j]++;
} elsif( $c2 =~ /^$GCChhars$/i ) {
$gc[$i][$j]++;
}
}
$gc[$i][$j] = ( $gc[$i][$j] /
($length - $gap[$i][$j]) );
$j++;
}
$i++;
}
for( $i = 0; $i < $seqct-1; $i++ ) {
# (diagonals) distance is 0 for same sequence
$dist{$names[$i]}->{$names[$i]} = [$i,$i];
$values[$i][$i] = sprintf($precisionstr,0);
for( $j = $i+1; $j < $seqct; $j++ ) {
my $pairwise = $aln->select_noncont($i+1,$j+1);
my $L = $self->pairwise_stats->number_of_comparable_bases($pairwise);
my $P = $self->transitions($pairwise) / $L;
my $Q = $self->transversions($pairwise) / $L;
my $C = $gc[$i][$j] + $gc[$j][$i]-
( 2 * $gc[$i][$j] * $gc[$j][$i] );
if( $P ) {
$P = $P / $C;
}
my $d = -($C * log(1- $P - $Q)) -(0.5* ( 1 - $C) * log(1 - 2 * $Q));
# fwd and rev lookup
$dist{$names[$i]}->{$names[$j]} = [$i,$j];
$dist{$names[$j]}->{$names[$i]} = [$i,$j];
$values[$j][$i] = $values[$i][$j] = sprintf($precisionstr,$d);
# (diagonals) distance is 0 for same sequence
$dist{$names[$j]}->{$names[$j]} = [$j,$j];
$values[$j][$j] = sprintf($precisionstr,0);
}
}
return Bio::Matrix::PhylipDist->new(-program => 'bioperl_DNAstats',
-matrix => \%dist,
-names => \@names,
-values => \@values);
}
=head2 D_F84
Title : D_F84
Usage : my $d = $stat->D_F84($aln)
Function: Calculates D (pairwise distance) between 2 sequences in an
alignment using the Felsenstein 1984 distance model.
Returns : L<Bio::Matrix::PhylipDist>
Args : L<Bio::Align::AlignI> of DNA sequences
[optional] double - gap penalty
=cut
sub D_F84 {
my ($self,$aln,$gappenalty) = @_;
return 0 unless $self->_check_arg($aln);
$self->throw_not_implemented();
# ambiguities ignored at this point
my (@seqs,@names,@values,%dist);
my $seqct = 0;
foreach my $seq ( $aln->each_seq) {
# if there is no name,
my $id = $seq->display_id;
if( ! length($id) || # deal with empty names
$id =~ /^\s+$/ ) {
$id = $seqct+1;
}
push @names, $id;
push @seqs, uc $seq->seq();
$seqct++;
}
my $precisionstr = "%.$Precision"."f";
for( my $i = 0; $i < $seqct-1; $i++ ) {
# (diagonals) distance is 0 for same sequence
$dist{$names[$i]}->{$names[$i]} = [$i,$i];
$values[$i][$i] = sprintf($precisionstr,0);
for( my $j = $i+1; $j < $seqct; $j++ ) {
}
}
}
# Tajima and Nei, Mol. Biol. Evol. 1984, 1, 269.
# Tajima-Nei correction used for multiple substitutions in the calc
# of the distance matrix. Nucleic acids only.
#
# D = p-distance = 1 - (matches/(posns_scored + gaps)
#
# distance = -b * ln(1-D/b)
#
=head2 D_TajimaNei
Title : D_TajimaNei
Usage : my $d = $stat->D_TajimaNei($aln)
Function: Calculates D (pairwise distance) between 2 sequences in an
alignment using the TajimaNei 1984 distance model.
Returns : L<Bio::Matrix::PhylipDist>
Args : Bio::Align::AlignI of DNA sequences
=cut
sub D_TajimaNei{
my ($self,$aln) = @_;
return 0 unless $self->_check_arg($aln);
# ambiguities ignored at this point
my (@seqs,@names,@values,%dist);
my $seqct = 0;
foreach my $seq ( $aln->each_seq) {
# if there is no name,
push @names, $seq->display_id;
push @seqs, uc $seq->seq();
$seqct++;
}
my $precisionstr = "%.$Precision"."f";
my ($i,$j,$bs);
# pairwise
for( $i =0; $i < $seqct -1; $i++ ) {
$dist{$names[$i]}->{$names[$i]} = [$i,$i];
$values[$i][$i] = sprintf($precisionstr,0);
for ( $j = $i+1; $j <$seqct;$j++ ) {
my ($matrix,$pfreq,$gaps) = $self->_build_nt_matrix($seqs[$i],
$seqs[$j]);
my $pairwise = $aln->select_noncont($i+1,$j+1);
my $slen = $self->pairwise_stats->number_of_comparable_bases($pairwise);
my $fij2 = 0;
for( $bs = 0; $bs < 4; $bs++ ) {
my $fi = 0;
map {$fi += $matrix->[$bs]->[$_] } 0..3;
my $fj = 0;
# summation
map { $fj += $matrix->[$_]->[$bs] } 0..3;
my $fij = ( $fi && $fj ) ? ($fi + $fj) /( 2 * $slen) : 0;
$fij2 += $fij**2;
}
my ($pair,$h) = (0,0);
for( $bs = 0; $bs < 3; $bs++ ) {
for(my $bs1 = $bs+1; $bs1 <= 3; $bs1++ ) {
my $fij = $pfreq->[$pair++] / $slen;
if( $fij ) {
my ($ci1,$ci2,$cj1,$cj2) = (0,0,0,0);
map { $ci1 += $matrix->[$_]->[$bs] } 0..3;
map { $cj1 += $matrix->[$bs]->[$_] } 0..3;
map { $ci2 += $matrix->[$_]->[$bs1] } 0..3;
map { $cj2 += $matrix->[$bs1]->[$_] } 0..3;
if( $fij ) {
$h += ( ($fij**2) / 2 ) /
( ( ( $ci1 + $cj1 ) / (2 * $slen) ) *
( ( $ci2 + $cj2 ) / (2 * $slen) )
);
}
$self->debug( "slen is $slen h is $h fij = $fij ci1 =$ci1 cj1=$cj1 ci2=$ci2 cj2=$cj2\n");
}
}
}
# just want diagonals which are matches (A matched A, C -> C)
my $m = ( $matrix->[0]->[0] + $matrix->[1]->[1] +
$matrix->[2]->[2] + $matrix->[3]->[3] );
my $D = 1 - ( $m / $slen);
my $d;
if( $h == 0 ) {
$d = -1;
} else {
my $b = (1 - $fij2 + (($D**2)/$h)) / 2;
my $c = 1- $D/ $b;
if( $c < 0 ) {
$d = -1;
} else {
$d = (-1 * $b) * log ( $c);
}
}
# fwd and rev lookup
$dist{$names[$i]}->{$names[$j]} = [$i,$j];
$dist{$names[$j]}->{$names[$i]} = [$i,$j];
$values[$j][$i] = $values[$i][$j] = sprintf($precisionstr,$d);
# (diagonals) distance is 0 for same sequence
$dist{$names[$j]}->{$names[$j]} = [$j,$j];
$values[$j][$j] = sprintf($precisionstr,0);
}
}
return Bio::Matrix::PhylipDist->new(-program => 'bioperl_DNAstats',
-matrix => \%dist,
-names => \@names,
-values => \@values);
}
# Jin and Nei, Mol. Biol. Evol. 82, 7, 1990.
=head2 D_JinNei
Title : D_JinNei
Usage : my $d = $stat->D_JinNei($aln)
Function: Calculates D (pairwise distance) between 2 sequences in an
alignment using the Jin-Nei 1990 distance model.
Returns : L<Bio::Matrix::PhylipDist>
Args : L<Bio::Align::AlignI> of DNA sequences
=cut
sub D_JinNei{
my ($self,@args) = @_;
$self->warn("JinNei implementation not completed");
return;
}
=head2 transversions
Title : transversions
Usage : my $transversions = $stats->transversion($aln);
Function: Calculates the number of transversions between two sequences in
an alignment
Returns : integer
Args : Bio::Align::AlignI
=cut
sub transversions{
my ($self,$aln) = @_;
return $self->_trans_count_helper($aln, $DNAChanges{'Transversions'});
}
=head2 transitions
Title : transitions
Usage : my $transitions = Bio::Align::DNAStatistics->transitions($aln);
Function: Calculates the number of transitions in a given DNA alignment
Returns : integer representing the number of transitions
Args : Bio::Align::AlignI object
=cut
sub transitions{
my ($self,$aln) = @_;
return $self->_trans_count_helper($aln, $DNAChanges{'Transitions'});
}
sub _trans_count_helper {
my ($self,$aln,$type) = @_;
return 0 unless( $self->_check_arg($aln) );
if( ! $aln->is_flush ) { $self->throw("must be flush") }
my (@tcount);
my ($first,$second) = ( uc $aln->get_seq_by_pos(1)->seq(),
uc $aln->get_seq_by_pos(2)->seq() );
my $alen = $aln->length;
for (my $i = 0;$i<$alen; $i++ ) {
my ($c1,$c2) = ( substr($first,$i,1),
substr($second,$i,1) );
if( $c1 ne $c2 ) {
foreach my $nt ( @{$type->{$c1}} ) {
if( $nt eq $c2) {
$tcount[$i]++;
}
}
}
}
my $sum = 0;
map { if( $_) { $sum += $_} } @tcount;
return $sum;
}
# this will generate a matrix which records across the row, the number
# of DNA subst
#
sub _build_nt_matrix {
my ($self,$seqa,$seqb) = @_;
my $basect_matrix = [ [ qw(0 0 0 0) ], # number of bases that match
[ qw(0 0 0 0) ],
[ qw(0 0 0 0) ],
[ qw(0 0 0 0) ] ];
my $gaps = 0; # number of gaps
my $pfreq = [ qw( 0 0 0 0 0 0)]; # matrix for pair frequency
my $len_a = length($seqa);
for( my $i = 0; $i < $len_a; $i++) {
my ($ti,$tj) = (substr($seqa,$i,1),substr($seqb,$i,1));
$ti =~ tr/U/T/;
$tj =~ tr/U/T/;
if( $ti =~ /^$GapChars$/) { $gaps++; next; }
if( $tj =~ /^$GapChars$/) { $gaps++; next }
my $ti_index = $NucleotideIndexes{$ti};
my $tj_index = $NucleotideIndexes{$tj};
if( ! defined $ti_index ) {
$self->warn("ti_index not defined for $ti\n");
next;
}
$basect_matrix->[$ti_index]->[$tj_index]++;
if( $ti ne $tj ) {
$pfreq->[$NucleotideIndexes{join('',sort ($ti,$tj))}]++;
}
}
return ($basect_matrix,$pfreq,$gaps);
}
sub _check_ambiguity_nucleotide {
my ($base1,$base2) = @_;
my %iub = Bio::Tools::IUPAC->iupac_iub();
my @amb1 = @{ $iub{uc($base1)} };
my @amb2 = @{ $iub{uc($base2)} };
my ($pmatch) = (0);
for my $amb ( @amb1 ) {
if( grep { $amb eq $_ } @amb2 ) {
$pmatch = 1;
last;
}
}
if( $pmatch ) {
return (1 / scalar @amb1) * (1 / scalar @amb2);
} else {
return 0;
}
}
sub _check_arg {
my($self,$aln ) = @_;
if( ! defined $aln || ! $aln->isa('Bio::Align::AlignI') ) {
$self->warn("Must provide a Bio::Align::AlignI compliant object to Bio::Align::DNAStatistics");
return 0;
} elsif( $aln->get_seq_by_pos(1)->alphabet ne 'dna' ) {
$self->warn("Must provide a DNA alignment to Bio::Align::DNAStatistics, you provided a " . $aln->get_seq_by_pos(1)->alphabet);
return 0;
}
return 1;
}
=head2 Data Methods
=cut
=head2 pairwise_stats
Title : pairwise_stats
Usage : $obj->pairwise_stats($newval)
Function:
Returns : value of pairwise_stats
Args : newvalue (optional)
=cut
sub pairwise_stats{
my ($self,$value) = @_;
if( defined $value) {
$self->{'_pairwise_stats'} = $value;
}
return $self->{'_pairwise_stats'};
}
=head2 calc_KaKs_pair
Title : calc_KaKs_pair
Useage : my $results = $stats->calc_KaKs_pair($alnobj,
$name1, $name2).
Function : calculates Nei-Gojobori statistics for pairwise
comparison.
Args : A Bio::Align::AlignI compliant object such as a
Bio::SimpleAlign object, and 2 sequence name strings.
Returns : a reference to a hash of statistics with keys as
listed in Description.
=cut
sub calc_KaKs_pair {
my ( $self, $aln, $seq1_id, $seq2_id) = @_;
$self->throw("Needs 3 arguments - an alignment object, and 2 sequence ids")
if @_!= 4;
$self->throw ("This calculation needs a Bio::Align::AlignI compatible object, not a [ " . ref($aln) . " ]object") unless $aln->isa('Bio::Align::AlignI');
my @seqs = (
#{id => $seq1_id, seq =>($aln->each_seq_with_id($seq1_id))[0]->seq},
#{id => $seq2_id, seq =>($aln->each_seq_with_id($seq2_id))[0]->seq}
{id => $seq1_id, seq => uc(($aln->each_seq_with_id($seq1_id))[0]->seq)},
{id => $seq2_id, seq => uc(($aln->each_seq_with_id($seq2_id))[0]->seq)}
) ;
if (length($seqs[0]{'seq'}) != length($seqs[1]{'seq'})) {
$self->throw(" aligned sequences must be of equal length!");
}
my $results = [];
$self->_get_av_ds_dn(\@seqs, $results);
return $results;
}
=head2 calc_all_KaKs_pairs
Title : calc_all_KaKs_pairs
Useage : my $results2 = $stats->calc_KaKs_pair($alnobj).
Function : Calculates Nei_gojobori statistics for all pairwise
combinations in sequence.
Arguments: A Bio::Align::ALignI compliant object such as
a Bio::SimpleAlign object.
Returns : A reference to an array of hashes of statistics of
all pairwise comparisons in the alignment.
=cut
sub calc_all_KaKs_pairs {
#returns a multi_element_array with all pairwise comparisons
my ($self,$aln) = @_;
$self->throw ("This calculation needs a Bio::Align::AlignI compatible object, not a [ " . ref($aln) . " ]object") unless $aln->isa('Bio::Align::AlignI');
my @seqs;
for my $seq ($aln->each_seq) {
push @seqs, {id => $seq->display_id, seq=>$seq->seq};
}
my $results ;
$results = $self->_get_av_ds_dn(\@seqs, $results);
return $results;
}
=head2 calc_average_KaKs
Title : calc_average_KaKs.
Useage : my $res= $stats->calc_average_KaKs($alnobj, 1000).
Function : calculates Nei_Gojobori stats for average of all
sequences in the alignment.
Args : A Bio::Align::AlignI compliant object such as a
Bio::SimpleAlign object, number of bootstrap iterations
(default 1000).
Returns : A reference to a hash of statistics as listed in Description.
=cut
sub calc_average_KaKs {
#calculates global value for sequences in alignment using bootstrapping
#this is quite slow (~10 seconds per 3 X 200nt seqs);
my ($self, $aln, $bootstrap_rpt) = @_;
$bootstrap_rpt ||= 1000;
$self->throw ("This calculation needs a Bio::Align::AlignI compatible object, not a [ " . ref($aln) . " ]object") unless $aln->isa('Bio::Align::AlignI');
my @seqs;
for my $seq ($aln->each_seq) {
push @seqs, {id => $seq->display_id, seq=>$seq->seq};
}
my $results ;
my ($ds_orig, $dn_orig) = $self->_get_av_ds_dn(\@seqs);
#print "ds = $ds_orig, dn = $dn_orig\n";
$results = {D_s => $ds_orig, D_n => $dn_orig};
$self->_run_bootstrap(\@seqs, $results, $bootstrap_rpt);
return $results;
}
############## primary internal subs for alignment comparisons ########################
sub _run_bootstrap {
### generates sampled sequences, calculates Ds and Dn values,
### then calculates variance of sampled sequences and add results to results hash
###
my ($self,$seq_ref, $results, $bootstrap_rpt) = @_;
my @seqs = @$seq_ref;
my @btstrp_aoa; # to hold array of array of nucleotides for resampling
my %bootstrap_values = (ds => [], dn =>[]); # to hold list of av values
#1st make alternative array of codons;
my $c = 0;
while ($c < length $seqs[0]{'seq'}) {
for (0..$#seqs) {
push @{$btstrp_aoa[$_]}, substr ($seqs[$_]{'seq'}, $c, 3);
}
$c+=3;
}
for (1..$bootstrap_rpt) {
my $sampled = _resample (\@btstrp_aoa);
my ($ds, $dn) = $self->_get_av_ds_dn ($sampled) ; # is array ref
push @{$bootstrap_values{'ds'}}, $ds;
push @{$bootstrap_values{'dn'}}, $dn;
}
$results->{'D_s_var'} = sampling_variance($bootstrap_values{'ds'});
$results->{'D_n_var'} = sampling_variance($bootstrap_values{'dn'});
$results->{'z_score'} = ($results->{'D_n'} - $results->{'D_s'}) /
sqrt($results->{'D_s_var'} + $results->{'D_n_var'} );
#print "bootstrapped var_syn = $results->{'D_s_var'} \n" ;
#print "bootstrapped var_nc = $results->{'D_n_var'} \n";
#print "z is $results->{'z_score'}\n"; ### end of global set up of/perm look up data
}
sub _resample {
my $ref = shift;
my $codon_num = scalar (@{$ref->[0]});
my @altered;
for (0..$codon_num -1) { #for each codon
my $rand = int (rand ($codon_num));
for (0..$#$ref) {
push @{$altered[$_]}, $ref->[$_][$rand];
}
}
my @stringed = map {join '', @$_}@altered;
my @return;
#now out in random name to keep other subs happy
for (@stringed) {
push @return, {id=>'1', seq=> $_};
}
return \@return;
}
sub _get_av_ds_dn {
# takes array of hashes of sequence strings and ids #
my $self = shift;
my $seq_ref = shift;
my $result = shift if @_;
my @caller = caller(1);
my @seqarray = @$seq_ref;
my $bootstrap_score_list;
#for a multiple alignment considers all pairwise combinations#
my %dsfor_average = (ds => [], dn => []);
for (my $i = 0; $i < scalar @seqarray; $i++) {
for (my $j = $i +1; $j<scalar @seqarray; $j++ ){
# print "comparing $i and $j\n";
if (length($seqarray[$i]{'seq'}) != length($seqarray[$j]{'seq'})) {
$self->warn(" aligned sequences must be of equal length!");
next;
}
my $syn_site_count = count_syn_sites($seqarray[$i]{'seq'}, $synsites);
my $syn_site_count2 = count_syn_sites($seqarray[$j]{'seq'}, $synsites);
# print "syn 1 is $syn_site_count , syn2 is $syn_site_count2\n";
my ($syn_count, $non_syn_count, $gap_cnt) = analyse_mutations($seqarray[$i]{'seq'}, $seqarray[$j]{'seq'});
#get averages
my $av_s_site = ($syn_site_count + $syn_site_count2)/2;
my $av_ns_syn_site = length($seqarray[$i]{'seq'}) - $gap_cnt- $av_s_site ;
#calculate ps and pn (p54)
my $syn_prop = $syn_count / $av_s_site;
my $nc_prop = $non_syn_count / $av_ns_syn_site ;
#now use jukes/cantor to calculate D_s and D_n, would alter here if needed a different method
my $d_syn = $self->jk($syn_prop);
my $d_nc = $self->jk($nc_prop);
#JK calculation must succeed for continuation of calculation
#ret_value = -1 if error
next unless $d_nc >=0 && $d_syn >=0;
push @{$dsfor_average{'ds'}}, $d_syn;
push @{$dsfor_average{'dn'}}, $d_nc;
#if not doing bootstrap, calculate the pairwise comparisin stats
if ($caller[3] =~ /calc_KaKs_pair/ || $caller[3] =~ /calc_all_KaKs_pairs/) {
#now calculate variances assuming large sample
my $d_syn_var = jk_var($syn_prop, length($seqarray[$i]{'seq'}) - $gap_cnt );
my $d_nc_var = jk_var($nc_prop, length ($seqarray[$i]{'seq'}) - $gap_cnt);
#now calculate z_value
#print "d_syn_var is $d_syn_var,and d_nc_var is $d_nc_var\n";
#my $z = ($d_nc - $d_syn) / sqrt($d_syn_var + $d_nc_var);
my $z = ($d_syn_var + $d_nc_var) ?
($d_nc - $d_syn) / sqrt($d_syn_var + $d_nc_var) : 0;
# print "z is $z\n";
push @$result , {S => $av_s_site, N=>$av_ns_syn_site,
S_d => $syn_count, N_d =>$non_syn_count,
P_s => $syn_prop, P_n=>$nc_prop,
D_s => @{$dsfor_average{'ds'}}[-1],
D_n => @{$dsfor_average{'dn'}}[-1],
D_n_var =>$d_nc_var, D_s_var => $d_syn_var,
Seq1 => $seqarray[$i]{'id'},
Seq2 => $seqarray[$j]{'id'},
z_score => $z,
};
$self->warn (" number of mutations too small to justify normal test for $seqarray[$i]{'id'} and $seqarray[$j]{'id'}\n- use Fisher's exact, or bootstrap a MSA")
if ($syn_count < 10 || $non_syn_count < 10 ) && $self->verbose > -1 ;
}#endif
}
}
#warn of failure if no results hashes are present
#will fail if Jukes Cantor has failed for all pairwise combinations
#$self->warn("calculation failed!") if scalar @$result ==0;
#return results unless bootstrapping
return $result if $caller[3]=~ /calc_all_KaKs/ || $caller[3] =~ /calc_KaKs_pair/;
#else if getting average for bootstrap
return( mean ($dsfor_average{'ds'}),mean ($dsfor_average{'dn'})) ;
}
sub jk {
my ($self, $p) = @_;
if ($p > 0.75) {
$self->warn( " Jukes Cantor won't work -too divergent!");
return -1;
}
return -1 * (3/4) * (log(1 - (4/3) * $p));
}
#works for large value of n (50?100?)
sub jk_var {
my ($p, $n) = @_;
return (9 * $p * (1 -$p))/(((3 - 4 *$p) **2) * $n);
}
# compares 2 sequences to find the number of synonymous/non
# synonymous mutations between them
sub analyse_mutations {
my ($seq1, $seq2) = @_;
my %mutator = ( 2=> {0=>[[1,2], # codon positions to be altered
[2,1]], # depend on which is the same
1=>[[0,2],
[2,0]],
2=>[[0,1],
[1,0]],
},
3=> [ [0,1,2], # all need to be altered
[1,0,2],
[0,2,1],
[1,2,0],
[2,0,1],
[2,1,0] ],
);
my $TOTAL = 0; # total synonymous changes
my $TOTAL_n = 0; # total non-synonymous changes
my $gap_cnt = 0;
my %input;
my $seqlen = length($seq1);
for (my $j=0; $j< $seqlen; $j+=3) {
$input{'cod1'} = substr($seq1, $j,3);
$input{'cod2'} = substr($seq2, $j,3);
#ignore codon if beeing compared with gaps!
if ($input{'cod1'} =~ /\-/ || $input{'cod2'} =~ /\-/){
$gap_cnt += 3; #just increments once if there is a pair of gaps
next;
}
my ($diff_cnt, $same) = count_diffs(\%input);
#ignore if codons are identical
next if $diff_cnt == 0 ;
if ($diff_cnt == 1) {
$TOTAL += $synchanges{$input{'cod1'}}{$input{'cod2'}};
$TOTAL_n += 1 - $synchanges{$input{'cod1'}}{$input{'cod2'}};
#print " \nfordiff is 1 , total now $TOTAL, total n now $TOTAL_n\n\n"
}
elsif ($diff_cnt ==2) {
my $s_cnt = 0;
my $n_cnt = 0;
my $tot_muts = 4;
#will stay 4 unless there are stop codons at intervening point
OUTER:for my $perm (@{$mutator{'2'}{$same}}) {
my $altered = $input{'cod1'};
my $prev= $altered;
# print "$prev -> (", $t[$CODONS->{$altered}], ")";
for my $mut_i (@$perm) { #index of codon mutated
substr($altered, $mut_i,1) = substr($input{'cod2'}, $mut_i, 1);
if ($t[$CODONS->{$altered}] eq '*') {
$tot_muts -=2;
#print "changes to stop codon!!\n";
next OUTER;
}
else {
$s_cnt += $synchanges{$prev}{$altered};
# print "$altered ->(", $t[$CODONS->{$altered}], ") ";
}
$prev = $altered;
}
# print "\n";
}
if ($tot_muts != 0) {
$TOTAL += ($s_cnt/($tot_muts/2));
$TOTAL_n += ($tot_muts - $s_cnt)/ ($tot_muts / 2);
}
}
elsif ($diff_cnt ==3 ) {
my $s_cnt = 0;
my $n_cnt = 0;
my $tot_muts = 18; #potential number of mutations
OUTER: for my $perm (@{$mutator{'3'}}) {
my $altered = $input{'cod1'};
my $prev= $altered;
# print "$prev -> (", $t[$CODONS->{$altered}], ")";
for my $mut_i (@$perm) { #index of codon mutated
substr($altered, $mut_i,1) = substr($input{'cod2'}, $mut_i, 1);
if ($t[$CODONS->{$altered}] eq '*') {
$tot_muts -=3;
# print "changes to stop codon!!\n";
next OUTER;
}
else {
$s_cnt += $synchanges{$prev}{$altered};
# print "$altered ->(", $t[$CODONS->{$altered}], ") ";
}
$prev = $altered;
}
# print "\n";
}#end OUTER loop
#calculate number of synonymous/non synonymous mutations for that codon
# and add to total
if ($tot_muts != 0) {
$TOTAL += ($s_cnt / ($tot_muts /3));
$TOTAL_n += 3 - ($s_cnt / ($tot_muts /3));
}
} #endif $diffcnt = 3
} #end of sequencetraversal
return ($TOTAL, $TOTAL_n, $gap_cnt);
}
sub count_diffs {
#counts the number of nucleotide differences between 2 codons
# returns this value plus the codon index of which nucleotide is the same when 2
#nucleotides are different. This is so analyse_mutations() knows which nucleotides
# to change.
my $ref = shift;
my $cnt = 0;
my $same= undef;
#just for 2 differences
for (0..2) {
if (substr($ref->{'cod1'}, $_,1) ne substr($ref->{'cod2'}, $_, 1)){
$cnt++;
} else {
$same = $_;
}
}
return ($cnt, $same);
}
=head2 get_syn_changes
Title : get_syn_changes
Usage : Bio::Align::DNAStatitics->get_syn_changes
Function: Generate a hashref of all pairwise combinations of codns
differing by 1
Returns : Symetic matrix using hashes
First key is codon
and each codon points to a hashref of codons
the values of which describe type of change.
my $type = $hash{$codon1}->{$codon2};
values are :
1 synonymous
0 non-syn
-1 either codon is a stop codon
Args : none
=cut
sub get_syn_changes {
#hash of all pairwise combinations of codons differing by 1
# 1 = syn, 0 = non-syn, -1 = stop
my %results;
my @codons = _make_codons ();
my $arr_len = scalar @codons;
for (my $i = 0; $i < $arr_len -1; $i++) {
my $cod1 = $codons[$i];
for (my $j = $i +1; $j < $arr_len; $j++) {
my $diff_cnt = 0;
for my $pos(0..2) {
$diff_cnt++ if substr($cod1, $pos, 1) ne substr($codons[$j], $pos, 1);
}
next if $diff_cnt !=1;
#synon change
if($t[$CODONS->{$cod1}] eq $t[$CODONS->{$codons[$j]}]) {
$results{$cod1}{$codons[$j]} =1;
$results{$codons[$j]}{$cod1} = 1;
}
#stop codon
elsif ($t[$CODONS->{$cod1}] eq '*' or $t[$CODONS->{$codons[$j]}] eq '*') {
$results{$cod1}{$codons[$j]} = -1;
$results{$codons[$j]}{$cod1} = -1;
}
# nc change
else {
$results{$cod1}{$codons[$j]} = 0;
$results{$codons[$j]}{$cod1} = 0;
}
}
}
return %results;
}
=head2 dnds_pattern_number
Title : dnds_pattern_number
Usage : my $patterns = $stats->dnds_pattern_number($alnobj);
Function: Counts the number of codons with no gaps in the MSA
Returns : Number of codons with no gaps ('patterns' in PAML notation)
Args : A Bio::Align::AlignI compliant object such as a
Bio::SimpleAlign object.
=cut
sub dnds_pattern_number{
my ($self, $aln) = @_;
return ($aln->remove_gaps->length)/3;
}
sub count_syn_sites {
#counts the number of possible synonymous changes for sequence
my ($seq, $synsite) = @_;
__PACKAGE__->throw("not integral number of codons") if length($seq) % 3 != 0;
my $S = 0;
for (my $i = 0; $i< length($seq); $i+=3) {
my $cod = substr($seq, $i, 3);
next if $cod =~ /\-/; #deal with alignment gaps
$S += $synsite->{$cod}{'s'};
}
#print "S is $S\n";
return $S;
}
sub get_syn_sites {
#sub to generate lookup hash for the number of synonymous changes per codon
my @nucs = qw(T C A G);
my %raw_results;
for my $i (@nucs) {
for my $j (@nucs) {
for my $k (@nucs) {
# for each possible codon
my $cod = "$i$j$k";
my $aa = $t[$CODONS->{$cod}];
#calculate number of synonymous mutations vs non syn mutations
for my $i (qw(0 1 2)){
my $s = 0;
my $n = 3;
for my $nuc (qw(A T C G)) {
next if substr ($cod, $i,1) eq $nuc;
my $test = $cod;
substr($test, $i, 1) = $nuc ;
if ($t[$CODONS->{$test}] eq $aa) {
$s++;
}
if ($t[$CODONS->{$test}] eq '*') {
$n--;
}
}
$raw_results{$cod}[$i] = {'s' => $s ,
'n' => $n };
}
} #end analysis of single codon
}
} #end analysis of all codons
my %final_results;
for my $cod (sort keys %raw_results) {
my $t = 0;
map{$t += ($_->{'s'} /$_->{'n'})} @{$raw_results{$cod}};
$final_results{$cod} = { 's'=>$t, 'n' => 3 -$t};
}
return \%final_results;
}
sub _make_codons {
#makes all codon combinations, returns array of them
my @nucs = qw(T C A G);
my @codons;
for my $i (@nucs) {
for my $j (@nucs) {
for my $k (@nucs) {
push @codons, "$i$j$k";
}
}
}
return @codons;
}
sub get_codons {
#generates codon translation look up table#
my $x = 0;
my $CODONS = {};
for my $codon (_make_codons) {
$CODONS->{$codon} = $x;
$x++;
}
return $CODONS;
}
#########stats subs, can go in another module? Here for speed. ###
sub mean {
my $ref = shift;
my $el_num = scalar @$ref;
my $tot = 0;
map{$tot += $_}@$ref;
return ($tot/$el_num);
}
sub variance {
my $ref = shift;
my $mean = mean($ref);
my $sum_of_squares = 0;
map{$sum_of_squares += ($_ - $mean) **2}@$ref;
return $sum_of_squares;
}
sub sampling_variance {
my $ref = shift;
return variance($ref) / (scalar @$ref -1);
}
1;