Bio::Tools
HMM
Summary
Bio::Tools::HMM - Perl extension to perform Hidden Markov Model calculations
Package variables
No package variables defined.
Inherit
Synopsis
use Bio::Tools::HMM;
use Bio::SeqIO;
use Bio::Matrix::Scoring;
# create a HMM object
# ACGT are the bases NC mean non-coding and coding
$hmm = new Bio::Tools::HMM('-symbols' => "ACGT", '-states' => "NC");
# initialize some training observation sequences
$seq1 = new Bio::SeqIO(-file => $ARGV[0], -format => 'fasta');
$seq2 = new Bio::SeqIO(-file => $ARGV[1], -format => 'fasta');
@seqs = ($seq1, $seq2);
# train the HMM with the observation sequences
$hmm->baum_welch_training(\@seqs);
# get parameters
$init = $hmm->init_prob; # returns an array reference
$matrix1 = $hmm->transition_prob; # returns Bio::Matrix::Scoring
$matrix2 = $hmm->emission_prob; # returns Bio::Matrix::Scoring
# initialize training hidden state sequences
$hs1 = "NCNCNNNNNCCCCNNCCCNNNNC";
$hs2 = "NCNNCNNNNNNCNCNCNNNCNCN";
@hss = ($hs1, $hs2);
# train the HMM with both observation sequences and hidden state
# sequences
$hmm->statistical_training(\@seqs, \@hss);
# with the newly calibrated HMM, we can use viterbi algorithm
# to obtain the hidden state sequence underlying an observation
# sequence
$hss = $hmm->viterbi($seq); # returns a string of hidden states
Description
Hidden Markov Model (HMM) was first introduced by Baum and his colleagues
in a series of classic papers in the late 1960s and 1970s. It was first
applied to the field of speech recognition with great success in the 1970s.
Explosion in the amount sequencing data in the 1990s opened the field
of Biological Sequence Analysis. Seeing HMM's effectiveness in detecing
signals in biological sequences, Krogh, Mian and Haussler used HMM to find
genes in E. coli DNA in a classical paper in 1994. Since then, there have
been extensive application of HMM to other area of Biology, for example,
multiple sequence alignment, CpG island detection and so on.
Methods
Methods description
Title : likelihood
Usage : $prob = $hmm->likelihood($seq)
Function: Calculate the probability of an observation sequence given an HMM
Returns : An floating point number that is the logarithm of the probability
of an observation sequence given an HMM
Args : The only argument is a string that is the observation sequence
you are interested in. Note that the sequence must not contain
any character that is not in the alphabet of observation symbols. |
Title : statistical_training
Usage : $hmm->statistical_training(\@seqs, \@hss)
Function: Estimate the parameters of an HMM given an array of observation
sequence and an array of the corresponding hidden state
sequences
Returns : Returns nothing. The parameters of the HMM will be set to the
estimated values
Args : The first argument is a reference to an array of observation
sequences. The second argument is a reference to an array of
hidden state sequences. Note that the lengths of an observation
sequence and a hidden state sequence must be the same. |
Title : baum_welch_training
Usage : $hmm->baum_welch_training(\@seqs)
Function: Estimate the parameters of an HMM given an array of observation
sequence
Returns : Returns nothing. The parameters of the HMM will be set to the
estimated values
Args : The only argument is a reference to an array of observation
sequences. |
Title : viterbi
Usage : $hss = $hmm->viterbi($seq)
Function: Find out the hidden state sequence that can maximize the
probability of seeing observation sequence $seq.
Returns : Returns a string that is the hidden state sequence that maximizes
the probability of seeing $seq.
Args : The only argument is an observation sequence. |
Title : symbols
Usage : $symbols = $hmm->symbols() #get
: $hmm->symbols($value) #set
Function : the set get for the observation symbols
Example :
Returns : symbols string
Arguments : new value |
Title : states
Usage : $states = $hmm->states() #get
: $hmm->states($value) #set
Function : the set get for the hidden states
Example :
Returns : states string
Arguments : new value |
Title : init_prob
Usage : $init = $hmm->init_prob() #get
: $hmm->transition_prob(\@init) #set
Function : the set get for the initial probability array
Example :
Returns : reference to double array
Arguments : new value |
Title : transition_prob
Usage : $transition_matrix = $hmm->transition_prob() #get
: $hmm->transition_prob($matrix) #set
Function : the set get for the transition probability mairix
Example :
Returns : Bio::Matrix::Scoring
Arguments : new value |
Title : emission_prob
Usage : $emission_matrix = $hmm->emission_prob() #get
: $hmm->emission_prob($matrix) #set
Function : the set get for the emission probability mairix
Example :
Returns : Bio::Matrix::Scoring
Arguments : new value |
Methods code
BEGIN { eval {
require Bio::Ext::HMM;
};
if ( $@ ) {
die("\nThe C-compiled engine for Hidden Markov Model (HMM) has not been installed.\n Please read the install the bioperl-ext package\n\n");
exit(1);} | sub new
{ my ($class, @args) = @_;
my $self = $class->SUPER::new(@args);
my ($symbols, $states, $init, $a_mat, $e_mat) = $self->_rearrange([qw(SYMBOLS
STATES
INIT
AMAT
EMAT
)], @args);
$self->throw("Observation Symbols are not defined!") unless defined $symbols;
$self->throw("Hidden States are not defined!") unless defined $states;
if (defined $symbols) {
if (scalar($symbols)) {
if ($self->symbols($symbols) < 0) {
$self->throw("Duplicate symbols!\n");
}
}
else {
$self->throw("We don't support list of symbols in this version.\n");
}
}
if (defined $states) {
if (scalar($states)) {
if ($self->states($states) < 0) {
$self->throw("Duplicate states!\n");
}
}
else {
$self->throw("We don't support list of states in this version.\n");
}
}
$self->{'hmm'} = new Bio::Ext::HMM::HMM($symbols, $states);
return $self;} | sub likelihood
{ my ($self, $seq) = @_;
my $valid_symbols;
if( ! defined $seq) {
$self->warn("Cannot calculate without supply an observation sequence!");
return undef;
}
my $s = $self->{'symbols'};
$_ = $seq;
$valid_symbols = eval "tr/$s//;";
if ($valid_symbols != length($seq)) {
$self->throw("Observation Sequence contains characters that is not in the
alphabet of observation symbols!\n");
}
return Bio::Ext::HMM->HMM_likelihood($self->{'hmm'}, $seq);} | sub statistical_training
{ my ($self, $seqs, $hss) = @_;
my $valid_symbols;
my $seq_cnt, $hs_cnt;
my $i;
if( ! defined $seqs or ! defined $hss) {
$self->warn("Cannot calculate without supply an observation and a hidden state sequence!");
return undef;
}
$seq_cnt = @{$seqs};
$hs_cnt = @{$seqs};
if ($seq_cnt != $hs_cnt) {
$self->throw("There must be the same number of observation sequences and
hidden state sequences!\n");
}
for ($i = 0; $i < $seq_cnt; ++$i) {
if (length(@{$seqs}[$i]) != length(@{$hss}[$i])) {
$self->throw("The corresponding observation sequences and hidden state sequences must be of the same length!\n");
}
}
foreach $seq (@{$seqs}) {
my $s = $self->{'symbols'};
$_ = $seq;
$valid_symbols = eval "tr/$s//;";
if ($valid_symbols != length($seq)) {
$self->throw("Observation Sequence contains characters that is not in the
alphabet of observation symbols!\n");
}
}
foreach $seq (@{$hss}) {
my $s = $self->{'states'};
$_ = $seq;
$valid_symbols = eval "tr/$s//;";
if ($valid_symbols != length($seq)) {
$self->throw("Hidden State Sequence contains characters that is not in the
alphabet of hidden states!\n");
}
}
Bio::Ext::HMM->HMM_statistical_training($self->{'hmm'}, $seqs, $hss);} | sub baum_welch_training
{ my ($self, $seqs) = @_;
my $valid_symbols;
if( ! defined $seqs) {
$self->warn("Cannot calculate without supply an observation sequence!");
return undef;
}
foreach $seq (@{$seqs}) {
my $s = $self->{'symbols'};
$_ = $seq;
$valid_symbols = eval "tr/$s//;";
if ($valid_symbols != length($seq)) {
$self->throw("Observation Sequence contains characters that is not in the
alphabet of observation symbols!\n");
}
}
Bio::Ext::HMM->HMM_baum_welch_training($self->{'hmm'}, $seqs);} | sub viterbi
{ my ($self, $seq) = @_;
my $valid_symbols;
if( ! defined $seq) {
$self->warn("Cannot calculate without supply an observation sequence!");
return undef;
}
my $s = $self->{'symbols'};
$_ = $seq;
$valid_symbols = eval "tr/$s//;";
if ($valid_symbols != length($seq)) {
$self->throw("Observation Sequence contains characters that is not in the
alphabet of observation symbols!\n");
}
return Bio::Ext::HMM->HMM_viterbi($self->{'hmm'}, $seq);} | sub symbols
{ my ($self,$val) = @_;
my %alphabets = ();
my $c;
if ( defined $val ) {
for ($i = 0; $i < length($val); ++$i) {
$c = substr($val, $i, 1);
if (defined $alphabets{$c}) {
$self->throw("Can't have duplicate symbols!");
}
else {
$alphabets{$c} = 1;
}
}
$self->{'symbols'} = $val;
}
return $self->{'symbols'};} | sub states
{ my ($self,$val) = @_;
my %alphabets = ();
my $c;
if ( defined $val ) {
for ($i = 0; $i < length($val); ++$i) {
$c = substr($val, $i, 1);
if (defined $alphabets{$c}) {
$self->throw("Can't have duplicate states!");
}
else {
$alphabets{$c} = 1;
}
}
$self->{'states'} = $val;
}
return $self->{'states'};} | sub init_prob
{ my ($self, $init) = @_;
my $i;
my @A;
if (defined $init) {
if (ref($init)) {
my $size = @{$init};
my $sum = 0.0;
foreach (@{$init}) {
$sum += $_;
}
if ($sum != 1.0) {
$self->throw("The sum of initial probability array must be 1.0!\n");
}
if ($size != length($self->{'states'})) {
$self->throw("The size of init array $size is different from the number of HMM's hidden states!\n");
}
for ($i = 0; $i < $size; ++$i) {
Bio::Ext::HMM::HMM->set_init_entry($self->{'hmm'}, substr($self->{'states'}, $i, 1), @{$init}[$i]);
}
}
else {
$self->throw("Initial Probability array must be a reference!\n");
}
}
else {
for ($i = 0; $i < length($self->{'states'}); ++$i) {
$A[$i] = Bio::Ext::HMM::HMM->get_init_entry($self->{'hmm'}, substr($self->{'states'}, $i, 1));
}
return\@ A;
}} | sub transition_prob
{ my ($self, $matrix) = @_;
my $i, $j;
my @A;
if (defined $matrix) {
if ($matrix->isa('Bio::Matrix::Scoring')) {
my $row = join("", $matrix->row_names);
my $col = join("", $matrix->column_names);
if ($row ne $self->{'states'}) {
$self->throw("Names of the rows ($row) is different from the states of HMM " . $self->{'states'});
}
if ($col ne $self->{'states'}) {
$self->throw("Names of the columns ($col) is different from the states of HMM " . $self->{'states'});
}
for ($i = 0; $i < length($self->{'states'}); ++$i) {
my $sum = 0.0;
my $a = substr($self->{'states'}, $i, 1);
for ($j = 0; $j < length($self->{'states'}); ++$j) {
my $b = substr($self->{'states'}, $j, 1);
$sum += $matrix->entry($a, $b);
}
if ($sum != 1.0) {
$self->throw("Sum of probabilities for each from-state must be 1.0!\n");
}
}
for ($i = 0; $i < length($self->{'states'}); ++$i) {
my $a = substr($self->{'states'}, $i, 1);
for ($j = 0; $j < length($self->{'states'}); ++$j) {
my $b = substr($self->{'states'}, $j, 1);
Bio::Ext::HMM::HMM->set_a_entry($self->{'hmm'}, $a, $b, $matrix->entry($a, $b));
}
}
}
else {
$self->throw("Transition Probability matrix must be of type Bio::Matrix::Scoring.\n");
}
}
else {
for ($i = 0; $i < length($self->{'states'}); ++$i) {
for ($j = 0; $j < length($self->{'states'}); ++$j) {
$A[$i][$j] = Bio::Ext::HMM::HMM->get_a_entry($self->{'hmm'}, substr($self->{'states'}, $i, 1), substr($self->{'states'}, $j, 1));
}
}
my @rows = split(//, $self->{'states'});
return $matrix = new Bio::Matrix::Scoring(-values =>\@ A, -rownames =>\@ rows, -colnames =>\@ rows);
}} | sub emission_prob
{ my ($self, $matrix) = @_;
my $i, $j;
my @A;
if (defined $matrix) {
if ($matrix->isa('Bio::Matrix::Scoring')) {
my $row = join("", $matrix->row_names);
my $col = join("", $matrix->column_names);
if ($row ne $self->{'states'}) {
$self->throw("Names of the rows ($row) is different from the states of HMM " . $self->{'states'});
}
if ($col ne $self->{'symbols'}) {
$self->throw("Names of the columns ($col) is different from the symbols of HMM " . $self->{'symbols'});
}
for ($i = 0; $i < length($self->{'states'}); ++$i) {
my $sum = 0.0;
my $a = substr($self->{'states'}, $i, 1);
for ($j = 0; $j < length($self->{'symbols'}); ++$j) {
my $b = substr($self->{'symbols'}, $j, 1);
$sum += $matrix->entry($a, $b);
}
if ($sum != 1.0) {
$self->throw("Sum of probabilities for each state must be 1.0!\n");
}
}
for ($i = 0; $i < length($self->{'states'}); ++$i) {
my $a = substr($self->{'states'}, $i, 1);
for ($j = 0; $j < length($self->{'symbols'}); ++$j) {
my $b = substr($self->{'symbols'}, $j, 1);
Bio::Ext::HMM::HMM->set_e_entry($self->{'hmm'}, $a, $b, $matrix->entry($a, $b));
}
}
}
else {
$self->throw("Emission Probability matrix must be of type Bio::Matrix::Scoring.\n");
}
}
else {
for ($i = 0; $i < length($self->{'states'}); ++$i) {
for ($j = 0; $j < length($self->{'symbols'}); ++$j) {
$A[$i][$j] = Bio::Ext::HMM::HMM->get_e_entry($self->{'hmm'}, substr($self->{'states'}, $i, 1), substr($self->{'symbols'}, $j, 1));
}
}
my @rows = split(//, $self->{'states'});
my @cols = split(//, $self->{'symbols'});
return $matrix = new Bio::Matrix::Scoring(-values =>\@ A, -rownames =>\@ rows, -colnames =>\@ cols);
}} | General documentation
This package comes with the main bioperl distribution. You also need
to install the lastest bioperl-ext package which contains the XS code
that implements the algorithms. This package won't work if you haven't
compiled the bioperl-ext package.
1.
Allow people to set and get the tolerance level in the EM algorithm.
2.
Allow people to set and get the maximum number of iterations
to run in the EM algorithm.
3.
A function to calculate the probability of an observation sequence
4.
A function to do posterior decoding, ie to find the probabilty of
seeing a certain observation symbol at position i.
User feedback is an integral part of the evolution of this and other
Bioperl modules. Send your comments and suggestions preferably to one
of the Bioperl mailing lists. Your participation is much appreciated.
bioperl-l@bioperl.org - General discussion
http://bioperl.org/MailList.shtml - About the mailing listsReport bugs to the Bioperl bug tracking system to help us keep track
the bugs and their resolution. Bug reports can be submitted via the
web:
http://bugzilla.bioperl.org/
This implementation was written by Yee Man Chan (ymc@yahoo.com).
Copyright (c) 2005 Yee Man Chan. All rights reserved. This program
is free software; you can redistribute it and/or modify it under
the same terms as Perl itself. All the code are written by Yee
Man Chan without borrowing any code from anywhere.