Reference

Bio::Tools::HMM(3pm)

Bio::Tools::HMM - Perl extension to perform Hidden Markov Model calculations

  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

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. 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. Allow people to set and get the tolerance level in the EM algorithm. Allow people to set and get the maximum number of iterations to run in the EM algorithm. A function to calculate the probability of an observation sequence A function to do posterior decoding, ie to find the probabilty of seeing a certain observation symbol at position i.
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        This implementation was written by Yee Man Chan ([email protected]).
        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.

likelihood

 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.

statistical_training

 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.

baum_welch_training

 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.

viterbi

 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.

symbols

 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

states

 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

init_prob

 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

transition_prob

 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

emission_prob

 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