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Bioinformatics Advance Access published online on January 29, 2004
Bioinformatics, doi:10.1093/bioinformatics/btg489
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received August 4, 2003
Revised September 30, 2003
Accepted October 15, 2003

Applications note

Algorithms for variable length Markov chain modeling

Gill Bejerano 1*

1 Center for Biomolecular Science and Engineering, School of Engineering, University of California, Santa Cruz CA 95064, USA

* To whom correspondence should be addressed. E-mail: jill{at}soe.ucsc.edu.


  Abstract

Summary: We present a general purpose implementation of variable length Markov models. Contrary to fixed order Markov models, these models are not restricted to a predefined uniform depth. Rather, by examining the training data, a model is constructed that fits higher order Markov dependencies where such contexts exist, while using lower order Markov dependencies elsewhere. As both theoretical and experimental results show, these models are capable of capturing rich signals from a modest amount of training data, without the use of hidden states.

Availability: The source code is freely available at http://www.soe.ucsc.edu/~jill/src/


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