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Bioinformatics Advance Access published online on March 23, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm096
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Unsupervised segmentation of continuous genomic data

Nathan Day a,*, Andrew Hemmaplardh a,*, Robert E. Thurman b,c, John A. Stamatoyannopoulos c and William S. Noble a,b,c,{dagger}

aDepartment of Computer Science and Engineering, bDivision of Medical Genetics, cDepartment of Genome Sciences, University of Washington, Seattle, WA, USA

{dagger}To whom correspondence should be addressed. William S. Noble, E-mail: noble{at}gs.washington.edu


  Abstract

Summary: The advent of high-density, high-volume genomic data has created the need for tools to summarize large datasets at multiple scales. HMMSeg is a command-line utility for the scale-specific segmentation of continuous genomic data using hidden Markov models (HMMs). Scale-specificity is achieved by an optional wavelet-based smoothing operation. HMMSeg is capable of handling multiple datasets simultaneously, rendering it ideal for integrative analysis of expression, phylogenetic, and functional genomic data.1

Availability: http://noble.gs.washington.edu/proj/hmmseg

* These authors contributed equally.

Associate Editor: Dr. Alex Bateman

Received on November 22, 2006; revised on February 9, 2007; accepted on March 7, 2007

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