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Bioinformatics Advance Access originally published online on March 23, 2007
Bioinformatics 2007 23(11):1424-1426; 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 1,{dagger}, Andrew Hemmaplardh 1,{dagger}, Robert E. Thurman 2,3,*, John A. Stamatoyannopoulos 3 and William S. Noble

1Department of Computer Science and Engineering, 2Division of Medical Genetics, and 3Department of Genome Sciences, University of Washington, Seattle, WA, USA

*To whom correspondence should be addressed.


  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.

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

Contact: rthurman{at}u.washington.edu

{dagger}The authors wish it to be know that, in their opinion, the first two authors should be regarded as First Authors.

Associate Editor: Alex Bateman

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

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