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PMID: 17384021 Published · ppublish English Journal Article Research Support, N.I.H., Extramural

Unsupervised segmentation of continuous genomic data.

Bioinformatics (Oxford, England) ·Vol. 23 ·No. 11 ·2007-06-01 ·Pages 1424-6

Day N, Hemmaplardh A, Thurman RE, Stamatoyannopoulos JA, Noble WS

Abstract

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

MeSH Terms
Algorithms Artificial Intelligence Chromosome Mapping/methods Computer Simulation Databases, Genetic Information Storage and Retrieval/methods Markov Chains Models, Genetic Models, Statistical Pattern Recognition, Automated/methods Sequence Analysis, DNA/methods
Authors & Affiliations
5 authors, click to expand affiliations / ORCID
Day Nathan
Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
Hemmaplardh Andrew
Thurman Robert E
Stamatoyannopoulos John A
Noble William S
Article Info
Journal
Bioinformatics (Oxford, England)
Abbr.
Bioinformatics
ISSN
1367-4811
Published
2007-06-01
Epub
2007-00-23
Pages
1424-6
Language
English
Region
England
NLM ID
9808944
Subset
IM
Grants
NHGRI NIH HHS · U54 HG004592 · United States
NIGMS NIH HHS · R01 GM071923 · United States
NIGMS NIH HHS · R01 GM71852 · United States
NHGRI NIH HHS · U01 HG003161 · United States
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