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PMID: 16533818 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't

BioHMM: a heterogeneous hidden Markov model for segmenting array CGH data.

Bioinformatics (Oxford, England) ·Vol. 22 ·No. 9 ·2006-05-01 ·Pages 1144-6

Marioni JC, Thorne NP, Tavaré S

Abstract

We have developed a new method (BioHMM) for segmenting array comparative genomic hybridization data into states with the same underlying copy number. By utilizing a heterogeneous hidden Markov model, BioHMM incorporates relevant biological factors (e.g. the distance between adjacent clones) in the segmentation process.

MeSH Terms

Algorithms Artificial Intelligence Base Sequence Chromosome Mapping/methods Gene Dosage/genetics In Situ Hybridization/methods Markov Chains Models, Genetic Models, Statistical Molecular Sequence Data Oligonucleotide Array Sequence Analysis/methods Pattern Recognition, Automated/methods Sequence Analysis, DNA/methods Software

Authors & Affiliations

3 authors, click to expand affiliations / ORCID

Marioni J C

Hutchison-MRC Research Centre, Department of Oncology, Computational Biology Group, University of Cambridge Hills Road, Cambridge. J.Marioni@damtp.cam.ac.uk

Thorne N P

Tavaré S

Article Info

Journal

Bioinformatics (Oxford, England)

Abbr.

Bioinformatics

ISSN

1367-4803

DOI

10.1093/bioinformatics/btl089

j.marioni@damtp.cam.ac.uk

Published

2006-05-01

Epub

2006-00-13

Pages

1144-6

Language

English

Region

England

NLM ID

9808944

Subset

External Links

PubMed source DOI: 10.1093/bioinformatics/btl089 View journal details

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BioHMM: a heterogeneous hidden Markov model for segmenting array CGH data.

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