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

J C Marioni; N P Thorne; S Tavaré

doi:10.1093/bioinformatics/btl089

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

Bioinformatics. 2006 May 1;22(9):1144-6. doi: 10.1093/bioinformatics/btl089. Epub 2006 Mar 13.

Authors

J C Marioni¹, N P Thorne, S Tavaré

Affiliation

¹ Hutchison-MRC Research Centre, Department of Oncology, Computational Biology Group, University of Cambridge Hills Road, Cambridge. J.Marioni@damtp.cam.ac.uk

PMID: 16533818
DOI: 10.1093/bioinformatics/btl089

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.

Publication types

Research Support, Non-U.S. Gov't

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*