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Bioinformatics Advance Access originally published online on March 13, 2006
Bioinformatics 2006 22(9):1144-1146; doi:10.1093/bioinformatics/btl089
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
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BioHMM: a heterogeneous hidden Markov model for segmenting array CGH data

J. C. Marioni 1,2,*, N. P. Thorne 1,2 and S. Tavaré 1,2

1 Hutchison-MRC Research Centre, Department of Oncology, Computational Biology Group, University of Cambridge Hills Road, Cambridge
2 Department of Applied Mathematics and Theoretical Physics,University of Cambridge Wilberforce Road, Cambridge

*To whom correspondence should be addressed.

Summary: 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.

Availability: BioHMM is available as part of the R library snapCGH which can be downloaded from http://www.bioconductor.org/packages/bioc/1.8/html/snapCGH.html

Contact: J.Marioni{at}damtp.cam.ac.uk

Supplementary information: Supplementary information is available at http://www.damtp.cam.ac.uk/user/jcm68/BioHMM.html

Received on November 24, 2005; revised on February 7, 2006; accepted on March 7, 2006

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