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

Authors:
J. C. Marioni;N. P. Thorne;S. Tavaré
Affiliations:
Hutchison-MRC Research Centre, Department of Oncology, Computational Biology Group, University of Cambridge Hills Road, Cambridge;Hutchison-MRC Research Centre, Department of Oncology, Computational Biology Group, University of Cambridge Hills Road, Cambridge;Hutchison-MRC Research Centre, Department of Oncology, Computational Biology Group, University of Cambridge Hills Road, Cambridge
Venue:
Bioinformatics
Year:
2006

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Abstract

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