BioHMM: a heterogeneous hidden Markov model for segmenting array CGH data.
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.
Hutchison-MRC Research Centre, Department of Oncology, Computational Biology Group, University of Cambridge Hills Road, Cambridge. J.Marioni@damtp.cam.ac.uk
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D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D018628:Gene Dosage / Q000235:genetics*; D017403:In Situ Hybridization / Q000379:methods*; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software