GPHMM (Global Parameter Hidden Markov Model) is a tool for identification of copy number variation (CNV) and loss of heterozygosity (LOH) in whole-genome single nucleotide (SNP) array data. The specific purpose of GPHMM is the analysis of tumor samples. The algorithm uses a hidden Markov model (HMM) to solve issues caused e.g., by a baseline shift of LRR signal, cell contamination, and genomic waves.
Li A, Liu Z, Lezon-Geyda K, Sarkar S, Lannin D, Schulz V, Krop I, Winer E, Harris L, Tuck D "GPHMM: an integrated hidden Markov model for identification of copy number alteration and loss of heterozygosity in complex tumor samples using whole genome SNP arrays." Nucleic Acids Res. 2011 Jul;39(12):4928-41. Epub 2011 Mar 11. https://doi.org/10.1093/nar/gkr014
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