hsegHMM: hidden Markov model-based allele-specific copy number alteration analysis accounting for hypersegmentation

hsegHMM: hidden Markov model-based allele-specific copy number alteration analysis accounting for hypersegmentation

Blog Article

Abstract Background Somatic copy number alternation (SCNA) is a common feature of the cancer genome and is associated with cancer etiology and crystal beaded candle holder prognosis.The allele-specific SCNA analysis of a tumor sample aims to identify the allele-specific copy numbers of both alleles, adjusting for the ploidy and the tumor purity.Next generation sequencing platforms produce abundant read counts at the base-pair resolution across the exome or whole genome which is susceptible to hypersegmentation, a phenomenon where numerous regions with very short length are falsely identified as SCNA.

Results We propose hsegHMM, a hidden Markov model approach that accounts for hypersegmentation for allele-specific SCNA analysis.hsegHMM provides statistical inference of copy number profiles by using an efficient E-M algorithm procedure.Through simulation and application studies, we found that hsegHMM handles hypersegmentation effectively with a t-distribution as a part of the emission probability distribution structure and a carefully defined state space.

We also compared hsegHMM with FACETS which is a current method for allele-specific SCNA analysis.For read more the application, we use a renal cell carcinoma sample from The Cancer Genome Atlas (TCGA) study.Conclusions We demonstrate the robustness of hsegHMM to hypersegmentation.

Furthermore, hsegHMM provides the quantification of uncertainty in identifying allele-specific SCNAs over the entire chromosomes.hsegHMM performs better than FACETS when read depth (coverage) is uneven across the genome.