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

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Abstract Background Somatic copy number alternation (SCNA) is a common feature of the cancer genome and is associated with cancer etiology and 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 silver lining herbs kidney support 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 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 yale law school colors 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.