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

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 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 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.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Choo-Wosoba H,Albert PS,Zhu B

doi

10.1186/s12859-018-2412-y

subject

Has Abstract

pub_date

2018-11-14 00:00:00

pages

424

issue

1

issn

1471-2105

pii

10.1186/s12859-018-2412-y

journal_volume

19

pub_type

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