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BMC Bioinformatics. 2018 Nov 14;19(1):424. doi: 10.1186/s12859-018-2412-y.

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

Author information

1
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
2
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA. bin.zhu@nih.gov.

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.

KEYWORDS:

Allele-specific somatic copy number alteration; Hidden Markov model; Hypersegmentation; Next-generation sequencing; The cancer genome Atlas study

PMID:
30428830
PMCID:
PMC6236906
DOI:
10.1186/s12859-018-2412-y
[Indexed for MEDLINE]
Free PMC Article

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