vi-HMM: a novel HMM-based method for sequence variant identification in short-read data

Human Genomics
Man TangXiaowei Wu

Abstract

Accurate and reliable identification of sequence variants, including single nucleotide polymorphisms (SNPs) and insertion-deletion polymorphisms (INDELs), plays a fundamental role in next-generation sequencing (NGS) applications. Existing methods for calling these variants often make simplified assumptions of positional independence and fail to leverage the dependence between genotypes at nearby loci that is caused by linkage disequilibrium (LD). We propose vi-HMM, a hidden Markov model (HMM)-based method for calling SNPs and INDELs in mapped short-read data. This method allows transitions between hidden states (defined as "SNP," "Ins," "Del," and "Match") of adjacent genomic bases and determines an optimal hidden state path by using the Viterbi algorithm. The inferred hidden state path provides a direct solution to the identification of SNPs and INDELs. Simulation studies show that, under various sequencing depths, vi-HMM outperforms commonly used variant calling methods in terms of sensitivity and F1 score. When applied to the real data, vi-HMM demonstrates higher accuracy in calling SNPs and INDELs.

References

Jan 11, 2000·Nucleic Acids Research·S T SherryK Sirotkin
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May 24, 2013·Nucleic Acids Research·Feng ZengTing Chen
Aug 20, 2015·Human Genomics·Mohammad Shabbir HasanLiqing Zhang

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Citations

Apr 6, 2019·Plant Biotechnology Journal·Joan Miquel Bernabé-OrtsDiego Orzaez

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Software Mentioned

PyroHMMsnp
SAMtools
BWA
wgsim
VarScan
FreeBayes
Dindel
GATK
SAM
GATK HaplotypeCaller

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