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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Man Tang, Mohammad Shabbir Hasan, Hongxiao Zhu, Liqing Zhang, Xiaowei Wu

ABSTRACT

BACKGROUND: 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). RESULTS AND CONCLUSION: 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. More... »

PAGES

9

Journal

TITLE

Human Genomics

ISSUE

1

VOLUME

13

Author Affiliations

From Grant

  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s40246-019-0194-6

    DOI

    http://dx.doi.org/10.1186/s40246-019-0194-6

    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30795817


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