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Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence

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Hidden Markov Models

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1552))

Abstract

The rapid development of next generation sequencing (NGS) technology provides a novel avenue for genomic exploration and research. Hidden Markov models (HMMs) have wide applications in pattern recognition as well as Bioinformatics such as transcription factor binding sites and cis-regulatory modules detection. An application of HMM is introduced in this chapter with the in-deep developing of NGS. Single nucleotide variants (SNVs) inferred from NGS are expected to reveal gene mutations in cancer. However, NGS has lower sequence coverage and poor SNV detection capability in the regulatory regions of the genome. A specific HMM is developed for this purpose to infer the genotype for each position on the genome by incorporating the mapping quality of each read and the corresponding base quality on the reads into the emission probability of HMM. The procedure and the implementation of the algorithm is presented in detail for understanding and programming.

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Author information

Authors and Affiliations

  1. School of Mathematics and Physics, China University of Geosciences, Wuhan, 430074, China

    Jiawen Bian

  2. Department of Diagnostic Radiology, Center for Bioinformatics & Systems Biology, Wake Forest University—School of Medicine, Winston-Salem, 27103, NC, USA

    Xiaobo Zhou

Authors
  1. Jiawen Bian
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  2. Xiaobo Zhou
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Corresponding author

Correspondence to Xiaobo Zhou .

Editor information

Editors and Affiliations

  1. University of Leeds School of Molecular and Cellular Biology, Leeds, United Kingdom

    David R. Westhead

  2. University of Leeds School of Cellular and Molecular Biology, Leeds, United Kingdom

    M. S. Vijayabaskar

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Bian, J., Zhou, X. (2017). Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence. In: Westhead, D., Vijayabaskar, M. (eds) Hidden Markov Models. Methods in Molecular Biology, vol 1552. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6753-7_9

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  • DOI: https://doi.org/10.1007/978-1-4939-6753-7_9

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6751-3

  • Online ISBN: 978-1-4939-6753-7

  • eBook Packages: Springer Protocols

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