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A fast and accurate SNP detection algorithm for next-generation sequencing data

Author

Listed:
  • Feng Xu

    (LKS Faculty of Medicine, The University of Hong Kong
    Shenzhen Institute of Research and Innovation, The University of Hong Kong)

  • Weixin Wang

    (LKS Faculty of Medicine, The University of Hong Kong
    Shenzhen Institute of Research and Innovation, The University of Hong Kong)

  • Panwen Wang

    (LKS Faculty of Medicine, The University of Hong Kong
    Shenzhen Institute of Research and Innovation, The University of Hong Kong)

  • Mulin Jun Li

    (LKS Faculty of Medicine, The University of Hong Kong
    Shenzhen Institute of Research and Innovation, The University of Hong Kong)

  • Pak Chung Sham

    (Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong
    LKS Faculty of Medicine, The University of Hong Kong
    State Key Laboratory in Cognitive and Brain Sciences, The University of Hong Kong)

  • Junwen Wang

    (LKS Faculty of Medicine, The University of Hong Kong
    Shenzhen Institute of Research and Innovation, The University of Hong Kong
    Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong
    HKU-BGI Bioinformatics Algorithms and Core Technology Research Laboratory, The University of Hong Kong)

Abstract

Various methods have been developed for calling single-nucleotide polymorphisms from next-generation sequencing data. However, for satisfactory performance, most of these methods require expensive high-depth sequencing. Here, we propose a fast and accurate single-nucleotide polymorphism detection program that uses a binomial distribution-based algorithm and a mutation probability. We extensively assess this program on normal and cancer next-generation sequencing data from The Cancer Genome Atlas project and pooled data from the 1,000 Genomes Project. We also compare the performance of several state-of-the-art programs for single-nucleotide polymorphism calling and evaluate their pros and cons. We demonstrate that our program is a fast and highly accurate single-nucleotide polymorphism detection method, particularly when the sequence depth is low. The program can finish single-nucleotide polymorphism calling within four hours for 10-fold human genome next-generation sequencing data (30 gigabases) on a standard desktop computer.

Suggested Citation

  • Feng Xu & Weixin Wang & Panwen Wang & Mulin Jun Li & Pak Chung Sham & Junwen Wang, 2012. "A fast and accurate SNP detection algorithm for next-generation sequencing data," Nature Communications, Nature, vol. 3(1), pages 1-9, January.
    • Handle: RePEc:nat:natcom:v:3:y:2012:i:1:d:10.1038_ncomms2256
    DOI: 10.1038/ncomms2256
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