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

Feng Xu, Weixin Wang, Panwen Wang, Mulin Jun Li, Pak Chung Sham and Junwen Wang ()
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Feng Xu: LKS Faculty of Medicine, The University of Hong Kong
Weixin Wang: LKS Faculty of Medicine, The University of Hong Kong
Panwen Wang: LKS Faculty of Medicine, The University of Hong Kong
Mulin Jun Li: LKS Faculty of Medicine, The University of Hong Kong
Pak Chung Sham: Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong
Junwen Wang: LKS Faculty of Medicine, The University of Hong Kong

Nature Communications, 2012, vol. 3, issue 1, 1-9

Abstract: 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.

Date: 2012
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DOI: 10.1038/ncomms2256

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