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

Feng Xu, Weixin Wang, Panwen Wang, Mulin Jun Li, Pak Chung Sham, Junwen Wang

Research output: Contribution to journalArticle

34 Scopus citations

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.

Original languageEnglish (US)
Article number1258
JournalNature communications
Volume3
DOIs
StatePublished - 2012

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

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