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

Nat Commun. 2012:3:1258. doi: 10.1038/ncomms2256.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Chromosomes, Human, Pair 21 / genetics
  • Chromosomes, Human, Pair 22 / genetics
  • Genome / genetics
  • Genomics / methods
  • Genotype
  • Humans
  • Neoplasms / genetics
  • Polymorphism, Single Nucleotide / genetics*
  • Sequence Analysis, DNA / methods*
  • Sequence Analysis, DNA / standards
  • Time Factors