期刊
NATURE COMMUNICATIONS
卷 3, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/ncomms2256
关键词
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资金
- Research Grants Council [781511M, 778609M, N_HKU752/10, AoE M-04/04]
- Food and Health Bureau of Hong Kong [10091262]
- NSFC of China [91229105]
- University of Hong Kong [10401206]
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.
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