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A new approach for detecting low-level mutations in next-generation sequence data

2012; BioMed Central; Volume: 13; Issue: 5 Linguagem: Inglês

10.1186/gb-2012-13-5-r34

1474-760X

Mingkun Li, Mark Stoneking,

Evolution and Genetic Dynamics

We propose a new method that incorporates population re-sequencing data, distribution of reads, and strand bias in detecting low-level mutations. The method can accurately identify low-level mutations down to a level of 2.3%, with an average coverage of 500×, and with a false discovery rate of less than 1%. In addition, we also discuss other problems in detecting low-level mutations, including chimeric reads and sample cross-contamination, and provide possible solutions to them.