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

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.

• Publication year

  2012

• Venue

  Genome Biology

• Publication date

  2012-05-01

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1186/gb-2012-13-5-r34PMID 22621726PMCID 3446287
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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