title: A new approach for detecting low-level mutations in next-generation sequence data.
abstract: 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.
authors.affiliation: Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, D04103, Leipzig, Germany. mingkun_li@eva.mpg.de
authors.forename: Mingkun
authors.initials: M
authors.lastname: Li
authors.forename: Mark
authors.initials: M
authors.lastname: Stoneking
mesh_terms: D004279:DNA, Viral / Q000737:chemistry; D005188:False Negative Reactions; D016678:Genome; D009154:Mutation; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D004279:DNA, Viral