sniper improved snp discovery by multiply mapping deep sequenced reads狙击手snp发现提高了乘映射深度测序读.pdfVIP

Simola and Kim Genome Biology 2011, 12:R55 /2011/12/6/R55 METHOD Open Access Sniper: improved SNP discovery by multiply mapping deep sequenced reads Daniel F Simola1,2 and Junhyong Kim1,3* Abstract SNP (single nucleotide polymorphism) discovery using next-generation sequencing data remains difficult primarily because of redundant genomic regions, such as interspersed repetitive elements and paralogous genes, present in all eukaryotic genomes. To address this problem, we developed Sniper, a novel multi-locus Bayesian probabilistic model and a computationally efficient algorithm that explicitly incorporates sequence reads that map to multiple genomic loci. Our model fully accounts for sequencing error, template bias, and multi-locus SNP combinations, maintaining high sensitivity and specificity under a broad range of conditions. An implementation of Sniper is freely available at /software/sniper.shtml. Background biases in sequencing coverage generated during genomic The advent of next-generation, short-read sequencing DNA library preparation [9]. These practical issues (NGS) technologies has enabled large-scale, whole-gen- make it difficult, even with sophisticated procedures, to ome resequencing studies that aim to discover novel discover true SNPs accurately without concurrently pre- SNPs and other population genetic variations. Perhaps dicting many false SNPs. A recent study evaluated the the most ambitious of these studies involves sequencing performance of three NGS technologies (Illumina, ABI over 1,000 individual human genomes in order to map Solid, 454) using near-saturating sequence coverage human genetic variation at a f