title: Sniper: improved SNP discovery by multiply mapping deep sequenced reads.
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 http://kim.bio.upenn.edu/software/sniper.shtml.
authors.affiliation: Department of Biology, University of Pennsylvania, 433 S, University Ave., Philadelphia, PA 19104, USA.
authors.forename: Daniel F
authors.initials: DF
authors.lastname: Simola
authors.forename: Junhyong
authors.initials: J
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping; D019295:Computational Biology; D015894:Genome, Human; D060005:Genotyping Techniques; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D015233:Models, Statistical; D015394:Molecular Structure; D020641:Polymorphism, Single Nucleotide; D012091:Repetitive Sequences, Nucleic Acid; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA; D012984:Software