4.7 Article

SNPGenie: estimating evolutionary parameters to detect natural selection using pooled next-generation sequencing data

Journal

BIOINFORMATICS
Volume 31, Issue 22, Pages 3709-3711

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv449

Keywords

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Funding

  1. NIH [Al077376, Al096882, Al084787]
  2. UW-Madison Molecular Biosciences Training [T32 GM07215]
  3. NSF GRF [DGE-0929297]
  4. University of South Carolina (USC) Presidential Fellowship
  5. USC Dept. Biol. Sci. Kathryn Hinnant-Johnson, M. D. Memorial Fellowship

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New applications of next-generation sequencing technologies use pools of DNA from multiple individuals to estimate population genetic parameters. However, no publicly available tools exist to analyse single-nucleotide polymorphism (SNP) calling results directly for evolutionary parameters important in detecting natural selection, including nucleotide diversity and gene diversity. We have developed SNPGenie to fill this gap. The user submits a FASTA reference sequence(s), a Gene Transfer Format (.GTF) file with CDS information and a SNP report(s) in an increasing selection of formats. The program estimates nucleotide diversity, distance from the reference and gene diversity. Sites are flagged for multiple overlapping reading frames, and are categorized by polymorphism type: nonsynonymous, synonymous, or ambiguous. The results allow single nucleotide, single codon, sliding window, whole gene and whole genome/population analyses that aid in the detection of positive and purifying natural selection in the source population.

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