SNPGenie Software Published in Bioinformatics
My dissertation’s SNPGenie software has now been published in the journal Bioinformatics, with co-authors Austin L. Hughes, my advisor, and Louise H. Moncla, a collaborator at the University of Wisconsin. As described in a previous post, the software accepts pooled sequencing data that describe genetic variation in a population of interest. We’ve been using this primarily for virus populations inside their hosts, that is, the organisms they infect. With this approach, we’re able to get snapshots of how the virus populations evolve over the course of infecting one individual, and between different individuals. Check out the abstract for the software, below.
ABSTRACT:
Summary: 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 analyze 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.
Availability and implementation: SNPGenie version 1.2 is a Perl program with no additional dependencies. It is free, open-source, and available for download at https://github.com/hugheslab/snpgenie.
Supplementary Information: Supplementary data are available at Bioinformatics online.
FULL STUDY:
Nelson CW, Moncla LH, Hughes AL [2015] SNPGenie: estimating evolutionary parameters to detect natural selection using pooled next-generation sequencing data. Bioinformatics 2015, doi: 10.1093/bioinformatics/btv449.