title: A bioinformatics workflow for detecting signatures of selection in genomic data.
abstract: The detection of "signatures of selection" is now possible on a genome-wide scale in many plant and animal species, and can be performed in a population-specific manner due to the wealth of per-population genome-wide genotype data that is available. With genomic regions that exhibit evidence of having been under selection shown to also be enriched for genes associated with biologically important traits, detection of evidence of selective pressure is emerging as an additional approach for identifying novel gene-trait associations. While high-density genotype data is now relatively easy to obtain, for many researchers it is not immediately obvious how to go about identifying signatures of selection in these data sets. Here we describe a basic workflow, constructed from open source tools, for detecting and examining evidence of selection in genomic data. Code to install and implement the pipeline components, and instructions to run a basic analysis using the workflow described here, can be downloaded from our public GitHub repository: http://www.github.com/smilefreak/selectionTools/
authors.affiliation: Department of Biochemistry, University of Otago Dunedin, New Zealand ; Virtual Institute of Statistical Genetics Rotorua, New Zealand.
authors.forename: Murray
authors.initials: M
authors.lastname: Cadzow
authors.affiliation: Department of Biochemistry, University of Otago Dunedin, New Zealand ; Virtual Institute of Statistical Genetics Rotorua, New Zealand.
authors.forename: James
authors.initials: J
authors.lastname: Boocock
authors.affiliation: Department of Biochemistry, University of Otago Dunedin, New Zealand ; Virtual Institute of Statistical Genetics Rotorua, New Zealand ; Department of Mathematics and Statistics, University of Otago Dunedin, New Zealand.
authors.forename: Hoang T
authors.initials: HT
authors.lastname: Nguyen
authors.affiliation: Department of Biochemistry, University of Otago Dunedin, New Zealand ; Virtual Institute of Statistical Genetics Rotorua, New Zealand ; New Zealand Forest Research Institute Ltd Rotorua, New Zealand.
authors.forename: Phillip
authors.initials: P
authors.lastname: Wilcox
authors.affiliation: Department of Biochemistry, University of Otago Dunedin, New Zealand ; Virtual Institute of Statistical Genetics Rotorua, New Zealand.
authors.forename: Tony R
authors.initials: TR
authors.lastname: Merriman
authors.affiliation: Department of Biochemistry, University of Otago Dunedin, New Zealand ; Virtual Institute of Statistical Genetics Rotorua, New Zealand.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Black
keywords: analysis pipeline; genome-wide; genomics; signatures of selection