Title |
A bioinformatics workflow for detecting signatures of selection in genomic data
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Published in |
Frontiers in Genetics, August 2014
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DOI | 10.3389/fgene.2014.00293 |
Pubmed ID | |
Authors |
Murray Cadzow, James Boocock, Hoang T. Nguyen, Phillip Wilcox, Tony R. Merriman, Michael A. Black |
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/ |
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Switzerland | 2 | 6% |
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India | 1 | 3% |
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Germany | 1 | 3% |
Hungary | 1 | 3% |
France | 1 | 3% |
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Unknown | 8 | 26% |
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Members of the public | 14 | 45% |
Mendeley readers
Geographical breakdown
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Spain | 4 | <1% |
Germany | 3 | <1% |
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Chile | 2 | <1% |
France | 2 | <1% |
Netherlands | 2 | <1% |
Australia | 2 | <1% |
New Zealand | 2 | <1% |
Other | 6 | 1% |
Unknown | 385 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 98 | 23% |
Researcher | 97 | 23% |
Student > Master | 58 | 14% |
Student > Bachelor | 34 | 8% |
Student > Postgraduate | 18 | 4% |
Other | 66 | 16% |
Unknown | 47 | 11% |
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Veterinary Science and Veterinary Medicine | 6 | 1% |
Social Sciences | 5 | 1% |
Other | 21 | 5% |
Unknown | 55 | 13% |