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RADpainter and fineRADstructure: population inference from RADseq data

View ORCID ProfileMilan Malinsky, View ORCID ProfileEmiliano Trucchi, View ORCID ProfileDaniel John Lawson, Daniel Falush
doi: https://doi.org/10.1101/057711
Milan Malinsky
1Wellcome Trust Sanger Institute, Cambridge, CB10 1SA, UK
2Gurdon Institute and Department of Genetics, University of Cambridge, Cambridge, CB2 1QN, UK
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  • For correspondence: millanek@gmail.com danielfalush@googlemail.com
Emiliano Trucchi
3Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria
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Daniel John Lawson
4School of Social and Community Medicine, University of Bristol, Bristol, BS8 2BN, UK
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Daniel Falush
5Institute of Life Science, Swansea University, Swansea, SA2 8PP, UK
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  • For correspondence: millanek@gmail.com danielfalush@googlemail.com
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Abstract

Powerful approaches to inferring recent or current population structure based on nearest neighbor haplotype ‘coancestry’ have so far been inaccessible to users without high quality genome-wide haplotypes. With a boom in non-model organism genomics, there is a pressing need to bring these approaches to communities without access to such data. Here we present RADpainter, a new program designed to infer the coancestry matrix from restriction-site-associated DNA sequencing (RADseq) data. We combine this program together with a previously published MCMC clustering algorithm into fineRADstructure - a complete, easy to use, and fast population inference package for RADseq data (https://github.com/millanek/fineRADstructure).

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted June 16, 2016.
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RADpainter and fineRADstructure: population inference from RADseq data
Milan Malinsky, Emiliano Trucchi, Daniel John Lawson, Daniel Falush
bioRxiv 057711; doi: https://doi.org/10.1101/057711
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RADpainter and fineRADstructure: population inference from RADseq data
Milan Malinsky, Emiliano Trucchi, Daniel John Lawson, Daniel Falush
bioRxiv 057711; doi: https://doi.org/10.1101/057711

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