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RADpainter and fineRADstructure: Population Inference from RADseq Data
by
Falush, Daniel
, Malinsky, Milan
, Trucchi, Emiliano
, Lawson, Daniel John
in
Algorithms
/ Clustering
/ Datasets
/ DNA sequencing
/ Gene sequencing
/ Genomes
/ Genomics
/ Haplotypes
/ Inference
/ Missing data
/ Population structure
2018
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RADpainter and fineRADstructure: Population Inference from RADseq Data
by
Falush, Daniel
, Malinsky, Milan
, Trucchi, Emiliano
, Lawson, Daniel John
in
Algorithms
/ Clustering
/ Datasets
/ DNA sequencing
/ Gene sequencing
/ Genomes
/ Genomics
/ Haplotypes
/ Inference
/ Missing data
/ Population structure
2018
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Do you wish to request the book?
RADpainter and fineRADstructure: Population Inference from RADseq Data
by
Falush, Daniel
, Malinsky, Milan
, Trucchi, Emiliano
, Lawson, Daniel John
in
Algorithms
/ Clustering
/ Datasets
/ DNA sequencing
/ Gene sequencing
/ Genomes
/ Genomics
/ Haplotypes
/ Inference
/ Missing data
/ Population structure
2018
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RADpainter and fineRADstructure: Population Inference from RADseq Data
Journal Article
RADpainter and fineRADstructure: Population Inference from RADseq Data
2018
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Overview
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 haplotype data. With a boom in nonmodel organism genomics, there is a pressing need to bring these methods 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; last accessed February 24, 2018). Finally, with two example data sets, we illustrate its use, benefits, and robustness to missing RAD alleles in double digest RAD sequencing.
Publisher
Oxford University Press
Subject
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