Defining loci in restriction-based reduced representation genomic data from nonmodel species: sources of bias and diagnostics for optimal clustering.

ABSTRACT: Next generation sequencing holds great promise for applications of phylogeography, landscape genetics, and population genomics in wild populations of nonmodel species, but the robustness of inferences hinges on careful experimental design and effective bioinformatic removal of predictable artifacts. Addressing this issue, we use published genomes from a tunicate, stickleback, and soybean to illustrate the potential for bioinformatic artifacts and introduce a protocol to minimize two sources of error expected from similarity-based de-novo clustering of stacked reads: the splitting of alleles into different clusters, which creates false homozygosity, and the grouping of paralogs into the same cluster, which creates false heterozygosity. We present an empirical application focused on Ciona sa

SUBMITTER: Ilut DC 

PROVIDER: S-EPMC4095725 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature