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Lost in parameter space: a road map for stacks
Author(s) -
Paris Josephine R.,
Stevens Jamie R.,
Catchen Julian M.
Publication year - 2017
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12775
Subject(s) - computer science , software , set (abstract data type) , visualization , data mining , sequence assembly , computational biology , biology , genetics , gene expression , transcriptome , gene , programming language
Summary Restriction site‐Associated DNA sequencing ( RAD ‐seq) has become a widely adopted method for genotyping populations of model and non‐model organisms. Generating a reliable set of loci for downstream analysis requires appropriate use of bioinformatics software, such as the program stacks . Using three empirical RAD ‐seq datasets, we demonstrate a method for optimising a de novo assembly of loci using stacks . By iterating values of the program's main parameters and plotting resultant core metrics for visualisation, researchers can gain a much better understanding of their dataset and select an optimal set of parameters; we present the 80% rule as a generally effective method to select the core parameters for stacks . Visualisation of the metrics plotted for the three RAD ‐seq datasets shows that they differ in the optimal parameters that should be used to maximise the amount of available biological information. We also demonstrate that building loci de novo and then integrating alignment positions is more effective than aligning raw reads directly to a reference genome. Our methods will help the community in honing the analytical skills necessary to accurately assemble a RAD ‐seq dataset.