Abstract
SummaryAncient whole-genome duplications (WGDs) have been uncovered in almost all major lineages of life on Earth and the search for traces or remnants of such events has become standard practice in most genome analyses. This is especially true for plants, where ancient WGDs are abundant. Common approaches to find evidence for ancient WGDs include the construction of KS distributions and the analysis of intragenomic colinearity. Despite the increased interest in WGDs and the acknowledgment of their evolutionary importance, user-friendly and comprehensive tools for their analysis are lacking. Here, we present an easy to use command-line tool for KS distribution construction named wgd. The wgd suite provides commonly used KS and colinearity analysis workflows together with tools for modeling and visualization, rendering these analyses accessible to genomics researchers in a convenient manner.Availability and implementationwgd is free and open source software implemented in Python and is available at https://github.com/arzwa/wgd.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
In this era of whole-genome sequencing, many ancient whole-genome duplication (WGD) events have been uncovered across the eukaryotic tree of life (Van de Peer et al, 2017)
One of the main approaches for revealing ancient WGDs using genomic data is the construction of whole paranome KS distributions (e.g. Blanc and Wolfe, 2004; Cui et al, 2006; Lynch and Conery, 2000; Vanneste et al, 2013), where KS is the synonymous distance or the estimated number of synonymous substitutions per synonymous site
Several issues compromise the use of KS distributions for WGD inference, and these were extensively addressed in Vanneste et al (2013)
Summary
In this era of whole-genome sequencing, many ancient whole-genome duplication (WGD) events have been uncovered across the eukaryotic tree of life (Van de Peer et al, 2017). WGDs are expected to leave large blocks with high intragenomic colinearity, and paralogs located in such colinear segments (anchor pairs) can be traced back more reliably to a particular event, enabling their use for downstream analyses such as molecular dating (Vanneste et al, 2014) or functional analysis. While these methods have been used frequently in genomics research, no comprehensive and user-friendly software is available to perform these analyses, and researchers have often resorted to custom pipelines. If a KS distribution is provided, KS-colored dotplots and anchor pair KS distributions are generated (Fig. 1)
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