Wham: Identifying Structural Variants of Biological Consequence |
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| Authors: | Zev N Kronenberg Edward J Osborne Kelsey R Cone Brett J Kennedy Eric T Domyan Michael D Shapiro Nels C Elde Mark Yandell |
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| Institution: | 1. Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah, United States of America.; 2. Utah Center for Genetic Discovery, University of Utah, Salt Lake City, Utah, United States of America.; 3. Department of Biology, University of Utah, Salt Lake City, Utah, United States of America.; UCSD, UNITED STATES, |
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| Abstract: | Existing methods for identifying structural variants (SVs) from short read datasets are inaccurate. This complicates disease-gene identification and efforts to understand the consequences of genetic variation. In response, we have created Wham (Whole-genome Alignment Metrics) to provide a single, integrated framework for both structural variant calling and association testing, thereby bypassing many of the difficulties that currently frustrate attempts to employ SVs in association testing. Here we describe Wham, benchmark it against three other widely used SV identification tools–Lumpy, Delly and SoftSearch–and demonstrate Wham’s ability to identify and associate SVs with phenotypes using data from humans, domestic pigeons, and vaccinia virus. Wham and all associated software are covered under the MIT License and can be freely downloaded from github (https://github.com/zeeev/wham), with documentation on a wiki (http://zeeev.github.io/wham/). For community support please post questions to https://www.biostars.org/.This is PLOS Computational Biology software paper. |
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