Wham: Identifying Structural Variants of Biological Consequence

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, in...

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Main Authors:	Kronenberg, Zev N., Osborne, Edward J., Cone, Kelsey R., Kennedy, Brett J., Domyan, Eric T., Shapiro, Michael D., Elde, Nels C., Yandell, Mark
Format:	Online
Language:	English
Published:	Public Library of Science 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4666669/

id	pubmed-4666669
recordtype	oai_dc
spelling	pubmed-46666692015-12-10 Wham: Identifying Structural Variants of Biological Consequence Kronenberg, Zev N. Osborne, Edward J. Cone, Kelsey R. Kennedy, Brett J. Domyan, Eric T. Shapiro, Michael D. Elde, Nels C. Yandell, Mark Research Article 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/. Public Library of Science 2015-12-01 /pmc/articles/PMC4666669/ /pubmed/26625158 http://dx.doi.org/10.1371/journal.pcbi.1004572 Text en © 2015 Kronenberg et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Kronenberg, Zev N. Osborne, Edward J. Cone, Kelsey R. Kennedy, Brett J. Domyan, Eric T. Shapiro, Michael D. Elde, Nels C. Yandell, Mark
spellingShingle	Kronenberg, Zev N. Osborne, Edward J. Cone, Kelsey R. Kennedy, Brett J. Domyan, Eric T. Shapiro, Michael D. Elde, Nels C. Yandell, Mark Wham: Identifying Structural Variants of Biological Consequence
author_facet	Kronenberg, Zev N. Osborne, Edward J. Cone, Kelsey R. Kennedy, Brett J. Domyan, Eric T. Shapiro, Michael D. Elde, Nels C. Yandell, Mark
author_sort	Kronenberg, Zev N.
title	Wham: Identifying Structural Variants of Biological Consequence
title_short	Wham: Identifying Structural Variants of Biological Consequence
title_full	Wham: Identifying Structural Variants of Biological Consequence
title_fullStr	Wham: Identifying Structural Variants of Biological Consequence
title_full_unstemmed	Wham: Identifying Structural Variants of Biological Consequence
title_sort	wham: identifying structural variants of biological consequence
description	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/.
publisher	Public Library of Science
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4666669/
_version_	1613508564408598528

Wham: Identifying Structural Variants of Biological Consequence

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