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sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data
Structural variants (SVs) are an important class of genetic variation implicated in a wide array of genetic diseases including cancer. Despite the advances in whole genome sequencing, comprehensive and accurate detection of SVs in short-read data still poses some practical and computational challeng...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6951283/ https://www.ncbi.nlm.nih.gov/pubmed/31934500 http://dx.doi.org/10.7717/peerj.8214 |
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author | Kuzniar, Arnold Maassen, Jason Verhoeven, Stefan Santuari, Luca Shneider, Carl Kloosterman, Wigard P. de Ridder, Jeroen |
author_facet | Kuzniar, Arnold Maassen, Jason Verhoeven, Stefan Santuari, Luca Shneider, Carl Kloosterman, Wigard P. de Ridder, Jeroen |
author_sort | Kuzniar, Arnold |
collection | PubMed |
description | Structural variants (SVs) are an important class of genetic variation implicated in a wide array of genetic diseases including cancer. Despite the advances in whole genome sequencing, comprehensive and accurate detection of SVs in short-read data still poses some practical and computational challenges. We present sv-callers, a highly portable workflow that enables parallel execution of multiple SV detection tools, as well as provide users with example analyses of detected SV callsets in a Jupyter Notebook. This workflow supports easy deployment of software dependencies, configuration and addition of new analysis tools. Moreover, porting it to different computing systems requires minimal effort. Finally, we demonstrate the utility of the workflow by performing both somatic and germline SV analyses on different high-performance computing systems. |
format | Online Article Text |
id | pubmed-6951283 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-69512832020-01-13 sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data Kuzniar, Arnold Maassen, Jason Verhoeven, Stefan Santuari, Luca Shneider, Carl Kloosterman, Wigard P. de Ridder, Jeroen PeerJ Bioinformatics Structural variants (SVs) are an important class of genetic variation implicated in a wide array of genetic diseases including cancer. Despite the advances in whole genome sequencing, comprehensive and accurate detection of SVs in short-read data still poses some practical and computational challenges. We present sv-callers, a highly portable workflow that enables parallel execution of multiple SV detection tools, as well as provide users with example analyses of detected SV callsets in a Jupyter Notebook. This workflow supports easy deployment of software dependencies, configuration and addition of new analysis tools. Moreover, porting it to different computing systems requires minimal effort. Finally, we demonstrate the utility of the workflow by performing both somatic and germline SV analyses on different high-performance computing systems. PeerJ Inc. 2020-01-06 /pmc/articles/PMC6951283/ /pubmed/31934500 http://dx.doi.org/10.7717/peerj.8214 Text en © 2020 Kuzniar et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Kuzniar, Arnold Maassen, Jason Verhoeven, Stefan Santuari, Luca Shneider, Carl Kloosterman, Wigard P. de Ridder, Jeroen sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data |
title | sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data |
title_full | sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data |
title_fullStr | sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data |
title_full_unstemmed | sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data |
title_short | sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data |
title_sort | sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6951283/ https://www.ncbi.nlm.nih.gov/pubmed/31934500 http://dx.doi.org/10.7717/peerj.8214 |
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