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iWhale: a computational pipeline based on Docker and SCons for detection and annotation of somatic variants in cancer WES data
Whole exome sequencing (WES) is a powerful approach for discovering sequence variants in cancer cells but its time effectiveness is limited by the complexity and issues of WES data analysis. Here we present iWhale, a customizable pipeline based on Docker and SCons, reliably detecting somatic variant...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8557746/ https://www.ncbi.nlm.nih.gov/pubmed/32436933 http://dx.doi.org/10.1093/bib/bbaa065 |
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author | Binatti, Andrea Bresolin, Silvia Bortoluzzi, Stefania Coppe, Alessandro |
author_facet | Binatti, Andrea Bresolin, Silvia Bortoluzzi, Stefania Coppe, Alessandro |
author_sort | Binatti, Andrea |
collection | PubMed |
description | Whole exome sequencing (WES) is a powerful approach for discovering sequence variants in cancer cells but its time effectiveness is limited by the complexity and issues of WES data analysis. Here we present iWhale, a customizable pipeline based on Docker and SCons, reliably detecting somatic variants by three complementary callers (MuTect2, Strelka2 and VarScan2). The results are combined to obtain a single variant call format file for each sample and variants are annotated by integrating a wide range of information extracted from several reference databases, ultimately allowing variant and gene prioritization according to different criteria. iWhale allows users to conduct a complex series of WES analyses with a powerful yet customizable and easy-to-use tool, running on most operating systems (macOs, GNU/Linux and Windows). iWhale code is freely available at https://github.com/alexcoppe/iWhale and the docker image is downloadable from https://hub.docker.com/r/alexcoppe/iwhale. |
format | Online Article Text |
id | pubmed-8557746 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-85577462021-11-01 iWhale: a computational pipeline based on Docker and SCons for detection and annotation of somatic variants in cancer WES data Binatti, Andrea Bresolin, Silvia Bortoluzzi, Stefania Coppe, Alessandro Brief Bioinform Problem Solving Protocol Whole exome sequencing (WES) is a powerful approach for discovering sequence variants in cancer cells but its time effectiveness is limited by the complexity and issues of WES data analysis. Here we present iWhale, a customizable pipeline based on Docker and SCons, reliably detecting somatic variants by three complementary callers (MuTect2, Strelka2 and VarScan2). The results are combined to obtain a single variant call format file for each sample and variants are annotated by integrating a wide range of information extracted from several reference databases, ultimately allowing variant and gene prioritization according to different criteria. iWhale allows users to conduct a complex series of WES analyses with a powerful yet customizable and easy-to-use tool, running on most operating systems (macOs, GNU/Linux and Windows). iWhale code is freely available at https://github.com/alexcoppe/iWhale and the docker image is downloadable from https://hub.docker.com/r/alexcoppe/iwhale. Oxford University Press 2020-05-20 /pmc/articles/PMC8557746/ /pubmed/32436933 http://dx.doi.org/10.1093/bib/bbaa065 Text en © The Author(s) 2020. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Problem Solving Protocol Binatti, Andrea Bresolin, Silvia Bortoluzzi, Stefania Coppe, Alessandro iWhale: a computational pipeline based on Docker and SCons for detection and annotation of somatic variants in cancer WES data |
title | iWhale: a computational pipeline based on Docker and SCons for detection and annotation of somatic variants in cancer WES data |
title_full | iWhale: a computational pipeline based on Docker and SCons for detection and annotation of somatic variants in cancer WES data |
title_fullStr | iWhale: a computational pipeline based on Docker and SCons for detection and annotation of somatic variants in cancer WES data |
title_full_unstemmed | iWhale: a computational pipeline based on Docker and SCons for detection and annotation of somatic variants in cancer WES data |
title_short | iWhale: a computational pipeline based on Docker and SCons for detection and annotation of somatic variants in cancer WES data |
title_sort | iwhale: a computational pipeline based on docker and scons for detection and annotation of somatic variants in cancer wes data |
topic | Problem Solving Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8557746/ https://www.ncbi.nlm.nih.gov/pubmed/32436933 http://dx.doi.org/10.1093/bib/bbaa065 |
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