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Viola: a structural variant signature extractor with user-defined classifications

SUMMARY: Here, we present Viola, a Python package that provides structural variant (SV; large scale genome DNA variations that can result in disease, e.g. cancer) signature analytical functions and utilities for custom SV classification, merging multi-SV-caller output files and SV annotation. We dem...

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Detalles Bibliográficos
Autores principales: Sugita, Itsuki, Matsuyama, Shohei, Dobashi, Hiroki, Komura, Daisuke, Ishikawa, Shumpei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8723148/
https://www.ncbi.nlm.nih.gov/pubmed/34534268
http://dx.doi.org/10.1093/bioinformatics/btab662
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author Sugita, Itsuki
Matsuyama, Shohei
Dobashi, Hiroki
Komura, Daisuke
Ishikawa, Shumpei
author_facet Sugita, Itsuki
Matsuyama, Shohei
Dobashi, Hiroki
Komura, Daisuke
Ishikawa, Shumpei
author_sort Sugita, Itsuki
collection PubMed
description SUMMARY: Here, we present Viola, a Python package that provides structural variant (SV; large scale genome DNA variations that can result in disease, e.g. cancer) signature analytical functions and utilities for custom SV classification, merging multi-SV-caller output files and SV annotation. We demonstrate that Viola can extract biologically meaningful SV signatures from publicly available SV data for cancer and we evaluate the computational time necessary for annotation of the data. AVAILABILITY AND IMPLEMENTATION: Viola is available on pip (https://pypi.org/project/Viola-SV/) and the source code is on GitHub (https://github.com/dermasugita/Viola-SV). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-87231482022-01-05 Viola: a structural variant signature extractor with user-defined classifications Sugita, Itsuki Matsuyama, Shohei Dobashi, Hiroki Komura, Daisuke Ishikawa, Shumpei Bioinformatics Applications Notes SUMMARY: Here, we present Viola, a Python package that provides structural variant (SV; large scale genome DNA variations that can result in disease, e.g. cancer) signature analytical functions and utilities for custom SV classification, merging multi-SV-caller output files and SV annotation. We demonstrate that Viola can extract biologically meaningful SV signatures from publicly available SV data for cancer and we evaluate the computational time necessary for annotation of the data. AVAILABILITY AND IMPLEMENTATION: Viola is available on pip (https://pypi.org/project/Viola-SV/) and the source code is on GitHub (https://github.com/dermasugita/Viola-SV). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-09-17 /pmc/articles/PMC8723148/ /pubmed/34534268 http://dx.doi.org/10.1093/bioinformatics/btab662 Text en © The Author(s) 2021. 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 (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 Applications Notes
Sugita, Itsuki
Matsuyama, Shohei
Dobashi, Hiroki
Komura, Daisuke
Ishikawa, Shumpei
Viola: a structural variant signature extractor with user-defined classifications
title Viola: a structural variant signature extractor with user-defined classifications
title_full Viola: a structural variant signature extractor with user-defined classifications
title_fullStr Viola: a structural variant signature extractor with user-defined classifications
title_full_unstemmed Viola: a structural variant signature extractor with user-defined classifications
title_short Viola: a structural variant signature extractor with user-defined classifications
title_sort viola: a structural variant signature extractor with user-defined classifications
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8723148/
https://www.ncbi.nlm.nih.gov/pubmed/34534268
http://dx.doi.org/10.1093/bioinformatics/btab662
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