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OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers
The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.o...
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/PMC7778899/ https://www.ncbi.nlm.nih.gov/pubmed/33179738 http://dx.doi.org/10.1093/nar/gkaa1033 |
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author | Wang, Tao Ruan, Shasha Zhao, Xiaolu Shi, Xiaohui Teng, Huajing Zhong, Jianing You, Mingcong Xia, Kun Sun, Zhongsheng Mao, Fengbiao |
author_facet | Wang, Tao Ruan, Shasha Zhao, Xiaolu Shi, Xiaohui Teng, Huajing Zhong, Jianing You, Mingcong Xia, Kun Sun, Zhongsheng Mao, Fengbiao |
author_sort | Wang, Tao |
collection | PubMed |
description | The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, ‘Mutation’, ‘Gene’, ‘Pathway’ and ‘Cancer’, to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine. |
format | Online Article Text |
id | pubmed-7778899 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77788992021-01-06 OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers Wang, Tao Ruan, Shasha Zhao, Xiaolu Shi, Xiaohui Teng, Huajing Zhong, Jianing You, Mingcong Xia, Kun Sun, Zhongsheng Mao, Fengbiao Nucleic Acids Res Database Issue The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, ‘Mutation’, ‘Gene’, ‘Pathway’ and ‘Cancer’, to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine. Oxford University Press 2020-11-12 /pmc/articles/PMC7778899/ /pubmed/33179738 http://dx.doi.org/10.1093/nar/gkaa1033 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. http://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/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Database Issue Wang, Tao Ruan, Shasha Zhao, Xiaolu Shi, Xiaohui Teng, Huajing Zhong, Jianing You, Mingcong Xia, Kun Sun, Zhongsheng Mao, Fengbiao OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers |
title | OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers |
title_full | OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers |
title_fullStr | OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers |
title_full_unstemmed | OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers |
title_short | OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers |
title_sort | oncovar: an integrated database and analysis platform for oncogenic driver variants in cancers |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7778899/ https://www.ncbi.nlm.nih.gov/pubmed/33179738 http://dx.doi.org/10.1093/nar/gkaa1033 |
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