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C(3): Consensus Cancer Driver Gene Caller
Next-generation sequencing has allowed identification of millions of somatic mutations in human cancer cells. A key challenge in interpreting cancer genomes is to distinguish drivers of cancer development among available genetic mutations. To address this issue, we present the first web-based applic...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6818389/ https://www.ncbi.nlm.nih.gov/pubmed/31465854 http://dx.doi.org/10.1016/j.gpb.2018.10.004 |
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author | Zhu, Chen-Yu Zhou, Chi Chen, Yun-Qin Shen, Ai-Zong Guo, Zong-Ming Yang, Zhao-Yi Ye, Xiang-Yun Qu, Shen Wei, Jia Liu, Qi |
author_facet | Zhu, Chen-Yu Zhou, Chi Chen, Yun-Qin Shen, Ai-Zong Guo, Zong-Ming Yang, Zhao-Yi Ye, Xiang-Yun Qu, Shen Wei, Jia Liu, Qi |
author_sort | Zhu, Chen-Yu |
collection | PubMed |
description | Next-generation sequencing has allowed identification of millions of somatic mutations in human cancer cells. A key challenge in interpreting cancer genomes is to distinguish drivers of cancer development among available genetic mutations. To address this issue, we present the first web-based application, consensus cancer driver gene caller (C(3)), to identify the consensus driver genes using six different complementary strategies, i.e., frequency-based, machine learning-based, functional bias-based, clustering-based, statistics model-based, and network-based strategies. This application allows users to specify customized operations when calling driver genes, and provides solid statistical evaluations and interpretable visualizations on the integration results. C(3) is implemented in Python and is freely available for public use at http://drivergene.rwebox.com/c3. |
format | Online Article Text |
id | pubmed-6818389 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-68183892019-11-01 C(3): Consensus Cancer Driver Gene Caller Zhu, Chen-Yu Zhou, Chi Chen, Yun-Qin Shen, Ai-Zong Guo, Zong-Ming Yang, Zhao-Yi Ye, Xiang-Yun Qu, Shen Wei, Jia Liu, Qi Genomics Proteomics Bioinformatics Application Note Next-generation sequencing has allowed identification of millions of somatic mutations in human cancer cells. A key challenge in interpreting cancer genomes is to distinguish drivers of cancer development among available genetic mutations. To address this issue, we present the first web-based application, consensus cancer driver gene caller (C(3)), to identify the consensus driver genes using six different complementary strategies, i.e., frequency-based, machine learning-based, functional bias-based, clustering-based, statistics model-based, and network-based strategies. This application allows users to specify customized operations when calling driver genes, and provides solid statistical evaluations and interpretable visualizations on the integration results. C(3) is implemented in Python and is freely available for public use at http://drivergene.rwebox.com/c3. Elsevier 2019-06 2019-08-26 /pmc/articles/PMC6818389/ /pubmed/31465854 http://dx.doi.org/10.1016/j.gpb.2018.10.004 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Application Note Zhu, Chen-Yu Zhou, Chi Chen, Yun-Qin Shen, Ai-Zong Guo, Zong-Ming Yang, Zhao-Yi Ye, Xiang-Yun Qu, Shen Wei, Jia Liu, Qi C(3): Consensus Cancer Driver Gene Caller |
title | C(3): Consensus Cancer Driver Gene Caller |
title_full | C(3): Consensus Cancer Driver Gene Caller |
title_fullStr | C(3): Consensus Cancer Driver Gene Caller |
title_full_unstemmed | C(3): Consensus Cancer Driver Gene Caller |
title_short | C(3): Consensus Cancer Driver Gene Caller |
title_sort | c(3): consensus cancer driver gene caller |
topic | Application Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6818389/ https://www.ncbi.nlm.nih.gov/pubmed/31465854 http://dx.doi.org/10.1016/j.gpb.2018.10.004 |
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