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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...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
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.
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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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