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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
Descripción
Sumario: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.