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 availab...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 webbased 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.展开更多
基金supported by the National Major Research and Innovation Program of China(Grant Nos.2017YFC0908500and 2016YFC1303205)National Natural Science Foundation of China(Grant No.61572361)+2 种基金Shanghai Rising-Star Program(Grant No.16QA1403900)Shanghai Natural Science Foundation Program(Grant No.17ZR1449400)Fundamental Research Funds for the Central Universities(Grant No.1501219106),China
文摘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 webbased 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.
