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 (C3), 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. C3 is implemented in Python and is freely available for public use at http://drivergene.rwebox.com/c3.
随着第二代测序技术（Next-generation sequencing，NGS）的飞速发展，人类癌症细胞基因组中的数百万的体细胞变异（Somatic Mutation）被鉴定发现。然而，区分癌症的驱动突变（Driver Mutation）和乘客突变（Passenger Mutation）对于该领域来说仍是一个挑战。针对这一挑战，我们开发了首个基于网页应用用户友好的癌症驱动基因整合分析平台（consensus cancer driver gene caller, C3）。该平台集成了六个基于不同策略的工具获得一致而可靠的驱动基因预测结果，这些工具各有特点又相互补充。同时，我们允许用户在一定范围内调整参数，并对分析结果进行整合分析和可视化。用户可以通过http://drivergene.rwebox.com/c3访问并使用C3。





PDF全文下载地址:

http://gpb.big.ac.cn/articles/download/713