Pediatric Neurosurgery, Department of Surgery, Cheng Hsin General Hospital, Taipei 11220, Taiwan, VGH-YM Genomic Research Center, National Yang-Ming University, Taipei 11221, Taiwan, Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan, Information Technology Office, Taipei Veterans General Hospital, Taipei 11217, Taiwan, Institute of Microbiology and Immunology, National Yang-Ming University, Taipei 11221, Taiwan and Department of Education and Research, Taipei City Hospital, Taipei 10341, Taiwan.
Nucleic Acids Res. 2014 Jan;42(Database issue):D1048-54. doi: 10.1093/nar/gkt1025. Epub 2013 Nov 7.
Exome sequencing (exome-seq) has aided in the discovery of a huge amount of mutations in cancers, yet challenges remain in converting oncogenomics data into information that is interpretable and accessible for clinical care. We constructed DriverDB (http://ngs.ym.edu.tw/driverdb/), a database which incorporates 6079 cases of exome-seq data, annotation databases (such as dbSNP, 1000 Genome and Cosmic) and published bioinformatics algorithms dedicated to driver gene/mutation identification. We provide two points of view, 'Cancer' and 'Gene', to help researchers to visualize the relationships between cancers and driver genes/mutations. The 'Cancer' section summarizes the calculated results of driver genes by eight computational methods for a specific cancer type/dataset and provides three levels of biological interpretation for realization of the relationships between driver genes. The 'Gene' section is designed to visualize the mutation information of a driver gene in five different aspects. Moreover, a 'Meta-Analysis' function is provided so researchers may identify driver genes in customer-defined samples. The novel driver genes/mutations identified hold potential for both basic research and biotech applications.
外显子组测序(exome-seq)已经帮助发现了大量癌症中的突变,但将肿瘤基因组学数据转化为可解释和可用于临床护理的信息仍然存在挑战。我们构建了 DriverDB(http://ngs.ym.edu.tw/driverdb/),这是一个数据库,其中包含 6079 例外显子组测序数据、注释数据库(如 dbSNP、1000 基因组和宇宙)以及专门用于驱动基因/突变识别的已发表生物信息学算法。我们提供了“癌症”和“基因”两个视角,以帮助研究人员可视化癌症和驱动基因/突变之间的关系。“癌症”部分总结了针对特定癌症类型/数据集的八种计算方法计算出的驱动基因的结果,并提供了三个层次的生物学解释,以实现驱动基因之间的关系。“基因”部分旨在从五个不同方面可视化驱动基因的突变信息。此外,还提供了“Meta-Analysis”功能,以便研究人员可以在客户定义的样本中识别驱动基因。新发现的驱动基因/突变可能对基础研究和生物技术应用都有潜在价值。