DriverDBv2: a database for human cancer driver gene research.
DriverDBv2: a database for human cancer driver gene research.
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DOI：
10.1093/nar/gkv1314
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发表时间：
2016-01-04
影响因子：
14.9
通讯作者：
Cheng WC
中科院分区：
文献类型：
--
作者：
Chung IF;Chen CY;Su SC;Li CY;Wu KJ;Wang HW;Cheng WC
我们之前介绍了DriverDB，这是一个数据库，除了注释数据库和已发表的生物信息学算法之外，还包含了20000例exome-seq数据，专门用于驱动基因/突变识别。该数据库提供了“癌症”和“基因”两个视角，以帮助研究人员可视化癌症与驱动基因/突变之间的关系。在本文提供的更新的DriverDBv 2数据库（http：//ngs.ym.edu.tw/driverdb）中，我们结合了来自癌症基因组图谱（TCGA）、国际癌症基因组联盟（ICGC）和已发表论文的>9500个癌症相关的RNA-seq数据集和>7000个外显子组-seq数据集。七个额外的计算算法（这意味着更新后的数据库总共包含15个），这些算法是为驱动基因识别而开发的，被纳入我们的分析流程，结果在“癌症”部分提供。此外，还有两个主要的新功能，“表达”和“热点”，在“基因”部分。“表达”显示基因的两个表达谱，分别根据样品类型和突变类型。“热点”表示根据四种生物信息学工具提供的结果，基因的热点突变区域。一个新的功能，“基因集”，允许用户调查一组基因，一个特定的数据集和临床特征的突变，表达水平和临床数据之间的关系。
We previously presented DriverDB, a database that incorporates ∼6000 cases of exome-seq data, in addition to annotation databases and published bioinformatics algorithms dedicated to driver gene/mutation identification. The database provides two points of view, ‘Cancer’ and ‘Gene’, to help researchers visualize the relationships between cancers and driver genes/mutations. In the updated DriverDBv2 database (http://ngs.ym.edu.tw/driverdb) presented herein, we incorporated >9500 cancer-related RNA-seq datasets and >7000 more exome-seq datasets from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and published papers. Seven additional computational algorithms (meaning that the updated database contains 15 in total), which were developed for driver gene identification, are incorporated into our analysis pipeline, and the results are provided in the ‘Cancer’ section. Furthermore, there are two main new features, ‘Expression’ and ‘Hotspot’, in the ‘Gene’ section. ‘Expression’ displays two expression profiles of a gene in terms of sample types and mutation types, respectively. ‘Hotspot’ indicates the hotspot mutation regions of a gene according to the results provided by four bioinformatics tools. A new function, ‘Gene Set’, allows users to investigate the relationships among mutations, expression levels and clinical data for a set of genes, a specific dataset and clinical features.