Identification of Cancer Driver Genes from a Custom Set of Next Generation Sequencing Data. [PMID: 30542988]
Shu-Hsuan Liu, Wei-Chung Cheng
Next generation sequencing (NGS) has become the norm of cancer genomic researches. Large-scale cancer sequencing projects seek to comprehensively uncover mutated genes that confer a selective advantage for cancer cells. Numerous computational algorithms have been developed to find genes that drive cancer based on their patterns of mutation in a patient cohort. It has been noted that the distinct features of driver gene alterations in different subgroups are based on clinical characteristics. Previously, we have developed a database, DriverDB, to integrate all public cancer sequencing data and to identify cancer driver genes according to bioinformatics tools. In this chapter, we describe the use of the function "Meta-Analysis" in DriverDB that offers a list of clinical characteristics to define samples and provides a high degree of freedom for researchers to utilize the huge amounts of sequencing data. Moreover, researchers can use the "Gene" section to explore a single driver gene in all cancers by different kinds of aspects after identifying the specific driver genes by "Meta-Analysis." DriverDB is available at http://ngs.ym.edu.tw/driverdb/ .
Methods Mol Biol 2019:1907()
0 Citations (from Europe PMC, 2019-06-08)
DriverDBv2: a database for human cancer driver gene research. [PMID: 26635391]
I-Fang Chung, Chen-Yang Chen, Shih-Chieh Su, Chia-Yang Li, Kou-Juey Wu, Hsei-Wei Wang, Wei-Chung Cheng
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. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
Nucleic Acids Res 2016:44(D1)
21 Citations (from Europe PMC, 2019-06-15)
DriverDB: an exome sequencing database for cancer driver gene identification. [PMID: 24214964]
Wei-Chung Cheng, I-Fang Chung, Chen-Yang Chen, Hsing-Jen Sun, Jun-Jeng Fen, Wei-Chun Tang, Ting-Yu Chang, Tai-Tong Wong, Hsei-Wei Wang
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.
Nucleic Acids Res 2014:42(Database issue)
29 Citations (from Europe PMC, 2019-06-15)