Publication | Open Access
DriverDBv3: a multi-omics database for cancer driver gene research
191
Citations
40
References
2019
Year
Cancer research requires integrative multi‑omics databases, as single‑dimension analyses are insufficient, prompting the development of DriverDB. DriverDBv3 aims to enhance integrative cancer omics research by identifying driver genes and advancing cancer biology. DriverDBv3 incorporates CNV, methylation, survival, and miRNA analyses, offers interactive visualizations and survival panels, and provides a redesigned interface with summary panels to facilitate multi‑omics exploration.
Abstract An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics’ sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the ‘Cancer’ and ‘Gene’ sections. The ‘Survival’ panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, ‘Survival Analysis’ in ‘Customized-analysis,’ allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics’ sophisticated information, and also constructed a Summary panel in the ‘Cancer’ and ‘Gene’ sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.
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