Format

Send to

Choose Destination
Nucleic Acids Res. 2020 Jan 8;48(D1):D863-D870. doi: 10.1093/nar/gkz964.

DriverDBv3: a multi-omics database for cancer driver gene research.

Author information

1
Graduate Institute of Biomedical Science, China Medical University, Taichung 40403, Taiwan.
2
Cytoaurora Biotechnologies, Inc. Hsinchu Science Park, Hsinchu 30261, Taiwan.
3
Department of Radiation Oncology, China Medical University Hospital, Taichung 40403, Taiwan.
4
Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 33302, Taiwan.
5
Department of Radiation Oncology, Chang Gung Memorial Hospital at Linkou, Taoyuan 33302, Taiwan.
6
Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan 33302, Taiwan.
7
Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.
8
Department of Medical Laboratory Science and Biotechnology, College of Health Sciences, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.
9
Center for Medical Genetics, Changhua Christian Hospital, Changhua 50006, Taiwan.
10
Institute of BioMedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan.
11
Research Center for Tumor Medical Science, China Medical University, Taichung 40403, Taiwan.

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.

PMID:
31701128
DOI:
10.1093/nar/gkz964

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center