DriverDBv3: A database for human cancer driver gene research

FAQ 1: What is different from DriverDBv3 to v2?

In this updated version, our goal is to interpret cancer omics’ sophisticated information through concise data visualization. There are four major improvements in this version:

  1. We collected ~11,000 copy number variation (CNV), ~12,000 methylation, and ~11,000 smRNA-seq datasets from the public domain. Further, 3,000 RNA-seq and 2,000 exome-seq datasets have been incorporated newly into DriverDBv3.
  2. We incorporated four computational tools that define CNV and methylation drivers, as well as multiple mutation tools into our analysis pipeline.
  3. Four new features, “CNV,” “methylation,” “survival,” and “miRNA,” in the “Cancer” and “Gene” sections allow users to obtain a more comprehensive picture of the relations from two perspectives. “CNV” and “methylation” display the tool-defined drivers in various cancers. “Survival” offers not only significant survival genes, but gene pairs that have been determined to have synergistic effects. In “miRNA,” cancer-related miRNAs are gathered to depict their interactions with driver genes.
  4. A new function, “Survival Analysis” in “Customized-analysis,” allows users to investigate user-defined gene(s)’ survival significance by mutation status or by expression in a specific group of patients users can define according to dozens of clinical criteria. This new function allows users to establish the connection between molecular events and clinical practice.

Moreover, we redesigned the DriverDB web interface and provided interactive figures that allow users to explore the information when the mouse moves to specific regions of an interactive plot. Users can investigate the data from different perspectives to produce views that are informative and easy to interpret. Further, our database incorporates the cancer-related genes that are defined in CGC (21) and NCG6.0 (22) to provide better illustrations of driver gene identification and increase our interpretation’s importance.

FAQ 2: How to perform and interpret networks in DriverDBv3?

Cancer summary network

  1. To reset the arrangement to the original state.
  2. Toggle CGC/ NCG6.0 /all for selecting dataset.
  3. To select the driver genes that are related to different functions. For example, if you want to view the driver genes which are associated with DNA methylation in the cancer that you selected in previous page, you can toggle ‘Methylation drivers’.
  4. By selecting ‘PPI’, you can see the protein-protein interactions; By selecting ‘synergistic effect’, you can see the synergistic effect between driver genes and miRNA. These interactions are shown in the grey line between nodes.
  5. After setting the conditions, click ‘Submit’ button.

Cancer/Gene survival network

  1. To reset the arrangement to the original state.
  2. Toggle CGC/ NCG6.0 /all for selecting dataset.
  3. To select the difference fold of hazard ratio between two genes and single gene.
  4. To select the direction of hazard ratio which indicates the high or low expression group of the target genes result in poor survival.
  5. After setting the conditions, click ‘Submit’ button.

Cancer/Gene miRNA network

  1. To reset the arrangement to the original state.
  2. Toggle CGC/ NCG6.0 /all for selecting dataset.
  3. To select number of prediction tools, which shown by the dash line in the edge of the network.
  4. To select validated interactions between miRNA and gene or not, which shown by the solid line in the edge of the network.
  5. After setting the conditions, click ‘Submit’ button.

FAQ 3: How to manipulate (download/select/zoom) interactive figures in DriverDBv3?

Download the figure:

Hover over the interactive figures to show tool bar. Press the camera icon on the far left.

Select specific sample types:

By clicking the sample types of the legend, the specific sample types will be hid. By clicking the sample types again, the selected sample types will be shown on the plot.

Adjust your zoom level:

  1. By clicking the second button from the left, users can circle the area that you want to zoom in.
  2. The ‘Plus’ button is for zoom in, while the ‘minus’ button is for zoom out.
  3. If you want to back to the original setting, press the house-like button to reset it.

FAQ 4: What kind of computational algorithms/tools are used in DriverDBv3?



Reimand J, Bader GD. Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Mol Syst Biol. 2013;9:637.


Vandin F, Upfal E, Raphael BJ. De novo discovery of mutated driver pathways in cancer. Genome Res. 2012 Feb;22(2):375-85.


Cerami E, Demir E, Schultz N, Taylor BS, Sander C. Automated network analysis identifies core pathways in glioblastoma. PLoS One. 2010 Feb 12;5(2):e8918.


Lawrence MS et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013 Jul 11;499(7457):214-218.


Ciriello G, Cerami E, Sander C, Schultz N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res. 2012 Feb;22(2):398-406.


Hou JP, Ma J. DawnRank: discovering personalized driver genes in cancer. Genome Med. 2014 Jul 31;6(7):56.


Bashashati A, Haffari G, Ding J, Ha G, Lui K, Rosner J, Huntsman DG, Caldas C, Aparicio SA, Shah SP. DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer. Genome Biol. 2012 Dec 22;13(12):R124.


Porta-Pardo E, Godzik A. e-Driver: a novel method to identify protein regions driving cancer. Bioinformatics. 2014 Nov 1;30(21):3109-14.


Ryslik GA, Cheng Y, Cheung KH, Modis Y, Zhao H. Utilizing protein structure to identify non-random somatic mutations. BMC Bioinformatics. 2013 Jun 13;14:190.


Jia P, Wang Q, Chen Q, Hutchinson KE, Pao W, Zhao Z. MSEA: detection and quantification of mutation hotspots through mutation set enrichment analysis. Genome Biol. 2014;15(10):489.


Tamborero D, Gonzalez-Perez A, Lopez-Bigas N. OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics. 2013 Sep 15;29(18):2238-44.


Leiserson MD, Wu HT, Vandin F, Raphael BJ. CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer. Genome Biol. 2015 Aug 8;16:160.


Babur, Özgün, et al. Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations. Genome biology 16.1 (2015): 45.


Han Y et al. DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies. Nucleic Acids Res. 2019 May 7;47(8):e45.



Lai, Y.P., Wang, L.B., Wang, W.A., Lai, L.C., Tsai, M.H., Lu, T.P. and Chuang, E.Y. (2017) iGC-an integrated analysis package of gene expression and copy number alteration. BMC Bioinformatics, 18, 35.


Alvarez, M.J., Chen, J.C. and Califano, A. (2015) DIGGIT: a Bioconductor package to infer genetic variants driving cellular phenotypes. Bioinformatics, 31, 4032-4034.



Cedoz, P.L., Prunello, M., Brennan, K. and Gevaert, O. (2018) MethylMix 2.0: an R package for identifying DNA methylation genes. Bioinformatics, 34, 3044-3046.


Silva, T.C., Coetzee, S.G., Gull, N., Yao, L., Hazelett, D.J., Noushmehr, H., Lin, D.C. and Berman, B.P. (2019) ELMER v.2: an R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles. Bioinformatics, 35, 1974-1977.

FAQ 5: How to visualize the Kaplan Meier plot of the specific gene/gene pair in ‘Survival’ function?

This table provides detailed survival information of a single gene in terms of hazard ratio (HR) and also serves as a control panel to produce the Kaplan-Meier plot for visualization. Select the desired cancer type on the table to generate a corresponding Kaplan-Meier plot below. Cancer types are abbreviated to OS (Overall survival), DFI (disease-free interval), PFI (progression-free interval), and DSS (disease-specific survival). The cut-off value can be either mean or median for analyses and plots.

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