DriverDBv4: A database for human cancer driver gene research



FAQ 1: What is different from DriverDBv4 to v3?

Introducing DriverDBv4, our latest version that brings substantial enhancements to address the evolving landscape of cancer biology research. With an expanded patient sample size, improved integration of multiple omics data, and innovative high-content visualizations, we empower researchers to gain deeper insights into the complex relationships within cancer between various omics.

This update includes five major improvements:
  1. Expanded datasets: The number of cohorts has increased from 33 to 93, resulting in a substantial rise in the sample size from approximately 12,000 to approximately 24,000.
  2. Incorporation of proteome data: In addition to the existing omics data, the proteome data has been included, expanding the scope of analysis within the database.
  3. Multi-omics integration: Eight published multi-omics tools utilizing matrix operations or dimension reduction methods have been incorporated. These tools enable the identification of multi-omics drivers in cancer by integrating heterogeneous data sources.
  4. New visualizations:
    • For original sections: Revamped figures for existing sections to accommodate the expanded datasets, such as the Mutation section in GENE.
    • For new sections: Introduction of novel visualizations to summarize high-content information and provide concise multi-omics results.
  5. Two new functions in Customized Analysis:
    • Multi-omics driver: This function facilitates the identification of multi-omics drivers associated with user-defined groups of patients.
    • Subgroup expression: This function visualizes gene expression patterns concerning specific clinical factors.

FAQ 2: 2. How do I perform and interpret network analyses in DriverDBv4?

DriverDBv4 provides several interactive network views. Below is a brief guide to each.

(a) Cancer Summary Network

What it shows:
Relationships between driver genes, functional categories (e.g., mutation, methylation), and optional interaction layers (PPI, synergistic effects).

How to use:
  1. Reset layout: click the reset button to restore the original network arrangement.
  2. Choose gene set: toggle between CGC, NCG6.0 and All to select which driver catalog to view.
  3. Select functional category: for example, turn on “Methylation drivers” to highlight driver genes associated with DNA methylation in the chosen cancer type.
  4. Add interaction layers:
    • PPI: show protein–protein interactions.
    • Synergistic effect: show synergistic relationships between driver genes and miRNA.
      Interactions are drawn as gray edges between nodes.
  5. Click “Submit” to update the network.


(b) Cancer Survival Synergy Network / Gene Survival Synergy Network

What it shows:
Relationships between genes (or gene pairs) and survival, based on hazard ratios.

How to use:
  1. Reset the network layout if needed.
  2. Choose CGC/ NCG6.0 / All to define the gene set.
  3. Select the fold difference of hazard ratio you want to focus on (e.g., strong vs moderate effects).
  4. Select the direction of the hazard ratio (whether high or low expression is associated with poor survival).
  5. Click “Submit” to apply the filters.

(c) miRNA-Gene Interaction Network

What it shows:
Predicted and validated interactions between driver genes and miRNAs.

How to use:
  1. Reset the network layout if needed.
  2. Choose CGC/ NCG6.0 / All to define the gene set.
  3. Set the minimum number of prediction tools supporting each miRNA–gene interaction (displayed by dashed edges).
  4. Choose whether to include validated interactions (shown as solid edges).
  5. Click “Submit” to update the network.

FAQ 3: 3. How do I manipulate (download/select/zoom) interactive figures?

Most plots in DriverDBv4 are interactive. You can:

Select / hide sample types
  • Click items in the legend (e.g., sample type) to hide or show those samples.
  • Clicking again toggles them back on.


Use the figure toolbar
When you move the mouse over a figure, a toolbar appears (usually at the top of the plot). Common tools:
  1. Download – save the current plot as a PNG image.
  2. Box zoom – draw a rectangle to zoom into a specific region.
  3. Zoom in – stepwise zoom in control.
  4. Zoom out – stepwise zoom out control.
  5. Reset axes – return to the original view.

These tools let you focus on specific sample subsets or export publication-ready figures.


Note: A 'Download PNG' button located at the bottom right corner of each figure provides higher-resolution images than the standard toolbar download option, making it better suited for publications or presentations.

FAQ 4: What kind of computational algorithms/tools are used in DriverDBv4?

Mutation

ActiveDriver

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

Dendrix

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

OncodriveFM

Gonzalez-Perez A and Lopez-Bigas N. 2012. Functional impact bias reveals cancer drivers. Nucleic Acids Res., 10.1093/nar/gks743.

MutSig2CV

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

e-Driver

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

MSEA

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.

OncodriveCLUST

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.

MUTEX

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

DriverML

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.

CNV

iGC

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.

diggit

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.

Methylation

Methylmix

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.

ELMER

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.

Multi-omics Integration

MOFA

Argelaguet R, Velten B, Arnol D, et al. Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol. 2018;14(6):e8124. Published 2018 Jun 20. doi:10.15252/msb.20178124.

DIABLO

Singh A, Shannon CP, Gautier B, et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics. 2019;35(17):3055-3062. doi:10.1093/bioinformatics/bty1054.

OPLSDA

Thévenot, E. A., Roux, A., Xu, Y., Ezan, E., & Junot, C. (2015). Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. Journal of Proteome Research, 14(8), 3322–3335.

SGCCA

Tenenhaus A, Philippe C, Guillemot V, Le Cao KA, Grill J, Frouin V. Variable selection for generalized canonical correlation analysis. Biostatistics. 2014;15(3):569-583. doi:10.1093/biostatistics/kxu001.

DriverSubNet

Zhang D, Bin Y. DriverSubNet: A Novel Algorithm for Identifying Cancer Driver Genes by Subnetwork Enrichment Analysis. Front Genet. 2021;11:607798. Published 2021 Feb 19. doi:10.3389/fgene.2020.607798.

SDGCCA

Moon S, Hwang J, Lee H. SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for Multi-Omics Integration. J Comput Biol. 2022;29(8):892-907. doi:10.1089/cmb.2021.0598.

dawnrank

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

drivernet

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.

comet

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.

FAQ 5: 5. How do I visualize Kaplan–Meier plots for a specific gene or gene pair in the “Survival” function?

The Survival interface contains a summary table and a KM plot panel.
Steps:
  1. Choose a survival type (OS, PFI, DFI, or DSS) and stratification method (mean or median expression).
  2. The summary table will display survival statistics (hazard ratio, p-values, etc.) for each cancer type based on the selected survival type and stratification method.
  3. Click a radio button for a row (cancer type) in the table to view the corresponding Kaplan–Meier plots.
  4. Both 5-year and all-year KM plots will display below, showing survival curves and numbers at risk for the selected gene, cancer type, survival type, and stratification method.

Use this function to quickly see how the expression of a single gene relates to patient survival across cancer types.


Citation

To support the continued development of this resource, please cite the following paper if you use DriverDBv4 in your work.

Chia-Hsin Liu , Yo-Liang Lai , Pei-Chun Shen , Hsiu-Cheng Liu , Meng-Hsin Tsai , Yu-De Wang , Wen-Jen Lin , Fang-Hsin Chen , Chia-Yang Li , Shu-Chi Wang , Mien-Chie Hung , Wei-Chung Cheng (2024).

DriverDBv4: a multi-omics integration database for cancer driver gene research. Nucleic Acids Research, 52(D1), D1246–D1252.

DOI Download citation (.nbib)

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