DriverDBv4

DriverDBv4 provides integrative analysis of multi-omics cancer datasets to identify and prioritize cancer driver genes across somatic mutations, RNA expression, miRNA expression, DNA methylation, copy number variation, proteomics, and clinical data.

Key Features:

• Extensive Data Coverage: Expanded cohort coverage from 33 to 70 cohorts comprising approximately 24,000 samples and including somatic mutations, RNA expression, miRNA expression, methylation profiles, copy number variations, clinical data, and proteomics.
• Multi-Omics Integration: Integrates multiple molecular layers (mutations, transcriptomics, miRNA, methylation, CNV, proteomics, clinical) to provide a holistic molecular view for cancer analysis.
• Advanced Analytical Tools: Employs various multi-omics algorithms specifically designed to identify and prioritize cancer driver genes from high-dimensional datasets.
• Enhanced Visualization Features: Implements visualization approaches to summarize complex multi-omics contexts and relationships across data types.
• Customized Analysis Functions: Provides two Customized Analysis functions: multi-omics driver identification and subgroup expression analysis.
• Facilitating Personalized Medicine: Enables cross-cohort multi-omics interpretation to inform cancer heterogeneity and support targeted therapeutic investigation.

Scientific Applications:

• Driver Gene Identification: Identification and prioritization of cancer driver genes through integrated analysis of mutations, expression, methylation, CNV, proteomics, and clinical data.
• Cancer Heterogeneity Characterization: Comparative multi-omics analyses across cohorts and subgroups to reveal molecular heterogeneity within and between cancer types.
• Precision Oncology Support: Integration of multi-omics and clinical data to inform subgroup-specific molecular profiles relevant to targeted therapies.

Methodology:

Integrates somatic mutation, RNA expression, miRNA expression, DNA methylation, copy number variation, proteomics, and clinical datasets and applies multi-omics algorithms for cancer-driver identification and subgroup expression analysis.