ConsensusPathDB

ConsensusPathDB integrates molecular interaction, biochemical pathway, and regulatory data to enable functional interpretation and network-based analysis of genes/proteins, metabolites, and drugs.

Key Features:

• Extensive Interaction Database: Integrates diverse molecular interactions from 31 human, 16 mouse, and 14 yeast databases, comprising over 859,848 human molecular interactions connecting 200,499 unique physical entities including genes/proteins, metabolites, and drugs.
• Pathway Analysis: Incorporates pathway data from 30 distinct sources totaling 46,601 pathways and supports pathway analysis of metabolite lists with visualization of functional gene and metabolite sets as overlap graphs.
• Gene Set and Network Analysis: Enables gene set analyses based on protein complexes and induced network modules and uses an integrated protein-protein interaction network as a scaffold for propagation methods and molecular neighborhood retrieval.
• Regulatory Interaction Integration: Includes regulatory datasets comprising transcription factor-, microRNA-, and enhancer-gene target interactions to inform overrepresentation and enrichment analyses.
• Advanced Visualization Tools: Implements graph visualization using the Cytoscape.js library and supports analysis of network topologies and inference of network characteristics from the integrated protein-protein interaction network.
• Confidence Assessment: Scores binary protein interactions with the IntScore tool to provide confidence assessments for interaction data.

Scientific Applications:

• Functional interpretation of gene lists: Performs overrepresentation and enrichment analyses using integrated pathway and interaction data.
• Network-based analysis of proteins and metabolites: Applies propagation methods, induced network modules, and protein-protein interaction scaffolds to retrieve molecular neighborhoods and analyze network topology.
• Exploration of regulatory networks: Analyzes regulatory relationships by integrating transcription factor, microRNA, and enhancer-gene target interaction datasets.

Methodology:

Integrates interaction and pathway data from multiple public databases (31 human, 16 mouse, 14 yeast; 30 pathway sources), incorporates regulatory datasets (transcription factor, microRNA, enhancer-gene targets), scores binary protein interactions with IntScore, and implements pathway/metabolite analyses, propagation methods, induced network modules, and overlap-graph visualizations using Cytoscape.js.