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. 2013 Jan;41(Database issue):D793-800.
doi: 10.1093/nar/gks1055. Epub 2012 Nov 11.

The ConsensusPathDB Interaction Database: 2013 Update

Free PMC article

The ConsensusPathDB Interaction Database: 2013 Update

Atanas Kamburov et al. Nucleic Acids Res. .
Free PMC article


Knowledge of the various interactions between molecules in the cell is crucial for understanding cellular processes in health and disease. Currently available interaction databases, being largely complementary to each other, must be integrated to obtain a comprehensive global map of the different types of interactions. We have previously reported the development of an integrative interaction database called ConsensusPathDB ( that aims to fulfill this task. In this update article, we report its significant progress in terms of interaction content and web interface tools. ConsensusPathDB has grown mainly due to the integration of 12 further databases; it now contains 215 541 unique interactions and 4601 pathways from overall 30 databases. Binary protein interactions are scored with our confidence assessment tool, IntScore. The ConsensusPathDB web interface allows users to take advantage of these integrated interaction and pathway data in different contexts. Recent developments include pathway analysis of metabolite lists, visualization of functional gene/metabolite sets as overlap graphs, gene set analysis based on protein complexes and induced network modules analysis that connects a list of genes through various interaction types. To facilitate the interactive, visual interpretation of interaction and pathway data, we have re-implemented the graph visualization feature of ConsensusPathDB using the Cytoscape.js library.


Figure 1.
Figure 1.
Histogram of the number of source databases per interaction in ConsensusPathDB.
Figure 2.
Figure 2.
Functional gene set overlap graph summarizing predefined gene sets (and their pairwise overlaps) that are over-represented in an input list of 410 genes differentially expressed after treatment of human hepatocite-like cells with the genotoxic chemical benzo[a]pyrene. Benzo[a]pyrene causes mutations in the DNA and leads to carcinogenesis (38). Each node in the overlap graph is a predefined gene set (blue label: curated pathway, purple label: Gene Ontology category, green label: NEST and orange label: protein complex). The node size reflects the size of the gene set and the node color—its P-value (deeper red means smaller P-value). Each edge denotes an overlap between gene sets (i.e. shared genes). The edge width reflects the size of the overlap and its color reflects the number of genes/metabolites from the input list that are contained in the overlap. Details are shown in tooltips.
Figure 3.
Figure 3.
Induced network module analysis of a cancer-related gene list. Each node represents a physical entity (gene, protein or compound). Nodes with black labels are from the input gene list (seed nodes) and nodes with a purple label are intermediate nodes that are not in the input list but connect seed nodes and have significantly many links in the induced network module. Each edge represents an interaction (physical, biochemical, regulatory or drug–target interaction). Numerical values can be overlaid on nodes (Supplementary Figure S1). This example network resulted from an induced network module analysis of 100 genes differentially expressed in metastatic prostate cancer as compared to non-metastatic primary prostate carcinoma and may represent a module that governs the metastatic potential of prostate cancer.

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