Since the previous database report (3
), the human interaction content of ConsensusPathDB has been increased significantly (, left panel). Due to the integration of six additional interaction data resources and updates on the previously integrated 12 resources, the human interaction data in ConsensusPathDB have more than doubled from 74
289 to 155
432 unique complex functional interactions. The newly integrated data include complex protein interactions from Corum (4
), large-scale protein interaction networks from IntAct (5
) (designated IntAct-LS), manually curated protein–protein interactions from MIPS-MPPI (6
), protein–protein interactions from the Pathogen Interaction Gateway (PIG) meta-database (7
), the Edinburgh Human Metabolic Network reconstruction (EHMN) (8
) and biological pathways from INOH (http://www.inoh.org
). We have additionally imported 5238 physical interactions between human transcription factors published recently in ref. 9
. Furthermore, pathway definitions in the form of lists of genes participating in biological pathways were imported from PharmGKB (10
) for use in pathway-based analysis of expression data. With the addition of PIG, 20
098 host–pathogenic protein–protein interactions were introduced into ConsensusPathDB involving proteins from 864 viral and bacterial species. Thus, the integrated ConsensusPathDB network can now additionally serve as explanatory basis in the context of infectious diseases.
Figure 1. ConsensusPathDB content and Web interface functionality. Content and features that have been described in our previous database report (3) are displayed in gray font, new items in black. The plot in the left panel shows the growth of the human interaction (more ...)
shows the number of human interactions imported from each database, as well as the pairwise overlaps of source databases. To assess these overlaps and to avoid redundant interactions in ConsensusPathDB, physical entities and functional interactions from source databases are mapped to each other. The mapping process is detailed in Supplementary Data
Pairwise overlaps between human interaction databases in terms of shared functional interactions as of September 2010
Apart from extending the human functional interaction network, we have created ConsensusPathDB instances for two more organisms: Saccharomyces cerevisiae
and Mus musculus
, integrating eight interaction resources each: Reactome (11
), KEGG (12
), BioCyc (13
), IntAct (5
), DIP (14
), MINT (15
), BioGRID (16
) and MIPS (6
). The mouse instance additionally includes 1145 interactions between mouse transcription factors obtained from ref. 9. As in the case of the human ConsensusPathDB instance, only metabolic reactions have been imported from KEGG in the mouse and yeast database instances. This is due to the fact that signaling reactions are not made available by KEGG in any computer-readable format. However, KEGG’s signaling pathways are stored in ConsensusPathDB in the form of gene lists for use in the context of gene expression analyses described below.
Overall, ConsensusPathDB currently contains 41
271 physical entities, 155
432 functional interactions and 2205 biological pathways in human; 14
532 physical entities, 194
480 functional interactions and 734 biological pathways in yeast; and 21
946 physical entities, 13
648 functional interactions and 1381 biological pathways in mouse. The numbers correspond to the content after integration, i.e. unique item counts (for example, the number of non-unique human interactions before integration is 306
003). Our meta-database is updated every 3 months with the newest releases of its interaction resources.
For the vast majority of functional interactions and physical entities, annotation in the form of literature references and sequence database identifiers, respectively, is imported from the source databases. Literature references are especially useful for protein–protein interactions, as they often serve for interaction confidence estimations. We do not make any judgments on the quality of interactions: all interactions from all source databases are treated equally. For example, physical interactions detected by both large-scale and small-scale experiments are accommodated in ConsensusPathDB without applying any interaction filtering. The ConsensusPathDB users can themselves opt to use filtering based, e.g. on the number of publications, the scale of the interaction detection method or the number of source databases per interaction, since this information is stored and provided in ConsensusPathDB.
For all physical entities, interactions and pathways, the different source databases are recorded and links to the original data are provided where applicable.