Interologous Interaction Database (I2D)

Category Cross-Omics>Knowledge Bases/Databases/Tools

Abstract I2D (Interologous Interaction Database) is an on-line database of known and predicted mammalian and eukaryotic protein-protein interactions.

To facilitate experimentation and integrated computational analysis with model organism Protein-Protein Interaction (PPI) networks, the manufacturers have integrated known, experimental and predicted PPIs for five (5) model organisms and human in the I2D database.

What makes I2D different from other interaction databases?

How can I access I2D?

I2D is searchable via a web interface or can be downloaded in its entirety.

What model organism data will I find in I2D?

I2D includes data for S. cerevisiae, C. elegans, D. melanogaster, R. norvegicus, M. musculus, and H. sapiens.

For the complete information on data sources and references, refer to the statistics page, located on the manufacturer’s web-site.

I2D Overall statistics as of December 2011 --

Source Interactions: 308,402

Predicted Interactions: 386,847

Total Interactions: 681,404

I2D as an extension of OPHID --

I2D is an extension of the manufacturer's earlier work on the OPHID database (see below...), and covers additional target organisms.

For instance, through this database the high-quality human interactome can be transferred to mouse, extending the mouse interactome by tens of thousands of protein interactions.

The data are provided for download in tab-delimited text or PSI-XML format, and can be viewed with an OpenGL-accelerated network visualization system called, NAViGaTOR (Network Analysis, Visualization and Graphing, TORonto) available for Windows, Linux, Solaris and OS X platforms.

Network Analysis, Visualization and Graphing, TORonto (NAViGaTOR) --

NAViGaTOR is an advanced graphing application for the 2D and 3D visualization of biological networks.

When I2D is queried, it can output data in several formats one of which is a NAViGaTOR compatible file.

This file can be opened up in NAViGaTOR for visualization and further analysis. I2D can also be queried from within NAViGaTOR - (see G6G Abstract Number 20686).

Online Predicted Human Interaction Database (OPHID) --

The Online Predicted Human Interaction Database (OPHID) is a web-based database of predicted interactions between human proteins.

It combines the literature-derived human PPI from BIND, HPRD and MINT, with predictions made from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Mus musculus.

The predicted interactions currently listed in OPHID are evaluated using protein domains, gene co-expression and Gene Ontology (GO) terms.

OPHID can be queried using single or multiple IDs and results can be visualized using the manufacturer's custom graph visualization program (NAViGaTOR).

OPHID generation --

OPHID was constructed by mapping model organism PPIs to human protein orthologs using BLASTP and the reciprocal best-hit approach.

Briefly, a database of model organism-to-human orthologs was constructed by BLASTing each model organism protein against the Swiss-Prot database filtered for human proteins.

Each model organism protein was translated to its human ortholog and a predicted human interaction was added if both proteins in the model organism interaction were conserved in humans.

Model organism PPIs were added from S. cerevisiae, C. elegans, D. melanogaster and M. musculus using this technique.

OPHID Domain co-occurrence dataset generation --

The literature-derived PPIs from BIND, DIP, HPRD, and MINT were used to create a domain-domain co-occurrence network using the InterPro domains obtained from Swiss-Prot.

OPHID Co-expression dataset --

Human gene expression data was obtained from the GeneAtlas Affymetrix dataset, which includes expression data for 44,775 human genes from 79 normal human tissues.

Gene co-expression was determined using the Pearson correlation coefficient between gene vectors for each protein in the interaction.

OPHID GO term similarity measure --

The manufacturer's used a modification of the semantic similarity measure to determine the relatedness of each interacting protein pair.

The semantic similarity method examines the frequency with which each Gene Ontology (GO) term appears in Swiss-Prot for human proteins and assigns a higher score to terms that appear less frequently (i.e. have greater ‘information content’).

OPHID Background distributions --

Statistically significant cutoffs for domain co-occurrence, gene co-expression and GO term similarity, were determined by estimating the background distributions using a bootstrap approach.

System Requirements

Contact manufacturer.


Manufacturer Web Site I2D

Price Contact manufacturer.

G6G Abstract Number 20687

G6G Manufacturer Number 104263