Abstract
BackgroundAlthough protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking.ResultsBy applying a probabilistic model, we integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict PPI networks in human. In addition to previously studied data types, we show that phenotypic distances and genetic interactions can also be integrated to predict PPIs. We further built an easy-to-use, updatable integrated PPI database, the Integrated Network Database (IntNetDB) online, to provide automatic prediction and visualization of PPI network among genes of interest. The networks can be visualized in SVG (Scalable Vector Graphics) format for zooming in or out. IntNetDB also provides a tool to extract topologically highly connected network neighborhoods from a specific network for further exploration and research. Using the MCODE (Molecular Complex Detections) algorithm, 190 such neighborhoods were detected among all the predicted interactions. The predicted PPIs can also be mapped to worm, fly and mouse interologs.ConclusionIntNetDB includes 180,010 predicted protein-protein interactions among 9,901 human proteins and represents a useful resource for the research community. Our study has increased prediction coverage by five-fold. IntNetDB also provides easy-to-use network visualization and analysis tools that allow biological researchers unfamiliar with computational biology to access and analyze data over the internet. The web interface of IntNetDB is freely accessible at . Visualization requires Mozilla version 1.8 (or higher) or Internet Explorer with installation of SVGviewer.
Highlights
Protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy
We examined the predictive power of new data types and datasets, created an Integrated Network Database (IntNetDB) and provided easy-to-use web-based visualization and data mining options
To allow researchers easy and rapid use of our prediction results, we assembled the data in a web-accessible Integrated Network Database, and we provide a graphic-userinterface for querying protein-protein interaction (PPI) among a group of query proteins/genes (Figure 1)
Summary
Protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. Largescale experimental studies have generated many PPI datasets in different model organisms by yeast two-hybrid (Y2H) screens [2,3,4,5,6,7,8] and by co-affinity purification (coAP) followed by mass spectrometry (MS) [9,10]. These studies have provided opportunities to examine cellular function at a network level. The simple integration scheme is very suitable for updating or including future datasets
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