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PMID: 17112386 Published · epublish English Journal Article Research Support, Non-U.S. Gov't

IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model.

BMC bioinformatics ·Vol. 7 ·2006-11-18 ·Pages 508

Xia K, Dong D, Han JD

Abstract

Although 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. By 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. IntNetDB 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 http://hanlab.genetics.ac.cn/IntNetDB.htm. Visualization requires Mozilla version 1.8 (or higher) or Internet Explorer with installation of SVGviewer.

MeSH Terms
Algorithms Animals Bayes Theorem Cluster Analysis Computer Graphics Databases, Genetic Gene Expression Regulation Humans Internet Models, Statistical Phenotype Protein Binding Protein Interaction Mapping Protein Structure, Tertiary Proteins/chemistry,genetics,metabolism Reproducibility of Results Systems Integration User-Computer Interface
Chemicals
Proteins
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Xia Kai
Chinese Academy of Sciences Key Laboratory of Developmental Biology, Center for Molecular Systems Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China. kxia@genetics.ac.cn
Dong Dong
Han Jing-Dong J
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Article Info
Journal
BMC bioinformatics
Abbr.
BMC Bioinformatics
ISSN
1471-2105
DOI
10.1186/1471-2105-7-508
Email
kxia@genetics.ac.cn
Published
2006-11-18
Epub
2006-00-18
Pages
508
Language
English
Region
England
NLM ID
100965194
PMCID
PMC1661597
Subset
IM
External Links
PubMed source DOI: 10.1186/1471-2105-7-508 PMC Full Text View journal details
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