Constructing a robust protein-protein interaction network by integrating multiple public databases

BMC Bioinformatics

Proceedings

Zhichao Liu¹  Weida Tong¹  Venkata-Swamy Martha²  Xiaowei Xu³  Hong Fang⁴  Yanbin Ye⁴  Don Ding⁴  Zhenqiang Su⁴  Li Guo⁵ 

[1] Center for Bioinformatics, Division of Systems Biology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, 72079, Jefferson, AR, USA;Department of Information Science, University of Arkansas at Little Rock, 2801 S. University Ave., 72204-1099, Little Rock, AR, USA;Department of Information Science, University of Arkansas at Little Rock, 2801 S. University Ave., 72204-1099, Little Rock, AR, USA;Center for Bioinformatics, Division of Systems Biology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, 72079, Jefferson, AR, USA;ICF International at FDA's National Center for Toxicological Research, 3900 NCTR Rd, 72079, Jefferson, AR, USA;State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, 100190, Beijing, P.R. China;

关键词: Cluster Result;    Functional Module;    KEGG Pathway;    Integrate Network;    Network Cluster;   

DOI  :  10.1186/1471-2105-12-S10-S7

来源: Springer

【 摘 要 】

BackgroundProtein-protein interactions (PPIs) are a critical component for many underlying biological processes. A PPI network can provide insight into the mechanisms of these processes, as well as the relationships among different proteins and toxicants that are potentially involved in the processes. There are many PPI databases publicly available, each with a specific focus. The challenge is how to effectively combine their contents to generate a robust and biologically relevant PPI network.MethodsIn this study, seven public PPI databases, BioGRID, DIP, HPRD, IntAct, MINT, REACTOME, and SPIKE, were used to explore a powerful approach to combine multiple PPI databases for an integrated PPI network. We developed a novel method called k-votes to create seven different integrated networks by using values of k ranging from 1-7. Functional modules were mined by using SCAN, a Structural Clustering Algorithm for Networks. Overall module qualities were evaluated for each integrated network using the following statistical and biological measures: (1) modularity, (2) similarity-based modularity, (3) clustering score, and (4) enrichment.ResultsEach integrated human PPI network was constructed based on the number of votes (k) for a particular interaction from the committee of the original seven PPI databases. The performance of functional modules obtained by SCAN from each integrated network was evaluated. The optimal value for k was determined by the functional module analysis. Our results demonstrate that the k-votes method outperforms the traditional union approach in terms of both statistical significance and biological meaning. The best network is achieved at k=2, which is composed of interactions that are confirmed in at least two PPI databases. In contrast, the traditional union approach yields an integrated network that consists of all interactions of seven PPI databases, which might be subject to high false positives.ConclusionsWe determined that the k-votes method for constructing a robust PPI network by integrating multiple public databases outperforms previously reported approaches and that a value of k=2 provides the best results. The developed strategies for combining databases show promise in the advancement of network construction and modeling.

【 授权许可】

Unknown   
© Swamy et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.