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

摘要: Abstract Background Protein-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. Methods In 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. Results Each 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 …