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

Abstract

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.

Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes

“…The public database BioGRID extracts and annotates in-depth physical and genetic interactions reported in the primary peer-reviewed literature and houses the data that is explicitly corroborated by experimental evidence in an organized form to enable various tasks such as computational analysis of biological networks and prediction of gene/protein function (http://thebiogrid.org/) [80]–[83]. We used physical and genetic interaction data sets for Homo sapiens in BioGRID v.3.1.89.…”

Pathway mapping and development of disease‐specific biomarkers: protein‐based network biomarkers

“…Human PPI network mapping is a crucial component of systematic approach for protein-based network biomarker discovery. Extensive efforts have been seen recently to increase the coverage of human PPI maps by high-throughput yeast two-hybrid (Y2H) interaction mating [2,3,[56][57][58], affinity purification mass spectrometry (AP-MS) [59], literature curation of published experiments [60][61][62][63][64][65][66][67][68][69][70] or computational integrating approaches [71][72][73][74][75][76]. Some of the major public PPI databases are summarized in Table 1.…”

Molecular interaction networks for the analysis of human disease: Utility, limitations, and considerations

“…The issue of how to generate robust and biologically relevant hMINs has been a longstanding challenge. Problematically, biases are introduced by all interaction mapping technologies and interactions in the literature are of varying quality.…”

“…To aid with the “ab initio” approach of combining interaction data from separate sources (as described in the examples above), Martha et al have developed a method for determining an optimal database content integration strategy. In sum, an optimal value for k —the number of times a particular interaction occurs among different databases of interest—is identified based on a number of quantitative statistical and biological measures, for example, the extent to which modules (groups of interacting molecules related by function) of each k ‐defined network are significantly enriched in KEGG pathways .…”