FACETS: multi-faceted functional decomposition of protein interaction networks.

ABSTRACT

MOTIVATION The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein-protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. RESULTS: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. SUPPLEMENTARY INFORMATION Supplementary data are available at the Bioinformatics online. AVAILABILITY: Our software is available freely for non-commercial purposes from http//www.cais.ntu.edu.sg/~assourav/Facets/