Detection of Locally Over-Represented GO Terms in Protein-Protein Interaction Networks

Authors:
Mathieu Lavallée-Adam;Benoit Coulombe;Mathieu Blanchette
Affiliations:
McGill Centre for Bioinformatics and School of Computer Science, 3775 University, Montreal, Canada H2J 3B9;Institut de recherches cliniques de Montréal, Montreal, Canada H2W 1R7;McGill Centre for Bioinformatics and School of Computer Science, 3775 University, Montreal, Canada H2J 3B9
Venue:
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Year:
2009

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Abstract

High-throughput methods for identifying protein-protein interactions produce increasingly complex and intricate interaction networks. These networks are extremely rich in information, but extracting biologically meaningful hypotheses from them and representing them in a human-readable manner is challenging. We propose a method to identify Gene Ontology terms that are locally over-represented in a subnetwork of a given biological network. Specifically, we propose two methods to evaluate the degree of clustering of proteins associated to a particular GO term and describe four efficient methods to estimate the statistical significance of the observed clustering. We show, using Monte Carlo simulations, that our best approximation methods accurately estimate the true p-value, for random scale-free graphs as well as for actual yeast and human networks. When applied to these two biological networks, our approach recovers many known complexes and pathways, but also suggests potential functions for many subnetworks.