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

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Research in Computational Molecular Biology (RECOMB 2009)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 5541))

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.

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Author information

Authors and Affiliations

  1. McGill Centre for Bioinformatics and School of Computer Science, 3775 University, room 332., Montreal, (Québec), H2J 3B9, Canada

    Mathieu Lavallée-Adam & Mathieu Blanchette

  2. Institut de recherches cliniques de Montréal, 110, Pine West., Montreal, (Québec), H2W 1R7, Canada

    Benoit Coulombe

Authors
  1. Mathieu Lavallée-Adam
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  2. Benoit Coulombe
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  3. Mathieu Blanchette
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Editor information

Editors and Affiliations

  1. Computer Science Department, James H. Clark Center, 318 Campus Drive, RM S266, CA 94305-5428,, Stanford, USA

    Serafim Batzoglou

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© 2009 Springer-Verlag Berlin Heidelberg

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Lavallée-Adam, M., Coulombe, B., Blanchette, M. (2009). Detection of Locally Over-Represented GO Terms in Protein-Protein Interaction Networks. In: Batzoglou, S. (eds) Research in Computational Molecular Biology. RECOMB 2009. Lecture Notes in Computer Science(), vol 5541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02008-7_23

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  • DOI: https://doi.org/10.1007/978-3-642-02008-7_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02007-0

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