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Open Access Highly Accessed Research article

Discover Protein Complexes in Protein-Protein Interaction Networks Using Parametric Local Modularity

Jongkwang Kim1 and Kai Tan12*

Author Affiliations

1 Department of Internal Medicine, The University of Iowa, 2294 CBRB, 285 Newton Road, Iowa City, IA 52242, USA

2 Department of Biomedical Engineering, The University of Iowa, 1402 Seamans Center, Iowa City, IA 52242, USA

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BMC Bioinformatics 2010, 11:521  doi:10.1186/1471-2105-11-521

Published: 19 October 2010

Abstract

Background

Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in multiple species and in both normal and diseased cells. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively distil network models from large-scale interactome data.

Results

We present an algorithm, miPALM (

    M
odule
    I
nference by
    P
arametric
    L
ocal
    M
odularity), to infer protein complexes in a protein-protein interaction network. The algorithm uses a novel graph theoretic measure, parametric local modularity, to identify highly connected sub-networks as candidate protein complexes. Using gold standard sets of protein complexes and protein function and localization annotations, we show our algorithm achieved an overall improvement over previous algorithms in terms of precision, recall, and biological relevance of the predicted complexes. We applied our algorithm to predict and characterize a set of 138 novel protein complexes in S. cerevisiae.

Conclusions

miPALM is a novel algorithm for detecting protein complexes from large protein-protein interaction networks with improved accuracy than previous methods. The software is implemented in Matlab and is freely available at http://www.medicine.uiowa.edu/Labs/tan/software.html webcite.