Abstract
BackgroundRecent 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.ResultsWe present an algorithm, miPALM (Module Inference by Parametric Local Modularity), 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.ConclusionsmiPALM 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.
Highlights
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
In this paper we introduce a novel algorithm to infer protein complexes by combining a parametric local modularity measure and a greedy search strategy
We evaluate our approach on the yeast protein-protein interactions (PPI) networks using two reference sets of protein complexes and additional functional annotations of yeast proteins
Summary
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. Recent advances in proteomics technologies such as two-hybrid system and mass spectrometry has allowed enormous amount of data on protein-protein interactions (PPI) to be released into the public domain [1]. There is a large body of research on computational methods for de novo protein complex detection in PPI networks. These methods can be roughly divided into only considering the immediate neighbors of a complex instead of the entire network. Applying it to the PPI network of E. coli, they showed that LQ was better at identifying small but biologically meaningful protein complexes
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