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BMC Bioinformatics

, 11:521

Networks analysis

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 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.

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.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-11-521 contains supplementary material, which is available to authorized users.

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Autor: Jongkwang Kim - Kai Tan

Fuente: https://link.springer.com/



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