Literature DB >> 17314122

Clustering by common friends finds locally significant proteins mediating modules.

Bill Andreopoulos1, Aijun An, Xiaogang Wang, Michalis Faloutsos, Michael Schroeder.   

Abstract

MOTIVATION: Much research has been dedicated to large-scale protein interaction networks including the analysis of scale-free topologies, network modules and the relation of domain-domain to protein-protein interaction networks. Identifying locally significant proteins that mediate the function of modules is still an open problem.
METHOD: We use a layered clustering algorithm for interaction networks, which groups proteins by the similarity of their direct neighborhoods. We identify locally significant proteins, called mediators, which link different clusters. We apply the algorithm to a yeast network.
RESULTS: Clusters and mediators are organized in hierarchies, where clusters are mediated by and act as mediators for other clusters. We compare the clusters and mediators to known yeast complexes and find agreement with precision of 71% and recall of 61%. We analyzed the functions, processes and locations of mediators and clusters. We found that 55% of mediators to a cluster are enriched with a set of diverse processes and locations, often related to translocation of biomolecules. Additionally, 82% of clusters are enriched with one or more functions. The important role of mediators is further corroborated by a comparatively higher degree of conservation across genomes. We illustrate the above findings with an example of membrane protein translocation from the cytoplasm to the inner nuclear membrane. AVAILABILITY: All software is freely available under Supplementary information.

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Year:  2007        PMID: 17314122     DOI: 10.1093/bioinformatics/btm064

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  7 in total

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Authors:  Georgios A Pavlopoulos; Charalampos N Moschopoulos; Sean D Hooper; Reinhard Schneider; Sophia Kossida
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5.  Triangle network motifs predict complexes by complementing high-error interactomes with structural information.

Authors:  Bill Andreopoulos; Christof Winter; Dirk Labudde; Michael Schroeder
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6.  Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks.

Authors:  Wen-Hsien Lin; Wei-Chung Liu; Ming-Jing Hwang
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7.  Unraveling protein networks with power graph analysis.

Authors:  Loïc Royer; Matthias Reimann; Bill Andreopoulos; Michael Schroeder
Journal:  PLoS Comput Biol       Date:  2008-07-11       Impact factor: 4.475

  7 in total

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