The Use of Edge-Betweenness Clustering to Investigate Biological Function in Protein Interaction Networks

BackgroundThis paper describes an automated method for finding clusters of interconnected proteins in protein interaction networks and retrieving protein annotations associated with these clusters.ResultsProtein interaction graphs were separated into subgraphs of interconnected proteins, using the JUNG implementation of Girvan and Newman's Edge-Betweenness algorithm. Functions were sought for these subgraphs by detecting significant correlations with the distribution of Gene Ontology terms which had been used to annotate the proteins within each cluster. The method was implemented using freely available software (JUNG and the R statistical package). Protein clusters with significant correlations to functional annotations could be identified and included groups of proteins know to cooperate in cell metabolism. The method appears to be resilient against the presence of false positive interactions.ConclusionThis method provides a useful tool for rapid screening of small to medium size protein interaction datasets.

[1]  R. Ozawa,et al.  A comprehensive two-hybrid analysis to explore the yeast protein interactome , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Anton J. Enright,et al.  Detection of functional modules from protein interaction networks , 2003, Proteins.

[3]  James R. Knight,et al.  A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae , 2000, Nature.

[4]  Ben Lehner,et al.  Analysis of a high-throughput yeast two-hybrid system and its use to predict the function of intracellular proteins encoded within the human MHC class III region. , 2004, Genomics.

[5]  Gary D. Bader,et al.  An automated method for finding molecular complexes in large protein interaction networks , 2003, BMC Bioinformatics.

[6]  P. Bork,et al.  Functional organization of the yeast proteome by systematic analysis of protein complexes , 2002, Nature.

[7]  J. Rothberg,et al.  Gaining confidence in high-throughput protein interaction networks , 2004, Nature Biotechnology.

[8]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[9]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[10]  Petter Holme,et al.  Subnetwork hierarchies of biochemical pathways , 2002, Bioinform..

[11]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  M. Tyers,et al.  Osprey: a network visualization system , 2003, Genome Biology.

[13]  Alain Guénoche,et al.  Clustering proteins from interaction networks for the prediction of cellular functions , 2004, BMC Bioinformatics.

[14]  Ben Lehner,et al.  A protein interaction framework for human mRNA degradation. , 2004, Genome research.

[15]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[16]  B. Snel,et al.  Comparative assessment of large-scale data sets of protein–protein interactions , 2002, Nature.

[17]  James R. Knight,et al.  A Protein Interaction Map of Drosophila melanogaster , 2003, Science.

[18]  L. Mirny,et al.  Protein complexes and functional modules in molecular networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Tatiana A. Tatusova,et al.  NCBI Reference Sequence Project: update and current status , 2003, Nucleic Acids Res..

[20]  Andreas D. Baxevanis,et al.  Bioinformatics - a practical guide to the analysis of genes and proteins , 2001, Methods of biochemical analysis.

[21]  Gary D Bader,et al.  BIND--The Biomolecular Interaction Network Database. , 2001, Nucleic acids research.