Detecting functional modules in the yeast protein--protein interaction network

  • Authors:

  • Jingchun Chen;Bo Yuan

  • Affiliations:

  • Integrated Biomedical Science Graduate Program, Department of Biomedical Informatics and Department of Pharmacology, The Ohio State University 333 W. 10th Avenue, Columbus, OH 43210, USA;Integrated Biomedical Science Graduate Program, Department of Biomedical Informatics and Department of Pharmacology, The Ohio State University 333 W. 10th Avenue, Columbus, OH 43210, USA

  • Venue:
  • Bioinformatics
  • Year:
  • 2006

Quantified Score

Hi-index 3.84

Visualization

Abstract

Motivation: Identification of functional modules in protein interaction networks is a first step in understanding the organization and dynamics of cell functions. To ensure that the identified modules are biologically meaningful, network-partitioning algorithms should take into account not only topological features but also functional relationships, and identified modules should be rigorously validated. Results: In this study we first integrate proteomics and microarray datasets and represent the yeast protein--protein interaction network as a weighted graph. We then extend a betweenness-based partition algorithm, and use it to identify 266 functional modules in the yeast proteome network. For validation we show that the functional modules are indeed densely connected subgraphs. In addition, genes in the same functional module confer a similar phenotype. Furthermore, known protein complexes are largely contained in the functional modules in their entirety. We also analyze an example of a functional module and show that functional modules can be useful for gene annotation. Contact: yuan.33@osu.edu Supplementary Information: Supplementary data are available at Bioinformatics online