A novel functional module detection algorithm for protein-protein interaction networks

Woochang Hwang,Murali Ramanathan,Aidong Zhang,Young-Rae Cho

doi:10.1186/1748-7188-1-24

Abstract

BackgroundThe sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM.ResultsSTM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be effective at detecting clusters with a diverse range of interaction structures that are significant on measures of biological relevance. The STM approach is compared to six competing approaches including the maximum clique, quasi-clique, minimum cut, betweeness cut and Markov Clustering (MCL) algorithms. The clusters obtained by each technique are compared for enrichment of biological function. STM generates larger clusters and the clusters identified have p-values that are approximately 125-fold better than the other methods on biological function. An important strength of STM is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches.ConclusionSTM outperforms competing approaches and is capable of effectively detecting both densely and sparsely connected, biologically relevant functional modules with fewer discards.

Highlights

The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging
Signal transduction model We propose to model the dynamic relationships between proteins in a protein-protein interactions (PPI) network using a signal transduction network model
To evaluate the biological significance of the most influential proteins, we annotated the lethality of each protein in the yeast PPI network according to the MIPS lethality data

Summary

Introduction

The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM. Discovering the functional roles of gene products after the completion of sequencing the Saccharomyces Cerevisiae genome has been in the spotlight of post-genomic era. Highthroughput techniques [2-5] for protein-protein interactions (PPI) detection have attracted researchers' attention since interacting proteins are likely to serve together as a group in cellular functions [6]. Highthroughput techniques in a genomic scale such as yeasttwo-hybrid, mass spectrometry, and protein chip technologies have multiplied the volume of protein interaction datasets exponentially and have provided us a genomic level view of molecular interactions. Effective computational systems for storage, management, visualization and analysis are necessary to cope with these fast growing complex datasets

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Conclusion