Exploring hierarchical and overlapping modular structure in the yeast protein interaction network

BMC Genomics

Proceedings

Jing Li¹  Changning Liu¹  Yi Zhao¹ 

[1] Bioinformatics Group, Key Laboratory of Intelligent Information Processing, Center for Advanced Computing Research, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, PR China;

关键词: Gene Ontology;    Cluster Coefficient;    Protein Interaction Network;    Gene Ontology Enrichment;    Border Node;   

DOI  :  10.1186/1471-2164-11-S4-S17

来源: Springer

【 摘 要 】

BackgroundDeveloping effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC) for clustering vertices of a protein interaction network using a novel subgraph density measurement.ResultsBy statistically evaluating several independent criteria, we found that ADHOC could significantly improve the outcome as compared with five previously reported density-dependent methods. We further applied ADHOC to investigate the hierarchical and overlapping modular structure in the yeast PPI network. Our method could effectively detect both protein modules and the overlaps between them, and thus greatly promote the precise prediction of protein functions. Moreover, by further assaying the intermodule layer of the yeast PPI network, we classified hubs into two types, module hubs and inter-module hubs. Each type presents distinct characteristics both in network topology and biological functions, which could conduce to the better understanding of relationship between network architecture and biological implications.ConclusionsOur proposed algorithm based on the novel subgraph density measurement makes it possible to more precisely detect hierarchical and overlapping modular structures in protein interaction networks. In addition, our method also shows a strong robustness against the noise in network, which is quite critical for analyzing such a high noise network.

【 授权许可】

CC BY   
© Liu et al; licensee BioMed Central Ltd. 2010