TY - JOUR
T1 - Modular organization of protein interaction networks
AU - Luo, Feng
AU - Yang, Yunfeng
AU - Chen, Chin Fu
AU - Chang, Roger
AU - Zhou, Jizhong
AU - Scheuermann, Richard H.
N1 - Funding Information:
The authors would like to thank Shankar Subramaniam, Trey Ideker, Nick Grishin and Stuart Johnson for helpful advice and critical review of this manuscript. The authors are also thankful to the anonymous reviewers for their great suggestion. The authors are thankful to Yanjun Qi for providing the confidence level data. This research was supported by the National Institutes of Health contracts N01-AI40076 and N01-AI40041, and by The United States Department of Energy under the Genomics: GTL program through Shewanella Federation, Office of Biological and Environmental Research, Office of Science. Oak Ridge National Laboratory is managed by University of Tennessee-Battelle LLC for the Department of Energy under contract DE-AC05-00OR22725. F.L. is supported by NSF EPSCoT grant EPS-0447660.
PY - 2007
Y1 - 2007
N2 - Motivation: Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules. Identifying these modules is essential to understand the organization of biological systems. Result: In this paper, we present a framework to identify modules within biological networks. In this approach, the concept of degree is extended from the single vertex to the sub-graph, and a formal definition of module in a network is used. A new agglomerative algorithm was developed to identify modules from the network by combining the new module definition with the relative edge order generated by the Girvan-Newman (G-N) algorithm. A JAVA program, MoNet, was developed to implement the algorithm. Applying MoNet to the yeast core protein interaction network from the database of interacting proteins (DIP) identified 86 simple modules with sizes larger than three proteins. The modules obtained are significantly enriched in proteins with related biological process Gene Ontology terms. A comparison between the MoNet modules and modules defined by Radicchi et al. (2004) indicates that MoNet modules show stronger co-clustering of related genes and are more robust to ties in betweenness values. Further, the MoNet output retains the adjacent relationships between modules and allows the construction of an interaction web of modules providing insight regarding the relationships between different functional modules. Thus, MoNet provides an objective approach to understand the organization and interactions of biological processes in cellular systems.
AB - Motivation: Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules. Identifying these modules is essential to understand the organization of biological systems. Result: In this paper, we present a framework to identify modules within biological networks. In this approach, the concept of degree is extended from the single vertex to the sub-graph, and a formal definition of module in a network is used. A new agglomerative algorithm was developed to identify modules from the network by combining the new module definition with the relative edge order generated by the Girvan-Newman (G-N) algorithm. A JAVA program, MoNet, was developed to implement the algorithm. Applying MoNet to the yeast core protein interaction network from the database of interacting proteins (DIP) identified 86 simple modules with sizes larger than three proteins. The modules obtained are significantly enriched in proteins with related biological process Gene Ontology terms. A comparison between the MoNet modules and modules defined by Radicchi et al. (2004) indicates that MoNet modules show stronger co-clustering of related genes and are more robust to ties in betweenness values. Further, the MoNet output retains the adjacent relationships between modules and allows the construction of an interaction web of modules providing insight regarding the relationships between different functional modules. Thus, MoNet provides an objective approach to understand the organization and interactions of biological processes in cellular systems.
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U2 - 10.1093/bioinformatics/btl562
DO - 10.1093/bioinformatics/btl562
M3 - Article
C2 - 17092991
AN - SCOPUS:33846681448
SN - 1367-4803
VL - 23
SP - 207
EP - 214
JO - Bioinformatics
JF - Bioinformatics
IS - 2
ER -