This article is part of the supplement: Selected articles from the 2009 IEEE International Conference on Bioinformatics and Biomedicine
Proceedings
Identification of functional hubs and modules by converting interactome networks into hierarchical ordering of proteins
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* Corresponding author: Young-Rae Cho young-rae_cho@baylor.edu
1 Department of Computer Science Baylor University, Waco, TX 76798, USA
2 Department of Computer Science and Engineering, State University of New York, Buffalo, NY 14260, USA
BMC Bioinformatics 2010, 11(Suppl 3):S3 doi:10.1186/1471-2105-11-S3-S3
Published: 29 April 2010Abstract
Background
Protein-protein interactions play a key role in biological processes of proteins within a cell. Recent high-throughput techniques have generated protein-protein interaction data in a genome-scale. A wide range of computational approaches have been applied to interactome network analysis for uncovering functional organizations and pathways. However, they have been challenged because ofcomplex connectivity. It has been investigated that protein interaction networks are typically characterized by intrinsic topological features: high modularity and hub-oriented structure. Elucidating the structural roles of modules and hubs is a critical step in complex interactome network analysis.
Results
We propose a novel approach to convert the complex structure of an interactome network into hierarchical ordering of proteins. This algorithm measures functional similarity between proteins based on the path strength model, and reveals a hub-oriented tree structure hidden in the complex network. We score hub confidence and identify functional modules in the tree structure of proteins, retrieved by our algorithm. Our experimental results in the yeast protein interactome network demonstrate that the selected hubs are essential proteins for performing functions. In network topology, they have a role in bridging different functional modules. Furthermore, our approach has high accuracy in identifying functional modules hierarchically distributed.
Conclusions
Decomposing, converting, and synthesizing complex interaction networks are fundamental tasks for modeling their structural behaviors. In this study, we systematically analyzed complex interactome network structures for retrievingfunctional information. Unlike previous hierarchical clustering methods, this approach dynamically explores the hierarchical structure of proteins in a global view. It is well-applicable to the interactome networks in high-level organisms because of its efficiency and scalability.