Refining Markov Clustering for protein complex prediction by incorporating core-attachment structure

Srihari, Sriganesh, Ning, Kang and Leong, Hon Wai (2009) Refining Markov Clustering for protein complex prediction by incorporating core-attachment structure. Genome Informatics, 23 1: 159-169.

Author Srihari, Sriganesh
Ning, Kang
Leong, Hon Wai
Title Refining Markov Clustering for protein complex prediction by incorporating core-attachment structure
Journal name Genome Informatics   Check publisher's open access policy
ISSN 0919-9454
Publication date 2009-10-01
Sub-type Article (original research)
Open Access Status Not Open Access
Volume 23
Issue 1
Start page 159
End page 169
Total pages 11
Place of publication Tokyo, Japan
Publisher Universal Academy Press
Language eng
Abstract Protein complexes are responsible for most of vital biological processes within the cell. Understanding the machinery behind these biological processes requires detection and analysis of complexes and their constituent proteins. A wealth of computational approaches towards detection of complexes deal with clustering of protein-protein interaction (PPI) networks. Among these clustering approaches, the Markov Clustering (MCL) algorithm has proved to be reasonably successful, mainly due to its scalability and robustness. However, MCL produces many noisy clusters, which either do not represent any known complexes or have additional proteins (noise) that reduce the accuracies of correctly predicted complexes. Consequently, the accuracies of these clusters when matched with known complexes are quite low. Refinement of these clusters to improve the accuracy requires deeper understanding of the organization of complexes. Recently, experiments on yeast by Gavin et al. (2006) revealed that proteins within a complex are organized in two parts: core and attachment. Based on these insights, we propose our method (MCL-CA), which couples core-attachment based refinement steps to refine the clusters produced by MCL. We evaluated the effectiveness of our approach on two different datasets and compared the quality of our predicted complexes with that produced by MCL. The results show that our approach significantly improves the accuracies of predicted complexes when matched with known complexes. A direct result of this is that MCL-CA is able to cover larger number of known complexes than MCL. Further, we also compare our method with two very recently proposed methods CORE and COACH, which also capitalize on the core-attachment structure. We also discuss several instances to show that our predicted complexes clearly adhere to the core-attachment structure as revealed by Gavin et al.
Keyword Protein complex
PPI network
Markov Clustering
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
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Created: Tue, 21 Aug 2012, 21:11:20 EST by Susan Allen on behalf of Institute for Molecular Bioscience