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Issue 7, 2013
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MGclus: network clustering employing shared neighbors

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Abstract

Network analysis is an important tool for functional annotation of genes and proteins. A common approach to discern structure in a global network is to infer network clusters, or modules, and assume a functional coherence within each module, which may represent a complex or a pathway. It is however not trivial to define optimal modules. Although many methods have been proposed, it is unclear which methods perform best in general. It seems that most methods produce far from optimal results but in different ways. MGclus is a new algorithm designed to detect modules with a strongly interconnected neighborhood in large scale biological interaction networks. In our benchmarks we found MGclus to outperform other methods when applied to random graphs with varying degree of noise, and to perform equally or better when applied to biological protein interaction networks. MGclus is implemented in Java and utilizes the JGraphT graph library. It has an easy to use command-line interface and is available for download from http://sonnhammer.sbc.su.se/download/software/MGclus/.

Graphical abstract: MGclus: network clustering employing shared neighbors

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Publication details

The article was received on 25 Oct 2012, accepted on 21 Jan 2013 and first published on 21 Jan 2013


Article type: Paper
DOI: 10.1039/C3MB25473A
Citation: Mol. BioSyst., 2013,9, 1670-1675
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    MGclus: network clustering employing shared neighbors

    O. Frings, A. Alexeyenko and E. L. L. Sonnhammer, Mol. BioSyst., 2013, 9, 1670
    DOI: 10.1039/C3MB25473A

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