MODA: An efficient algorithm for network motif discovery in biological networks

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  • Omidi Saeed
    Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics and Center of Excellence in Biomathematics, University of Tehran
  • Schreiber Falk
    Institute of Computer Science, Martin Luther University Halle-Wittenberg Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)
  • Masoudi-Nejad Ali
    Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics and Center of Excellence in Biomathematics, University of Tehran

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In recent years, interest has been growing in the study of complex networks. Since <bibref label = "Erdos">Erdös and Rényi (1960)</bibref> proposed their random graph model about 50 years ago, many researchers have investigated and shaped this field. Many indicators have been proposed to assess the global features of networks. Recently, an active research area has developed in studying local features named motifs as the building blocks of networks. Unfortunately, network motif discovery is a computationally hard problem and finding rather large motifs (larger than 8 nodes) by means of current algorithms is impractical as it demands too much computational effort. In this paper, we present a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently. We have tested our algorithm and found it able to identify larger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms. While most of the algorithms rely on induced subgraphs as motifs of the networks, MODA is able to extract both induced and non-induced subgraphs simultaneously. The MODA source code is freely available at: http://LBB.ut.ac.ir/Download/LBBsoft/MODA/<br>

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