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CombiMotif: A new algorithm for network motifs discovery in protein–protein interaction networks

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  • Luo, Jiawei
  • Li, Guanghui
  • Song, Dan
  • Liang, Cheng

Abstract

Discovering motifs in protein–protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

Suggested Citation

  • Luo, Jiawei & Li, Guanghui & Song, Dan & Liang, Cheng, 2014. "CombiMotif: A new algorithm for network motifs discovery in protein–protein interaction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 309-320.
    • Handle: RePEc:eee:phsmap:v:416:y:2014:i:c:p:309-320
    DOI: 10.1016/j.physa.2014.08.062
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    References listed on IDEAS

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    1. Sahand Khakabimamaghani & Iman Sharafuddin & Norbert Dichter & Ina Koch & Ali Masoudi-Nejad, 2013. "QuateXelero: An Accelerated Exact Network Motif Detection Algorithm," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-15, July.
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