Research Article Open Access

A Selection-based MCL Clustering Algorithm for Motif Discovery

Chunxiao Sun1, Zhiyong Zhang1, Jinglei Tang1 and Shuai Liu1
  • 1 Northwest A&F University, China


As motif discovery plays an important role in the understanding of the relationship of gene regulation, this paper puts forward a selection-based MCL clustering refinement algorithm (SMCLR) aiming at solving the planted (l, d) motif search (PMS) problem. Firstly, we divide the DNA dataset into different subsets through selection of reference sequence and screen parts of eligible subsets by setting thresholds under selection project. Then MCL clustering algorithm is used for refinement. The experiment resulted on simulation data shows that SMCLR algorithm has higher prediction accuracy in a reasonable time than these existing motif discovery algorithms like Project, MEME, MCL-WMR and VINE. Moreover, the experiment resulted on real biological data demonstrates the effectiveness of SMCLR algorithm.

American Journal of Biochemistry and Biotechnology
Volume 14 No. 4, 2018, 298-306


Submitted On: 5 September 2018 Published On: 1 January 2019

How to Cite: Sun, C., Zhang, Z., Tang, J. & Liu, S. (2018). A Selection-based MCL Clustering Algorithm for Motif Discovery. American Journal of Biochemistry and Biotechnology, 14(4), 298-306.

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  • Motif Discovery
  • MCL Clustering
  • Gene Regulation
  • Bioinformatics