Research on Algorithms for Planted (l,d) Motif Search

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 950)


As one of the most challenging problems in bioinformatics, motif search has important implications for gene discovery and understanding of gene regulatory relationships. Planted (l,d) motif searching (PMS) is a widely accepted issue model in the field of motif search. Solving PMS issues involve computer science, bioinformatics and other related knowledge. Of course, a huge amount of computation is probably unavoidable. Designing an effective and optimized method is particularly important for solving the puzzle.

This article mainly introduces and implements related algorithms for planted (l,d) motif search. Firstly, according to the different implementation methods, it can be divided into two categories: enumeration algorithm and local search algorithm. Then four algorithms are proposed to solve the issue, pseudocodes are also available. Finally, in the experimental part, PMSP algorithm has the best performance through programing implementation and operating results evaluation. In the following work, by combining the idea of MapReduce parallel computing, the design of PMSPMR algorithm can achieve more excellent operational results.


Planted (l,d) motif search Execution time MapReduce 


  1. 1.
    Davila, J., Balla, S., Rajasekaran, S.: Space and time efficient algorithms for planted motif search. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006 Part II. LNCS, vol. 3992, pp. 822–829. Springer, Heidelberg (2006). Scholar
  2. 2.
    Zambelli, F., Pesole, G., Pavesi, G.: Motif discovery and transcription factor binding sites before and after the next-generation sequencing era. Brief Bioinform. 14(2), 225–238 (2013)CrossRefGoogle Scholar
  3. 3.
    Mrazek, J.: Finding sequence motifs in prokaryotic Genomes-brief practical guide for a micro-biologist. Brief. Bioinform. 10(5), 525–536 (2009)CrossRefGoogle Scholar
  4. 4.
    D’haeseleer, P.: What are DNA sequence motif. Nat. Biotechnol. 24(4), 423–425 (2006)CrossRefGoogle Scholar
  5. 5.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  6. 6.
    Hu, J., Li, B., Kihara, D.: Limitations and potentials of current motif discovery algorithms. Nucleic Acids Res. 33(15), 4899–4913 (2005)CrossRefGoogle Scholar
  7. 7.
    Rampášek, L., Jimenez, R.M., Lupták, A., et al.: RNA motif search with data-driven element ordering. BMC Bioinform. 17(1), 1–10 (2016)CrossRefGoogle Scholar
  8. 8.
    Davila, J., Balla, S., Rajasekaran, S.: Fast and practical algorithms for planted (l,d) motif Search. IEEE/ACM Trans. Comput. Biol. Bioinf. 4(4), 544–552 (2007)CrossRefGoogle Scholar
  9. 9.
    Yu, Q., Huo, H., Zhao, R., et al.: RefSelect: a reference sequence selection algorithm for planted (l,d) motif search. BMC Bioinformatics 17(9), 266–281 (2016)CrossRefGoogle Scholar
  10. 10.
    He, B., Fang, W., et al.: Mars: a MapReduce framework on graphics processors. In: International Conference on Parallel Architectures and Compilation Techniques, pp. 260–269. IEEE (2017)Google Scholar
  11. 11.
    Hu, J., Li, B., Kihara, D.: Limitations and potentials of current motif discovery algorithms. Nucleic Acids Res. 33(15), 4899–4913 (2005)CrossRefGoogle Scholar
  12. 12.
    Tanaka, S.: Improved Exact Enumerative Algorithms for the Planted(l,d)-Motif Search Problem. IEEE Computer Society Press, Washington, D.C. (2014)Google Scholar
  13. 13.
    Rajasekaran, S., Dinh, H.: A speedup technique for (l,d)-Motif finding algorithms. BMC Res. Notes 4(1), 1–7 (2011)CrossRefGoogle Scholar
  14. 14.
    Peng, X., Pal, S., Rajasekaran, S.: qPMS10: a randomized algorithm for efficiently solving quorum Planted Motif Search problem. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 670–675. IEEE (2017)Google Scholar
  15. 15.
    Xu, Y., Yang, J., Zhao, Y., et al.: An improved voting algorithm for planted (l,d) motif search. Inf. Sci. 237, 305–312 (2013)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Tong, E., et al.: Bloom filter-based workflow management to enable QoS guarantee in wireless sensor networks. J. Netw. Comput. Appl. 39, 38–51 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Computer ScienceXi’an Shiyou UniversityXi’anChina

Personalised recommendations