Skip to main content

Advertisement

SpringerLink
Log in

Freezing firefly algorithm for efficient planted (ℓ, d) motif search

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

The detection of inimitable patterns (motif) occurring in a set of biological sequences could elevate new biological discoveries. Its application in recognition of transcription factors and their binding sites have demonstrated the necessity to attain knowledge of gene function, human diseases, and drug design. The literature identifies (ℓ, d) motif search as the widely studied problem in PMS (Planted Motif Search). This paper proposes an efficient optimization algorithm named “Freezing FireFly (FFF)” to solve (ℓ, d) motif search problem. The new strategy freezing such as local and global was added to increase the performance of the basic Firefly algorithm. It freezes the best possible out coming positions even in the lesser brighter one. The performance of the proposed algorithm is experienced on simulated and real datasets. The experimental results show that the proposed algorithm resolves the instance (50, 21) within 1.47 min in the simulated dataset. For real (such as ChIP-seq (Chromatin Immunoprecipitation)) and synthetic datasets, the proposed algorithm runs much faster in comparison to existing state-of-the-art optimization algorithms, including Samselect, TraverStringRef, PMS8, qPMS9, AlignACE, FMGA, and GSGA.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Pal S, Rajasekaran S (2015) Improved algorithms for finding edit distance based motifs.  Proc. - 2015 IEEE Int. Conf. Bioinforma. Biomed. BIBM 2015, no. 1, pp. 537–542, https://doi.org/10.1109/BIBM.2015.7359740.

  2. Rajasekaran S, Balla S, Huang CH (2005) Exact algorithms for planted motif challenge problems. Ser Adv Bioinforma Comput Biol 1(8):249–259. https://doi.org/10.1142/9781860947322_0025

    Article  CAS  Google Scholar 

  3. Pevzner S-HS, Pavel A (2000) Combinatorial approaches to finding subtle signals in DNA sequences. ISMB (8):21–29

  4. Yu Q, Wei D, Huo H (2018) SamSelect: A sample sequence selection algorithm for quorum planted motif search on large DNA datasets. BMC Bioinformatics 19(1):1–16. https://doi.org/10.1186/s12859-018-2242-y

    Article  Google Scholar 

  5. Nicolae M, Rajasekaran S (2015) QPMS9: An efficient algorithm for quorum planted motif search. Sci Rep 5:1–8. https://doi.org/10.1038/srep07813

    Article  CAS  Google Scholar 

  6. Sheng X, Wang K (2017) Motif identification method based on Gibbs sampling and genetic algorithm. Cluster Comput 20(1):33–41. https://doi.org/10.1007/s10586-016-0699-x

    Article  Google Scholar 

  7. Chin HCL, Francis YL (2005) Voting algorithms for discovering long motifs. in 3rd Asia-Pacific Bioinformatics Conference, pp. 261–271.

  8. Pisanti, Nadia, “RISOTTO: Fast extraction of motifs with mismatches,” Lat. Am. Symp. Theor. Informatics., pp. 757–768, 2006.

  9. Davila J, Balla S, Rajasekaran S (2007) Fast and practical algorithms for planted (l, d) motif search. IEEE/ACM Trans Comput Biol Bioinforma 4(4):544–552. https://doi.org/10.1109/TCBB.2007.70241

    Article  CAS  Google Scholar 

  10. Dinh H, Rajasekaran S, Kundeti VK (2011) PMS5: An efficient exact algorithm for the (ℓ, d)-motif finding problem. BMC Bioinformatics 12:1–10. https://doi.org/10.1186/1471-2105-12-410

    Article  Google Scholar 

  11. Dinh H, Rajasekaran S, Davila J (2012) qPMS7: A fast algorithm for finding (ℓ, d)-motifs in DNA and protein sequences. PLoS One 7(7) https://doi.org/10.1371/journal.pone.0041425.

  12. SanguthevarRajasekaran M (2014) Efficient sequential and parallel algorithms for planted motif search. BMC Bioinformatics 15(1):34

    Article  Google Scholar 

  13. Bailey CE, Timothy L (1995) The value of prior knowledge in discovering motifs with meme. Ismb 3:1–29

  14. Reid JE, Wernisch L (2011) STEME: efficient EM to find motifs in large data sets. Nucleic Acids Res 39(18): e126–e126

  15. Quang D, Xie X (2014) EXTREME: an online EM algorithm for motif discovery. Bioinformatics 30(12): 1667–1673

  16. Lawrence CE et al. (1993) Detecting Subtle Sequence Signals : A Gibbs Sampling Strategy for Multiple Alignment Published by : American Association for the Advancement of Science Detecting Subtle Sequence Signals : A Gibbs Sampling Strategy for Multiple Alignment. Science (80-) 262(5131): 208–214

  17. Mc G (2000) Jason DHughes, Preston WEstep, SaeedTavazoie, “Computational identification of Cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae.” JMB 296(5):1205–1214

    Article  Google Scholar 

  18. Liu XLJ, Brutlag DL (2001) Bioprospector: discovering conserved dna motifs in upstream regulatory regions of co-expressed genes. Biocomput. World Sci: 127–138

  19. Krause J, Cordeiro J, Parpinelli RS, Lopes HSA (2013) A Survey of Swarm Algorithms Applied to Discrete Optimization Problems. Swarm Intell Bio-Inspired Comput: 169–191. https://doi.org/10.1016/B978-0-12-405163-8.00007-7.

  20. Shi EY (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546) pp. 81–86.

  21. ARUSR, Arock (2010) Planted (l, d) - Motif Finding using Particle Swarm Optimization. Int J Comput Appl ecot(2): 51–56. https://doi.org/10.5120/1541-144

  22. van Laarhoven PJM, Aarts EHL (1987) Chapter 2 Simulated annealing 2.1 Introduction of the algorithm. Simulated Annealing Theory Appl, p. 7, [Online]. Available: https://link-springer-com.ezproxy2.library.colostate.edu/content/pdf/10.1007%2F978-94-015-7744-1_2.pdf.

  23. Yang XS (2009) Firefly algorithms for multimodal optimization. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics), vol. 5792 LNCS, pp. 169–178. https://doi.org/10.1007/978-3-642-04944-6_14.

  24. Yang X-S (2010) Firefy algorithm, lévy fights and global optimization. In: Bramer M, Ellis R. Petridis M(eds.) Res Dev Intell Syst XXVI. Springer, London, pp 209–218

    Google Scholar 

  25. Dos Santos Coelho L, De Andrade Bernert DL, Mariani VC (2011) A chaotic firefly algorithm applied to reliability-redundancy optimization. 2011 IEEE Congr Evol Comput CEC 2011, pp. 517–521. https://doi.org/10.1109/CEC.2011.5949662.

  26. Subutic M, Tuba M, Stanarevic N (2012) Parallelization of the firefly algorithm for unconstrained optimization problems. Latest Adv. Inf. …, 264–269, [Online]. Available: http://www.wseas.us/e-library/conferences/2012/Singapore/ACCIDS/ACCIDS-43.pdf.

  27. Husselmann AV and Hawick KA (2012) Parallel Parametric Optimisation with Firefly Algorithms on Graphical Processing Units. Proc. Int. Conf. Genet. Evol. Methods no. January, pp. 77–83.

  28. Yang XS, Deb S (2010) Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. Stud Comput Intell 284:101–111. https://doi.org/10.1007/978-3-642-12538-6_9

    Article  Google Scholar 

  29. Senthilnath J, Omkar SN, Mani V (2011) Clustering using firefly algorithm: Performance study. Swarm Evol Comput 1(3):164–171. https://doi.org/10.1016/j.swevo.2011.06.003

    Article  Google Scholar 

  30. Fister I, Yang XS, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46. https://doi.org/10.1016/j.swevo.2013.06.001

    Article  Google Scholar 

  31. Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98. https://doi.org/10.1016/j.cnsns.2012.06.009

    Article  Google Scholar 

  32. Kaveh A, Javadi SM (2019) Chaos-based firefly algorithms for optimization of cyclically large-size braced steel domes with multiple frequency constraints. Comput Struct 214:28–39. https://doi.org/10.1016/j.compstruc.2019.01.006

    Article  Google Scholar 

  33. Jain L, Katarya R (2019) Discover opinion leader in online social network using firefly algorithm. Expert Syst Appl 122:1–15. https://doi.org/10.1016/j.eswa.2018.12.043

    Article  Google Scholar 

  34. Zubair AF, Abu Mansor MS (2019) Embedding firefly algorithm in optimization of CAPP turning machining parameters for cutting tool selections. Comput Ind Eng 135(September 2018): 317–325. https://doi.org/10.1016/j.cie.2019.06.006.

  35. Tilahun SL, Ngnotchouye JMT (2017) Firefly algorithm for discrete optimization problems: A survey. KSCE J Civ Eng 21(2):535–545. https://doi.org/10.1007/s12205-017-1501-1

    Article  Google Scholar 

  36. Tilahun SL, Ngnotchouye JMT, Hamadneh NN (2019) Continuous versions of firefly algorithm: a review. Artif Intell Rev 51(3):445–492. https://doi.org/10.1007/s10462-017-9568-0

    Article  Google Scholar 

  37. Hashim FA, Houssein EH (2020) A modified Henry gas solubility optimization for solving motif discovery problem. Neural Comput Appl 32(14):10759–10771. https://doi.org/10.1007/s00521-019-04611-0

    Article  Google Scholar 

  38. Mabrouk MS (2014) Adaptation of cuckoo search algorithm for the Motif Finding problem. 10th Int. Comput Eng Conf (ICENCO), 87–91

  39. Reddy US, Arock M, Reddy AV (2013) A particle swarm optimization solution for challenging planted(l, d)-Motif problem. Proc IEEE Symp Comput Intell Bioinforma Comput Biol CIBCB, pp. 222–229

  40. Hashim F, Mabrouk MS, Al-Atabany W, GWOMF: Grey Wolf Optimization for motif finding. ICENCO 2017 - 13th Int. Comput. Eng. Conf. Boundless Smart Soc., vol. 2018-Janua, pp. 141–146.https://doi.org/10.1109/ICENCO.2017.8289778.

  41. Singh V (2012) ZMDABC: Motif Discovery Using Artificial Bee Colony Algorithm.  J Inf Technol Res 5(5.4 ): 30–47. https://doi.org/10.4018/jitr.2012100103.

  42. Kumar S, Rafiqul S, Mredul I (2020) DNA motif discovery using chemical reaction optimization. Evol Intell no. 0123456789, https://doi.org/10.1007/s12065-020-00444-2.

  43. Nabos JQ (2013) Finding Planted DNA Motifs Using Gibbs Sampling with Simulated Annealing Neighborhood Search. no. September 2015

  44. Kaya M (2009) MOGAMOD : Multi-objective genetic algorithm for motif discovery. Expert Syst Appl 36(2):1039–1047. https://doi.org/10.1016/j.eswa.2007.11.008

    Article  Google Scholar 

  45. Machhi V, Degama J (2015) Motif Finding with Application to the Transcription Factor Binding Sites Motif Finding with Application to the Transcription Factor Binding Sites Problem. Int J Comput Appl 120(15), https://doi.org/10.5120/21301-3918.

  46. Fratkin E, Naughton BT, Brutlag DL, Batzoglou S (2006) MotifCut: Regulatory motifs finding with maximum density subgraphs. Bioinformatics 22(14):150–157. https://doi.org/10.1093/bioinformatics/btl243

    Article  Google Scholar 

  47. Lee NK, Wang D (2011) SOMEA: Self-organizing map based extraction algorithm for DNA motif identification with heterogeneous model. BMC Bioinform 12(1). https://doi.org/10.1186/1471-2105-12-S1-S16.

  48. Mahony S, Hendrix D, Golden A, Smith TJ, Rokhsar DS (2005) Transcription factor binding site identification using the self-organizing map. Bioinformatics 21(9):1807–1814. https://doi.org/10.1093/bioinformatics/bti256

    Article  CAS  PubMed  Google Scholar 

  49. Liang KC, Wang X, Anastassiou D (2008) A profile-based deterministic sequential Monte Carlo algorithm for motif discovery. Bioinformatics 24(1):46–55. https://doi.org/10.1093/bioinformatics/btm543

    Article  CAS  PubMed  Google Scholar 

  50. Yang XS, He X (2013) Firefly algorithm: recent advances and applications. Int J Swarm Intell 1(1):36. https://doi.org/10.1504/ijsi.2013.055801

    Article  Google Scholar 

  51. Lohrer MF (2013) A Comparison Between the Firefly Algorithm and Particle Swarm Optimization. pp. 1–49

  52. Cheung NJ, Ding XM, Bin Shen H (2014) Adaptive firefly algorithm: Parameter analysis and its application. PLoS One 9(11). https://doi.org/10.1371/journal.pone.0112634

  53. Xu Q, Wang L, Wang N, Hei X, Zhao L (2014) A review of opposition-based learning from 2005 to 2012. Eng Appl Artif Intell 29:1–12. https://doi.org/10.1016/j.engappai.2013.12.004

    Article  CAS  Google Scholar 

  54. Ergezer M, Simon D (2015) Probabilistic properties of fitness-based quasi-reflection in evolutionary algorithms. Comput Oper Res 63:114–124. https://doi.org/10.1016/j.cor.2015.03.013

    Article  Google Scholar 

  55. Tanaka S (2014) Improved Exact Enumerative Algorithms for the Planted (l, d)-Motif Search Problem. IEEE/ACM Trans Comput Biol Bioinforma 11(2):361–374

    Article  Google Scholar 

  56. Liu FFM, Tsai JJP, Chen RM, Chen SN, Shih SH (2004) FMGA: Finding motifs by genetic algorithm. Proc. - Fourth IEEE Symp. Bioinforma. Bioeng. BIBE 2004, pp. 459–466, 2004, https://doi.org/10.1109/BIBE.2004.1317378.

  57. Kheradpour P, Kellis M (2014) Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Res 42(5):2976–2987. https://doi.org/10.1093/nar/gkt1249

    Article  CAS  PubMed  Google Scholar 

  58. Chen X et al (2008) Integration of External Signaling Pathways with the Core Transcriptional Network in Embryonic Stem Cells. Cell 133(6):1106–1117. https://doi.org/10.1016/j.cell.2008.04.043

    Article  CAS  PubMed  Google Scholar 

  59. Sharov AA, Ko MSH (2009) Exhaustive search for over-represented DNA sequence motifs with cisfinder. DNA Res 16(5):261–273. https://doi.org/10.1093/dnares/dsp014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Crooks GE, Hon G, Chandonia JM, Brenner SE (2004) WebLogo: A sequence logo generator. Genome Res 14(6):1188–1190. https://doi.org/10.1101/gr.849004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamilnadu, India

    P. Theepalakshmi

  2. Machine Learning and Data Analytics Lab, Center of Excellence in Artificial Intelligence, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamilnadu, India

    U. Srinivasulu Reddy

Authors
  1. P. Theepalakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. U. Srinivasulu Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Theepalakshmi.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Theepalakshmi, P., Reddy, U.S. Freezing firefly algorithm for efficient planted (ℓ, d) motif search. Med Biol Eng Comput 60, 511–530 (2022). https://doi.org/10.1007/s11517-021-02468-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-021-02468-x

Keywords

Search

Navigation