Abstract
Motifs are short patterns in Deoxyribonucleic Acid (DNA) that indicate the presence of certain biological characteristics. Motifs finding is the process of successfully finding meaningful motifs in large DNA sequences. Nature-inspired algorithms have been recently gaining much popularity in solving complex and large real-world optimization problems similar to the motif finding problem. This work aims on investigating the application of nature-inspired algorithms in motif finding problem. The investigation methodology is divided into three main approaches; the first is to apply well-known nature-inspired algorithms in solving the problem, then the enhancement of an algorithm is investigated, and finally the hybridization between two algorithms is investigated. Experiments are performed on synthetic as well as real data sets. The results show that the combination provides the best results, however, individual and modified algorithms provide also good results compared to some state-of-the-art tools.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
D’haeseleer, P.: What are DNA sequence motifs? Nat. Biotechnol. 24(4), 423–425 (2006)
Marbrouk, M., Hamdy, M., Mamdouh, M., Aboelfotoh, M., Kadah, Y.M.: BIOINFTool: bioinformatics and sequence data analysis in molecular biology using Mat Lab. In: Proceedings of Cairo International Biomedical Engineering Conference, 01–09 October 2006
Zelinka, I.: A survey on evolutionary algorithms dynamics and its complexity–Mutual relations, past, present and future. Swarm Evol. Comput. 25, 2–14 (2015)
Smolinski, T.G., Milanova, M.M., Hassanien, A.E.: Applications of Computational Intelligence in Biology: Current Trends and Open Problems. Studies in Computational Intelligence Springer, Heidelberg (2008)
Smolinski, T.G., Milanova, M.M., Hassanien, A.E.: Applications of Computational Intelligence in Bioinformatics and Biomedicine: Current Trends and Open Problems. Springer, Heidelberg (2008)
Abdelhalim, M.B., Habib, S.E.D.: Particle swarm optimization for HW/SW partitioning. In: Lazinica, A. (ed.) Particle Swarm Optimization, pp. 49–76. Tech Education and Publishing, New York (2009)
Wei, W., Xiao-Dan, Yu.: Comparative analysis of regulatory motif discovery tools for transcription factor binding sites. Genom. Proteomics Bioinf. 5(2), 131–142 (2007)
Eskin, E., Pevzner, P.A.: Finding composite regulatory patterns in DNA sequences. Bioinformatics 18(suppl 1), S354–S363 (2002)
Pavesi, G., et al.: Weeder web: discovery of transcription factor binding sites in a set of sequences from co-regulared genes. Nucl. Acids Res. 32, 199–203 (2004)
Bailley, T., Elkan, C.: Unsupervised learning of multiple motifs in biopolymers using expectation maximization. Mach. Learn. 21(1–2), 51–80 (1995)
Roth, P., et al.: Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat. Biotechnol. 16(10), 939–945 (1998)
Vijayvargiya, S., Shukla, P.: Identification of transcription factor binding sites using genetic algorithm. Int. J. Res. Rev. Comput. Sci. 2(2), 100–107 (2011)
Basha Gutierrez, J., Frith, M., Nakai, K.: A genetic algorithm for motif finding based on statistical significance. In: Ortuño, F., Rojas, I. (eds.) IWBBIO 2015, Part I. LNCS, vol. 9043, pp. 438–449. Springer, Heidelberg (2015)
Reddy, U., et al.: A particle swarm solution for planted(l, d)-Motif problem. In: IEEE Symposium in Bioinformatics and Computational Biology (CIBCB), pp. 222–229 (2013)
Lei, C., Ruan, J.: A particle swarm optimization-based algorithm for finding gapped motifs. BioData Min. 3(1), 3–9 (2010)
Das, M.K., Dai, H.K.: A survey of DNA motif finding algorithms. BMC Bioinf. 8(7), 1 (2007)
Yang, X., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), pp. 210–214. IEEE Publications (2009)
Zhang, Y., Wang, L., Wu, Q.: Modifed adaptive cuckoo search (MACS) algorithm and formal description for global optimisation. Int. J. Comput. Appl. Technol. 44(2), 73–79 (2012)
Sinaie, S.: Solving shortest path problem using gravitational search algorithm and neural networks (Doctoral dissertation, Universiti Teknologi Malaysia) (2010)
Zhang, Yu., Wu, L., Zhang, Y., Wang, J.: Immune gravitation inspired optimization algorithm. In: Huang, D.-S., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2011. LNCS, vol. 6838, pp. 178–185. Springer, Heidelberg (2011)
González-Álvarez, D.L., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Applying a multiobjective gravitational search algorithm (MO-GSA) to discover motifs. In: International Work-Conference on Artificial Neural Networks, pp. 372–379 (2011)
Pevzner, P.A., Sze, S.H.: Combinatorial approaches to finding subtle signals in DNA sequences. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, California USA, pp. 269–278 (2000)
Tompa, M., et al.: Assessing computational tools for the discovery of transcription factor binding sites. Nat. Biotechnol. 23(1), 137–144 (2005)
Chan, T.M. et al.: TFBS identification by position and consensus-led genetic algorithm with local filtering. In: GECCO 2007: Proceedings of the 2007 Conference on Genetic and Evolutionary Computation, pp. 377–384. ACM, London, England (2007)
Hassanien, A.E., Alamry, E.: Swarm Intelligence: Principles, Advances, and Applications. CRC – Taylor & Francis Group (2015). ISBN 9781498741064 - CAT# K26721
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Elewa, E.S., Abdelhalim, M.B., Mabrouk, M.S. (2017). An Efficient System for Finding Functional Motifs in Genomic DNA Sequences by Using Nature-Inspired Algorithms. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-48308-5_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48307-8
Online ISBN: 978-3-319-48308-5
eBook Packages: EngineeringEngineering (R0)