Abstract
In bioinformatics, Multiple Sequence Alignment (MSA) is an NP-complete problem. This alignment problem is important in computational biology due to its usefulness in extracting and representing biological importance among sequences by finding similar regions. MSA is also helpful for finding the secondary or tertiary structure of the protein and using it critical anonymousness motives of DNA or Protein can also be found. For solving the problem, we have proposed a method based on Chemical Reaction Optimization (CRO). We have redesigned the basic four operators of CRO and three new operators have been designed to solve the problem. The additional operators are needed in order to arrange the base symbols properly. For testing the efficiency of our proposed method DNA sequences have taken from the different sources. We have compared the experimental results of the proposed method with clustal-omega and got better results for DNA sequences.
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References
Notredame, C., Higgins, D.G., Heringa, J.: T-coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 302(1), 205–217 (2000)
Hassanien, A.E., Milanova, M.G., Smolinski, T.G., Abraham, A.: Computational intelligence in solving bioinformatics problems: reviews, perspectives, and challenges. In: Computational Intelligence in Biomedicine and Bioinformatics, pp. 3–47. Springer, Berlin (2008)
Thompson, J.D., Higgins, D.G., Gibson, T.J.: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22(22), 4673–4680 (1994)
Katoh, K., Misawa, K., Kuma, K.I., Miyata, T.: MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30(14), 3059–3066 (2002)
Edgar, R.C.: MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinf. 5(1), 1 (2004)
Kaya, M., Kaya, B., Alhajj, R.: A novel multi-objective genetic algorithm for multiple sequence alignment. Int. J. Data Min. Bioinf. 14(2), 139–158 (2016)
Zhang, Z., Schwartz, S., Wagner, L., Miller, W.: A greedy algorithm for aligning DNA sequences. J. Comput. Biol. 7(1–2), 203–214 (2000)
Lee, Z.J., Su, S.F., Chuang, C.C., Liu, K.H.: Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment. Appl. Soft Comput. 8(1), 55–78 (2008)
Lam, A.Y., Li, V.O.: Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans. Evol. Comput. 14(3), 381–399 (2010)
Blum, C.: Ant colony optimization: introduction and recent trends. Phys. Life Rev. 2(4), 353–373 (2005)
Chen, J., et al.: Partitioned optimization algorithms for multiple sequence alignment. In: 2006 20th International Conference on Advanced Information Networking and Applications, AINA 2006, vol. 2. IEEE (2006)
Xu, F., Chen, Y.: A method for multiple sequence alignment based on particle swarm optimization. In: Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence, pp. 965–973. Springer, Berlin (2009)
Lei, X.J., Sun, J.J., Ma, Q.Z.: Multiple sequence alignment based on chaotic PSO. In: International Symposium on Intelligence Computation and Applications, pp. 351–360. Springer, Berlin, October 2009
Jagadamba, P.V.S.L., Babu, M.S.P., Rao, A.A., Rao, P.K.S.: An improved algorithm for multiple sequence alignment using particle swarm optimization. In: 2011 IEEE 2nd International Conference on Software Engineering and Service Science, pp. 544–547. IEEE, July 2011
Sievers, F., Wilm, A., Dineen, D., Gibson, T.J., Karplus, K., Li, W., Lopez, R., McWilliam, H., Remmert, M., Söding, J., Thompson, J.D., Higgins, D.G.: Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7(1), 539 (2011)
Huang, D., Zhu, X.: A novel method based on chemical reaction optimization for pairwise sequence alignment. In: International Conference on Parallel Computing in Fluid Dynamics, pp. 429–439. Springer, Berlin, May 2013
Clustal Omega \( < \) Multiple Sequence Alignment \( < \) EMBL-EBI. http://www.ebi.ac.uk/Tools/msa/clustalo/
Horng, J.T., Wu, L.C., Lin, C.M., Yang, B.H.: A genetic algorithm for multiple sequence alignment. Soft Comput. 9(6), 407–420 (2005)
HAlign: Fast Multiple Similar DNA/RNA Sequence Alignment based on Center Star Strategy. http://datamining.xmu.edu.cn/software/halign
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Wadud, M.S., Islam, M.R., Kundu, N., Kabir, M.R. (2020). Multiple Sequence Alignment Using Chemical Reaction Optimization Algorithm. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_104
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DOI: https://doi.org/10.1007/978-3-030-16660-1_104
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