Abstract
This paper presents a genetic algorithm (GA) to predict the secondary structure of RNA molecules, where the secondary structure is encoded as a permutation. More specifically the proposed algorithm predicts which specific canonical base pairs will form hydrogen bonds and build helices, also known as stem loops. Since RNA is involved in both transcription and translation and also has catalytic and structural roles in the cell, knowing its structure is of fundamental importance since it will determine the function of the RNA molecule. We discuss results on RNA sequences of lengths 76, 681, and 785 nucleotides and present several improvements to our algorithm. We show that the Keep-Best Reproduction operator has similar benefits as in the TSP domain. In addition, a comparison of several crossover operators is provided, demonstrating that CX, an operator that is marginal in the TSP domain, performs very well in the RNA folding domain.
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References
Davis, L.: Job Shop Scheduling with Genetic Algorithms. In: Proceedings of the 1st International Conf. on Genetic Algorithms, ICGA 1985, pp. 136–140 (1985)
Chen, J.H., Shi-Yun, L., Maizel, J.V.: Prediction of common secondary structures of RNAs: A genetic Algorithm Approach. Nucleic Acids Research 28(4), 991–999 (2000)
Currey, K.M., Shapiro, B.: Secondary structure computer prediction of the poliovirus 5’ non-coding region is improved by a genetic algorithm. Computer Applications in the Biosciences 13(1), 1–12 (1997)
Goldberg, D.E., Lingle Jr., R.: Alleles, Loci, and the Traveling Salesman Problem. In: Proceedings of the 1st International Conference on Genetic Algorithms ICGA 1985, pp. 154–159 (1985)
Gultyaev, A.P., VanBatenburg, F.H.D., Pleij, C.W.A.: RNA folding dynamics: Computer simulations by a genetic algorithm. In: Molecular Modeling of Nucleic Acids. ACS Symposium Series, vol. 682, pp. 229–245 (1998)
Higgs, P.G.: RNA secondary structure: Physical and computational aspects. Quarterly Reviews for Biophysics 33(3), 199–253 (2000)
Holland, J.H.: Adaptation in Natural and Artificial Systems. Michigan University Press, Ann Arbor (1975)
Shapiro, B.A., Wu, J.C., Bengali, D., Potts, M.J.: The massively parallel genetic algorithm for RNA folding: MIMD implementation and population variation. Bioinformatics 17(2), 137–148 (2001)
Shapiro, B.A., Wu, J.C.: An annealing mutation operator in the genetic algorithms for RNA folding. Comput. Appl. Biosci. 12(3), 171–180 (1996)
Shapiro, B.A., Navetta, J.: A massively parallel genetic algorithm for RNA secondary structure prediction. Journal of Supercomputing 8(3), 195–207 (1994)
VanBatenburg, F.H.D., Gultyaev, A.P., Pleij, C.W.A.: An APLprogrammed Genetic Algorithm for the Prediction of RNA Secondary Structure. Journal of theoretical Biology 174(3), 269–280 (1995)
Oliver, I.M., Smith, D.J., Holland, J.R.C.: A Study on Permutation Crossover Operators on the Traveling Salesman Problem. In: Proceedings of the 2nd International Conf. on Genetic Algorithms ICGA-8, pp. 224–230 (1987)
Wiese, K.C., Goodwin, S.D.: Keep-Best Reproduction: A Local Family Competition Selection Strategy and the Environment it Flourishes in Constraints, vol. 6, pp. 399–422. Kluwer Academic Publishers, Dordrecht (2001)
Wiese, K.C., Goodwin, S.D., Nagarajan, S.: ASERC – A Genetic Sequencing Operator for Asymmetric Permutation Problems. In: Hamilton, H.J. (ed.) Canadian AI 2000. LNCS (LNAI), vol. 1822, pp. 201–213. Springer, Heidelberg (2000)
Wiese, K.C., Glen, E.: A Permutation Based Genetic Algorithm for RNA Secondary Structure Prediction. In: Abraham, A., Ruiz-del-Solar, J., Köppen, M. (eds.) Frontiers in Artificial Intelligence and Applications, vol. 87, pp. 173–182. IOS Press, Amsterdam (2002)
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Wiese, K.C., Glen, E. (2003). RNA Structure as Permutation: A GA Approach Comparing Different Genetic Sequencing Operators. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science(), vol 2871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39592-8_73
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DOI: https://doi.org/10.1007/978-3-540-39592-8_73
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