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RNA Secondary Structure an Overview

  • Abdelhakim El Fatmi
  • Arakil Chentoufi
  • M. Ali Bekri
  • Said Benhlima
  • Mohamed Sabbane
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 37)

Abstract

It was believed that the single role of the Ribonucleic acid (RNA) is to carry the information necessitate to build a specific protein. Now it discovered that RNA has important and essential roles in many gene regulatory networks and many other cellular functions. Thus, the prediction of RNA structures becomes the subject of many studies in the last few years.

Determining the secondary structure of an RNA from its primary sequence is a challenging computational task. Various methods have been proposed to handle this problem. Initially, there are physical methods such as X-Ray, Crystallography, and Nuclear Magnetic Resonance. These methods are too costly, and they necessitate a lot of effort and so much time consuming. Therefore, the bioinformatics methods become highly needed.

In this paper, we will review the usually used approaches to predict RNA secondary structure counting the dynamic programming approach, the soft computing approach, the comparative approach, and the grammatical approach. Finally as perspective, we propose a method based on Genetic Algorithm principle and Greedy Randomized Adaptive Search Procedure (GRASP) method.

Keywords

Bioinformatics Ribonucleic acid (RNA) RNA secondary structure 

References

  1. 1.
    Osman, M.N., Abdullah, R., AbdulRashid, N.: RNA secondary structure prediction using dynamic programming algorithm – a review and proposed work. In: 2010 International Symposium in Information Technology (ITSim), vol. 2, pp. 551–556. IEEE, June 2010Google Scholar
  2. 2.
    Nussinov, R., Pieczenik, G., Griggs, J.R., Kleitman, D.J.: Algorithms for loop matchings. SIAM J. Appl. Math. 35(1), 68–82 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Zuker, M.: Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 31(13), 3406–3415 (2003)CrossRefGoogle Scholar
  4. 4.
    Tinoco, I., Borer, P.N., Dengler, B., Levine, M.D., Uhlenbeck, O.C., Crothers, D.M., Gralla, J.: Improved estimation of secondary structure in ribonucleic acids. Nature 246(150), 40–41 (1973)Google Scholar
  5. 5.
    Rivas, E., Eddy, S.R.: A dynamic programming algorithm for RNA structure prediction including pseudoknots. J. Mol. Biol. 285(5), 2053–2068 (1999)CrossRefGoogle Scholar
  6. 6.
    Dirks, R.M., Pierce, N.A.: A partition function algorithm for nucleic acid secondary structure including pseudoknots. J. Comput. Chem. 24(13), 1664–1677 (2003)CrossRefGoogle Scholar
  7. 7.
    Ray, S.S., Pal, S.K.: RNA secondary structure prediction using soft computing. IEEE/ACM Trans. Comput. Biol. Bioinf. 10(1), 2–17 (2013)CrossRefGoogle Scholar
  8. 8.
    Holland, J.H.: Outline for a logical theory of adaptive systems. J. ACM (JACM) 9(3), 297–314 (1962)CrossRefzbMATHGoogle Scholar
  9. 9.
    Van Batenburg, F.H.D., Gultyaev, A.P., Pleij, C.W.: An APL-programmed genetic algorithm for the prediction of RNA secondary structure. J. Theor. Biol. 174(3), 269–280 (1995)CrossRefGoogle Scholar
  10. 10.
    Wiese, K.C., Deschenes, A.A., Hendriks, A.G.: RnaPredict—an evolutionary algorithm for RNA secondary structure prediction. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 5(1), 25–41 (2008)CrossRefGoogle Scholar
  11. 11.
    Tong, K.K., Cheung, K.Y., Lee, K.H., Leung, K.S.: GAknot: RNA secondary structures prediction with pseudoknots using genetic algorithm. In: 2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 136–142. IEEE, April 2013Google Scholar
  12. 12.
    Shapiro, B.A., Navetta, J.: A massively parallel genetic algorithm for RNA secondary structure prediction. J. Supercomput. 8(3), 195–207 (1994)CrossRefzbMATHGoogle Scholar
  13. 13.
    Liu, Q., Ye, X., Zhang, Y.: A Hopfield neural network based algorithm for RNA secondary structure prediction. In: First International Multi-Symposiums on Computer and Computational Sciences, 2006, IMSCCS 2006, vol. 1, pp. 10–16. IEEE, June 2006Google Scholar
  14. 14.
    Koessler, D.R., Knisley, D.J., Knisley, J., Haynes, T.: A predictive model for secondary RNA structure using graph theory and a neural network. BMC Bioinform. 11(6), S21 (2010)CrossRefGoogle Scholar
  15. 15.
    Han, K., Kim, H.J.: Prediction of common folding structures of homologous RNAs. Nucleic Acids Res. 21(5), 1251–1257 (1993)CrossRefGoogle Scholar
  16. 16.
    Sato, K., Kato, Y., Akutsu, T., Asai, K., Sakakibara, Y.: DAFS: simultaneous aligning and folding of RNA sequences via dual decomposition. Bioinformatics 28(24), 3218–3224 (2012)CrossRefGoogle Scholar
  17. 17.
    Harmanci, A.O., Sharma, G., Mathews, D.H.: TurboFold: iterative probabilistic estimation of secondary structures for multiple RNA sequences. BMC Bioinform. 12(1), 108 (2011)CrossRefGoogle Scholar
  18. 18.
    Xu, X., Ji, Y., Stormo, G.D.: RNA Sampler: a new sampling based algorithm for common RNA secondary structure prediction and structural alignment. Bioinformatics 23(15), 1883–1891 (2007)CrossRefGoogle Scholar
  19. 19.
    Jiwan, A., Singh, S.: A review on RNA pseudoknot structure prediction techniques. In: 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET), pp. 975–978. IEEE, March 2012Google Scholar
  20. 20.
    Garca, R.: Prediction of RNA pseudoknotted secondary structure using Stochastic Context Free Grammars (SCFG). CLEI Electron. J. 9(2) (2006)Google Scholar
  21. 21.
    Sakakibara, Y., Brown, M., Hughey, R., Mian, I.S., Sjlander, K., Underwood, R.C., Haussler, D.: Stochastic context-free grammars for tRNA modeling. Nucleic Acids Res. 22(23), 5112–5120 (1994)CrossRefGoogle Scholar
  22. 22.
    Anderson, J.W., Tataru, P., Staines, J., Hein, J., Lyngs, R.: Evolving stochastic context-free grammars for RNA secondary structure prediction. BMC Bioinform. 13(1), 78 (2012)CrossRefGoogle Scholar
  23. 23.
    Kato, Y., Seki, H., Kasami, T.: RNA pseudoknotted structure prediction using stochastic multiple context-free grammar. IPSJ Digit. Courier 2, 655–664 (2006)CrossRefGoogle Scholar
  24. 24.
    Mizoguchi, N., Kato, Y., Seki, H.: A grammar-based approach to RNA pseudoknotted structure prediction for aligned sequences. In: 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), pp. 135–140. IEEE, February 2011Google Scholar
  25. 25.
    Knudsen, B., Hein, J.: Pfold: RNA secondary structure prediction using stochastic context-free grammars. Nucleic Acids Res. 31(13), 3423–3428 (2003)CrossRefGoogle Scholar
  26. 26.
    Hofacker, I.L., Fekete, M., Stadler, P.F.: Secondary structure prediction for aligned RNA sequences. J. Mol. Biol. 319(5), 1059–1066 (2002)CrossRefGoogle Scholar
  27. 27.
    Sükösd, Z., Knudsen, B., Værum, M., Kjems, J., Andersen, E.S.: Multithreaded comparative RNA secondary structure prediction using stochastic context-free grammars. BMC Bioinform. 12(1), 103 (2011)CrossRefGoogle Scholar
  28. 28.
    Doose, G., Metzler, D.: Bayesian sampling of evolutionarily conserved RNA secondary structures with pseudoknots. Bioinformatics 28(17), 2242–2248 (2012)CrossRefGoogle Scholar
  29. 29.
    Seetin, M.G., Mathews, D.H.: TurboKnot: rapid prediction of conserved RNA secondary structures including pseudoknots. Bioinformatics 28(6), 792–798 (2012)CrossRefGoogle Scholar
  30. 30.
    Sato, K., Kato, Y., Hamada, M., Akutsu, T., Asai, K.: IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming. Bioinformatics 27(13), i85–i93 (2011)CrossRefGoogle Scholar
  31. 31.
    Bindewald, E., Kluth, T., Shapiro, B.A.: CyloFold: secondary structure prediction including pseudoknots. Nucleic Acids Res. 38(suppl 2), W368–W372 (2010)CrossRefGoogle Scholar
  32. 32.
    Bon, M., Orland, H.: TT2NE: a novel algorithm to predict RNA secondary structures with pseudoknots. Nucleic Acids Res. 39(14), e93 (2011)CrossRefGoogle Scholar
  33. 33.
    Engelen, S., Tahi, F.: Tfold: efficient in silico prediction of non-coding RNA secondary structures. Nucleic Acids Res. 38(7), 2453–2466 (2010)CrossRefGoogle Scholar
  34. 34.
    Zou, Q., Zhao, T., Liu, Y., Guo, M.: Predicting RNA secondary structure based on the class information and Hopfield network. Comput. Biol. Med. 39(3), 206–214 (2009)CrossRefGoogle Scholar
  35. 35.
    Markham, N.R., Zuker, M.: UNAFold: software for nucleic acid folding and hybridization. In: Bioinformatics: Structure, Function and Applications, pp. 3–31 (2008)Google Scholar
  36. 36.
    Seemann, S.E., Gorodkin, J., Backofen, R.: Unifying evolutionary and thermodynamic information for RNA folding of multiple alignments. Nucleic Acids Res. 36(20), 6355–6362 (2008)CrossRefGoogle Scholar
  37. 37.
    Andersen, E.S., Lind-Thomsen, A., Knudsen, B., Kristensen, S.E., Havgaard, J.H., Torarinsson, E., Gorodkin, J.: Semiautomated improvement of RNA alignments. RNA 13(11), 1850–1859 (2007)CrossRefGoogle Scholar
  38. 38.
    Meyer, I.M., Mikls, I.: SimulFold: simultaneously inferring RNA structures including pseudoknots, alignments, and trees using a Bayesian MCMC framework. PLoS Comput. Biol. 3(8), e149 (2007)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Zhang, T., Guo, M., Zou, Q.: RNA secondary structure prediction based on forest representation and genetic algorithm. In: Third International Conference on Natural Computation, 2007, ICNC 2007, vol. 4, pp. 370–374. IEEE, August 2007Google Scholar
  40. 40.
    Bindweed, E., Shapiro, B.A.: RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers. RNA 12(3), 342–352 (2006)CrossRefGoogle Scholar
  41. 41.
    Tan, G., Feng, S., Sun, N.: Locality and parallelism optimization for dynamic programming algorithm in bioinformatics. In: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, p. 78. ACM, November 2006Google Scholar
  42. 42.
    Namsrai, O.E., Jung, K.S., Kim, S., Ryu, K.H.: RNA secondary structure prediction with simple pseudo knots based on dynamic programming. In: International Conference on Intelligent Computing, pp. 303–311. Springer, Berlin, August 2006Google Scholar
  43. 43.
    Mathews, D.H., Disney, M.D., Childs, J.L., Schroeder, S.J., Zuker, M., Turner, D.H.: Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl. Acad. Sci. U.S.A. 101(19), 7287–7292 (2004)CrossRefGoogle Scholar
  44. 44.
    Reeder, J., Giegerich, R.: Design, implementation and evaluation of a practical pseudoknot folding algorithm based on thermodynamics. BMC Bioinform. 5(1), 104 (2004)CrossRefGoogle Scholar
  45. 45.
    Hofacker, I.L.: Vienna RNA secondary structure server. Nucleic Acids Res. 31(13), 3429–3431 (2003)CrossRefGoogle Scholar
  46. 46.
    Feo, T.A., Resende, M.G.: Greedy randomized adaptive search procedures. J. Global Optim. 6(2), 109–133 (1995)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Abdelhakim El Fatmi
    • 1
    • 2
  • Arakil Chentoufi
    • 1
    • 2
  • M. Ali Bekri
    • 1
    • 2
  • Said Benhlima
    • 1
    • 2
  • Mohamed Sabbane
    • 1
    • 2
  1. 1.Faculty of ScienceMoulay Ismail UniversityMeknesMorocco
  2. 2.MACS Laboratory, Computer Science Department, Faculty of ScienceMoulay Ismail UniversityMeknesMorocco

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