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RNA Secondary Structure Prediction with Simple Pseudoknots Based on Dynamic Programming

  • Oyun-Erdene Namsrai
  • Kwang Su Jung
  • Sunshin Kim
  • Keun Ho Ryu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)

Abstract

RNA molecules are sequences of nucleotides that serve as more than mere intermediaries between DNA and proteins, e.g. as catalytic molecules. The sequence of nucleotides of an RNA molecule constitutes its primary structure, and the pattern of pairing between nucleotides determines the secondary structure of an RNA. Computational prediction of RNA secondary structure is among the few structure prediction problems that can be solved satisfactory in polynomial time. Most work has been done to predict structures that do not contain pseudoknots. Pseudoknots have generally been excluded from the prediction of RNA secondary structures due to its difficulty in modelling. In this paper, we present a computation the maximum number of base pairs of an RNA sequence with simple pseudoknots. Our approach is based on pseudoknot technique proposed by Akutsu. We show that a structure with the maximum possible number of base pairs could be deduced by a improved Nussinov’s trace-back procedure. In our approach we also considered wobble base pairings (G·U). We introduce an implementation of RNA secondary structure prediction with simple pseudoknots based on dynamic programming algorithm. To evaluate our method we use the 15 sequences with simple pseudoknots of variable size from 19 to 25 nucleotides. We get our experimental data set from PseudoBase. Our program predicts simple pseudoknots with correct or almost correct structure for 53% sequences.

Keywords

Nature Publishing Group Pseudoknotted Structure Structure Prediction Problem Wobble Base Pairing Nucleic Acid Secondary Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Eddy, S.R.: What is Dynamic Programming? Nature BioTechnology 22(7) (2004) Nature Publishing Group, November, JulyGoogle Scholar
  2. 2.
    Robin, D.D.: Prepared under the Direction of Sean R. Eddy.: RNA Structural Alignment Using Stochastic Context-free Grammars. Ph.D thesis presented to the Sever Institute of Washington University (December 2004)Google Scholar
  3. 3.
    Stephen McCauley advised by Ian Holmes.: An Analysis of the Relative Efficacy of the Nussinov-Felsenstein, and the Knudsen-Hein, RNA Secondary Structure Prediction, Ph.D thesis presented in October 6 (2003)Google Scholar
  4. 4.
    Eddy, S.R.: How do RNA Folding Algorithms Works? Nature BioTechnology, vol. 22. Nature Publishing Group (2004)Google Scholar
  5. 5.
    Pipas, J., McMahon, J.: A Method for Predicting RNA Secondary Structure. Proc Natl. Acad. Sci. 72, 2017–2021 (1975)CrossRefGoogle Scholar
  6. 6.
    Sankoff, D.: Simultaneous Solution of the RNA Folding, Alignment, and Protosequence Problems. SIAM J. Appl. Math. 45, 810–825 (1985)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Nussinov, R.P., Eddy, S.R., Griggs, J.R., Kleitman, D.J.: Algorithms for Loop Matching. SIAM Journal of Applied Mathematics 35, 68–82 (1978)zbMATHCrossRefGoogle Scholar
  8. 8.
    Nussinov, R., Jacobson, A.: Fast Algorithm for Predicting the Secondary Structure of Single-stranded RNA. Proc. Natl. Acad. Sci. 77, 6309–6313 (1980)CrossRefGoogle Scholar
  9. 9.
    Waterman, M.S., Smith, T.F.: RNA Secondary Structure: A Complete Mathematical Analysis. Mathematical Bioscience 42, 257–266 (1978)zbMATHCrossRefGoogle Scholar
  10. 10.
    Zuker, M., Stiegler, P.: Optimal Computer Folding of Large RNA Sequences Using Thermodynamics and Auxiliary Information. Nucleic Acids Res. 9(133) (1981)Google Scholar
  11. 11.
    Zuker, M., Sankoff, D.: RNA Secondary Structures and Their Prediction. Mathematical Bioscience 46, 591–621 (1984)zbMATHGoogle Scholar
  12. 12.
    Zuker, M.: On Finding All Suboptimal Foldings of an RNA Molecule. Science 244, 48–52 (1989)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Zuker, M., Mathews, D.H., Turner, D.H.: Algorithms and Thermodynamics for RNA Secondary Structure Prediction: A Practical Guide in RNA Biochemistry and Biotechnology. In: Barciszewski, J., Clark, B. (eds.) NATO ASI Series, Kluwer Academic Publishers, Dordrecht (1999)Google Scholar
  14. 14.
    Eddy, S.R., Durbin, R.: RNA Sequence Analysis Using Covariance Models. Nucl. Acids. Res. 22, 2079–2088 (1994)CrossRefGoogle Scholar
  15. 15.
    Gorodkin, J., Heyer, L.J., Stormo, G.D.: Finding the Most Significant Common Sequence and Structure Motifs in a set of RNA Sequences. Nucl. Acids. Res. 25, 3724–3732 (1997)CrossRefGoogle Scholar
  16. 16.
    Samuel, I., Ming-Yang, K.: Predicting RNA Secondary Structures with Arbitrary Pseudoknots by Maximizing the Number of Stacking Pairs. Journal of Computational biology 10(6), 981–995 (2003)CrossRefGoogle Scholar
  17. 17.
    Tabaska, J., Stormo, G.: Automated Alignment of RNA Sequences to Pseudoknotted Structures. In: Fifth International Conference on Intelligent Systems for Molecular Biology, pp. 311–318. The AAAI Press, Menlo Park (1997)Google Scholar
  18. 18.
    Tabaska, J.E., Cary, R.B., Gabow, H.N., Stormo, G.D.: An RNA Folding Method Capable of Identifying Pseudoknots and Base Triples. Bioinformatics 14, 691–699 (1998)CrossRefGoogle Scholar
  19. 19.
    Rivas, E., Eddy, S.: A Dynamic Programming Algorithm for RNA Structure Prediction Including Pseudoknots. Journal of Molecular Biology 285, 2053 (1999)CrossRefGoogle Scholar
  20. 20.
    Rivas, E., Eddy, S.R.: Noncoding RNA Gene Detection Using Comparative Sequence Analysis. BioMedCentral 2(8), Bioinformatics (2001)Google Scholar
  21. 21.
    Akutsu, T.: Dynamic Programming Algorithm for RNA Secondary Structure Prediction with Pseudoknots. Discrete Appl. Math. 104, 45–62 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Uemura, Y., Hasegawa, A., Kobayashi, S., Yokomori, T.: Tree Adjoining Grammars for RNA Structure Prediction. Theoretical Computer Science 210, 277–303 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  23. 23.
    Dirks, R.M., Pierce, N.A.: A Partition Function Algorithm for Nucleic Acid Secondary Structure Including Pseudoknots. J. Comput. Chem. 24, 1664–1677 (2003)CrossRefGoogle Scholar
  24. 24.
    Crick, F.H.: Codon–anticodon Pairing: The Wobble Hypothesis. J. Mol.Biol. 19, 548–555 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Oyun-Erdene Namsrai
    • 1
  • Kwang Su Jung
    • 1
  • Sunshin Kim
    • 1
  • Keun Ho Ryu
    • 1
  1. 1.Database/BioInformatics lab, School of Electrical & Computer EngineeringChungbuk National UniversityCheongju, ChungbukKorea

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