A Local Structural Prediction Algorithm for RNA Triple Helix Structure

  • Bay-Yuan Hsu
  • Thomas K. F. Wong
  • Wing-Kai Hon
  • Xinyi Liu
  • Tak-Wah Lam
  • Siu-Ming Yiu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7986)

Abstract

Secondary structure prediction (with or without pseudoknots) of an RNA molecule is a well-known problem in computational biology. Most of the existing algorithms have an assumption that each nucleotide can interact with at most one other nucleotide. This assumption is not valid for triple helix structure (a pseudoknotted structure with tertiary interactions). As these structures are found to be important in many biological processes, it is desirable to develop a prediction tool for these structures. We provide the first structural prediction algorithm to handle triple helix structures. Our algorithm runs in O(n 3) time where n is the length of input RNA sequence. The accuracy of the prediction is reasonably high, with average sensitivity and specificity over 80% for base pairs, and over 70% for tertiary interactions.

Keywords

Single Base Active Node Adjunct Tree Triple Helix Nonterminal Symbol 
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.

References

  1. 1.
    Lyngso, R., Pedersen, C.: A dynamic programming algorithm for RNA structure prediction including pseudoknots. In: Proc. of the Fourth Annual International Conferences on Compututational Molecular Biology (RECOMB 2000). ACM Press (2000)Google Scholar
  2. 2.
    Akutsu, T.: Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots. Discrete Applied Mathematics 104, 45–62 (2000)MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Chen, H., Condon, A., Jabbari, H.: An O(n 5) algorithm for MFE prediction of kissing hairpins and 4- chains in nucleic acids. Journal of Computational Biology 16(6), 803–815 (2009)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Dirks, R., Pierce, N.: A partition function algorithm for nucleic acid secondary structure including pseudoknots. Journal of Comput. Chem. 24(13), 1664–1677 (2003)CrossRefGoogle Scholar
  5. 5.
    Reeder, J., Giegerich, R.: Design, implementation and evaluation of a practical pseudoknot folding algorithm based on thermodynamics. BMC Bioinformatics 5, 104 (2004)Google Scholar
  6. 6.
    Rivas, E., Eddy, S.: A dynamic programming algorithm for RNA structure prediction including pseudoknots. Journal of Molecular Biology 285(5), 2053–2068 (1999)CrossRefGoogle Scholar
  7. 7.
    Uemura, Y., Hasegawa, A., Kobayashi, S., Yokomori, T.: Tree adjoining grammars for RNA structure prediction. Theoretical Computer Science 210, 277–303 (1999)MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    Qiao, F., Cech, T.R.: Triple-helix structure in telomerase RNA contributes to catalysis. Nature Structural and Molecular Biology 15(6), 634–640 (2008)CrossRefGoogle Scholar
  9. 9.
    Chen, J.L., Greider, C.W.: Functional analysis of the pseudoknot structure in human telomerase RNA. Proc. Natl. Acad. Sci. USA 102, 8080–8085 (2005)CrossRefGoogle Scholar
  10. 10.
    Theimer, C.A., Blois, C.A., Feigon, J.: Structure of the human telomerase RNA pseudoknot reveals conserved tertiary interactions essential for function. Molecular Cell 17, 671–682 (2005)CrossRefGoogle Scholar
  11. 11.
    Su, L., Chen, L., Egli, M., Berger, J.M., Rich, A.: Minor groove RNA triplex in the crystal structure of a ribosomal frameshifting viral pseudoknot. Nature Structural Biology 6(3), 285–292 (1999)CrossRefGoogle Scholar
  12. 12.
    Chen, X., Chamorro, M., Lee, S.I., Shen, L.X., Hines, J.V., Tinoco Jr., I., Varmus, H.E.: Structural and functional studies of retroviral RNA pseudoknots involved in ribosomal frameshifting: nucleotides at the junction of the two stems are important for efficient ribosomal frameshifting. EMBO 14(4), 842–852 (1995)Google Scholar
  13. 13.
    Siederdissen, C., Bernhart, S., Stadler, P., Hofacker, I.: A folding algorithm for extended RNA secondary structures. Bioinformatics 27, i29–i36 (2011) (ISMB 2011)Google Scholar
  14. 14.
    Wong, T.K., Lam, T., Yiu, S.: Structural alignment of RNA with triple helix structure. Journal of Computational Biology 19(4), 365–378 (2012)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Matsui, H., Sato, K., Sakakibara, Y.: Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures. Bioinformatics 21, 2611–2617 (2005)CrossRefGoogle Scholar
  16. 16.
    Dowell, R.D., Eddy, S.R.: Evaluation of several lightweight stochastic context-free grammars for rna secondary structure prediction. BMC Bioinformatics 5, 71 (2004)Google Scholar
  17. 17.
    Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G.: Covariance models: SCFG-based RNA profiles. In: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press (1998)Google Scholar
  18. 18.
    Chastain, M., Tinoco, I.J.: A base-triple structural domain in RNA. Biochemistry 31, 12733–12741 (1992)CrossRefGoogle Scholar
  19. 19.
    Dawson, W.K., Fujiwara, K., Kawai, G.: Prediction of RNA pseudoknots using heuristic modeling with mapping and sequential folding. PLoS One 2(9), e905 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bay-Yuan Hsu
    • 1
  • Thomas K. F. Wong
    • 2
  • Wing-Kai Hon
    • 1
  • Xinyi Liu
    • 3
  • Tak-Wah Lam
    • 3
  • Siu-Ming Yiu
    • 3
  1. 1.Department of Computer ScienceNational Tsing Hua UniversityTaiwan
  2. 2.Ecosystem Sciences, CSIROAustralian Capital TerritoryCanberraAustralia
  3. 3.Department of Computer ScienceThe University of Hong KongHong Kong

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