Two-Step Genetic Programming for Optimization of RNA Common-Structure

  • Jin-Wu Nam
  • Je-Gun Joung
  • Y. S. Ahn
  • Byoung-Tak Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3005)


We present an algorithm for identifying putative non-coding RNA (ncRNA) using an RCSG (RNA Common-Structural Grammar) and show the effectiveness of the algorithm. The algorithm consists of two steps: structure learning step and sequence learning step. Both steps are based on genetic programming. Generally, genetic programming has been applied to learning programs automatically, reconstructing networks, and predicting protein secondary structures. In this study, we use genetic programming to optimize structural grammars. The structural grammars can be formulated as rules of tree structure including function variables. They can be learned by genetic programming. We have defined the rules on how structure definition grammars can be encoded into function trees. The performance of the algorithm is demonstrated by the results obtained from the experiments with RCSG of tRNA and 5S small RNA.


Function Tree miRNA Precursor Nucleic Acid Research Learning Step Expression Rule 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Rivas, E., Eddy, S.R.: A dynamic programming algorithm for RNA structure prediction including pseudoknots. J. Mol. Biol. 285, 2053–2068 (1999)CrossRefGoogle Scholar
  2. 2.
    Zuker, M.: On finding all suboptimal foldings of an RNA molecule. Science 244, 48–52 (1989)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Eddy, S.R., Durbin, R.: RNA sequence analysis using covariance models. Nucleic Acids Research 22, 2079–2088 (1994)CrossRefGoogle Scholar
  4. 4.
    Gorodkin, J., Stricklin, S.L., Stormo, G.D.: Discovering common stem-loop motifs in unaligned RNA sequences. Nucleic Acids Reearch 29, 2135–2144 (2001)CrossRefGoogle Scholar
  5. 5.
    Perriquet, O., Touzet, H., Dauchet, M.: Finding the common structure shared by two homolous RNAs. Bioinformatics 19, 108–116 (2003)CrossRefGoogle Scholar
  6. 6.
    Fogel, G.B., William Porto, V., Weekers, D.G., Fogel, D.B., Griffey, R.H., McNeil, J.A., Lesnik, E., Ecker, D.J., Sampath, R.: Discovery of RNA structural elements using evolutionary computation. Nucleic Acids Research 30, 5310–5317 (2002)CrossRefGoogle Scholar
  7. 7.
    Chen, J.-H., Le, S.-Y., Maizel, J.V.: Prediction of common secondary structures of RNAs: a genetic algorithm approach. Nucleic Acids Research 28, 991–999 (2000)CrossRefGoogle Scholar
  8. 8.
    Sakakibara, Y.: Pair Hidden Markov models on tree structures Bioinformatics. Bioinformatics 19, i232– i240 (2003)CrossRefGoogle Scholar
  9. 9.
    Cai, L., Malmberg, R.L., Wu, Y.: Stochastic modeling or RNA pseudoknotted structures: a grammatical approach. Bioinformatics 19, i66–i73 (2003)CrossRefGoogle Scholar
  10. 10.
    Sakakibara, Y., Brwon, M., Hughey, R., Mian, I.S., Sjolander, K., Underwood, R.C., Haussler, D.: Stochastic context-free grammars for tRNA modeling. Nucleic Acids Research 22, 5112–5120 (1994)CrossRefGoogle Scholar
  11. 11.
    Knudsen, B., Hein, J.: RNA secondary structure prediction using stochastic context-free grammars and evolutionary history. Bioinformatics 15, 446–454 (1999)CrossRefGoogle Scholar
  12. 12.
    Macke, T.J., Ecker, D.J., Gutell, R.R., Gautheret, D., Case, D.A., Sampath, R.: RNAMotif, an RNA secondary structure definition and search algorithms. Nucleic Acids Research 29, 4724–4735 (2001)CrossRefGoogle Scholar
  13. 13.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  14. 14.
    Zhang, B.-T., Ohm, P., Mühlenbein, H.: Evolutionary neural trees for modeling and predicting complex systems. Engineering Applications of Artificial Intelligence 10, 473–483 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jin-Wu Nam
    • 1
    • 2
  • Je-Gun Joung
    • 1
    • 2
  • Y. S. Ahn
    • 4
  • Byoung-Tak Zhang
    • 1
    • 2
    • 3
  1. 1.Graduate Program in Bioinformatics 
  2. 2.Center for Bioinformation Technology (CBIT) 
  3. 3.Biointelligence LaboratorySchool of Computer Science and Engineering Seoul National UniversitySeoulKorea
  4. 4.Altenia CorporationKorea

Personalised recommendations