Advertisement

Computational Methods for Identification of Human microRNA Precursors

  • Jin-Wu Nam
  • Wha-Jin Lee
  • Byoung-Tak Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3157)

Abstract

MicroRNA (miRNA), one of non-coding RNAs (ncRNAs), regulates gene expression directly by arresting the messenger RNA (mRNA) translation, which is important for identifying putative miRNAs. In this study, we suggest a searching procedure for human miRNA precursors using genetic programming that automatically learn common structures of miRNAs from a set of known miRNA precursors. Our method consists of three-steps. At first, for each miRNA precursor, we adopted genetic programming techniques to optimize the RNA Common-Structural Grammar (RCSG) of populations until certain fitness is achieved. In this step, the specificity and the sensitivity of a RCSG for the training data set were used as the fitness criteria. Next, for each optimized RCSG, we collected candidates of matching miRNA precursors with the corresponding grammar from genome databases. Finally, we selected miRNA precursors over a threshold (= 365) of scoring model from the candidates. This step would reduce false positives in the candidates. To validate the effectiveness of our miRNA method, we evaluated the learned RCSG and the scoring model with test data. Here, we obtained satisfactory results, with high specificity (= 51/64) and proper sensitivity (= 51/82) using human miRNA precursors as a test data set.

Keywords

Genetic Programming Function Tree miRNA Precursor Negative Training 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Huttenhofer, A., Brosius, J., Bachellerie, J.P.: RNomics: identification and function of small, non-messenger RNAs. Current Opinion in Chemical Biology 6, 835–843 (2002)CrossRefGoogle Scholar
  2. 2.
    Ambros, V.: Tiny regulators with great potential. Cell 107, 823–826 (2001)CrossRefGoogle Scholar
  3. 3.
    Gottesman, S.: Genes & Development 16, 2829–2842 (2002)CrossRefGoogle Scholar
  4. 4.
    Zamore, P.D.: Ancient Pathways programmed by small RNAs. Science 296, 1265–1269 (2002)CrossRefGoogle Scholar
  5. 5.
    Lagos-Quintana, M., Rauhut, R., Lendeckel, W., Tuschl, T.: Identification of novel genes coding for small expressed RNAs. Science 294, 853–858 (2001)CrossRefGoogle Scholar
  6. 6.
    Lim, L.P., Glasner, M.E., Yekta, S., Burge, C.B., Bartel, D.P.: Vertebrate microRNA genes. Science 299, 1540 (2003)CrossRefGoogle Scholar
  7. 7.
    Lagos-Quintana, M., Rauhut, R., Meyer, J., Borkhardt, A., Tuschl, T.: New microRNAs form mouse and human. RNA 9, 175–179 (2003)CrossRefGoogle Scholar
  8. 8.
    Dostie, J., Mourelatos, Z., Yang, M., Sharma, A., Dreyfuss, G.: Numerous microRNPs in neuronal cell containing novel microRNA. RNA 9, 180–186 (2003)CrossRefGoogle Scholar
  9. 9.
    Lai, E.C., Tomancak, P., Williams, R.W., Rubin, G.M.: Computational identification of Drosophila microRNA genes. Genome Biology 4, R42 (2003)CrossRefGoogle Scholar
  10. 10.
    Koza, J.R.: Genetic programming: On the programming of computers by means of natural selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  11. 11.
    Angeline, P.J., Kinnear, K.E.: Advances in Genetic Programming, vol. 2. Jr MIT Press, Cambridge (1996)Google Scholar
  12. 12.
    Macke, T.J., Ecker, D.J., Gutell, R.R., Gautheret, D.: Case and Rangarajan Sampath. RNAmotif, an RNA secondary structure definition and search algorithms. Nucleic Acids Research 29, 4724–4735 (2001)CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    Siebert, S., Backofen, R.: MARNA: A Server for Multiple Alignment of RNAs. In: Proceedings of the German Conference on Bioinformatics, pp. 135–140 (2003)Google Scholar
  15. 15.
    Weiss, S.M., Kapouleas, I.: An empirical comparison of pattern recognition neural nets and machine learning classification methods. In: Proceedings of the 11th International Joint Conference on Artificial Intelligence. Detroit, Mich: IJCA1, pp. 234–237 (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jin-Wu Nam
    • 1
  • Wha-Jin Lee
    • 1
  • Byoung-Tak Zhang
    • 1
  1. 1.Graduate Program in Bioinformatics Center for Bioinformation Technology Biointelligence LaboratorySchool of Computer Science and Engineering Seoul National UniversitySeoulKorea

Personalised recommendations