Dynamic De-Novo Prediction of microRNAs Associated with Cell Conditions: A Search Pruned by Expression

  • Chaya Ben-Zaken Zilberstein
  • Michal Ziv-Ukelson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3692)


Biological background: Plant microRNAs (miRNAs) are short RNA sequences that bind to target genes (mRNAs) and change their expression levels by redirecting their stabilities and marking them for cleavage. In Arabidopsis thaliana, microRNAs have been shown to regulate development and are believed to impact expression both under many conditions, such as stress and stimuli, as well as in various tissue types.

Methods: mirXdeNovo is a novel prototype tool for the de-novo prediction of microRNAs associated with a given cell condition. The work of mirXdeNovo is composed of two off-line preprocessing stages, which are executed only once per genome in the database, and a dynamic online main stage, which is executed again and again for each newly obtained expression profile. During the preprocessing stages, a set of candidate microRNAs is computed for the genome of interest and then each microRNA is associated with a set of mRNAs which are its predicted targets.

Then, during the main stage, given a newly obtained cell condition represented by a vector describing the expression level of each of the genes under this condition, the tool will efficiently compute the subset of microRNA candidates which are predicted to be active under this condition. The efficiency of the main stage is based in a novel branch-and-bound search of a tree constructed over the microRNA candidates and annotated with the corresponding predicted targets. This search exploits the monotonicity of the target prediction decision with respect to microRNA prefix size in order to apply an efficient yet admissible pruning. Our testing indicates that this pruning results in a substantial speed up over the naive search.

Biological Results: We employed mirXdeNovo to conduct a study, using the plant Arabidopsis thaliana as our model organism and the subject of our ”hunt for microRNAs”. During the preprocessing stage, 2000 microRNA precursor candidates were extracted from the genome. Our study included the 3’UTRs of 5800 mRNAs. 380 different conditions were analyzed including various tissues and hormonal treatments. This led to the discovery of some interesting and statistically significant newly predicted microRNAs, annotated with their potential condition of activity.


Cell Condition microRNA Target Prototype Tool Stable Duplex microRNA Precursor 
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.
    Adai, A., Johnson, C., Mlotshwa, S., Archer-Evans, S., Manocha, V., Vance, V., Sundaresan, V.: Computational prediction of miRNAs in arabidopsis thaliana. Proc. Natl. Acad. Sci. U S A 15, 78–91 (2005)Google Scholar
  2. 2.
    Bonnet, E., Wuyts, J., Rouze, P., Van de Peer, Y.: Detection of 91 potential conserved plant microRNAs in Arabidopsis thaliana and Oryza sativa identifies important target genes. Proc. Natl. Acad. Sci. U S A 101, 11511–11516 (2004)CrossRefGoogle Scholar
  3. 3.
    Cabrera, C.V., Lee, J.J., Ellison, J.W., Britten, R.J., Davidson, E.H.: Regulation of cytoplasmic mRNA prevalence in sea urchin embryos. Rates of appearance and turnover for specific sequences. J Mol Biol 174(1), 85–111 (1984)CrossRefGoogle Scholar
  4. 4.
    Casey, J.L., Hentze, M.W., Koeller, D.M., Caughman, S.W., Rouault, T.A., Klausner, R.D., Harford, J.B.: Iron-responsive elements: regulatory RNA sequences that control mRNA levels and translation. Science 240, 924–928 (1988)CrossRefGoogle Scholar
  5. 5.
    Enright, A.J., et al.: MicroRNA targets in drosophila. Genome Biol., Pubmed 5(1), 12 (2003)Google Scholar
  6. 6.
    Stark, A., et al.: Identification of Drosophila microRNA targets. Plos. Biol., Pubmed 1(3) (2003)Google Scholar
  7. 7.
    Tang, G., et al.: Framework for RNA silencing in plants. Genes Dev. 17, 49–63 (2003)CrossRefGoogle Scholar
  8. 8.
    Kasschau, K.D., et al.: P1/HC-Pro, a viral suppressor of RNA silencing, interferes with Arabidopsis development and miRNA function. Dev. Cell 4, 205–217 (2003)CrossRefGoogle Scholar
  9. 9.
    Rhoades, M.W., et al.: Prediction of plant microRNA targets. Cell 23, 513–520 (2002)CrossRefGoogle Scholar
  10. 10.
    Yekta, S., et al.: MicroRNA-directed cleavage of HOXB8 mRNA. Science 304, 594–596 (2004)CrossRefGoogle Scholar
  11. 11.
    Grad, Y., Aach, J., Hayes, G.D., Reinhart, B.J., Church, G.M., Ruvkun, G., Kim, J.: Computational and experimental identification of C. elegans microRNAs. Mol Cell 11, 1253–1263 (2003)CrossRefGoogle Scholar
  12. 12.
    Heintz, N., Sive, H.L., Roeder, R.G.: Regulation of human histone gene expression: kinetics of accumulation and changes in the rate of synthesis and in the half-lives of individual histone mRNAs during the hela cell cycle. Mol Cell Biol 3(4), 539–550 (1983)Google Scholar
  13. 13.
    Jack, H.M., Wabl, M.: Immunoglobulin mRNA stability varies during B lymphocyte differentiation. EMBO J. 7(4), 1041–1046 (1988)Google Scholar
  14. 14.
    Zhang, B.T., Nam, J.W., Lee, W.J.: Computational methods for identification of human microrna precursors. In: Zhang, C., Guesgen, H.W., Yeap, W.-K. (eds.) PRICAI 2004. LNCS (LNAI), vol. 3157, pp. 732–741. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Krowczynska, A., Yenofsky, R., Brawerman, G.: Regulation of messenger RNA stability in mouse erythroleukemia cells. J Mol Biol 181(2), 231–239 (1985)CrossRefGoogle Scholar
  16. 16.
    Lai, E.C.: Predicting and validating microRNA targets. Genome Biol 5, 115 (2004)CrossRefGoogle Scholar
  17. 17.
    Legendre, M., Lambert, A., Gautheret, D.: Profile-based detection of microrna precursors in animal genomes. Bioinformatics 21(7), 841–845 (2005)CrossRefGoogle Scholar
  18. 18.
    Lim, L.P., Lau, N.C., Weinstein, E.G., Abdelhakim, A., Yekta, S., Rhoades, M.W., Burge, C.B., Bartel, D.P.: The microRNAs of C. elegans. Genes and Development 17, 991–1008 (2003)CrossRefGoogle Scholar
  19. 19.
    Llave, C., Xie, Z., Kasschau, K.D., Carrington, J.C.: Cleavage of scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science 297, 2053–2056 (2002)CrossRefGoogle Scholar
  20. 20.
    Mathews, D.H., Sabina, J., Zuker, M., Turner, D.H.: Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol. 288, 911–940 (1999)CrossRefGoogle Scholar
  21. 21.
    Rajewsky, N., Socci, N.C.: Computational identification of microRNA targets. Genome Biology, 5 (2004)Google Scholar
  22. 22.
    Rehmsmeier, M., Steffen, P., Hochsmann, M., Giegerich, R.: Fast and effective prediction of microRNA target duplexes. RNA 10, 1507–1517 (2004)CrossRefGoogle Scholar
  23. 23.
    Reinhart, B.J., Slack, F.J., Basson, M., Pasquinelli, A.E., Bettinger, J.C., Rougvie, A.E., Horvitz, H.R., Ruvkun, G.: The 21-nucleotide let-7 RNA regulates developmental timing in caenorhabditis elegans. Nature 403, 901–906 (2000)CrossRefGoogle Scholar
  24. 24.
    Ross, J.: mRNA stability in mammalian cells. Microbiol Rev 59(3), 423–450 (1995)Google Scholar
  25. 25.
    Ross, J.: Control of messenger RNA stability in higher eukaryotes. Trends. Genet. 12(5), 171–175 (1996)CrossRefGoogle Scholar
  26. 26.
    Sorenson, C.M., Hart, P.A., Ross, J.: Analysis of herpes simplex virus-induced mRNA destabilizing activity using an in vitro mRNA decay system. Nucleic Acids Res. 19, 4459–4465 (1991)CrossRefGoogle Scholar
  27. 27.
    Thomson, A.M., Rogers, J.T., Leedman, P.J.: Iron-regulatory proteins, iron-responsive elements and ferritin mRNA translation. Int J Biochem Cell Biol 31(10), 1139–1152 (1999)CrossRefGoogle Scholar
  28. 28.
    Warburton, P.E., Giordano, J., Cheung, F., Gelfand, Y., Benson, G.: Inverted repeat structure of the human genome: the x-chromosome contains a preponderance of large, highly homologous inverted repeats that contain testes genes. Genome Res, 1861–1869 (2004)Google Scholar
  29. 29.
    Zilberstein, C., Ukelson, M., Pinter, R., Yakhini, Z.: A high-throughput approach for associating micrornas with their activity conditions. In: The ninth annual international conference on research in computational molecular biology (2005)Google Scholar
  30. 30.
    Zuker, M.: Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 31(13), 3406–3415 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Chaya Ben-Zaken Zilberstein
    • 1
  • Michal Ziv-Ukelson
    • 1
  1. 1.Dept. of Computer ScienceTechnion – Israel Institute of TechnologyHaifaIsrael

Personalised recommendations