RNA-RNA Interaction Prediction and Antisense RNA Target Search

  • Can Alkan
  • Emre Karakoç
  • Joseph H. Nadeau
  • S. Cenk Şahinalp
  • Kaizhong Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3500)

Abstract

Recent studies demonstrating the existence of special non-coding “antisense” RNAs used in post-transcriptional gene regulation have received considerable attention. These RNAs are synthesized naturally to control gene expression in C.elegans, Drosophila and other organisms; they are known to regulate plasmid copy numbers in E.coli as well. Small RNAs have also been artificially constructed to knock-out genes of interest in humans and other organisms for the purpose of finding out more about their functions.

Although there are a number of algorithms for predicting the secondary structure of a single RNA molecule, no such algorithm exists for reliably predicting the joint secondary structure of two interacting RNA molecules, or measuring the stability of such a joint structure. In this paper, we describe the RNA-RNA interaction prediction (RIP) problem between an antisense RNA and its target mRNA and develop efficient algorithms to solve it. Our algorithms minimize the joint free-energy between the two RNA molecules under a number of energy models with growing complexity. Because the computational resources needed by our most accurate approach is prohibitive for long RNA molecules, we also describe how to speed up our techniques through a number of heuristic approaches while experimentally maintaining the original accuracy. Equipped with this fast approach, we apply our method to discover targets for any given antisense RNA in the associated genome sequence.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Akutsu, T.: Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots. Discrete Applied Mathematics 104, 45–62 (2000)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Andronescu, M., Aguirre-Hernandes, R., Condon, A., Hoos, H.: RNAsoft: a suite of RNA secondary structure prediction and design software tools. Nucleic Acids Research 31(13), 3416–3422 (2003)CrossRefGoogle Scholar
  3. 3.
    Condon, A., Davy, B., Rastegari, B., Zhao, S., Tarrant, F.: Classifying RNA pseudoknotted structures. Theoretical Computer Science 320(1), 35–50 (2004)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Collins, G., Le, S., Zhang, K.: A new algorithm for computing similarity between RNA structures. In: Proc. 5th Joint Conf. on Information Science, Atlantic City, NJ, March 2000, vol. 2, pp. 761–765 (2000)Google Scholar
  5. 5.
    Kim, C.-H., Tinoco Jr., I.: A Retroviral RNA Kissing Complex Containing Only Two G-C Base Pairs. Proc. Nat. Acad. Sci. USA 97, 93–96 (2000)Google Scholar
  6. 6.
    Kolb, F.A., Engdahl, H.M., Slagter-Jager, J.G., Ehresmann, B., Ehresmann, C., Westhof, E., Wagner, E.G.H., Romby, P.: Progression of a loop-loop complex to a four-way junction is crucial for the activity of a regulatory antisense RNA. EMBO Journal 19(21), 5905–5915 (2000)CrossRefGoogle Scholar
  7. 7.
    Lagos-Quintana, M., Rauhut, R., Lendeckel, W., Tuschl, T.: Identification of novel genes coding for small expressed RNAs. Science 294, 853–857 (2001)CrossRefGoogle Scholar
  8. 8.
    Lau, N.C., Lim, L.P., Weinstein, E.G., Bartel, D.P.: An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294, 858–862 (2001)CrossRefGoogle Scholar
  9. 9.
    Lyngso, R.B., Zuker, M., Pedersen, C.N.S.: Fast evaluation of internal loops in RNA secondary structure prediction. Bioinformatics 15, 440–445 (1999)CrossRefGoogle Scholar
  10. 10.
    Mathews, D., Sabina, J., Zuker, M., Turner, D.: Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. Journal of Molecular Biology 288, 911–940 (1999)CrossRefGoogle Scholar
  11. 11.
    McManus, M.T., Sharp, P.A.: Gene silencing in mammals by small interfering RNAs. Nature Reviews Genetics 10, 737–747 (2002)CrossRefGoogle Scholar
  12. 12.
    Moss, E.G.: RNA interference: It’s a small RNA world. Current Biology 11, R772–R775 (2001)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Moss, E.G.: MicroRNAs: Hidden in the Genome. Current Biology 12, R138–R140 (2002)CrossRefGoogle Scholar
  14. 14.
    Notredame, C., O’Brien, E.A., Higgins, D.G.: RAGA: RNA sequence alignment by genetic algorithm. Nucleic Acids Research 25(22), 4570–4580 (1997)CrossRefGoogle Scholar
  15. 15.
    Nussinov, R., Jacobson, A.: Fast algorithm for predicting the secondary structure of single stranded RNA. PNAS 77, 6309–6313 (1980)CrossRefGoogle Scholar
  16. 16.
  17. 17.
    Pervouchine, D.D.: IRIS: Intermolecular RNA Interaction Search. In: 15th Int. Conf. Genome Informatics (2004)Google Scholar
  18. 18.
    Peyret, N., SantaLucia, J.: HYTHERTM version 1.0. Wayne State University, http://ozone2.chem.wayne.edu/Hyther/hythermenu.html
  19. 19.
    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
  20. 20.
    Rivas, E., Eddy, S.R.: A dynamic programming algorithm for RNA structure prediction including pseudoknots. J. Mol. Biol. 285(5), 2053–2068 (1999)CrossRefGoogle Scholar
  21. 21.
    Wagner, E.G.H., Flardh, K.: Antisense RNAs everywhere? TRENDS in Genetics 18(5), 223–226 (2002)CrossRefGoogle Scholar
  22. 22.
    Zhang, K., Wang, L., Ma, B.: Computing similarity between RNA structures. Theoretical Computer Sciences 276(1-2), 111–132 (2002)MATHCrossRefMathSciNetGoogle Scholar
  23. 23.
    Zuker, M., Stiegler, P.: Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 9, 133–148 (1981)CrossRefGoogle Scholar
  24. 24.
    Zuker, M.: On finding all suboptimal foldings of an RNA molecule. Science 244, 48–52 (1989)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Can Alkan
    • 1
    • 2
  • Emre Karakoç
    • 2
  • Joseph H. Nadeau
    • 3
  • S. Cenk Şahinalp
    • 2
  • Kaizhong Zhang
    • 4
  1. 1.Department of EECSCase Western Reserve UniversityClevelandUSA
  2. 2.School of Computing ScienceSimon Fraser UniversityBurnabyCanada
  3. 3.Department of GeneticsCase Western Reserve UniversityClevelandUSA
  4. 4.Department of Computer ScienceUniversity of Western OntarioLondonCanada

Personalised recommendations