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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Akutsu, T.: Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots. Discrete Applied Mathematics 104, 45–62 (2000)
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)
Condon, A., Davy, B., Rastegari, B., Zhao, S., Tarrant, F.: Classifying RNA pseudoknotted structures. Theoretical Computer Science 320(1), 35–50 (2004)
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)
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)
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)
Lagos-Quintana, M., Rauhut, R., Lendeckel, W., Tuschl, T.: Identification of novel genes coding for small expressed RNAs. Science 294, 853–857 (2001)
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)
Lyngso, R.B., Zuker, M., Pedersen, C.N.S.: Fast evaluation of internal loops in RNA secondary structure prediction. Bioinformatics 15, 440–445 (1999)
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)
McManus, M.T., Sharp, P.A.: Gene silencing in mammals by small interfering RNAs. Nature Reviews Genetics 10, 737–747 (2002)
Moss, E.G.: RNA interference: It’s a small RNA world. Current Biology 11, R772–R775 (2001)
Moss, E.G.: MicroRNAs: Hidden in the Genome. Current Biology 12, R138–R140 (2002)
Notredame, C., O’Brien, E.A., Higgins, D.G.: RAGA: RNA sequence alignment by genetic algorithm. Nucleic Acids Research 25(22), 4570–4580 (1997)
Nussinov, R., Jacobson, A.: Fast algorithm for predicting the secondary structure of single stranded RNA. PNAS 77, 6309–6313 (1980)
NCBI web site, http://www.ncbi.nlm.nih.gov
Pervouchine, D.D.: IRIS: Intermolecular RNA Interaction Search. In: 15th Int. Conf. Genome Informatics (2004)
Peyret, N., SantaLucia, J.: HYTHERTM version 1.0. Wayne State University, http://ozone2.chem.wayne.edu/Hyther/hythermenu.html
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)
Rivas, E., Eddy, S.R.: A dynamic programming algorithm for RNA structure prediction including pseudoknots. J. Mol. Biol. 285(5), 2053–2068 (1999)
Wagner, E.G.H., Flardh, K.: Antisense RNAs everywhere? TRENDS in Genetics 18(5), 223–226 (2002)
Zhang, K., Wang, L., Ma, B.: Computing similarity between RNA structures. Theoretical Computer Sciences 276(1-2), 111–132 (2002)
Zuker, M., Stiegler, P.: Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 9, 133–148 (1981)
Zuker, M.: On finding all suboptimal foldings of an RNA molecule. Science 244, 48–52 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alkan, C., Karakoç, E., Nadeau, J.H., Şahinalp, S.C., Zhang, K. (2005). RNA-RNA Interaction Prediction and Antisense RNA Target Search. In: Miyano, S., Mesirov, J., Kasif, S., Istrail, S., Pevzner, P.A., Waterman, M. (eds) Research in Computational Molecular Biology. RECOMB 2005. Lecture Notes in Computer Science(), vol 3500. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11415770_12
Download citation
DOI: https://doi.org/10.1007/11415770_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25866-7
Online ISBN: 978-3-540-31950-4
eBook Packages: Computer ScienceComputer Science (R0)