Abstract
mRNA molecules are tightly regulated, mostly through interactions with proteins and other RNAs, but the mechanisms that confer the specificity of such interactions are poorly understood. It is clear, however, that this specificity is determined by both the nucleotide sequence and secondary structure of the mRNA. We developed RNApromo, an efficient computational tool for identifying structural elements within mRNAs that are involved in specifying post-transcriptional regulations. Using RNApromo, we predicted putative motifs in sets of mRNAs with substantial experimental evidence for common post-transcriptional regulation, including mRNAs with similar decay rates, mRNAs that are bound by the same RNA binding protein, and mRNAs with a common cellular localization. Our new RNA motif discovery tool reveals unexplored layers of post-transcriptional regulations in groups of RNAs, and is therefore an important step toward a better understanding of the regulatory information conveyed within RNA molecules.
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Arava, Y., et al., Genome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A, 2003. 100(7): p. 3889–94.
Shepard, K.A., et al., Widespread cytoplasmic mRNA transport in yeast: identification of 22 bud–localized transcripts using DNA microarray analysis. Proc Natl Acad Sci U S A, 2003. 100(20): p. 11429–34.
Wang, Y., et al., Precision and functional specificity in mRNA decay. Proc Natl Acad Sci U S A, 2002. 99(9): p. 5860–5.
Anantharaman, V., E.V. Koonin, and L. Aravind, Comparative genomics and evolution of proteins involved in RNA metabolism. Nucleic Acids Res, 2002. 30(7): p. 1427–64.
Hentze, M.W., M.U. Muckenthaler, and N.C. Andrews, Balancing acts: molecular control of mammalian iron metabolism. Cell, 2004. 117(3): p. 285–97.
Olivier, C., et al., Identification of a conserved RNA motif essential for She2p recognition and mRNA localization to the yeast bud. Mol Cell Biol, 2005. 25(11): p. 4752–66.
Krol, A., Evolutionarily different RNA motifs and RNA-protein complexes to achieve selenoprotein synthesis. Biochimie, 2002. 84(8): p. 765–74.
Kertesz, M., et al., The role of site accessibility in microRNA target recognition. Nat Genet, 2007. 39(10): p. 1278–84.
Robins, H., Y. Li, and R.W. Padgett, Incorporating structure to predict microRNA targets. Proc Natl Acad Sci U S A, 2005. 102(11): p. 4006–9.
Long, D., et al., Potent effect of target structure on microRNA function. Nat Struct Mol Biol, 2007. 14(4): p. 287–94.
Zhao, Y., E. Samal, and D. Srivastava, Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis. Nature, 2005. 436(7048): p. 214–20.
Rabani, M., M. Kertesz, and E. Segal, Computational prediction of RNA structural motifs involved in posttranscriptional regulatory processes. Proc Natl Acad Sci U S A, 2008. 105(39): p. 14885–90.
Hofacker L.I., F.W., Stadler P.F., Bonhoeffer L.S., Tacker M., Schuster P., Fast Folding and Comparison of RNA Secondary Structures. Monatshefte fur Chemie, 1994. 125: p. 167–88.
Wuchty, S., et al., Complete suboptimal folding of RNA and the stability of secondary structures. Biopolymers, 1999. 49(2): p. 145–65.
Do, C.B., D.A. Woods, and S. Batzoglou, CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics, 2006. 22(14): p. e90–8.
Bleasby A. Rice P., Longden I. EMBOSS: The european molecular biology open software suite. Trends in Genetics, 16(6):276–277, 2000.
Eddy, S.R. and R. Durbin, RNA sequence analysis using covariance models. Nucleic Acids Res, 1994. 22(11): p. 2079–88.
Sakakibara, Y., et al., Stochastic context-free grammars for tRNA modeling. Nucleic Acids Res, 1994. 22(23): p. 5112–20.
Holmes, I., Accelerated probabilistic inference of RNA structure evolution. BMC Bioinformatics, 2005. 6: p. 73.
Yao, Z., Z. Weinberg, and W.L. Ruzzo, CMfinder--a covariance model based RNA motif finding algorithm. Bioinformatics, 2006. 22(4): p. 445–52.
Wiese, K.C. and A. Hendriks, Comparison of P-RnaPredict and mfold–algorithms for RNA secondary structure prediction. Bio-informatics, 2006. 22(8): p. 934–42.
He, L. and G.J. Hannon, MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet, 2004. 5(7): p. 522–31.
van Dongen S. Bateman A. Enright A.J. Griffiths-Jones S., Grocock R.J. miRBase: microRNA sequences, targets and gene nomenclature. nuc. acid res., 34:D140–4.
Griffiths-Jones S. The microRNA registry. nuc. acid res., 32:D109–11.
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Rabani, M., Kertesz, M., Segal, E. (2011). Computational Prediction of RNA Structural Motifs Involved in Post-Transcriptional Regulatory Processes. In: Gerst, J. (eds) RNA Detection and Visualization. Methods in Molecular Biology, vol 714. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-61779-005-8_28
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DOI: https://doi.org/10.1007/978-1-61779-005-8_28
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