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Exact Learning of RNA Energy Parameters from Structure

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Research in Computational Molecular Biology (RECOMB 2014)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8394))

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Abstract

We consider the problem of exact learning of parameters of a linear RNA energy model from secondary structure data. A necessary and sufficient condition for learnability of parameters is derived, which is based on computing the convex hull of union of translated Newton polytopes of input sequences [15]. The set of learned energy parameters is characterized as the convex cone generated by the normal vectors to those facets of the resulting polytope that are incident to the origin. In practice, the sufficient condition may not be satisfied by the entire training data set; hence, computing a maximal subset of training data for which the sufficient condition is satisfied is often desired. We show that problem is NP-hard in general for an arbitrary dimensional feature space. Using a randomized greedy algorithm, we select a subset of RNA STRAND v2.0 database that satisfies the sufficient condition for separate A-U, C-G, G-U base pair counting model. The set of learned energy parameters includes experimentally measured energies of A-U, C-G, and G-U pairs; hence, our parameter set is in agreement with the Turner parameters.

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References

  1. Andronescu, M., Bereg, V., Hoos, H.H., Condon, A.: RNA STRAND: the RNA secondary structure and statistical analysis database. BMC Bioinformatics 9, 340 (2008)

    Article  Google Scholar 

  2. Andronescu, M., Condon, A., Hoos, H.H., Mathews, D.H., Murphy, K.P.: Computational approaches for RNA energy parameter estimation. RNA 16, 2304–2318 (2010)

    Article  Google Scholar 

  3. Backofen, R., Tsur, D., Zakov, S., Ziv-Ukelson, M.: Sparse RNA folding: Time and space efficient algorithms. In: Kucherov, G., Ukkonen, E. (eds.) CPM 2009 Lille. LNCS, vol. 5577, pp. 249–262. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  4. Bartel, D.P.: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116(2), 281–297 (2004)

    Article  Google Scholar 

  5. Bernhart, S.H., Tafer, H., Mückstein, U., Flamm, C., Stadler, P.F., Hofacker, I.L.: Partition function and base pairing probabilities of RNA heterodimers. Algorithms Mol. Biol. 1, 3 (2006)

    Article  Google Scholar 

  6. Brantl, S.: Antisense-RNA regulation and RNA interference. Bioch. Biophys. Acta 1575(1-3), 15–25 (2002)

    Google Scholar 

  7. Burge, S.W., Daub, J., Eberhardt, R., Tate, J., Barquist, L., Nawrocki, E.P., Eddy, S.R., Gardner, P.P., Bateman, A.: Rfam 11.0: 10 years of RNA families. Nucleic Acids Res. 41(database issue), D226–D232 (2013)

    Google Scholar 

  8. Chitsaz, H., Backofen, R., Sahinalp, S.C.: biRNA: Fast RNA-RNA binding sites prediction. In: Salzberg, S.L., Warnow, T. (eds.) WABI 2009. LNCS, vol. 5724, pp. 25–36. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  9. Chitsaz, H., Salari, R., Cenk Sahinalp, S., Backofen, R.: A partition function algorithm for interacting nucleic acid strands. Bioinformatics 25(12), i365–i373 (2009); Also ISMB/ECCB proceedings

    Google Scholar 

  10. Dirks, R.M., Pierce, N.A.: A partition function algorithm for nucleic acid secondary structure including pseudoknots. Journal of Computational Chemistry 24(13), 1664–1677 (2003)

    Article  Google Scholar 

  11. Do, C.B., Woods, D.A., Batzoglou, S.: CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics 22, 90–98 (2006)

    Article  Google Scholar 

  12. Dyer, M.E.: The Complexity of Vertex Enumeration Methods. Mathematics of Operations Research 8(3), 381–402 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  13. Faber, C., Scharpf, M., Becker, T., Sticht, H., Rosch, P.: The structure of the coliphage HK022 Nun protein-lambda-phage boxB RNA complex. Implications for the mechanism of transcription termination. J. Biol. Chem. 276(34), 32064–32070 (2001)

    Article  Google Scholar 

  14. Finger, L.D., Trantirek, L., Johansson, C., Feigon, J.: Solution structures of stem-loop RNAs that bind to the two N-terminal RNA-binding domains of nucleolin. Nucleic Acids Res. 31(22), 6461–6472 (2003)

    Article  Google Scholar 

  15. Forouzmand, E., Chitsaz, H.: The RNA Newton polytope and learnability of energy parameters. Bioinformatics 29(13), i300–i307 (2013); Also ISMB/ECCB proceedings

    Google Scholar 

  16. Gibson, D.G., Glass, J.I., Lartigue, C., Noskov, V.N., Chuang, R.-Y., Algire, M.A., Benders, G.A., Montague, M.G., Ma, L., Moodie, M.M., Merryman, C., Vashee, S., Krishnakumar, R., Assad-Garcia, N., Andrews-Pfannkoch, C., Denisova, E.A., Young, L., Qi, Z.-Q., Segall-Shapiro, T.H., Calvey, C.H., Parmar, P.P., Hutchison, C.A., Smith, H.O., Craig Venter, J.: Creation of a bacterial cell controlled by a chemically synthesized genome. Science 329(5987), 52–56 (2010)

    Article  Google Scholar 

  17. Gottesman, S.: Micros for microbes: non-coding regulatory RNAs in bacteria. Trends in Genetics 21(7), 399–404 (2005)

    Article  Google Scholar 

  18. Hannon, G.J.: RNA interference. Nature 418(6894), 244–251 (2002)

    Article  Google Scholar 

  19. Honer zu Siederdissen, C., Bernhart, S.H., Stadler, P.F., Hofacker, I.L.: A folding algorithm for extended RNA secondary structures. Bioinformatics 27(13), i129–i136 (2011)

    Google Scholar 

  20. Huang, F.W.D., Qin, J., Reidys, C.M., Stadler, P.F.: Target prediction and a statistical sampling algorithm for RNA-RNA interaction. Bioinformatics 26(2), 175–181 (2010)

    Article  Google Scholar 

  21. 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)

    Article  Google Scholar 

  22. McCaskill, J.S.: The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers 29, 1105–1119 (1990)

    Article  Google Scholar 

  23. Nussinov, R., Piecznik, G., Grigg, J.R., Kleitman, D.J.: Algorithms for loop matchings. SIAM Journal on Applied Mathematics 35, 68–82 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  24. Rivas, E., Eddy, S.R.: A dynamic programming algorithm for RNA structure prediction including pseudoknots. J. Mol. Biol. 285(5), 2053–2068 (1999)

    Article  Google Scholar 

  25. Rivas, E., Lang, R., Eddy, S.R.: A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more. RNA 18(2), 193–212 (2012)

    Article  Google Scholar 

  26. Seeman, N.C., Lukeman, P.S.: Nucleic acid nanostructures: bottom-up control of geometry on the nanoscale. Reports on Progress in Physics 68, 237–270 (2005)

    Article  Google Scholar 

  27. Seeman, N.C.: From genes to machines: DNA nanomechanical devices. Trends Biochem. Sci. 30, 119–125 (2005)

    Article  Google Scholar 

  28. Simmel, F.C., Dittmer, W.U.: DNA nanodevices. Small 1, 284–299 (2005)

    Article  Google Scholar 

  29. Staple, D.W., Butcher, S.E.: Solution structure and thermodynamic investigation of the HIV-1 frameshift inducing element. J. Mol. Biol. 349(5), 1011–1023 (2005)

    Article  Google Scholar 

  30. Storz, G.: An expanding universe of noncoding RNAs. Science 296(5571), 1260–1263 (2002)

    Article  Google Scholar 

  31. Tinoco, I., Borer, P.N., Dengler, B., Levin, M.D., Uhlenbeck, O.C., Crothers, D.M., Bralla, J.: Improved estimation of secondary structure in ribonucleic acids. Nature New Biol. 246(150), 40–41 (1973)

    Article  Google Scholar 

  32. Venkataraman, S., Dirks, R.M., Rothemund, P.W., Winfree, E., Pierce, N.A.: An autonomous polymerization motor powered by DNA hybridization. Nat. Nanotechnol. 2, 490–494 (2007)

    Article  Google Scholar 

  33. Wagner, E.G., Flardh, K.: Antisense RNAs everywhere? Trends Genet. 18, 223–226 (2002)

    Article  Google Scholar 

  34. Waterman, M.S., Smith, T.F.: RNA secondary structure: A complete mathematical analysis. Math. Biosc. 42, 257–266 (1978)

    Article  MATH  Google Scholar 

  35. Yin, P., Hariadi, R.F., Sahu, S., Choi, H.M., Park, S.H., Labean, T.H., Reif, J.H.: Programming DNA tube circumferences. Science 321, 824–826 (2008)

    Article  Google Scholar 

  36. Zakov, S., Goldberg, Y., Elhadad, M., Ziv-Ukelson, M.: Rich parameterization improves RNA structure prediction. In: Bafna, V., Sahinalp, S.C. (eds.) RECOMB 2011. LNCS, vol. 6577, pp. 546–562. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  37. Zamore, P.D., Haley, B.: Ribo-gnome: the big world of small RNAs. Science 309(5740), 1519–1524 (2005)

    Article  Google Scholar 

  38. Zuker, M., Stiegler, P.: Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Research 9(1), 133–148 (1981)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Wayne State University, Detroit, MI, 48202, USA

    Hamidreza Chitsaz & Mohammad Aminisharifabad

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  1. Hamidreza Chitsaz
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  2. Mohammad Aminisharifabad
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Editors and Affiliations

  1. School of Computer Science, Tel Aviv University, 69978, Tel Aviv, Israel

    Roded Sharan

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Chitsaz, H., Aminisharifabad, M. (2014). Exact Learning of RNA Energy Parameters from Structure. In: Sharan, R. (eds) Research in Computational Molecular Biology. RECOMB 2014. Lecture Notes in Computer Science(), vol 8394. Springer, Cham. https://doi.org/10.1007/978-3-319-05269-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-05269-4_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05268-7

  • Online ISBN: 978-3-319-05269-4

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