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Methods in molecular biology (Clifton, N.J.)
2023;2586 89-105.

RNA Secondary Structure Prediction Based on Energy Models.

Manato Akiyama, Kengo Sato
Author Information
  1. Manato Akiyama: Department of Biosciences and Informatics, Keio University, Yokohama, Japan.
  2. Kengo Sato: School of System Design and Technology, Tokyo Denki University, Tokyo, Japan. satoken@mail.dendai.ac.jp.
PMID: 36705900 DOI: 10.1007/978-1-0716-2768-6_6

Abstract

This chapter introduces the RNA secondary structure prediction based on the nearest neighbor energy model, which is one of the most popular architectures of modeling RNA secondary structure without pseudoknots. We discuss the parameterization and the parameter determination by experimental and machine learning-based approaches as well as an integrated approach that compensates each other's shortcomings. Then, folding algorithms for the minimum free energy and the maximum expected accuracy using the dynamic programming technique are introduced. Finally, we compare the prediction accuracy of the method described so far with benchmark datasets.

Keywords

Machine learning Maximum expected accuracy Minimum free energy Nearest neighbor model RNA secondary structure prediction Thermodynamic parameters

References

  1. Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31:3406–3415 [DOI: 10.1093/nar/gkg595]
  2. Markham NR, Zuker M (2008) UNAFold: software for nucleic acid folding and hybridization. Methods Mol Biol 453:3–31 [DOI: 10.1007/978-1-60327-429-6_1]
  3. Hofacker IL (2003) Vienna RNA secondary structure server. Nucleic Acids Res 31:3429–3431 [DOI: 10.1093/nar/gkg599]
  4. Lorenz R, Bernhart SH, Höner Zu Siederdissen C et al (2011) ViennaRNA package 2.0. Algorithms Mol Biol 6:26 [DOI: 10.1186/1748-7188-6-26]
  5. Mathews DH, Andre TC, Kim J et al (1997) An updated recursive algorithm for RNA secondary structure prediction with improved thermodynamic parameters. Mol Model Nucleic Acids 15:246–257. https://doi.org/10.1021/bk-1998-0682.ch015 [DOI: 10.1021/bk-1998-0682.ch015]
  6. Reuter JS, Mathews DH (2010) RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinformatics 11:129 [DOI: 10.1186/1471-2105-11-129]
  7. Hamada M, Kiryu H, Sato K et al (2009) Prediction of RNA secondary structure using generalized centroid estimators. Bioinformatics 25:465–473 [DOI: 10.1093/bioinformatics/btn601]
  8. Sato K, Hamada M, Asai K et al (2009) CentroidFold: A web server for RNA secondary structure prediction. Nucleic Acids Res 37:277–280 [DOI: 10.1093/nar/gkp367]
  9. Do CB, Woods DA, Batzoglou S (2006) CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics 22:e90–e98 [DOI: 10.1093/bioinformatics/btl246]
  10. Foo CS, Do CB, Ng AY (2008) Efficient multiple hyperparameter learning for log-linear models. In: Platt JC, Koller D, Singer Y et al (eds) Advances in neural information processing systems, vol 20, pp 377–384
  11. Zakov S, Goldberg Y, Elhadad M et al (2011) Rich parameterization improves RNA structure prediction. J Comput Biol 18:1525–1542 [DOI: 10.1089/cmb.2011.0184]
  12. Rivas E (2013) The four ingredients of single-sequence RNA secondary structure prediction. A unifying perspective. RNA Biol 10:1185–1196 [DOI: 10.4161/rna.24971]
  13. Andronescu M, Condon A, Hoos HH et al (2007) Efficient parameter estimation for RNA secondary structure prediction. Bioinformatics 23:i19–i28 [DOI: 10.1093/bioinformatics/btm223]
  14. Andronescu M, Condon A, Hoos HH et al (2010) Computational approaches for RNA energy parameter estimation. RNA 16:2304–2318 [DOI: 10.1261/rna.1950510]
  15. Akiyama M, Sato K, Sakakibara Y (2018) A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model. J Bioinform Comput Biol 16:1840025 [DOI: 10.1142/S0219720018400255]
  16. Borer PN, Dengler B, Tinoco I Jr et al (1974) Stability of ribonucleic acid double-stranded helices. J Mol Biol 86:843–853 [DOI: 10.1016/0022-2836(74)90357-X]
  17. Xia T, SantaLucia J Jr, Burkard ME et al (1998) Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs. Biochemistry 37:14719–14735 [DOI: 10.1021/bi9809425]
  18. Mathews DH, Sabina J, Zuker M et al (1999) Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J Mol Biol 288:911–940 [DOI: 10.1006/jmbi.1999.2700]
  19. Bloomfield VA, Crothers DM (2000) Nucleic acids: structures, properties and functions. J Med Chem 43(24):4721–4722
  20. Mathews DH, Disney MD, Childs JL et al (2004) Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc Natl Acad Sci U S A 101:7287–7292 [DOI: 10.1073/pnas.0401799101]
  21. Turner DH, Mathews DH (2010) NNDB: the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure. Nucleic Acids Res 38:D280–D282 [DOI: 10.1093/nar/gkp892]
  22. Turner DH, Sugimoto N, Freier SM (1988) RNA structure prediction. Annu Rev Biophys Biophys Chem 17:167–192 [DOI: 10.1146/annurev.bb.17.060188.001123]
  23. Sakakibara Y, Brown M, Hughey R et al (1994) Stochastic context-free grammars for tRNA modeling. Nucleic Acids Res 22:5112–5120 [DOI: 10.1093/nar/22.23.5112]
  24. Eddy SR, Durbin R (1994) RNA sequence analysis using covariance models. Nucleic Acids Res 22:2079–2088 [DOI: 10.1093/nar/22.11.2079]
  25. Rivas E, Lang R, Eddy SR (2012) A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more. RNA 18:193–212 [DOI: 10.1261/rna.030049.111]
  26. Nussinov R, Jacobson AB (1980) Fast algorithm for predicting the secondary structure of single-stranded RNA. Proc Natl Acad Sci U S A 77:6309–6313 [DOI: 10.1073/pnas.77.11.6309]
  27. Zuker M, Stiegler P (1981) Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res 9:133–148 [DOI: 10.1093/nar/9.1.133]
  28. Tsochantaridis I, Joachims T, Hofmann T et al (2005) Large margin methods for structured and interdependent output variables. J Mach Learn Res 6:1453–1484
  29. McCaskill JS (1990) The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers 29:1105–1119 [DOI: 10.1002/bip.360290621]
  30. Gardner PP, Daub J, Tate J, Moore BL, Osuch IH, Griffiths-Jones S, Finn RD, Nawrocki EP, Kolbe DL, Eddy SR, Bateman A (2011) Rfam: Wikipedia, clans and the “decimal” release. Nucleic Acids Res 39(Database issue):D141–D145. https://doi.org/10.1093/nar/gkq1129 [DOI: 10.1093/nar/gkq1129]
  31. Lucks JB, Mortimer SA, Trapnell C et al (2011) Multiplexed RNA structure characterization with selective 2′-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). Proc Natl Acad Sci U S A 108:11063–11068 [DOI: 10.1073/pnas.1106501108]
  32. Rouskin S, Zubradt M, Washietl S et al (2014) Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo. Nature 505:701–705 [DOI: 10.1038/nature12894]
  33. Underwood JG, Uzilov AV, Katzman S et al (2010) FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing. Nat Method 7:995–1001 [DOI: 10.1038/nmeth.1529]
  34. Kertesz M, Wan Y, Mazor E et al (2010) Genome-wide measurement of RNA secondary structure in yeast. Nature 467:103–107 [DOI: 10.1038/nature09322]
  35. Deigan KE, Li TW, Mathews DH et al (2009) Accurate SHAPE-directed RNA structure determination. Proc Natl Acad Sci U S A 106:97–102 [DOI: 10.1073/pnas.0806929106]
  36. Ouyang Z, Snyder MP, Chang HY (2013) SeqFold: genome-scale reconstruction of RNA secondary structure integrating high-throughput sequencing data. Genome Res 23:377–387 [DOI: 10.1101/gr.138545.112]
  37. Lorenz R, Wolfinger MT, Tanzer A et al (2016) Predicting RNA secondary structures from sequence and probing data. Methods 103:86–98 [DOI: 10.1016/j.ymeth.2016.04.004]

MeSH Term

RNA
Nucleic Acid Conformation
RNA Folding
Entropy
Algorithms
Thermodynamics

Chemicals

RNA
Journal Article Research Support, Non-U.S. Gov't

Word Cloud

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