Energy-Based RNA Consensus Secondary Structure Prediction in Multiple Sequence Alignments

  • Stefan Washietl
  • Stephan H. Bernhart
  • Manolis Kellis
Part of the Methods in Molecular Biology book series (MIMB, volume 1097)


Many biologically important RNA structures are conserved in evolution leading to characteristic mutational patterns. RNAalifold is a widely used program to predict consensus secondary structures in multiple alignments by combining evolutionary information with traditional energy-based RNA folding algorithms. Here we describe the theory and applications of the RNAalifold algorithm. Consensus secondary structure prediction not only leads to significantly more accurate structure models, but it also allows to study structural conservation of functional RNAs.

Key words

RNA structure Consensus structure Structure prediction Functional RNA 



Stefan Washietl was supported by an Erwin Schrödinger Fellowship of the Austrian Fonds zur Förderung der Wissenschaftlichen Forschung. Stephan H. Bernhart was funded by the Austrian GEN-AU project “Noncoding RNA.” We thank Ivo Hofacker and Benjamin Holmes for comments on the manuscript.


  1. 1.
    Washietl S, Pedersen JS, Korbel JO, Stocsits C, Gruber AR, Hackermüller J, Hertel J, Lindemeyer M, Reiche K, Tanzer A, Ucla C, Wyss C, Antonarakis SE, Denoeud F, Lagarde J, Drenkow J, Kapranov P, Gingeras TR, Guigó R, Snyder M, Gerstein MB, Reymond A, Hofacker IL, Stadler PF (2007) Structured RNAs in the ENCODE selected regions of the human genome. Genome Res 17: 852–864. doi:10.1101/gr.5650707Google Scholar
  2. 2.
    Gardner PP, Giegerich R (2004) A comprehensive comparison of comparative RNA structure prediction approaches. BMC Bioinformatics 5: 140. doi:10.1186/1471-2105-5-140PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Gardner PP, Wilm A, Washietl S (2005) A benchmark of multiple sequence alignment programs upon structural RNAs. Nucleic Acids Res 33: 2433–2439. doi:10.1093/nar/gki541PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Wilm A, Mainz I, Steger G (2006) An enhanced RNA alignment benchmark for sequence alignment programs. Algorithms Mol Biol 1: 19. doi:10.1186/1748-7188-1-19PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Höchsmann M, Voss B, Giegerich R (2004) Pure multiple RNA secondary structure alignments: a progressive profile approach. IEEE/ACM Trans Comput Biol Bioinform 1: 53–62. doi:10.1109/TCBB.2004.11PubMedCrossRefGoogle Scholar
  6. 6.
    Sankoff D (1985) Simultaneous solution of the RNA folding, alignment and protosequence problems. SIAM J Appl Math 45: 810–825CrossRefGoogle Scholar
  7. 7.
    Noller HF, Kop J, Wheaton V, Brosius J, Gutell RR, Kopylov AM, Dohme F, Herr W, Stahl DA, Gupta R, Woese CR (1981) Secondary structure model for 23s ribosomal RNA. Nucleic Acids Res 9 (22): 6167–6189. doi:10.1093/nar/9.22.6167PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Hofacker IL, Fekete M, Stadler PF (2002) Secondary structure prediction for aligned RNA sequences. J Mol Biol 319: 1059–1066. doi:10.1016/S0022-2836(02)00308-XPubMedCrossRefGoogle Scholar
  9. 9.
    Zuker M, Stiegler P (1981) Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res 9: 133–148.PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Bernhart SH, Hofacker IL, Will S, Gruber AR, Stadler PF (2008) RNAalifold: improved consensus structure prediction for RNA alignments. BMC Bioinformatics 9: 474. doi:10.1186/1471-2105-9-474PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Klein RJ, Eddy SR (2003) RSEARCH: finding homologs of single structured RNA sequences. BMC Bioinformatics 4: 44. doi:10.1186/1471-2105-4-44PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    McCaskill JS (1990) The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers 29: 1105–1119. doi:10.1002/bip.360290621PubMedCrossRefGoogle Scholar
  13. 13.
    Ding Y, Lawrence CE (1999) A bayesian statistical algorithm for RNA secondary structure prediction. Comput Chem 23 (3–4): 387–400.PubMedCrossRefGoogle Scholar
  14. 14.
    Ding Y, Chan CY, Lawrence CE (2005) RNA secondary structure prediction by centroids in a boltzmann weighted ensemble. RNA 11 (8): 1157–1166. doi:10.1261/rna.2500605PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Hofacker IL, Priwitzer B, Stadler PF (2004) Prediction of locally stable RNA secondary structures for genome-wide surveys. Bioinformatics 20: 186–190. doi:10.1093/bioinformatics/btg388PubMedCrossRefGoogle Scholar
  16. 16.
    Washietl S, Hofacker IL (2004) Consensus folding of aligned sequences as a new measure for the detection of functional RNAs by comparative genomics. J Mol Biol 342: 19–30. doi:10.1016/j.jmb.2004.07.018PubMedCrossRefGoogle Scholar
  17. 17.
    Gruber AR, Bernhart SH, Hofacker IL, Washietl S (2008) Strategies for measuring evolutionary conservation of RNA secondary structures. BMC Bioinformatics 9: 122. doi:10.1186/1471-2105-9-122PubMedCentralPubMedCrossRefGoogle Scholar
  18. 18.
    Gesell T, Washietl S (2008) Dinucleotide controlled null models for comparative RNA gene prediction. BMC Bioinformatics 9: 248. doi:10.1186/1471-2105-9-248PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Gruber AR, Findeiß S, Washietl S, Hofacker IL, Stadler PF (2010) RNAz 2.0: improved noncoding RNA detection. Pac Symp Biocomput 15: 69–79Google Scholar
  20. 20.
    Washietl S (2007) Prediction of structural noncoding RNAs with RNAz. Methods Mol Biol 395: 503–526PubMedCrossRefGoogle Scholar
  21. 21.
    Knudsen B, Hein J (2003) Pfold: RNA secondary structure prediction using stochastic context-free grammars. Nucleic Acids Res 31: 3423–3428PubMedCentralPubMedCrossRefGoogle Scholar
  22. 22.
    Seemann SE, Gorodkin J, Backofen R (2008) Unifying evolutionary and thermodynamic information for RNA folding of multiple alignments. Nucleic Acids Res 36: 6355–6362. doi:10.1093/nar/gkn544PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    Do CB, Woods DA, Batzoglou S (2006) CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics 22: e90–e98. doi:10.1093/ bioinformatics/btl246PubMedCrossRefGoogle Scholar
  24. 24.
    Lu ZJ, Gloor JW, Mathews DH (2009) Improved RNA secondary structure prediction by maximizing expected pair accuracy. RNA 15: 1805–1813. doi:10.1261/rna.1643609PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Kiryu H, Kin T, Asai K (2007) Robust prediction of consensus secondary structures using averaged base pairing probability matrices. Bioinformatics 23: 434–441. doi:10.1093/bioinformatics/btl636PubMedCrossRefGoogle Scholar
  26. 26.
    Hamada M, Kiryu H, Sato K, Mituyama T, Asai K (2009) Prediction of RNA secondary structure using generalized centroid estimators. Bioinformatics 25: 465–473. doi:10.1093/bioinformatics/btn601PubMedCrossRefGoogle Scholar
  27. 27.
    Hamada M, Sato K, Asai K (2011) Improving the accuracy of predicting secondary structure for aligned RNA sequences. Nucleic Acids Res 39: 393–402. doi:10.1093/ nar/ gkq792PubMedCentralPubMedCrossRefGoogle Scholar
  28. 28.
    Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32: 1792–1797. doi:10.1093/nar/gkh340PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Stefan Washietl
    • 1
  • Stephan H. Bernhart
    • 2
  • Manolis Kellis
    • 1
  1. 1.Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of Computer Science and Interdisciplinary Center for BioinformaticsUniversity of LeipzigLeipzigGermany

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