Skip to main content

Structural Analysis of Single-Point Mutations Given an RNA Sequence: A Case Study with RNAMute


We introduce here for the first time the RNAMute package, a pattern-recognition-based utility to perform mutational analysis and detect vulnerable spots within an RNA sequence that affect structure. Mutations in these spots may lead to a structural change that directly relates to a change in functionality. Previously, the concept was tried on RNA genetic control elements called "riboswitches" and other known RNA switches, without an organized utility that analyzes all single-point mutations and can be further expanded. The RNAMute package allows a comprehensive categorization, given an RNA sequence that has functional relevance, by exploring the patterns of all single-point mutants. For illustration, we apply the RNAMute package on an RNA transcript for which individual point mutations were shown experimentally to inactivate spectinomycin resistance in Escherichia coli. Functional analysis of mutations on this case study was performed experimentally by creating a library of point mutations using PCR and screening to locate those mutations. With the availability of RNAMute, preanalysis can be performed computationally before conducting an experiment.


  1. 1.

    Waterman MS, Smith TF: RNA secondary structure: a complete mathematical analysis. Mathematical Biosciences 1978, 42(3–4):257–266. 10.1016/0025-5564(78)90099-8

    Article  Google Scholar 

  2. 2.

    Zuker M: Calculating nucleic acid secondary structure. Current Opinion in Structural Biology 2000, 10(3):303–310. 10.1016/S0959-440X(00)00088-9

    Article  Google Scholar 

  3. 3.

    Nussinov R, Jacobson AB: Fast algorithm for predicting the secondary structure of single-stranded RNA. Proceedings of the National Academy of Sciences 1980, 77(11):6309–6313. 10.1073/pnas.77.11.6309

    Article  Google Scholar 

  4. 4.

    Zuker M, Stiegler P: Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Research 1981, 9(1):133–148. 10.1093/nar/9.1.133

    Article  Google Scholar 

  5. 5.

    Zuker M, Sankoff D: RNA secondary structures and their prediction. Bulletin of Mathematical Biology 1984, 46(4):591–621.

    Article  Google Scholar 

  6. 6.

    Zuker M: Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Research 2003, 31(13):3406–3415. 10.1093/nar/gkg595

    Article  Google Scholar 

  7. 7.

    Hofacker IL: Vienna RNA secondary structure server. Nucleic Acids Research 2003, 31(13):3429–3431. 10.1093/nar/gkg599

    Article  Google Scholar 

  8. 8.

    Mathews DH, Sabina J, Zuker M, Turner DH: Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. Journal of Molecular Biology 1999, 288(5):911–940. 10.1006/jmbi.1999.2700

    Article  Google Scholar 

  9. 9.

    Gutell RR, Lee JC, Cannone JJ: The accuracy of ribosomal RNA comparative structure models. Current Opinion in Structural Biology 2002, 12(3):301–310. 10.1016/S0959-440X(02)00339-1

    Article  Google Scholar 

  10. 10.

    Zimmerman JM, Maher LJ III: In vivo selection of spectinomycin-binding RNAs. Nucleic Acids Research 2002, 30(24):5425–5435. 10.1093/nar/gkf687

    Article  Google Scholar 

  11. 11.

    Barash D, Comaniciu D: A common viewpoint on broad kernel filtering and nonlinear diffusion. Proceedings of the 4th International Conference on Scale-Space Theories in Computer Vision (Scale-Space '03), June 2003, Isle of Skye, UK, Lecture Notes in Computer Science 2695: 683–698.

    MATH  Google Scholar 

  12. 12.

    Shokoufandeh A, Macrini D, Dickinson S, Siddiqi K, Zucker SW: Indexing hierarchical structures using graph spectra. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005, 27(7):1125–1140. Special issue on syntactic and structural pattern recognition

    Article  Google Scholar 

  13. 13.

    Barash D: Second eigenvalue of the Laplacian matrix for predicting RNA conformational switch by mutation. Bioinformatics 2004, 20(12):1861–1869. 10.1093/bioinformatics/bth157

    Article  Google Scholar 

  14. 14.

    Hofacker IL, Fontana W, Stadler PF, Bonhoeffer LS, Tacker M, Schuster P: Fast folding and comparison of RNA secondary structures. Monatshefte für Chemie 1994, 125(2):167–188. 10.1007/BF00818163

    Article  Google Scholar 

  15. 15.

    Le S-Y, Nussinov R, Maizel JV: Tree graphs of RNA secondary structures and their comparisons. Computers and Biomedical Research 1989, 22(5):461–473. 10.1016/0010-4809(89)90039-6

    Article  Google Scholar 

  16. 16.

    Shapiro BA: An algorithm for comparing multiple RNA secondary structures. Computer Applications in the Biosciences 1988, 4(3):387–393.

    Google Scholar 

  17. 17.

    Margalit H, Shapiro BA, Oppenheim AB, Maizel JV: Detection of common motifs in RNA secondary structures. Nucleic Acids Research 1989, 17(12):4829–4845. 10.1093/nar/17.12.4829

    Article  Google Scholar 

  18. 18.

    Jiang T, Lin G, Ma B, Zhang K: A general edit distance between RNA structures. Journal of Computational Biology 2002, 9(2):371–388. 10.1089/10665270252935511

    Article  Google Scholar 

  19. 19.

    Kitagawa J, Futamura Y, Yamamoto K: Analysis of the conformational energy landscape of human snRNA with a metric based on tree representation of RNA structures. Nucleic Acids Research 2003, 31(7):2006–2013. 10.1093/nar/gkg288

    Article  Google Scholar 

  20. 20.

    Shapiro BA, Zhang K: Comparing multiple RNA secondary structures using tree comparisons. Computer Applications in the Biosciences 1990, 6(4):309–318.

    Google Scholar 

  21. 21.

    Fiedler M: Algebraic connectivity of graphs. Czechoslovak Mathematical Journal 1973, 23: 298–305.

    MathSciNet  MATH  Google Scholar 

  22. 22.

    Grone R, Merris R, Sunder VS: The Laplacian spectrum of a graph. SIAM Journal on Matrix Analysis and Applications 1990, 11(2):218–238. 10.1137/0611016

    MathSciNet  Article  Google Scholar 

  23. 23.

    Grone R, Merris R: Algebraic connectivity of trees. Czechoslovak Mathematical Journal 1987, 37(4):660–670.

    MathSciNet  MATH  Google Scholar 

  24. 24.

    Merris R: Characteristic vertices of trees. Linear and Multilinear Algebra 1987, 22: 115–131. 10.1080/03081088708817827

    MathSciNet  Article  Google Scholar 

  25. 25.

    Smith DB, Simmonds P: Characteristics of nucleotide substitution in the hepatitis C virus genome: constraints on sequence change in coding regions at both ends of the genome. Journal of Molecular Evolution 1997, 45(3):238–246. 10.1007/PL00006226

    Article  Google Scholar 

  26. 26.

    You S, Stump DD, Branch AD, Rice CM: A cis -acting replication element in the sequence encoding the NS5B RNA-dependent RNA polymerase is required for Hepatitis C virus RNA replication. Journal of Virology 2004, 78(3):1352–1366. 10.1128/JVI.78.3.1352-1366.2004

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Alexander Churkin.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Churkin, A., Barash, D. Structural Analysis of Single-Point Mutations Given an RNA Sequence: A Case Study with RNAMute. EURASIP J. Adv. Signal Process. 2006, 056246 (2006).

Download citation


  • Escherichia Coli
  • Structural Change
  • Information Technology
  • Structural Analysis
  • Functional Analysis