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10.8: Advanced topics, Summary and key points, Further Reading, Bibliography

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    There still remain a host of other problems that need to be solved by studying RNA structure. This section will profile some of them.

    Other problems

    Observe some of the problems depicted graphically below:

    Figure 10.15: We can study kinetics and folding pathways in further depth. © Stefan Washietl. All rights reserved. This content is excluded from our Creative Commons license. For more information, see
    Figure 10.16: We can investigate pseudoknots. © Stefan Washietl. All rights reserved. This content is excluded from our Creative Commons license. For more information, see
    Figure 10.17: We can try to better understand RNA-RNA interactions. © Stefan Washietl. All rights reserved. This content is excluded from our Creative Commons license. For more information, see


    There are plenty of RNAs inside the cell aside from mRNAs, tRNAs and rRNAs. The question is what is the relevance of all this non-coding RNA? Some believe it is noise resulted through experiment, some think its just biological noise that doesn't have a meaning in the living organism. On the other hand some believe junk RNA might actually have an important role as signals inside the cell and all of it is actually functional, the truth probably lies somewhere in between.

    Current research

    There are conserved regions in the genome that do not code any proteins, and now Stefans et al. are looking into them to see if they have structures that are stable enough to form functional RNAs. It turns out that around 6% of these regions have hallmarks of good RNA structure, which is still 30000 structural elements. The group has annotated some of these elements, but there is still a long way to go. a lot of miRNA, snowRNAs have been found and of course lots of false positives. But there exciting results coming up in this topic! so the final note is, it’s a very good area to work in!

    Summary and key points

    1. The functional spectrum of RNAs is practically unlimited

    (a) RNAs similar to contemporary Ribozymes and Riboswitches might have existed in an RNA world. Some of them still exist as living fossils in current cells.

    (b) Evolutionarily younger RNAs including miRNAs and many long ncRNAs form a non-protein based regulatory layer.

    1. RNA structure is critical for their function and can be predicted computationally

      (a) Nussinov/Zuker: Minimum Free Energy structure (b) McCaskill: Partition function and pair probabilities

      (c) CYK/Inside-Outside: probabilistic solution to the problem using SCFGs

    2. Phylogenetic information can improve structure prediction
    3. Computational biology of RNAs is an active eld of research with many hard algorithmic problems still open

    10.10 Further reading

    • Overview
    – Washietl S, Will S. et al. Computational analysis of noncoding RNAs. Wiley Interdiscip Rev RNA. 2012, 10.1002/wrna.1134
    • RNA function: review papers by John Mattick

    • Single sequence RNA folding

    – Nussinov R, Jacobson AB, Fast algorithm for predicting the secondary structure of single-stranded RNA.Proc Natl Acad Sci U S A. 1980 Nov; 77:(11)6309-13

    – Zuker M, Stiegler P Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res. 1981 Jan; 9:(1)133-48

    – McCaskill JS The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers. 1990; 29:(6-7)1105-19

    – Dowell RD, Eddy SR, Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction. BMC Bioinformatics. 2004 Jun; 5:71

    – Do CB, Woods DA, Batzoglou S, CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics. 2006 Jul; 22:(14)e90-8

    • Consensus RNA folding

    – Hofacker IL, Fekete M, Stadler PF, Secondary structure prediction for aligned RNA sequences. J Mol Biol. 2002 Jun; 319:(5)1059-66

    – Knudsen B, Hein J, RNA secondary structure prediction using stochastic context-free grammars and evolutionary history. Bioinformatics. 1999 Jun; 15:(6)446-54

    • RNA gene finding

    – Pedersen JS, Bejerano G, Siepel A, Rosenbloom K, Lindblad-Toh K, Lander ES, Kent J, Miller W, Haussler D Identication and classication of conserved RNA secondary structures in the human genome. PLoS Comput Biol. 2006 Apr; 2:(4)e33

    – Washietl S, Hofacker IL, Stadler PF, Fast and reliable prediction of noncoding RNAs. Proc Natl Acad Sci U S A. 2005 Feb; 102:(7)2454-9


    1. [1] R Durbin. Biological Sequence Analysis.
    2. [2] W. Gilbert. ”origin of life: The rna world”. Nature., 319(6055):618, 1986.
    3. [3] Rachel Sealfon, 2012. Extra information taken from Recitation 5 slides.
    4. [4] Z. Wang, M. Gestein, and M. Snyder. Rna-seq: a revolutionary tool for transcriptomics. Nat Rev Genet., 10(1):57–63, 2009.
    5. [5] Stefan Washietl, 2012. All pictures/formulas courtesy of Stefan’s slides.
    6. [6] R. Weaver. Molecular Biology. 3rd edition.

    This page titled 10.8: Advanced topics, Summary and key points, Further Reading, Bibliography is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Manolis Kellis et al. (MIT OpenCourseWare) via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.