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. 2004 Sep 15;32(16):4843-51.
doi: 10.1093/nar/gkh779. Print 2004.

Abstract shapes of RNA

Robert Giegerich  1 , Björn Voss, Marc Rehmsmeier
Affiliations

Abstract shapes of RNA

Robert Giegerich et al. Nucleic Acids Res. .

Abstract

The function of a non-protein-coding RNA is often determined by its structure. Since experimental determination of RNA structure is time-consuming and expensive, its computational prediction is of great interest, and efficient solutions based on thermodynamic parameters are known. Frequently, however, the predicted minimum free energy structures are not the native ones, leading to the necessity of generating suboptimal solutions. While this can be accomplished by a number of programs, the user is often confronted with large outputs of similar structures, although he or she is interested in structures with more fundamental differences, or, in other words, with different abstract shapes. Here, we formalize the concept of abstract shapes and introduce their efficient computation. Each shape of an RNA molecule comprises a class of similar structures and has a representative structure of minimal free energy within the class. Shape analysis is implemented in the program RNAshapes. We applied RNAshapes to the prediction of optimal and suboptimal abstract shapes of several RNAs. For a given energy range, the number of shapes is considerably smaller than the number of structures, and in all cases, the native structures were among the top shape representatives. This demonstrates that the researcher can quickly focus on the structures of interest, without processing up to thousands of near-optimal solutions. We complement this study with a large-scale analysis of the growth behaviour of structure and shape spaces. RNAshapes is available for download and as an online version on the Bielefeld Bioinformatics Server.

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Figures

Figure 1
Figure 1
Representations of RNA secondary structure. (a) Primary sequence, (b) Vienna, (c) ASCII-tree, (d) squiggle, (e) shape and (f) tree.
Figure 2
Figure 2
Predicted shreps for N.pharaonis tRNA-alanine in an energy range of 5 kcal/mol above the mfe. This energy range holds 199 structures. (a) 1st shrep: −35.9 kcal/mol; (b) 2nd shrep: −32.2 kcal/mol; and (c) 3rd shrep: −31.7 kcal/mol; (d) output of RNAshapes.
Figure 3
Figure 3
Subset of 19 predicted shreps for HIV1-leader in an energy range of 6 kcal/mol above the mfe. (a) 1st shrep: −108.3 kcal/mol, [][[][[][]]], S1; (b) 2nd shrep: −107.9 kcal/mol, [][[[][[][]]][]]; (c) 3rd shrep: −106.8 kcal/mol [[][][[[][[][]]][]]]; (d) 12th shrep: −102.8 kcal/mol, [][][[[[][[][]]][]][]], S2.
Figure 4
Figure 4
Predicted shreps for human U2 snRNA in an energy range of 3 kcal/mol above the mfe. (a) 1st shrep: −69.12 kcal/mol, [][][][]; (b) 2nd shrep: −68.02 kcal/mol, [][][[][]][]; and (c) 3rd shrep: −67.32 kcal/mol, [][][[[][]][]].
Figure 5
Figure 5
Comparison of folding space and shape space. (a) Growth of structure space, respective shape space with sequence length (energy range 5 kcal/mol). (b) Growth of structure space, respective shape space with sequence length (energy range 5 kcal/mol, log-scale). (c) Shape/structure ratio for growing sequence length (energy range 5 kcal/mol). (d) Shape/structure ratio for growing energy range (n = 100). (e) Overall number of structures, respective shapes (log-scale) with growing sequence length.
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References

    1. Zuker M. and Stiegler,P. (1981) Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res., 9, 133–148. - PMC - PubMed
    1. Mathews D.H., Sabina,J., Zuker,M. and Turner,D.H. (1999) Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol., 288, 911–940. - PubMed
    1. McCaskill J.S. (1990) The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers, 29, 1105–1119. - PubMed
    1. Zuker M. (1989) On finding all suboptimal foldings of an RNA molecule. Science, 244, 48–52. - PubMed
    1. Wuchty S., Fontana,W., Hofacker,I.L. and Schuster,P. (1999) Complete suboptimal folding of RNA and the stability of secondary structures. Biopolymers, 49, 145–169. - PubMed

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