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Structural analysis of aligned RNAs.

Voss B - Nucleic Acids Res. (2006)

Bottom Line: For correct characterization of such classes it is therefore of great importance to analyse the structural features in great detail.RNAlishapes makes use of an extended thermodynamic model and covariance scoring, which allows to reward covariation of paired bases.Applying the algorithm to a set of bacterial trp-operon leaders using shape abstraction it was able to identify the two alternating conformations of this attenuator.

View Article: PubMed Central - PubMed

Affiliation: Experimental Bioinformatics, Institute of Biology II, Freiburg University, Schänzlestrasse 1, 79104 Freiburg, Germany. bjoern.voss@biologie.uni-freiburg.de

ABSTRACT
The knowledge about classes of non-coding RNAs (ncRNAs) is growing very fast and it is mainly the structure which is the common characteristic property shared by members of the same class. For correct characterization of such classes it is therefore of great importance to analyse the structural features in great detail. In this manuscript I present RNAlishapes which combines various secondary structure analysis methods, such as suboptimal folding and shape abstraction, with a comparative approach known as RNA alignment folding. RNAlishapes makes use of an extended thermodynamic model and covariance scoring, which allows to reward covariation of paired bases. Applying the algorithm to a set of bacterial trp-operon leaders using shape abstraction it was able to identify the two alternating conformations of this attenuator. Besides providing in-depth analysis methods for aligned RNAs, the tool also shows a fairly well prediction accuracy. Therefore, RNAlishapes provides the community with a powerful tool for structural analysis of classes of RNAs and is also a reasonable method for consensus structure prediction based on sequence alignments. RNAlishapes is available for online use and download at http://rna.cyanolab.de.

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Analysis of aligned tRNAs. A ClustalW alignment of 10 arbitrarily chosen tRNAs from Rfam was analysed with RNAlishapes. (A) The consensus structure predicted by RNAlishapes drawn as a squiggle plot using RNAplot from the Vienna RNA package (62). The sequence corresponds to the sequence of the most frequent base at each position. Colours indicate different stems (see B). (B) The alignment produced by ClustalW. Additionally the consensus structure is given on the last line together with the score in parentheses. The different stems are colour coded in the alignment as well as in the consensus structure. Note, that helical regions do not need to have the same length in all sequences. (C) Output of RNAlishapes, when running in shape probabilistic mode. Four consensus shapes with a probability >10−6 have been predicted. For each the free energy and the dot-bracket representation of the shrep (both on the first line), the probability of the shape and the shape notation (both on the second line) are computed.
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fig1: Analysis of aligned tRNAs. A ClustalW alignment of 10 arbitrarily chosen tRNAs from Rfam was analysed with RNAlishapes. (A) The consensus structure predicted by RNAlishapes drawn as a squiggle plot using RNAplot from the Vienna RNA package (62). The sequence corresponds to the sequence of the most frequent base at each position. Colours indicate different stems (see B). (B) The alignment produced by ClustalW. Additionally the consensus structure is given on the last line together with the score in parentheses. The different stems are colour coded in the alignment as well as in the consensus structure. Note, that helical regions do not need to have the same length in all sequences. (C) Output of RNAlishapes, when running in shape probabilistic mode. Four consensus shapes with a probability >10−6 have been predicted. For each the free energy and the dot-bracket representation of the shrep (both on the first line), the probability of the shape and the shape notation (both on the second line) are computed.

Mentions: The concept of shape abstraction for RNA secondary structures was introduced in (27). Abstract shapes are defined by means of abstraction functions preserving varying amounts of structural detail. Up to now, the common feature of these functions is that they abstract from the length of helical and unpaired regions. This means that shape abstraction retains only the nesting and adjacency pattern of helical and unpaired regions. The most widely used and also most abstract function [described as level-5 in (27)] totally abstracts from unpaired regions and retains only the nesting pattern of multiloops and hairpins. An abstract shape is defined by the shape in shape notation and the energetically most favourable structure attaining this shape, the shape representative (shrep). The shape notation can be seen as a derivative of the dot-bracket-notation for RNA secondary structures. It makes use of the underscore character ‘_’ representing unpaired bases and pairs of square brackets ‘[’ ‘]’ representing helical regions. For example, the shape for the tRNA-cloverleaf using level-5 abstraction, which does not retain unpaired regions, is ‘[[][][]]’. This representation shows that this structure encompasses three hairpins (the three ‘[]’s) which are enclosed by a multiloop (the two outermost square brackets). Retaining information on unpaired bases (level-4 abstraction) would result in the shape ‘[_[_]_[_]_[_]]_’ for the structure shown in Figure 1A.


Structural analysis of aligned RNAs.

Voss B - Nucleic Acids Res. (2006)

Analysis of aligned tRNAs. A ClustalW alignment of 10 arbitrarily chosen tRNAs from Rfam was analysed with RNAlishapes. (A) The consensus structure predicted by RNAlishapes drawn as a squiggle plot using RNAplot from the Vienna RNA package (62). The sequence corresponds to the sequence of the most frequent base at each position. Colours indicate different stems (see B). (B) The alignment produced by ClustalW. Additionally the consensus structure is given on the last line together with the score in parentheses. The different stems are colour coded in the alignment as well as in the consensus structure. Note, that helical regions do not need to have the same length in all sequences. (C) Output of RNAlishapes, when running in shape probabilistic mode. Four consensus shapes with a probability >10−6 have been predicted. For each the free energy and the dot-bracket representation of the shrep (both on the first line), the probability of the shape and the shape notation (both on the second line) are computed.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC1636479&req=5

fig1: Analysis of aligned tRNAs. A ClustalW alignment of 10 arbitrarily chosen tRNAs from Rfam was analysed with RNAlishapes. (A) The consensus structure predicted by RNAlishapes drawn as a squiggle plot using RNAplot from the Vienna RNA package (62). The sequence corresponds to the sequence of the most frequent base at each position. Colours indicate different stems (see B). (B) The alignment produced by ClustalW. Additionally the consensus structure is given on the last line together with the score in parentheses. The different stems are colour coded in the alignment as well as in the consensus structure. Note, that helical regions do not need to have the same length in all sequences. (C) Output of RNAlishapes, when running in shape probabilistic mode. Four consensus shapes with a probability >10−6 have been predicted. For each the free energy and the dot-bracket representation of the shrep (both on the first line), the probability of the shape and the shape notation (both on the second line) are computed.
Mentions: The concept of shape abstraction for RNA secondary structures was introduced in (27). Abstract shapes are defined by means of abstraction functions preserving varying amounts of structural detail. Up to now, the common feature of these functions is that they abstract from the length of helical and unpaired regions. This means that shape abstraction retains only the nesting and adjacency pattern of helical and unpaired regions. The most widely used and also most abstract function [described as level-5 in (27)] totally abstracts from unpaired regions and retains only the nesting pattern of multiloops and hairpins. An abstract shape is defined by the shape in shape notation and the energetically most favourable structure attaining this shape, the shape representative (shrep). The shape notation can be seen as a derivative of the dot-bracket-notation for RNA secondary structures. It makes use of the underscore character ‘_’ representing unpaired bases and pairs of square brackets ‘[’ ‘]’ representing helical regions. For example, the shape for the tRNA-cloverleaf using level-5 abstraction, which does not retain unpaired regions, is ‘[[][][]]’. This representation shows that this structure encompasses three hairpins (the three ‘[]’s) which are enclosed by a multiloop (the two outermost square brackets). Retaining information on unpaired bases (level-4 abstraction) would result in the shape ‘[_[_]_[_]_[_]]_’ for the structure shown in Figure 1A.

Bottom Line: For correct characterization of such classes it is therefore of great importance to analyse the structural features in great detail.RNAlishapes makes use of an extended thermodynamic model and covariance scoring, which allows to reward covariation of paired bases.Applying the algorithm to a set of bacterial trp-operon leaders using shape abstraction it was able to identify the two alternating conformations of this attenuator.

View Article: PubMed Central - PubMed

Affiliation: Experimental Bioinformatics, Institute of Biology II, Freiburg University, Schänzlestrasse 1, 79104 Freiburg, Germany. bjoern.voss@biologie.uni-freiburg.de

ABSTRACT
The knowledge about classes of non-coding RNAs (ncRNAs) is growing very fast and it is mainly the structure which is the common characteristic property shared by members of the same class. For correct characterization of such classes it is therefore of great importance to analyse the structural features in great detail. In this manuscript I present RNAlishapes which combines various secondary structure analysis methods, such as suboptimal folding and shape abstraction, with a comparative approach known as RNA alignment folding. RNAlishapes makes use of an extended thermodynamic model and covariance scoring, which allows to reward covariation of paired bases. Applying the algorithm to a set of bacterial trp-operon leaders using shape abstraction it was able to identify the two alternating conformations of this attenuator. Besides providing in-depth analysis methods for aligned RNAs, the tool also shows a fairly well prediction accuracy. Therefore, RNAlishapes provides the community with a powerful tool for structural analysis of classes of RNAs and is also a reasonable method for consensus structure prediction based on sequence alignments. RNAlishapes is available for online use and download at http://rna.cyanolab.de.

Show MeSH
Related in: MedlinePlus