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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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Consensus structure of T-box leader. T-box leader sequences from 16 species have been aligned using ClustalW. The resulting alignment has an average percentage identity of ∼59.1% and shows gap-rich regions. Consensus structures and their score (in parentheses) computed by RNAlishapes and RNAalifold are shown on the second last and last line, respectively.
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fig3: Consensus structure of T-box leader. T-box leader sequences from 16 species have been aligned using ClustalW. The resulting alignment has an average percentage identity of ∼59.1% and shows gap-rich regions. Consensus structures and their score (in parentheses) computed by RNAlishapes and RNAalifold are shown on the second last and last line, respectively.

Mentions: A major advantage of the algorithm presented here is the gap-aware energy evaluation as described in the methods section. To my opinion, this is a major advantage compared to RNAalifold, especially when analysing sequences with low pairwise identities. An example for this are T-box sequences which have been analysed in (53). The multiple sequence alignment (see Figure 3) of these 16 sequences shows an average percentage identity of ∼59.1%. RNAalifold predicted only a 3 bp hairpin (see Figure 3, last line), while RNAlishapes was more successful (see Figure 3, second last line) and was able to predict at least one conformation of the T-box switch. The interesting fact about this structure is, that an internal loop is predicted, whose 3′-unpaired region consists mainly of gaps due to an insert in only one sequence. The scoring of RNAalifold penalizes all sequences with this length and, therefore, favours another structure without this internal loop. In response to the ‘bad’ alignment, RNAlishapes was unable to predict the second functional conformation of the T-box switch, e.g. the sequestor hairpin, which prohibits binding of the ribosome to the ribosome binding site.


Structural analysis of aligned RNAs.

Voss B - Nucleic Acids Res. (2006)

Consensus structure of T-box leader. T-box leader sequences from 16 species have been aligned using ClustalW. The resulting alignment has an average percentage identity of ∼59.1% and shows gap-rich regions. Consensus structures and their score (in parentheses) computed by RNAlishapes and RNAalifold are shown on the second last and last line, respectively.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC1636479&req=5

fig3: Consensus structure of T-box leader. T-box leader sequences from 16 species have been aligned using ClustalW. The resulting alignment has an average percentage identity of ∼59.1% and shows gap-rich regions. Consensus structures and their score (in parentheses) computed by RNAlishapes and RNAalifold are shown on the second last and last line, respectively.
Mentions: A major advantage of the algorithm presented here is the gap-aware energy evaluation as described in the methods section. To my opinion, this is a major advantage compared to RNAalifold, especially when analysing sequences with low pairwise identities. An example for this are T-box sequences which have been analysed in (53). The multiple sequence alignment (see Figure 3) of these 16 sequences shows an average percentage identity of ∼59.1%. RNAalifold predicted only a 3 bp hairpin (see Figure 3, last line), while RNAlishapes was more successful (see Figure 3, second last line) and was able to predict at least one conformation of the T-box switch. The interesting fact about this structure is, that an internal loop is predicted, whose 3′-unpaired region consists mainly of gaps due to an insert in only one sequence. The scoring of RNAalifold penalizes all sequences with this length and, therefore, favours another structure without this internal loop. In response to the ‘bad’ alignment, RNAlishapes was unable to predict the second functional conformation of the T-box switch, e.g. the sequestor hairpin, which prohibits binding of the ribosome to the ribosome binding site.

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