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Updated: Jun 19, 2026
10:34
Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
Published on: December 9, 2022
From consensus structure prediction to RNA gene finding
Stephan H Bernhart1, Ivo L Hofacker
1Department of Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Wien, Austria. berni@tbi.univie.ac.at
Briefings in Functional Genomics & Proteomics
|October 17, 2009
Summary
Predicting RNA secondary structures improves with consensus structure analysis of related sequences. This method aids in detecting non-coding RNAs by leveraging sequence variation for better RNA structure prediction.
Area of Science:
- Bioinformatics
- Computational Biology
- Molecular Biology
Background:
- Accurate RNA structure prediction is crucial for bioinformatics.
- Single RNA sequence structure prediction has limited accuracy.
- Consensus structure prediction from related RNA sequences enhances accuracy.
Purpose of the Study:
- To review strategies for predicting consensus secondary structures of RNA.
- To demonstrate the utility of consensus structure prediction for identifying non-coding RNA genes.
- To improve RNA structure prediction by exploiting sequence variation.
Main Methods:
- Exploiting patterns of sequence (co-)variation in related RNA sequences.
- Computing consensus secondary structures.
- Utilizing conserved RNA structures under selective pressure.
Main Results:
- Consensus structure prediction significantly improves RNA structure prediction accuracy.
- Sequence variation patterns are key to enhancing structure prediction.
- Consensus structure prediction serves as a starting point for de novo non-coding RNA detection.
Conclusions:
- Consensus structure prediction is a powerful approach for RNA analysis.
- This method enhances the detection of structured non-coding RNAs.
- Leveraging sequence evolution improves RNA gene discovery.
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