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Related Experiment Video

Updated: May 2, 2026

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Energy-based RNA consensus secondary structure prediction in multiple sequence alignments.

Stefan Washietl¹, Stephan H Bernhart, Manolis Kellis

¹Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|March 19, 2014

Summary

This summary is machine-generated.

RNAalifold predicts RNA secondary structures using evolutionary data and energy-based folding. This approach enhances accuracy and enables the study of structural conservation in functional RNAs.

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Published on: March 12, 2012

Area of Science:

Bioinformatics
Computational Biology
Molecular Biology

Background:

Biologically significant RNA structures exhibit evolutionary conservation, resulting in distinct mutational patterns.
RNAalifold is a popular tool for predicting consensus secondary structures from multiple sequence alignments.

Purpose of the Study:

To describe the theory and applications of the RNAalifold algorithm.
To highlight the benefits of consensus secondary structure prediction for RNA modeling and analysis.

Main Methods:

Combines evolutionary information with traditional energy-based RNA folding algorithms.
Utilizes multiple sequence alignments to infer conserved RNA structures.

Main Results:

Consensus secondary structure prediction yields significantly more accurate RNA structure models.
The method facilitates the investigation of structural conservation in functional RNAs.

Conclusions:

RNAalifold provides a powerful approach for accurate RNA secondary structure prediction.
The algorithm aids in understanding the evolutionary basis of RNA structure and function.