RNA structure prediction with ViennaRNA
The tutorials on this site on RNA secondary structure prediction with the ViennaRNA Package, in reading order: reading RNAfold output, folding with SHAPE and chemical probing data, what deep learning predictors learn, and the background papers.
This page collects the posts on this site that explain how to predict the secondary structure of an RNA with the ViennaRNA Package and how far such a prediction can be trusted, in the order in which I would read them. They are written for researchers who use structure predictions in their own work, i.e. for experimentalists and computational biologists alike, and each tutorial works through a complete example with all commands included, so that the results can be reproduced on any computer with ViennaRNA installed.
Tutorials
- RNAfold tutorial: MFE, pair probabilities and ensemble output. What RNAfold writes to the terminal and to its output files, i.e. the minimum free energy (MFE) structure, the pair probabilities and the dot plot, the centroid and MEA structures, and the suboptimal structures from RNAsubopt. The example is yeast tRNA-Phe, and the question throughout is how to tell whether the MFE structure is a robust prediction or one of many that the sequence could form.
- SHAPE and chemical probing tutorial: folding with reactivities in RNAfold. How SHAPE reactivities go into RNAfold, the input format, what the three methods in ViennaRNA (Deigan, Zarringhalam and Washietl) do with the data, and what probing data change in a prediction and what they do not. Same tRNA, so the two tutorials can be read side by side.
Background
- AI for RNA structure prediction: what deep learning models learn. What neural networks trained on known structures compute, why the usual benchmarks overstate their accuracy, what a test on synthetic data revealed about their outputs, and what has changed since 2022.
- Caveats to deep learning approaches to RNA secondary structure prediction (Flamm et al., Front Bioinform 2022). The paper behind the explainer, with the synthetic data experiments.
- Predicting RNA secondary structures from sequence and probing data (Lorenz et al., Methods 2016). The methods paper for the probing data support in ViennaRNA that the SHAPE tutorial uses.
- SHAPE directed RNA folding (Lorenz et al., Bioinformatics 2016). The comparison of the three methods on a set of RNAs with known structures.
Reference
The glossary defines the terms used in the tutorials, e.g. MFE, partition function, dot-bracket notation and soft constraints. Every entry has its own address and can be linked directly.
Questions and further tutorials
Each tutorial ends with a short form for questions that the text left open; these questions decide what gets written next. Further tutorials are in preparation on reading dot plots and dot-bracket strings, on accessibility with RNAplfold, on consensus structures from alignments with RNAalifold, and on comparing predicted structures. A series of hands-on ViennaRNA training modules and workshops is planned for 2027.