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ViennaRNA Package 2.0: High-Performance RNA Secondary Structure Prediction, Partition Functions and RNA-RNA Interaction
The ViennaRNA Package 2.0 is a widely used software toolkit for RNA secondary structure prediction and analysis. The 2011 software article in Algorithms for Molecular Biology, by Ronny Lorenz, Ivo L. Hofacker and colleagues (Hofacker is at the University of Vienna), describes a major overhaul of a package that had by then been in use for nearly two decades.
RNA function depends on structure, and the structure of an RNA is largely set by which bases pair with which. The package predicts secondary structure with thermodynamic models and dynamic programming, from single minimum free energy folds to full ensembles of structures.
The 2.0 release updated the default energy parameters to the Turner 2004 set, made the core library RNAlib thread-safe so it can use multiple cores through OpenMP, and added several new analyses. The authors state that efficiency was preserved and backward compatibility was maintained where possible.
Property | Detail |
|---|---|
Energy model | Nearest-neighbor model; Turner 2004 parameters by default, with Turner 1999 and Andronescu sets also available |
Core algorithms | Minimum free energy (MFE) folding, McCaskill partition function and base pair probabilities, suboptimal structures, stochastic sampling |
Interfaces | Command-line programs, C/C++ library (RNAlib), Perl bindings |
Benchmark set | 1,817 sequence and structure pairs from the RNAstrand database, excluding pseudoknotted references |
Source and manual | tbi.univie.ac.at/RNA |
Key Capabilities of the ViennaRNA Package for RNA Folding
Programs in the Package
RNAfold: MFE structure and partition function, including base pairing probabilities.
RNAsubopt: enumeration and stochastic sampling of suboptimal structures.
RNALfold and RNAplfold: local folding and locally stable structures.
RNAcofold, RNAup, RNAplex and RNAduplex: RNA-RNA interaction prediction.
RNAalifold: consensus structures for alignments.
RNAinverse: sequence design for a target structure.
RNAPKplex: pseudoknot prediction.
RNA2Dfold: two-dimensional projection of the structure landscape.
New Analyses Added in Version 2.0
These include restricted structure ensembles, centroid and maximum expected accuracy (MEA) structures, z-scores for locally stable structures, and FASTA input.
Benchmark Results and Performance of ViennaRNA 2.0
On the 1,817 non-multimer sequences drawn from RNAstrand, RNAfold 2.0 performed slightly better on average than RNAstructure 5.2 and UNAFold 3.8, and better than RNAfold 1.8.5. Runtime and memory use were nearly unchanged from RNAfold 1.8.5 despite the larger parameter set. The authors caution that performance varies widely across RNA families and that no single implementation is consistently best.
Program | Practical notes from the paper |
|---|---|
RNAfold 2.0 | Slightly better average accuracy than RNAstructure 5.2 and UNAFold 3.8 on the benchmark |
RNAplfold and RNALfold | Practical up to roughly 400 to 600 nucleotides |
RNA2Dfold | O(n^7) time and O(n^4) memory |
What is Tamarind Bio?
Tamarind Bio is a no-code bioinformatics platform built to give life scientists and researchers access to powerful computational tools. Many cutting-edge machine learning models are hard to deploy and use. Tamarind provides an intuitive, web-based environment that removes the complexity of high-performance computing, software dependencies and command-line interfaces.
The platform is designed for biologists, chemists and other researchers who may not have a background in programming or cloud infrastructure but want to run models on their own data. Key features include:
A user-friendly graphical interface for setting up and launching experiments
A robust API for integration into existing research pipelines
An automated system for managing and scaling computational resources
Tamarind treats information and data security as a top priority, as detailed in its Trust Center and Terms of Service.
Accelerating Discovery with ViennaRNA on Tamarind Bio
Secondary structure prediction: compute MFE structures and base pairing probabilities for RNA sequences.
RNA-RNA interaction analysis: model duplexes and co-folding between two RNAs.
Consensus structure analysis: derive structures conserved across an alignment.
RNA design: search for sequences that fold into a target structure.
How to Use ViennaRNA on Tamarind Bio
Log in: go to tamarind.bio and open the ViennaRNA tool.
Provide the sequence: enter or upload one or more RNA sequences, for example in FASTA format.
Choose the program: if exposed, select the analysis such as folding, suboptimal structures or interaction prediction.
Set parameters: if exposed, pick the energy parameter set and dangling-end option.
Run: submit the job and wait for the prediction to finish.
Download outputs: collect predicted structures, free energies and base pairing probabilities.
Interpret: compare the MFE structure with ensemble results to judge how well defined the fold is.
Use downstream: use the structures for design, probing experiments or further analysis.
Things to Keep in Mind
Only RNAPKplex predicts pseudoknots explicitly.
The energy model omits several special loop cases for efficiency, though the authors report no measurable loss of accuracy on their test set.
RNALfold has no partition function version.
The benchmark covers MFE prediction on a single dataset and is not a broad comparison against non-thermodynamic methods.
Command-line syntax changed in version 2.0, and parameter files from version 1.8.5 and earlier need conversion.
Source: Lorenz R, Bernhart SH, Höner zu Siederdissen C, Tafer H, Flamm C, Stadler PF, Hofacker IL (the page's author list may continue). ViennaRNA Package 2.0. Algorithms for Molecular Biology 6:26 (2011). DOI 10.1186/1748-7188-6-26. Software and manual: www.tbi.univie.ac.at/RNA.