ViennaRNA Structure Prediction

Overview

ViennaRNA is the gold-standard toolkit for RNA secondary structure prediction based on thermodynamic nearest-neighbor parameters. It predicts the minimum free energy (MFE) structure and dot-bracket notation for a given RNA sequence, computes the full partition function to obtain base pair probabilities, and models RNA-RNA interactions via co-folding and duplex prediction. The Python bindings (import RNA) expose the full ViennaRNA C library with sequence-level and fold-compound APIs. Command-line programs (RNAfold, RNAalifold, RNAduplex) are also available and demonstrated here.

When to Use

• Predicting the minimum free energy secondary structure of an RNA sequence (mRNA, lncRNA, miRNA precursor, aptamer)
• Computing base pair probability matrices to assess structural uncertainty and identify well-defined stem-loops
• Designing or evaluating siRNA accessibility by folding the target mRNA region and checking for double-stranded structure
• Assessing sgRNA targeting efficiency by predicting guide RNA secondary structure that may reduce on-target activity
• Modeling RNA-RNA interactions (co-folding or duplex prediction) for miRNA-target binding or antisense oligonucleotide design
• Calculating folding free energies for a set of sequences to compare thermodynamic stability
• Use mfold (web server) or RNAstructure instead when you need Mfold algorithm predictions specifically or need the Efold partition function; ViennaRNA uses the Turner 2004 nearest-neighbor parameters and is the standard for research-grade thermodynamic prediction

Prerequisites