Institute of Computer Science
University Freiburg

antaRNA - Ant Colony Optimized RNA Sequence Design



antaRNA applies the principle of Ant Colony optimization (ACO) to the problem of inverse folding a RNA structure i.e. finding a suitable sequence, which can fold into that structure. Besides the structural constraint, antaRNA realizes the usage of sequence constraints and provides the user to specify a GC value constraint.

Requirements & Installation



Download a provided python file of antaRNA and save it to your favorite place. Go and execute!

antaRNA project repository on GitHub.

Direct .py downloads:

Web Services

Freiburg RNA Tools

antaRNA is available through the Freiburg RNA Tools

Galaxy Docker Constainer

antaRNA is available through the RNA-workbench Docker Image which is part of the Galaxy Docker Image.

Users, which already host a Galaxy Instance, antaRNA can be obtained through its repository.

How to use ?


Once downloaded and having installed all dependencies, you can execute antaRNA from the shell.
Optional you can also include the the program to your PYTHONPATH, so that you can use antaRNA from within python or call functions from other python scripts.

Example for generating a sequence wild-card constrained instances comprising a desired target GC-content of 50%:

python -Cstr "....(((((...)))))...(((((...)))))..." -tGC 0.5

In order to use the pseudoknot option within antaRNA, the flags -p and -pkPar have to be used.

Example for generating a sequence wild-card constrained instances comprising a desired target GC-content of 50% using pkiss as folding prediction and using the parametrized parameters for pseudoknots:

python -Cstr "....(((((.[[[[.))))).........]]]]..." -tGC 0.5 -p -pkPar

A regular call of antaRNA will produce an output in classical FASTA format: A header and the output sequence of the program.

The option -v, however, induces a three lined verbose output: In the first line some stats about the run and qualities of the result are added; in the second line the solution structure is listed; in the third line, the solution sequence is listed:


RFAM v.11.0 dataset: [training] and [test]

Pseudobase++ dataset (as of 2014/12): [training] and [test]

antaRNA Auxiliary Illustrations and Explanations

GC Management

GC target Value Handling

Terrain Graph

Terrain Vertices' and Edges' Features
Terrain Initialization
Structure/Sequence Constraint Dependent Terrain Modification
GC Constraint Dependent Terrain Modification


IUPAC Nucleotide Definition


antaRNA Variables or type python -h


If you employ antaRNA in your work, please cite: