recipe eternafold

RNA structure prediction algorithm improved through crowdsourced training data

Homepage:

https://github.com/eternagame/EternaFold

Documentation:

https://eternagame.github.io/EternaFold

License:

BSD / BSD-3-Clause

Recipe:

/eternafold/meta.yaml

EternaFold performs multitask learning to improve RNA structure prediction. Its training tasks include 1) predicting single structures, 2) maximizing the likelihood of structure probing data, and 3) predicting experimentally-measured affinities of RNA molecules to proteins and small molecules. Described in the paper https://www.nature.com/articles/s41592-022-01605-0

package eternafold

versions:

1.3.1-0

depends libgcc-ng:

>=12

depends libstdcxx-ng:

>=12

requirements:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install eternafold

and update with::

   mamba update eternafold

To create a new environment, run:

mamba create --name myenvname eternafold

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull quay.io/biocontainers/eternafold:<tag>

(see `eternafold/tags`_ for valid values for ``<tag>``)

Download stats

Link to this page

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/eternafold/README.html)