recipe bioconductor-crisprscoredata

Pre-trained models for the crisprScore package






Provides an interface to access pre-trained models for on-target and off-target gRNA activity prediction algorithms implemented in the crisprScore package. Pre-trained model data are stored in the ExperimentHub database. Users should consider using the crisprScore package directly to use and load the pre-trained models.

package bioconductor-crisprscoredata

(downloads) docker_bioconductor-crisprscoredata



depends bioconductor-annotationhub:


depends bioconductor-data-packages:


depends bioconductor-experimenthub:


depends curl:

depends r-base:




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 bioconductor-crisprscoredata

and update with::

   mamba update bioconductor-crisprscoredata

To create a new environment, run:

mamba create --name myenvname bioconductor-crisprscoredata

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<tag>

(see `bioconductor-crisprscoredata/tags`_ for valid values for ``<tag>``)

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