recipe bioconductor-geneattribution

Identification of candidate genes associated with genetic variation







biotools: geneattribution, doi: 10.1093/bioinformatics/btw698

Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in the UCSC .BED file format.

package bioconductor-geneattribution

(downloads) docker_bioconductor-geneattribution



depends bioconductor-biocgenerics:


depends bioconductor-genomeinfodb:


depends bioconductor-genomicfeatures:


depends bioconductor-genomicranges:


depends bioconductor-iranges:




depends bioconductor-rtracklayer:


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-geneattribution

and update with::

   mamba update bioconductor-geneattribution

To create a new environment, run:

mamba create --name myenvname bioconductor-geneattribution

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-geneattribution/tags`_ for valid values for ``<tag>``)

Download stats