recipe bioconductor-cageminer

Candidate Gene Miner






This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments.

package bioconductor-cageminer

(downloads) docker_bioconductor-cageminer



depends bioconductor-bionero:


depends bioconductor-genomeinfodb:


depends bioconductor-genomicranges:


depends bioconductor-ggbio:


depends bioconductor-iranges:


depends r-base:


depends r-ggplot2:

depends r-ggtext:

depends r-reshape2:

depends r-rlang:



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-cageminer

and update with::

   mamba update bioconductor-cageminer

To create a new environment, run:

mamba create --name myenvname bioconductor-cageminer

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

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