recipe bioconductor-trena

Fit transcriptional regulatory networks using gene expression, priors, machine learning






Methods for reconstructing transcriptional regulatory networks, especially in species for which genome-wide TF binding site information is available.

package bioconductor-trena

(downloads) docker_bioconductor-trena



depends bioconductor-annotationdbi:


depends bioconductor-biomart:


depends bioconductor-biostrings:


depends bioconductor-bsgenome:


depends bioconductor-bsgenome.hsapiens.ucsc.hg19:


depends bioconductor-bsgenome.hsapiens.ucsc.hg38:


depends bioconductor-bsgenome.mmusculus.ucsc.mm10:


depends bioconductor-genomicranges:


depends bioconductor-motifdb:




depends bioconductor-snplocs.hsapiens.dbsnp150.grch38:


depends r-base:


depends r-dbi:

depends r-glmnet:


depends r-lassopv:

depends r-randomforest:

depends r-rmysql:

depends r-rpostgresql:

depends r-rsqlite:

depends r-wgcna:

depends r-xgboost:



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-trena

and update with::

   mamba update bioconductor-trena

To create a new environment, run:

mamba create --name myenvname bioconductor-trena

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

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