recipe bioconductor-monalisa

Binned Motif Enrichment Analysis and Visualization



GPL (>= 3)



Useful functions to work with sequence motifs in the analysis of genomics data. These include methods to annotate genomic regions or sequences with predicted motif hits and to identify motifs that drive observed changes in accessibility or expression. Functions to produce informative visualizations of the obtained results are also provided.

package bioconductor-monalisa

(downloads) docker_bioconductor-monalisa



depends bioconductor-biocgenerics:


depends bioconductor-biocparallel:


depends bioconductor-biostrings:


depends bioconductor-bsgenome:


depends bioconductor-complexheatmap:


depends bioconductor-genomeinfodb:


depends bioconductor-genomicranges:


depends bioconductor-iranges:


depends bioconductor-s4vectors:


depends bioconductor-summarizedexperiment:


depends bioconductor-tfbstools:


depends bioconductor-xvector:


depends r-base:


depends r-circlize:

depends r-glmnet:

depends r-stabs:

depends r-vioplot:



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-monalisa

and update with::

   mamba update bioconductor-monalisa

To create a new environment, run:

mamba create --name myenvname bioconductor-monalisa

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

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