recipe bioconductor-rlseq

RLSeq: An analysis package for R-loop mapping data






RLSeq is a toolkit for analyzing and evaluating R-loop mapping datasets. RLSeq serves two primary purposes: (1) to facilitate the evaluation of dataset quality, and (2) to enable R-loop analysis in the context of publicly-available data sets from RLBase. The package is intended to provide a simple pipeline, called with the `RLSeq()` function, which performs all main analyses. Individual functions are also accessible and provide custom analysis capabilities. Finally an HTML report is generated with `report()`.

package bioconductor-rlseq

(downloads) docker_bioconductor-rlseq



depends bioconductor-annotationhub:


depends bioconductor-complexheatmap:


depends bioconductor-genomeinfodb:


depends bioconductor-genomicfeatures:


depends bioconductor-genomicranges:


depends bioconductor-regioner:


depends bioconductor-rlhub:


depends bioconductor-rtracklayer:


depends r-aws.s3:

depends r-base:


depends r-callr:

depends r-caretensemble:

depends r-circlize:

depends r-dplyr:

depends r-ggplot2:

depends r-ggplotify:

depends r-ggprism:

depends r-pheatmap:

depends r-rcolorbrewer:

depends r-valr:

depends r-venndiagram:



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-rlseq

and update with::

   mamba update bioconductor-rlseq

To create a new environment, run:

mamba create --name myenvname bioconductor-rlseq

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

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