recipe bioconductor-profileplyr

Visualization and annotation of read signal over genomic ranges with profileplyr



GPL (>= 3)



Quick and straightforward visualization of read signal over genomic intervals is key for generating hypotheses from sequencing data sets (e.g. ChIP-seq, ATAC-seq, bisulfite/methyl-seq). Many tools both inside and outside of R and Bioconductor are available to explore these types of data, and they typically start with a bigWig or BAM file and end with some representation of the signal (e.g. heatmap). profileplyr leverages many Bioconductor tools to allow for both flexibility and additional functionality in workflows that end with visualization of the read signal.

package bioconductor-profileplyr

(downloads) docker_bioconductor-profileplyr



depends bioconductor-biocgenerics:


depends bioconductor-biocparallel:


depends bioconductor-chipseeker:


depends bioconductor-complexheatmap:


depends bioconductor-enrichedheatmap:


depends bioconductor-genomeinfodb:


depends bioconductor-genomicfeatures:


depends bioconductor-genomicranges:


depends bioconductor-iranges:






depends bioconductor-rgreat:


depends bioconductor-rsamtools:


depends bioconductor-rtracklayer:


depends bioconductor-s4vectors:


depends bioconductor-soggi:


depends bioconductor-summarizedexperiment:


depends bioconductor-txdb.hsapiens.ucsc.hg19.knowngene:


depends bioconductor-txdb.hsapiens.ucsc.hg38.knowngene:


depends bioconductor-txdb.mmusculus.ucsc.mm10.knowngene:


depends bioconductor-txdb.mmusculus.ucsc.mm9.knowngene:


depends r-base:


depends r-circlize:

depends r-dplyr:

depends r-ggplot2:

depends r-magrittr:

depends r-pheatmap:

depends r-r.utils:

depends r-rjson:

depends r-rlang:

depends r-tidyr:

depends r-tiff:



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-profileplyr

and update with::

   mamba update bioconductor-profileplyr

To create a new environment, run:

mamba create --name myenvname bioconductor-profileplyr

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

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