recipe bioconductor-ramr

Detection of Rare Aberrantly Methylated Regions in Array and NGS Data






ramr is an R package for detection of low-frequency aberrant methylation events in large data sets obtained by methylation profiling using array or high-throughput bisulfite sequencing. In addition, package provides functions to visualize found aberrantly methylated regions (AMRs), to generate sets of all possible regions to be used as reference sets for enrichment analysis, and to generate biologically relevant test data sets for performance evaluation of AMR/DMR search algorithms.

package bioconductor-ramr

(downloads) docker_bioconductor-ramr



depends bioconductor-biocgenerics:


depends bioconductor-genomicranges:


depends bioconductor-iranges:


depends bioconductor-s4vectors:


depends r-base:


depends r-doparallel:

depends r-dorng:

depends r-envstats:

depends r-extdist:

depends r-foreach:

depends r-ggplot2:

depends r-matrixstats:

depends r-reshape2:



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-ramr

and update with::

   mamba update bioconductor-ramr

To create a new environment, run:

mamba create --name myenvname bioconductor-ramr

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

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