recipe bioconductor-segmenter

Perform Chromatin Segmentation Analysis in R by Calling ChromHMM






Chromatin segmentation analysis transforms ChIP-seq data into signals over the genome. The latter represents the observed states in a multivariate Markov model to predict the chromatin's underlying states. ChromHMM, written in Java, integrates histone modification datasets to learn the chromatin states de-novo. The goal of this package is to call chromHMM from within R, capture the output files in an S4 object and interface to other relevant Bioconductor analysis tools. In addition, segmenter provides functions to test, select and visualize the output of the segmentation.

package bioconductor-segmenter

(downloads) docker_bioconductor-segmenter



depends bioconductor-bamsignals:


depends bioconductor-chipseeker:


depends bioconductor-chromhmmdata:


depends bioconductor-complexheatmap:


depends bioconductor-genomicranges:


depends bioconductor-iranges:


depends bioconductor-s4vectors:


depends bioconductor-summarizedexperiment:


depends r-base:




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-segmenter

and update with::

   mamba update bioconductor-segmenter

To create a new environment, run:

mamba create --name myenvname bioconductor-segmenter

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

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