recipe bioconductor-ichip

Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models



GPL (>= 2)




biotools: ichip, doi: 10.1093/bioinformatics/btq032

Hidden Ising models are implemented to identify enriched genomic regions in ChIP-chip data. They can be used to analyze the data from multiple platforms (e.g., Affymetrix, Agilent, and NimbleGen), and the data with single to multiple replicates.

package bioconductor-ichip

(downloads) docker_bioconductor-ichip



depends bioconductor-limma:


depends bioconductor-limma:


depends libblas:


depends libgcc-ng:


depends liblapack:


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-ichip

and update with::

   mamba update bioconductor-ichip

To create a new environment, run:

mamba create --name myenvname bioconductor-ichip

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

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