recipe bioconductor-hummingbird

Bayesian Hidden Markov Model for the detection of differentially methylated regions



GPL (>=2)



A package for detecting differential methylation. It exploits a Bayesian hidden Markov model that incorporates location dependence among genomic loci, unlike most existing methods that assume independence among observations. Bayesian priors are applied to permit information sharing across an entire chromosome for improved power of detection. The direct output of our software package is the best sequence of methylation states, eliminating the use of a subjective, and most of the time an arbitrary, threshold of p-value for determining significance. At last, our methodology does not require replication in either or both of the two comparison groups.

package bioconductor-hummingbird

(downloads) docker_bioconductor-hummingbird



depends bioconductor-genomicranges:


depends bioconductor-iranges:


depends bioconductor-summarizedexperiment:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-rcpp:



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-hummingbird

and update with::

   mamba update bioconductor-hummingbird

To create a new environment, run:

mamba create --name myenvname bioconductor-hummingbird

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

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