recipe bioconductor-yarn

YARN: Robust Multi-Condition RNA-Seq Preprocessing and Normalization







biotools: yarn, doi: 10.1101/086587

Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments.

package bioconductor-yarn

(downloads) docker_bioconductor-yarn



depends bioconductor-biobase:


depends bioconductor-biomart:


depends bioconductor-edger:


depends bioconductor-limma:


depends bioconductor-preprocesscore:


depends bioconductor-quantro:


depends r-base:


depends r-downloader:

depends r-gplots:

depends r-matrixstats:

depends r-rcolorbrewer:

depends r-readr:



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-yarn

and update with::

   mamba update bioconductor-yarn

To create a new environment, run:

mamba create --name myenvname bioconductor-yarn

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

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