recipe bioconductor-bionar

Biological Network Analysis in R






the R package BioNAR, developed to step by step analysis of PPI network. The aim is to quantify and rank each protein’s simultaneous impact into multiple complexes based on network topology and clustering. Package also enables estimating of co-occurrence of diseases across the network and specific clusters pointing towards shared/common mechanisms.

package bioconductor-bionar

(downloads) docker_bioconductor-bionar



depends bioconductor-annotationdbi:


depends bioconductor-fgsea:


depends bioconductor-go.db:




depends r-base:


depends r-clustercons:

depends r-cowplot:

depends r-data.table:

depends r-dplyr:

depends r-ggplot2:

depends r-ggrepel:

depends r-igraph:


depends r-latex2exp:

depends r-matrix:

depends r-minpack.lm:

depends r-powerlaw:

depends r-rdpack:

depends r-rspectra:

depends r-rspectral:

depends r-scales:

depends r-stringr:

depends r-viridis:

depends r-wgcna:



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-bionar

and update with::

   mamba update bioconductor-bionar

To create a new environment, run:

mamba create --name myenvname bioconductor-bionar

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

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