recipe bioconductor-msnbase

Base Functions and Classes for Mass Spectrometry and Proteomics







biotools: msnbase

MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data.

package bioconductor-msnbase

(downloads) docker_bioconductor-msnbase



depends bioconductor-affy:


depends bioconductor-biobase:


depends bioconductor-biocgenerics:


depends bioconductor-biocparallel:


depends bioconductor-impute:


depends bioconductor-iranges:


depends bioconductor-mscoreutils:


depends bioconductor-mzid:


depends bioconductor-mzr:


depends bioconductor-pcamethods:


depends bioconductor-protgenerics:


depends bioconductor-s4vectors:


depends bioconductor-vsn:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-digest:

depends r-ggplot2:

depends r-lattice:

depends r-maldiquant:


depends r-mass:

depends r-plyr:

depends r-rcpp:

depends r-scales:

depends r-xml:



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-msnbase

and update with::

   mamba update bioconductor-msnbase

To create a new environment, run:

mamba create --name myenvname bioconductor-msnbase

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

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