recipe bioconductor-rnamodr.ml

Detecting patterns of post-transcriptional modifications using machine learning

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/RNAmodR.ML.html

License:

Artistic-2.0

Recipe:

/bioconductor-rnamodr.ml/meta.yaml

RNAmodR.ML extend the functionality of the RNAmodR package and classical detection strategies towards detection through machine learning models. RNAmodR.ML provides classes, functions and an example workflow to establish a detection stratedy, which can be packaged.

package bioconductor-rnamodr.ml

versions:

1.16.0-0,  1.14.0-0,  1.12.0-0,  1.8.0-0,  1.6.0-0,  1.4.0-1,  1.4.0-0,  1.2.0-0,  1.0.0-0

depends bioconductor-biocgenerics:

>=0.48.0,<0.49.0

depends bioconductor-genomicranges:

>=1.54.0,<1.55.0

depends bioconductor-iranges:

>=2.36.0,<2.37.0

depends bioconductor-rnamodr:

>=1.16.0,<1.17.0

depends bioconductor-s4vectors:

>=0.40.0,<0.41.0

depends r-base:

>=4.3,<4.4.0a0

depends r-ranger:

requirements:

Installation

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-rnamodr.ml

and update with::

   mamba update bioconductor-rnamodr.ml

To create a new environment, run:

mamba create --name myenvname bioconductor-rnamodr.ml

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 quay.io/biocontainers/bioconductor-rnamodr.ml:<tag>

(see `bioconductor-rnamodr.ml/tags`_ for valid values for ``<tag>``)

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