recipe rnachipintegrator

Analyse genes against peak data, and vice versa

Homepage:

https://github.com/fls-bioinformatics-core/RnaChipIntegrator

Documentation:

https://rnachipintegrator.readthedocs.io

License:

OTHER / Artistic License

Recipe:

/rnachipintegrator/meta.yaml

RnaChipIntegrator is a utility that performs integrated analyses of 'gene' data (a set of genes or other genomic features) with 'peak' data (a set of regions, for example ChIP peaks) to identify the genes nearest to each peak, and vice versa

package rnachipintegrator

(downloads) docker_rnachipintegrator

Versions:

3.0.0-02.0.0-12.0.0-01.2.0-01.1.0-01.0.3-11.0.3-0

Depends:
  • on python

  • on xlsxwriter >=0.8.4

Additional platforms:

Installation

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install rnachipintegrator

to add into an existing workspace instead, run:

pixi add rnachipintegrator

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install rnachipintegrator

Alternatively, to install into a new environment, run:

conda create -n envname rnachipintegrator

with envname being the name of the desired environment.

Container

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/rnachipintegrator:<tag>

(see rnachipintegrator/tags for valid values for <tag>).

Integrated deployment

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

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