Package Recipe 'rmats-long'

recipe rmats-long

Differential isoform analysis using long-read RNA-seq data.

Homepage:: https://github.com/Xinglab/rMATS-long
License:: BSD / BSD-2-Clause
Recipe:: /rmats-long/meta.yaml

package rmats-long¶

Versions:

2.0.1-0, 1.0.0-0

Depends:

on bioconductor-biocparallel
on bioconductor-drimseq
on libgcc >=14
on matplotlib-base >=3.7
on networkx >=2.8
on numpy >=1.24
on pandas >=2
on pydot >=3
on python >=3.11,<3.12.0a0
on python_abi 3.11.* *_cp311
on r-base
on r-cowplot
on r-ggplot2
on r-ggrepel
on r-ggvenndiagram
on r-mclogit
on r-stringi >=1.7.8
on r-this.path
on rpy2 >=3.5
on samtools
on threadpoolctl >=3.6

Additional platforms:

linux-aarch64

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 rmats-long

to add into an existing workspace instead, run:

pixi add rmats-long

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 rmats-long

Alternatively, to install into a new environment, run:

conda create -n envname rmats-long

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/rmats-long:<tag>

(see rmats-long/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.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/rmats-long/README.html)