recipe pycistarget

pycistarget is a python module to perform motif enrichment analysis in sets of regions with different tools and identify high confidence TF cistromes.

Homepage:

https://github.com/aertslab/pycistarget

License:

OTHER

Recipe:

/pycistarget/meta.yaml

package pycistarget

(downloads) docker_pycistarget

versions:

1.1-0

depends ctxcore:

>=0.2.0,<0.3.0

depends h5py:

>=3.10.0,<4.0.0

depends ipython:

>=8.0.0

depends numpy:

<2.0.0

depends pandas:

>=1.5.0,<2.0.0

depends pyranges:

>=0.0.111,<0.1.0

depends python:

>=3.9,<3.12

depends scikit-learn:

>=1.3.2,<2.0.0

requirements:

additional platforms:

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 pycistarget

and update with::

   mamba update pycistarget

To create a new environment, run:

mamba create --name myenvname pycistarget

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/pycistarget:<tag>

(see `pycistarget/tags`_ for valid values for ``<tag>``)

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Link to this page

Render an install-with-bioconda 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/pycistarget/README.html)