recipe deepaccess

A package for training and interpreting an ensemble of neural networks for chromatin accessibility

Homepage:

https://github.com/gifford-lab/deepaccess-package

Documentation:

https://pypi.org/project/deepaccess/

License:

MIT / MIT

Recipe:

/deepaccess/meta.yaml

Links:

doi: 10.1101/2021.02.26.433073

package deepaccess

versions:

0.1.3-0,  0.1.2-0,  0.1.1-0,  0.1.0-0

depends bedtools:

>=2.29.2

depends keras:

>=2.4.3

depends matplotlib-base:

>=3.3.3

depends numpy:

>=1.19.0

depends python:

>=3.6

depends scikit-learn:

>=0.24.1

depends scipy:

>=1.6.2

depends tensorflow:

>=2.4

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 deepaccess

and update with::

   mamba update deepaccess

To create a new environment, run:

mamba create --name myenvname deepaccess

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/deepaccess:<tag>

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

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