recipe psap

CLI interface for the PSAP classifier. PSAP implements a RandomForest approach to predict the probability of proteins to mediate protein phase separation (PPS).

Homepage:

https://github.com/vanheeringen-lab/psap

License:

MIT

Recipe:

/psap/meta.yaml

package psap

versions:

1.0.7-0,  1.0.6-0

depends biopython:

>=1.73

depends black:

>=20.8b1

depends loguru:

>=0.5.3

depends matplotlib-base:

>=3.3.4

depends pandas:

>=1.0.1

depends python:

>=3.7

depends scikit-learn:

>=0.21.3

depends scipy:

>=1.2.0

depends seaborn:

>=0.11.1

depends sklearn-json:

>=0.1.0

depends tqdm:

>=4.38.0

depends versioneer:

>=0.19

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 psap

and update with::

   mamba update psap

To create a new environment, run:

mamba create --name myenvname psap

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/psap:<tag>

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

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