recipe percolator

Semi-supervised learning for peptide identification from shotgun proteomics datasets.

Homepage:

https://github.com/percolator/percolator

Documentation:

http://percolator.ms

License:

APACHE / Apache-2.0

Recipe:

/percolator/meta.yaml

Links:

biotools: Percolator, doi: 10.1007/s13361-016-1460-7, usegalaxy-eu: percolator

package percolator

(downloads) docker_percolator

versions:
3.7.1-0,  3.6.5-0,  3.5-1,  3.5-0,  3.4-1,  3.4-0,  3.1-4,  3.1-3,  3.1-2, 

3.7.1-0,  3.6.5-0,  3.5-1,  3.5-0,  3.4-1,  3.4-0,  3.1-4,  3.1-3,  3.1-2,  3.0-1,  3.0-0

depends bzip2:

>=1.0.8,<2.0a0

depends libgcc:

>=12

depends libsqlite:

>=3.46.0,<4.0a0

depends libstdcxx:

>=12

depends sqlite:

depends xerces-c:

>=3.2.3,<3.3.0a0

depends xsd:

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 percolator

and update with::

   mamba update percolator

To create a new environment, run:

mamba create --name myenvname percolator

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/percolator:<tag>

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

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Link to this page

Render an install-with-bioconda badge with the following MarkDown:

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