recipe metacerberus

Versatile Functional Ontology Assignments for Metagenomes via Hidden Markov Model (HMM) searching with environmental focus of shotgun meta'omics data

Homepage:

https://github.com/raw-lab/metacerberus

License:

BSD / BSD-3-Clause

Recipe:

/metacerberus/meta.yaml

package metacerberus

versions:

1.1-1,  1.1-0,  1.0-1,  1.0-0,  0.2-1,  0.2-0,  0.1-0

depends bbmap:

depends configargparse:

depends dominate:

depends fastp:

depends fastqc:

depends flash2:

depends grpcio:

1.43.*

depends hmmer:

depends metaomestats:

depends pandas:

depends plotly:

depends porechop:

depends prodigal:

depends psutil:

depends python:

>=3.8

depends python-kaleido:

depends ray-core:

depends ray-dashboard:

depends ray-default:

depends ray-tune:

depends scikit-learn:

requirements:

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 metacerberus

and update with::

   mamba update metacerberus

To create a new environment, run:

mamba create --name myenvname metacerberus

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/metacerberus:<tag>

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

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