- recipe mgkit
Metagenomics Framework
- Homepage:
- License:
GPL2 / GPL-2.0-or-later
- Recipe:
- Links:
biotools: mgkit, doi: 10.6084/m9.figshare.1588384
- package mgkit¶
-
- Versions:
0.5.8-4,0.5.8-3,0.5.8-2,0.5.8-1,0.5.8-0,0.5.6-1,0.5.6-0,0.5.5-0,0.5.4-0,0.5.8-4,0.5.8-3,0.5.8-2,0.5.8-1,0.5.8-0,0.5.6-1,0.5.6-0,0.5.5-0,0.5.4-0,0.5.3-0,0.5.2-0,0.5.1-0,0.5.0-0,0.4.3-1,0.4.3-0,0.4.2-0,0.4.1-0,0.4.0-0,0.3.4-0,0.3.3-0,0.3.0-0,0.2.2-0- Depends:
on click
>=6on future
on htseq
>=0.9.1on libgcc
>=13on matplotlib-base
>=2on msgpack-python
>=0.5.6on networkx
on numpy
>=1.9.2on pandas
>=1.1.3on pyarrow
>=2.0.0on pymongo
>=3.1.1on pysam
>=0.14on pytables
>=3.4.2on python
>=3.9,<3.10.0a0on python_abi
3.9.* *_cp39on pyvcf
>=0.6.0on requests
on scipy
>=0.15.1on semidbm
>=0.5.1on statsmodels
>=0.12on tqdm
>=4.0
- Additional platforms:
linux-aarch64
Installation¶
You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).
Pixi¶
With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:
pixi global install mgkit
to add into an existing workspace instead, run:
pixi add mgkit
In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:
pixi workspace channel add conda-forge
pixi workspace channel add bioconda
Conda¶
With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:
conda install mgkit
Alternatively, to install into a new environment, run:
conda create -n envname mgkit
with envname being the name of the desired environment.
Container¶
Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:
docker pull quay.io/biocontainers/mgkit:<tag>
(see mgkit/tags for valid values for <tag>).
Integrated deployment¶
Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.
Download stats¶
Link to this page¶
Render an badge with the following MarkDown:
[](http://bioconda.github.io/recipes/mgkit/README.html)