- recipe strainify
Strain-level abundance analysis tool for short-read metagenomics
- Homepage:
- License:
MIT
- Recipe:
- package strainify¶
-
- Versions:
1.2.0-0- Depends:
on bcftools
>=1.21on biopython
>=1.85on bwa
>=0.7.18on clarabel
>=0.10.0on coincbc
on configargparse
>=1.7.1on cvxpy
>=1.6.5on ecos
>=2.0.14on fastani
>=1.34on fasttree
>=2.1.11on git-filter-repo
>=2.47.0on gitpython
>=3.1on harvesttools
>=1.2on htslib
>=1.21on jinja2
>=3.1on joblib
>=1.5.0on jsonschema
>=4.23on mash
>=2.3on numpy
>=2.2on osqp
>=1.0.4on pandas
>=2.2on parallel
on parsnp
>=2.1.4on phipack
>=1.1on psutil
>=7.0on pulp
>=2.8on pysam
>=0.23.0on pyspoa
>=0.2.1on python
>=3.12on pyyaml
>=6.0on raxml
>=8.2.13on samtools
>=1.21on scikit-learn
>=1.6.1on scipy
>=1.15.3on scs
>=3.2.7on snakemake
>=9.3on tabulate
>=0.9on threadpoolctl
>=3.6.0on tqdm
>=4.67on typer
>=0.15on wgatools
>=1.1.0
- Additional platforms:
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 strainify
to add into an existing workspace instead, run:
pixi add strainify
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 strainify
Alternatively, to install into a new environment, run:
conda create -n envname strainify
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/strainify:<tag>
(see strainify/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/strainify/README.html)