- recipe alphagenome
Python SDK for interacting with and visualizing AlphaGenome genomic models
- Homepage:
- Documentation:
- License:
APACHE / Apache-2.0
- Recipe:
AlphaGenome is a unified DNA sequence model from Google DeepMind for regulatory variant-effect prediction. This package provides the Python SDK for interacting with the AlphaGenome API and visualizing predictions across gene expression, splicing, chromatin features, and contact maps.
- package alphagenome¶
- Versions:
0.6.1-0,0.6.0-0,0.5.1-0- Depends:
on absl-py
on anndata
on grpcio
>=1.67.1on immutabledict
on intervaltree
on jaxtyping
on matplotlib-base
on ml_dtypes
on numpy
on pandas
on protobuf
>=5.28.3on pyarrow
on python
>=3.10on scipy
on seaborn
on tqdm
on typeguard
on typing-extensions
on zstandard
- 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 alphagenome
to add into an existing workspace instead, run:
pixi add alphagenome
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 alphagenome
Alternatively, to install into a new environment, run:
conda create -n envname alphagenome
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/alphagenome:<tag>
(see alphagenome/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/alphagenome/README.html)
