- recipe famus
Functional Annotation Method Using Siamese neural networks (FAMUS)
- Homepage:
- License:
MIT / MIT
- Recipe:
FAMUS is a Siamese Neural Network based framework that annotates protein sequences with function using pre-trained models or custom training.
NOTE: PyTorch must be installed separately according to your system configuration. Visit https://pytorch.org/get-started/locally/ for installation instructions.
- package famus¶
- versions:
0.1.2-0,0.1.1-0- depends biopython:
>=1.76- depends hmmer:
- depends mafft:
- depends matplotlib-base:
>=3.7.0- depends mmseqs2:
- depends numpy:
>=1.26.4,<2.0- depends pandas:
>=2.2.3- depends python:
>=3.12,<3.13.0a0- depends pyyaml:
>=5.0- depends scikit-learn:
>=1.3.0- depends scipy:
>=1.10.0- depends seaborn:
>=0.13.2- depends seqkit:
- depends tqdm:
>=4.66.2- 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 famus and update with:: mamba update famus
To create a new environment, run:
mamba create --name myenvname famus
with
myenvnamebeing 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/famus:<tag> (see `famus/tags`_ for valid values for ``<tag>``)
Download stats¶
Link to this page¶
Render an 
badge with the following MarkDown:
[](http://bioconda.github.io/recipes/famus/README.html)
