- recipe deepchopper
A Genomic Language Model for Chimera Artifact Detection in Nanopore Direct RNA Sequencing.
- Homepage:
- License:
Apache / Apache-2.0
- Recipe:
DeepChopper is a genomic language model designed to identify artificial sequences. It provides functionality for encoding FASTQ files, making predictions, and chopping sequences.
- package deepchopper¶
- versions:
1.2.6-0,1.2.5-1,1.2.5-0,1.2.4-0- depends datasets:
2.14.2- depends deepchopper-cli:
>=1.2.5- depends evaluate:
>=0.4.1- depends fastapi:
0.112.2- depends gradio:
5.0.1- depends hydra-core:
>=1.3.2- depends libgcc:
>=12- depends lightning:
>=2.1.2- depends omegaconf:
>=2.3.0- depends python:
>=3.10,<3.11.0a0- depends python-multipart:
0.0.12- depends python_abi:
3.10.* *_cp310- depends pytorch:
>=2.1.0- depends rich:
>=13.7.0- depends safetensors:
>=0.4.2- depends scikit-learn:
>=1.5.2- depends torchmetrics:
>=1.2.0- depends transformers:
>=4.37.2- depends typer:
>=0.12.0- 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 deepchopper and update with:: mamba update deepchopper
To create a new environment, run:
mamba create --name myenvname deepchopper
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/deepchopper:<tag> (see `deepchopper/tags`_ for valid values for ``<tag>``)
Download stats¶
Link to this page¶
Render an 
badge with the following MarkDown:
[](http://bioconda.github.io/recipes/deepchopper/README.html)
