Installation

Note

Supported Python versions are 3.12, 3.13 and 3.14.

Conda (recommended)

This is the recommended and most straightforward way to install CAGEcleaner. It should work on any Linux or MacOS system. Create a fresh conda environment as well to keep it from meddling with your other tools. You can install CAGEcleaner either directly from Bioconda

conda create -n cagecleaner -c bioconda -c conda-forge cagecleaner

or by using the conda yml environment file in this repo.

conda env create -f env.yml

Then start using it by activating the conda environment.

conda activate cagecleaner

Docker

CAGEcleaner is also available as a Docker image from DockerHub. This is one of the recommended ways to run CAGEcleaner on Windows (the other one being running it using Windows’ WSL feature).

docker pull lucodevro/cagecleaner

There is no entrypoint set up so running CAGEcleaner requires prepending your CAGEcleaner command with the appropriate Docker commands.

docker run lucodevro/cagecleaner -v <your-cblaster-session>:session.json -v <your-output-folder>:output cagecleaner -s session.json -o output

GitHub

Alternatively, it is possible to install the latest semi-stable development version by cloning this repository and running the following command at the root of your local copy of this repository.

pip install .

PyPi

CAGEcleaner is also installable from PyPi using pip, yet we do not recommend using this approach as some dependencies are not available from PyPi (NCBI Datasets CLI, Entrez Direct, any2fasta, MMseqs2) and therefore should be installed beforehand. So either make sure you have installed these dependencies separately, or use one of the other installation options.

pip install cagecleaner

Warning

We do not recommend using this approach as some non-Python dependencies are not available from PyPi (NCBI Datasets CLI, any2fasta, MMseqs2) and therefore should be installed beforehand. Check out CAGEcleaner’s dependencies in the Bioconda recipe for more details.