refineGEMs - A Python-based toolbox to facilitate curation of genome-scale metabolic models (GEMS)
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refineGEMs is a python package intended to help with the curation of genome-scale metabolic models (GEMS).
Currently refineGEMs can be used for the investigation of a genome-scale metabolic model (GEM)/multiple GEMs, it can complete the following tasks:
COBRApy and libSBMLOther applications of refineGEMs to curate a given model include:
The toolbox refineGEMscan be installed via pip or via Docker.
You can install refineGEMs via pip:
pip install refineGEMs
or to a local conda environment where refineGEMs is distributed via this GitHub repository and all dependencies are denoted in the pyproject.toml file:
# clone or pull the latest source code
git clone https://github.com/draeger-lab/refinegems.git
cd refinegems
conda create -n <EnvName> python=3.10 (at least but < 3.13)
conda activate <EnvName>
# check that pip comes from <EnvName>
which pip
pip install .
Optional features require additional packages that are not needed for the base installation:
# ChEBI lookups
pip install "refineGEMs[chebi]"
# SBO label lookup via OLS
pip install "refineGEMs[ols]"
# SBO annotation
pip install "refineGEMs[sbo]"
# install all optional dependencies
pip install "refineGEMs[optional]"
Caution
Some connected tools are optional and currently need to be installed directly from GitHub before using the corresponding refineGEMs workflow step. If they are missing, refineGEMs reports the missing dependency and skips the affected optional step where possible.# For MCC pip install "masschargecuration@git+https://github.com/Biomathsys/MassChargeCuration" # For BOFdat, our fork with hot fix(es): pip install "bofdat@git+https://github.com/draeger-lab/BOFdat" # ModelPolisher client: pip install "model-polisher@git+https://github.com/draeger-lab/MPClient"
refineGEMs can also be used via Docker.
You can pull the latest image from (a) Docker Hub or (b) build it locally.
To pull the image from Docker Hub, simply use:
docker pull biodatalab/refinegems:<tag>
To build the Docker image locally, firstly clone the repository:
git clone "https://github.com/draeger-lab/refinegems.git"
Then change into the directory and build the image:
cd refinegems
docker build -t refinegems .
The default image installs the runtime optional dependency group from
pyproject.toml, but excludes the documentation dependencies. Optional
connected tools that are currently installed directly from GitHub are included
by default and can be disabled for a smaller image:
# build without the optional connected GitHub tools
docker build \
--build-arg INSTALL_EXTERNAL_TOOLS=false \
-t refinegems:runtime .
The full default can also be made explicit:
docker build \
--build-arg INSTALL_EXTERNAL_TOOLS=true \
-t refinegems:full .
Note
To provide the input files and retrieve the output files mount one folder as workspace folder to the Docker image with `-v`.
The default command executed by the image is refinegems -h and provides the help information for the CLI of
refineGEMs.
docker run refinegems -h
To use the image interactively and open a bash shell, run the following command:
docker run -it --entrypoint bash refinegems
To use the image for specific commands, you can simply use every of the CLI commands as entrypoint. For example, to curate a (draft) model, run:
docker run --name <container_name> -v <user_folder>:/rg_cont refinegems analyse stats ./path/to/model.xml
Contributions are welcome from users, developers, model curators, and documentation writers. Please read the contributing guide before opening larger changes. It describes the recommended development workflow, coding and documentation standards, media database notes, review process, and pull request checklist.
GitHub issue forms are available for
bug reports
and
feature requests.
Pull requests should target the dev branch and use the repository
pull request template. Please also follow
the project code of conduct.
The refineGEMs source code is distributed under the MIT licence. Bundled third-party data, database identifiers, adapted code, and connected external tools remain under their own licences or terms; see THIRD_PARTY_LICENSES.md for details.
The refineGEMs logos are licensed separately from the source code under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). The asset-side notice is stored at docs/source/images/logos/LOGO_LICENSE.md.
For colours, minimum size, clear space, and acceptable modifications, see the brand usage guide.
Project-created documentation graphics are licensed separately from the source code under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). The asset-side notice is stored at docs/source/images/GRAPHICS_LICENSE.md. Third-party graphics are excluded unless explicitly listed there or accompanied by a compatible notice.
When using refineGEMs, please cite the latest publication:
Famke Bäuerle, Gwendolyn O. Döbel, Laura Camus, Simon Heilbronner, and Andreas Dräger. Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium striatum. Front. Bioinform., oct 2023. doi:10.3389/fbinf.2023.1214074.
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docker pull biodatalab/refinegems:2.0.0b3