- Overview
- Documentation
- System Requirements
- Installation Guide
- Quick Start
- Demo
- Attribution
- For Developers
fermo_core is a tool to perform hypothesis-driven prioritization on metabolomics data.
Besides its use as backend processing tool of the FERMO dashboard, fermo_core can be used as a command line interface (CLI) for large-scale data processing and analysis.
This README specifies the use of fermo_core as CLI. For a more user-friendly version of FERMO, see FERMO Online.
For general information on FERMO, see the FERMO Metabolomics GitHub Organization page.
The high-level documentation can be found HERE.
For the Sphinx-generated library documentation, see HERE.
fermo_core can be run on a standard computer and does not have any special requirements.
Local installation of this package is only supported for Linux (tested on Ubuntu 20.04 and 22.04).
Dependencies including exact versions are specified in the pyproject.toml file.
Depending on the chosen installation option, install time can vary from a few seconds (uv) to a few minutes (conda).
Nota bene: we recommend installing fermo_core in some kind of virtual environment.
pip install fermo_core
Once installed, run as specified in Run with pip
Assumes that uv is installed
git clone git@github.com:fermo-metabolomics/fermo_core.git
cd fermo_core
uv sync
Once installed, run as specified in Run with uv
Assumes that conda is installed
conda create --name fermo_core python=3.11 -y
conda activate fermo_core
git clone git@github.com:fermo-metabolomics/fermo_core.git
cd fermo_core
pip install -e .
Once installed, run as specified in Run with conda
As minimal requirement, fermo_core takes LC-MS(/MS) metabolomics data, which it can integrate with a range of optional orthogonal data formats.
Compatible formats are described in the Documentation.
fermo_core requires all parameters to be described in a parameters file. This file must follow specifications outlined in the JSON Schema file. For an example, see case_study_parameters.json.
For a more user-friendly version of FERMO, see FERMO Online.
fermo_core --parameters <your_parameter_file.json>
uv run fermo_core --parameters <your_parameter_file.json>
python3 fermo_core/main.py --parameters <your_parameter_file.json>
To demonstrate the functionality of fermo_core, we provide an example dataset sourced from this publication.
It describes a set of extracts from strains belonging to the bacterial genus Planomonospora grown in the same condition, showing differential antibiotic activity against Staphylococcus aureus.
fermo_core can be used to investigate and prioritize the phenotype-associated and differentially abundant molecular features.
Calculation of the Phenotype Score results in the selection of a group of molecular features annotated as siomycins, thiopeptides with known anti-Gram positive antibiotic activity.
Details on the experimental conditions can be found in the Wiki.
All parameters and input data are specified in a parameters.json file.
Execution time is hardware-dependent but usually takes only a few minutes. On a machine running Ubuntu 22.04 with Intel® Core™ i5-7200U CPU @ 2.50GHz x 4 with 8 GiB Memory, execution time was 104 seconds.
Nota bene: the exact command depends on the type of installation as specified in the Installation Guide.
uv run fermo_core --parameters ./example_data/case_study_parameters.json
After successful completion of the run, all results files can be found in example_data/results.
The out.fermo.session.json file can be visualized in FERMO online or inspected with a text viewer.
Alternatively, the spreadsheet format file out.fermo.abbrev.csv can be inspected.
Antibiotic activity is attributable to the thiopeptide siomycin and congeners (e.g. feature ID 83).
fermo_core is an open source tool licensed under the MIT license (see LICENSE).
See CITATION.cff or FERMO online for information on citing fermo_core.
Nota bene: for details on how to contribute to the FERMO project, please refer to CONTRIBUTING.
Instructions for setting up a development environment.
Assumes that hatch is installed
hatch env create dev
hatch run dev:pre-commit install
hatch run dev:pytest --run_slow
Instructions on setting up and deploying the automated documentation found here.
The documentation rebuilds and deploys automatically on every release using GitHub Actions.
Assumes that hatch is installed
hatch env create doc
hatch run doc:sphinx-build -b html docs/source/ docs/_build