fermo_core

Contents

• Overview
• Documentation
• System Requirements
• Installation Guide
• Quick Start
• Demo
• Attribution
• For Developers

Overview

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.

Documentation

The high-level documentation can be found HERE.

For the Sphinx-generated library documentation, see HERE.

System Requirements

Hardware requirements

fermo_core can be run on a standard computer and does not have any special requirements.

Software requirements

OS Requirements

Local installation of this package is only supported for Linux (tested on Ubuntu 20.04 and 22.04).

Python dependencies

Dependencies including exact versions are specified in the pyproject.toml file.

Installation Guide

Depending on the chosen installation option, install time can vary from a few seconds (uv) to a few minutes (conda).

With pip from PyPI

Nota bene: we recommend installing fermo_core in some kind of virtual environment.

commandline pip install fermo_core

Once installed, run as specified in Run with pip

With uv from GitHub

Assumes that uv is installed

commandline git clone git@github.com:fermo-metabolomics/fermo_core.git cd fermo_core uv sync

Once installed, run as specified in Run with uv

With conda from GitHub

Assumes that conda is installed

commandline 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

Quick Start

Running fermo_core on your data

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 casestudyparameters.json.

For a more user-friendly version of FERMO, see FERMO Online.

Run with pip

commandline fermo_core --parameters <your_parameter_file.json>

Run with uv

commandline uv run fermo_core --parameters <your_parameter_file.json>

Run with conda

commandline python3 fermo_core/main.py --parameters <your_parameter_file.json>

Demo

Overview

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.

Setup

All parameters and input data are specified in a parameters.json file.

Run the example

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.

Run command

Nota bene: the exact command depends on the type of installation as specified in the Installation Guide.

commandline uv run fermo_core --parameters ./example_data/case_study_parameters.json

Results and Interpretation

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).

Attribution

License

fermo_core is an open source tool licensed under the MIT license (see LICENSE).

Publications

See CITATION.cff or FERMO online for information on citing fermo_core.

For Developers

Nota bene: for details on how to contribute to the FERMO project, please refer to CONTRIBUTING.

Development

Instructions for setting up a development environment.

Package Installation

Assumes that hatch is installed

commandline hatch env create dev hatch run dev:pre-commit install hatch run dev:pytest --run_slow

Documentation

Instructions on setting up and deploying the automated documentation found here.

The documentation rebuilds and deploys automatically on every release using GitHub Actions.

Package Installation

Assumes that hatch is installed

commandline hatch env create doc hatch run doc:sphinx-build -b html docs/source/ docs/_build