Disclaimer: Early Development / Prototype Notice
Fiora is an algorithm in its early stages of development and is provided as a prototype. Performance is not guaranteed, functionality may be incomplete, and usability was not a central concern during this phase of development. Users should exercise caution.
Fiora is an in silico fragmentation algorithm for small compounds that produces simulated tandem mass spectra (MS/MS). The framework employs a graph neural network to predict bond cleavages and fragment ion intensities via edge prediction. Additionally, Fiora can estimate retention times (RT) and collision cross sections (CCS) of the compounds.
Developed and tested with the following systems and versions:
- Debian GNU/Linux 11 (bullseye)
- Python 3.10.8
- GCC 11.2.0
Installation guide for the Fiora Python package (under 10 minutes):
Clone the project folder
git clone https://github.com/BAMeScience/fiora.git
(Optional) Create a new conda environment
conda create -n fiora python=3.10.8
conda activate fiora
Change into the project directory (cd fiora). Then, install the package by using the setup.py via
pip install .
(Optional) You may want to test that the package works as intended. This can be done by running the sripts in the tests directory or by using pytest (requires: pip install pytest)
pytest -v tests
Use spectral prediction function as follows:
fiora-predict [-h] -i INPUT -o OUTPUT [--model MODEL] [--rt | --no-rt] [--ccs | --no-ccs] [--annotation | --no-annotation]
An input csv file must be provided and an output file specified (mgf or msp format).
Input files are expected to be in csv format. With a header defining the columns: "Name", "SMILES", "Precursor_type", "CE", "Instrument_type" and rows listing individual queries. See example input file.
Predicted spectra are provided in standard msp and mgf format.
Run the fiora-predict from within this directory
fiora-predict -i examples/example_input.csv -o examples/example_spec.mgf
By default, an open-source model is selected automatically, and predictions typically complete within a few seconds. For faster performance, specify a GPU device using the --dev option (e.g., --dev cuda:0). The output file (e.g., examples/example_spec.mgf) can be compared with the expected results to verify model accuracy. This verification is automatically performed by running pytest (as described above).