pyGecko

pyGecko an open-source Python library for the parsing, processing and analysis of GC-MS and GC-FID raw data.

With increasing amounts of analytical and metadata generated in HTE, data processing and analysis quickly become a workflow's limiting step if conducted manually. The automated processing of analytical data opens up time for chemists to focus on relevant outcomes, enables the standardized storage of reaction data, and facilitates the integration of analytical methods into closed-loop systems. Herein we present pyGecko, an open-source Python library for the parsing, processing and analysis of GC-MS and GC-FID raw data. pyGecko offers a variety of analysis tools for the automated or semi-automated handling of GC measurements and sequences. This includes the interpretation of measurements in the context of the experiment, the automatic identification of internal standards and compound identifications based on retention times, the mass of a molecular ion or fragment and spectral comparison. Quantification relative to an internal standard can be performed for GC-FID measurements. Results of an analysis as well as chromatograms and spectra can be visualized and reported in standardized formats like the Open Reaction Database (ORD) schema. pyGecko is designed to be easily integrated into automated workflows and can be used as a stand-alone tool or as a python library.

Preprint: https://chemrxiv.org/engage/chemrxiv/article-details/66adfc465101a2ffa8001761
Paper: https://doi.org/10.1039/D4DD00347K

Installation

[!IMPORTANT] To read vendor files you need to install the msConvert tool from ProteoWizard. You can download it from here. You need to specify the path to the msConvert.exe before the first run of pyGecko.

pyGecko requires Python 3.10 or newer and is published on PyPI as pygecko-gc (the import name stays pygecko):

pip install pygecko-gc

Optional extras: pip install "pygecko-gc[ord]" adds Open Reaction Database export (Reaction_Parser), "pygecko-gc[test]" the test dependencies and "pygecko-gc[docs]" the documentation build.

To work on pyGecko itself, install an editable checkout instead:

git clone https://github.com/FelixKatz77/pyGecko.git
cd pyGecko
pip install -e ".[test]"

To install the exact, pinned set of dependency versions instead of the newest compatible ones, use uv with the committed lock file:

uv sync

Configuring msConvert

pyGecko looks for the msConvert executable each time a vendor file is converted, in this order:

1. the PYGECKO_MSCONVERT environment variable, set to the full path of the executable
2. an msconvert found on your PATH

Set the variable once for your user account, e.g. on Windows via Start → "Edit environment variables for your account" → New (name PYGECKO_MSCONVERT, value C:\path\to\msconvert.exe), or on Linux/macOS by adding export PYGECKO_MSCONVERT=/path/to/msconvert to your shell profile. Inside a script or notebook you can also set it for the current session before calling pyGecko:

import os
os.environ["PYGECKO_MSCONVERT"] = r"C:\path\to\msconvert.exe"

Without msConvert, open formats (.mzML, .mzXML, .cdf, .xy, .csv) still work.

Documentation

The documentation for pyGecko can be found here.

Running the tests

pip install -e ".[test,ord]"
pytest

Two integration tests load Agilent .D directories and therefore need a configured msConvert executable; they fail without one. To skip them, run pytest -m "not msconvert".

The normal offline suite includes small, attributed .xy and mzML excerpts from the pyGecko study. It also checks ORD/PDF export using the corresponding plate metadata. Run it with the coverage gate:

pytest -m "not msconvert and not slow" --cov=pygecko --cov-report=term-missing --cov-fail-under=80

Full study-data regressions

The complete plate tests use release 1.2 of the study dataset, pinned to Zenodo record 14316687. Raw archives and extracted files are kept in the ignored .test-data/ directory and are never added to Git. Download all three checksum-verified archives, then run the plate regressions:

python -m tests.support.fetch_zenodo
PYGECKO_REAL_DATA_DIR="$PWD/.test-data/zenodo/14316687" \
  pytest tests/real_data -m "realdata and slow"

These regressions process all 96 FID and all 96 mzML injections for thiolation, Buchwald–Hartwig, and AD-HoC. Thiolation and AD-HoC reproduce the checked-in yields, retention times, and analyte assignments exactly. Buchwald–Hartwig reproduces every assignment and retention time; 26 of its 27 reported yields are exact. Well C9 is expected to be 67% with the current peak overlap-border correction rather than the paper's 74%, and the test requires its overlap flag. The same full run is scheduled weekly in CI and can be started with workflow_dispatch.

Usage

For non-automated workflows pyGecko is best used with jupyter notebooks. The notebooks folder of the repository contains examples for the usage of pyGecko for the quantitative analysis of reaction outcomes and spectral matching. The Python scripts used to perform the data processing for the publication can be found in the examples folder. GC-MS and GC-FID raw data for all experiments is available on Zenodo.

Split-GC: single-injection FID + MS

For instruments that split one GC column post-column to both an MS and a Polyarc-FID detector, both traces come from a single injection and share a retention-time axis. SplitGC_Parser.load_sequence reads such an OpenLab .rslt/.sirslt folder and returns paired FID/MS sequences. Because the detectors share a time axis, FID and MS peaks can be matched directly by nearest retention time (matching='rt' in Analysis.calc_plate_yield / Analysis.calc_plate_conv), so no retention-index alkane standard is required; the legacy two-machine retention-index workflow remains available via matching='ri' (the default). If the result folder's .acaml metadata file is missing (e.g. an incomplete export), the FID injections are enumerated directly from the AIA/*_FID1A.cdf files.

Starting-material conversion and remaining starting material

In addition to product yields (Analysis.calc_plate_yield), pyGecko can quantify a starting material relative to the internal standard:

• Analysis.calc_plate_conv reports conversion (100 - remaining%). By default it assumes the substrate was charged at the same loading as the internal standard (1 equiv); for a substrate charged in excess pass equivalents (e.g. equivalents=1.5) so its conversion is referenced to its actual starting amount instead of reading as a negative conversion. The result is floored at 0.
• Analysis.calc_plate_rsm reports the remaining starting material (the raw carbon-normalised area relative to the internal standard, in percent). It is reported as measured and never clamped, so an excess substrate can read above 100%.

See examples/split_gc/ for a worked split-GC plate.

Supported File Formats

pyGecko supports the following file formats:

[!NOTE] To achieve the best performance, we recommend using the .mzML file format for GC-MS data.

Exporting Data

Processed injections and sequences can be written back out to open formats. MS data goes to mzML, FID data to ANDI/AIA netCDF:

from pygecko.parsers import (write_injection_to_mzml, write_sequence_to_mzml,
                             write_injection_to_cdf, write_sequence_to_cdf)

write_injection_to_mzml(ms_injection, 'FKB-FA-060-A1.mzML')
write_sequence_to_mzml(ms_sequence, 'exported/')       # one file per injection

write_injection_to_cdf(fid_injection, 'FBS-FA-033-A1.cdf')
write_sequence_to_cdf(fid_sequence, 'exported/')

mzML is written with psims and netCDF with netCDF4; both ship with the default install.

[!IMPORTANT] An export is a record of the injection as pyGecko holds it, not a copy of the original vendor file. pyGecko's readers round m/z to nominal integer mass and keep no polarity, instrument or acquisition metadata, so the MS1/centroid/positive terms in the written mzML are the writer's defaults rather than values from the source. Data written by pyGecko reads back through pyGecko's own readers unchanged; it is not a faithful round-trip of the raw file.

FID data is written as netCDF rather than mzML deliberately. The PSI-MS controlled vocabulary has no term for a flame ionization detector, and none of its chromatogram types describes one, so an mzML export of FID data would be schema-valid but semantically wrong. ANDI/AIA (ASTM E1947/E1948) is the chromatography standard for a detector trace, and pyGecko already reads it.

How to Cite

If you use pyGecko in your research, please cite the following publication:

Calibration-free quantification and automated data analysis for high-throughput reaction screening Felix Katzenburg, et al. Digital Discovery, 2025, 4, 384-394. DOI: 10.1039/D4DD00347K