Authors: Joe Mason, Francesco Rianjongdee, Harry Wilders, David Fallon
This script will read Liquid Chromatography Mass Spectrometry (LCMS) data in the Waters OpenLynx™ browser report (.rpt) or Shimadzu .daml file formats, and assign peaks to compounds specified in a .csv platemap. This assignment is then used to generate heatmaps and other visualisations to compare and contrast different LCMS runs. It was designed specifically for the analysis of data generated from high-throughput chemistry, and is suitable for reaction optimisations, parallel synthesis, library validation experiments and direct-to-biology.
Check out the Github Pages site! Here you'll find full documentation, including a walkthrough using the example data set provided!
You can also find our published user-guide for chemists at our peer-reviewed article at Digital Discovery
python PyParse.py example_rpt.rpt example_platemap.csv -o new_output_directory
(Saves all output tables, data and visualisations to "new_output_directory".)
Publications which make use of PyParse to aid analysis of high-throughput LC-MS data should cite the peer-reviewed article:
Mason J., Wilders H., Fallon D.J., Thomas R.P., Bush J.T., Tomkinson N.C.O., Rianjongdee, F.; Automated LC-MS Analysis and Data Extraction for High-Throughput Chemistry; Digital Discovery (2023), 2, 1894 - 1899; https://doi.org/10.1039/D3DD00167A
(An earlier version of this manuscript was published on ChemRxiv (2023), https://doi.org/10.26434/chemrxiv-2023-1x288)
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