Year: 2024
Type: article
Abstract: A major bottleneck of chromatography-based analytics has been the elusive fully automated identification and integration of peak data without the need of extensive human supervision.The presented Python package PeakPerformance applies Bayesian inference to chromatographic peak fitting, and provides an automated approach featuring model selection and uncertainty quantification.Regarding peak acceptance, it improves on vendor software solutions with more sophisticated, multi-layered metrics for decision making based on convergence of the parameter estimation as well as the uncertainties of peak parameters.Currently, its application is focused on data from targeted liquid chromatography tandem mass spectrometry (LC-MS/MS), but its design allows for an expansion to other chromatographic techniques and accommodates users with little programming experience by supplying convenience functions and relying on Microsoft Excel for data input and reporting.PeakPerformance is implemented in Python, its source code is available on GitHub, and a thorough documentation is available under https://peak-performance.rtfd.io.It is unit-tested on Linux and Windows and accompanied by example notebooks.
DOI: 10.21105/joss.07313
Institutions: Forschungszentrum Jülich, RWTH Aachen University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4405370120