PeakPerformance - A tool for Bayesian inference-based fitting of LC-MS/MS peaks

The Journal of Open Source Software, vol. 9, pp. 7313

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.

Authors 5

1. Jochen Nießer Aachen

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, Jülich, Germany

   RWTH Aachen University, Aachen, Germany

2. Michael Osthege

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, Jülich, Germany

3. Eric von Lieres Aachen Computational Systems Biotechnology

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Computational Systems Biotechnology, RWTH Aachen University, Aachen, Germany

   Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, Jülich, Germany

4. Wolfgang Wiechert Aachen Computational Systems Biotechnology

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Computational Systems Biotechnology, RWTH Aachen University, Aachen, Germany

   Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, Jülich, Germany

5. Stephan Noack

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, Jülich, Germany