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Open AccessArticle

WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data

1
Core Unit Bioinformatics, Berlin Institute of Health, 10178 Berlin, Germany
2
Berlin Institute of Health Metabolomics Platform, 10178 Berlin, Germany
3
Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany
4
Integrative Proteomics and Metabolomics, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany
5
Charité—Universitätsmedizin Berlin, 10178 Berlin, Germany
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Université Clermont Auvergne, INRA, UNH, Plateforme d’Exploration du Métabolisme, MetaboHUB Clermont, 63000 Clermont-Ferrand, France
7
LABERCA, Oniris, INRA, Université Bretagne—Loire, 44307 Nantes, France
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Metabolites 2019, 9(9), 171; https://doi.org/10.3390/metabo9090171
Received: 17 July 2019 / Revised: 18 August 2019 / Accepted: 20 August 2019 / Published: 21 August 2019
Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Medium- and low-quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset, we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results in integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence. View Full-Text
Keywords: gas chromatography-mass spectrometry (GC-MS); metabolomics; peak detection; peak classification; pre-processing workflow; parameter optimisation; machine learning; support vector machine gas chromatography-mass spectrometry (GC-MS); metabolomics; peak detection; peak classification; pre-processing workflow; parameter optimisation; machine learning; support vector machine
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Borgsmüller, N.; Gloaguen, Y.; Opialla, T.; Blanc, E.; Sicard, E.; Royer, A.-L.; Le Bizec, B.; Durand, S.; Migné, C.; Pétéra, M.; Pujos-Guillot, E.; Giacomoni, F.; Guitton, Y.; Beule, D.; Kirwan, J. WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data. Metabolites 2019, 9, 171.

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