ArticleMetabolites2019
WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data.
Article in Metabolites, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Mass Spectrometry-Based Chromatographic and Computational Workflows for Biomarker and Therapeutic Target Discovery: A Comprehensive Review.Biomedical chromatography : BMC · 2026Review
- Review
- Current Advancements in Untargeted Metabolomics Analysis and Testing Driven by Machine Learning: Prospects for Artificial Intelligence in Patient-Centric Healthcare Transformation.Applied biochemistry and biotechnology · 2026Review
- The isolation strategy and chemical analysis of oil cells from Asari Radix et Rhizoma.Plant methods · 2024Article
- Optimizing XCMS parameters for GC-MS metabolomics data processing: a case study.Metabolomics : Official journal of the Metabolomic Society · 2023Article
- AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications.Cell reports. Physical science · 2022Article
- Deep Learning-Assisted Peak Curation for Large-Scale LC-MS Metabolomics.Analytical chemistry · 2022Article
- Comprehensive Peak Characterization (CPC) in Untargeted LC-MS Analysis.Metabolites · 2022Article
- An actionable annotation scoring framework for gas chromatography-high-resolution mass spectrometry.Exposome · 2022Article
- MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data.Metabolomics : Official journal of the Metabolomic Society · 2020Article
- Machine Learning Applications for Mass Spectrometry-Based Metabolomics.Metabolites · 2020Review
- Software tools, databases and resources in metabolomics: updates from 2018 to 2019.Metabolomics : Official journal of the Metabolomic Society · 2020Review
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Authors and funding
15 authors.
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Abstract
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.
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