Reproducible MS/MS library cleaning pipeline in matchms

  • Mass spectral libraries have proven to be essential for mass spectrum annotation, both for library matching and training new machine learning algorithms. A key step in training machine learning models is the availability of high-quality training data. Public libraries of mass spectrometry data that are open to user submission often suffer from limited metadata curation and harmonization. The resulting variability in data quality makes training of machine learning models challenging. Here we present a library cleaning pipeline designed for cleaning tandem mass spectrometry library data. The pipeline is designed with ease of use, flexibility, and reproducibility as leading principles.Scientific contributionThis pipeline will result in cleaner public mass spectral libraries that will improve library searching and the quality of machine-learning training datasets in mass spectrometry. This pipeline builds on previous work by adding new functionality for curating and correcting annotatedMass spectral libraries have proven to be essential for mass spectrum annotation, both for library matching and training new machine learning algorithms. A key step in training machine learning models is the availability of high-quality training data. Public libraries of mass spectrometry data that are open to user submission often suffer from limited metadata curation and harmonization. The resulting variability in data quality makes training of machine learning models challenging. Here we present a library cleaning pipeline designed for cleaning tandem mass spectrometry library data. The pipeline is designed with ease of use, flexibility, and reproducibility as leading principles.Scientific contributionThis pipeline will result in cleaner public mass spectral libraries that will improve library searching and the quality of machine-learning training datasets in mass spectrometry. This pipeline builds on previous work by adding new functionality for curating and correcting annotated libraries, by validating structure annotations. Due to the high quality of our software, the reproducibility, and improved logging, we think our new pipeline has the potential to become the standard in the field for cleaning tandem mass spectrometry libraries.show moreshow less

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Author:Niek F. de JongeORCiD, Helge Hecht, Michael Strobel, Mingxun Wang, Justin J. J. van der HooftORCiD, Florian HuberORCiD
Qualitätssicherung:peer reviewed
open access:Gold - Erstveröffentlichung mit Lizenzhinweis
agreement:DEAL Springer Nature
Institutes:Zentrum für Digitalisierung und Digitalität (ZDD)
Fachbereich/Einrichtung:Hochschule Düsseldorf / Fachbereich - Medien
Document Type:Article
Year of Completion:2024
Language of Publication:English
Publisher:Springer Nature
Parent Title (English):Journal of Cheminformatics
Volume:16
Issue:1
Article Number:88
URN:urn:nbn:de:hbz:due62-opus-46491
DOI:https://doi.org/10.1186/s13321-024-00878-1
ISSN:1758-2946
Tag:DFG Publikationskosten; HSD Publikationsfonds
Library cleaning
GND Keyword:Massenspektrometrie; Metabolomik; Metadaten; Python (Programmiersprache)
Corresponding Author:Florain Huber
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2024/10/24
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