TY - JOUR A1 - de Jonge, Niek F. A1 - Hecht, Helge A1 - Strobel, Michael A1 - Wang, Mingxun A1 - van der Hooft, Justin J. J. A1 - Huber, Florian T1 - Reproducible MS/MS library cleaning pipeline in matchms T2 - Journal of Cheminformatics N2 - 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 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. KW - Massenspektrometrie KW - Metabolomik KW - Metadaten KW - Python (Programmiersprache) KW - Library cleaning KW - HSD Publikationsfonds KW - DFG Publikationskosten Y1 - 2024 UR - https://opus4.kobv.de/opus4-hs-duesseldorf/frontdoor/index/index/docId/4649 UR - https://nbn-resolving.org/urn:nbn:de:hbz:due62-opus-46491 SN - 1758-2946 VL - 16 IS - 1 PB - Springer Nature ER -