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.

Bioinformatics Group Wageningen University and Research 6708 PB Wageningen the Netherlands

Centre for Digitalisation and Digitality Düsseldorf University of Applied Sciences 40476 Düsseldorf Germany

Department of Biochemistry University of Johannesburg Auckland Park Johannesburg 2006 South Africa

Department of Computer Science and Engineering University of California Riverside 900 University Ave Riverside CA 92521 USA

Faculty of Science RECETOX Masaryk University Kotlářská 2 Brno Czech Republic

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