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. 2024 Jul 29;16(1):88.
doi: 10.1186/s13321-024-00878-1.

Reproducible MS/MS library cleaning pipeline in matchms

Niek F de Jonge  1 , Helge Hecht  2 , Michael Strobel  3 , Mingxun Wang  3 , Justin J J van der Hooft  4   5 , Florian Huber  6
Affiliations

Reproducible MS/MS library cleaning pipeline in matchms

Niek F de Jonge et al. J Cheminform. .

Abstract

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.

Keywords: Library cleaning; Mass spectrometry; Metabolomics; Metadata; Python Package.

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Conflict of interest statement

MW is a cofounder of Ometa Labs LLC. JJJvdH is currently a member of the Scientific Advisory Board of NAICONS Srl., Milano, Italy, and is consulting for Corteva Agriscience, Indianapolis, IN, USA. All other authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Graphical overview of the library cleaning pipeline
Fig. 2
Fig. 2
Visualization of the number of spectra in the GNPS library affected by the different filters. The central stacked bar graph splits the spectra into 3 groups. The orange group represents spectra that were completely removed by a filter, since they did not pass a metadata requirement. The blue group represents spectra that were repaired by at least one of the newly added repair functions focused on repairing the annotation. The grey group represents all other spectra, for which the metadata was harmonized, but the annotation or metadata was not affected by the newly added filters
Fig. 3
Fig. 3
Two real examples of GNPS library spectra that were cleaned in multiple ways by the library cleaning pipeline. The colors indicate the type of changes that were made to the metadata
See this image and copyright information in PMC

References

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