Untargeted high-resolution mass spectrometry is a key tool in clinical metabolomics, natural product discovery and exposomics, with compound identification remaining the major bottleneck. Currently, the standard workflow applies spectral library matching against tandem mass spectrometry (MS2) fragmentation data. Multi-stage fragmentation (MSn) yields more profound insights into substructures, enabling validation of fragmentation pathways; however, the community lacks open MSn reference data of diverse natural products and other chemicals. Here we describe MSnLib, a machine learning-ready open resource of >2 million spectra in MSn trees of 30,008 unique small molecules, built with a high-throughput data acquisition and processing pipeline in the open-source software mzmine.

Department of Biochemistry University of California Riverside Riverside CA USA

Department of Cell Biology Faculty of Science Charles University Prague Czechia

Department of Pharmaceutical Sciences Faculty of Life Sciences University of Vienna Vienna Austria

Institute of Inorganic and Analytical Chemistry University of Münster Münster Germany

Institute of Microbiology of the Czech Academy of Sciences Prague Czechia

Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Prague Czechia

Interdisciplinary Institute for Artificial Intelligence Côte d'Azur Sophia Antipolis Valbonne France

Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego San Diego CA USA

Université Côte d'Azur CNRS ICN Nice France

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Stein, S. Mass spectral reference libraries: an ever-expanding resource for chemical identification. PubMed

Blaženović, I. et al. Structure annotation of all mass spectra in untargeted metabolomics. PubMed PMC

Bittremieux, W., Wang, M. & Dorrestein, P. C. The critical role that spectral libraries play in capturing the metabolomics community knowledge. PubMed PMC

Sorokina, M., Merseburger, P., Rajan, K., Yirik, M. A. & Steinbeck, C. COCONUT online: Collection of Open Natural Products database. PubMed PMC

Zdrazil, B. et al. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. PubMed PMC

Kim, S. et al. PubChem 2023 update. PubMed PMC

de Jonge, N. F. et al. Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools. PubMed PMC

van der Hooft, J. J. J., Vervoort, J., Bino, R. J. & de Vos, R. C. H. Spectral trees as a robust annotation tool in LC–MS based metabolomics.

Kasper, P. T. et al. Fragmentation trees for the structural characterisation of metabolites. PubMed PMC

Vaniya, A. & Fiehn, O. Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics. PubMed PMC

Waridel, P. et al. Evaluation of quadrupole time-of-flight tandem mass spectrometry and ion-trap multiple-stage mass spectrometry for the differentiation of C-glycosidic flavonoid isomers. PubMed

Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. PubMed PMC

Horai, H. et al. MassBank: a public repository for sharing mass spectral data for life sciences. PubMed

Guijas, C. et al. METLIN: a technology platform for identifying knowns and unknowns. PubMed PMC

Schmid, R. et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. PubMed PMC

Probst, D. & Reymond, J.-L. Visualization of very large high-dimensional data sets as minimum spanning trees. PubMed PMC

Nothias, L.-F. et al. Feature-based molecular networking in the GNPS analysis environment. PubMed PMC

Bento, A. P. et al. An open source chemical structure curation pipeline using RDKit. PubMed PMC

Kretschmer, F., Seipp, J., Ludwig, M., Klau, G. W. & Böcker, S. Coverage bias in small molecule machine learning. PubMed PMC

Empirically establishing drug exposure records directly from untargeted metabolomics data