Molecular networking connects mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. As a result, the fragmentation spectra of these ion species often remain unconnected in tandem mass spectrometry-based molecular networks, leading to redundant and disconnected sub-networks of the same compound classes. To overcome this bottleneck, we develop Ion Identity Molecular Networking (IIMN) that integrates chromatographic peak shape correlation analysis into molecular networks to connect and collapse different ion species of the same molecule. The new feature relationships improve network connectivity for structurally related molecules, can be used to reveal unknown ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral reference libraries. IIMN is integrated into various open source feature finding tools and the GNPS environment. Moreover, IIMN-based spectral libraries with a broad coverage of ion species are publicly available.

Chair for Bioinformatics Friedrich Schiller University Jena Germany

Chiba University UC San Diego Center for Mucosal Immunology Allergy and Vaccines La Jolla CA USA

CMFI Cluster of Excellence Interfaculty Institute of Microbiology and Medicine University of Tübingen Tübingen Germany

Collaborative Mass Spectrometry Innovation Center University of California San Diego La Jolla San Diego CA USA

Department of Biotechnology and Life Science Tokyo University of Agriculture and Technology Koganei shi Tokyo Japan

Department of Pharmaceutical Sciences College of Pharmacy Oregon State University Corvallis OR USA

Department of Psychiatry University of California San Diego San Diego CA USA

Division of Host Microbe Systems and Therapeutics Department of Pediatrics University of California San Diego La Jolla CA USA

Institute for Biomedicine Eurac Research Affiliated Institute of the University of Lübeck Bolzano Italy

Institute of Biomedical Sciences Universidade de São Paulo São Paulo SP Brazil

Institute of Food Chemistry University of Münster Münster Germany

Institute of Inorganic and Analytical Chemistry University of Münster Münster Germany

Institute of Microbiology Czech Academy of Sciences Prague Czech Republic

Institute of Organic Chemistry and Biochemistry Czech Academy of Sciences Prague Czech Republic

IRNASUS Universidad Católica de Córdoba CONICET Facultad de Ciencias Agropecuarias Córdoba Argentina

NuBBE Institute of Chemistry São Paulo State University Araraquara SP Brazil

RIKEN Center for Integrative Medical Sciences Yokohama Kanagawa Japan

RIKEN Center for Sustainable Resource Science Yokohama Kanagawa Japan

School of Pharmaceutical Sciences of Ribeirão Preto Universidade de São Paulo Ribeirão Preto SP Brazil

Scripps Institution of Oceanography University of California San Diego La Jolla CA USA

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

Univ Grenoble Alpes CNRS Grenoble INP CHU Grenoble Alpes TIMC IMAG Grenoble France

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A universal language for finding mass spectrometry data patterns

Discovery of metabolites prevails amid in-source fragmentation

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

Empirically establishing drug exposure records directly from untargeted metabolomics data

Reproducible mass spectrometry data processing and compound annotation in MZmine 3

Characterization of Electrospray Ionization Complexity in Untargeted Metabolomic Studies

Functional metabolomics of the human scalp: a metabolic niche for Staphylococcus epidermidis

Evaluation of Data-Dependent MS/MS Acquisition Parameters for Non-Targeted Metabolomics and Molecular Networking of Environmental Samples: Focus on the Q Exactive Platform

Integrative analysis of multimodal mass spectrometry data in MZmine 3