Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment
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2021-12-01
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Schmid, Robin
Petras, Daniel
Nothias, Louis-Félix
Wang, Mingxun
Aron, Allegra T.
Jagels, Annika
Tsugawa, Hiroshi
Rainer, Johannes
Garcia-Aloy, Mar
Dührkop, Kai
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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.
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Nature Communications, v. 12, n. 1, 2021.