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June 1, 2026Analytical Chemistry

MS-Net: Multi-Similarity-Based Network Annotation for Untargeted Metabolomics

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Authors

VFVictor Pereira FranciscoUniversité Fédérale de Toulouse Midi-PyrénéesSDSimon DuthenCentre National de la Recherche ScientifiqueECElise CrossayUniversité Fédérale de Toulouse Midi-Pyrénées

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Implication

Randomized trial demonstrates improved metabolite annotation accuracy in ultra-complex samples, suggesting enhanced biological insights.

Key Points

  • The study aims to improve the accuracy of metabolite annotation in untargeted LC-MS metabolomics using a novel workflow.
  • Developed MS-Net, integrating mass spectral similarity networks and structural similarity metrics for metabolite annotation.
  • Applied to Cannabis sativa extracts, filtering from 2595 detected features to 1297 after initial analysis.
  • Utilized a composite Link Score for iterative propagation and correction of lower-ranked annotations.
  • Assigned 1275 compounds from a candidate pool of over 118,000 structures, achieving high-confidence annotations.
  • Rescued 53% of final annotations from ranks 2–50, correcting initial in silico ranking inaccuracies.
  • Successfully reconstructed known cannabinoid biosynthetic pathways, validating biological relevance.

Cite This Study

Francisco et al. (2026) studied this question.

synapsesocial.com/papers/6a1d226d02fbce91306382dfhttps://doi.org/10.1021/acs.analchem.6c01026
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