#AI reads Urine# TidyMass2: advancing LC-MS untargeted metabolomics through metabolite origin inference and metabolic feature-based functional module analysis
Published 25 March, 2026
TidyMass2 is an enhanced computational framework for Liquid Chromatography-Mass Spectrometry (LC-MS) untargeted metabolomics, featuring three core innovations compared to its predecessor. Firstly, it integrates 11 metabolite databases to construct MetOriginDB, which contains 532,488 metabolites with source information, enabling the tracing of metabolites to origins such as human, microbial, dietary, pharmaceutical, and environmental sources. Secondly, it proposes a metabolic feature-based functional module analysis approach that leverages metabolic network topology to extract biological insights from unannotated metabolic features, breaking through the annotation bottleneck. Thirdly, it provides an intuitive graphical interface, making advanced metabolomics analyses accessible to researchers without programming expertise. Maintaining an open-source nature, the framework was applied to longitudinal urine metabolomics data from human pregnancy, successfully identifying diverse metabolites from multiple origins and 27 dysregulated metabolic modules. It increased the proportion of biologically interpretable metabolic features from 5.8% to 58.8%, revealing coordinated changes in steroid hormone biosynthesis, carbohydrate metabolism, and amino acid processing. As a more comprehensive and efficient metabolomics analysis tool, TidyMass2 contributes significantly to multiple biomedical research fields.
Nat Commun. 2026 Jan 17. doi: 10.1038/s41467-026-68464-7.
Youhe Gao
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