TidyMass2: advancing LC-MS untargeted metabolomics through metabolite origin inference and metabolic feature-based functional module analysis
In a groundbreaking collaboration between Nanyang Technological University, Singapore (NTU Singapore) and international collaborators, A/Prof Shen Xiao Tao introduced significant innovations that redefines the capabilities of its predecessor.
Figure 1: Development and enhancements in TidyMass2
TidyMass2 introduces a novel metabolite origin inference strategy to distinguish whether detected compounds arise from human metabolism, microbiota, diet, drugs, or environmental exposure, enabling deeper insights into host–microbiome–environment interactions. In addition, it implements metabolic feature-based functional module analysis to reduce pathway redundancy and extract coherent biological themes even when metabolite annotations are incomplete (e.g., MSI Level 3). Built with a tidyverse-compatible and reproducible design, TidyMass2 provides a powerful and user-friendly solution for translating large-scale metabolomics data into meaningful biological discoveries.
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Figure 1: Reprinted from “TidyMass2: advancing LC-MS untargeted metabolomics through metabolite origin inference and metabolic feature-based functional module analysis” by Wang, X., Liu, Y., Jiang, C. et al., Nature Communications 17, 1755 (2026). https://doi.org/10.1038/s41467-026-68464-7 Licensed under CC BY 4.0
