TidyMass2: Advancing LC-MS Untargeted Metabolomics Through Metabolite Origin Inference and Metabolic Feature-based Functional Module Analysis Article Swipe
Xiao Wang
,
Yijiang Liu
,
Chao Jiang
,
Zinuo Huang
,
Yan Hong
,
Grace Lai‐Hung Wong
,
Caroline H. Johnson
,
Jingxiang Zhang
,
Ying Ge
,
Feifan Zhang
,
Renchun Lai
,
Peng Gao
,
Xuebin Zhang
,
Xiaotao Shen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.05.09.652992
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.05.09.652992
Untargeted metabolomics provides a direct window into biochemical activities but faces critical challenges in determining metabolite origins and interpreting unannotated metabolic features. Here, we present TidyMass2, an enhanced computational framework for Liquid Chromatography-Mass Spectrometry (LC-MS) untargeted metabolomics that addresses these limitations. TidyMass2 introduces three major innovations compared its predecessor, TidyMass: (1) a comprehensive metabolite origin inference capability that traces metabolites to human, microbial, dietary, pharmaceutical, and environmental sources through integration of 11 metabolite databases containing 532,488 metabolites with source information; (2) a metabolic feature-based functional module analysis approach that bypasses the annotation bottleneck by leveraging metabolic network topology to extract biological insights from unannotated metabolic features; and (3) an intuitive graphical interface that makes advanced metabolomics analyses accessible to researchers without programming expertise. Applied to longitudinal urine metabolomics data from human pregnancy, TidyMass2 identified diverse metabolites originating from human, microbiome, and environment, and uncovered 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. By expanding biological interpretation beyond conventionally MS 2 spectra-based annotated metabolites, TidyMass2 enables more comprehensive metabolic phenotyping while upholding strict open-source principles of reproducibility, traceability, and transparency.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.05.09.652992
- https://www.biorxiv.org/content/biorxiv/early/2025/05/14/2025.05.09.652992.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 107
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410454966
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- OpenAlex ID
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https://openalex.org/W4410454966Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.05.09.652992Digital Object Identifier
- Title
-
TidyMass2: Advancing LC-MS Untargeted Metabolomics Through Metabolite Origin Inference and Metabolic Feature-based Functional Module AnalysisWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-05-14Full publication date if available
- Authors
-
Xiao Wang, Yijiang Liu, Chao Jiang, Zinuo Huang, Yan Hong, Grace Lai‐Hung Wong, Caroline H. Johnson, Jingxiang Zhang, Ying Ge, Feifan Zhang, Renchun Lai, Peng Gao, Xuebin Zhang, Xiaotao ShenList of authors in order
- Landing page
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https://doi.org/10.1101/2025.05.09.652992Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2025/05/14/2025.05.09.652992.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2025/05/14/2025.05.09.652992.full.pdfDirect OA link when available
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Metabolomics, Metabolite, Inference, Computational biology, Metabolite profiling, Feature (linguistics), Chemistry, Computer science, Biology, Chromatography, Biochemistry, Artificial intelligence, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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