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Related Experiment Video

Updated: Jul 12, 2026

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Reverse Biotransformation-Guided Annotation of Untargeted MS/MS Features: A Computational Framework for Candidate

Smaroki Smruti Rekha1,2, Palok Aich1,2

  • 1School of Biological Sciences, National Institute of Science Education and Research (NISER), Bhubaneswar 752050, Odisha, India.

Journal of Proteome Research
|July 10, 2026
PubMed
Summary

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This study introduces a new computational workflow to identify enzymes and genes linked to unknown metabolites detected in untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments. The method enhances biological interpretation of metabolomics data by inferring precursor reactions and mapping them to candidate genes.

Area of Science:

  • Metabolomics
  • Bioinformatics
  • Systems Biology

Background:

  • Untargeted LC-MS/MS generates vast metabolic data, but most features lack annotation due to database limitations.
  • Accurate annotation is crucial for understanding biological pathways and disease mechanisms.

Purpose of the Study:

  • To develop a modular computational workflow for annotating uncharacterized MS/MS features.
  • To infer plausible precursor reactions and identify candidate enzymes and genes associated with these features.
  • To enhance the biological interpretation of metabolomics data.

Main Methods:

  • Identifies structural analogs for MS/MS features.
  • Applies rule-based reverse biotransformation to infer precursor reactions.
  • Maps reactions to candidate enzymes and genes using confidence-aware scoring.
Keywords:
MS/MS spectral annotationenzyme-gene mappingpathway enrichmentreverse biotransformationuntargeted metabolomics

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  • Evaluates performance using reference datasets and sensitivity analyses.

    • Main Results:

    • The workflow successfully recapitulated known metabolite-gene associations and pathways.
    • It demonstrated stronger pathway enrichment compared to baseline methods.
    • Application to new datasets yielded biologically coherent pathway enrichments.
    • Inferred genes for a poorly characterized metabolite were significantly enriched in glutathione metabolism and oxidative stress pathways.

    Conclusions:

    • The developed framework provides a reproducible and interpretable method for generating enzyme and gene hypotheses from MS/MS data.
    • It significantly enhances the biological interpretation of metabolomics studies, particularly for unannotated features.
    • This approach advances the discovery potential of metabolomics by linking spectral data to biological functions.