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MetaboGraph: A Framework for Metabolomics and Lipidomics Annotation and Pathway Network Analysis
Oluwatosin Daramola1, Judith Nwaiwu1, Odunayo Oluokun1
1Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas79409-1061, United States.
Analytical Chemistry
|August 11, 2026
Summary
MetaboGraph is a Python tool for analyzing metabolomics and lipidomics data, enabling pathway interpretation. It reveals metabolic differences in breast cancer cells, linking them to metastatic potential.
Area of Science:
- Biochemistry
- Bioinformatics
- Systems Biology
Background:
- Untargeted metabolomics and lipidomics generate complex, high-dimensional data.
- Biological interpretation of these datasets, especially at pathway and network levels, is challenging.
- Existing analytical methods often lack the depth for comprehensive pathway analysis.
Purpose of the Study:
- To present MetaboGraph, a Python-based workflow for end-to-end metabolomics and lipidomics analysis.
- To enable pathway-level interpretation of small-molecule data.
- To facilitate biologically interpretable pathway and network analyses beyond conventional enrichment approaches.
Main Methods:
- MetaboGraph integrates automated data cleaning, multidatabase metabolite/lipid annotation, and pathway mapping.
- It employs direction-aware pathway inference by combining fold changes with pathway structure.
- The platform supports multiomics integration and comparative analysis across studies.
Main Results:
- MetaboGraph was demonstrated on untargeted LC-MS/MS data from two breast cancer cell lines (MCF7/HTB22 and MDA-MB-453/HTB131) with differing metastatic potential.
- HTB131 cells showed coordinated metabolic remodeling compared to HTB22 cells, including alterations in amino acid, nitrogen, and energy metabolism, alongside lipid remodeling.
- Pathway-level alterations observed were consistent with metabolic adaptations associated with increased cancer aggression.
Conclusions:
- MetaboGraph provides a robust framework for pathway-level interpretation of metabolomics and lipidomics data.
- The tool supports reproducible, biologically grounded insights by enabling consistent analysis across different datasets and omics types.
- MetaboGraph enhances the analytical toolbox for small-molecule biology, particularly in cancer research.
