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Updated: May 6, 2026
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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
Published on: May 20, 2013
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Effective data visualization strategies in untargeted metabolomics
Kevin Mildau1, Henry Ehlers2, Mara Meisenburg3
1Bioinformatics Group, Wageningen University & Research, Wageningen, The Netherlands. kevin.mildau@wur.nl.
Natural Product Reports
|December 2, 2024
Summary
This review bridges untargeted metabolomics and information visualization, offering a roadmap to visual tools for data analysis. It highlights best practices and future research for better data interpretation and communication.
Area of Science:
- Computational Biology
- Bioinformatics
- Data Visualization
Background:
- LC-MS/MS-based untargeted metabolomics generates complex data requiring advanced computational tools.
- Existing visualization tools are numerous, leading to challenges in identifying suitable options for researchers and developers.
- There's a gap in cross-pollination between data visualization and metabolomics research.
Purpose of the Study:
- To bridge the gap between untargeted metabolomics and information visualization.
- To provide a primer on cutting-edge visualization research for metabolomics.
- To offer a practical roadmap to visual tools within the untargeted metabolomics workflow.
Main Methods:
- Review of the untargeted metabolomics workflow from an information visualization perspective.
- Introduction to data visualization concepts and best practices for metabolomics.
- Overview of visual strategies and tools for computational analysis stages in metabolomics.
Main Results:
- Identified data visualization as crucial for inspection, evaluation, and sharing in metabolomics.
- Presented a roadmap of visual tools and strategies for various metabolomics analysis stages.
- Highlighted promising areas for future research and development in visual analytics for metabolomics.
Conclusions:
- Emphasizes the need for dedicated research into data visualization for metabolomics.
- Recommends best practices for effective and transparent communication of metabolomics results using visualizations.
- Encourages greater integration of visualization techniques into the metabolomics workflow.
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