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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
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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.

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Summary
This summary is machine-generated.

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.

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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.