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Data Analysis in Extreme Resolution Mass Spectrometry Untargeted Metabolomics.

Marta Sousa Silva1, Francisco Traquete1, João Luz1

  • 1FT-ICR and Structural Mass Spectrometry Laboratory, Biosystems and Integrative Sciences Institute (BioISI), Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.

Advances in Experimental Medicine and Biology
|May 3, 2026
PubMed
Summary
This summary is machine-generated.

Mass spectrometry (MS)-based metabolomics offers insights into biological systems. This chapter details data analysis methods, from preprocessing to bioinformatics, addressing challenges posed by large datasets from high-resolution instruments.

Keywords:
Compound identificationData processing and treatmentFT-ICR-MSUntargeted metabolomics

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Analytical Chemistry

Background:

  • Mass spectrometry (MS)-based metabolomics is crucial for understanding biochemical processes and identifying biomarkers.
  • High-resolution mass spectrometers generate vast datasets, presenting significant data processing and interpretation challenges.

Purpose of the Study:

  • To provide a comprehensive overview of current methodologies in MS-based metabolomics data analysis.
  • To address challenges in data processing and interpretation for high-resolution MS data.

Main Methods:

  • Data preprocessing and pretreatment techniques.
  • m/z extraction and annotation strategies.
  • Univariate and multivariate statistical analyses and data visualization.

Main Results:

  • Discussion of key considerations for ensuring data quality in metabolomics.
  • Highlighting the role of bioinformatics in pathway analysis and metabolite identification.
  • Emphasis on the impact of FT-ICR mass spectrometry for extreme resolution and mass accuracy.

Conclusions:

  • MS-based metabolomics data analysis requires robust computational workflows.
  • Emerging trends like AI and real-time processing are shaping the future of metabolomics.
  • Effective data interpretation is vital for advancing biological understanding and biomarker discovery.