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Pathway Analysis for Targeted and Untargeted Metabolomics.

Alla Karnovsky1, Shuzhao Li2

  • 1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

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|January 19, 2020
PubMed
Summary

This study reviews pathway analysis tools for large metabolomics datasets generated by techniques like liquid chromatography-mass spectrometry (LC-MS). It explains methods such as Mummichog for biological context and disease relevance.

Keywords:
MetScapeMetabolic networkMetabolomicsMummichogPathway analysisUntargeted metabolomics

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Systems Biology

Background:

  • Analytical techniques like liquid chromatography-mass spectrometry (LC-MS) produce large, complex metabolomics datasets.
  • Interpreting these datasets requires tools to place experimental observations into biological or disease contexts.
  • Pathway analysis is crucial for understanding the biological significance of metabolomics data.

Purpose of the Study:

  • To provide an overview of general concepts and common tools for pathway analysis in metabolomics.
  • To explain practical applications of pathway mapping, MetScape, and Mummichog.
  • To serve as a tutorial and survey for label-free metabolomics data analysis.

Main Methods:

  • Overview of general pathway analysis concepts.
  • Explanation of common pathway analysis tools.
  • Demonstration of Mummichog for untargeted metabolomics.
  • Examples of pathway mapping and MetScape.

Main Results:

  • Mummichog is highlighted as a tool for untargeted metabolomics pathway analysis.
  • Pathway mapping and MetScape are explained with examples.
  • The chapter provides a practical tutorial and survey of pathway analysis methods.

Conclusions:

  • Pathway analysis tools are essential for interpreting complex metabolomics data.
  • Mummichog, MetScape, and pathway mapping offer valuable approaches for label-free metabolomics.
  • This work serves as a guide for researchers analyzing metabolomics data.