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Related Experiment Video

Updated: May 23, 2026

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

Metabolomics in agriculture.

K D Nadella1, Soma S Marla, P Ananda Kumar

  • 1National Bureau of Plant Genetic Resources, ICAR, New Delhi, India.

Omics : a Journal of Integrative Biology
|March 22, 2012
PubMed
Summary

This review explores plant metabolomics, analyzing metabolic pathways and genetic diversity using Omics data. It highlights bioinformatics tools for identifying genes related to plant traits and stress responses.

Area of Science:

  • Plant Science
  • Metabolomics
  • Bioinformatics

Background:

  • The plant metabolome encompasses all metabolites derived from the plant genome via biochemical pathways.
  • Omics data, particularly from plant genome sequencing, has advanced understanding of metabolic substance synthesis and regulation.
  • Metabolic profiling is crucial for assessing crop phenotypes, genetic diversity, and understanding plant responses to growth, development, and stress.

Purpose of the Study:

  • To review recent advancements in plant metabolomics analysis.
  • To discuss bioinformatics techniques and databases for comparative pathway analysis.
  • To explore the application of metabolic quantitative trait loci (mQTLs) in plants.

Main Methods:

  • Review of current literature on plant metabolomics.

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Last Updated: May 23, 2026

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  • Analysis of bioinformatics tools and databases for pathway analysis.
  • Examination of mQTL studies in plant systems.
  • Main Results:

    • Significant progress in analyzing plant metabolic profiles and their genetic underpinnings.
    • Availability of sophisticated bioinformatics tools and databases aids comparative pathway analysis.
    • mQTLs provide a powerful approach for linking metabolic variation to genetic loci.

    Conclusions:

    • Plant metabolomics, integrated with systems biology, is vital for identifying candidate genes.
    • Bioinformatics and mQTL analysis are key to unlocking the potential of plant metabolic data.
    • Further research in these areas will enhance crop improvement and understanding of plant biology.