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Updated: May 13, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

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Published on: March 14, 2013

Leveraging non-targeted metabolite profiling via statistical genomics.

Miaoqing Shen¹, Corey D Broeckling, Elly Yiyi Chu

¹Boyce Thompson Institute for Plant Research, Ithaca, New York, United States of America.

|March 8, 2013

Summary

Systems biology integrates diverse data for a holistic view. This study uses network analysis of maize kernel metabolomics and genomics to reveal genetic control of biochemical networks, enhancing biological interpretation.

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Area of Science:

Systems biology
Metabolomics
Genomics
Bioinformatics

Background:

Integrating multi-omics data is a key challenge in systems biology.
Maize kernels are a model system for genomic studies and crucial for the agroeconomy.

Purpose of the Study:

To develop a network analysis framework for integrating maize kernel metabolomics data.
To identify genetic control of biochemical networks through genome-wide association studies.

Main Methods:

Mass spectrometry-based profiling of maize kernels from 210 varieties.
Network analysis to organize detected features into distinct modules.
Calculation of module eigenvalues for genome-wide association studies.

Main Results:

A single network framework incorporated 97.5% of detected features.
47.1% of compounds organized into 48 distinct network modules.
Nineteen modules showed significant associations, indicating genetic control of biochemical networks.

Conclusions:

Leveraging genome-metabolome correlations enhances annotation and biological interpretation.
The developed method is applicable to other organisms with adequate bioinformatic resources.
This approach provides insights into the genetic architecture of maize kernel biochemistry.