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Updated: Apr 1, 2026
07:34
Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
Published on: March 14, 2013
13.6K
Systems-Wide High-Dimensional Data Acquisition and Informatics Using Structural Mass Spectrometry Strategies
Stacy D Sherrod1, John A McLean2
1Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN.
Clinical Chemistry
|October 11, 2015
Summary
Untargeted multiomics analysis integrates chromatography with ion mobility-mass spectrometry (IM-MS) for systems biology. New data mining rapidly prioritizes features for discovery in synthetic and chemical biology applications.
Area of Science:
- Systems Biology
- Synthetic Biology
- Chemical Biology
Background:
- Untargeted multiomics data generation using chromatography and ion mobility-mass spectrometry (IM-MS).
- Challenges in handling massive datasets from multiomics analyses.
Purpose of the Study:
- To present big data approaches for data mining of massive multiomics datasets.
- To highlight strategies for organizing and prioritizing data for identification.
Main Methods:
- Integration of chromatographic separations with IM-MS.
- Application of bioinformatics and self-organizing map strategies for data mining.
- Analysis of separation timescales for different chromatographic techniques.
Main Results:
- Demonstration of data mining for rapid filtering and highlighting unique features.
- Examples in longitudinal analyses of synthetic biology and human liver acetaminophen exposure.
- Application in natural product discovery from bacterial biomes.
Conclusions:
- Chromatography and IM-MS provide multiomics selectivity for systems-wide analyses.
- Novel data mining strategies enable rapid feature prioritization and discovery.
- Effective applications across systems, synthetic, and chemical biology.
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