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Related Experiment Video

Updated: Jun 4, 2026

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

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

Published on: March 14, 2013

Bioinformatics for mass spectrometry-based metabolomics.

David P Enot¹, Bernd Haas, Klaus M Weinberger

¹BIOCRATES life sciences AG, Innsbruck, Austria.

Methods in Molecular Biology (Clifton, N.J.)

|March 4, 2011

Summary

Integrating metabolomics with genomics and proteomics offers a comprehensive view of biological systems. This study outlines practical bioinformatics strategies for analyzing mass spectrometry data, aiding researchers in interpreting metabolomics results.

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

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Last Updated: Jun 4, 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

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Area of Science:

Biochemistry
Systems Biology
Bioinformatics

Background:

A comprehensive understanding of biological systems requires integrating multi-omics data, including genomics, proteomics, and metabolomics.
Metabolomics quantifies small molecules in biological samples, providing insights into cellular states.
Mass spectrometry (MS) is crucial for high-throughput metabolomic analysis.

Purpose of the Study:

To describe strategies for translating raw mass spectrometry data into metabolite features.
To provide resources for navigating the metabolomics pipeline.
To emphasize practical solutions for interpreting metabolomics experimental outcomes.

Main Methods:

Utilizing mass spectrometry (MS) for high-throughput metabolomic data generation.
Employing bioinformatics approaches to process and analyze MS data.
Applying signal processing and statistical methods for data interpretation.

Main Results:

Development of strategies to convert crude MS information into metabolite-specific features.
Identification of available resources to support scientists in the metabolomics workflow.
Focus on pragmatic solutions for interpreting metabolomics data.

Conclusions:

Bioinformatics is essential for bridging technological advancements and domain expertise in metabolomics.
Effective interpretation of metabolomics data involves signal processing, statistical analysis, and biochemical understanding.
This work provides guidance for researchers utilizing metabolomics in biological studies.