Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Interleukin-17A mediates cardiorenal injury in oxalate nephropathy.

Cardiovascular research·2026
Same author

Divergent skeletal muscle metabolite exchange in insulin-like growth factor-1-stimulated myotubes and resistance-exercised human muscle.

Experimental physiology·2026
Same author

A comprehensive assessment of lifecourse and mortality of Parkinson's disease in the German National Cohort.

NPJ Parkinson's disease·2026
Same author

Taurine supplementation at the crossroads of metabolism, inflammation and aging: mechanistic and nutritional perspectives.

Food & function·2026
Same author

Hepatokines lipocalin 2 and osteopontin drive muscle atrophy in MASH.

Molecular metabolism·2026
Same author

Lowered Abundance of Gut Bacteriophage Species Is Associated With Human Cancer Cachexia.

Journal of cachexia, sarcopenia and muscle·2026

Related Experiment Video

Updated: Jul 29, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
07:34

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

Published on: March 14, 2013

12.8K

Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples.

Andre Märtens1,2, Johannes Holle3, Brit Mollenhauer4,5

  • 1Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology, Technische Universität Braunschweig, 38118 Braunschweig, Germany.

Metabolites
|May 26, 2023
PubMed
Summary

Untargeted metabolomics studies require robust data processing to address instrumental drifts. This study recommends a workflow using quality control (QC) samples and finds TIGER batch-effect correction superior for high-quality biomarker discovery.

Keywords:
analytical variationbatch effectsmetabolomicsquality control

More Related Videos

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

3.8K
Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
11:00

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

Published on: May 20, 2013

22.6K

Related Experiment Videos

Last Updated: Jul 29, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
07:34

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

Published on: March 14, 2013

12.8K
An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

3.8K
Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
11:00

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

Published on: May 20, 2013

22.6K

Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Biotechnology

Background:

  • Untargeted metabolomics is vital for biomarker discovery, drug development, and precision medicine.
  • Instrumental drifts (retention time, signal intensity) pose challenges in large-scale mass spectrometry-based metabolomics.
  • Ensuring data quality requires accounting for these variations during data processing.

Purpose of the Study:

  • To recommend an optimal data processing workflow for untargeted metabolomics using intrastudy quality control (QC) samples.
  • To identify and mitigate errors caused by instrumental drifts.
  • To compare the performance of different batch-effect correction methods.

Main Methods:

  • Developed a data processing workflow incorporating intrastudy QC samples.
  • Evaluated three popular batch-effect correction methods.
  • Utilized QC-based metrics and a machine learning approach on biological samples for performance evaluation.

Main Results:

  • The TIGER method exhibited the best performance in batch-effect correction.
  • TIGER significantly reduced the relative standard deviation of QCs and dispersion ratio.
  • TIGER achieved the highest area under the receiver operating characteristic curve with multiple classifiers.

Conclusions:

  • The recommended workflow enhances data quality for untargeted metabolomics.
  • Effective batch-effect correction is crucial for reliable biomarker discovery and precision medicine.
  • The TIGER method is a highly effective tool for improving metabolomics data integrity.