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

Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

853
Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and signal-to-noise ratio for the analyte. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.
Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called collision-induced...
853
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

6.4K
Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
6.4K
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

687
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
687

You might also read

Related Articles

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

Sort by
Same author

Agentic AI for Structural Elucidation and Discovery of Drug Metabolites from Mass Spectrometry Data.

bioRxiv : the preprint server for biology·2026
Same author

MALDI Tandem Mass Spectrometry for Colony-Based Dereplication of Natural Products.

bioRxiv : the preprint server for biology·2026
Same author

Predicting Discrete Structural Transformations in Small Molecules from Tandem Mass Spectrometry.

bioRxiv : the preprint server for biology·2026
Same author

Glucuronidation metabolomic fingerprinting to map host-microbe metabolism.

Nature communications·2026
Same author

Structure-centric searching enables global mapping of the public metabolome.

Nature biotechnology·2026
Same author

Pan-Metabolomics Repository Mapping of the Carnitine Landscape.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: May 24, 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

12.6K

MS-RT: A Method for Evaluating MS/MS Clustering Performance for Metabolomics Data.

Xianghu Wang1, Yasin El Abiead2, Deepa D Acharya3

  • 1Department of Computer Science and Engineering, University of California Riverside, 900 University Avenue, Riverside, California 92521, United States.

Journal of Proteome Research
|March 5, 2025
PubMed
Summary
This summary is machine-generated.

We developed a new method to evaluate tandem mass spectra (MS/MS) clustering in metabolomics. This approach helps select the best tools for analyzing complex biological samples.

Keywords:
MS-RT methodbenchmark clusteringclustering toolscompletenessmetabolomicspuritytandem mass spectrometry

More Related Videos

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

20.9K
2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes
08:23

2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes

Published on: August 6, 2018

11.3K

Related Experiment Videos

Last Updated: May 24, 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

12.6K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

20.9K
2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes
08:23

2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes

Published on: August 6, 2018

11.3K

Area of Science:

  • Analytical Chemistry
  • Computational Biology
  • Metabolomics

Background:

  • Tandem mass spectrometry (MS/MS) clustering is vital for deduplicating data in untargeted metabolomics.
  • High-throughput mass spectrometers generate vast amounts of MS/MS data, necessitating efficient clustering.

Purpose of the Study:

  • To address the lack of robust MS/MS clustering evaluation methods in metabolomics.
  • To introduce and validate a novel MS1-retention time (MS-RT) method for assessing MS/MS clustering performance.
  • To benchmark existing MS/MS clustering tools for metabolomics applications.

Main Methods:

  • Development of the MS1-retention time (MS-RT) method for MS/MS clustering evaluation.
  • Validation of MS-RT by comparison with established proteomics clustering assessment techniques.
  • Performance evaluation of multiple MS/MS clustering tools using metabolomics datasets.

Main Results:

  • The MS-RT method provides a reliable approach to assess MS/MS clustering in metabolomics.
  • Benchmarking revealed the strengths and weaknesses of various MS/MS clustering tools.
  • The study offers practical recommendations for tool selection in metabolomics data analysis.

Conclusions:

  • The MS-RT method is a significant advancement for evaluating MS/MS clustering in metabolomics.
  • This work provides crucial guidance for researchers using untargeted metabolomics.
  • Future advancements in metabolomics MS/MS clustering are facilitated by this study.