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

924
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...
924
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

733
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...
733
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 Analyzers: Overview01:13

Mass Analyzers: Overview

611
The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
611
Mass Spectrometers01:16

Mass Spectrometers

5.2K
This lesson details the instrumentation of a mass spectrometer—a physical instrument to perform mass spectrometry on analyte molecules and record the characteristic mass spectra. This is achieved via three chief functions:
5.2K

You might also read

Related Articles

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

Sort by
Same author

Characterizing the effect of short wavelengths on the floral flavonoid metabolome of medicinal cannabis using a comparative computational metabolomics workflow.

Metabolomics : Official journal of the Metabolomic Society·2026
Same author

Elementary vectors reveal minimal interactions in microbial communities.

Journal of the Royal Society, Interface·2026
Same author

Cortical development dynamics across autism spectrum disorder mouse models.

Nature·2026
Same author

AnnoMe: user-defined classification of HR-MS/MS spectra for natural product discovery.

Bioinformatics advances·2026
Same author

Large-scale multi-omics profiling reveals environmental and evolutionary drivers of fungal phylogeographic and metabolic diversity.

Nature communications·2026
Same author

Cross ionization mode chemical similarity prediction between tandem mass spectra in metabolomics.

Nature communications·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 11, 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

12.7K

Combined LC-MS/MS feature grouping, statistical prioritization, and interactive networking in msFeaST.

Kevin Mildau1,2,3, Christoph Büschl4, Jürgen Zanghellini2

  • 1Bioinformatics Group, Department of Plant Sciences, Wageningen University & Research, Radix Building, Droevendaalsesteeg 1, Wageningen, 6708PB, the Netherlands.

Bioinformatics (Oxford, England)
|September 30, 2024
PubMed
Summary
This summary is machine-generated.

We developed msFeaST, a novel workflow for untargeted metabolomics data analysis. This tool enhances metabolite feature prioritization by integrating spectral similarity with statistical testing for improved biological insights.

More Related Videos

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems
14:42

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Published on: September 23, 2021

4.7K
Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
09:04

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

12.4K

Related Experiment Videos

Last Updated: Jun 11, 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

12.7K
Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems
14:42

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Published on: September 23, 2021

4.7K
Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
09:04

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

12.4K

Area of Science:

  • Metabolomics
  • Computational Biology
  • Bioinformatics

Background:

  • Untargeted metabolomics generates large datasets requiring efficient analysis.
  • Organizing and prioritizing metabolite features is a significant challenge.
  • Current methods often rely on mass fragmentation-based spectral similarity grouping.

Purpose of the Study:

  • To present msFeaST, a feature-set testing and visualization workflow for LC-MS/MS untargeted metabolomics data.
  • To streamline the organization and prioritization of metabolite features.
  • To integrate experimental data with mass-spectral structural information for enhanced analysis.

Main Methods:

  • Utilized k-medoids clustering for spectral similarity-based feature grouping.
  • Applied feature-set testing using the globaltest package for group-wise statistical analysis.
  • Developed an interactive workflow for integrating experimental and spectral data.

Main Results:

  • msFeaST enables statistically assessing differential abundance patterns for metabolite feature groups.
  • The workflow leverages spectral clustering to group potentially related metabolites.
  • Provides enhanced prioritization of features and feature sets in exploratory data analysis.

Conclusions:

  • msFeaST revolutionizes untargeted metabolomics by automating feature organization and prioritization.
  • The workflow enhances the interpretation of metabolomics data through integrated analysis.
  • Facilitates the discovery of meaningful biological insights from complex datasets.