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

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

18.7K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
18.7K
Comparative Excretory Systems02:24

Comparative Excretory Systems

26.6K
Animals have evolved different strategies for excretion, the removal of waste from the body. Most waste must be dissolved in water to be excreted, so an animal’s excretory strategy directly affects its water balance.
26.6K
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

6.0K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
6.0K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

602
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
602
Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes02:16

Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes

16.1K
The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...
16.1K
Comparing Intermolecular Forces: Melting Point, Boiling Point, and Miscibility02:34

Comparing Intermolecular Forces: Melting Point, Boiling Point, and Miscibility

51.5K
Intermolecular forces are attractive forces that exist between molecules. They dictate several bulk properties, such as melting points, boiling points, and solubilities (miscibilities) of substances. Molar mass, molecular shape, and polarity affect the strength of different intermolecular forces, which influence the magnitude of physical properties across a family of molecules.
Temporary attractive forces like dispersion are present in all molecules, whether they are polar or nonpolar. They...
51.5K

You might also read

Related Articles

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

Sort by
Same author

Differential ErbB receptor dimerization modulates the ability of EGF receptor ligands to regulate metabolic flux.

The Journal of biological chemistry·2026
Same author

Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate.

Cancer discovery·2026
Same author

Accelerating discovery of cancer causes for prevention in the era of rising early-onset cancers.

Cell·2026
Same author

SLC33A1 exports oxidized glutathione to maintain endoplasmic reticulum redox homeostasis.

Nature cell biology·2026
Same author

Metabolites from plasma-like medium fuel nitrogen metabolism and influence proliferation in <i>Leptospira interrogans</i>.

bioRxiv : the preprint server for biology·2026
Same author

Cell-specific isotope labeling identifies <i>myo</i>-inositol transfer between neurons and oligodendroglia to support myelin repair.

bioRxiv : the preprint server for biology·2026
Same journal

Tracking Synthetic Adhesins on Bacterial Surfaces with Immunofluorescence Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Post-Selection Methods for Analyzing mRNA Display Selections and Optimization of Hits.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

High-Performance Computing in Tandem Mass Spectrometry (MS/MS) Peptide Identification.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Engineering and Adapting Disulfide-Containing Proteins to Enable Intracellular Functionality.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

AI-Driven Protein Research: From Prediction to Design.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for the In Vitro Selection of Protein and Peptide Libraries Using mRNA Display.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Feb 4, 2026

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

4.2K

A Protocol to Compare Methods for Untargeted Metabolomics.

Lingjue Wang1, Fuad J Naser1, Jonathan L Spalding1,2

  • 1Department of Chemistry, Washington University, St. Louis, MO, USA.

Methods in Molecular Biology (Clifton, N.J.)
|October 14, 2018
PubMed
Summary
This summary is machine-generated.

Researchers need a reliable way to compare untargeted metabolomics methods. This study introduces a credentialing protocol to identify and remove artifacts, ensuring accurate global metabolite profiling and method optimization.

Keywords:
CredentialingLiquid chromatographyMass spectrometryMetabolismMetabolite profilingUntargeted metabolomics

More Related Videos

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

10.8K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.7K

Related Experiment Videos

Last Updated: Feb 4, 2026

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

4.2K
Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

10.8K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.7K

Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Metabolomics

Background:

  • Thousands of liquid chromatography/mass spectrometry (LC/MS) methods exist for metabolite profiling.
  • Few methods are evaluated for global metabolite coverage, complicating untargeted metabolomics research.
  • Distinguishing true metabolite signals from contamination is challenging in untargeted experiments.

Purpose of the Study:

  • To develop a protocol for assessing and optimizing untargeted metabolomics methods.
  • To enable reliable comparison of different experimental workflows.
  • To improve the accuracy of global metabolite profiling by removing artifacts.

Main Methods:

  • Presentation of a novel 'credentialing protocol' for untargeted metabolomic datasets.
  • The protocol effectively removes signals originating from contamination and artifacts.
  • No metabolite structure identification is required for protocol application.

Main Results:

  • The credentialing protocol successfully distinguishes true metabolite signals from artifacts.
  • It provides a standardized approach to evaluate LC/MS-based metabolomics methods.
  • The protocol is applicable across various stages of the metabolomic workflow.

Conclusions:

  • The credentialing protocol is essential for accurate global metabolite coverage assessment.
  • It empowers researchers to confidently compare and optimize untargeted metabolomics workflows.
  • This method enhances the reliability of untargeted metabolomics data analysis.