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

Rocuronium Dose and First-Attempt Intubation Success in the Critically Ill: Secondary Analysis of Two Multicenter Trials.

American journal of respiratory and critical care medicine·2026
Same author

A unified photosensitizer platform for <i>in situ</i> DNA-, RNA-, and protein-directed proximity labeling.

bioRxiv : the preprint server for biology·2026
Same author

Survey of the human proteostasis network: the ubiquitin-proteasome system.

bioRxiv : the preprint server for biology·2026
Same author

United Global Advocacy Drives Updates to World Health Organization Essential Medicines List.

Haemophilia : the official journal of the World Federation of Hemophilia·2026
Same author

Impact of measurable residual disease on outcomes using a modified DFCI protocol for adults with BCR-ABL negative acute lymphoblastic leukemia.

Leukemia research·2026
Same author

Stratifying Risk and Treatment Benefit: A Model Predicting Overall Survival in Men with Metastatic De Novo Hormone-sensitive Prostate Cancer in Trials Investigating Docetaxel (the STOPCAP Collaboration).

European urology focus·2026

Related Experiment Video

Updated: Jul 4, 2026

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources
12:47

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Published on: January 22, 2018

Matching isotopic distributions from metabolically labeled samples.

Sean McIlwain1, David Page, Edward L Huttlin

  • 1Department of Computer Sciences, University of Wisconsin, Madison, WI, USA. mcilwain@cs.wisc.edu

Bioinformatics (Oxford, England)
|July 1, 2008
PubMed
Summary
This summary is machine-generated.

This study presents an algorithm for matching metabolic labeling in mass spectrometry data, improving quantitative proteomic analysis. The novel approach accurately identifies peptide pairs, even with unknown sequences, enhancing high-throughput experiments.

More Related Videos

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing
07:41

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing

Published on: February 4, 2017

Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage
08:07

Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage

Published on: September 16, 2025

Related Experiment Videos

Last Updated: Jul 4, 2026

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources
12:47

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Published on: January 22, 2018

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing
07:41

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing

Published on: February 4, 2017

Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage
08:07

Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage

Published on: September 16, 2025

Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Computational Biology

Background:

  • Stable isotopic labeling is crucial for quantitative proteomics.
  • Metabolic labeling offers excellent internal controls but presents data analysis challenges due to variable peptide sequence-dependent labeling.
  • Automated matching of labeled and unlabeled peptide pairs from mass spectrometry data is needed, especially for unknown sequences.

Purpose of the Study:

  • To develop and evaluate an algorithm for identifying isotopic distributions and matching metabolically labeled peptide pairs in mass spectrometry data.
  • To address the challenge of analyzing quantitative proteomic data where peptide sequences are unknown.
  • To improve the accuracy and efficiency of high-throughput quantitative proteomic and metabolomic experiments.

Main Methods:

  • A two-stage algorithm combining a modified IDM algorithm for isotopic peak annotation and a probabilistic classifier with dynamic programming for matching labeled pairs.
  • Utilized expert and machine-selected peaks for evaluating algorithm performance.
  • Employed 10-fold cross-validation on 41 mass spectra (800-4000 m/z) with hand-annotated isotopic distributions and matched pairs.

Main Results:

  • The dynamic programming approach achieved high true positive rates (99% with perfect annotations, 77% with expert peaks, 36% with machine peaks) and low false positive rates (1%).
  • Performance improved when only requiring monoisotopic peak existence (45% true positive rate with machine data), further enhanced to 65% true positive rate with algorithm refinements.
  • Results highlight the impact of annotation accuracy on matching performance.

Conclusions:

  • The developed algorithm effectively annotates isotopic peaks and matches metabolically labeled peptide pairs, even with unknown sequences.
  • The dynamic programming approach shows significant promise for advancing quantitative proteomic and metabolomic analyses.
  • Further refinements can improve performance in noisy, real-world experimental conditions.