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

Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. 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...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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...

You might also read

Related Articles

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

Sort by
Same author

Phosphatidylserine and RhoB connect PI4P and PA metabolism to maintain plasma membrane identity.

The Journal of cell biology·2026
Same author

Predictions of protein-protein interactions and co-complex models with deep learning.

Current opinion in chemical biology·2026
Same author

Research progress on extraction and purification, structural characteristics, biological activities, and applications from Millettia speciosa Champ polysaccharides: a review.

Carbohydrate research·2026
Same author

Interactome mapping in human excitatory neurons reveals novel risk genes and pathways in Alzheimer's disease.

bioRxiv : the preprint server for biology·2026
Same author

Cystinosin/Ers1 functions in redox homeostasis in the early secretory pathway.

bioRxiv : the preprint server for biology·2026
Same author

Cooperative and competitive interactions among transcription regulatory elements modulate transcription output.

bioRxiv : the preprint server for biology·2026
Same journal

A temporal phospho-acetylome atlas of human myogenesis identifies coordinated post-translational regulation.

Molecular & cellular proteomics : MCP·2026
Same journal

Temporal proteomic characterization of SARS-CoV-2 infected mouse lungs.

Molecular & cellular proteomics : MCP·2026
Same journal

Platelet proteome links metabolism to reactivity in Essential Thrombocythemia.

Molecular & cellular proteomics : MCP·2026
Same journal

Genetic rescue of disrupted synaptic protein interaction network dynamics following SYNGAP1 reactivation.

Molecular & cellular proteomics : MCP·2026
Same journal

ASAP-ID: Proximity labelling with small tags.

Molecular & cellular proteomics : MCP·2026
Same journal

Proteome profiling reveals NQO2 activity contributing to proteasome inhibitor resistance in multiple myeloma cell lines.

Molecular & cellular proteomics : MCP·2026
See all related articles

Related Experiment Video

Updated: Jun 1, 2026

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
14:51

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Published on: November 13, 2021

A bayesian mixture model for comparative spectral count data in shotgun proteomics.

James G Booth1, Kirsten E Eilertson, Paul Dominic B Olinares

  • 1Department of Biological Statistics and Computational Biology, Cornell University, Comstock Hall, Ithaca, NY 14853, USA. jim.booth@cornell.edu

Molecular & Cellular Proteomics : MCP
|May 24, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian model for analyzing large-scale shotgun proteomics data. The method accurately identifies proteins with differing abundance across experimental conditions, outperforming existing techniques.

More Related Videos

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot
10:12

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot

Published on: October 28, 2021

Related Experiment Videos

Last Updated: Jun 1, 2026

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
14:51

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Published on: November 13, 2021

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot
10:12

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot

Published on: October 28, 2021

Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Mass-spectrometry-based shotgun proteomics enables large-scale proteome profiling.
  • Identifying proteins with differential abundance across conditions is crucial in many proteomic studies.
  • Existing quantitative methods have limitations in accuracy and scalability.

Purpose of the Study:

  • To develop and implement a novel Bayesian model for simultaneous differential abundance analysis of thousands of proteins.
  • To improve the accuracy and reliability of quantitative proteomics.

Main Methods:

  • Utilized spectral counting data from mass spectrometry.
  • Developed a Bayesian statistical model inspired by microarray analysis techniques.
  • Calculated posterior probabilities of differential protein abundance.

Main Results:

  • The developed Bayesian model demonstrated uniformly superior performance compared to existing methods.
  • Successfully applied to analyze thousands of proteins simultaneously.
  • Provided accurate identification of differentially abundant proteins.

Conclusions:

  • The Bayesian model offers a robust and high-performing approach for differential proteomics.
  • This method enhances the analysis of complex proteomes and facilitates biological discovery.