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

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

8.8K
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...
8.8K
Proteomics01:33

Proteomics

10.2K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
10.2K

You might also read

Related Articles

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

Sort by
Same author

MLMarker: a machine learning framework for tissue inference and biomarker discovery.

Genome biology·2026
Same author

DiaReport: Reproducible workflow for differential expression analysis and interactive reporting in DIA-based proteomics.

Bioinformatics (Oxford, England)·2026
Same author

iDeepLC: Chemical Structure Information Yields Improved Retention Time Prediction of Peptides with Unseen Modifications.

Analytical chemistry·2026
Same author

Harnessing genomic and bioinformatics for surveillance of pathogens in Africa: a scoping review of existing training and gaps in training.

BMC infectious diseases·2026
Same author

Project ODIN: advancing environmental genomic surveillance for public health across sub-Saharan Africa.

The Lancet. Microbe·2026
Same author

omicsGMF: a multi-tool for dimensionality reduction, batch correction and imputation in bulk- and single-cell proteomics.

Nature communications·2026

Related Experiment Video

Updated: Apr 15, 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

6.3K

Summarization vs Peptide-Based Models in Label-Free Quantitative Proteomics: Performance, Pitfalls, and Data Analysis

Ludger J E Goeminne1, Andrea Argentini, Lennart Martens

  • 1∥Department of Plant Systems Biology, VIB, Ghent University, 9052 Ghent, Belgium.

Journal of Proteome Research
|April 2, 2015
PubMed
Summary
This summary is machine-generated.

Quantitative proteomics analysis requires careful data interpretation. Peptide-based models offer superior performance over summarization pipelines, and diagnostic plots aid in assessing differential expression in complex samples.

Keywords:
data analysisdifferential proteomicslinear model

More Related Videos

Quantitative Analysis of Chromatin Proteomes in Disease
08:11

Quantitative Analysis of Chromatin Proteomes in Disease

Published on: December 28, 2012

13.8K
Sample Preparation and Relative Quantitation using Reductive Methylation of Amines for Peptidomics Studies
08:00

Sample Preparation and Relative Quantitation using Reductive Methylation of Amines for Peptidomics Studies

Published on: November 4, 2021

2.8K

Related Experiment Videos

Last Updated: Apr 15, 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

6.3K
Quantitative Analysis of Chromatin Proteomes in Disease
08:11

Quantitative Analysis of Chromatin Proteomes in Disease

Published on: December 28, 2012

13.8K
Sample Preparation and Relative Quantitation using Reductive Methylation of Amines for Peptidomics Studies
08:00

Sample Preparation and Relative Quantitation using Reductive Methylation of Amines for Peptidomics Studies

Published on: November 4, 2021

2.8K

Area of Science:

  • Proteomics
  • Quantitative Mass Spectrometry
  • Bioinformatics

Background:

  • Quantitative label-free mass spectrometry is a powerful tool for proteome analysis.
  • Selecting appropriate data analysis methods is crucial but challenging for complex samples.

Purpose of the Study:

  • To rigorously compare peptide-based models and peptide-summarization-based pipelines for quantitative proteomics.
  • To evaluate the impact of highly abundant proteins on differential expression analysis.
  • To assess the utility of imputation strategies in differential expression detection.

Main Methods:

  • Comparative analysis of peptide-based models versus peptide-summarization pipelines.
  • Evaluation of false discovery rate (FDR) cutoff performance with varying protein abundance.
  • Assessment of diagnostic plots for quality control.
  • Analysis of imputation strategies under the "missing by low abundance" assumption.

Main Results:

  • Peptide-based models demonstrated superior sensitivity, specificity, accuracy, and precision compared to summarization pipelines.
  • Predefined FDR cutoffs can be problematic for highly abundant differentially expressed proteins.
  • Diagnostic plots are effective for assessing differential expression and fold-change estimates.
  • Imputation benefits low-abundance protein detection but negatively impacts moderately to highly abundant proteins.

Conclusions:

  • Peptide-based models are recommended for quantitative proteomics data analysis.
  • Caution is advised when interpreting data with highly abundant proteins or using standard imputation methods.
  • Diagnostic plots are valuable tools for robust data interpretation and quality assessment.