Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry

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

The proper splicing of RNAi factors is critical for pericentric heterochromatin assembly in fission yeast.

PLoS genetics·2014

Same author

Reelin induces Erk1/2 signaling in cortical neurons through a non-canonical pathway.

The Journal of biological chemistry·2014

Same author

Dynactin integrity depends upon direct binding of dynamitin to Arp1.

Molecular biology of the cell·2014

Same author

Quantitative proteomic profiling reveals differentially regulated proteins in cystic fibrosis cells.

Journal of proteome research·2014

Same author

Isolation of chromatin from dysfunctional telomeres reveals an important role for Ring1b in NHEJ-mediated chromosome fusions.

Cell reports·2014

Same author

Identification of small ubiquitin-like modifier substrates with diverse functions using the Xenopus egg extract system.

Molecular & cellular proteomics : MCP·2014

Search research articles

Related Experiment Video

Updated: May 22, 2026

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

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

Published on: November 13, 2021

Improving the TFold test for differential shotgun proteomics.

Paulo C Carvalho¹, John R Yates, Valmir C Barbosa

¹Carlos Chagas Institute, Fiocruz, Paraná, Brazil. paulo@pcarvalho.com

Bioinformatics (Oxford, England)

|April 28, 2012

Summary

This study updates TFold software to better identify differentially expressed proteins in shotgun proteomics. The new version improves sensitivity by optimizing protein identification with a flexible fold-change cutoff and stringency criteria.

More Related Videos

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

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

Published on: October 28, 2021

Related Experiment Videos

Last Updated: May 22, 2026

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

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

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

Published on: October 28, 2021

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Shotgun proteomics experiments generate large datasets for protein identification.
Accurate identification of differentially expressed proteins is crucial for biological discovery.
Existing software may have limitations in sensitivity for detecting low-abundance proteins.

Purpose of the Study:

To present an updated version of the TFold software.
To enhance the identification of differentially expressed proteins in shotgun proteomics.
To improve the sensitivity of protein identification algorithms.

Main Methods:

Utilizing a theoretical False Discovery Rate (FDR) estimator.
Implementing a fold-change cutoff that varies with the t-test P-value using a power law.
Incorporating a stringency criterion to detect lowly abundant proteins.

Main Results:

The updated TFold software demonstrates significant improvements in sensitivity.
The new approach maximizes protein identifications under specified FDR and fold-change constraints.
Enhanced detection of low-abundance differentially expressed proteins is achieved.

Conclusions:

The updated TFold software offers superior performance for differential protein expression analysis.
The refined methodology enhances the discovery of biologically relevant proteins.
This advancement aids researchers in more sensitive and accurate proteomics data interpretation.