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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...

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Updated: May 2, 2026

Quantitative Analysis of Chromatin Proteomes in Disease

Quantitative Analysis of Chromatin Proteomes in Disease

Published on: December 28, 2012

Statistical methods for quantitative mass spectrometry proteomic experiments with labeling.

Ann L Oberg¹, Douglas W Mahoney

¹Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. oberg.ann@mayo.edu

BMC Bioinformatics

|November 27, 2012

Summary

This summary is machine-generated.

This study introduces statistical experimental design for mass spectrometry labeling, minimizing variability and bias in protein abundance analysis. It provides a framework for efficient data export, normalization, and reliable differential protein detection using iTRAQ 4-plex.

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Quantitative Analysis of Chromatin Proteomes in Disease

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Published on: December 28, 2012

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Area of Science:

Proteomics
Analytical Chemistry
Biostatistics

Background:

Mass spectrometry (MS) with labeling enables simultaneous analysis of multiple specimens, reducing inter-experiment variability.
Statistical experimental design principles are crucial for robust and reproducible MS-based proteomic studies.

Purpose of the Study:

To integrate statistical experimental design concepts into MS labeling workflows.
To minimize variability and avoid biases in quantitative proteomic data.
To provide a practical guide for data analysis, including normalization and differential expression testing.

Main Methods:

Application of statistical experimental design principles within a mass spectrometry labeling framework (iTRAQ 4-plex).
Demonstration of efficient data export formats for statistical analysis.
Methods for assessing, performing, and validating data normalization.
Building statistical models for differential protein abundance testing.

Main Results:

Successful integration of statistical design reduces variability and potential biases in MS experiments.
Effective strategies for data normalization and assessment of its performance are presented.
Reliable methods for testing differential protein abundance and interpreting results are demonstrated.

Conclusions:

Statistical experimental design is essential for maximizing the utility of MS labeling techniques.
The described framework enhances the reliability and accuracy of quantitative proteomic analyses.
This approach facilitates robust discovery of differentially abundant proteins across multiple conditions.