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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: Sep 9, 2025

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Published on: April 18, 2025

Understanding Data Analysis Steps in Mass-Spectrometry-Based Proteomics Is Key to Transparent Reporting.

Nadezhda T Doncheva¹, Veit Schwämmle², Marie Locard-Paulet^3,4

¹Novo Nordisk Foundation Center for Protein Research, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.

Journal of Proteome Research

|September 3, 2025

Summary

This summary is machine-generated.

Reporting mass spectrometry (MS)-based proteomics data analysis is crucial for reproducibility. This work outlines best practices for transparently documenting MS proteomics analysis workflows to enhance data reusability.

Keywords:

data analysis mass-spectrometry proteomics reporting reproducibility statistics transparency

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Area of Science:

Proteomics
Bioinformatics
Data Science

Background:

Mass spectrometry (MS)-based proteomics generates complex data requiring multi-stage analysis.
Reproducibility and reusability of proteomics data depend on comprehensive reporting of analysis workflows.

Purpose of the Study:

To report good practices for describing MS-based proteomics data analysis.
To advocate for increased transparency in reporting data analysis workflows within the proteomics community.

Main Methods:

Review and synthesis of current practices in MS-based proteomics data analysis reporting.
Discussion of the importance of detailed workflow documentation.

Main Results:

Identification of key stages in MS proteomics data analysis that require thorough reporting (QC, cleaning, normalization, statistical, functional analysis, visualization).
Highlighting the challenges and potential errors in exhaustive reporting.

Conclusions:

Adopting standardized good practices for reporting MS proteomics data analysis is essential.
Enhanced transparency in data analysis workflows will improve data sharing and scientific collaboration.