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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
Published on: August 19, 2025
Guidelines for reporting quantitative mass spectrometry based experiments in proteomics
Salvador Martínez-Bartolomé1, Eric W Deutsch, Pierre-Alain Binz
1Proteomics Facility, Centro Nacional de Biotecnología-Consejo Superior de Investigaciones Científicas (CNB-CSIC), ProteoRed ISCIII, Madrid, Spain.
Journal of Proteomics
|March 19, 2013
Summary
New guidelines standardize quantitative proteomics data from mass spectrometry. These MIAPE Quant guidelines ensure critical analysis and reproducibility of proteomics studies.
Area of Science:
- Proteomics
- Mass Spectrometry
- Data Standardization
Background:
- Mass spectrometry is a key tool for protein identification and quantitative proteomics.
- Methodological advancements enable large-scale protein abundance measurements.
- Various quantitative strategies exist, each with unique data analysis needs.
Purpose of the Study:
- To introduce the MIAPE Quant guidelines for reporting quantitative mass spectrometry data.
- To standardize data handling, representation, and sharing in proteomics.
- To facilitate critical analysis and reproducibility of quantitative proteomics experiments.
Main Methods:
- Development of Minimum Information About a Proteomics Experiment (MIAPE) Quant guidelines by the HUPO Proteomics Standards Initiative (HUPO-PSI).
- Creation of the mzQuantML data exchange format.
- Inclusion of labeled, label-free, and targeted approaches like Selected Reaction Monitoring (SRM).
Main Results:
- The MIAPE Quant guidelines define essential data and metadata for quantitative proteomics.
- These guidelines support data submission to databases and journals.
- The guidelines aim for broad adoption by journals and public proteomics databases.
Conclusions:
- The MIAPE Quant guidelines represent a consensus view for reporting quantitative proteomics data.
- Standardization through these guidelines will improve data quality and reproducibility.
- Adoption is expected to impact proteomics laboratories globally, enhancing data sharing and analysis.

