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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
Published on: August 19, 2025
912
Fast Proteome Identification and Quantification from Data-Dependent Acquisition-Tandem Mass Spectrometry (DDA MS/MS)
Jesse G Meyer1,2,3
1Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.
Methods and Protocols
|April 23, 2019
Summary
This study introduces a streamlined method for identifying and quantifying proteins using data-dependent acquisition tandem mass spectrometry. The flexible protocol enables accurate proteome analysis in various conditions, offering a statistically rigorous approach.
Area of Science:
- Proteomics
- Biochemistry
- Computational Biology
Background:
- Protein identification via data-dependent acquisition (DDA) tandem mass spectrometry is standard for small genomes.
- Quantifying identified proteins often necessitates multiple software packages, complicating analysis.
Purpose of the Study:
- To present a flexible strategy for label-free protein identification and quantification from bottom-up proteomics experiments.
- To enable the analysis of proteome remodeling in response to stimuli like drug treatment or gene knockout.
Main Methods:
- Utilizes a flexible strategy for protein identification and label-free quantification.
- Applies to any DDA dataset with high-resolution precursor scans.
- Employs the latest, fastest, freely-available software for statistical rigor.
Main Results:
- Enables quantification of all detectable proteins in DDA datasets.
- Provides a statistically rigorous method for proteome analysis.
- The entire protocol can be completed in a few hours for yeast proteome analysis.
Conclusions:
- The described protocol offers a robust and efficient method for comprehensive proteome quantification.
- Facilitates the study of dynamic proteome changes in response to experimental perturbations.
- Streamlines complex protein quantification processes using accessible software.
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