Complete Data Analysis Workflow for Quantitative DIA Mass Spectrometry Using Nextflow
Mats Perk1, Sami Pietilä1, Tommi Välikangas1
1Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.
Journal of Proteome Research
|February 6, 2026
Summary
We developed glaDIAtor-nf, a Nextflow workflow for analyzing complex data-independent acquisition (DIA) mass spectrometry proteomics data. This tool efficiently reanalyzes public datasets, revealing hidden proteome patterns, such as in breast cancer research.
Area of Science:
- Proteomics
- Mass Spectrometry
- Bioinformatics
Background:
- Data-independent acquisition (DIA) mass spectrometry offers comprehensive protein profiling but generates large, complex datasets.
- Analyzing DIA data requires robust computational tools to manage complex pipelines and high-performance computing.
Purpose of the Study:
- To introduce glaDIAtor-nf, a Nextflow-based workflow for untargeted DIA mass spectrometry proteomics data analysis.
- To demonstrate the utility of glaDIAtor-nf in reanalyzing public datasets and uncovering previously hidden biological insights.
Main Methods:
- Development of glaDIAtor-nf using the Nextflow workflow management system.
- Rigorous technical validation using gold-standard datasets.
- Application to public breast cancer proteomics data.
Main Results:
- glaDIAtor-nf demonstrated technical accuracy in analyzing DIA mass spectrometry data.
- Reanalysis of public breast cancer data using glaDIAtor-nf revealed previously undetected proteome patterns.
- The study highlights the potential of reanalyzing existing public data with efficient tools.
Conclusions:
- glaDIAtor-nf provides an efficient and automated solution for large-scale DIA proteomics data analysis.
- The workflow facilitates the discovery of novel biological insights from public repositories.
- There is a significant need for user-friendly tools to enable widespread reanalysis of public proteomics data.
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