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Mingxuan Gao1,2,3,4, Shubham Gupta1,2,5, Wenxian Yang6
1Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, Canada.
Communications Chemistry
|November 20, 2025
Summary
DreamDIAlignR improves data-independent acquisition (DIA) proteomics by integrating peptide elution behavior across runs. This deep learning tool enhances protein quantification consistency and accuracy, outperforming existing methods.
Area of Science:
- Proteomics
- Mass Spectrometry
- Bioinformatics
Background:
- Data-independent acquisition (DIA) proteomics utilizes peptide-centric strategies for MS2 spectra analysis.
- Current DIA tools often use single-run data, causing inconsistent quantification across datasets.
- Existing match-between-runs (MBR) algorithms lack statistical control, leading to false positives and reduced reproducibility.
Purpose of the Study:
- To develop a novel cross-run peptide-centric tool for DIA proteomics.
- To enhance peptide peak identification and alignment across multiple runs.
- To improve quantitative consistency and reproducibility in DIA analysis.
Main Methods:
- Introduced DreamDIAlignR, a tool integrating peptide elution behavior across runs.
- Employed a deep learning model for peptide peak identification and alignment.
- Implemented FDR-controlled scoring for peak quality assessment.
Main Results:
- DreamDIAlignR identified 21.2% more changing proteins in a benchmark dataset.
- Achieved a 36.6% increase in protein identification in a cancer dataset compared to state-of-the-art MBR methods.
- Demonstrated improved quantitative accuracy and reproducibility.
Conclusions:
- DreamDIAlignR offers a robust and statistically sound MBR methodology for DIA proteomics.
- The tool enhances overall DIA analysis quality and protein quantification.
- DreamDIAlignR is compatible with existing DIA analysis workflows.
