DiaReport: Reproducible workflow for differential expression analysis and interactive reporting in DIA-based proteomics

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2026
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Abstract
MOTIVATION: Data-independent acquisition (DIA) has become the preferred data acquisition method for mass spectrometry-based proteomics, yet, reproducible workflows for differential expression (DE) analysis and results reporting remain limited. We present DiaReport, an R package that performs precursor- and protein-level DE analysis from DIA-NN output using MSqRob and QFeatures, while generating high-quality, interactive HTML reports through Quarto. DiaReport integrates precursor data, filtering of missing values, normalization, protein summarization and statistical modeling within a single function, supporting both simple pairwise as well as complex experimental designs. The package provides structured outputs and configuration files to ensure computational reproducibility across different studies. To accommodate diverse research needs, DiaReport includes multiple reporting templates tailored to different proteomic applications. Applying DiaReport to an extracellular vesicle (EV) proteomics dataset demonstrates its ability to efficiently analyze DIA data and provide rapid insights into sample quality and protein level differences. AVAILABILITY: DiaReport is an open-source R package available at https://github.com/Gevaert-Lab/diareport (DOI: 10.5281/zenodo.20120604). The package is platform-independent and distributed under the MIT license. Reports are generated using Quarto and require only standard R dependencies. Detailed documentation, installation guides and usage vignettes are provided within the repository. The interactive HTML reports discussed in this study, including the UPS2 benchmark and EV case study, are archived on Zenodo (DOI: 10.5281/zenodo.20122506 and 10.5281/zenodo.20123378). SUPPLEMENTARY INFORMATION: Figure S1 (Benchmarking performance of DiaReport); Table S1 (Guidance for missing value filtering strategies); and Table S2 (Indicative runtimes across different cohort sizes and storage configurations) are available at Bioinformatics online.
Reference Key
openalex_W7165638431 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Andrea Argentini, Esperanza Fernández, Jarne Pauwels, Kris Gevaert
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag435
URL
https://doi.org/10.1093/bioinformatics/btag435
Keywords Keywords not found

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