ArticleNature communications2025
iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AI proteomics: from protein identification to virtual cells.Nature methods · 2026Review
- DIA proteomics of FFPE renal biopsies reveals two molecular subtypes of lupus nephritis and identifies APOL1 as candidate biomarker for stratification.Lupus science & medicine · 2026Article
- dECM bioinks for 3D bioprinted tumor models: Advances, challenges, and drug screening.iScience · 2026Review
- Advances in the application of artificial intelligence in mass spectrometry-based analysis of traditional Chinese medicine: compound identification and metabolic pathway elucidation.Analytical and bioanalytical chemistry · 2026Review
- Complete Data Analysis Workflow for Quantitative DIA Mass Spectrometry Using Nextflow.Journal of proteome research · 2026Article
- Spectral Cruncher: A Visualization Tool Integrating Manual Curation, Ion-Intensity Prediction, and De Novo Tag Generation.Journal of the American Society for Mass Spectrometry · 2026Article
- Emerging strategies to enhance microbial natural product-based drug discovery.Current opinion in biotechnology · 2025Review
Corrections and comments
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Authors and funding
36 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collect 2754 files acquired by data independent acquisition (DIA) and paired 2638 DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data demonstrate that DIA-based LC-MS/MS-related consensus QC metrics exhibit higher sensitivity compared to DDA-based QC metrics in detecting changes in LC-MS status. We then prioritize 15 metrics and invite 21 experts to manually assess the quality of 2754 DIA files based on those metrics. We develop an AI model for DIA-based QC using 2110 training files. It achieves AUCs of 0.91 (LC) and 0.97 (MS) in the first validation dataset (n = 528), and 0.78 (LC) and 0.94 (MS) in an independent validation dataset (n = 116). Finally, we develop an offline software called iDIA-QC for convenient adoption of this methodology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.