PXD051878

PXD051878 is an original dataset announced via ProteomeXchange.

Title	iDIA-QC: AI-empowered Data-Independent Acquisition Mass Spectrometry-based Quality Control
Description	Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collected 2638 files acquired by data independent acquisition (DIA) and paired DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data showed that DIA-based LC-MS/MS related consensus QC metric is more sensitive than DDA-based QC in detecting MS status changes. We then optimized 15 DIA-QC metrics, and invited to manually assess the quality of 2638 DIA files generated by 21 mass spectrometers based on each metric. Based on the annotation results, we developed an AI model for DIA-based QC in the training set of 2059 DIA files, and predicted the liquid chromatography (LC) performance with an AUC of 0.91 and the MS performance with an AUC of 0.97 in an independent validation dataset (n = 523). Finally, we developed an offline software called iDIA-QC for convenient adoption of this methodology for LC-MS QC
HostingRepository	PRIDE
AnnounceDate	2025-05-06
AnnouncementXML	Submission_2025-05-06_15:05:07.935.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Tiannan Guo
SpeciesList	scientific name: Mus musculus (Mouse); NCBI TaxID: 10090;
ModificationList	monohydroxylated residue
Instrument	Q Exactive HF; timsTOF Pro; TripleTOF 5600; Q Exactive

Revision	Datetime	Status	ChangeLog Entry
0	2024-04-30 02:26:25	ID requested
⏵ 1	2025-05-06 15:05:08	announced

Gao H, Zhu Y, Wang D, Nie Z, Wang H, Wang G, Liang S, Xie Y, Sun Y, Jiang W, Dong Z, Qian L, Wang X, Liang M, Chen M, Fang H, Zeng Q, Tian J, Sun Z, Xue J, Li S, Chen C, Liu X, Lyu X, Guo Z, Qi Y, Wu R, Du X, Tong T, Kong F, Han L, Wang M, Zhao Y, Dai X, He F, Guo T, iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control. Nat Commun, 16(1):892(2025) [pubmed]

10.1038/s41467-024-54871-1;

submitter keyword: Quality Control

Data independent acquisition

machine learning

Tiannan Guo
contact affiliation	School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
contact email	gaohuanhuan@westlake.edu.cn
lab head
Tiannan Guo
contact affiliation	Westlake University
contact email	guotiannan@westlake.edu.cn
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2025/05/PXD051878

PRIDE project URI

• PRIDE
  1. PXD051878
     1. Label: PRIDE project
     2. Name: iDIA-QC: AI-empowered Data-Independent Acquisition Mass Spectrometry-based Quality Control