PXD055173

PXD055173 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 demonstrated 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 optimized 15 metrics and invited 21 experts to manually assess the quality of 2638 DIA files based on those metrics. Based on the annotation results, we developed an AI model for DIA-based QC in the training set of 2110 DIA files. This model 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 = 528). Finally, we developed an offline software called iDIA-QC for convenient adoption of this methodology for LC-MS QC.
HostingRepository	PRIDE
AnnounceDate	2025-05-07
AnnouncementXML	Submission_2025-05-07_01:48:30.243.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	欢 高
SpeciesList	scientific name: Mus musculus (Mouse); NCBI TaxID: 10090;
ModificationList	iodoacetamide derivatized residue
Instrument	Q Exactive HF

Revision	Datetime	Status	ChangeLog Entry
0	2024-08-24 21:33:59	ID requested
⏵ 1	2025-05-07 01:48:32	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: machine learning, DIA,quality control, SWATH, mouse liver, diaPASEF

Tiannan Guo
contact affiliation	Westlake University
contact email	guotiannan@westlake.edu.cn
lab head
欢 高
contact affiliation	Westlake University
contact email	gaohuanhuan@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/PXD055173

PRIDE project URI

• PRIDE
  1. PXD055173
     1. Label: PRIDE project
     2. Name: iDIA-QC: AI-empowered Data-Independent Acquisition Mass Spectrometry-based Quality Control