@article {doi:10.1021/acs.analchem.3c02689,
	title = {Data-Driven Tool for Cross-Run Ion Selection and Peak-Picking in Quantitative Proteomics with Data-Independent Acquisition LC{\textendash}MS/MS},
	journal = {Analytical Chemistry},
	year = {2023},
	note = {PMID: 37906674},
	month = {11/2023},
	pages = {in press},
	type = {Research},
	abstract = {<p>Proteomics provides molecular bases of biology and disease, and liquid chromatography\&ndash;tandem mass spectrometry (LC\&ndash;MS/MS) is a platform widely used for bottom-up proteomics. Data-independent acquisition (DIA) improves the run-to-run reproducibility of LC\&ndash;MS/MS in proteomics research. However, the existing DIA data processing tools sometimes produce large deviations from true values for the peptides and proteins in quantification. Peak-picking error and incorrect ion selection are the two main causes of the deviations. We present a cross-run ion selection and peak-picking (CRISP) tool that utilizes the important advantage of run-to-run consistency of DIA and simultaneously examines the DIA data from the whole set of runs to filter out the interfering signals, instead of only looking at a single run at a time. Eight datasets acquired by mass spectrometers from different vendors with different types of mass analyzers were used to benchmark our CRISP-DIA against other currently available DIA tools. In the benchmark datasets, for analytes with large content variation among samples, CRISP-DIA generally resulted in 20 to 50\% relative decrease in error rates compared to other DIA tools, at both the peptide precursor level and the protein level. CRISP-DIA detected differentially expressed proteins more efficiently, with 3.3 to 90.3\% increases in the numbers of true positives and 12.3 to 35.3\% decreases in the false positive rates, in some cases. In the real biological datasets, CRISP-DIA showed better consistencies of the quantification results. The advantages of assimilating DIA data in multiple runs for quantitative proteomics were demonstrated, which can significantly improve the quantification accuracy.</p>},
	doi = {10.1021/acs.analchem.3c02689},
	url = {https://doi.org/10.1021/acs.analchem.3c02689},
	author = {Yan, Binjun and Shi, Mengtian and Cai, Siyu and Su, Yuan and Chen, Renhui and Huang, Chiyuan and Chen, David D. Y.}
}
