A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics

Lei Xin, Rui Qiao, Xin Chen, Hieu Tran, Shengying Pan, Sahar Rabinoviz, Haibo Bian, Xianliang He, Brenton Morse, Baozhen Shan () and Ming Li ()
Additional contact information
Lei Xin: Bioinformatics Solutions Inc.
Rui Qiao: Bioinformatics Solutions Inc.
Xin Chen: Bioinformatics Solutions Inc.
Hieu Tran: University of Waterloo
Shengying Pan: Bioinformatics Solutions Inc.
Sahar Rabinoviz: Bioinformatics Solutions Inc.
Haibo Bian: Bioinformatics Solutions Inc.
Xianliang He: Bioinformatics Solutions Inc.
Brenton Morse: Bioinformatics Solutions Inc.
Baozhen Shan: Bioinformatics Solutions Inc.
Ming Li: University of Waterloo

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches can enable highly sensitive mass spectrometry, especially for imunnopeptidomics applications. Here we report a streamlined platform for both DDA and DIA data analysis. The platform integrates deep learning-based solutions of spectral library search, database search, and de novo sequencing under a unified framework, which not only boosts the sensitivity but also accurately controls the specificity of peptide identification. Our platform identifies 5-30% more peptide precursors than other state-of-the-art systems on multiple benchmark datasets. When evaluated on immunopeptidomics datasets, we identify 1.7-4.1 and 1.4-2.2 times more peptides from DDA and DIA data, respectively, than previously reported results. We also discover six T-cell epitopes from SARS-CoV-2 immunopeptidome that might represent potential targets for COVID-19 vaccine development. The platform supports data formats from all major instruments and is implemented with the distributed high-performance computing technology, allowing analysis of tera-scale datasets of thousands of samples for clinical applications.

Date: 2022
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DOI: 10.1038/s41467-022-30867-7

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