A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics
peer-reviewed · Nature Communications · 2022
| Date | 2022-06-07 |
| Type | peer-reviewed |
| Venue | Nature Communications |
| Publisher | Springer Science and Business Media LLC |
| Contribution | algorithm |
| DOI | 10.1038/s41467-022-30867-7 |
| Citations (OpenAlex) | 111 |
| Venue 2-year citedness | 17.60 |
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.
Methods and tools
- PEAKS: Commercial DP-based de novo