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) | 98 |
| 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
Data used
- Surpassing 10,000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and dat (as deposited) · PXD013658
Cites (8)
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Cited by (10)
- Ancient proteins identify various Denisovan remains from Southwest China (2026) both
- Transformer Architectures for De Novo Peptide Sequencing and Peptide Property Prediction in LC–MS/MS Proteomics (2026) both
- Enamel proteins from six Homo erectus specimens across China (2026) both
- The proteome of the late Middle Pleistocene Harbin individual (2025) crossref
- De Novo sequencing-assisted homology search for DIA data analysis enables low abundance peptide variants discovery (2025) crossref
- InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments (2025) both
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref
- Functional peptidomics analysis of Saccharomyces pastorianus protein hydrolysates based on different enzyme treatments (2023) crossref
- De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments (2023) both
- Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review (2023) crossref