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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Lei Xin & Rui Qiao & Xin Chen & Hieu Tran & Shengying Pan & Sahar Rabinoviz & Haibo Bian & Xianliang He & Brenton Morse & Baozhen Shan & Ming Li, 2022. "A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30867-7
    DOI: 10.1038/s41467-022-30867-7
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    1. Mathias Wilhelm & Daniel P. Zolg & Michael Graber & Siegfried Gessulat & Tobias Schmidt & Karsten Schnatbaum & Celina Schwencke-Westphal & Philipp Seifert & Niklas Andrade Krätzig & Johannes Zerweck &, 2021. "Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Mathias Wilhelm & Daniel P. Zolg & Michael Graber & Siegfried Gessulat & Tobias Schmidt & Karsten Schnatbaum & Celina Schwencke-Westphal & Philipp Seifert & Niklas Andrade Krätzig & Johannes Zerweck &, 2021. "Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics," Nature Communications, Nature, vol. 12(1), pages 1-1, December.
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    Cited by:

    1. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. David Gomez-Zepeda & Danielle Arnold-Schild & Julian Beyrle & Arthur Declercq & Ralf Gabriels & Elena Kumm & Annica Preikschat & Mateusz Krzysztof Łącki & Aurélie Hirschler & Jeewan Babu Rijal & Chris, 2024. "Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    3. Wen-Feng Zeng & Xie-Xuan Zhou & Sander Willems & Constantin Ammar & Maria Wahle & Isabell Bludau & Eugenia Voytik & Maximillian T. Strauss & Matthias Mann, 2022. "AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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