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DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis

Author

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  • Zhiwei Liu

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University)

  • Pu Liu

    (Westlake Omics (Hangzhou) Biotechnology Co., Ltd.)

  • Yingying Sun

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University)

  • Zongxiang Nie

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University)

  • Xiaofan Zhang

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University)

  • Yuqi Zhang

    (Westlake University
    Westlake University)

  • Yi Chen

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University)

  • Tiannan Guo

    (Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine
    Westlake University
    Westlake Omics (Hangzhou) Biotechnology Co., Ltd.)

Abstract

Data-independent acquisition mass spectrometry (DIA-MS) has become increasingly pivotal in quantitative proteomics. In this study, we present DIA-BERT, a software tool that harnesses a transformer-based pre-trained artificial intelligence (AI) model for analyzing DIA proteomics data. The identification model was trained using over 276 million high-quality peptide precursors extracted from existing DIA-MS files, while the quantification model was trained on 34 million peptide precursors from synthetic DIA-MS files. When compared to DIA-NN, DIA-BERT demonstrated a 51% increase in protein identifications and 22% more peptide precursors on average across five human cancer sample sets (cervical cancer, pancreatic adenocarcinoma, myosarcoma, gallbladder cancer, and gastric carcinoma), achieving high quantitative accuracy. This study underscores the potential of leveraging pre-trained models and synthetic datasets to enhance the analysis of DIA proteomics.

Suggested Citation

  • Zhiwei Liu & Pu Liu & Yingying Sun & Zongxiang Nie & Xiaofan Zhang & Yuqi Zhang & Yi Chen & Tiannan Guo, 2025. "DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58866-4
    DOI: 10.1038/s41467-025-58866-4
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    References listed on IDEAS

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    1. Brian C. Searle & Kristian E. Swearingen & Christopher A. Barnes & Tobias Schmidt & Siegfried Gessulat & Bernhard Küster & Mathias Wilhelm, 2020. "Generating high quality libraries for DIA MS with empirically corrected peptide predictions," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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