TIPs: a deep learning-guided proteogenomic framework to expand the landscape of transposable element-derived antigens with immunopeptidomics

peer-reviewed · Genome Biology · 2026

peer-reviewed · Genome Biology · 2026. Qian Wu et al. Transposable elements (TEs) represent an abundant and important source of HLA-presented antigens, but their…
Date 2026-07-11
Type peer-reviewed
Venue Genome Biology
Publisher Springer Science and Business Media LLC
Contribution downstream-application
DOI 10.1186/s13059-026-04191-y
Citations (OpenAlex) 0

Abstract

Transposable elements (TEs) represent an abundant and important source of HLA-presented antigens, but their immunopeptidomic characterization remains challenging due to the inflated search space. We present TIPs (TE-derived Immunopeptidomic Search), a deep learning-guided proteogenomic framework that integrates de novo sequencing, database refinement, multiple search engines and stringent FDR controls. Across various cell lines and cancer types, TIPs identified 20-fold more TE-derived peptides on average than conventional approaches. It further revealed many recurrent, tumor-specific antigens from TEs, including candidates induced by epigenetic therapy. These findings highlight the potential of TIPs to expand the antigenic landscape beyond canonical sources.

Authors

  1. Qian Wu · Shanghai Jiao Tong University, State Key Laboratory of Microbial Metabolism
  2. Xinyue Zhou (Shanghai Jiao Tong University) · Shanghai Jiao Tong University, State Key Laboratory of Microbial Metabolism
  3. Qizhen Feng · Shanghai Jiao Tong University, State Key Laboratory of Microbial Metabolism
  4. Zixiang Shang · Shanghai Jiao Tong University, State Key Laboratory of Microbial Metabolism
  5. Jiayi Shen · Shanghai Jiao Tong University, State Key Laboratory of Microbial Metabolism
  6. Xiaoxiang Huang · Shanghai Jiao Tong University, State Key Laboratory of Microbial Metabolism
  7. Xiaobing Liu · Shanghai Jiao Tong University, Shanghai Ninth People’s Hospital
  8. Wenguang Shao · Shanghai Jiao Tong University, State Key Laboratory of Microbial Metabolism

Methods and tools

  • TIPs: Deep-learning-guided proteogenomic framework that uses de novo sequencing to refine the search space for transposable-element-derived HLA antigens, finding 20-fold more than conventional searches.

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