Benchmarking deep learning models and classical de novo sequencing tools for immunopeptidomics
peer-reviewed · Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics · 2023
| Date | 2023-09-03 |
| Type | peer-reviewed |
| Venue | Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics |
| Publisher | ACM |
| Contribution | benchmark |
| DOI | 10.1145/3584371.3613055 |
| Citations (OpenAlex) | 0 |
Abstract
The identification of neo-peptide antigens is essential for the development of immunotherapies like chimeric antigen receptor T-cell therapy (CART), mRNA, and peptide vaccine. While mass spectrometry-based peptidomics is powerful for identifying peptides, its utility in discovery of non-canonical antigens is yet to achieve the full potential. Some challenges of such application include the constraints of the matching the resulting spectra against reference databases which may not represent the neo-peptide sequence, lower statistical power with exploded searching space, as well as low throughput due to computation power requirement[1].
Methods and tools
- Immunopeptidomics de novo benchmark: Benchmarks deep-learning de novo sequencing models against classical tools on immunopeptidomics data, where non-canonical neoantigens fall outside the reference database.