Neighborhood enrichment for the identification of antigen-specific T-cell receptors
Jazyk angličtina Země Velká Británie, Anglie Médium print
Typ dokumentu časopisecké články
Grantová podpora
075-15-2025-598, June 25, 2025
Ministry of Science and Higher Education of the Russian Federation (the Federal Scientific-technical programme for genetic technologies development for 2019-2030
PubMed
40996146
PubMed Central
PMC12461701
DOI
10.1093/bib/bbaf495
PII: 8263580
Knihovny.cz E-zdroje
- Klíčová slova
- TCR repertoire, TCR specificity, immunoinformatics, software,
- MeSH
- algoritmy * MeSH
- antigeny * imunologie MeSH
- lidé MeSH
- Mycobacterium tuberculosis imunologie MeSH
- myši MeSH
- receptory antigenů T-buněk * imunologie genetika MeSH
- virus lymfocytární choriomeningitidy imunologie MeSH
- výpočetní biologie * metody MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- antigeny * MeSH
- receptory antigenů T-buněk * MeSH
Understanding T-cell receptor (TCR) specificity is not only essential for fundamental research, but could open up novel avenues for diagnostics, cancer immunotherapy, and the targeted treatment of autoimmune diseases. The immune system responds to challenges through groups of T-cells with similar TCR sequences. In recent years, searching for TCRs with an enrichment of similar sequences - neighbors - in a TCR repertoire has become a standard procedure for antigen-specific TCR identification. This study provides a systematic comparison of computational algorithms-ALICE, TCRNET, GLIPH2, and tcrdist3-that leverage neighborhood enrichment for antigen-specific TCR identification. Using published murine datasets from Lymphocytic choriomeningitis virus (LCMV) infection and novel datasets from Sputnik V vaccination and Mycobacterium tuberculosis (Mtb) infection, we evaluated the performance of these algorithms. To facilitate reproducible analysis, we developed TCRgrapher, an R library that integrates these pipelines into a user-friendly framework. TCRgrapher enables efficient identification of antigen-specific TCRs from single repertoire snapshots and supports flexible parameter customization. Our comparative analysis revealed that ALICE and TCRNET consistently outperformed GLIPH2 and tcrdist3 across most datasets, achieving higher area under precision-recall curve. While murine datasets provide valuable insights into algorithm performance, caution is advised when extrapolating these results to other species or different experimental conditions. TCRgrapher is freely available on GitHub (https://github.com/KseniaMIPT/tcrgrapher), offering researchers a robust tool for investigating TCR specificity and advancing immunological studies.
Abu Dhabi Stem Cells Center 25 Mahdar Qutouf Street Rowdhat Abu Dhabi United Arab Emirates
Central European Institute of Technology Žerotínovo nám 617 9 601 77 Brno Czech Republic
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