Benchmarking unsupervised methods for inferring TCR specificity.

ABSTRACT: Identifying T-cell receptor (TCR) specificity is crucial for advancing the understanding of adaptive immunity. Despite the development of computational methods to infer TCR specificity, their clustering behavior has not been thoroughly compared. We addressed this by curating a unified database of 190 670 human TCRs with known specificities for 2313 epitopes across 121 organisms, combining data from IEDB, McPAS-TCR, and VDJdb. We asked whether widely used TCR clustering methods produce comparable results on the same high-confidence dataset. We hypothesized that shared assumptions about conserved CDR3 motifs would yield similar patterns, with differences reflecting algorithmic design. Nine methods for clustering TCRs based on similarity were benchmarked against this dataset. DeepTCR demonstr

SUBMITTER: Jouannet C 

PROVIDER: S-EPMC12629845 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature