ABSTRACT
MOTIVATION: The T-cell receptor (TCR) determines the specificity of a T-cell towards an epitope. As of yet, the rules for antigen recognition remain largely undetermined. Current methods for grouping TCRs according to their epitope specificity remain limited in performance and scalability. Multiple methodologies have been developed, but all of them fail to efficiently cluster large datasets exceeding 1 million sequences. To account for this limitation, we developed ClusTCR, a rapid TCR clustering alternative that efficiently scales up to millions of CDR3 amino acid sequences, without knowledge about their antigen specificity. RESULTS: Benchmarking comparisons revealed similar accuracy of ClusTCR as compared to other TCR clustering methods, as measured by cluster retention, purity and consistency. ClusTCR offers a drastic improvement in clustering speed, which allows the clustering of millions of TCR sequences in just a few minutes through ultraefficient similarity searching and sequence hashing. AVAILABILITY AND IMPLEMENTATION: ClusTCR was written in Python 3. It is available as an anaconda package (https://anaconda.org/svalkiers/clustcr) and on github (https://github.com/svalkiers/clusTCR). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
