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ClusTCR: a python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity
Sebastiaan Valkiers1,2, Max Van Houcke1, Kris Laukens1,2
1Adrem Data Lab, Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium.
Bioinformatics (Oxford, England)
|June 16, 2021
Summary
ClusTCR rapidly clusters millions of T-cell receptor (TCR) sequences, overcoming scalability limitations of existing methods. This tool efficiently groups TCRs by sequence similarity without needing antigen specificity data.
Area of Science:
- Immunology
- Bioinformatics
- Computational Biology
Background:
- T-cell receptors (TCRs) dictate T-cell specificity for epitopes, but antigen recognition rules are largely unknown.
- Current methods for clustering TCRs by epitope specificity struggle with large datasets (over 1 million sequences), limiting performance and scalability.
Purpose of the Study:
- To develop a scalable and rapid method for clustering T-cell receptor (TCR) sequences.
- To address the limitations of existing TCR clustering techniques for large-scale datasets.
Main Methods:
- Developed ClusTCR, a novel computational tool for TCR sequence clustering.
- Implemented ultraefficient similarity searching and sequence hashing for rapid processing of millions of CDR3 amino acid sequences.
Main Results:
- ClusTCR demonstrates comparable accuracy to existing methods in cluster retention, purity, and consistency.
- Achieved a significant improvement in clustering speed, enabling the analysis of millions of TCR sequences within minutes.
Conclusions:
- ClusTCR provides a scalable and efficient solution for clustering large TCR sequence datasets.
- The tool facilitates deeper insights into TCR repertoire analysis without prior knowledge of antigen specificity.
