ClusTCR: a python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity
ClusTCR: a python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity
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DOI：
10.1093/bioinformatics/btab446
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发表时间：
2021-06-16
期刊：
影响因子：
5.8
通讯作者：
Meysman, Pieter
中科院分区：
文献类型：
--
作者：
Valkiers, Sebastiaan;Van Houcke, Max;Meysman, Pieter
动机：T细胞受体（TCR）决定T细胞对表位的特异性。到目前为止，抗原识别的规则在很大程度上仍然没有确定。目前用于根据TCR的表位特异性对TCR进行分组的方法在性能和可扩展性方面仍然有限。已经开发了多种方法，但它们都未能有效地聚类超过100万个序列的大型数据集。为了解决这一问题，我们开发了CIusTCR，一种快速的TCR聚类替代方案，有效地扩展到数百万的CDR3氨基酸序列，而不知道他们的抗原specificity.Results：基准比较显示类似的准确性CIusTCR相比，其他TCR聚类方法，如集群保留，纯度和一致性。CIusTCR在聚类速度上有了显著的提高，通过超高效的相似性搜索和序列散列，在短短几分钟内就可以对数百万个TCR序列进行聚类。
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 CIusTCR, 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 CIusTCR as compared to other TCR clustering methods, as measured by cluster retention, purity and consistency. CIusTCR 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.