clusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences
clusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences
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clusTCR：用于快速聚类大量 CDR3 序列的 Python 接口
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
P. Meysman
中科院分区：
文献类型：
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作者：
Sebastiaan Valkiers;Max Van Houcke;K. Laukens;P. Meysman
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，但是它们都不能有效地聚类超过100万个序列的大数据集。为了解决这一限制，我们开发了clusTCR，这是一种快速的TCR聚类替代方案，可以有效地扩展到数百万个CDR 3氨基酸序列。基准比较显示clustTCR与其他TCR聚类方法相似的准确性。clustTCR在聚类速度方面提供了巨大的改进，它允许通过高效的相似性搜索和序列散列在短短几分钟内对数百万个TCR序列进行聚类。clustTCR是用Python 3编写的。它可以作为一个anaconda软件包（https：//anaconda.org/svalkiers/clustcr）和github（https：//github.com/svalkiers/clustTCR）获得。
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 data sets 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. Benchmarking comparisons revealed similar accuracy of clusTCR with other TCR clustering methods. clusTCR offers a drastic improvement in clustering speed, which allows clustering of millions of TCR sequences in just a few minutes through efficient similarity searching and sequence hashing. 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).
影响因子：
5.6
作者：
SCHNEIDER, TD;STORMO, GD;EHRENFEUCHT, A
通讯作者：
EHRENFEUCHT, A
影响因子：
5.8
作者：
Sethna, Zachary;Elhanati, Yuval;Mora, Thierry
通讯作者：
Mora, Thierry