Division of Infection and Immunity, UCL, WC1E 6BT, London, UK.
CoMPLEX, Department of Computer Science, UCL, WC1E 7JG, London, UK.
Bioinformatics. 2021 May 5;37(6):876-878. doi: 10.1093/bioinformatics/btaa758.
Analysis of the T-cell receptor repertoire is rapidly entering the general toolbox used by researchers interested in cellular immunity. The annotation of T-cell receptors (TCRs) from raw sequence data poses specific challenges, which arise from the fact that TCRs are not germline encoded, and because of the stochastic nature of the generating process.
In this study, we report the release of Decombinator V4, a tool for the accurate and fast annotation of large sets of TCR sequences. Decombinator was one of the early Python software packages released to analyse the rapidly increasing flow of T-cell receptor repertoire sequence data. The Decombinator package now provides Python 3 compatibility, incorporates improved sequencing error and PCR bias correction algorithms, and provides output which conforms to the international standards proposed by the Adaptive Immune Receptor Repertoire Community.
The entire Decombinator suite is freely available at: https://github.com/innate2adaptive/Decombinator.
Supplementary data are available at Bioinformatics online.
分析 T 细胞受体（TCR）库正在迅速成为对细胞免疫感兴趣的研究人员使用的通用工具。从原始序列数据中注释 TCR 会带来一些特定的挑战，这是因为 TCR 不是胚系编码的，并且由于生成过程的随机性。
在这项研究中，我们报告了 Decombinator V4 的发布，这是一种用于准确快速注释大量 TCR 序列的工具。combinator 是最早发布的用于分析不断增加的 T 细胞受体库序列数据的 Python 软件包之一。combinator 包现在提供了 Python 3 的兼容性，合并了改进的测序误差和 PCR 偏差校正算法，并提供符合适应性免疫受体库国际标准的输出。
整套 Decombinator 套件可在以下网址免费获得：https://github.com/innate2adaptive/Decombinator。
补充数据可在 Bioinformatics 在线获得。
