An ultra-sensitive T-cell receptor detection method for TCR-Seq and RNA-Seq data
An ultra-sensitive T-cell receptor detection method for TCR-Seq and RNA-Seq data
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TCR-Seq 和 RNA-Seq 数据的超灵敏 T 细胞受体检测方法
DOI：
10.1093/bioinformatics/btaa432
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发表时间：
2020-08-01
期刊：
影响因子：
5.8
通讯作者：
Guo, An-Yuan
中科院分区：
文献类型：
--
作者：
Chen, Si-Yi;Liu, Chun-Jie;Guo, An-Yuan
动机：T细胞受体（TCR）的功能是识别抗原，并在T细胞免疫学中发挥重要作用。通过表征互补决定区3（CDR 3）来调查TCR库是一个关键问题。由于CDR 3的高度多样性和技术的限制，准确的表征CDR 3 repertoires仍然是一个巨大的challenges.Results：我们提出了一种计算方法命名为CATT超灵敏和精确的TCR CDR 3序列检测。CATT可应用于TCR测序、RNA-Seq和单细胞TCR（RNA）-Seq数据以表征CDR 3库。CATT集成了de Bruijn图微组装算法、数据驱动纠错模型和贝叶斯推理算法，自适应、超灵敏地表征高性能的CDR 3库。计算机模拟和实验数据的基准测试结果表明，CATT与现有工具相比具有上级召回率和精确度，特别是对于短读段长度和小尺寸的数据以及单细胞测序数据。因此，CATT将成为肿瘤和免疫学研究中TCR分析的有用工具。
Motivation: T-cell receptors (TCRs) function to recognize antigens and play vital roles in T-cell immunology. Surveying TCR repertoires by characterizing complementarity-determining region 3 (CDR3) is a key issue. Due to the high diversity of CDR3 and technological limitation, accurate characterization of CDR3 repertoires remains a great challenge.Results: We propose a computational method named CATT for ultra-sensitive and precise TCR CDR3 sequences detection. CATT can be applied on TCR sequencing, RNA-Seq and single-cell TCR(RNA)-Seq data to characterize CDR3 repertoires. CATT integrated de Bruijn graph-based micro-assembly algorithm, data-driven error correction model and Bayesian inference algorithm, to self-adaptively and ultra-sensitively characterize CDR3 repertoires with high performance. Benchmark results of datasets from in silico and experimental data demonstrated that CATT showed superior recall and precision compared with existing tools, especially for data with short read length and small size and single-cell sequencing data. Thus, CATT will be a useful tool for TCR analysis in researches of cancer and immunology.