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TRIg:一种用于非规则T细胞受体和免疫球蛋白序列的强大比对流程。

TRIg: a robust alignment pipeline for non-regular T-cell receptor and immunoglobulin sequences.

作者信息

Hung Sheng-Jou, Chen Yi-Lin, Chu Chia-Hung, Lee Chuan-Chun, Chen Wan-Li, Lin Ya-Lan, Lin Ming-Ching, Ho Chung-Liang, Liu Tsunglin

机构信息

Department of Biotechnology and Bioindustry Sciences, National Cheng Kung University, Tainan City, Taiwan.

Molecular Diagnostic Laboratory, Department of Pathology, National Cheng Kung University Hospital, Tainan City, Taiwan.

出版信息

BMC Bioinformatics. 2016 Oct 26;17(1):433. doi: 10.1186/s12859-016-1304-2.

Abstract

BACKGROUND

T cells and B cells are essential in the adaptive immunity via expressing T cell receptors and immunoglogulins respectively for recognizing antigens. To recognize a wide variety of antigens, a highly diverse repertoire of receptors is generated via complex recombination of the receptor genes. Reasonably, frequencies of the recombination events have been shown to predict immune diseases and provide insights into the development of immunity. The field is further boosted by high-throughput sequencing and several computational tools have been released to analyze the recombined sequences. However, all current tools assume regular recombination of the receptor genes, which is not always valid in data prepared using a RACE approach. Compared to the traditional multiplex PCR approach, RACE is free of primer bias, therefore can provide accurate estimation of recombination frequencies. To handle the non-regular recombination events, a new computational program is needed.

RESULTS

We propose TRIg to handle non-regular T cell receptor and immunoglobulin sequences. Unlike all current programs, TRIg does alignments to the whole receptor gene instead of only to the coding regions. This brings new computational challenges, e.g., ambiguous alignments due to multiple hits to repetitive regions. To reduce ambiguity, TRIg applies a heuristic strategy and incorporates gene annotation to identify authentic alignments. On our own and public RACE datasets, TRIg correctly identified non-regularly recombined sequences, which could not be achieved by current programs. TRIg also works well for regularly recombined sequences.

CONCLUSIONS

TRIg takes into account non-regular recombination of T cell receptor and immunoglobulin genes, therefore is suitable for analyzing RACE data. Such analysis will provide accurate estimation of recombination events, which will benefit various immune studies directly. In addition, TRIg is suitable for studying aberrant recombination in immune diseases. TRIg is freely available at https://github.com/TLlab/trig .

摘要

背景

T细胞和B细胞在适应性免疫中至关重要,它们分别通过表达T细胞受体和免疫球蛋白来识别抗原。为了识别各种各样的抗原,通过受体基因的复杂重组产生了高度多样化的受体库。合理地,重组事件的频率已被证明可预测免疫疾病并为免疫发育提供见解。高通量测序进一步推动了该领域的发展,并且已经发布了几种计算工具来分析重组序列。然而,所有当前工具都假定受体基因的重组是规则的,这在使用RACE方法制备的数据中并不总是有效的。与传统的多重PCR方法相比,RACE没有引物偏差,因此可以提供重组频率的准确估计。为了处理不规则的重组事件,需要一个新的计算程序。

结果

我们提出了TRIg来处理不规则的T细胞受体和免疫球蛋白序列。与所有当前程序不同,TRIg将序列与整个受体基因进行比对,而不仅仅是与编码区进行比对。这带来了新的计算挑战,例如由于对重复区域的多次匹配而导致的模糊比对。为了减少模糊性,TRIg应用了一种启发式策略并结合基因注释来识别真实的比对。在我们自己的和公开的RACE数据集上,TRIg正确地识别出了当前程序无法实现的不规则重组序列。TRIg对规则重组序列也同样适用。

结论

TRIg考虑了T细胞受体和免疫球蛋白基因的不规则重组,因此适用于分析RACE数据。这样的分析将提供重组事件的准确估计,这将直接有利于各种免疫研究。此外,TRIg适用于研究免疫疾病中的异常重组。TRIg可在https://github.com/TLlab/trig上免费获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b634/5080739/2205d6910d00/12859_2016_1304_Fig1_HTML.jpg

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