Obradovic Aleksandar, Shen Yufeng, Sykes Megan, Fu Jianing
Columbia Center for Translational Immunology, Department of Medicine; Columbia University, New York, NY 10032, United States.
Department of Systems Biology; Columbia University, New York, NY 10032, United States.
Softw Impacts. 2021 Nov;10. doi: 10.1016/j.simpa.2021.100142. Epub 2021 Sep 23.
We have developed a suite of tools for integrated analysis of T-Cell-Receptor Sequencing data to define and track alloreactive T-cells in human transplant studies. This has enabled discovery of sequences and patterns of T-cell enrichment and deletion associated with clinical outcomes such as transplant rejection and tolerance. The codebase includes user-friendly default analyses with customizable parameters which greatly accelerate computational workflows and provide robust statistics comparing post-transplant specimens to pre-transplant baseline. It also includes helper functions for robust characterization of T-cell-repertoire diversity, sample-to-sample divergence, resolution of sample-of-origin ambiguity in pooled assays, and functions to output all sequences defined as alloreactive.
我们开发了一套用于T细胞受体测序数据综合分析的工具，以在人类移植研究中定义和追踪同种异体反应性T细胞。这使得能够发现与移植排斥和耐受等临床结果相关的T细胞富集和缺失的序列及模式。该代码库包括具有可定制参数的用户友好型默认分析，极大地加速了计算工作流程，并提供了将移植后标本与移植前基线进行比较的可靠统计数据。它还包括用于T细胞库多样性的稳健表征、样本间差异、混合检测中起源样本模糊性的解析以及输出所有定义为同种异体反应性序列的辅助函数。
