NoisET：噪声学习和T细胞受体扩增检测
NoisET: Noise learning and Expansion detection of T-cell receptors
摘要：
T细胞和B细胞受体的高通量测序使得在不同组织、急性和慢性疾病以及健康个体中跨时间追踪免疫库成为可能。然而，由于取样、文库制备和表达噪声，每个受体克隆型的读段计数的变异性混淆了库之间的定量比较。我们审查的方法占生物和实验噪声，并提出了一个易于使用的Python包NoisET，实现和推广以前开发的贝叶斯方法。它可以用来学习实验噪声模型，用于重复的库测序，并检测刺激后的响应克隆。我们在不同的库测序技术和数据集上测试该软件包。我们回顾了如何使用这种方法来确定疫苗接种和疾病数据中的响应克隆型。可用性：NoisET可以在www.example.com上免费使用源代码。
英文摘要：
High-throughput sequencing of T- and B-cell receptors makes it possible to track immune repertoires across time, in different tissues, in acute and chronic diseases and in healthy individuals. However quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. We review methods for accounting for both biological and experimental noise and present an easy-to-use python package NoisET that implements and generalizes a previously developed Bayesian method. It can be used to learn experimental noise models for repertoire sequencing from replicates, and to detect responding clones following a stimulus. We test the package on different repertoire sequencing technologies and datasets. We review how such approaches have been used to identify responding clonotypes in vaccination and disease data. Availability: NoisET is freely available to use with source code at github.com/statbiophys/NoisET.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
