标题： 使用重组的数据驱动模型预测TCR库共享的光谱 显示英文标题

标题： Predicting the spectrum of TCR repertoire sharing with a data-driven model of recombination

摘要： 尽管T细胞受体（TCR）库具有极高的多样性，但在大量个体小鼠和人类中发现了许多相同的TCR序列。 这些广泛共享的序列，通常被称为“公共”序列，被认为由于其潜在的免疫功能或通过V(D)J重组生成的容易性而被过度代表。 在这里，我们表明，即使对于大规模队列，个体之间TCR序列的共享程度也可以通过一个模型很好地预测，该模型考虑了生成过程中的已知定量统计偏差，以及一个简单的胸腺选择模型。 序列是否被多个个体共享被预测取决于查询的个体数量、采样深度以及序列本身，这与数据一致。 我们引入了在查询队列大小和采样库大小条件下的公共性程度。 基于这些观察，我们提出了一种公共/私有序列分类器，“PUBLIC”（Public Universal Binary Likelihood Inference Classifier），基于生成概率，即使在小队列规模下也能表现出色。 显示英文摘要

摘要： Despite the extreme diversity of T cell repertoires, many identical T-cell receptor (TCR) sequences are found in a large number of individual mice and humans. These widely-shared sequences, often referred to as `public', have been suggested to be over-represented due to their potential immune functionality or their ease of generation by V(D)J recombination. Here we show that even for large cohorts the observed degree of sharing of TCR sequences between individuals is well predicted by a model accounting for by the known quantitative statistical biases in the generation process, together with a simple model of thymic selection. Whether a sequence is shared by many individuals is predicted to depend on the number of queried individuals and the sampling depth, as well as on the sequence itself, in agreement with the data. We introduce the degree of publicness conditional on the queried cohort size and the size of the sampled repertoires. Based on these observations we propose a public/private sequence classifier, `PUBLIC' (Public Universal Binary Likelihood Inference Classifier), based on the generation probability, which performs very well even for small cohort sizes.