B- and T-cell receptor repertoires of the adaptive immune system have become a key target for diagnostics and therapeutics research. Consequently, there is a rapidly growing number of bioinformatics tools for immune repertoire analysis. Benchmarking of such tools is crucial for ensuring reproducible and generalizable computational analyses. Currently, however, it remains challenging to create standardized ground truth immune receptor repertoires for immunoinformatics tool benchmarking. Therefore, we developed immuneSIM, an R package that allows the simulation of native-like and aberrant synthetic full-length variable region immune receptor sequences by tuning the following immune receptor features: (i) species and chain type (BCR, TCR, single and paired), (ii) germline gene usage, (iii) occurrence of insertions and deletions, (iv) clonal abundance, (v) somatic hypermutation and (vi) sequence motifs. Each simulated sequence is annotated by the complete set of simulation events that contributed to its in silico generation. immuneSIM permits the benchmarking of key computational tools for immune receptor analysis, such as germline gene annotation, diversity and overlap estimation, sequence similarity, network architecture, clustering analysis and machine learning methods for motif detection.

中文翻译：

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immuneSIM：B细胞和T细胞受体库的可调多特征模拟，用于免疫信息学基准测试。

适应性免疫系统的B细胞和T细胞受体库已成为诊断和治疗研究的关键目标。因此，用于免疫库分析的生物信息学工具数量迅速增长。此类工具的基准测试对于确保可重现和通用的计算分析至关重要。但是，当前，为免疫信息学工具基准测试创建标准化的地面真相免疫受体库仍然具有挑战性。因此，我们开发了immuneSIM，这是一个R包，它可以通过调节以下免疫受体特征来模拟天然的和异常的合成全长可变区免疫受体序列：（i）种类和链型（BCR，TCR，单链和单链）配对），（ii）种系基因用法，（iii）插入和缺失的出现，（iv）克隆丰度，（v）体细胞超突变和（vi）序列基序。每个模拟序列都由有助于其序列的完整模拟事件集来注释。在计算机时代。immuneSIM允许对用于免疫受体分析的关键计算工具进行基准测试，例如种系基因注释，多样性和重叠估计，序列相似性，网络架构，聚类分析和用于主题检测的机器学习方法。