Developing robust and scalable genomics tools and databases to analyze immune receptor repertoires across diverse populations

摘要 高通量测序技术的最新进展使得能够经济有效地表征 并为研究适应性免疫受体谱(AIRR)提供了新的机会 人口规模。特别是，AIRR分析提供了对免疫系统复杂性的重要洞察 涉及多种人类疾病，包括传染病、癌症、自身免疫疾病和 神经退行性疾病。常用的基于化验的方法(即AIRR-SEQ)提供了详细的视图 通过利用从变量中扩增的DNA或RNA的深度测序来实现适应性免疫系统的 T和B细胞受体(TCR和BCR)基因座的区域。然而，调查的样本数量有限， AIRR-Seq方法限制了跨种族检测新的人群特异性V(D)J基因等位基因的能力 多样化和混杂的种群。非定向下一代测序(NGS)(例如WGS)有望填补 通过提供跨越各种祖先组的数十万个NGS数据集，消除现有的数据差距。 然而，还没有开发可靠和可扩展的生物信息学算法来利用非靶向的NGS 组装新的群体特异性等位基因的技术，这些等位基因将支持效应大小的异质性 祖先。缺乏全面的针对人群的等位基因免疫基因组学参考数据库。 这一空白加剧了现有的健康差距，因为医学免疫基因组学的发现继续是 为欧洲血统的人提供特权和利益。目前最先进的数据库是建立在 以欧洲血统的个体为基础的遗传结构，因此不能捕获跨 不同的人群。适应性免疫受体谱系社区(AIRR-C)正在进行的倡议 改进参考数据库(例如OGRDB和VDJbase)中不同群体的表示方式 非欧洲血统的人，只有极少数的欧洲人 下降。我们建议利用数据科学的方法来研究人类适应性免疫的变异。 在真正的全球范围内，改善对免疫健康和疾病的研究，并减少健康 差距。在这项研究中，我们将开发强大且可扩展的生物信息学工具和数据库，以利用 覆盖不同祖先个体的最大数据集由50多万个NGS样本组成 跨越AIRR-Seq、RNA-Seq和WGS技术。我们将进行严格的基准测试 开发了基于模拟和真实数据的生物信息学方法，以论证使用 基于NGS的新V(D)J等位基因组装方法。可获得大量不同种族的 样本将使我们能够发现新的群体特有的V(D)J等位基因，这将丰富现有的 具有特定人群免疫等位基因的免疫基因组学数据库。以促进传播《 获得的结果，新的等位基因和组装的受体序列将作为一个易于使用的数据库共享 具有丰富的功能集。

Abstract The recent advances in high-throughput sequencing technologies enable cost-effective characterization of the immune system and provide novel opportunities to study adaptive immune receptor repertoire (AIRR) at the population scale. In particular, AIRR analysis provides essential insight into the complexity of the immune system across a large variety of human diseases, including infectious diseases, cancer, autoimmune conditions, and neurodegenerative diseases. A commonly used assay-based approach (i.e. AIRR-Seq) provides a detailed view of the adaptive immune system by leveraging the deep sequencing of amplified DNA or RNA from the variable region of the T and B cell receptors (TCR and BCR) loci. However, the limited number of samples probed by the AIRR-Seq approach restricts the ability to detect novel population-specific V(D)J gene alleles across ethnically diverse and admixed populations. Non-targeted next-generation sequencing (NGS) (e.g. WGS) promises to fill the existing data gap by providing hundreds of thousands of NGS datasets across various ancestry groups. However, reliable and scalable bioinformatics algorithms have yet to be developed to utilize non-targeted NGS technologies to assemble novel population-specific alleles that would support effect-size heterogeneity across ancestries. There's a lack of comprehensive population-specific allelic immunogenomics reference databases. This void exacerbates existing health disparities, as discoveries in medical immunogenomics continue to be a privilege and benefit for populations of European ancestry. The current state-of-the-art databases were built on the genetic architecture based on individuals of European ancestry and thus fail to capture allelic variation across diverse populations. Ongoing initiatives by the Adaptive Immune Receptor Repertoire Community (AIRR-C) to improve the representation of diverse populations in reference databases (e.g. OGRDB and VDJbase) ignore individuals of non-European ancestry and only incorporate an extremely small number of individuals of European descent. We propose to utilize a data science approach for studying the variation of the human adaptive immune system at a truly global scale, improving studies of immunological health and diseases, and reducing health disparities. In this study, we will develop robust and scalable bioinformatics tools and databases able to leverage the largest datasets covering individuals of various ancestries composed of over half a million NGS samples spanning the AIRR-Seq, RNA-Seq, and WGS technologies. We will perform rigorous benchmarking of the developed bioinformatics methods based on both simulated and real data to demonstrate the feasibility of using NGS-based approaches to assemble novel V(D)J alleles. The availability of large and ethnically diverse sets of samples will allow us to discover novel population-specific V(D)J alleles, which will enrich existing immunogenomics databases with population-specific immune alleles. To promote the dissemination of the obtained results, the novel alleles and assembled receptor sequences will be shared as an easy-to-use database with a rich set of functionalities.