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

开发强大且可扩展的基因组学工具和数据库来分析不同人群的免疫受体库

• 批准号: 10656981
• 负责人: SERGHEI MANGUL
• 金额: $ 82.38万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-10 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10656981
• 关键词: Adaptive Immune System; Algorithms; Alleles; Autoimmune Diseases; B-Cell Antigen Receptor; B-Lymphocytes; Benchmarking; Bioinformatics; Biological Assay; Cataloging; Certification; Collection; Communicable Diseases; Communities; DNA amplification; Data; Data Science; Data Set; Databases; Ethnic Population; European; European ancestry; Genes; Genetic; Heterogeneity; High-Throughput Nucleotide Sequencing; Human; Immune; Immune System Diseases; Immune system; Immunogenetics; Immunogenomics; Immunologic Receptors; Individual; Information Dissemination; Information Systems; International; Malignant Neoplasms; Medical; Methods; Neurodegenerative Disorders; Population; Population Heterogeneity; RNA; Reduce health disparities; Sampling; T cell receptor repertoire sequencing; T-Lymphocyte; T-cell receptor repertoire; Technology; Variant; adaptive immune response; aggregation database; bioinformatics tool; cost effective; deep sequencing; ethnic diversity; genetic architecture; genomic tools; health disparity; human disease; immune health; improved; insight; next generation sequencing; novel; receptor; tool; transcriptome sequencing

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.

抽象的 高通量测序技术的最新进展使得能够具有成本效益的表征 免疫系统并提供新的机会来研究适应性免疫受体库（AIRR） 人口规模。特别是，AIRR分析提供了对免疫系统复杂性的基本见解 在各种各样的人类疾病中，包括传染病，癌症，自身免疫性疾病和 神经退行性疾病。一种常用的基于测定的方法（即AIRR-SEQ）提供了详细的视图 通过利用从变量中的放大DNA或RNA的深度测序来进行自适应免疫系统 T和B细胞受体（TCR和BCR）基因座的区域。但是，由 AIRR-SEQ方法限制了检测新型种群特异性V（D）J基因等位基因的能力 多样化和混杂的人群。非目标的下一代测序（NGS）（例如WGS）有望填补 现有的数据差距通过在各个祖先群体中提供数十万个NGS数据集。 但是，尚未开发出可靠且可扩展的生物信息学算法来利用非目标NGS 组装新型人群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。缺乏全面的人群特异性等位基因免疫基因组学参考数据库。 由于医学免疫学学的发现继续是一种，这种空白加剧了现有的健康差异 欧洲血统人群的特权和利益。当前最新数据库建立在 基于欧洲血统个体的遗传结构，因此无法捕获跨越等位基因的变化 多样化的人群。自适应免疫受体曲目社区（AIRR-C）的持续计划 改善参考数据库（例如OGRDB和VDJBASE）中不同人群的表示 非欧洲血统的个人，仅包含欧洲的少数人 下降。我们建议利用数据科学方法来研究人类适应性免疫的变化 在全球范围内的真正范围内的系统，改善对免疫健康和疾病的研究，并降低健康状况 差异。在这项研究中，我们将开发可实现的强大而可扩展的生物信息学工具和数据库 最大的数据集涵盖了由超过一百万个NGS样本组成的各种祖先的个人 跨越Airr-Seq，RNA-Seq和WGS技术。我们将执行严格的基准测试 基于模拟和实际数据开发的生物信息学方法，以证明使用的可行性 基于NGS的方法来组装新颖的V（D）J等位基因。大型和种族多元化集的可用性 样本将使我们能够发现新颖的人群特异性V（d）J等位基因，这将丰富现有 具有人群特异性免疫等位基因的免疫基因组学数据库。促进传播 获得的结果，新颖的等位基因和组装的受体序列将作为易于使用的数据库共享 具有丰富的功能。