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: 10910354
• 负责人: SERGHEI MANGUL
• 金额: $ 10.21万
• 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/10910354
• 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经济有效地表征 免疫系统，并为研究适应性免疫受体库（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见解 涵盖多种人类疾病，包括传染病、癌症、自身免疫性疾病和 神经退行性疾病。常用的基于分析的方法（即 AIRR-Seq）提供了详细的视图 通过利用变量中扩增的 DNA 或 RNA 的深度测序来评估适应性免疫系统 T 细胞和 B 细胞受体（TCR 和 BCR）基因座区域。但由于检测样本数量有限 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 多样化和混合的人群。非靶向下一代测序 (NGS)（例如 WGS）有望填补这一空白 通过提供不同血统群体的数十万个 NGS 数据集来弥补现有的数据差距。 然而，尚未开发出可靠且可扩展的生物信息学算法来利用非靶向 NGS 组装新的群体特异性等位基因的技术将支持跨群体的效应大小异质性 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。 这种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仍然是一个难题 欧洲血统人口的特权和利益。目前最先进的数据库是建立在 基于欧洲血统个体的遗传结构，因此无法捕获跨区域的等位基因变异 不同的人群。适应性免疫受体库社区 (AIRR-C) 正在采取的举措 提高参考数据库（例如 OGRDB 和 VDJbase）中不同人群的代表性 非欧洲血统的个体，只包含极少数欧洲人 血统。我们建议利用数据科学方法来研究人类适应性免疫的变异 真正的全球范围内的系统，改进免疫健康和疾病的研究，并减少健康 差异。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，能够利用 涵盖不同血统个体的最大数据集，由超过 50 万个 NGS 样本组成 涵盖 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，以证明使用的可行性 基于 NGS 的方法组装新型 V(D)J 等位基因。大量且种族多样化的群体的可用性 样本将使我们能够发现新的人群特异性 V(D)J 等位基因，这将丰富现有的 具有人群特异性免疫等位基因的免疫基因组数据库。为促进传播 获得的结果，新的等位基因和组装的受体序列将作为易于使用的数据库共享 具有丰富的功能。