i-AKC: Integrated AIRR Knowledge Commons

i-AKC：综合 AIRR 知识共享

• 批准号: 10712558
• 负责人: LINDSAY G. COWELL
• 金额: $ 99.93万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10712558
• 关键词: Acceleration; Adaptive Immune System; Address; Adult; Age; Algorithms; Alleles; Antibodies; Antibody Formation; Antigens; Archives; Artificial Intelligence; Autoimmune; Autoimmune Diseases; Automation; Awareness; B-Cell Antigen Receptor; B-Lymphocytes; Base Sequence; Binding; Bioinformatics; Biological Assay; Biomedical Research; Cell physiology; Clinical; Common Data Element; Communicable Diseases; Communities; Complex; Computational algorithm; Data; Data Commons; Data Element; Data Science; Data Set; Databases; Demography; Development; Diagnostic; Disease; Ensure; Epitopes; Ethics; FAIR principles; FDA approved; Frequencies; Genbank; Generations; Genes; Genetic; Genetic Recombination; Genotype; Goals; Graft Rejection; Haplotypes; Human; Immune; Immunologic Monitoring; Immunologic Receptors; Immunologist; Influenza; Kidney Transplantation; Knowledge; Lymphocyte; Malignant Neoplasms; Maps; Meta-Analysis; Metadata; Monitor; Ontology; Outcome; Patient Care; Patient Monitoring; Population; Preparation; Procedures; Process; Production; Protocols documentation; PubMed; Public Health; Publications; Readiness; Research; Research Personnel; Residual Neoplasm; Resources; Risk; Sampling; Shapes; Specificity; Stem cell transplant; System; T-Cell Receptor; T-Lymphocyte; Techniques; Therapeutic Monoclonal Antibodies; Training; Transplantation; Update; Vaccine Design; Validation; Visualization; Work; adaptive immune response; antigen binding; application programming interface; clinical application; community building; data acquisition; data complexity; data integration; data modeling; data repository; data sharing; design; genomic locus; graft vs host disease; graphical user interface; immune reconstitution; immunosenescence; improved; innovation; interoperability; knowledge commons; knowledge integration; knowledgebase; meetings; metadata standards; neutralizing antibody; novel; novel therapeutics; novel vaccines; pathogen; preservation; receptor; receptor binding; repository; response; sex; skills; therapy design; tool; usability

PROJECT SUMMARY/ABSTRACT: Immune receptors of the adaptive immune system (antibody/B-cell or T-cell receptors, or AIRR data) are designed to recognize and remove pathogens, and also recognize and preserve self-molecules. Therefore, these receptors have to be highly variable; it has been estimated that the number of possible human B-cell receptors approaches 1013. In addition to the diversity of these receptor sequences, the genes that underlie the production of these receptor molecules are highly diverse and complicated, and the data describing how these receptors bind to antigens (such as influenza) are also highly complex. Repositories to curate, analyze, and share these data are necessary to characterize B/T cell function in disease, as well as facilitate the discovery of new vaccine leads and therapeutic monoclonal antibodies to suppress autoimmune disease and cancer. Such data repositories are available, but they tend to focus on only one aspect of the data. Given that these repositories typically have been developed independently, the primary data and associated metadata (age, demography, sex, etc.) of the samples are stored in non-compatible forms, and in addition, the enormous size and complexity of the data make data sharing and integrated analyses extremely challenging. The goal of the proposed research is to establish the integrated AIRR Knowledge Commons (i-AKC), a novel knowledgebase that will allow seamless access, exploration, analysis, querying, and downloading of these various data types from a single point of entry. Our approach will be based on the very successful AIRR Community initiative, a group of immunologists, bioinformaticians, and experts in ethics and data sharing who have worked together since 2015 to develop protocols and standards for analysis and data sharing tools. One of the outcomes of the AIRR Community is the AIRR Data Commons, a set of data repositories that store the immense immune receptor repertoires that underlie the adaptive immune response. The proposed research takes the next important step of integrating (1) the AIRR Data Commons with repositories of (2) antigen/receptor binding and (3) germline immune genes. Steps to producing the i-AKC are (1) develop a common data model and establish common data elements relying on existing ontologies and community standards, (2) integrate the data using innovative algorithms and automation tools and enrich it with new knowledge derived from algorithms operating on the integrated data, and (3) community building. Using the i-AKC, researchers will, for example, be able to discover receptor sequences based on metadata or sequence searches, seamlessly examine information on the germline genes underlying these receptor sequences or examine what is known about the binding targets of these receptors. This novel and innovative knowledgebase will facilitate data and knowledge exploration that would be prohibitively difficult using sets of “siloed” repositories and will greatly accelerate biomedical research in autoimmune disease, infectious disease, transplantation, and cancer and directly improve patient care.

项目摘要/摘要： 适应性免疫系统的免疫受体(抗体/B细胞或T细胞受体，或AIRR数据)是 旨在识别和清除病原体，也识别和保护自身分子。因此，这些 受体必须高度可变；据估计，可能的人类B细胞受体的数量 接近1013。除了这些受体序列的多样性外，构成产物的基因 这些受体分子是高度多样化和复杂的，描述这些受体如何 与抗原(如流感)的结合也非常复杂。用于管理、分析和共享这些内容的存储库 数据对于确定B/T细胞在疾病中的功能以及促进新疫苗的发现是必要的 用于抑制自身免疫性疾病和癌症的先导和治疗性单抗。这样的数据 存储库是可用的，但它们往往只关注数据的一个方面。鉴于这些储存库 通常是独立开发的，主要数据和关联元数据(年龄、人口统计、 性爱等)的样本以不兼容的形式存储，此外，巨大的大小和复杂性 数据的多样性使得数据共享和综合分析极具挑战性。拟议研究的目标是 是建立综合AIRR知识共享(I-AKC)，这是一个新的知识库，将允许 从单个数据库中无缝访问、探索、分析、查询和下载各种数据类型 入口点。我们的方法将基于非常成功的AIRR社区倡议，这是一组 自2015年以来一直在一起工作的免疫学家、生物信息学家以及伦理和数据共享专家 为分析和数据共享工具制定协议和标准。AIRR的一个结果是 社区是AIRR数据共享，这是一组存储巨大免疫受体的数据存储库 作为适应性免疫反应基础的曲目。拟议中的研究采取了下一个重要步骤 集成(1)AIRR数据共享与(2)抗原/受体结合和(3)种系的存储库 免疫基因。生成I-AKC的步骤是(1)开发公共数据模型并建立公共数据 依赖于现有本体和社区标准的元素，(2)使用创新的 算法和自动化工具，并用来自于在 整合数据；(3)社区建设。例如，使用i-AKC，研究人员将能够发现 基于元数据或序列搜索的受体序列，无缝检查生殖系上的信息 这些受体序列背后的基因，或者检查关于这些受体结合靶标的已知情况 感受器。这个新颖和创新的知识库将促进数据和知识探索，这将是 令人望而却步的是，使用多套“孤岛”储存库将极大地加速生物医学研究 自身免疫性疾病、传染病、移植和癌症，并直接改善患者护理。