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.

项目总结/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 用来识别和清除病原体，也用来识别和保存自身分子。因此这些 受体必须是高度可变的;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， 这些受体分子是高度多样和复杂的，描述这些受体如何 与抗原（如流感）的结合也是高度复杂的。存储库来管理、分析和共享这些 数据对于描述疾病中B/T细胞的功能以及促进新疫苗的发现是必要的 导致和治疗性单克隆抗体抑制自身免疫性疾病和癌症。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。鉴于这些仓库 通常是独立开发的，原始数据和相关的元数据（年龄，人口统计学， 性别等）的样本以不兼容的形式存储，此外， 数据的复杂性使得数据共享和综合分析极具挑战性。拟议研究的目标 是建立集成的AIRR知识共享（i-AKC），这是一个新的知识库， 无缝访问、探索、分析、查询和下载这些不同的数据类型， 进入点。我们的方法将基于非常成功的AIRR社区倡议， 免疫学家，生物信息学家，以及自2015年以来一直合作的伦理和数据共享专家 制定分析和数据共享工具的协议和标准。AIRR的成果之一是 社区是AIRR数据共享区，这是一组存储巨大免疫受体的数据存储库 适应性免疫反应的基础。拟议中的研究迈出了下一个重要步骤 整合（1）AIRR数据共享与（2）抗原/受体结合和（3）生殖系 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 元素依赖于现有的本体和社区标准，（2）使用创新的 算法和自动化工具，并丰富它与新的知识，从算法上运行的 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， 这些受体序列的基础基因或检查关于这些受体序列的结合靶点的已知信息。 受体。这个新颖和创新的知识库将促进数据和知识探索， 这将大大加速生物医学研究， 自身免疫性疾病、传染病、移植和癌症，并直接改善患者护理。