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： 自适应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 旨在识别和去除病原体，并识别和保存自分子。因此，这些 受体必须是高度变化的；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外，生产的基因 这些受体分子中有高度多样和复杂，数据描述了这些受体如何 与抗原（例如影响力）结合也很复杂。存储库来策划，分析和分享这些 对于疾病中的B/T细胞功能的表征是必要的，并有助于发现新的疫苗 铅和热单克隆抗体抑制自身免疫性疾病和癌症。这样的数据 存储库可用，但它们倾向于仅关注数据的一个方面。鉴于这些存储库 通常是独立开发的，主要数据和相关的元数据（年龄，删除术， 性别等样本以非兼容的形式存储，此外，巨大的尺寸和复杂性 数据中的数据共享和集成分析极为挑战。拟议研究的目标 是建立综合AIRR知识共享（I-AKC），这是一个新颖的知识基础，将允许 从单个数据类型的这些各种数据类型的无缝访问，探索，分析，查询和下载 进入点。我们的方法将基于非常成功的AIRR社区倡议，这是一群 自2015年以来共同努力的伦理和数据共享专家免疫学家，生物信息学家和专家 为分析和数据共享工具开发协议和标准。 AIRR的结果之一 社区是AIRR数据共享，这是一组数据存储库，它们存储了巨大的免疫接收器 曲目是自适应免疫反应的基础。拟议的研究采取了下一个重要步骤 （1）与（2）抗原/受体结合和（3）种系的存储库集成（1） 免疫基因。生产I-AKC的步骤是（1）开发一个通用数据模型并建立常见数据 依靠现有本体和社区标准的要素，（2）使用创新的数据集成了数据 算法和自动化工具，并通过从运营的算法中得出的新知识来丰富它 集成数据，以及（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，无缝检查种系的信息 这些受体序列或检查有关这些受体序列的基因 接收者。这个小说和创新的知识基础将促进数据和知识探索，这将是 使用一组“孤立的”存储库非常困难，并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。