Core D: Biostatistics and Computational Analysis Core

核心 D：生物统计学和计算分析核心

• 批准号: 10731280
• 负责人: Fei Zou
• 金额: $ 14.17万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10731280
• 关键词: Address; Adjuvant; Affinity; Archives; B-Lymphocytes; Bioinformatics; Biological; Biological Response Modifiers; Biometry; Biostatistical Methods; Biostatistics Core; Communities; Computer Analysis; Data; Data Analyses; Data Science; Data Science Core; Development; Dimensions; Early identification; Ensure; Experimental Designs; FAIR principles; Generations; Genes; Goals; HIV; HIV envelope protein; HIV vaccine; Human Resources; Immune; Infant; Knowledge; Lead; Mediation; Mediator; Metabolic Pathway; Methodology; Methods; Modality; Modeling; Molecular; Molecular Profiling; Multiomic Data; Output; Pathway Analysis; Pathway interactions; Plasma; Proteome; Quality Control; Reproducibility; Research; Research Personnel; Research Project Grants; Research Support; Role; Statistical Data Interpretation; Statistical Methods; Sum; Time; Vaccination; Vaccine Adjuvant; Vaccine Design; Vaccinee; Work; analytical tool; computerized tools; data integration; data management; design; differential expression; high dimensionality; host microbiota; immunoregulation; innovation; insight; interoperability; longitudinal analysis; microbial signature; microbiome; microbiome analysis; microbiome signature; multidimensional data; multimodality; multiple omics; neutralizing antibody; novel; programs; quality assurance; response; single-cell RNA sequencing; statistics; tool; transcriptomics; vaccine response; vaccine strategy; verification and validation

ABSTRACT - Biostatistics and Computational Analysis Core (BCAC) The Biostatistics and Computational Analysis Core (BCAC) is a critical supporting core for the proposed project. The BCAC will work closely with all project investigators to meet their bioinformatic, statistical, and data science needs. By making data Findable, Accessible, Interoperable and Reusable (FAIR), the BCAC will maximize the impact and optimize the path to identifying high impact insights on the initial steps of the induction of bNAb precursors, and help to address the knowledge gap whether vaccine adjuvant-induced innate responses impact bNAb precursor development and affinity maturation which are expected to inform the development of new targeted immunomodulatory approaches for HIV vaccine design. The program will generate a suite of longitudinal data with a broad data types, including transcriptomics, single cell (sc) RNA-sequencing, immunome analysis by CyTOF, proteome analysis of soluble immune mediators in plasma, and bacterial presence/abundance in the gut. Such rich data provides unprecedented opportunities to gain insights into innate responses, but also creates analytically challenges to dissect underlying mechanisms of bNAb precursor development, which are intricate with many factors interweaving with each other and jointly impacting bNAb precursor development. The BCAC investigators are recognized experts in biostatistics, data integration across multi-omics, and HIV research, which allows them to address the challenges by providing state-of-the art expertise in a wide range of data science fields. The core will ensure effective data management and data integration. More importantly, the core will develop a suite of innovative approaches to integrate longitudinal immune, microbiome, and molecular signatures to predict the development of bNAb precursors. The sum of the combined results of Projects and Cores provided by the BACA in this Program will be greater than data output that could be achieved by each Program component alone. By integrating the data obtained in Project 1 with microbial signatures identified in Project 2, we expect to make more precise prediction on bNAb precursor development, as defined by the B Cell Core.

摘要 - 生物统计学和计算分析核心 (BCAC) 生物统计和计算分析核心（BCAC）是关键的支持核心 拟议的项目。 BCAC 将与所有项目调查人员密切合作，以满足他们的要求 生物信息、统计和数据科学需求。通过使数据可查找、可访问， 可互操作和可重用 (FAIR)，BCAC 将最大限度地发挥影响并优化实现目标的路径 确定 bNAb 前体诱导初始步骤的高影响力见解，并帮助 填补疫苗佐剂诱导的先天反应是否影响 bNAb 的知识空白 前体开发和亲和力成熟预计将为 用于艾滋病毒疫苗设计的新的靶向免疫调节方法。 该程序将生成一套具有广泛数据类型的纵向数据，包括 转录组学、单细胞 (sc) RNA 测序、CyTOF 免疫组分析、蛋白质组 分析血浆中的可溶性免疫介质以及肠道中细菌的存在/丰度。 如此丰富的数据提供了前所未有的机会来深入了解先天反应，但是 还为剖析 bNAb 前体的潜在机制带来了分析挑战 发展是一个错综复杂的过程，是多种因素相互交织、共同作用的结果 影响 bNAb 前体的开发。 BCAC 调查员是公认的专家 生物统计学、跨多组学的数据集成和艾滋病毒研究，这使他们能够解决 通过在广泛的数据科学领域提供最先进的专业知识来应对挑战。这 core将确保有效的数据管理和数据集成。更重要的是，核心将 开发一套创新方法来整合纵向免疫、微生物组和 预测 bNAb 前体发育的分子特征。合并后的总和 BACA 在本计划中提供的项目和核心的结果将大于数据 每个计划组成部分单独可实现的产出。通过整合数据 在项目 1 中获得的微生物特征在项目 2 中确定，我们希望能够制造更多 根据 B Cell Core 的定义，精确预测 bNAb 前体发育。