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） 生物统计学和计算分析核心（BCAC）是生物统计学和计算分析的关键支持核心。 拟议项目。BCAC将与所有项目调查人员密切合作， 生物信息学、统计学和数据科学的需求。通过使数据可查找，可解释， 互操作和可重用（FAIR），BCAC将最大限度地发挥影响，并优化路径， 确定对诱导bNAb前体的初始步骤具有高度影响的见解，并帮助 解决疫苗免疫诱导的先天性应答是否影响bNAb的知识缺口 前体发展和亲和力成熟，预计将告知发展 HIV疫苗设计的新靶向免疫调节方法。 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，免疫组分析，蛋白质组 血浆中可溶性免疫介质和肠道中细菌存在/丰度的分析。 如此丰富的数据为深入了解先天反应提供了前所未有的机会， 也为剖析bNAb前体的潜在机制带来了分析上的挑战 发展是多种因素相互交织、共同作用的复杂过程 影响bNAb前体的发展。BCAC调查人员是公认的专家， 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。的 核心小组将确保有效的数据管理和数据整合。更重要的是，核心将 开发一套创新方法，整合纵向免疫，微生物组， 分子特征来预测bNAb前体的发展。组合的总和 巴卡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以实现的产出。通过整合数据 在项目1中获得了项目2中鉴定的微生物特征，我们希望能够获得更多 精确预测bNA B前体发育，如B细胞核心所定义。