Data Management and Analysis Core

数据管理与分析核心

• 批准号: 10731712
• 负责人: DENNIS J. HARTIGAN-O'CONNOR
• 金额: $ 23.54万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-22 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10731712
• 关键词: Address; Affinity; Aftercare; Area; Binding; Bioinformatics; Biological Process; Biometry; Cellular biology; Clinical; Clinical Data; Complex; Custom; Data; Data Analyses; Data Management Resources; Data Science; Data Set; Data Storage and Retrieval; Dimensions; Ensure; Flow Cytometry; Goals; HIV; Health; Human; Human Resources; Immune; Immune response; Infection Control; Infrastructure; Ingestion; Interruption; Joints; Kinetics; Mediating; Metabolic; Metadata; Modality; Modeling; Nature; Online Systems; Outcome; Participant; Phenotype; Process; Regimen; Research; Research Personnel; Resolution; Secure; Specificity; Staging; Standardization; Statistical Data Interpretation; Structure; System; T cell receptor repertoire sequencing; T cell response; T-Lymphocyte; Techniques; Testing; Vaccine Therapy; Vaccines; Viral; Viremia; Work; data harmonization; data ingestion; data library; data management; data modeling; data sharing; experience; improved; insight; member; metabolomics; multidimensional data; multimodal data; multimodality; multiple omics; nonhuman primate; pathogen; programs; public database; repository; shared database; single-cell RNA sequencing; therapeutic vaccine; therapy outcome; transcriptome sequencing; vaccine response

The Data Management and Analysis Core (DSC) will be responsible for performing and supporting biostatistical analyses and management of multidimensional and subject metadata sets generated in the OmiT- HIV program. A custom project will be created and maintained on the UCSF Data Library platform to store, secure, curate, and make accessible all raw and processed biological data and de-identified clinical data associated with the research program. We will interface with each project team for data ingestion and provide statistical and bioinformatic support to each. We will additionally perform multi-modal integrative analyses, including cross-species analysis, to answer questions within and across each project toward better understanding how vaccine regimens and metabolic conditions influence T-cell responses to and virologic outcomes of therapeutic vaccination. We will accomplish these goals with the following specific aims: Aim 1: Develop and maintain a centralized system of data management and sharing using the UCSF Data Library. - We will leverage the UCSF Data Library (UCDL) to facilitate the ingestion, curation, and secure storage of the diverse high-dimensional data types and metadata or de-identified clinical data generated across the projects. - We will apply shared data schemas per modality that store raw and processed data and annotations. - The UCDL supports web-based data sharing across all members of project and core teams as well as serves as a staging area for the transfer of all raw and processed data and metadata to ImmPort and other public databases. Aim 2: Provide statistical analysis and bioinformatic support for testing project hypotheses relevant to vaccine regimens, T-cell features, metabolomics, and rebound kinetics. - We will apply standardized and custom approaches to harmonize datasets for use by all teams. - We will work collaboratively with each project and core team to perform described analyses including dimensionality reduction, differential analysis, and association with outcomes. Aim 3: Integrate multi-modal data to develop a model of joint immune and metabolomic influence over vaccine responses and control over viremia in ATI. - We will leverage the complementary nature of these projects and state-of-the-art integrative analysis techniques, including cross-species analysis, to construct models relating multi-modal factors to longitudinal immune and virologic outcomes. - We will address the Bridging Hypotheses described in the Overall component.

数据管理和分析核心（DSC）将负责执行和支持 OmiT-中生成的多维和主题元数据集的生物统计分析和管理 艾滋病毒计划。将在 UCSF 数据库平台上创建和维护一个自定义项目来存储、 保护、管理并提供所有原始和处理后的生物数据以及去识别化的临床数据 与研究计划相关。我们将与每个项目团队进行接口以进行数据摄取并提供 为每个人提供统计和生物信息支持。我们还将进行多模态综合分析， 包括跨物种分析，回答每个项目内部和跨项目的问题，以更好地 了解疫苗方案和代谢条件如何影响 T 细胞反应和病毒学 治疗性疫苗接种的结果。 我们将通过以下具体目标来实现这些目标： 目标 1：使用 UCSF 开发和维护数据管理和共享的集中式系统 数据库。 - 我们将利用 UCSF 数据库 (UCDL) 来促进摄取、管理和安全存储 各种高维数据类型和元数据或去识别的临床数据生成 项目。 - 我们将根据存储原始和处理后的数据和注释的模式应用共享数据模式。 - UCDL 支持项目和核心团队的所有成员以及基于网络的数据共享 作为将所有原始和处理后的数据和元数据传输到 ImmPort 的暂存区 其他公共数据库。 目标 2：为测试相关项目假设提供统计分析和生物信息学支持 疫苗方案、T 细胞特征、代谢组学和反弹动力学。 - 我们将应用标准化和定制方法来协调数据集以供所有团队使用。 - 我们将与每个项目和核心团队合作进行所描述的分析，包括 降维、差异分析以及与结果的关联。 目标 3：整合多模态数据，开发联合免疫和代谢组影响的模型 ATI 中的疫苗反应和病毒血症控制。 - 我们将利用这些项目的互补性和最先进的综合分析 技术，包括跨物种分析，构建与多模态因素相关的模型 纵向免疫和病毒学结果。 - 我们将讨论总体部分中描述的桥接假设。