Data Management and Analysis Core

数据管理与分析核心

• 批准号: 10584579
• 负责人: Purveshkumar Khatri
• 金额: $ 31.73万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-07 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10584579
• 关键词: ATAC-seq; Address; Adjuvant; Area; B-Lymphocytes; Bioinformatics; Biological; COVID-19 vaccine; Cells; Clinical; Cohort Analysis; Collaborations; Computational Biology; Data; Data Analyses; Data Security; Data Set; Data Storage and Retrieval; Databases; Development; Doctor of Philosophy; Funding; Gene Expression; Gene Expression Profile; Generations; Genetic Transcription; Goals; Health Insurance Portability and Accountability Act; Heterogeneity; Human; Immune; Immune response; Immune system; Immunity; Immunologist; Immunology; Individual; Infection; Innate Immune Response; Knowledge; Link; Masks; Measures; Mediating; Medicine; Messenger RNA; Methods; Modeling; Molecular Profiling; Multiomic Data; National Institute of Allergy and Infectious Disease; Natural Immunity; Pathway interactions; Peripheral Blood Mononuclear Cell; Plasma; Population; Recording of previous events; Research; Role; Sampling; Scientist; Serum; Services; Statistical Data Interpretation; System; T cell response; T-Lymphocyte; Techniques; Testing; Transcript; United States National Institutes of Health; Universities; Vaccination; Vaccinee; Vaccines; Virus Diseases; adaptive immune response; adaptive immunity; bioinformatics tool; biological systems; biomedical data science; cohort; complex data; computational suite; computer framework; computer science; computerized tools; cytokine; data de-identification; data integration; data management; data sharing; data submission; differential expression; experience; human data; improved; member; metabolic profile; metabolomics; microbiome; mid-career faculty; multimodality; multiple omics; neutralizing antibody; novel; public repository; repository; response; single-cell RNA sequencing; tool; transcription factor; transcriptome; vaccine response; vaccine trial

ABSTRACT – Data Management and Analysis Core. Identifying the biological features of the human immune response that correlate with and predict the development of an effective immune response to vaccination is an overarching goal of this U19 consortium. The Data Management and Analysis Core (DMAC) in this proposal has two roles to support this overarching goal. First, the core will provide reliable data management service for all the data generated by the U19 study. It will provide management of large amounts of de-identified data, along with timely data submission to NIH databases. Second, the DMAC will apply a suite of computational tools to analyze data from human samples of serum, plasma, or peripheral blood mononuclear cell (PBMC), and individual immune cells obtained before and after vaccination to create new knowledge about the biological basis for effective vaccine-mediated immunity. To achieve these goals, we have assembled a team of computational biologists and immunologists with deep expertise in the generation and analysis of highly complex datasets of transcript abundance and metabolic profiles, who will support Projects 1, 2, and 3 in the following aims: Aim 1. To provide data management service for the data generated by Stanford HIPC. The DMAC will provide efficient HIPAA-compliant data storage, backup, and transfer with adequate data security and the protection of subject identity. It will also facilitate data sharing between the Projects, and with public by submitting data to appropriate public repositories such as the NIH GEO and ImmPort. Aim 2: Provide bioinformatics support to Projects 1, 2 and 3 for analyzing systems immunology data generated in those projects. The DMAC will assist the Projects in this proposal with various statistical and bioinformatics analysis including differential expression, pathway, transcription factor, interaction network and other statistical analyses as needed. Aim 3: Integrative analysis of multiomics signatures of vaccine immunity from orthogonal data sets, and from public datasets of similar studies. Developing a holistic and system-level view of immune responses to vaccines requires (1) tools that can accurately capture interactions across the diverse components of the immune system as they coordinate during response to vaccination and (2) cohorts representative of the heterogeneity observed in the real world. We will satisfy both criteria in in this aim.

摘要 - 数据管理和分析核心。 确定与人类免疫激素的生物学特征，该特征与 对疫苗接种的有效免疫响应的发展是该U19联盟的总体目标。 该提案中的数据管理和分析核心（DMAC）有两个角色来支持这一总体 目标。首先，核心将为U19研究生成的所有数据提供可靠的数据管理服务。 它将提供大量去识别数据的管理，以及及时提交NIH的数据 数据库。其次，DMAC将应用一套计算工具来分析人类样本的数据 血清，血浆或外周血单核细胞（PBMC）以及在之前和 疫苗后，以创建有关有效疫苗介导的免疫力的生物基础的新知识。 为了实现这些目标，我们将一组计算生物学家和免疫学家组成 在高度复杂的成绩单丰度和代谢数据集的生成和分析方面的专业知识 个人资料，将在以下目的中支持项目1、2和3的个人资料： AIM 1。为Stanford HIPC生成的数据提供数据管理服务。 DMAC会 提供有效的符合HIPAA的数据存储，备份和传输，并提供足够的数据安全性和 保护主题身份。它还将促进项目之间的数据共享，并在公众之间通过 将数据提交给适当的公共存储库，例如NIH Geo和Immport。 目标2：为分析系统免疫学数据提供项目1、2和3的生物信息学支持 在这些项目中产生。 DMAC将通过各种统计和 生物信息学分析包括差异表达，途径，转录因子，相互作用网络和 根据需要进行其他统计分析。 AIM 3：从正交数据集的疫苗免疫史的多组学特征的综合分析， 以及类似研究的公共数据集。开发免疫的整体和系统级别的观点 对疫苗的反应需要（1）可以准确捕获潜水员互动的工具 免疫系统的组件在对疫苗接种响应期间的协调和（2）人群时进行协调 代表在现实世界中观察到的异质性。我们将在此目标中满足这两个标准。