Data Management & Analysis Core: IDEAL shapes vaccine response, susceptibility to respiratory infectious disease and asthma

数据管理

• 批准号: 10589803
• 负责人: AL OZONOFF
• 金额: $ 21.78万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-10 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10589803
• 关键词: Architecture; Archives; Articulation; Asthma; Bioinformatics; Biological Assay; Biological Markers; Biometry; Child Health; Childhood; Clinical; Clinical Data; Collaborations; Communicable Diseases; Communities; Computer software; Data; Data Analyses; Data Analytics; Data Files; Data Set; Data Sources; Data Storage and Retrieval; Deposition; Development; Disease; Disparate; Environment; Experimental Designs; FAIR principles; Gene Expression; Goals; Health; Human Resources; Immune; Immune system; In Vitro; Infrastructure; Institution; Intervention; Knowledge; Life; Link; Metadata; Methods; National Institute of Allergy and Infectious Disease; Outcome; Pathway interactions; Personnel Staffing; Predisposition; Process; Productivity; Proteomics; Quality Control; Reproducibility; Reproduction; Research; Research Project Grants; Resources; Respiratory Tract Infections; Sampling; Secure; Security; Services; Shapes; Source; Statistical Data Interpretation; Systems Analysis; Systems Biology; United States National Institutes of Health; Vaccines; Work; archive data; biosignature; clinically relevant; cloud based; cloud storage; cohort; computerized tools; computing resources; data access; data acquisition; data archive; data infrastructure; data integration; data management; data sharing; data standards; demographics; design; digital; epigenomics; experimental analysis; heterogenous data; high dimensionality; improved; innovation; insight; metabolomics; microbiome; operation; predict clinical outcome; prognostic; public repository; quality assurance; relational database; repository; respiratory; response; sample collection; synergism; transcriptome sequencing; transcriptomics; user-friendly; vaccine response

PROJECT SUMMARY: IDEAL Data Management Core (DMC) The overarching goal of the proposed immune development in early life (IDEAL) project is to employ systems biology to characterize childhood immune trajectories in order to define endotypes of key clinical outcomes such as vaccine responsiveness, respiratory infection and asthma. In support of this effort, the Data Management Core (DMC) will establish and maintain a cloud-based discovery environment, consisting of data storage and computational tools to perform integrative systems analyses of clinical data, lab sample management, and high dimensional assay results. The DMC will facilitate collaborations between the IDEAL Projects and Cores including data sharing, submission to public repositories, and engagement with the external research community. The broad goals of the DMC are reliable data capture and retention, ongoing quality assurance (QA), and access/computational resources for integrative analyses. We articulate these goals through three Specific Aims, including establishment of digital infrastructure for data capture, management, and analysis; rigorous quality control (QC) and QA; and biostatistical and bioinformatics expertise for analysis. Our data management architecture is designed on the basis of improved institutional capabilities for reliable cloud-based clinical data management, experimental assay QC/QA, and analytic support. We have worked closely with the BCH Research Computing Department to implement a system for IDEAL collaborators that will combine security and reliability with accessibility and state-of-the-art computational tools and resources. Our approach offers many advantages over more traditional server-based architecture, not the least of which is a fruitful collaborative computing environment for integrative analyses and scientific discovery. Any scientific endeavor of this scope and scale requires robust data management plans, infrastructure, and operations. Indeed, there can be no practical integration of data without an environment to link these data across platforms. The DMC’s integrative function is necessary to leverage the distinct strengths of the IDEAL projects and cores into scientific synergies. We expect our integrated discovery environment will augment the scientific value and productivity of the overall IDEAL study. The DMC will enable us to discover endotype-specific molecules and pathways, that may serve as prognostic and/or actionable biomarkers to predict clinical outcomes and/or that represent targets for interventions that redirect IDEAL towards health.

项目摘要：理想数据管理核心 (DMC) 拟议的生命早期免疫发展（IDEAL）项目的总体目标是利用系统 生物学来表征儿童免疫轨迹，以确定关键临床结果的内型，例如 如疫苗反应、呼吸道感染和哮喘等。为了支持这一努力，数据管理。 Core（DMC）将建立和维护一个基于云的发现环境，包括数据存储和 用于对临床数据、实验室样本管理和高通量进行综合系统分析的计算工具 DMC 将促进 IDEAL 项目和 Cores 之间的合作。 包括数据共享、向公共存储库提交以及与外部研究界的合作。 DMC 的广泛目标是可靠的数据捕获和保留、持续的质量保证 (QA) 以及 我们通过三个具体目标阐明这些目标， 包括建立用于数据采集、管理和分析的数字基础设施； 控制（QC）和质量保证；以及用于分析的生物统计和生物信息学专业知识。 我们的数据管理架构是在改进机构能力的基础上设计的，以确保可靠 基于云的临床数据管理、实验分析 QC/QA 和分析支持。 与 BCH 研究计算部门密切合作，为 IDEAL 合作者实施一个系统，该系统将 将安全性和可靠性与可访问性和最先进的计算工具和资源结合起来。 与更传统的基于服务器的体系结构相比，该方法具有许多优点，其中最重要的是 用于综合分析和科学发现的富有成效的协作计算环境。 任何这种范围和规模的科学努力都需要强大的数据管理计划、基础设施和 事实上，如果没有链接这些数据的环境，就不可能进行实际的数据集成。 DMC 的综合功能对于利用 IDEAL 项目的独特优势是必要的。 我们期望我们的综合发现环境将增强科学协同作用。 整个 IDEAL 研究的价值和生产力将使我们能够发现内型特异性。 分子和途径，可以作为预后和/或可操作的生物标志物来预测临床结果 和/或代表将 IDEAL 转向健康的干预措施目标。