Core B: Data Management and Analysis

摘要 临床医学和公共卫生的一个基本挑战是了解是什么导致了 传染病严重程度的异质性和在人群中的传播，以最大限度地提高 预防、诊断和治疗每一种感染，保护人口。解决这一 这一挑战将需要整合宿主和病原体之间复杂的动态相互作用。数据 由Steven Kleinstein博士领导的管理和分析核心将支持数据库，生物信息学分析 以及项目1、2和3的复杂数据分析需求。此外，核心将与项目合作， 研究人员的实验设计和报告，并为项目人员提供培训环境 关于软件工具和方法的原则以及结果的解释。核心还将执行 代谢组学分析实验。具体而言，数据管理和分析核心将： (1)为临床和研究数据提供数据管理系统。 (2)通过提交到NIAID ImmPort储存库，HIPC ImmuneSpace，支持数据共享 数据库和其他相关公共存储库（例如，SRA，dbGAP，代谢组学（Metabolomics）。 (3)继续在重债穷国内努力制定和采用共同数据标准。 (4)提供生物统计分析支持和咨询。 (5)开展代谢组学分析实验和生物信息学分析，以识别免疫特征 与不同的临床反应有关。 (6)与Core C（单细胞表型分析）合作提交CyTOF，多光谱成像细胞仪 （MuSIC），将单细胞RNA-seq数据导入数据管理系统。 (7)与Core C（单细胞表型分析）合作，整合CyTOF分析结果， 多光谱成像细胞术（MuSIC）和单细胞RNA-seq数据进入系统级分析。 (8)跨项目集成数据，以检测常见和独特的感染特征

ABSTRACT A fundamental challenge in clinical medicine and public health is to understand what accounts for the heterogeneity in infectious disease severity and spread through populations in order to maximize the prevention, diagnosis and treatment of each individual infection and protection of populations. Addressing this challenge will require integration of the complex dynamic interactions among hosts and pathogens. The Data Management and Analysis Core led by Dr. Steven Kleinstein will support the database, bioinformatics analysis and complex data analysis needs of Projects 1, 2 and 3. In addition, the core will collaborate with project investigators on experimental design and reporting, and provide a training environment for project personnel on software tools and principles of methods and interpretation of results. The core will also carry out metabolomics profiling experiments. Specifically, the Data Management and Analysis Core will: (1) Provide data management systems for clinical and research data. (2) Support sharing of data by submission to the NIAID ImmPort repository, the HIPC ImmuneSpace database and other relevant public repositories (e.g., SRA, dbGAP, Metabolomics Workbench). (3) Continue our efforts within HIPC towards the development and adoption of common data standards. (4) Provide biostatistical analysis support and consultation. (5) Carry out metabolomics profiling experiments and bioinformatics analysis to identify immune signatures associated with differential clinical responses. (6) Collaborate with Core C (Single Cell Phenotyping) to submit CyTOF, multispectral imaging cytometry (MuSIC), single-cell RNA-seq data into the data management system. (7) Collaborate with Core C (Single Cell Phenotyping) to integrate results from the analysis of CyTOF, multispectral imaging cytometry (MuSIC) and single-cell RNA-seq data into the systems-level analysis. (8) Integrate data across projects to detect common and unique signatures of infection