Core B: Data Management and Analysis

核心 B：数据管理和分析

• 批准号: 10317008
• 负责人: Steven H. Kleinstein
• 金额: $ 107.73万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-10 至 2021-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10317008
• 关键词: Address; Adoption; Age; Arboviruses; Bioinformatics; Biological; Biometry; Borrelia burgdorferi; Cells; Characteristics; Classification; Clinical; Clinical Medicine; Clinical Research; Collection; Communicable Diseases; Communities; Complex; Consultations; Data; Data Analyses; Data Management Resources; Data Set; Databases; Deposition; Development; Diagnosis; Disease; Ensure; Environment; Equation; Experimental Designs; Gene Expression; Gene Expression Profile; Goals; Heterogeneity; Human; Human Resources; Image Cytometry; Immune; Immune response; Immune system; Immunologics; Immunophenotyping; Individual; Infection; Influenza vaccination; Left; Lipids; Liquid Chromatography; Manuals; Manuscripts; Maps; Measurable; Measures; Metabolic; Methods; Modeling; Molecular Profiling; National Institute of Allergy and Infectious Disease; Non-linear Models; Outcome; Participant; Pathway Analysis; Pathway interactions; Pattern; Phenotype; Population; Preparation; Prevention; Process; Public Health; Regulation; Reporting; Research Personnel; Research Project Grants; Resources; Risk Factors; Sample Size; Sampling; Sequence Read Archive; Serum; Severity of illness; Software Tools; Statistical Data Interpretation; Structure; Supervision; System; Techniques; Testing; TimeLine; Training; Triglycerides; Vaccination; Vaccines; West Nile virus; Work; cohort; complex data; computerized data processing; data management; data modeling; data repository; data sharing; data standards; data submission; database of Genotypes and Phenotypes; experimental study; heterogenous data; human pathogen; large datasets; metabolomics; organic acid; outcome prediction; pathogen; power analysis; programs; public repository; repository; response; single-cell RNA sequencing; success; supervised learning; tandem mass spectrometry

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

摘要 临床医学和公共卫生的一个根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，以便最大限度地提高 预防、诊断和治疗每一个个体感染和保护人群。解决这个问题 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、3的复杂数据分析需求。此外，核心将与项目协作 研究人员进行实验设计和报告，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。核心还将开展 代谢组学图谱实验。具体而言，数据管理和分析核心将： (1)提供临床和研究数据管理系统。 (2)支持通过提交到NIAID ImmPort储存库--重债穷国免疫空间--共享数据 数据库和其他相关公共储存库(例如，SRA、DBGaP、代谢组学工作台)。 (3)继续在重债穷国内部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)开展代谢组学图谱实验和生物信息学分析，识别免疫特征 与不同的临床反应有关。 (6)与Core C(单细胞表型鉴定)合作提交CyTOF，多光谱成像细胞仪 (MUSIC)、单细胞RNA-seq数据输入数据管理系统。 (7)与Core C(单细胞表型鉴定)合作，整合细胞周期蛋白分析结果， 多光谱成像细胞仪(MUSIC)和单细胞RNA-SEQ数据进入系统水平进行分析。 (8)整合跨项目的数据，以检测感染的共同和独特特征