Data Management and Bioinformatics

项目摘要/摘要-数据管理和生物信息学核心 特应性生物标记物早期(Babe)U19提案寻求比较免疫系统 在生命的第一年，高风险人群与低风险人群相比，过敏疾病的风险较高，重点是 粘膜小室、过敏原暴露部位和微生物组的免疫。尤其是， 该计划比较了城市罗切斯特婴儿；那些患有特应性疾病的婴儿和受保护的罗切斯特婴儿 从特应性疾病和旧秩序门诺派(OOM)，一个实行传统的单一家庭农业的人口 AD和FA发生率低，作为传统保护性免疫的外部对照 发育，在出生队列中。Babe由三个单独的项目和两个核心组成，Cohort Admin和 生物信息库核心和数据管理和生物信息学(DMB)核心。贝贝将收集大规模的高 来自我们之前招募的队列和第一年内多个时间点的新队列的吞吐量数据 以及临床终点。高通量检测将在脐带血和婴儿粪便上进行， 血液、婴儿皮肤拭子和胶带以及婴儿口腔拭子。收集的数据包括代谢物， 蛋白质组学、转录组学、ATACseq、免疫表型、细胞因子反应和微生物组(16S和 元基因组学)。由Juilee Thakar博士领导的DMB将支持数据管理、生物信息学分析 项目1、2和3以及队列管理和生物库核心的复杂数据分析需求。此外, 核心将在实验设计和报告方面与项目调查人员合作，并提供培训 为项目人员提供关于软件工具、方法原理和结果解释的环境。通过 使数据可查找、可访问、可互操作和可重复使用(公平)，DMB将最大限度地发挥影响和 优化识别高影响力洞察力的途径。具体来说，DMB将：(1)提供数据收集、格式化 和存储基础设施，以促进参与的学术中心之间的数据分析和分发。(2) 支持通过提交到ImmPort储存库和其他相关公共储存库来共享数据(例如， SRA、DBGaP、代谢学工作台)。(3)为实验设计提供生物信息和统计支持 和数据分析。(4)支持各种数据类型之间的跨项目数据集成。(五)发展 早期特应性疾病综合评分(ISEAD)。(6)发展监督和半监督预测 特应性和食物过敏结果的模型。(7)建立婴幼儿免疫机制动力学模型 发展和皮肤健康。因此，DMB将在确保Babe的成功方面发挥关键的支持作用。

PROJECT SUMMARY/ABSTRACT – DATA MANAGEMENT & BIOINFORMATICS CORE The Biomarkers of Atopy Beginning Early (“BABE”) U19 proposal seeks to compare how the immune system develops in the first year of life in populations at high risk vs. low risk for allergic disease with an emphasis on immunity at mucosal compartment, the site of exposure to allergens and the microbiome. Particularly, the program compares urban Rochester infants; those who develop atopic diseases with Rochester infants protected from atopic diseases and Old Order Mennonites (OOM), a population practicing traditional, single-family farming with a low rate of AD and FA serve as an external comparison group for traditional, protective immune development, in a birth cohort. The BABE consists of three individual projects and two cores, Cohort Admin and Biorepository core and Data Management and Bioinformatics (DMB) core. BABE will collect large-scale high- throughput data from our previously recruited cohort and a new cohort at multiple time-points within the first year of life along with clinical endpoints. The high-throughput assays will be performed on cord blood, and infant stool, blood, infant skin swabs and tape strips and infant buccal swabs. The data collected includes metabolites, proteomics, transcriptomics, ATACseq, immune phenotyping, cytokine response, and microbiome (16S and metagenomics). The DMB led by Dr. Juilee Thakar will support the data management, bioinformatics analysis and complex data analysis needs of Projects 1, 2 and 3, and Cohort Admin and Biorepository core. 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. By making data Findable, Accessible, Interoperable and Reusable (FAIR), the DMB will maximize the impact and optimize the path to identifying high impact insights. Specifically, DMB will: (1) Provide data collection, formatting and storage infrastructure to facilitate data analysis and distribution across participating academic centers. (2) Support sharing of data by submission to the ImmPort repository and other relevant public repositories (e.g., SRA, dbGAP, Metabolomics Workbench). (3) Provide bioinformatic and statistical support in experimental design and data analysis. (4) Provide support for data integration across projects between each data type. (5) Develop Integrated Score for Early Atopic Diseases (ISEAD). (6) Develop supervised and semi-supervised predictive models for atopic and food allergy outcome. (7) Develop mechanistic dynamic models of infant immune development and skin health. Thus, DMB will play a critical support role in ensuring success of BABE.