Data Management and Bioinformatics

数据管理和生物信息学

基本信息

批准号：
10633367
负责人：
Juilee Thakar
金额：
$ 13.09万
依托单位：
UNIVERSITY OF ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-07 至 2028-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10633367
关键词：
ATAC-seq Affect Age Months Allergens Allergic Allergic Disease Allergic rhinitis Asthma Atopic Dermatitis Bioinformatics Biological Assay Biological Markers Birth Blood Child Chronic Clinical Collaborations Complex Data Data Analyses Data Collection Data Coordinating Center Data Storage and Retrieval Database Management Systems Databases Development Diet Dimensions Disease Eczema Ensure Environment Environmental Exposure Experimental Designs Exposure to FAIR principles Family Farm Feces Flow Cytometry Food Food Hypersensitivity Health Health Status Human Resources Hypersensitivity Immune Immune signaling Immune system Immunity Immunology Individual Infant Inflammation Informatics Infrastructure Learning Life Longitudinal cohort Measurement Medical Mennonite Metadata Metagenomics Methods Modeling Molecular Mucous Membrane Outcome Output Participant Pathway interactions Phenotype Play Population Prevalence Prevention strategy Primary Prevention Procedures Proteome Proteomics Quality Control Questionnaires Reporting Reproducibility Research Personnel Resource Sharing Risk Role Sampling Scheme Secure Site Skin Software Tools Standardization Structure Surveys Swab System Techniques Testing Time Training Umbilical Cord Blood Universities Unmarried person Visit Work atopy biobank biomarker identification classification algorithm clinical predictive model cohort comparison group complex data computerized data processing cytokine data acquisition data integration data integrity data management data sharing data standards data submission database of Genotypes and Phenotypes early childhood fecal microbiome gut microbiome high risk high throughput screening infant gut microbiome insight metabolomics microbiome multiple data types novel predictive modeling programs public repository random forest recruit repository response sample collection skin barrier skin microbiome statistical learning statistics success tool transcriptome transcriptomics

项目摘要

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.

项目摘要/摘要 - 数据管理和生物信息学核心特应早期开始的生物标志物（“宝贝”）U19提案试图比较免疫系统如何在高风险和过敏性疾病的低风险的人群中，在生命的第一年发展，重点粘膜室的免疫力，暴露于过敏原和微生物组的部位。特别是计划比较城市罗切斯特婴儿；那些患有罗切斯特婴儿受到保护的人患特应疾病的人从特应性疾病和旧秩序mennonites（OOM），这是一种传统的，单户农业的人口 AD和FA的速度较低，作为传统，受保护的免疫的外部比较组发展，在出生队列中。宝贝由三个单独的项目和两个核心组成生物验证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度 - 我们先前招募的队列中的吞吐量数据和第一年内以多个时间点的新队列生命以及临床终点。高通量测定法将在脐带血和婴儿凳子上进行血液，婴儿皮肤拭子，胶带条以及婴儿颊拭子。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16s 宏基因组学）。由Juilee Thakar博士领导的DMB将支持数据管理，生物信息学分析以及项目1、2和3的复杂数据分析需求，以及队列管理和生物座席核心。此外，核心将与项目调查人员合作进行实验设计和报告，并提供培训项目人员的环境软件工具和方法原理和结果解释。经过使数据可识别，可访问，可互操作和可重复使用（公平），DMB将最大程度地发挥影响力和优化识别高影响力见解的路径。具体而言，DMB将：（1）提供数据收集，格式化和存储基础架构，以促进参与学术中心的数据分析和分布。（2）通过提交给Immport存储库和其他相关公共存储库来支持数据共享（例如 SRA，DBGAP，代谢组学工作台）。（3）在实验设计中提供生物信息学和统计支持和数据分析。（4）为每种数据类型之间的项目之间的数据集成提供支持。（5）发展早期特应疾病的综合分数（ISEAD）。（6）开发和半监督预测特应性和食物过敏结果的模型。（7）开发婴儿免疫的机械动态模型发育和皮肤健康。那就是DMB将在确保宝贝成功方面发挥关键的支持作用。