Data Repository and Curation

数据存储和管理

• 批准号: 8667882
• 负责人: Chris Cotsapas
• 金额: $ 9.57万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8667882
• 关键词: Cells; Collaborations; Computing Methodologies; Data; Data Set; Databases; Elements; Housing; Immune; Immune response; Immunology; Institutes; Methods; Modeling; Population; Preparation; Quality Control; Research Infrastructure; Research Personnel; System; Vaccinated; Vaccination; Writing; base; member; novel; programs; response; sample collection

This U19 project brings together a large number of investigators of varied expertise generating different forms of data to characterize immune responses to vaccinated and infected subjects in several sample collections. The resulting multidimensional dataset will be an extremely rich substrate for systematic analyses that not only answer specific questions but also investigate the possibility of coordinated responses to vaccination across multiple platforms. Leveraging existing database infrastructure at the Broad Institute, we will build a database for this U19 project by adding functionality that accommodates the unique features of different immunophenotypic data. In particular, we will implement an automated pipeline for cytometric data preparation, quality control, and analysis based on our FLAME program. This novel method models multidimensional data and extracts parameters such as geometric descriptions of cell population clusters and the orientation of these clusters in multidimensional space. This method illustrates the cutting-edge computational methods that we will deploy in this project. Additional methods will be written and implemented as needed in close collaboration with the Analysis Team, whose members are also physically located at the Broad Institute.

该U19项目汇集了大量具有不同专业知识的研究人员，产生不同形式的数据，以表征对若干样本收集中接种疫苗和受感染受试者的免疫反应。由此产生的多维数据集将成为系统分析的极其丰富的基础，不仅可以回答具体问题，还可以调查跨多个平台协调应对疫苗接种的可能性。利用Broad研究所现有的数据库基础设施，我们将通过添加功能来适应不同免疫表型数据的独特特征，为这个U19项目建立一个数据库。特别是，我们将基于FLAME程序实现细胞数据准备、质量控制和分析的自动化流水线。该方法对多维数据进行建模，提取细胞群簇的几何描述及其在多维空间中的方向等参数。这个方法说明了我们将在这个项目中部署的尖端计算方法。额外的方法将根据需要与分析小组密切合作编写和实施，分析小组的成员也在Broad研究所。