DATA MANAGEMENT AND RESOURCE REPOSITORY FOR THE exRNA ATLAS PHASE II

exRNA ATLAS II 期的数据管理和资源库

• 批准号: 10466423
• 负责人: DAVID J. GALAS
• 金额: $ 99.2万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10466423
• 关键词: Achievement; Address; Atlases; Awareness; Biogenesis; Biology; Blood; Blood specimen; Cells; Clinic; Collaborations; Communication; Communication Programs; Communities; Complement; Data; Data Analyses; Data Collection; Data Commons; Data Management Resources; Data Set; Diagnosis; Disease; Education and Outreach; Educational workshop; Effectiveness; Ensure; Evaluation; FAIR principles; Funding; Generations; Genetic; Genome; Geography; Goals; Health; Human; Knowledge; Life; Link; Malignant Neoplasms; Metadata; Methodology; Monitor; Network-based; Persons; Pilot Projects; Process; Protocols documentation; Quality Control; RNA; Reporting; Research; Research Personnel; Resources; Role; Services; Structure; Technology; United States National Institutes of Health; Update; Work; base; blood disorder diagnosis; clinically relevant; computerized data processing; data archive; data integration; data management; data quality; data repository; data reuse; data sharing; data submission; extracellular; federated computing; genome sequencing; improved; indexing; interest; meetings; metadata standards; new technology; novel; outreach; physical separation; precision medicine; repository; social media; success; text searching; tool; tool development; transcriptome sequencing; user-friendly; virtual

Summary Generation of high-volume sequence data has become a routine part of genome research, genetics and the nascent field of precision medicine. With increasing interest in exRNA biology, the ability of researchers to make discoveries and extract clinically relevant knowledge from these data requires access to new types of tools and analytical approaches. However, the specialized tools and the expertise required for to develop and implement effective solutions are beyond reach of most researchers. To address this problem, we have assembled a team comprised of accomplished investigators to further host exRNA Portal resources and tools for exRNA biology. Building on the success of ERCC Stage 1, we will explore novel mechanisms to collect, identify, and characterize exRNA biotypes. Our team, in collaboration with Stage 2 investigators, will explore the use of computational deconvolution to complement UG3/UH3 physical separation and isolation methodologies to better characterize exRNA carriers and their cargo. By further extending our metadata standards for Stage 1/Stage 2, exRNA Atlas will become a unique data repository utilizing the emerging federated Data Commons based upon the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) data. ERCC data will be linked with geographically distributed resources, thereby contributing to a continuous cycle of expanding knowledge and free flow of information. An agile DMRR governance structure will ensure responsiveness to the input from all ERCC Stage 2 stakeholders and to the needs of the community. Outreach and training will be targeted to various levels of expertise to ensure a lasting engagement with a diverse community of users and contributors. By accomplishing these goals, DMRR will empower researchers to make discoveries using exRNA datasets, and to extract from these datasets actionable information and apply new knowledge to improve human health.

摘要 产生大量的序列数据已经成为基因组研究、遗传学和 精准医学的新兴领域。随着人们对exRNA生物学的兴趣与日俱增，研究人员能够 从这些数据中发现并提取临床相关知识需要访问新类型的 工具和分析方法。然而，开发和开发所需的专门工具和专业知识 实施有效的解决方案是大多数研究人员无法企及的。为了解决这个问题，我们有 组建了一个由经验丰富的研究人员组成的团队，以进一步托管exRNA门户资源和工具 外源核糖核酸生物学。在ERCC第一阶段成功的基础上，我们将探索新的机制来收集、 鉴定和鉴定exRNA生物型。我们的团队将与第二阶段的调查人员合作，探索 利用计算反卷积补充UG3/UH3物理分离和分离 更好地确定exRNA载体及其货物特征的方法。通过进一步扩展我们的元数据 阶段1/阶段2的标准，exRNA图谱将成为一个独特的数据存储库，利用新兴的 基于可查找、可访问、可互操作和可重用原则的联合数据共享 (公平)数据。ERCC数据将与地理上分布的资源相联系，从而有助于 不断扩大知识和信息自由流动的循环。敏捷的DMRR治理结构 将确保对所有ERCC第二阶段利益攸关方的投入和 社区。外联和培训将针对不同水平的专业知识，以确保持久的 与不同的用户和贡献者社区互动。通过实现这些目标，DMRR将 使研究人员能够使用exRNA数据集进行发现，并从这些数据集中提取 提供可操作的信息和应用新知识，以改善人类健康。