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: 10471812
• 负责人: DAVID J. GALAS
• 金额: $ 50万
• 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 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10471812
• 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.

总结 高容量序列数据的产生已经成为基因组研究、遗传学和基因组学的常规部分。 精准医疗的新兴领域随着人们对exRNA生物学的兴趣越来越大，研究人员能够 从这些数据中发现和提取临床相关知识需要获得新型的 工具和分析方法。然而，开发和使用所需的专门工具和专业知识， 实施有效解决方案超出了大多数研究人员的能力范围。为了解决这个问题，我们 组建了一个由资深研究人员组成的团队，以进一步托管exRNA门户资源和工具 exRNA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， 鉴定和表征exRNA生物型。我们的团队将与第二阶段的研究人员合作， 使用计算反褶积来补充UG 3/UH 3物理分离和隔离 更好地表征exRNA载体及其货物的方法。通过进一步扩展元数据 作为第1阶段/第2阶段的标准，exRNA Atlas将成为一个独特的数据库， 基于可查找、可扩展、可互操作和可重用原则的联邦数据共享 （FAIR）数据。ERCC数据将与地理上分布的资源相联系，从而有助于 知识的不断扩展和信息的自由流动。灵活的DMRR治理结构 将确保对所有ERCC第二阶段利益相关者的投入和 社区外联和培训将针对各级专门知识，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 可操作的信息和应用新知识来改善人类健康。

项目成果

The NanoFlow Repository.

• DOI: 10.1093/bioinformatics/btad368
• 发表时间: 2023-06-01
• 期刊: Bioinformatics (Oxford, England)

ApoE Modifier Alleles for Alzheimer's Disease Discovered by Information Theory Dependency Measures: MIST Software Package.

通过信息理论依赖度量发现的阿尔茨海默氏病的ApoE修饰等位基因：雾软件包。

• DOI: 10.1089/cmb.2022.0185
• 发表时间: 2023-03
• 期刊: Journal of computational biology : a journal of computational molecular cell biology

Open Problems in Extracellular RNA Data Analysis: Insights From an ERCC Online Workshop.

• DOI: 10.3389/fgene.2021.778416
• 发表时间: 2021
• 期刊: Frontiers in genetics
• 影响因子: 3.7
• 作者: Alexander RP;Kitchen RR;Tosar JP;Roth M;Mestdagh P;Max KEA;Rozowsky J;Kaczor-Urbanowicz KE;Chang J;Balaj L;Losic B;Van Nostrand EL;LaPlante E;Mateescu B;White BS;Yu R;Milosavljevic A;Stolovitzky G;Spengler RM
• 通讯作者: Spengler RM