Data-Analysis-Core

数据分析核心

• 批准号: 10385164
• 负责人: Constantin F. Aliferis
• 金额: $ 26.61万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10385164
• 关键词: Data; Analysis; Core

Project Summary The Data Analysis Core (DAC) of the Minnesota Tissue Mapping Center (MN TMC) of Senescent Cells (SnCs) is co-directed by Constantin Aliferis, an expert in biomedical data science and bioinformatics modeling with a long track record in successfully leading large-scale informatics cores; Jinhua Wang, a senior bioinformaticist specializing in single cell data and integrative genomics; and Steve Johnson, an expert in data management, data quality and informatics services, and collaborative science. The DAC also includes experts in causal and predictive modeling (Dr. Kummerfeld), omics imaging (Dr. Pengo), modeling of cell dynamics and cell movement (Dr. Odde), and statistical planning, quality control measures, and statistical hypothesis testing (Dr. Guan).The overall goal of the DAC is to be the final step in the construction of a MN TMC 4D SnC atlas for healthy human adipose, liver, skeletal muscle, and ovarian tissues to be delivered (along with all supporting data) to the SenNet Consortium Organization and Data Coordinating Center (CODCC) for the construction of a human 4D SnC Atlas. Healthy human tissues over a range of ages will be analyzed with both bulk and single cell characterization and spatio-temporal analysis by the MN TMC Biological Analysis Core (BAC) using samples provided by the Biospecimen Core (BSP). The DAC will be responsible for data ingestion from BSP and BAC, mapping to interoperable and searchable ontologies, annotation, curation, and analysis. It will build data storage, search, retrieval, analysis, and visualization tools and link human specimens to a rich set of de-identified health metadata from corresponding electronic health records. In collaboration with the SenNet consortium, DAC will establish benchmarks, contribute to standard operating procedures and standards development, and ultimately prepare and share datasets with the CODCC to enable a final 4D human SnC atlas with healthy aging. DAC will leverage cutting-edge informatics, high performance computing, expert faculty, and advanced data storage and management capabilities at the University of Minnesota (UMN). It will use existing data/metadata standards, software tools, and analysis methods that ensure reproducibility and usability. DAC will deploy ontology and analytic standards widely accepted in the fields of high throughput omics and data capture, harmonization, transfer, security, and analysis and that are germane to the task of creating an atlas of SnCs. The DAC will also work closely with the other TMCs and the CODCC to develop and implement customized SenNet-wide standards fine-tuned to the needs of the consortium; data quality metrics, ontologies, and data elements; integration of imaging and omics data; analytical tools for visualization, segmentation, and annotation; SOPs; Common Data Elements (CDEs); and the network's public data sharing policy. The DAC will finally conduct a preliminary study in collaboration with Mayo Clinic (Drs. LeBrasseur and Mielke) that will comprehensively illustrate how the resulting data can be utilized to build a functional SnC atlas and establish a set of SnC biomarkers.

项目摘要 明尼苏达州组织映射中心（MN TMC）的数据分析核心（DAC）（SNC） 由康斯坦丁·阿利弗利斯（Constantin Aliferis）共同指导，他是生物医学数据科学和生物信息学建模专家 成功领导大规模信息核心的长期记录；金华·王（Jinhua Wang），高级生物信息学家 专门研究单细胞数据和综合基因组学；数据管理专家史蒂夫·约翰逊（Steve Johnson） 数据质量和信息学服务以及协作科学。 DAC还包括因果专家和 预测建模（Kummerfeld博士），Omics成像（Pengo博士），细胞动力学和细胞的建模 运动（Odde博士）以及统计计划，质量控制措施和统计假设检验（博士 Guan）。DAC的总体目标是成为建造MN TMC 4D SNC地图集的最后一步 健康的人类脂肪，肝脏，骨骼肌和卵巢组织要递送（以及所有支持 数据）向Sennet联盟组织和数据协调中心（CODCC）进行建设 人类4D SNC地图集。整个年龄范围内的健康人体组织将通过批量和单一分析 通过MN TMC生物分析核心（BAC）使用样品通过MN TMC生物分析核心（BAC）进行细胞表征和时空分析 由Biospecimen Core（BSP）提供。 DAC将负责BSP和BAC的数据摄入数据 映射到可互操作和可搜索的本体，注释，策展和分析。它将建立数据存储， 搜索，检索，分析和可视化工具，并将人类标本连接到一套不含识别的健康 来自相应的电子健康记录的元数据。与Sennet财团合作，DAC将 建立基准，为标准操作程序和标准开发做出贡献，并最终 与CODCC准备并共享数据集，以使最终的4D人类SNC地图集具有健康的衰老。 DAC会 利用尖端信息学，高性能计算，专家教职员工和高级数据存储以及 明尼苏达大学（UMN）的管理能力。它将使用现有的数据/元数据标准， 软件工具以及确保可重复性和可用性的分析方法。 DAC将部署本本学和 在高吞吐量法和数据捕获，协调的领域中广泛接受的分析标准， 转移，安全性和分析以及创建SNC地图集的任务。 DAC也将 与其他TMC和CODCC紧密合作，以制定和实施定制的SENNET范围标准 对财团的需求进行了微调；数据质量指标，本体和数据元素；整合 成像和OMICS数据；可视化，分割和注释的分析工具； sops;常见数据 元素（CDE）;以及网络的公共数据共享策略。 DAC最终将进行初步研究 与Mayo Clinic（Lebrasseur和Mielke博士）合作，将全面说明如何 可以将结果数据用于构建功能性SNC地图集并建立一组SNC生物标志物。