Data-Analysis-Core

数据分析核心

• 批准号: 10682553
• 负责人: Constantin F. Aliferis
• 金额: $ 32.06万
• 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/10682553
• 关键词: Address; Adipose tissue; Age; Atlases; Benchmarking; Bioinformatics; Biologic Characteristic; Biological; Biological Assay; Biological Markers; Cell model; Cells; Clinic; Collaborations; Common Data Element; Computer Models; Custom; Data; Data Analyses; Data Commons; Data Coordinating Center; Data Element; Data Set; Data Storage and Retrieval; Development; Disease; Electronic Health Record; Elements; Ensure; Faculty; Future; Generations; Genomics; Goals; Health; Health Services; High Performance Computing; Human; Image; Individual; Informatics; Ingestion; Link; Liver; Maps; Measurement; Measures; Metadata; Methods; Mining; Minnesota; Modeling; Muscle; Ontology; Ovarian Tissue; Pathway interactions; Patients; Policies; Preparation; Procedures; Process; Protocols documentation; Quality Control; Readiness; Recommendation; Reproducibility; Research; Resources; Retrieval; Sampling; Science; Secure; Security; Services; Software Tools; Specific qualifier value; Specimen; System; Systems Biology; Testing; Tissues; United States National Institutes of Health; United States National Library of Medicine; Universities; Validation; Visualization; Visualization software; Work; analytical tool; biomedical data science; causal model; cell motility; cell type; clinically actionable; computerized data processing; data ingestion; data integration; data management; data quality; data sharing; data sharing networks; data standards; data warehouse; design; ethical, legal, and social implication; health data; health information technology; healthy aging; human subject; human tissue; innovation; interoperability; metadata standards; multiple omics; novel marker; predictive modeling; senescence; skeletal tissue; spatiotemporal; technology development; tissue mapping; tool; usability

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.

项目概要 明尼苏达组织图谱中心 (MN TMC) 衰老细胞 (SnC) 的数据分析核心 (DAC) 由生物医学数据科学和生物信息学建模专家 Constantin Aliferis 共同指导， 长期成功领导大型信息学核心的记录；王金华，高级生物信息学家 专注于单细胞数据和综合基因组学；和数据管理专家史蒂夫约翰逊， 数据质量和信息学服务以及协作科学。 DAC 还包括因果和 预测建模（Kummerfeld 博士）、组学成像（Pengo 博士）、细胞动力学和细胞建模 运动（Odde 博士）、统计规划、质量控制措施和统计假设检验（Dr. Odde）。 DAC 的总体目标是成为构建 MN TMC 4D SnC 图集的最后一步 待输送的健康人体脂肪、肝脏、骨骼肌和卵巢组织（以及所有支持性组织） 数据）到 SenNet 联盟组织和数据协调中心（CODCC），以构建 人类 4D SnC Atlas。将对不同年龄段的健康人体组织进行批量和单一分析 MN TMC 生物分析核心 (BAC) 使用样本进行细胞表征和时空分析 由生物样本核心 (BSP) 提供。 DAC 将负责从 BSP 和 BAC 获取数据， 映射到可互操作和可搜索的本体、注释、管理和分析。它将构建数据存储， 搜索、检索、分析和可视化工具，并将人类标本与丰富的去识别健康信息联系起来 来自相应电子健康记录的元数据。 DAC 将与 SenNet 联盟合作 建立基准，为标准操作程序和标准制定做出贡献，最终 准备数据集并与 CODCC 共享，以实现健康老龄化的最终 4D 人类 SnC 图集。 DAC 将 利用尖端信息学、高性能计算、专家团队和先进的数据存储和 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， 确保可重复性和可用性的软件工具和分析方法。 DAC将部署本体并 高通量组学和数据采集、协调、协调等领域广泛接受的分析标准 传输、安全性和分析，这些都与创建 SnC 图集的任务密切相关。 DAC 还将 与其他 TMC 和 CODCC 密切合作，开发和实施定制的 SenNet 标准 根据联盟的需求进行微调；数据质量指标、本体和数据元素；整合 成像和组学数据；用于可视化、分割和注释的分析工具；标准操作程序；通用数据 要素（CDE）；以及网络的公共数据共享政策。 DAC最终将进行初步研究 与 Mayo Clinic（LeBrasseur 博士和 Mielke 博士）合作，将全面说明如何 所得数据可用于构建功能性 SnC 图谱并建立一组 SnC 生物标志物。