Data Analysis Core

数据分析核心

• 批准号: 10675023
• 负责人: Jinhua Wang
• 金额: $ 50.11万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-02 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10675023
• 关键词: Adipose tissue; Age; Animals; Atlases; Benchmarking; Bioinformatics; Biologic Characteristic; Biological; Biological Assay; Biological Markers; Cells; Center Core Grants; Clinic; Collaborations; Common Data Element; Computer Models; Custom; Data; Data Analyses; Data Analytics; Data Commons; Data Coordinating Center; Data Element; Data Set; Data Storage and Retrieval; Database Management Systems; Development; Elements; Ensure; Equipment; Factor Analysis; Faculty; Generations; Genes; Genetic Markers; Genome; Genomics; Goals; Guidelines; Heterogeneity; High Performance Computing; Histology; Human; Human BioMolecular Atlas Program; Image; Informatics; Ingestion; Institution; Joints; Link; Liver; Lung; Maps; Measures; Metadata; Methods; Midwestern United States; Minnesota; Modality; Modeling; Molecular; Multiomic Data; Mus; Neural Network Simulation; Online Systems; Ontology; Pathway interactions; Pattern; Policies; Preparation; Procedures; Process; Productivity; Proteomics; Protocols documentation; Quality Control; Recommendation; Reproducibility; Research; Research Personnel; Resources; Retrieval; Sampling; Secure; Security; Skeletal Muscle; Software Tools; Stains; System; Systems Biology; Testing; Tissue Sample; Tissues; United States National Institutes of Health; Universities; Validation; Visualization software; Work; analysis pipeline; analytical tool; biomarker discovery; biomarker selection; causal model; cell type; cellular imaging; comparative genomics; computing resources; data ingestion; data management; data portal; data quality; data sharing; data sharing networks; data tools; data visualization; data warehouse; deep neural network; graphical user interface; high dimensionality; human data; human tissue; imaging Segmentation; innovation; interoperability; metabolomics; metadata standards; multimodal data; multimodality; multiple omics; novel; novel marker; predictive modeling; proteogenomics; quality assurance; senescence; synergism; tissue mapping; tool; tool development; transcriptome; transcriptome sequencing; transcriptomics; usability; user-friendly

PROJECT SUMMARY The Data Analysis Core (DAC) of the Midwest Murine Tissue Mapping Center (MM-TMC) of Senescent Cells (SnCs) will build upon extensive computational resources to meet all the Center’s informatics and data analytics needs. The DAC MPIs are Jinhua Wang, an expert in genome informatics and bioinformatics modeling with a long track record in successfully building and leading informatics cores for center grants at the University of Minnesota (UMN), and Alexander Misharin, a senior researcher specializing in single cell data and integrative genomics at Northwestern University. The DAC also includes experts in cross-species comparative genomics, transcriptomic analysis, gene pathway modeling, genetic biomarker selection, proteomics/metabolomics data analysis and tool development, deep neural network modeling of cellular imaging, and statistical planning, quality control measures, and statistical hypothesis testing. The overall goal of the DAC is to perform multi-scale and multi-modality analysis of the collected data (for SnC identification, novel SnC biomarker discovery, SnC spatial pattern discovery, and SnC cellular states dynamics modeling) and prepare it for the SenNet Consortium Organization and Data Coordinating Center (CODCC) for the construction of a murine SnC 4D Atlas. The DAC MPIs will work closely with Yale and UMN human TMC DAC centers to inform and help advance the ongoing analysis carried out in human tissues. Notably, DAC MPI Wang has had productive collaborations with Yale and UMN human DAC directors for more than a decade. Select murine tissues (liver, lung, skeletal muscle, and adipose) over a range of ages, strains, and perturbations will be analyzed with both bulk and single cell profiling and spatial analysis by the MM-TMC Biological Analysis Core (BAC). The DAC will be responsible for data ingestion from the BAC, mapping to interoperable and searchable ontologies, annotation, curation, and analysis. We will 1) build or use the best practice tools for data storage, search, retrieval, analysis, and multi-omics data joint embedding; 2) create a comprehensive murine SnC biomarker set, including both known and novel biomarkers; and 3) establish a cross-comparison procedure to bridge murine and human SnC analyses. In collaboration with the SenNet Consortium, the DAC will establish benchmarks, contribute to standard operating procedures and standards development, and prepare and share datasets with the CODCC to enable a murine SnC 4D atlas. The DAC will leverage cutting-edge informatics, high performance computing, expert faculty, and advanced data analytics, data storage and management capabilities at MM-TMC institutions. 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 including: 1) data quality metrics, ontologies, and data elements; 2) integration of imaging and omics data analytical tools for visualization, segmentation, and annotation; 3) SOPs; 4) Common Data Elements; and 5) a network public data sharing policy.

项目摘要 中西部小鼠衰老细胞组织图谱中心（MM-TMC）数据分析核心（DAC） （SnCs）将建立在广泛的计算资源，以满足中心的所有信息学和数据分析 需求DAC MPI是基因组信息学和生物信息学建模专家， 在成功地建立和领导信息学核心的中心赠款在大学的长期跟踪记录 明尼苏达大学（UMN）和亚历山大Misharin，一个专门从事单细胞数据和综合研究的高级研究员， 西北大学的基因组学。DAC还包括跨物种比较基因组学专家， 转录组学分析、基因通路建模、遗传生物标志物选择、蛋白质组学/代谢组学数据 分析和工具开发，细胞成像的深度神经网络建模，统计规划，质量 控制措施和统计假设检验。DAC的总体目标是执行多尺度和 对收集的数据进行多模态分析（用于SnC鉴定、新的SnC生物标志物发现、SnC空间分布、生物标志物识别和生物标志物识别）。 模式发现和SnC细胞状态动力学建模），并为SenNet Consortium做准备 组织和数据协调中心（CODCC），用于构建小鼠SnC 4D图谱。的DAC MPI将与耶鲁大学和UMN人类TMC DAC中心密切合作，为正在进行的 在人体组织中进行的分析。值得注意的是，DAC MPI Wang与耶鲁大学进行了富有成效的合作， UMN人类DAC董事十多年。选择小鼠组织（肝、肺、骨骼肌和 脂肪）在一系列年龄、品系和扰动下进行分析， 和MM-TMC生物分析核心（BAC）的空间分析。DAC将负责数据 从BAC摄取，映射到可互操作和可搜索的本体，注释，策展和分析。 我们将1）构建或使用数据存储、搜索、检索、分析和多组学数据的最佳实践工具 联合包埋; 2）创建全面鼠SnC生物标志物组，包括已知的和新的 生物标志物;和3）建立交叉比较程序以桥接鼠和人SnC分析。在 通过与SenNet联盟的合作，DAC将建立基准，促进标准操作， 程序和标准的发展，并准备和共享数据集与CODCC，使鼠 SnC 4D图谱。DAC将利用尖端的信息学，高性能计算，专家教师， MM-TMC机构的高级数据分析、数据存储和管理能力。DAC还将 与其他TMC和CODCC密切合作，开发和实施定制的SenNet范围标准 根据联盟的需求进行微调，包括：1）数据质量度量、本体和数据元素; 2） 用于可视化、分割和注释的成像和组学数据分析工具的集成; 3）SOP; 4)公共数据元素;以及5）网络公共数据共享策略。