Data Analysis Core

数据分析核心

基本信息

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
项目类别：
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

项目摘要

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.

项目概要中西部衰老细胞鼠组织图谱中心 (MM-TMC) 的数据分析核心 (DAC) (SnCs) 将建立在广泛的计算资源之上，以满足该中心的所有信息学和数据分析需求 DAC MPI 是王金华，一位基因组信息学和生物信息学建模专家。在成功建立和领导大学中心资助的信息学核心方面有着长期的记录明尼苏达州（UMN）和亚历山大·米沙林（Alexander Misharin），一位专门从事单细胞数据和综合研究的高级研究员 DAC 还包括西北大学基因组学专家，转录组分析、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以及统计规划、质量 DAC 的总体目标是执行多尺度和统计假设检验。对收集的数据进行多模态分析（用于 SnC 识别、新型 SnC 生物标志物发现、SnC 空间模式发现和 SnC 细胞状态动力学建模）并为 SenNet 联盟做好准备组织和数据协调中心 (CODCC) 用于构建小鼠 SnC 4D Atlas DAC。 MPI 将与耶鲁大学和 UMN 人类 TMC DAC 中心密切合作，为正在进行的项目提供信息并帮助推进值得注意的是，DAC MPI Wang 与耶鲁大学和 UMN 人类 DAC 主管十多年。将通过批量和单细胞分析来分析不同年龄、应变和扰动的脂肪） MM-TMC 生物分析核心 (BAC) 负责数据分析和空间分析。从 BAC 摄取，映射到可互操作和可搜索的本体、注释、管理和分析。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嵌入；2) 创建全面的小鼠 SnC 生物标志物集，包括已知的和新颖的生物标志物；3) 建立交叉比较程序以连接小鼠和人类 SnC 分析。 DAC 将与 SenNet 联盟合作建立基准，为标准运营做出贡献程序和标准开发，并与 COCDCC 准备和共享数据集，以使小鼠能够 SnC 4D 图集将利用尖端信息学、高性能计算、专家团队和 MM-TMC 机构还拥有先进的数据分析、数据存储和管理功能。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，包括：1) 数据质量指标、本体和数据元素；2) 集成成像和组学数据分析工具，用于可视化、分割和注释 3) SOP； 4) 通用数据元素；以及 5) 网络公共数据共享政策。