Data Processing, Analysis and Modeling Unit

数据处理、分析和建模单元

• 批准号: 10904041
• 负责人: Li Ding
• 金额: $ 17.24万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-04 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10904041
• 关键词: 3-Dimensional; Atlases; Bioinformatics; Cataloging; Cells; Censuses; Classification; Clinical; Clonality; Communities; Copy Number Polymorphism; Data; Data Analyses; Database Management Systems; Databases; Digital Imaging and Communications in Medicine; Dimensions; FAIR principles; Foundations; Genes; Genomics; Goals; Human; Image; Immune; Individual; Infrastructure; Length; Link; Location; Magnetic Resonance Imaging; Malignant Neoplasms; Measures; Meta-Analysis; Methodology; Modeling; Monitor; Mutate; Mutation; Pathology; Pathway interactions; Patients; Phenotype; Picture Archiving and Communication System; Ploidies; Positron-Emission Tomography; Post-Translational Protein Processing; Process; Proteomics; Reporting; Research; Resolution; Sampling; Secure; Shapes; Site; Solid Neoplasm; Standardization; System; Text; Time; Universities; Washington; automated segmentation; bioinformatics tool; cancer type; cell type; cohesion; computerized data processing; data dissemination; data integration; data sharing; data streams; density; diverse data; driver mutation; ex vivo imaging; image processing; indexing; information organization; metabolome; metabolomics; multidimensional data; neoplastic cell; relational database; scaffold; single cell sequencing; single-cell RNA sequencing; statistics; tool; triple-negative invasive breast carcinoma; tumor; web portal

Project Summary/Abstract: Data Analysis Unit The over-arching goal of the Data Analysis Unit for the Washington University Human Tumor Atlas Research Center (WU-HTARC) is to provide bioinformatics tools and processing/analysis infrastructure for in-depth analyses of the data generated in the Characterization Unit. Most importantly, we will integrate data across both the methodological (omics/imaging/phenotypic analyses) and the dimensional (1D/2D/3D/time) spectrums into coherent and accessible tumor atlases for each of the three cancer types: GBM, PDAC, and BRCA/TNBC. At the basic level, each atlas will consist of first cataloging a variety of numerically-computed metrics for cell types, including fractions (1D), density, dispersion, and location measures for individual cell types and Euclidean measures of spatial interspersedness of different cell types, e.g. immune and tumor cells (2D and 3D), and how these metrics change with time. We will then correlate this information with both genomic analyses, such as mutation signatures, clonality, and significantly mutated genes/regions/pathways, proteomic and metabolomics analyses, and image-derived data. The core of the atlas will be a MySQL relational database that not only stores all collected data, but links them along these different dimensions. Users will interface with the atlas through a sophisticated viewer/query browser-based web portal that will support both traditional text-based queries, as well as spatial-based queries (shape, feature locations, etc.). The cohesion among the three atlases, in terms of the spectrum of data used and the approaches of their construction, will allow users to generate new types of hypotheses not now possible and to perform pan-cancer analyses to reveal commonalities and differences in the three representative solid tumors, and to potentially extrapolate these findings to other tumors.

项目摘要/摘要：数据分析单元 华盛顿大学人类肿瘤图谱研究数据分析部门的总体目标 中心（WU-HTARC）旨在为深入研究提供生物信息学工具和处理/分析基础设施。 分析表征单元中生成的数据。最重要的是，我们将整合跨领域的数据 方法学（组学/成像/表型分析）和维度（1D/2D/3D/时间）谱 为三种癌症类型中的每一种形成连贯且可访问的肿瘤图谱：GBM、PDAC 和 BRCA/TNBC。 在基础层面上，每个图谱将首先对细胞的各种数值计算指标进行编目 类型，包括单个细胞类型的分数 (1D)、密度、分散度和位置测量 不同细胞类型的空间散布度的欧几里得测量，例如免疫细胞和肿瘤细胞（2D 和 3D），以及这些指标如何随时间变化。然后我们会将这些信息与基因组相关联 分析，例如突变特征、克隆性和显着突变的基因/区域/途径、蛋白质组学 代谢组学分析以及图像衍生数据。该图集的核心将是 MySQL 关系型数据库 数据库不仅存储所有收集的数据，而且将它们沿着这些不同的维度链接起来。用户将 通过基于浏览器的复杂查看器/查询门户网站与地图集进行交互，该门户网站将支持这两种功能 传统的基于文本的查询，以及基于空间的查询（形状、特征位置等）。凝聚力 在这三个地图集中，就所使用的数据范围及其构建方法而言，将 允许用户产生现在不可能的新型假设，并进行泛癌分析 揭示三种代表性实体瘤的共性和差异，并有可能推断 这些发现适用于其他肿瘤。