WU-SN-TMC Data Analysis Core

WU-SN-TMC数据分析核心

• 批准号: 10685424
• 负责人: Li Ding
• 金额: $ 22.19万
• 依托单位: WASHINGTON UNIVERSITY
• 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/10685424
• 关键词: Age; Analysis of Variance; Anatomy; Atlases; Back; Biological; Biological Assay; Bone Marrow; Breast; Breeding; Categories; Cell Aging; Cells; Chromatin; Colon; Communities; DNA; Data; Data Analyses; Data Commons; Data Coordinating Center; Development; Dimensions; Evolution; FAIR principles; Generations; Genome; Goals; Heterogeneity; Image; Imaging Techniques; Imaging technology; Individual; Informatics; Intake; Learning; Licensing; Light; Link; Liver; Machine Learning; Maps; Metadata; Methods; Microscopy; Mission; Modality; Modeling; Molecular; Oncogenes; Organ; Philosophy; Population; Procedures; Process; Property; Proteomics; Publishing; Recording of previous events; Research; Research Infrastructure; Running; Specimen; System; Technology; The Cancer Genome Atlas; Time; Tissues; Tumor Suppressor Proteins; Universities; Visualization software; Washington; age group; analytical tool; computerized data processing; computerized tools; data analysis pipeline; data format; data harmonization; data integration; data management; data sharing; data standards; data warehouse; demographics; experience; insight; interoperability; large scale data; open data; open source; process improvement; programs; public database; senescence; single nucleus RNA-sequencing; statistics; success; tissue mapping; tool; transcriptome sequencing; transcriptomics; user-friendly

Data Analysis Core Summary/Abstract Our group has a well-established history of large-scale omics and imaging data management, processing, analysis, and interpretation in the context of consortium projects. We have built, used, and maintained data warehouses and the associated tracking, meta-data, and QC machinery for specimens and raw data and have developed a large number of computational tools for processing, analyzing, and interpreting such data. The primary mission of the Data Analysis Core at the Washington University Cellular Senescence Network Tissue Mapping Center (WU-SN-TMC) will take place along 4 primary dimensions: 1) providing data support to the Biospecimen and Biological Analysis Cores, 2) processing raw assay data from the Biological Analysis Core and marrying these with data from other SN-TMCs using cutting-edge informatics pipeline and integrated approaches, 3) applying processed data to build organ-specific cellular senescence atlases, with a focus on differences/evolution among demographic groups (especially by age) and within the individual (between organs and longitudinally), and 4) finally managing delivery of data and research products to the Consortium Organization and Data Coordinating Center (CODCC) and coordinating further activities toward amalgamation of findings across the SenNet project. Data Analysis Core will promote cooperation through development of common formats and interoperable tools for advanced omics and imaging technologies within the SenNet program, harmonize tools in larger, scalable, user-friendly pipelines and distribute tools to the larger scientific community under open-source licensing, develop senescence signatures and models using integrated data, and investigate how nascent technologies will be brought into the research infrastructure to improve processes and results for senescence research. In conclusion, Data Analysis Core will be a crucial cog within WU-SN- TMC and serve as a strong link between WU-SN-TMC, CODCC, and other TMCs within SenNet.

数据分析核心摘要/摘要 我们的团队在大规模组学和成像数据管理、处理， 分析，并在联合体项目的背景下解释。我们建立、使用和维护数据 仓库和相关的跟踪，元数据，以及用于标本和原始数据的QC机器， 开发了大量的计算工具来处理、分析和解释这些数据。的 华盛顿大学细胞衰老网络数据分析核心的主要使命 制图中心（WU-SN-TMC）将沿着沿着4个主要方面进行：1）为 生物样本和生物分析核心，2）处理来自生物分析核心的原始测定数据 并将这些数据与来自其他SN-TMC的数据结合起来， 方法，3）应用处理后的数据建立器官特异性细胞衰老图谱，重点是 人口群体之间（特别是按年龄）和个人内部（ 机构和纵向），以及4）最终管理向联盟交付数据和研究产品 组织和数据协调中心（CODCC），并协调进一步的合并活动 在SenNet项目中的发现。数据分析核心将通过开发 SenNet内先进组学和成像技术的通用格式和互操作工具 在更大的、可扩展的、用户友好的管道中编程、协调工具，并将工具分发给更大的科学机构。 社区在开源许可下，使用集成数据开发衰老特征和模型， 并研究如何将新生技术引入研究基础设施，以改善流程 和衰老研究的结果。总之，数据分析核心将是WU-SN的关键齿轮， TMC是WU-SN-TMC、CODCC和SenNet内其他TMC之间的强大链接。