Thinking outside the cell: Leveraging HuBMAP data to build the human ECM atlas

细胞外思考：利用 HuBMAP 数据构建人类 ECM 图谱

• 批准号: 10816692
• 负责人: Yu Gao
• 金额: $ 15万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10816692
• 关键词: Acceleration; Administrative Supplement; Adopted; Atlases; Biological Process; Cells; Collaborations; Data; Data Analyses; Data Set; Development; Ensure; Extracellular Matrix; Extracellular Space; FAIR principles; Future; Genetic Transcription; Goals; Guidelines; Heterogeneity; Human; Human BioMolecular Atlas Program; Human body; Mass Spectrum Analysis; Measurement; Measures; Metadata; Methods; Modeling; Nucleic Acids; Parents; Positioning Attribute; Post-Translational Protein Processing; Preparation; Process; Production; Protein Isoforms; Proteins; Proteomics; Proxy; Quality Control; RNA; Research; Research Personnel; Resolution; Resources; Sampling; Software Tools; Source; Surveys; Techniques; Technology; Thinking; Time; Tissue Banks; Tissue Sample; Tissues; United States National Institutes of Health; Validation; Work; complex data; computerized data processing; data acquisition; data analysis pipeline; data format; data integration; data interoperability; data quality; data reuse; design; diverse data; experience; experimental study; improved; instrument; interoperability; ionization; machine learning method; programs; protein degradation; protein expression; repository; stem; success; tissue mapping; tool; tool development; transcriptome sequencing; validation studies

Abstract: NIH's Human BioMolecular Atlas Program (HuBMAP) is an ambitious endeavor striving to create a comprehensive, high-resolution atlas of the human body. It relies on collaborations among multiple research groups and the integration of diverse, complex datasets. Adhering to FAIR principles (Findability, Accessibility, Interoperability, and Reusability) is essential for enabling seamless sharing and reuse of valuable data among researchers and clinicians. This becomes increasingly important as the HuBMAP program expands, and more collaborations emerge from the project. In this proposal, we aim to enhance the data interoperability and reusability of existing mass spectrometry-based data within HuBMAP. Our proposed efforts serve as a natural extension of our current work in the parent U01 demo project (Thinking outside the cell: Leveraging HuBMAP data to build the human ECM atlas), which focuses on constructing the first extracellular matrix atlas for HuBMAP. The first aim plans to develop quality control tools and a unified proteomics data processing pipeline to examine the current landscape of HuBMAP data and facilitate comparisons across various experiments and tissue types. This effort will help us to better evaluate the data quality and identify key aspects for improvement. Our second aim concentrates on the cross-validation of diverse protein quantitation technologies currently utilized in HuBMAP. The extensive scope and depth of current HuBMAP datasets uniquely position us to compare various protein quantitation methods employed by different tissue mapping centers. This effort will help us to better understand the robustness, interchangeability, and suitability of these techniques. Collectively, our proposed research will significantly improve data interoperability and reusability within the HuBMAP project and provide guidelines for best practices in future data acquisition.

抽象的： NIH的人类生物分子地图集计划（Hubmap）是一项雄心勃勃的努力，努力创造 人体的全面，高分辨率的地图集。它依赖于合作 多个研究小组以及各种复杂数据集的整合。公平地遵守 原理（可访问性，可访问性，互操作性和可重复性）对于实现 研究人员和临床医生之间无缝共享和重复使用有价值的数据。这变成了 随着Hubmap计划的扩展，越来越重要 项目。在此提案中，我们旨在增强现有的数据互操作性和可重复性 Hubmap中的基于质谱的数据。我们提出的努力是自然的扩展 我们目前在父级U01演示项目中的工作（在单元外思考：利用Hubmap 构建人类ECM Atlas的数​​据，该数据侧重于构建第一个细胞外基质 Hubmap的地图集。第一个目标计划开发质量控制工具和统一的蛋白质组学 数据处理管道以检查Hubmap数据的当前格局并促进 在各种实验和组织类型上进行比较。这项努力将帮助我们更好 评估数据质量并确定改进的关键方面。我们的第二个目标集中 关于目前在Hubmap中使用的各种蛋白质定量技术的交叉验证。 当前Hubmap数据集的广泛范围和深度独特地定位了我们以比较 不同组织映射中心采用的各种蛋白质定量方法。这项努力 将帮助我们更好地了解这些的鲁棒性，互换性和适用性 技术。总的来说，我们提出的研究将显着改善数据互操作性和 Hubmap项目中的可重复使用性，并为未来数据中的最佳实践提供指南 获得。