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

摘要： 美国国立卫生研究院的人类生物分子图谱计划(HuBMAP)是一项雄心勃勃的努力，努力创造 一种全面的、高分辨率的人体图谱。它依赖于 多个研究小组和整合不同、复杂的数据集。坚持公平 原则(可查找性、可访问性、互操作性和可重用性)是实现 在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。这就变成了 随着HuBMAP计划的扩展和更多的合作 这个项目。在这项建议中，我们的目标是加强现有数据的互操作性和可重用性 HuBMAP内基于质谱学的数据。我们提议的努力是自然的延伸 我们当前在父U01演示项目中的工作(在单元之外思考：利用HuBMAP 构建人类细胞外基质图谱的数据)，其重点是构建第一个细胞外基质 HuBMAP地图集。第一个目标是开发质量控制工具和统一的蛋白质组学 数据处理流水线，用于检查当前HuBMAP数据的情况并提供便利 对各种实验和组织类型进行比较。这一努力将帮助我们更好地 评估数据质量并确定需要改进的关键方面。我们的第二个目标集中在 关于HuBMAP目前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为我们提供了独特的比较条件 不同组织定位中心采用的不同蛋白质定量方法。这一努力 将帮助我们更好地理解这些组件的健壮性、互换性和适用性 技巧。总的来说，我们建议的研究将显著改善数据互操作性和 HuBMAP项目中的可重用性，并为未来数据中的最佳实践提供指导 收购。

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.