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.

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