Thinking outside the cell: Leveraging HuBMAP data to build the human ECM atlas
Thinking outside the cell: Leveraging HuBMAP data to build the human ECM atlas
批准号:
10816692
负责人:
Yu Gao
金额:
$15.0万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-07-01 至 2026-04-30
关键词:
AccelerationAdministrative SupplementAdoptedAtlasesBiological ProcessCellsCollaborationsDataData AnalysesData SetDevelopmentEnsureExtracellular MatrixExtracellular SpaceFAIR principlesFutureGenetic TranscriptionGoalsGuidelinesHeterogeneityHumanHuman BioMolecular Atlas ProgramHuman bodyMass Spectrum AnalysisMeasurementMeasuresMetadataMethodsModelingNucleic AcidsParentsPositioning AttributePost-Translational Protein ProcessingPreparationProcessProductionProtein IsoformsProteinsProteomicsProxyQuality ControlRNAResearchResearch PersonnelResolutionResourcesSamplingSoftware ToolsSourceSurveysTechniquesTechnologyThinkingTimeTissue BanksTissue SampleTissuesUnited States National Institutes of HealthValidationWorkcomplex datacomputerized data processingdata acquisitiondata analysis pipelinedata formatdata integrationdata interoperabilitydata qualitydata reusedesigndiverse dataexperienceexperimental studyimprovedinstrumentinteroperabilityionizationmachine learning methodprogramsprotein degradationprotein expressionrepositorystemsuccesstissue mappingtooltool developmenttranscriptome sequencingvalidation studies
中文摘要
文摘:
英文摘要
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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