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Our ultimate goal is to deliver complex chromatin interaction network maps in the context of 3D genome structures from which the dynamics of individual genomic elements can be monitored and referenced. Here, we propose to develop a Nucleome Positioning System (NPS)-comprised of 1) a robust genome- wide mapping technology platform, 2) advanced computational modeling algorithms and 3) state-of-the-art nuclear imaging methods-that will allow users community-wide to uncover the regulatory functions of 3D genome organization in human cells. NPS will be based upon the established ChIA-PET method (1,2), enhanced by process optimizations-i.e., microfluidic-based miniaturization and Tn5-transposase-based library preparation-to facilitate the study of chromatin interactions mediated by protein factors across a broader range of human cell types (Aim 1, see also Mapping Technology Development Component). We will also optimize RICh-PET for the comprehensive mapping of chromatin interactions mediated by non-coding RNAs (Aim 1). The high-quality mapping data generated through these optimization efforts will be analyzed by a new computational platform (Three-Dimensional Nucleome Modeling Engine, or 3D-NOME) that makes use of hierarchical multi-scaling to model 3D genome structures (Aim 2, Data Analysis and Modeling component). We will also complement the 3D modeling with transcriptome, epigenome and SNP data associated with genetic diseases (GWAS) to provide functional annotation to structural units (Aim 2). We will continue by developing strategies to validate the nucleome geometry predicted by 3D-NOME both structurally, using new nuclear imaging technologies, and functionally, using cutting-edge genome- and epigenome-editing approaches, in both human cell lines and mouse models (Aim 3, Biological Validation Component). Finally, we will implement NPS to generate pilot 3D genome maps from a wide range of human cell lines and primary immune cells sorted from whole blood, to elucidate the spatiotemporal dynamics of human genome organization over major developmental and hematopoietic cell lineages, as well as among differentiating lymphocytes involved in the immune response (Aim 4, Data Generation Component). Together, these efforts will yield a powerful set of sophisticated, high-quality tools and mapping data for the larger research community, and will help establish the standards for future 3D/4D nucleome studies. 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If you are intending to use these data for a \npublication, we ask that you please contact the data \ngenerating lab to discuss possible coordinated publication. \nIn your manuscript, please cite the 4DN White Paper \n([doi:10.1038/nature23884](https://doi.org/10.1038/nature23884)) \nand the 4DN Data Portal paper \n([doi:10.1038/s41467-022-29697-4](https://doi.org/10.1038/s41467-022-29697-4)), \nand please acknowledge the 4DN lab which generated the data. 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If you are intending to use these data for a \npublication, we ask that you please contact the data \ngenerating lab to discuss possible coordinated publication. \nIn your manuscript, please cite the 4DN White Paper \n([doi:10.1038/nature23884](https://doi.org/10.1038/nature23884)) \nand the 4DN Data Portal paper \n([doi:10.1038/s41467-022-29697-4](https://doi.org/10.1038/s41467-022-29697-4)), \nand please acknowledge the 4DN lab which generated the data. Please direct any questions to the [Data Coordination and Integration Center](mailto:support@4dnucleome.org).", "filetype": "md", "content_as_html": "
Data Use Guidelines: This is a data set generated by the \n4DN Network and made freely available to the scientific \ncommunity. If you are intending to use these data for a \npublication, we ask that you please contact the data \ngenerating lab to discuss possible coordinated publication. \nIn your manuscript, please cite the 4DN White Paper \n(doi:10.1038/nature23884) \nand the 4DN Data Portal paper \n(doi:10.1038/s41467-022-29697-4), \nand please acknowledge the 4DN lab which generated the data. Please direct any questions to the Data Coordination and Integration Center.
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