Computational methods for studying single-cell 3D genome

研究单细胞 3D 基因组的计算方法

• 批准号: 10392079
• 负责人: Zhijun Duan
• 金额: $ 55.76万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-11 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10392079
• 关键词: 3-Dimensional; Address; Algorithms; Biological; Biological Assay; Biological Process; Biology; Catalogs; Cell Nucleus; Cells; Chromatin; Communities; Complex; Computing Methodologies; Data; Data Analyses; Data Set; Data Sources; Development; Disease; Evaluation; Excision; Formulation; Gene Expression; Genome; Genomics; Health; Hematopoiesis; Heterogeneity; Hi-C; Human; Image; Individual; Intuition; Knowledge; Methods; Mission; Modeling; Outcome; Population; Public Health; Research; Research Personnel; Resolution; Resources; Role; Series; Structure; Technology; Tissues; United States National Institutes of Health; Ursidae Family; Validation; Variant; Visualization; Work; base; cell type; computerized data processing; computerized tools; data exploration; diverse data; epigenomics; functional genomics; functional outcomes; gene function; genomic data; graph neural network; hematopoietic differentiation; imaging modality; improved; multimodal data; multimodality; population based; predictive modeling; simulation; single cell analysis; supervised learning; three dimensional structure; tool; user-friendly

PROJECT SUMMARY The three-dimensional (3D) genome organization in the nucleus is of vital importance to genome function. The vast majority of the existing 3D genome studies, however, are based on population-based assays that are unable to unveil the functional roles of 3D genome structures at single-cell resolution in complex tissues. Recent advent in single-cell Hi-C (scHi-C) technologies has enabled genomic mapping of chromatin interactions in individual cells, but the analysis of scHi-C data remains a significant challenge. In particular, computational methods that can effectively analyze scHi-C data to extract multiscale 3D genome features are significantly lacking, limiting our ability to reveal the variability of structure and function connections in heterogeneous cell populations. The overall objective of this proposal is to develop state-of-the-art computational tools for scHi-C data analysis that effectively identify multiscale single-cell 3D genome features and connect them to genome function. Specifically, we will (1) develop algorithms for scHi-C data processing and imputation to delineate multiscale 3D genome features; (2) develop computational methods to connect 3D genome structure and function in heterogeneous cell population; and (3) develop an integrative visualization platform to navigate single-cell 3D genome organization. The methods developed in this project can be applied to all types of scHi-C data generated by different single-cell chromatin interaction assays to reveal 3D genome features at multiple scales, quantifying their variability and predicting their functional outcomes. The new tools and resources from this project will be publicly accessible through our new visualization platform that provides integrative and interactive navigation of scHi-C data and other data types. Overall, our project will greatly facilitate the use of scHi-C data by the broad scientific community and be of high value to a diverse group of biomedical researchers.

项目摘要 细胞核中的三维（3D）基因组组织对于基因组功能至关重要。这 但是，绝大多数现有的3D基因组研究基于基于人群的测定 无法揭示3D基因组结构在复杂组织中单细胞分辨率中的功能作用。 单细胞HI-C（SCHI-C）技术的最新出现已启用染色质的基因组映射 单个细胞中的相互作用，但是对SCHI-C数据的分析仍然是一个重大挑战。尤其， 可以有效分析SCHI-C数据以提取多尺度3D基因组特征的计算方法是 显着缺乏，限制了我们揭示结构和功能连接可变性的能力 异质细胞群。该提案的总体目的是开发最新的 用于SCHI-C数据分析的计算工具，可有效识别多尺度单细胞3D基因组特征 并将它们连接到基因组功能。具体而言，我们将（1）开发用于SCHI-C数据处理的算法 并插图描绘多尺度3D基因组特征； （2）开发连接3D的计算方法 基因组结构和异质细胞群中的功能； （3）开发一个集成的可视化 导航单细胞3D基因组组织的平台。可以应用该项目中开发的方法 对于由不同的单细胞染色质相互作用测定产生的所有类型的SCHI-C数据，以揭示3D基因组 在多个尺度上的特征，量化它们的变异性并预测其功能结果。新工具 该项目的资源将通过我们提供的新可视化平台公开访问 SCHI-C数据和其他数据类型的集成和交互式导航。总体而言，我们的项目将大大 促进广泛科学界使用SCHI-C数据，并对多样化的一组具有很高的价值 生物医学研究人员。