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: 10570830
• 负责人: Zhijun Duan
• 金额: $ 54.49万
• 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/10570830
• 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; Maps; Methods; Mission; Modeling; Outcome; Population; Public Health; Research; Research Personnel; Resolution; Resources; Role; Series; Structure; Technology; Tissues; United States National Institutes of Health; Validation; Variant; Visualization; Work; cell type; computerized data processing; computerized tools; data exploration; diverse data; empowerment; 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.

项目总结