Computational methods for studying single-cell 3D genome

项目总结 细胞核中的三维基因组组织对基因组功能至关重要。这个 然而，现有的大多数3D基因组研究都是基于基于群体的分析，这些分析是 无法在单细胞分辨率下揭示3D基因组结构在复杂组织中的功能作用。 最近出现的单细胞Hi-C(Schi-C)技术使染色质的基因组图谱成为可能 在单个细胞之间的相互作用，但Schi-C数据的分析仍然是一个巨大的挑战。特别是， 可以有效地分析Schi-C数据以提取多尺度3D基因组特征的计算方法是 显著缺乏，限制了我们揭示结构和功能联系的可变性的能力 异质细胞群体。这项提议的总体目标是发展最先进的 有效识别多尺度单细胞3D基因组特征的Schi-C数据分析计算工具 并将它们与基因组功能联系起来。具体地说，我们将(1)开发Schi-C数据处理算法 和归类来描述多尺度的3D基因组特征；(2)开发计算方法来连接3D 异质细胞群体的基因组结构和功能；以及(3)发展综合可视化 导航单细胞3D基因组组织的平台。本项目中开发的方法可以应用 通过不同的单细胞染色质相互作用分析产生的所有类型的Schi-C数据来揭示3D基因组 多个尺度上的特征，量化它们的变异性并预测它们的功能结果。新工具 来自该项目的资源将通过我们新的可视化平台公开访问，该平台提供 Schi-C数据和其他数据类型的集成和交互导航。总体而言，我们的项目将大大 促进广大科学界使用Schi-C数据，并对不同群体具有很高的价值 生物医学研究人员。

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.