Integrated approaches to decipher 3D genomes

项目摘要 基因组研究的一个巨大挑战是使用DNA邻近连接方法（如Hi-C）的数据 与其他可用数据一起创建3D核组织的准确预测模型， 基因组和揭示不同基因组组织的功能意义。的限制和 数据的不完整性使这成为一项具有挑战性的任务。例如，Hi-C数据仅描述平均值 基因组构象在一个大的整体细胞，但空间基因组组织是高度动态的， 在同一样品中的单个细胞之间可变。此外，数据不能揭示任何更高的顺序， 信息，例如同一细胞中相互作用的共同出现。即使是单细胞Hi-C方法， 由于每个小区的交互覆盖率低，以及整个小区的统计相关性抽样有限， 基因组之间的构象变异性。此外，基因组的动态性质使其非常 通过功能性挖掘找到基因组结构/功能关系的全面描述具有挑战性 相关的结构染色质模式。因此，迫切需要计算方法， 适当地解释Hi-C数据，用于3D基因组建模和分析，并将该数据与任何其他数据整合。 关于基因组组织的可用信息，例如来自成像和其他技术。我们 提出了一种新的基于种群的建模方法，它重新定义了优化基因组的问题 结构化总体作为最大后验概率估计问题。我们的方法可以反卷积 将基于集成的Hi-C数据整合到基因组结构群体中，这些基因组结构在统计学上与 输入数据，并描述给定可用的真实基因组结构群体的最佳近似。 数据我们的概率方法为全面整合所有可用数据提供了一个框架， 包括整体平均和单细胞Hi-C数据，以及其他实验数据源（例如成像）， 以增加预测基因组模型的覆盖率、准确性和分辨率。我们还开发了一个图表 用于在基因组结构的集合中发现染色质模式的挖掘方法并关联这些模式 许多核过程，如转录、易位和DNA复制。

Project summary A grand challenge of genome research is to use data from DNA proximity ligation methods (such as Hi-C) together with other available data to create accurate, predictive models of the 3D nuclear organization of the genome and reveal the functional implications of different genome organizations. The limitations and incompleteness of the data make this a challenging task. For instance, Hi-C data describe only the average genome conformation over a large ensemble of cells, but the spatial genome organization is highly dynamic and variable among individual cells in the same sample. Moreover, the data cannot reveal any higher order information such as co-occurrences of interactions in the same cell. Even single-cell Hi-C approaches are hampered by low interaction coverage per cell and limited sampling with statistical relevance across the large conformational variability among genomes. In addition, the dynamic nature of the genome makes it very challenging to find a comprehensive description of genome structure/function relationships by mining functionally relevant structural chromatin patterns. Therefore, there is urgent demand for computational methods that can appropriately interpret Hi-C data for 3D genome modeling and analysis and integrate this data with any other available information about the genome organization, for example from imaging and other technologies. We propose a new population-based modeling approach, which reframes the problem of optimizing a genome structure population as a maximum a posteriori probability estimation problem. Our method can deconvolute ensemble-based Hi-C data into a population of genome structures that are altogether statistically consistent with the input data and describe the best approximation of the true genome structure population given the available data. Our probabilistic approach provides a framework for comprehensive integration of all available data, including ensemble-average and single-cell Hi-C data, as well as other experimental data sources (e.g. imaging), to increase the coverage, accuracy and resolution of the predictive genome models. We also develop a graph mining approach for chromatin pattern discovery in an ensemble of genome structures and relate these patterns to a variety of nuclear processes, such as transcription, translocation, and DNA replication.