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Advanced algorithms to infer and analyze 3D genome structures

用于推断和分析 3D 基因组结构的先进算法

基本信息

批准号：
10708000
负责人：
Zheng Wang
金额：
$ 36.67万
依托单位：
UNIVERSITY OF MIAMI CORAL GABLES
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-08-31
项目状态：
未结题

项目摘要

Project Summary For the past decade, the population-cell Hi-C technique has significantly improved our ability to discover genome-wide DNA proximities. However, because population Hi-C is based on a pool of cells, it will not help us reveal each single cell's 3D genome structure or understand cell-to-cell variability in terms of 3D genome structure and gene regulation. It is also difficult to achieve a high resolution, such as 1 Kbp, with population Hi- C; therefore, when finding and analyzing the spatial interactions for the promoter or enhancer regions typically associated with biologically-important regulatory elements, population Hi-C data's resolution is too low to be useful. Moreover, while we know that the CTCF-cohesin complex plays a key role in the formation of genome 3D structures, the question is whether long non-coding RNAs (lncRNAs) are involved in the process since lncRNAs have been found to recruit proteins needed for chromatin remodeling, and our preliminary research has found that lncRNA LINC00346 directly interacts with CTCF. Finally, while members of the bioinformatics community, including the PI, have developed many algorithms to reconstruct 3D genome structures based on population Hi-C data, important questions still must be answered regarding how 3D genome structures are involved in gene regulation and whether there are relationships between 3D genome structures and genetic and epigenetic features. The PI proposes to conduct leading research to overcome these challenges and address these questions. During the next five years, the PI will develop algorithms to reconstruct the 3D whole- genome structures for single cells and analyze cell-to-cell variabilities in terms of 3D genome structure and gene regulation. The PI will develop a deep learning algorithm to enhance the resolution of population Hi-C data to that of Capture Hi-C data (1 Kbp) so that we can make good use of the large amount of Hi-C data accumulated in the past decade. An online database will be built to allow the community to access both population and single-cell 3D genome structures in an integrated way. The PI will work with a cancer biologist to discover any lncRNAs that function as a scaffold to fine-tune the CTCF-cohesin protein complex, as well as two neuron scientists to develop a more complete understanding of gene regulation while considering 3D genome and other genetic and epigenetic features. Given the PI's track record and productivity, having three computational goals and two collaborative goals is not only feasible but computationally and biologically rewarding. In five years, once the proposed studies are accomplished, the PI should have established a uniquely independent place in the field of 3D genome, maintaining leading positions in inferring single-cell 3D genome structures, enhancing Hi-C data resolution, and building 3D genome databases, while establishing similar positions in reconstructing high-resolution 3D genome structures, finding lncRNAs' roles in the formation of genome structures, and understanding how 3D genome structures are involved in gene regulation.

项目摘要在过去的十年里，群体细胞Hi-C技术显著提高了我们发现全基因组的DNA接近度然而，由于群体Hi-C是基于细胞池的，我们揭示了每个单细胞的3D基因组结构，或了解细胞与细胞之间的3D基因组变异性结构和基因调控。在人口高的情况下，也难以实现高分辨率，例如1 Kbp。因此，当发现和分析启动子或增强子区域的空间相互作用时，与生物学上重要的调控元素相关，人口Hi-C数据的分辨率太低，有用的.此外，虽然我们知道CTCF-cohesin复合物在基因组的形成中起着关键作用， 3D结构，问题是长链非编码RNA（lncRNA）是否参与了这一过程，已经发现lncRNA可以募集染色质重塑所需的蛋白质，我们的初步研究已经发现lncRNA LINC 00346直接与CTCF相互作用。最后，虽然生物信息学的成员社区，包括PI，已经开发了许多算法来重建3D基因组结构，人口Hi-C数据，重要的问题仍然必须回答关于3D基因组结构是如何参与基因调控，以及3D基因组结构与基因表达之间是否存在关系。和表观遗传特征。PI建议进行领先的研究，以克服这些挑战，回答这些问题。在接下来的五年里，PI将开发算法来重建3D整体- 单细胞的基因组结构，并根据3D基因组结构分析细胞间变异性，基因调控PI将开发一种深度学习算法，以提高群体Hi-C的分辨率数据到捕获Hi-C数据的数据（1 Kbp），以便我们可以很好地利用大量Hi-C数据过去十年积累的。将建立一个在线数据库，使社区能够访问这两个群体和单细胞3D基因组结构。私家侦探会和一位癌症生物学家合作发现任何作为支架的lncRNA，以微调CTCF-粘附蛋白复合物，以及两名神经科学家在考虑3D的同时更全面地了解基因调控基因组和其他遗传和表观遗传特征。考虑到PI的记录和生产力，有三个计算目标和两个合作目标不仅是可行的，而且在计算和生物学上奖励在五年内，一旦完成了拟议的研究，PI应该已经建立了一个在3D基因组领域独占鳌头，在单细胞3D推断领域保持领先地位基因组结构，提高Hi-C数据分辨率，建立3D基因组数据库，同时建立在重建高分辨率3D基因组结构中的类似位置，发现lncRNA在基因组中的作用。基因组结构的形成，并了解3D基因组结构如何参与基因调控。