Computational modeling of spatial genome organization and gene regulation

项目摘要/摘要 基因组的三维(3D)组织在基因组稳定性、基因调控、 以及许多疾病，包括癌症。高通量染色质构象捕获技术的研究进展 (hi-C)及其变异体为研究高阶染色质结构提供了前所未有的机会。 尽管研究3D基因组组织的资源迅速积累，但我们对 基因组组织的调节机制和功能在很大程度上仍然不完整。高密度脂蛋白分析和 3D基因组研究仍处于早期阶段，并面临着几个挑战。第一，高分辨率染色质 接触图谱需要极深的测序，因此只有几个细胞系才能实现。第二, 用一维(1D)基因组和 表观基因组学特征。第三，最近的研究刚刚开始推断染色质相互作用之间的联系。 和遗传变异，并在全基因组范围内确定这些变异的潜在目标基因。vt.给出 这些挑战和我独特的多学科训练，使我的长期研究目标是开拓创新 揭示3D基因组结构和功能之间相互作用的计算和统计方法。 具体地说，在接下来的几年里，我将i)开发计算方法来提高现有fifi的分辨率 HI-C数据，并研究fiNe尺度3D基因组结构及其时空动力学和II)建立 利用一维表观基因组数据预测细胞类型的可扩展和可解释的机器学习模型--fic 3D染色质相互作用与基因表达及3D基因组组织在基因中的作用 监管和人类疾病。拟议工作的完成将加深我们对3D基因组的了解 结构及其在基因调控和疾病中的功能。

PROJECT SUMMARY/ABSTRACT The three-dimensional (3D) organization of the genome plays an essential role in genome stability, gene regulation, and many diseases, including cancer. The recent development of high-throughput chromatin conformation capture (Hi-C) and its variants provide an unprecedented opportunity to investigate higher-order chromatin organization. Despite the rapidly accumulating resources for investigating 3D genome organization, our understanding of the regulatory mechanisms and functions of the genome organization remain largely incomplete. Hi-C analyses and 3D genome research are still in their early stage and face several challenges. First, high-resolution chromatin contact maps require extremely deep sequencing and hence have been achieved only for a few cell lines. Second, it is computationally challenging to complement 3D genome structure with one-dimensional (1D) genomic and epigenomic features. Third, recent studies have just begun to infer associations between chromatin interactions and genetic variants and to identify potential target genes of those variants at the genome-wide scale. Given these challenges and my unique multi-disciplinary training, my long-term research goal is to develop innovative computational and statistical methods to uncover the interplay between 3D genome structure and function. Specifically, in the next five years, I will i) develop computational approaches to enhance the resolution of existing Hi-C data and investigate fine-scale 3D genome architecture as well as its spatiotemporal dynamics and ii) build scalable and interpretable machine learning models that leverage 1D epigenomic data to predict cell type-specific 3D chromatin interactions and gene expression and elucidate the function of 3D genome organization in gene regulation and human diseases. The completion of the proposed work will deepen our knowledge of 3D genome architecture as well as its functions in gene regulation and disease.