Sequence models of genome regulatory architecture in 3D

项目摘要 解释个体基因组序列和任何序列变异的后果对研究至关重要 疾病的遗传基础和通往精准医学的道路。基因组序列是以 高度相互交织的多层次基因组调控，包括染色质蛋白结合，3D 基因组结构和基因转录。直接从序列中解码这些调控功能将 为变异效应的可扩展预测和碱基对水平的查询提供计算平台 序列与“在电子诱变中”一起起作用。在解码调控基因组方面取得了进展 序列，包括随着深度学习序列模型的发展。然而，序列的基础 如果不考虑基因组，转录调控等复杂现象将无法完全解决 结构和远程3D序列背景。多个空间尺度的基因组结构，包括启动子- 增强子相互作用，转录缩合物，拓扑相关结构域，染色质隔间， 核体可以对转录调控产生强烈的影响。使用的数据和技术具有 只有现在足够应对这一挑战，我们才会研究这些现象并追踪复杂的监管 通过开发3D基因组调控的序列模型来输出回序列依赖的基础 建筑。我们将开发一个具有以下能力的深度学习序列模型的计算框架 建模多尺度3D基因组相互作用和整合远程序列 信息，用于全面解释基因组序列的调控功能。拟议中的工作 将为解释和应用序列的结构和转录影响开辟新的可能性 变异，包括询问遗传因素，如大的结构变异，如何通过 健康和疾病状态下基因组结构组织的重塑。

Project Summary Interpreting individual genome sequence and the consequence of any sequence variation is critical for the study of the genetic basis of diseases and the path toward precision medicine. Genomic sequence is at the basis of multiple levels of genome regulation which are highly intertwined, including chromatin protein binding, 3D genome architecture, and gene transcription. Decoding these regulatory functions directly from the sequence will provide a computational platform for scalable prediction of variant effects and interrogation of base pair-level sequence functions with “in silico mutagenesis”. Progress has been made in decoding regulatory genomic sequence, including with the development of deep learning sequence models. However, the sequence-basis of complex phenomena such as transcriptional regulation will not be fully resolved without accounting for genome structure and long-range 3D sequence context. Genome structures at multiple spatial scales, including promoter- enhancer interactions, transcriptional condensates, topologically associating domains, chromatin compartments, and nuclear bodies can have strong impacts on transcriptional regulation. With data and techniques that have only now become sufficient to tackle this challenge, we will study these phenomena and trace complex regulatory output back to the basis of sequence dependencies by developing sequence models of 3D genome regulatory architecture. We will develop a computational framework of deep learning sequence models with the capability of modeling multiscale 3D genome interactions and integrating long-range sequence information, for comprehensively interpreting the regulatory functions of genome sequence. The proposed work will open up new possibilities for interpreting and applying structural and transcriptional impacts of sequence variations, including asking how genetic factors, such as large structural variants, affect gene expression through remodeling of genome structural organization in healthy and disease states.