Sequence models of genome regulatory architecture in 3D

3D 基因组调控架构的序列模型

• 批准号: 10242561
• 负责人: Jian Zhou
• 金额: $ 147.6万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-23 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10242561
• 关键词: 3-Dimensional; Accounting; Affect; Architecture; Back; Base Pairing; Binding Proteins; Chromatin; Complex; Computer Models; Data; Dependence; Development; Disease; Enhancers; Gene Expression; Gene Expression Regulation; Genetic; Genetic Diseases; Genetic Transcription; Genetic study; Genome; Genomics; Human body; Individual; Modeling; Mutagenesis; Nuclear; Output; Predisposition; Regulation; Role; Structural Genes; Structure; Techniques; Therapeutic; Transcriptional Regulation; Variant; Work; chromatin protein; computational platform; computer framework; deep learning; in silico; multi-scale modeling; organizational structure; precision medicine; promoter; response; sequence learning; three-dimensional modeling

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.

项目摘要 解释单个基因组序列和任何序列变化的后果对于研究至关重要 疾病的遗传基础和通往精确医学的道路。基因组序列是 高度相互交织的多个水平的基因组调节，包括染色质蛋白结合，3D 基因组结构和基因转录。直接从序列解码这些调节函数将 提供一个计算平台，用于可扩展的变异效应和基础配对级的询问 序列功能“在硅诱变中”。在解码调节基因组方面取得了进展 序列，包括发展深度学习序列模型。但是，序列基础 如果不考虑基因组，就不会完全解决诸如转录调控之类的复杂现象 结构和远程3D序列上下文。基因组结构在多个空间尺度上，包括启动子 - 增强剂相互作用，转录冷凝物，拓扑关联的域，染色质区室， 核体可以对转录调节产生强大的影响。具有具有的数据和技术 直到现在才能应对这一挑战，我们将研究这些现象并追踪复杂的调节 通过开发调节3D基因组调节的序列模型，输出回到序列依赖性的基础 建筑学。我们将开发具有能力的深度学习序列模型的计算框架 建模多尺度3D基因组相互作用并整合远程序列 信息，用于全面解释基因组序列的调节功能。拟议的工作 将为序列解释和应用结构和转录影响开辟新的可能性 变化，包括询问遗传因素（例如大结构变异）如何通过 在健康和疾病状态下基因组结构组织的重塑。