Coevolution and Functional Interactions in the Non-Coding Genome

项目摘要/摘要 生物学中的一个中心问题是了解基因组变异如何影响基因组功能 表型。关键的挑战和机遇在于将基因组变异与表型、人类健康和 疾病。因为在所有环境中对所有感兴趣的基因组变体进行实验探测是不可行的， 改进计算方法以准确预测未知基因组变异的影响是必要的。 这项研究计划的目的是从机制上了解功能基因组相互作用和 最终开发计算方法来建模和预测变异、泛函 元素、基因组功能和表型。最近收购的两项关键资产将用于推断远端 DNA元素之间的功能相互作用：i)3D基因组数据和ii)多基因组比对。高- 分辨率接触映射实验(Hi-C和类似方法)表明，结构系综 染色体是流动的，但对细胞类型和生命阶段是特有的1。这些部分组织的乐团 结构使相隔很长基因组距离的DNA片段在空间上非常接近，并发挥了 在控制基因转录中的重要作用2，3.通过测量DNA之间物理接触的频率 元素，DNA-DNA邻近连接分析提供了对存在功能相互作用的洞察 相同的元素，即使相互作用的性质未知。在过去的几年里，一直有 旨在组装许多物种的基因组的爆炸性活动35-37。数百个新的 组装的端到端基因组构成了在研究一般情况下具有变革性重要性的数据集 利用进化信息研究生命之树上基因组的运行原理。这项建议旨在 将邻近连接分析的数据与从多个基因组中提取的共同进化信息相结合 比对，以推断DNA元件之间的功能相互作用网络。计算方法将 基于直接耦合分析29-32(DCA)和其他机器学习方法。PI之前已经 使用DCA来研究基因组结构33以及在其他上下文34中，并且已经使重要的 对3D基因组学领域的贡献。

Project Summary / Abstract A central problem in biology is to understand how genomic variation affects genome function to influence phenotypes. Key challenges and opportunities lie in linking genomic variants to phenotypes, human health, and disease. Because it is not feasible to experimentally probe all genomic variants of interest in all contexts, improved computational methods to accurately predict the impact of unknown genomic variants are necessary. The aim of this research proposal is to gain mechanistic understanding of functional genomic interactions and ultimately to develop computational approaches to model and predict relationships among variation, functional elements, genome function, and phenotype. Two recently acquired key assets will be used to infer distal functional interactions among DNA elements: i) 3D genomics data and ii) multiple genome alignments. High- resolution contact mapping experiments (Hi-C and similar methods) have shown that the structural ensembles of chromosomes are fluid and yet specific to cell type and phase of life1. These ensembles of partially organized structures bring sections of DNA separated by great genomic distance into close spatial proximity and play an important role in controlling gene transcription 2,3. By measuring the frequency of physical contacts among DNA elements, DNA-DNA proximity ligation assays offer insight into the existence of functional interactions among the same elements, even when the nature of the interaction is unknown. In the last few years, there has been an explosion of activity directed toward assembling the genomes of many species 35–37. Hundreds of newly assembled end-to-end genomes constitute a dataset of transformative importance in studying the general operating principles of genomes across the tree of life using evolutionary information. This proposal aims to combine data from proximity ligation assays and coevolutionary information extracted from multiple genome alignments to infer the network of functional interactions among DNA elements. The computational approach will be based on Direct Coupling Analysis 29–32 (DCA) and other machine learning methods. The PI has previously employed DCA to study genome architecture 33 as well as in other contexts 34, and has already made important contributions to the field of 3D genomics.