Coevolution and Functional Interactions in the Non-Coding Genome

项目总结/摘要 生物学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 疾病因为在所有情况下实验性地探测所有感兴趣的基因组变体是不可行的， 需要改进计算方法以准确预测未知基因组变异的影响。 这项研究计划的目的是获得功能基因组相互作用的机制理解， 最终开发计算方法来模拟和预测变异、功能性 元件、基因组功能和表型。两个最近收购的关键资产将用于推断远端 DNA元件之间的功能相互作用：i）3D基因组学数据和ii）多基因组比对。高- 分辨率接触映射实验（Hi-C和类似的方法）表明， 染色体是流动的，但特定于细胞类型和生命阶段1。这些部分组织起来的 结构使基因组距离很远的DNA片段在空间上接近， 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， 元件，DNA-DNA邻近连接试验提供了深入了解存在的功能相互作用， 相同的元素，即使相互作用的性质是未知的。在过去的几年里， 许多物种的基因组组装活动的爆发35-37。数百名新 组装的端到端基因组构成了一个数据集，在研究基因组的一般特性方面具有变革性的重要性。 基因组在生命之树上的运作原理。这项建议旨在 联合收割机将来自邻近连接测定数据和从多个基因组提取的共同进化信息结合 比对来推断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。此前，PI 使用DCA研究基因组结构33以及在其他背景下34，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.