Elucidation of the organizing principles of the regulatory genome through large-scale data integration

项目总结 人类基因组包含活细胞的结构和操作指令，但这些指令 指令是什么，以及它们是如何在初级基因组序列中使用和编码的，人们对此知之甚少。 可以说，基因组中唯一被人们熟知的部分是蛋白质编码区，这些区域组成的蛋白质少于 2%的基因组。越来越清楚的是，非编码基因组编码了大量的 调控元件对于以特定细胞类型的方式控制基因表达水平很重要。此外， 由全基因组关联确定的绝大多数与疾病和性状相关的变异 研究(GWAS)位于基因组的非编码区，并且富含调控元件。 尽管有这种明确的相关性，但我们仍然缺乏对全球组织原则的完全了解 调控基因组，如调控元件如何在基因组中分布，它们发生了什么 模式跨越细胞类型，以及它们在基因组序列中的编码方式。我们假设 我们理解有限的主要原因不是缺乏数据，而是大多数数据集是生成和 最终被孤立地分析，限制了它们的全部潜力。为了加深我们对组织的理解 关于调控基因组的原理，因此采取​整体方法进行数据分析是至关重要的， 在大量的观测中利用这种动态。在这个项目中，我们将使用这个概念来 开发定义第一个全面和实用的人类调控基因组的方法 基于跨数百种细胞类型的调控元件的协调出现模式的注释 和州政府。除了个别元素，我们将定义共享监管活动的数千个碱基域， 这将揭示围绕基因和更高级别的监管领域的监管格局。在……里面 此外，我们将基于功能基因组学将调控注释与正交信息相结合 染色质状态数据，以得出调控基因组的丰富复合视图。最后，我们将开发 第一个完全由数据驱动的系统，用于设计和验证特定背景的合成监管要素。我们 预计我们的结果将为人类调控基因组提供一个新的视角，这将打开新的 系统和合成生物学领域的研究途径，最终有助于理解 以及人类疾病的治疗。我们决心为基因组学社区提供务实的 有用的调控基因组注释和工具，以利用这些资源。

PROJECT SUMMARY The human genome contains the structural and operational instructions for living cells, yet exactly what these instructions are and how they are utilized and encoded in the primary genomic sequence is poorly understood. Arguably the only well-understood portions of the genome are protein-coding regions, which make up less than 2% of the genome. It has become increasingly clear that the non-coding genome encodes vast numbers of regulatory elements important for controlling gene expression levels in a cell type specific manner. Moreover, the overwhelming majority of disease- and trait-associated variants identified by genome-wide association studies (GWAS) lie in non-coding regions of the genome, and are strongly enriched in regulatory elements. Despite this clear relevance, we still lack a complete understanding of the global organizing principles of the regulatory genome, such as how regulatory elements are distributed across the genome, what their occurrence patterns are across cell types, and how they are encoded in the genomic sequence. We hypothesize that the main reason for our limited understanding is not lack of data, but that most data sets are generated and ultimately analyzed in isolation, limiting their full potential. To further our understanding of the organizing principles of the regulatory genome, it is therefore essential to take an ​en masse approach to data analysis, exploiting the dynamics across large numbers of observations. In this project, we will use this notion to develop methods for defining the first comprehensive and pragmatically useful human regulatory genome annotation based on the coordinated occurrence patterns of regulatory elements across hundreds of cell types and states. Beyond individual elements, we will define multi-kilobase domains of shared regulatory activity, which will shed light on the regulatory landscapes around genes and higher-order regulatory domains. In addition, we will integrate regulatory annotations with orthogonal information based on functional genomics chromatin state data to arrive at a rich composite view of the regulatory genome. Lastly, we will develop the first fully data-driven system for designing and validating context-specific synthetic regulatory elements. We anticipate that our results will provide a new lens on the human regulatory genome, which will open up new research avenues in the areas of systems and synthetic biology, ultimately contributing to the understanding and treatment of human disease. We are determined to provide the genomics community with pragmatically useful regulatory genome annotations and tools to utilize these resources.