Predicting 3D physical gene-enhancer interactions through integration of GTEx and 4DN data

项目总监/首席调查员(梁杰)： 项目摘要/摘要 我们将开发有助于研究基本关系的计算工具 基因表达和基因组拓扑之间的关系。具体来说，我们将开发机器学习工具 能够在全基因组范围内将增强子与其靶基因连接起来。建立的能力 增强子和它们的靶基因之间的关系至关重要，因为它将有助于我们的 理解基因调控和建立非编码风险变量之间的关系 从GWAS的研究到潜在的因果基因。我们的方法将基于3D聚合物模型 染色质相互作用源自共同基金4D核组(4DN)数据库中的Hi-C数据， 并将整合来自共同基金支持的基因-组织表达(GTEx)的数据 数据库，以及来自ENCODE数据库的数据。我们将1)构建一个值得信赖的高级数据库- 候选增强子-基因靶点对的质量数据库。然后我们将使用这个数据库来训练一个 机器学习预测器，可以在基因组范围内预测增强子-基因靶点对。对于1)， 我们将开发一个渠道来确定一小部分关键的特定染色质3D相互作用 三维染色质系综的大规模折叠模拟。这一小组特定交互将 检测染色质折叠的充分性。然后，我们将基于以下内容确定计算增强器 来自ENCODE的表观遗传组蛋白修饰和染色质可及性数据 路线图表观基因组学计划。然后，我们将从GTEx中选择包含eQTL的增强子 数据库，这是已知的影响目标基因的表达。最终的结果将是一个高- 高质量和值得信赖的增强基因对数据库，将由预测提供 连接含有eQTL的增强子和 靶基因。对于2)，我们将开发一种预测增强基因的机器学习预测器 基因组、表观基因组和Hi-C数据在全基因组范围内的相互作用。我们将联合起来 以基因组信息(如转录因子的序列基序)为特征的表观遗传学数据。到时候我们会的 通过坚持和交叉验证构建的机器学习预测器 来自1)的增强子-靶基因对的数据库。然后将评估预测器的有效性。 以CRISPRi-FlowFISH数据为金标准。然后我们将进行大规模的 并将为所选细胞构建预测的增强子-基因关系数据库 类型。总体而言，我们将展示集成两个重要的公共 并将为基金数据资源提供工具，以便于了解 基因组拓扑学和基因表达。我们的计算工具将引导我们对 基因组结构与基因组功能的关系对改善人类健康具有重要意义。 0925-0001(03/16版)页面续格式页面

Program Director/Principal Investigator (Liang, Jie): PROJECT SUMMARY/ABSTRACT We will develop computational tools that facilitate investigation of the fundamental relationship between gene expression and genome topology. Specifically, we will develop machine learning tools that can link enhancer and its targeted gene at genome wide scale. The ability of establishing relationship between enhancers and their target genes is critically important, as it will aid in our understanding of gene regulation and in establishing the relationship between noncoding risk variants from GWAS studies to potential causal genes. Our approach will be based on 3D polymer models of chromatin interactions derived from Hi-C data in the common fund 4D Nucleome (4DN) database, and will integrate data from the common fund supported Genotype-Tissue Expression (GTEx) databaseas, as well as data from ENCODE database. We will 1) construct a database of trusted high- quality database of candidate enhancer-gene target pairs. We will then 2) use this database to train a machine learning predictor that can predict enhancer-gene target pairs at genome wide scale. For 1), we will develop a pipeline to identify a small set of critical specific chromatin 3D interactions through simulation of large scale folding of 3D chromatin ensembles. The small set of specific interactions will be tested for sufficiency of chromatin folding. We will then identify computationally enhancers based on epigenetic histone modifications and chromatin accessibility data from ENCODE as well as the Roadmap Epigenomics Project. We will then select enhancers containing eQTLs from the GTEx databases, which are known to affect the expression of the target gene. The end result will be a high- quality and trustworthy database of enhance-gene pairs, which will be provided by the predicted critical specific 3D physical chromatin interactions connecting the eQTL-containing enhancer and the target gene. For 2), we will develop a machine-learning predictor that predicts enhancer-gene interactions from genomic, epigenomic, and Hi-C data at genome-wide scale. We will combine epigenetic data with genomic information (such as sequence motifs of TFs) as features. We will then train a machine learning predictor through hold-outs and cross-validations of the constructed database of enhancer-target gene pairs from 1). The efficacy of the predictor will then be assessed with the gold-standard of the CRISPRi-FlowFISH data. We will then carry out large scale computational and will construct databases of predicted enhancer-gene relationship for selected cell types. Overall, we will demonstrate significant added-power of integrating two important Common Fund data resources and will provide tools to facilitate understanding the relationship between genome topology and gene expression. Our computational tools will lead to new insight into the relationship of genome structure and genome function important for improving human health. 0925-0001 (Rev. 03/16) Page Continuation Format Page