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

通过整合 GTEx 和 4DN 数据预测 3D 物理基因增强子相互作用

• 批准号: 10776871
• 负责人: Jie Liang
• 金额: $ 29.82万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2024-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10776871
• 关键词: 3-Dimensional; ATAC-seq; Address; Affect; Algorithms; Biological Assay; CRISPR interference; Cells; ChIP-seq; Chromatin; Chromatin Modeling; Chromosomes; Computer Analysis; DNA; DNase I hypersensitive sites sequencing; Data; Databases; Elements; Enhancers; Epigenetic Process; Frequencies; Funding; Gene Expression; Gene Expression Regulation; Gene Targeting; Genes; Genome; Genomics; Genotype; Genotype-Tissue Expression Project; Health; Hi-C; Human; Investigation; Link; Location; Machine Learning; Maps; Methods; Modeling; Molecular Conformation; Polymers; Principal Investigator; Quantitative Trait Loci; Regulatory Element; Resources; Structure; Techniques; Testing; Tissues; Training; Trust; Untranslated RNA; Validation; Variant; causal variant; cell type; chromosome conformation capture; computerized tools; cost; data integration; data resource; data standards; deep learning; deep learning model; epigenomics; gene interaction; genetic variant; genome sequencing; genome wide association study; genome-wide; genome-wide analysis; histone modification; improved; innovation; insight; large scale simulation; machine learning prediction; programs; risk variant; simulation; tool; transcriptome sequencing; trustworthiness; whole genome

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

项目负责人/主要研究者（梁杰）： 项目摘要/摘要 我们将开发计算工具，促进调查的基本关系， 基因表达和基因组拓扑之间的联系具体来说，我们将开发机器学习工具 可以在全基因组范围内连接增强子和其靶基因。建立能力 增强子和它们的靶基因之间的关系是至关重要的，因为它将有助于我们 了解基因调控和建立非编码风险变异之间的关系 从GWAS研究到潜在的致病基因。我们的方法将基于3D聚合物模型， 来自共同基金4D Nucleome（4DN）数据库中Hi-C数据的染色质相互作用， 并将整合来自共同基金支持的基因型-组织表达（GTEx）的数据 数据库以及ENCODE数据库中的数据。我们将1）建立一个可信度高的数据库- 候选增强子-基因靶对的质量数据库。然后，我们将使用这个数据库来训练一个 机器学习预测器，可以在全基因组范围内预测增强子-基因靶对。对于1）， 我们将开发一个管道，通过以下方式识别一小组关键的特异性染色质3D相互作用： 3D染色质系综的大规模折叠模拟。一小部分特定的相互作用将 检测染色质折叠的充分性。然后，我们将确定计算增强剂的基础上， 关于表观遗传组蛋白修饰和来自ENCODE的染色质可及性数据以及 路线图表观基因组学项目。然后，我们将从GTEx中选择含有eQTL的增强子， 数据库，已知其影响靶基因的表达。最终结果将是一个高- 高质量和可靠的增强基因对数据库，这将由预测的 关键的特异性3D物理染色质相互作用，连接含eQTL的增强子和 靶基因对于2），我们将开发一个机器学习预测器，预测增强子基因 从基因组，表观基因组和Hi-C数据在全基因组范围内的相互作用。我们将联合收割机 以基因组信息（例如TF的序列基序）作为特征的表观遗传数据。然后我们将 通过对所构建的模型的保留和交叉验证来训练机器学习预测器， 来自1）的增强子-靶基因对的数据库。然后将评估预测因子的有效性 CRISPRi-FlowFISH数据的黄金标准。我们将进行大规模的 计算并将构建所选细胞的预测增强子-基因关系的数据库 类型总的来说，我们将展示整合两个重要的通用 基金的数据资源，并将提供工具，以促进了解之间的关系， 基因组拓扑学和基因表达。我们的计算工具将带来对 基因组结构和基因组功能的关系对改善人类健康很重要。 0925-0001（Rev. 03/16）Page Continuation Format Page