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

计划总监/首席研究员（Liang，Jie）： 项目摘要/摘要 我们将开发计算工具，以促进调查基本关系 在基因表达和基因组拓扑之间。具体来说，我们将开发机器学习工具 这可以在基因组大规模上将增强子及其靶向基因联系起来。建立能力 增强子与其靶基因之间的关系至关重要，因为它将有助于我们 了解基因调节并在建立非编码风险变体之间的关系时 从GWAS研究到潜在的因果基因。我们的方法将基于3D聚合物模型 染色质相互作用来自普通基金4D核心（4DN）数据库中的HI-C数据， 并将整合来自普通基金支持的基因型组织表达（GTEX）的数据 databaseas以及来自编码数据库的数据。我们将1）构建一个可信赖的数据库 候选者 - 基因目标对的质量数据库。然后，我们将2）使用此数据库训练 机器学习预测指标可以预测基因组大规模的增强剂基因目标对。 1）， 我们将开发一条管道，以通过 模拟3D染色质集合的大规模折叠。一组特定的互动将 可以测试染色质折叠的充分性。然后，我们将确定基于计算增强器的 关于表观遗传组蛋白的修饰和染色质访问性数据， 路线图表观基因组学项目。然后，我们将选择来自GTEX的EQTL的增强器 数据库，已知会影响靶基因的表达。最终结果将是一个高 增强基因对的质量和值得信赖的数据库，这将由预测提供 关键的特异性3D物理染色质相互作用，连接含EQTL的增强剂和 靶基因。对于2），我们将开发一个机器学习预测变量，以预测增强器基因 基因组，表观基因组和HI-C数据的相互作用在全基因组范围内。我们将结合 具有基因组信息（例如TFS的序列基序）的表观遗传数据作为特征。然后我们会 训练机器学习预测指标，通过固定和交叉验证 1）的增强子目标基因对数据库。然后将评估预测变量的功效 带有CRISPRI-FLOWFISH数据的金标准。然后，我们将进行大规模 计算，将构建针对选定单元格的预测增强子基因关系的数据库 类型。总体而言，我们将展示综合两个重要常见的重大添加功能 资助数据资源，并将提供工具以促进了解 基因组拓扑和基因表达。我们的计算工具将导致对 基因组结构和基因组的关系对于改善人类健康很重要。 0925-0001（修订版03/16）页面延续格式页面