Ultra-high resolution 3D genome maps for multiple human tissues

Abstract The diversity of human tissues and cell types is controlled by differential regulation of gene expression. Enhancers are the primary units of gene expression control in humans, which physically interact with the target genes to activate them. To understand the mechanism of transcriptional regulation, several landmarking consortia

have accumulated large amounts of genomic data. The NIH Common Fund GTEx project has revealed tissue- specific gene expression and transcriptional regulation. The NIH Common Fund 4D Nucleome (4DN) project has characterized 3D chromatin interactions in many human tissues and cell types. The ENCODE project and

the Roadmap Epigenomic project have profiled tissue-specific epigenomic states and annotated the regulatory elements accordingly. However, our understanding of human transcriptional regulation remains limited. A major challenge in studying Enhancer-Promoter (E-P) interactions is that enhancers are often located tens to hundreds of kilobases distal to

their target genes, yet must physically interact with their target genes to activate them. Therefore, to understand gene regulation in human, a necessary first step is to precisely map all E-P interactions. However, current 3D maps of the human genome remain sparse and noisy and fail to detect the vast majority of E-P interactions. In

addition, the datasets generated from the Common Fund consortia are isolated in terms of cell types and tissue types covered, how the data are stored, and the resolution of the genomic data, resulting in additional barriers for integrative analysis. Here we propose to combine the experimental approach, Region Capture Micro-C, and the deep learning

algorithms to accurately quantify E-P interactions. These results enhance the current 3D maps from 4DN and ENCODE projects. We also plan to integrate experimental and imputed E-P interactions with NIH Common Fund data in a knowledge graph. Our knowledge graph will support efficient cross-modality queries, graph visualization,

and customized computational modeling for investigating quantitative rules in transcriptional regulation. Not only would this accomplishment have an enormous positive impact on the utility and usage of the Common Fund datasets, but it would also help to promote open science and reproducible research in the areas of computational

genomics and data science.