HiChIPdb: a comprehensive database of HiChIP regulatory interactions

Abstract

Elucidating the role of 3D architecture of DNA in gene regulation is crucial for understanding cell differentiation, tissue homeostasis and disease development. Among various chromatin conformation capture methods, HiChIP has received increasing attention for its significant improvement over other methods in profiling of regulatory (e.g. H3K27ac) and structural (e.g. cohesin) interactions. To facilitate the studies of 3D regulatory interactions, we developed a HiChIP interactions database, HiChIPdb (http://health.tsinghua.edu.cn/hichipdb/). The current version of HiChIPdb contains ∼262M annotated HiChIP interactions from 200 high-throughput HiChIP samples across 108 cell types. The functionalities of HiChIPdb include: (i) standardized categorization of HiChIP interactions in a hierarchical structure based on organ, tissue and cell line and (ii) comprehensive annotations of HiChIP interactions with regulatory genes and GWAS Catalog SNPs. To the best of our knowledge, HiChIPdb is the first comprehensive database that utilizes a unified pipeline to map the functional interactions across diverse cell types and tissues in different resolutions. We believe this database has the potential to advance cutting-edge research in regulatory mechanisms in development and disease by removing the barrier in data aggregation, preprocessing, and analysis.

Heritability enrichment in context-specific regulatory networks improves phenotype-relevant tissue identification

“…Our CNCC-specific regulations further prioritized only two SNPs (‘16:87237097’, ‘16:87236947’) located in this CNCC-specific RE, which may influence the expression of FOXC2 and the brain shape phenotypes. We checked the chromatin loops in the database of HiChIP ( Zeng et al, 2022 ) and found that this SNP-associated regulation of FOXC2 was supported by a HiChIP loop in brain tissues to link this SNP locus and FOXC2 promoter. This example showed the power of SpecVar to interpret the genetic variants’ association with phenotypes by detailed regulatory networks in relevant tissues.…”

DeepDrug: A general graph‐based deep learning framework for drug‐drug interactions and drug‐target interactions prediction

“…Second, the current interaction predictions (e.g., DTIs) do not consider the causal interaction where one drug is involved in a biological or biochemical process to directly or indirectly affect a protein. Identifying such direct interactions and indirect interactions by causal inference method [64] could help us better understand the related biological or chemical pathways or mechanisms. Furthermore, based on complex geneprotein-drug-disease heterogeneous networks constructed from multiple genomics databases [65][66][67], combining sequence and structural features of proteins/ targets with association features in complex graphs through heterogeneous graph convolution networks would be a direction that could be improved.…”

The dynamic role of cohesin in maintaining human genome architecture

“…Such Example is ChromLoops [ 119 ] [https://3dgenomics.hzau.edu.cn/chromloops], an integrated dataset for high‐quality chromatin loops from 1030 datasets across 366 samples from 13 species from ChIA‐PET, HiChIP and PLAC‐Seq datasets. HiChIPdb [ 120 ] [http://health.tsinghua.edu.cn/hichipdb/] contains more than 260 million HiChIP interactions from 200 HiChIP experiments for 208 cell type at variable 1/5/10/50 kb resolution processed by FitHiChIP [ 121 ] and Hichipper [ 122 ] computational pipeline for loop calling. CohesinDB [ 123 ] [https://cohesindb.iqb.u-tokyo.ac.jp] is a multi‐omics database summarising information for Cohesin Protein in human cells.…”