Back

A cohesin-centric gene regulatory network resource with regulatory site annotations

Ding, J.; Chen, H.; Fu, Y.; Gao, W.; Fang, Z.; Wang, J.

2026-01-07 bioinformatics
10.64898/2026.01.06.697865 bioRxiv
Show abstract

Cohesin is a central regulator of transcription and chromatin organization, binding to the majority of cis-regulatory elements (CREs) and mediating enhancer-promoter communication through three-dimensional genome architecture. Although gene regulatory networks (GRNs) provide an interpretable framework for modeling transcriptional regulation, most existing GRN analyses focus on transcription factor (TF)-gene relationships and largely ignore regulatory sites. Consequently, cohesin-associated regulatory contexts are rarely incorporated into GRN reconstruction. Here, we present a cohesin-centric gene regulatory network database that explicitly integrates TF binding, regulatory sites, and gene targets into unified TF-site-gene regulatory paths. Building upon our previously developed multiomics resource CohesinDB, we mapped TF binding to cohesin-associated regulatory sites and linked these sites to their downstream target genes. The resulting Cohesin-GRN module in CohesinDB (http://cohesindb.wangjklab.com/) (http://120.24.147.32/)comprises 61,222,502 TF-cohesin site links and 2,228,634 cohesin site-gene links, collectively forming over 270 million TF-site-gene regulatory paths. By enabling a CRE-informed and site-aware representation of gene regulation, Cohesin-GRN bridges conventional TF-gene GRNs with regulatory site-centric mechanisms. Given the pervasive roles of cohesin in enhancer activity, transcriptional control, and human disease, Cohesin-GRN provides a valuable resource for exploring transcriptional dysregulation and gene regulatory networks.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.