CRMIPred: Identifying the spatial interactions among cis-regulatory modules via considering their cross-attended epigenetic profiles

Background and Objective: Modular DNA elements known as cis -regulatory modules (CRMs) play central roles in transcriptional regulation in metazoan species. Beyond their individual functions, CRMs can physically interact with one another to cooperatively regulate target gene expression, forming an additional layer of transcription regulation. Methods: Experimental identification of such interactions typically relies on chromosome conformation capture technologies coupled with sequencing (HiC), which require high sequencing depths to achieve sufficient resolution for CRM-level analysis, resulting in substantial cost. Computational approaches, therefore, provide an economic strategy for pre-screening potential CRM interactions. Nonetheless, existing tools often lack sufficient resolution and are restricted to limited CRM types. Some tools even suffer from data contamination caused by improper data partitioning. Here, we presented CRMIPred (CRM Interaction Predictor), a deep learning framework for CRM interaction identification built upon a chromosome-based, data-snooping-free partitioning scheme. CRMIPred models epigenetic crosstalk between CRMs using a cross-attention architecture to capture biologically meaningful interactions between multi-track epigenetic profiles. Results: On a strictly held-out test set, CRMIPred achieved an auROC of 87.7% and an auPRC of 89.3% in recognizing interacting CRM pairs, outperforming all currently available tools and baseline methods by over 10.3% and 7.2% in auROC and auPRC, respectively. Moreover, the model demonstrated robustness to input design choices, and further analyses confirmed that its performance gains stem from the biologically grounded cross-attention mechanism. Conclusions: Beyond its use as a pre-screening tool, CRMIPred also provides a computational framework for investigating the mechanistic relationship between epigenetic codes and chromatin interactions, offering insight into how epigenetic crosstalk mediates CRM–CRM communication. CRMIPred is available at https://github.com/cobisLab/CRMIPred .