Multimodal regulatory genomics for inferring causal gene-regulatory mechanisms

The field of regulatory genomics has evolved rapidly, with increasing complexity in understanding how genes are regulated within the three-dimensional context of the genome. Traditional approaches have often focused on isolated modalities, such as methylation or chromatin structure, leaving significant gaps in mechanistic insight. Recent technological advances now allow integration of 3D genome contact maps, diverse epigenomic marks, expression data, and genomic sequence information, offering an unprecedented opportunity to decode the causal relationships between regulatory elements and gene activity. However, deciphering how enhancers and other distal elements exert their influence over specific genes remains a major challenge, particularly in light of extensive cell type variability and the context-dependent nature of gene regulation. Emerging studies utilizing perturbation-informed models and causal graphs demonstrate the potential to overcome these limitations, but a comprehensive framework for integrating all available modalities is still lacking.This Research Topic aims to facilitate a deeper understanding of causal gene-regulatory mechanisms through integrated multimodal analysis. The main objective is to uncover how multiple layers of genome organization and activity, combining 3D contact architecture, epigenomic states, transcriptional output, and underlying nucleotide sequence, cooperatively link enhancers to their functional target genes. Key research questions include how to develop robust models that move beyond correlative associations, how causal relationships can be validated experimentally or computationally, and how these frameworks generalize across diverse cell types or tissues. By prioritizing causal inference, this initiative seeks to move the field beyond descriptive catalogs toward predictive and mechanistic models of genome regulation.To gather further insights in the causal mechanisms of gene regulation using multimodal data integration, we welcome articles addressing, but not limited to, the following themes:- Computational frameworks for integrating 3D genome, epigenomic, expression, and sequence data- Causal graphs and inference techniques applied to regulatory genomics- Perturbation-informed modeling and validation of enhancer–gene relationships- Generalization of gene-regulatory models across cell types, states, or species- Comparative and benchmark studies of multimodal regulatory inference methods- Experimental approaches for validating causal regulatory predictions- Limitations, challenges, and future opportunities in multimodal regulatory genomics Topic editors: Hasan Zulfiqar, JIAN ZHANG, Tao Jiang Source: https://www.frontiersin.org/research-topics/78529/multimodal-regulatory-genomics-for-inferring-causal-gene-regulatory-mechanisms