Ren, L.; Debnath, I.; Duren, Z.

Regulatory genomics faces a depth-breadth gap: deep multi-omics provides regulatory detail but is difficult to scale, whereas broad expression datasets often lack the regulatory structure needed for mechanistic Gene Regulatory Network (GRN) analysis. We developed Regulatory Elements Guided Analysis (REGA), an interpretable framework that uses reference Regulatory Element (RE) catalogs to infer transcription factor (TF)-RE-gene programs from gene expression data. Across ChIP-seq, knockdown, Hi-C, cis- and trans-eQTL benchmarks, REGA prioritized functional REs, improved RE-gene and TF-gene inference over existing baselines, including methods using more data, and recovered coherent regulatory modules. In PsychENCODE snRNA-seq, REGA identified disease-associated modules and TF activities, linked regulatory dysregulation to genetic risk, and detected cross-cell-type neuronal-glial programs. In spatial transcriptomics, REGA linked cell-intrinsic regulatory programs with intercellular ligand-receptor communication; in Perturb-seq, it mapped perturbation responses to trait-associated regulatory architectures. REGA enables scalable, interpretable GRN analysis across expression datasets.