RegFM: an interpretable context-aware foundation model for human transcriptional regulation

Abstract

Transcriptional regulation is governed by interactions between cis-regulatory elements (CREs) and trans-acting regulators in a context-specific manner. Although DNA and single-cell foundation models have enabled modeling regulatory biology at scale, most represent either sequence or cellular state alone, limiting their ability to capture context-dependent gene regulation. Here we present RegFM, a context-aware foundation model for human transcriptional regulation. RegFM treats transcriptional regulation as a dialogue between cis-regulatory sequences (e.g., CREs) and trans-acting regulators (e.g., transcription factors (TFs) and chromatin regulators (CRs)) by coupling long-range CRE representations with TFs and CRs activity. Trained on large-scale ENCODE and CELLxGENE transcriptomic profiles, RegFM learns gene-centered regulatory representations that generalize across unseen cellular contexts. In a wide range of tasks, including gene expression prediction, cis-regulatory element annotation, bivalent promoter and dosage-sensitivity classification, and perturbation-response prediction, RegFM consistently improves over existing methods. RegFM emerges as a scalable and interpretable framework for modeling human transcriptional regulation and provides insights into context-dependent gene regulatory programs.