Decoding the Mechanistic Impact of Genetic Variation on Regulatory Sequences with Deep Learning

Abstract

Non-coding DNA encodes complex cis-regulatory mechanisms that govern gene expression by orchestrating transcription factor binding within specific sequence contexts. While deep learning has advanced our understanding of these mechanisms, how genetic variation reconfigures them remains an open challenge. Here, we introduce SEAM, an AI-driven tool that systematically investigates how mutations reshape regulatory mechanisms. By mapping sequences into a mechanism space and clustering them based on shared features, SEAM reveals how specific mutations can reprogram regulatory DNA, driving mechanistic and functional diversity. SEAM highlights the remarkable evolvability of human regulatory elements, disentangles transcription factor-specific effects from broader sequence context, and provides a powerful framework for decoding the cis-regulatory code. By enabling systematic, unbiased exploration of reprogrammable mechanisms, SEAM illuminates evolutionary pathways and informs the rational design of synthetic sequences with tailored functions.