Quantifying the impact of genetic mutations on enhancer dynamics

Transcriptional regulation is mediated by enhancers, yet how genetic perturbations alter enhancer activity and gene expression remains unclear. We develop UDI-UMI-STARR-seq, integrating dual indexes and unique molecular identifiers, and combine it with RNA-seq to profile perturbation-associated changes in enhancer activity and gene expression. We apply this approach to 253,632 fragments representing 46,142 cell type-specific candidate enhancers and assess the impact of CRISPR/Cas9-mediated deletion of six transcription factors (ATF2, CTCF, FOXA1, LEF1, TCF7L2, and SCRT1). We identify responsive fragments that are repressed or induced and enriched for p53 family motifs. Enhancer-gene mapping reveals associations between responsive fragments and gene expression changes, including Wnt/p53 targets after ATF2 or LEF1 depletion. A deep learning model recapitulates enhancer grammar, including cooperative motif syntax and flanking sequence context. Applying this framework to 16p12.1 deletion identifies responsive fragments associated with genes involved in neuronal function, providing a scalable readout of enhancer dynamics across genetic perturbations.