Quantifying the impact of genetic mutations on enhancer dynamics [RNAseq]

We developed UDI-UMI-STARR-seq, integrating dual indexes and unique molecular identifiers, and combined it with RNA-seq to profile perturbation-induced changes in enhancer activity and target gene expression. We applied this approach to 253,632 fragments representing 46,142 cell type-specific candidate enhancer regions and assessed the impact of CRISPR/Cas9-mediated deletion of six transcription factors (ATF2, CTCF, FOXA1, LEF1, TCF7L2, and SCRT1).

We identified fragments that were repressed or induced, often through p53 family motifs. Enhancer-gene mapping revealed TF-specific programs, including repression of Wnt/p53 targets after ATF2 or LEF1 loss. A deep learning model trained on enhancer sequences recapitulated enhancer grammar, including cooperative motif syntax and flanking sequence context.