MPRA-powered CNN reveals the cell-type grammar of human promoters and enables synthetic promoter design
PARM, a cell-type-specific convolutional neural network trained on massively parallel reporter assays, predicts autonomous promoter activity from DNA sequences across multiple cell types and stimuli. It identifies functional TF motifs, uncovers a position-dependent regulatory grammar around the transcription start site, distinguishes activating and repressing factors, and can design synthetic promoters. The approach validates predictions with ISM and motif-insertion experiments, offering mechanistic insights into promoter regulation and potential applications in disease research and personalized medicine.