Plasticity of regulatory grammar in human promoters uncovered by MPRA-trained deep learning
One of the major challenges in genomics is to build computational models that accurately predict genome-wide gene expression from the sequences of regulatory elements. Promoters play a key role in gene regulation, yet their regulatory logic remains incompletely understood. Here, we present PARM, a cell-type specific deep learning model trained on specially designed massively parallel reporter assays that query human promoter sequences. PARM is computationally light-weight and reliably predicts autonomous promoter activity across the genome from DNA sequence alone, in multiple cell types. PARM can also design purely synthetic strong promoters. We leveraged PARM to systematically identify binding sites of transcription factors (TFs) binding sites that are likely to contribute to the activity of each natural human promoter, and to detect the rewiring of these regulatory interactions upon various stimuli to the cells. We also uncovered and experimentally confirmed striking positional preferences of TFs that differ between activating and repressive regulatory functions, as well as a complex grammar of motif-motif interactions. Our approach provides a foundation towards a deeper understanding of the dynamic regulation of human promoters by TFs Overall design: Various cell lines were transfected with various different massively parrallel reporter assay (MPRA) libraries. Data contain raw read counts as well as normalized data.