Predicting enhancer‐promoter interaction from genomic sequence with deep neural networks

Background In the human genome, distal enhancers are involved in regulating target genes through proximal promoters by forming enhancer‐promoter interactions. Although recently developed high‐throughput experimental approaches have allowed us to recognize potential enhancer‐promoter interactions genome‐wide, it is still largely unclear to what extent the sequence‐level information encoded in our genome help guide such interactions. Methods Here we report a new computational method (named “SPEID”) using deep learning models to predict enhancer‐promoter interactions based on sequence‐based features only, when the locations of putative enhancers and promoters in a particular cell type are given. Results Our results across six different cell types demonstrate that SPEID is effective in predicting enhancer‐promoter interactions as compared to state‐of‐the‐art methods that only use information from a single cell type. As a proof‐of‐principle, we also applied SPEID to identify somatic non‐coding mutations in melanoma samples that may have reduced enhancer‐promoter interactions in tumor genomes. Conclusions This work demonstrates that deep learning models can help reveal that sequence‐based features alone are sufficient to reliably predict enhancer‐promoter interactions genome‐wide.