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

摘要

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 enhancerpromoter 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.