Cross-species analysis of enhancer logic using deep learning

Deciphering the genomic regulatory code of enhancers is a key challenge in biology as this code underlies cellular identity. A better understanding of how enhancers work will improve the interpretation of noncoding genome variation, and empower the generation of cell type-specific drivers for gene therapy. Here we explore the combination of deep learning and cross-species chromatin accessibility profiling to build explainable enhancer models. We apply this strategy to decipher the enhancer code in melanoma, a relevant case study due to the presence of distinct melanoma cell states. We trained and validated a deep learning model, called DeepMEL, using chromatin accessibility data of 26 melanoma samples across six different species.