Enhancers are short DNA sequences that modulate gene expression patterns. Recent studies have shown that enhancer elements could be enriched for certain histone modification combinatorial codes, leading to interest in developing computational models to predict enhancer locations. Here we present EP-DNN, a protocol for predicting enhancers based on chromatin features, in two different cell types, a human embryonic (H1) and a human lung fibroblast (IMR90) cell line. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures. We train EP-DNN using distal p300 binding sites, as enhancers, and TSS and random non-DNase-I hypersensitivity sites, as non-enhancers. We find that EP-DNN has superior accuracy relative to other state-of-the-art algorithms, such as DEEP-EN and RFECS, and also scales well to large number of predictions. Then, we surmount the problem that DNN results are not interpretable and develop a method to interpret which histone modifications are important, and within that, which spatial features proximal or distal to the enhancer site, are important. We uncover that the important histone modifications vary between cell types. Further, whether the important features are clustered around the enhancer peak or more spread out also differs among the different histone modifications. Thus, we bring forth a new paradigm for automatically determining the important features and the important histone modifications, rather than the current computational standard of using the same fixed number of features from all the histone modifications for all cell types. Our results have implications for computational scientists who can now do feature selection for their classification task and for biologists who can now experimentally collect data only for the relevant histone modifications.
['Seong Gon Kim', 'Nawanol Theera-Ampornpunt', 'Ananth Grama', 'Somali Chaterji']
Interpretable deep neural networks for enhancer prediction