CHilEnPred: CNN Model With Hilbert Curve Representation of DNA Sequence For Enhancer Prediction

Motivation: Enhancers are distal cis-acting regulating regions that play a vital role in gene transcription. However, due to the inherent nature of enhancers being linearly distant from the affected gene in an irregular manner while being spatially close at the same time, systematically predicting enhancers has been a challenging task. Although several computational predictor models through both epigenetic marker analysis and sequence-based analysis have been proposed, they lack generalization capacity across different enhancer datasets and have feature dependency. On the other hand, the recent proliferation of deep learning methods has opened previously unknown avenues of approach for sequence analysis tasks which eliminates feature dependency and achieves greater generalization. Therefore, harnessing the power of deep learning based sequence analysis techniques to develop a more generalized model than the ones developed before to predict enhancer region in a DNA sequence is a topic of interest in bioinformatics. Results: In this study, we develop the predictor model CHilEnPred that has been trained with the visual representation of the DNA sequences with Hilbert Curve. We report our computational prediction result on FANTOM5 dataset where CHilEnPred achieves an accuracy of 94.97% and AUC of 0.987 on test data.