A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome

Abstract

Many deep learning approaches have been proposed to connect DNA sequence, epigenetic profiles, chromatin organization, and transcription activity. While these approaches achieve satisfactory performance in predicting one modality from another, the learned representations are not generalizable across predictive tasks or across cell types. In this paper, we propose a deep learning approach named EPCOT which employs a pre-training and fine-tuning framework, and comprehensively predicts epigenome, chromatin organization, transcriptome, and enhancer activity in one framework, which is also generalizable to new cell/tissue types. EPCOT is the first framework proposed to connect all of these genome modalities and achieves superior predictive performance in individual modality prediction. EPCOT also maps from DNA sequence and chromatin accessibility profiles to vectors of generic representations which are generalizable across different modalities and characterize the connections among these modalities. Interpreting EPCOT model also allows us to provide a number of tools and services to the research community including mapping between different genomic modalities, identifying TF sequence binding patterns, and analyzing cell-type specific TF impacts to enhancer activity.