Many deep learning approaches have been proposed to connect DNA sequence, epigenetic profiles, chromatin organization and transcription activities. While these approaches achieve satisfactory performance in predicting one modality from another, the representations learned 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 types. EPCOT not only achieves superior predictive performance in individual predictive tasks, it also produces globally optimized sequence representations that are generalizable across different predictive tasks. 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.

Many deep learning approaches have been proposed to predict 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 is able to accurately and comprehensively predict multiple modalities including epigenome, chromatin organization, transcriptome, and enhancer activity for new cell types, by only requiring cell-type specific chromatin accessibility profiles. Many of these predicted modalities, such as Micro-C and ChIA-PET, are quite expensive to get in practice, and the in silico prediction from EPCOT should be quite helpful. Furthermore, this pre-training and fine-tuning framework allows EPCOT to identify generic representations generalizable across different predictive tasks. Interpreting EPCOT models also provides biological insights including mapping between different genomic modalities, identifying TF sequence binding patterns, and analyzing cell-type specific TF impacts on enhancer activity.

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