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Incorporating gene expression in genome-wide prediction of chromatin accessibility via deep learning

By Qiao Liu, Wing Hung Wong, Rui Jiang

Posted 28 Oct 2019
bioRxiv DOI: 10.1101/610642

Regulatory elements (REs) in human genome are major sites of non-coding transcription which lack adequate interpretation. Although computational approaches have been complementing high- throughput biological experiments towards the annotation of the human genome, it remains a big challenge to systematically and accurately characterize REs in the context of a specific cell type. To address this problem, we proposed DeepCAGE, an open-source deep learning framework that incorporates transcriptome profile of human transcription factors (TFs) for accurately predicting the activities of cell type-specific REs. Our approach automatically learns the regulatory code of input DNA sequence and allocates attention to TFs with cell type-specific expression. In a series of systematic comparison with existing methods, we show the superior performance of our model in not only the classification of accessible regions, but also the regression of DNase-seq signals. A typical scenario of usage for our method is to predict the activities of REs in new cell types, especially where the chromatin accessibility data is not available. Our study provides a fascinating insight into disclosing complex regulatory mechanism by integrating transcriptomic profile of human TFs.

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