Jul 19, 2019

Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network

Bioinformatics
Wanwen ZengRui Jiang

Abstract

Interactions among cis-regulatory elements such as enhancers and promoters are main driving forces shaping context-specific chromatin structure and gene expression. Although there have been computational methods for predicting gene expression from genomic and epigenomic information, most of them neglect long-range enhancer-promoter interactions, due to the difficulty in precisely linking regulatory enhancers to target genes. Recently, HiChIP, a novel high-throughput experimental approach, has generated comprehensive data on high-resolution interactions between promoters and distal enhancers. Moreover, plenty of studies suggest that deep learning achieves state-of-the-art performance in epigenomic signal prediction, and thus promoting the understanding of regulatory elements. In consideration of these two factors, we integrate proximal promoter sequences and HiChIP distal enhancer-promoter interactions to accurately predict gene expression. We propose DeepExpression, a densely connected convolutional neural network, to predict gene expression using both promoter sequences and enhancer-promoter interactions. We demonstrate that our model consistently outperforms baseline methods, not only in the classification of binary gene expr...Continue Reading

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Mentioned in this Paper

Study
Classification
Genes
Insulin-like Growth Factor Binary Complex
Genomic Stability
Promoter
Cross Validation
SPT6 Protein
Gene Expression
Neural Stem Cells

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