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

doi:10.1093/bioinformatics/btz562

CSpace > 应用数学研究所

	Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network
	Zeng, Wanwen 1; Wang, Yong2,3 ; Jiang, Rui 1
	2020-01-15
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	36 期号:2 页码:496-503
摘要	Motivation: 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. Results: 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 expression status but also in regression of continuous gene expression levels, in both cross-validation experiments and cross-cell line predictions. We show that the sequential promoter information is more informative than the experimental enhancer information; meanwhile, the enhancer-promoter interactions within +/- 100 kbp around the TSS of a gene are most beneficial. We finally visualize motifs in both promoter and enhancer regions and show the match of identified sequence signatures with known motifs. We expect to see a wide spectrum of applications using HiChIP data in deciphering the mechanism of gene regulation.
DOI	10.1093/bioinformatics/btz562
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFC0910404] ; National Natural Science Foundation of China[61873141] ; National Natural Science Foundation of China[61721003] ; National Natural Science Foundation of China[61573207] ; Tsinghua-Fuzhou Institute for Data Technology
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000526660300021
出版者	OXFORD UNIV PRESS
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/51358
专题	应用数学研究所
通讯作者	Wang, Yong; Jiang, Rui
作者单位	1.Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Automat, MOE Key Lab Bioinformat, Beijing 100084, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, CEMS,NCMIS,MDIS, Beijing 100080, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China
推荐引用方式 GB/T 7714	Zeng, Wanwen,Wang, Yong,Jiang, Rui. Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network[J]. BIOINFORMATICS,2020,36(2):496-503.
APA	Zeng, Wanwen,Wang, Yong,&Jiang, Rui.(2020).Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network.BIOINFORMATICS,36(2),496-503.
MLA	Zeng, Wanwen,et al."Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network".BIOINFORMATICS 36.2(2020):496-503.