Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network
文献类型：期刊论文
| 作者 | Zeng, Wanwen1; Wang, Yong2,3 ; Jiang, Rui1
|
| 刊名 | BIOINFORMATICS
 |
| 出版日期 | 2020-01-15 |
| 卷号 | 36期号:2页码:496-503 |
| ISSN号 | 1367-4803 |
| DOI | 10.1093/bioinformatics/btz562 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000526660300021 |
| 出版者 | OXFORD UNIV PRESS |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/51357]  |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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