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Liang J, Cui Z, Wu C, Yu Y, Tian R, Xie H, Jin Z, Fan W, Xie W, Huang Z, Xu W, Zhu J, You Z, Guo X, Qiu X, Ye J, Lang B, Li M, Tan S, Hu Z. DeepEBV: A deep learning model to predict Epstein-Barr virus (EBV) integration sites. Bioinformatics 2021; 37:3405-3411. [PMID: 34009299 DOI: 10.1093/bioinformatics/btab388] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 03/26/2021] [Accepted: 05/17/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Epstein-Barr virus (EBV) is one of the most prevalent DNA oncogenic viruses. The integration of EBV into the host genome has been reported to play an important role in cancer development. The preference of EBV integration showed strong dependence on the local genomic environment, which enables the prediction of EBV integration sites. RESULTS An attention-based deep learning model, DeepEBV, was developed to predict EBV integration sites by learning local genomic features automatically. First, DeepEBV was trained and tested using the data from the dsVIS database. The results showed that DeepEBV with EBV integration sequences plus Repeat peaks and 2 fold data augmentation performed the best on the training dataset. Furthermore, the performance of the model was validated in an independent dataset. In addition, the motifs of DNA-binding proteins could influence the selection preference of viral insertional mutagenesis. Furthermore, the results showed that DeepEBV can predict EBV integration hotspot genes accurately. In summary, DeepEBV is a robust, accurate and explainable deep learning model, providing novel insights into EBV integration preferences and mechanisms. AVAILABILITY DeepEBV is available as open-source software and can be downloaded from https://github.com/JiuxingLiang/DeepEBV.gitSupplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiuxing Liang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Zifeng Cui
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Canbiao Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Yao Yu
- Department of Urology, The First Medical Center of Chinese PLA General Hospital, Beijing 100853 China.,School of Medicine, Nankai University, Tianjin 300071, China
| | - Rui Tian
- Center for Translational Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Hongxian Xie
- STech Company Bio-X Lab, Zhuhai 519000, Guangdong, China
| | - Zhuang Jin
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Weiwen Fan
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Weiling Xie
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Zhaoyue Huang
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Wei Xu
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Jingjing Zhu
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Zeshan You
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Xiaofang Guo
- Department of Medical Oncology of the Eastern Hospital, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510700, China
| | - Xiaofan Qiu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Jiahao Ye
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China.,School of Computer Science, South China Normal University, Guangzhou 510631, China
| | - Bin Lang
- School of Health Sciences and Sports, Macao Polytechnic Institute, China
| | - Mengyuan Li
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Songwei Tan
- School of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zheng Hu
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China.,Department of Obstetrics and Gynaecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
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