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DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites

BACKGROUND: The hepatitis B virus (HBV) is one of the main causes of viral hepatitis and liver cancer. HBV integration is one of the key steps in the virus-promoted malignant transformation. RESULTS: An attention-based deep learning model, DeepHBV, was developed to predict HBV integration sites. By...

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Autores principales: Wu, Canbiao, Guo, Xiaofang, Li, Mengyuan, Shen, Jingxian, Fu, Xiayu, Xie, Qingyu, Hou, Zeliang, Zhai, Manman, Qiu, Xiaofan, Cui, Zifeng, Xie, Hongxian, Qin, Pengmin, Weng, Xuchu, Hu, Zheng, Liang, Jiuxing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8261932/
https://www.ncbi.nlm.nih.gov/pubmed/34233610
http://dx.doi.org/10.1186/s12862-021-01869-8
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author Wu, Canbiao
Guo, Xiaofang
Li, Mengyuan
Shen, Jingxian
Fu, Xiayu
Xie, Qingyu
Hou, Zeliang
Zhai, Manman
Qiu, Xiaofan
Cui, Zifeng
Xie, Hongxian
Qin, Pengmin
Weng, Xuchu
Hu, Zheng
Liang, Jiuxing
author_facet Wu, Canbiao
Guo, Xiaofang
Li, Mengyuan
Shen, Jingxian
Fu, Xiayu
Xie, Qingyu
Hou, Zeliang
Zhai, Manman
Qiu, Xiaofan
Cui, Zifeng
Xie, Hongxian
Qin, Pengmin
Weng, Xuchu
Hu, Zheng
Liang, Jiuxing
author_sort Wu, Canbiao
collection PubMed
description BACKGROUND: The hepatitis B virus (HBV) is one of the main causes of viral hepatitis and liver cancer. HBV integration is one of the key steps in the virus-promoted malignant transformation. RESULTS: An attention-based deep learning model, DeepHBV, was developed to predict HBV integration sites. By learning local genomic features automatically, DeepHBV was trained and tested using HBV integration site data from the dsVIS database. Initially, DeepHBV showed an AUROC of 0.6363 and an AUPR of 0.5471 for the dataset. The integration of genomic features of repeat peaks and TCGA Pan-Cancer peaks significantly improved model performance, with AUROCs of 0.8378 and 0.9430 and AUPRs of 0.7535 and 0.9310, respectively. The transcription factor binding sites (TFBS) were significantly enriched near the genomic positions that were considered. The binding sites of the AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra, and Foxo3 were highlighted by DeepHBV in both the dsVIS and VISDB datasets, revealing a novel integration preference for HBV. CONCLUSIONS: DeepHBV is a useful tool for predicting HBV integration sites, revealing novel insights into HBV integration-related carcinogenesis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12862-021-01869-8.
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spelling pubmed-82619322021-07-07 DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites Wu, Canbiao Guo, Xiaofang Li, Mengyuan Shen, Jingxian Fu, Xiayu Xie, Qingyu Hou, Zeliang Zhai, Manman Qiu, Xiaofan Cui, Zifeng Xie, Hongxian Qin, Pengmin Weng, Xuchu Hu, Zheng Liang, Jiuxing BMC Ecol Evol Research BACKGROUND: The hepatitis B virus (HBV) is one of the main causes of viral hepatitis and liver cancer. HBV integration is one of the key steps in the virus-promoted malignant transformation. RESULTS: An attention-based deep learning model, DeepHBV, was developed to predict HBV integration sites. By learning local genomic features automatically, DeepHBV was trained and tested using HBV integration site data from the dsVIS database. Initially, DeepHBV showed an AUROC of 0.6363 and an AUPR of 0.5471 for the dataset. The integration of genomic features of repeat peaks and TCGA Pan-Cancer peaks significantly improved model performance, with AUROCs of 0.8378 and 0.9430 and AUPRs of 0.7535 and 0.9310, respectively. The transcription factor binding sites (TFBS) were significantly enriched near the genomic positions that were considered. The binding sites of the AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra, and Foxo3 were highlighted by DeepHBV in both the dsVIS and VISDB datasets, revealing a novel integration preference for HBV. CONCLUSIONS: DeepHBV is a useful tool for predicting HBV integration sites, revealing novel insights into HBV integration-related carcinogenesis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12862-021-01869-8. BioMed Central 2021-07-07 /pmc/articles/PMC8261932/ /pubmed/34233610 http://dx.doi.org/10.1186/s12862-021-01869-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wu, Canbiao
Guo, Xiaofang
Li, Mengyuan
Shen, Jingxian
Fu, Xiayu
Xie, Qingyu
Hou, Zeliang
Zhai, Manman
Qiu, Xiaofan
Cui, Zifeng
Xie, Hongxian
Qin, Pengmin
Weng, Xuchu
Hu, Zheng
Liang, Jiuxing
DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites
title DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites
title_full DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites
title_fullStr DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites
title_full_unstemmed DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites
title_short DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites
title_sort deephbv: a deep learning model to predict hepatitis b virus (hbv) integration sites
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8261932/
https://www.ncbi.nlm.nih.gov/pubmed/34233610
http://dx.doi.org/10.1186/s12862-021-01869-8
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