VISDB: a manually curated database of viral integration sites in the human genome

Tang, Deyou; Li, Bingrui; Xu, Tianyi; Hu, Ruifeng; Tan, Daqiang; Song, Xiaofeng; Jia, Peilin; Zhao, Zhongming

doi:10.1093/nar/gkz867

Cited by 47 publications

(53 citation statements)

References 37 publications

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“…Whether this might represent one of several components for risk stratification remains to be determined. Our results, together with a recent study [58], have shown that viral integrations may also occur in other genetic elements that are involved in regulation of gene expression, such as ncRNA and UTRs. and HPV18 are to a certain degree associated with different types of invasive cervical cancers [3,4] and may utilise different molecular mechanisms to induce carcinogenesis.…”

Section: Discussionsupporting

confidence: 78%

HPV16 and HPV18 type-specific APOBEC3 and integration profiles in different diagnostic categories of cervical samples

Lagström

Løvestad

Umu

et al. 2020

Preprint

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Human papillomavirus 16 and 18 are the most predominant types in cervical cancer. Only a small fraction of HPV infections progress to cancer, indicating that genomic factors, such as minor nucleotide variation caused by APOBEC3 and chromosomal integration, contribute to the carcinogenesis.We analysed minor nucleotide variants (MNVs) and integration in HPV16 and HPV18 positive cervical samples with different morphology. Samples were sequenced using an HPV whole genome sequencing protocol TaME-seq. A total of 80 HPV16 and 51 HPV18 positive cervical cell samples passed the sequencing depth criteria of 300× reads, showing the following distribution: non-progressive disease (HPV16 n=21, HPV18 n=12); cervical intraepithelial neoplasia (CIN) grade 2 (HPV16 n=27, HPV18 n=9); CIN3/adenocarcinoma in situ (AIS) (HPV16 n=27, HPV18 n=30); cervical cancer (HPV16 n=5).Similar rates of MNVs in HPV16 and HPV18 samples were observed for most viral genes but for HPV16, the non-coding region (NCR) showed a trend towards increasing variation with increasing lesion severity. APOBEC3 signatures were observed in HPV16 lesions, while similar mutation patterns were not detected for HPV18. The proportion of samples with integration was 13% for HPV16 and 59% for HPV18 positive samples, with a noticeable portion located within or close to cancer-related genes.

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Section: Discussionsupporting

confidence: 78%

HPV16 and HPV18 type-specific APOBEC3 and integration profiles in different diagnostic categories of cervical samples

Lagström

Løvestad

Umu

et al. 2020

Preprint

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“…The major sources of data in ViMIC were derived from abundant published literatures, the Cistrome Data Browser (18,19), VISDB (21) and GEO database (20). Viral annotation information was retrieved from a large collection of published literature through our text-mining system, which was followed by a manual curation process.…”

Section: Data Sourcesmentioning

confidence: 99%

“…Therefore, it is quite challenging for researchers to quickly search, visualize, and re-use such knowledge for their studies of virus-host interactions. Several databases, such as the Viral Integration Site DataBase (VISDB) (21), Dr.VIS (22), the Retrovirus Integration Database (RID) (23) and the ISDB (24) have been developed. However, to our knowledge, these databases focused on virus integration, and none of existing databases or web-servers are designed for systematically evaluating VMs and functionally annotating of VISs.…”

Section: Introductionmentioning

confidence: 99%

ViMIC: A Database of Human Disease-related Virus Mutations, Integration Sites and Cis-effects

Wang

Tong

Zhang

et al. 2020

Preprint

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Molecular mechanisms of virus-related diseases involve multiple factors, including viral mutation accumulation and integration of a viral genome into the host DNA. With increasing attention being paid to virus-mediated pathogenesis and the development of many useful technologies to identify virus mutations (VMs) and viral integration sites (VISs), abundant literatures on these topics are available in PubMed. However, knowledge of VMs and VISs is widely scattered in numerous published papers, and the association of VMs with VISs in the viral genome or the functional annotation of VISs still lacks integration and curation. To address these challenges, we built a database of human disease-related Virus Mutations, Integration sites and Cis-effects (ViMIC), which specialize in three features: virus mutation sites, viral integration sites and target genes. In total, the ViMIC provides information on 6,461 VMs, 79,089 VISs, and 15,056 viral target genes of 8 viruses in 65 human diseases obtained from literatures. Furthermore, in ViMIC, users are allowed to explore the cis-effects of virus-host interactions by surveying 78 histone modifications, binding of 1,358 transcription regulators, and chromatin accessibility on these VISs. We believe ViMIC will become a valuable resource for the virus research community. The database is available at http://bmtongji.cn/ViMIC/index.php.

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“…It is necessary of DeepHBV to be applied on general datasets, we tested the pre-trained DeepHBV models (DeepHBV with HBV integration sequences + repeat peaks and DeepHBV with HBV integration sequences + TCGA Pan Cancer peaks) on the HBV integration sites dataset in another viruses integration sites (VIS) database VISDB [19]. We found that in the model trained with HBV integration sequences + repeat sequences showed an AUROC of 0.6657 and an AUPR of 0.5737, while the model trained with HBV integrated sequences + TCGA Pan Cancer showed an AUROC of 0.7603 and an AUPR of 0.6189.…”

Section: Resultsmentioning

confidence: 99%

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

Guo

et al. 2021

Preprint

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Hepatitis B virus (HBV) is one of the main causes for viral hepatitis and liver cancer. Previous studies showed HBV can integrate into host genome and further promote malignant transformation. In this study, we developed an attention-based deep learning model DeepHBV to predict HBV integration sites by learning local genomic features automatically. We trained and tested DeepHBV using the HBV integration sites data from dsVIS database. Initially, DeepHBV showed AUROC of 0.6363 and AUPR of 0.5471 on the dataset. Adding repeat peaks and TCGA Pan Cancer peaks can significantly improve the model performance, with an AUROC of 0.8378 and 0.9430 and an AUPR of 0.7535 and 0.9310, respectively. On independent validation dataset of HBV integration sites from VISDB, DeepHBV with HBV integration sequences plus TCGA Pan Cancer (AUROC of 0.7603 and AUPR of 0.6189) performed better than HBV integration sequences plus repeat peaks (AUROC of 0.6657 and AUPR of 0.5737). Next, we found the transcriptional factor binding sites (TFBS) were significantly enriched near genomic positions that were paid attention to by convolution neural network. The binding sites of AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra and Foxo3 were highlighted by DeepHBV attention mechanism in both dsVIS dataset and VISDB dataset, revealing the HBV integration preference. In summary, DeepHBV is a robust and explainable deep learning model not only for the prediction of HBV integration sites but also for further mechanism study of HBV induced cancer.Author summaryHepatitis B virus (HBV) is one of the main causes for viral hepatitis and liver cancer. Previous studies showed HBV can integrate into host genome and further promote malignant transformation. In this study, we developed an attention-based deep learning model DeepHBV to predict HBV integration sites by learning local genomic features automatically. The performance of DeepHBV model significantly improves after adding genomic features, with an AUROC of 0.9430 and an AUPR of 0.9310. Furthermore, we enriched the transcriptional factor binding sites of proteins by convolution neural network. In summary, DeepHBV is a robust and explainable deep learning model not only for the prediction of HBV integration sites but also for the further study of HBV integration mechanism.

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VISDB: a manually curated database of viral integration sites in the human genome

Cited by 47 publications

References 37 publications

HPV16 and HPV18 type-specific APOBEC3 and integration profiles in different diagnostic categories of cervical samples

HPV16 and HPV18 type-specific APOBEC3 and integration profiles in different diagnostic categories of cervical samples

ViMIC: A Database of Human Disease-related Virus Mutations, Integration Sites and Cis-effects

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

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