Hepatitis B virus (HBV) can integrate into the human genome, contributing to genomic instability and hepatocarcinogenesis. Here by conducting high-throughput viral integration detection and RNA sequencing, we identify 4,225 HBV integration events in tumour and adjacent non-tumour samples from 426 patients with HCC. We show that HBV is prone to integrate into rare fragile sites and functional genomic regions including CpG islands. We observe a distinct pattern in the preferential sites of HBV integration between tumour and non-tumour tissues. HBV insertional sites are significantly enriched in the proximity of telomeres in tumours. Recurrent HBV target genes are identified with few that overlap. The overall HBV integration frequency is much higher in tumour genomes of males than in females, with a significant enrichment of integration into chromosome 17. Furthermore, a cirrhosis-dependent HBV integration pattern is observed, affecting distinct targeted genes. Our data suggest that HBV integration has a high potential to drive oncogenic transformation.

Background: Integration of HBV DNA into the human genome could progressively contribute to hepatocarcinogenesis. Both intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular-cholangiocarcinoma (CHC) are known to be associated with HBV infection. However, the integration of HBV and mechanism of HBV-induced carcinogenesis in ICC and CHC remains unclear. Methods: 41 patients with ICC and 20 patients with CHC were recruited in the study. We conducted HIVID analysis on these 61 samples to identify HBV integration sites in both the tumor tissues and adjacent non-tumor liver tissues. To further explore the effect of HBV integration on gene alteration, we selected paired tumors and adjacent non-tumor liver tissues from 3 ICC and 4 CHC patients for RNA-seq and WGS. Results: We detected 493 HBV integration sites in ICC patients, of which 417 were from tumor samples and 76 were from non-tumor samples. And 246 HBV integration sites were detected in CHC patients, of which 156 were located in the genome of tumor samples and 90 were in non-tumor samples. Recurrent HBV integration events were detected in ICC including TERT, ZMAT4, MET, ANKFN1, PLXNB2, and in CHC like TERT, ALKBH5. Together with our established data of HBV-infected hepatocellular carcinoma, we found that HBV preferentially integrates into the specific regions which may affect the gene expression and regulation in cells and involved in carcinogenesis. We further performed genomic and transcriptomic sequencing of three ICC and four CHC patients, and found that HBV fragments could integrate near some important oncogene like TERT, causing large-scale genome variations on nearby genomic sequences, and at the same time changing the expression level of the oncogenes. Conclusion: Comparative analysis demonstrates numerous newly discovered mutational events in ICC and CHC resulting from HBV insertions in the host genome. Our study provides an in-depth biological and clinical insights into HBV-induced ICC and CHC.

In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the smooth operation of ships. Currently, ship communication status analysis heavily relies on large-scale, multi-source heterogeneous data with spatio-temporal and multi-modal features, which presents challenges for ship communication quality prediction tasks. To address this issue, this paper constructs a multi-modal spatio-temporal ontology and a multi-modal spatio-temporal knowledge graph for ship communication, guided by existing ontologies and domain knowledge. This approach effectively integrates multi-modal spatio-temporal data, providing support for subsequent efficient data analysis and applications. Taking the scenario of fishing vessel communication activities as an example, the query tasks for ship communication knowledge are successfully performed using a graph database, and we combine the spatio-temporal knowledge graph with graph convolutional neural network technology to achieve real-time communication quality prediction for fishing vessels, further validating the practical value of the multi-modal spatio-temporal knowledge graph.

Virus integration in the host genome is frequently reported to be closely associated with many human diseases, and the detection of virus integration is a critically challenging task. However, most existing tools show limited specificity and sensitivity. Therefore, the objective of this study is to develop a method for accurate detection of virus integration into host genomes. Herein, we report a novel method termed HIVID2 that is a significant upgrade of HIVID. HIVID2 performs a paired-end combination (PE-combination) for potentially integrated reads. The resulting sequences are then remapped onto the reference genomes, and both split and discordant chimeric reads are used to identify accurate integration breakpoints with high confidence. HIVID2 represents a great improvement in specificity and sensitivity, and predicts breakpoints closer to the real integrations, compared with existing methods. The advantage of our method was demonstrated using both simulated and real datasets. HIVID2 uncovered novel integration breakpoints in well-known cervical cancer-related genes, including FHIT and LRP1B, which was verified using protein expression data. In addition, HIVID2 allows the user to decide whether to automatically perform advanced analysis using the identified virus integrations. By analyzing the simulated data and real data tests, we demonstrated that HIVID2 is not only more accurate than HIVID but also better than other existing programs with respect to both sensitivity and specificity. We believe that HIVID2 will help in enhancing future research associated with virus integration. HIVID2 can be accessed at https://github.com/zengxi-hada/HIVID2/. Supplementary data are available at Bioinformatics online.