ViroISDC: a method for calling integration sites of hepatitis B virus based on feature encoding

Sep 21, 2026

Uploaded by Aurora

Hepatitis B virus (HBV) integration into human chromosomes can trigger genomic instability and hepatocarcinogenesis, yet existing integration-site detection tools often suffer from limited accuracy and unstable performance. ViroISDC presents a deep learning pipeline that generates grammar rules and encodes language-data characteristics to predict integration sites. Benchmarking against Lumpy, Pindel, Seeksv, and SurVirus shows improved overall performance and reduced sensitivity to sequencing depth and integration length. Downstream analyses reveal integration preferences, cancer-related tendencies, and higher male integration frequency, supporting robustness and clinical relevance for HCC research.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Abstract

Background

Results

Conclusions

Background

Global burden of liver cancer and HCC risk factors

Biological significance of HBV integration

Integration-site detection and existing limitations

Qiao et al. BMC Bioinformatics (2024) 25:177 BMC Bioinformatics [https://doi.org/10.1186/s12859-024-05763-0](https://doi.org/10.1186/s12859-024-05763-0) RESEARCH Open Access ViroISDC: a method for calling integration sites of hepatitis B virus based on feature encoding Lei Qiao1†, Chang Li1†, Wei Lin2†, Xiaoqi He2, Jia Mi1, Yigang Tong2* and Jingyang Gao1* | †Lei Qiao, Chang Li and Wei Lin have contributed equally to this work | | --- | | *Correspondence:
[tongyigang@mail.buct.edu.cn](tongyigang@mail.buct.edu.cn); [gaojy@mail.buct.edu.cn](gaojy@mail.buct.edu.cn) | 1 College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China 2 College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China Abstract Background: Hepatitis B virus (HBV) integrates into human chromosomes and can lead to genomic instability and hepatocarcinogenesis. Current tools for HBV integration site detection lack accuracy and stability. Results: This study proposes a deep learning-based method, named ViroISDC, for detecting integration sites. ViroISDC generates corresponding grammar rules and encodes the characteristics of the language data to predict integration sites accurately. Compared with Lumpy, Pindel, Seeksv, and SurVirus, ViroISDC exhibits better overall performance and is less sensitive to sequencing depth and integration sequence length, displaying good reliability, stability, and generality. Further downstream analysis of integrated sites detected by ViroISDC reveals the integration patterns and features of HBV. It is observed that HBV integration exhibits specific chromosomal preferences and tends to integrate into cancerous tissue. Moreover, HBV integration frequency was higher in males than females, and high-frequency integration sites were more likely to be present on hepatocarcinogenesis-and anti-cancer-related genes, validating the reliability of the ViroISDC. Conclusions: ViroISDC pipeline exhibits superior precision, stability, and reliability across various datasets when compared to similar software. It is invaluable in exploring HBV infection in the human body, holding significant implications for the diagnosis, treatment, and prognosis assessment of HCC. Keywords: HBV integration, Detect tools, Deep learning, Integration patterns and features Background Liver cancer, as reported by the World Health Organization [1], accounts for approximately 905,677 new cases and 830,180 deaths globally each year, ranking sixth in global cancer incidence and third in global cancer death rates. Its mechanism is complex and may involve multiple factors, such as viral infections [2], genetic factors [3], environmental factors, lifestyle habits, and diet [4] . There are two types of liver cancer: primary liver cancer and metastatic liver cancer. Primary liver cancer refers to © The Author(s) 2024. Open Access This 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://](http://)[ ](http://)[creativecommons.org/licenses/by/4.0/](creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[. The Creative Commons Public Domain Dedication waiver (](creativecommons.org/licenses/

FAQs

What problem does ViroISDC address in HBV integration research?

ViroISDC targets the insufficient accuracy and stability of existing tools for detecting HBV integration sites, aiming for more reliable and general performance across datasets.

How does ViroISDC perform integration-site prediction?

ViroISDC uses a deep learning approach that generates grammar rules and encodes the characteristics of language-like data to predict integration sites accurately.

What biological insights were found from ViroISDC-detected integration sites?

The study reports chromosomal preferences, a tendency to integrate into cancerous tissue, higher integration frequency in males than females, and enrichment of high-frequency sites on hepatocarcinogenesis- and anti-cancer-related genes.

Sep 21, 2026

Uploaded by Aurora