Evidence map›Paper›PMID 38704528›Full record

ArticleBMC bioinformatics2024

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

Lei Qiao, Chang Li, Wei Lin, Xiaoqi He, Jia Mi, Yigang Tong, Jingyang Gao

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In one paragraph

Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Lei Qiao *College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
Chang Li *College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
Wei Lin *College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
Xiaoqi HeCollege of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
Jia MiCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
Yigang TongCollege of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China. tongyigang@mail.buct.edu.cn.
Jingyang GaoCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China. gaojy@mail.buct.edu.cn.

Funding

Ministry of Science and Technology of the People´s Republic of China 2022FY101104Ministry of Science and Technology of the People´s Republic of China No. ZK20230149
6 · The paper itself

Abstract

backgroundHepatitis 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.

resultsThis 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.

conclusionsViroISDC 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.

Indexed as

Hepatitis B virusVirus IntegrationComputational BiologyDeep LearningFemaleHepatitis BHumansLiver NeoplasmsMaleSoftwareDeep learningDetect toolsHBV integrationIntegration patterns and features

Identifiers

PMID38704528
PMCPMC11070082

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