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Related Experiment Video

Updated: Jan 18, 2026

Detection of Low Copy Number Integrated Viral DNA Formed by In Vitro Hepatitis B Infection

Detection of Low Copy Number Integrated Viral DNA Formed by In Vitro Hepatitis B Infection

Published on: November 7, 2018

Decoding the interconnected splicing patterns of hepatitis B virus and host using large language and deep learning

Chun Shen Lim^1,2,3, Chris M Brown^1,2,3

¹Department of Biochemistry, Faculty of Biomedical Sciences, University of Otago, Dunedin, New Zealand.

Microbial Genomics

|January 16, 2026

Summary

This summary is machine-generated.

Hepatitis B virus (HBV) splicing efficiency, not just the amount of spliced RNA, strongly correlates with disease progression. Artificial intelligence models reveal unique HBV splicing patterns, aiding understanding of viral persistence.

Keywords:

artificial intelligence deep sequencing large language model splicing efficiency virus–host coevolution

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Area of Science:

Virology
Genomics
Bioinformatics

Background:

Hepatitis B virus (HBV) infection is a leading cause of liver cancer and death.
HBV's compact genome utilizes extensive alternative splicing, contributing to immune evasion and hindering functional cures.
Understanding HBV splicing is crucial for developing effective treatments.

Purpose of the Study:

To investigate the correlation between HBV splicing efficiency and disease progression.
To decode the sequence determinants of HBV splicing using AI models.
To analyze context- and genotype-specific splicing patterns in HBV.

Main Methods:

Quantified HBV splicing efficiency from 279 RNA-sequencing libraries.
Applied AI models (SpliceBERT, OpenSpliceAI) to 4,706 HBV genomes.
Performed motif conservation and splicing propensity analyses.

Main Results:

HBV splicing efficiency correlates more strongly with disease progression than the proportion of spliced HBV RNA.
HBV splice donor sites resemble host sites, while acceptor sites are more cryptic.
Identified context- and genotype-specific splicing patterns, suggesting sequence-based regulation.

Conclusions:

HBV splicing efficiency is a more significant indicator of disease progression than previously thought.
AI models effectively decode viral splicing patterns, revealing unique HBV splice site characteristics.
HBV splicing patterns may reflect coevolution with hosts, supporting viral persistence and immune evasion.