Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data

Area of Science:

• Genomics
• Bioinformatics
• Oncology

Background:

• Viral integration into the human genome is linked to tumorigenesis.
• Detecting viral integration breakpoints from short-read sequencing is challenging due to technical limitations.

Purpose of the Study:

• To develop a sensitive and efficient workflow, Exogene, for detecting viral integrations from paired-end next-generation sequencing data.
• To validate Exogene's performance against long-read sequencing and existing methods.

Main Methods:

• Exogene employs specific read filtering and breakpoint detection strategies.
• The workflow was validated on 6 TCGA Hepatocellular Carcinoma (HCC) samples using paired-end sequencing.
• Exogene was further applied to targeted capture data from 426 HCC samples.

Main Results:

• Exogene identified Hepatitis B virus integrations in HCC samples with high concordance to long-read validation.
• The workflow achieved 98.9% concordance with existing methods on 426 HCC samples.
• Exogene identified 238 novel high-confidence viral integrations in HCC.

Conclusions:

• Exogene is a sensitive and efficient tool for detecting viral integrations from various paired-end sequencing data types.
• The workflow enhances the accurate identification of viral integration breakpoints, aiding cancer research.
• Exogene has the potential to uncover new viral associations in cancer through large-scale data analysis.