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

The integration of viruses into the human genome is known to be associated with tumorigenesis in many cancers, but the accurate detection of integration breakpoints from short read sequencing data is made difficult by human-viral homologies, viral genome heterogeneity, coverage limitations, and othe...

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Bibliographic Details
Published in:PloS one 2021-09, Vol.16 (9), p.e0250915
Main Authors: Stephens, Zachary, O'Brien, Daniel, Dehankar, Mrunal, Roberts, Lewis R, Iyer, Ravishankar K, Kocher, Jean-Pierre
Format: Article
Language:English
Subjects:
Analysis
Biology and Life Sciences
Breakpoints
Cancer
Carcinoma, Hepatocellular - genetics
Carcinoma, Hepatocellular - virology
Computational Biology - methods
Computer engineering
Exome Sequencing
Gene expression
Genomes
Health sciences
Hepatitis B
Hepatitis B - genetics
Hepatitis B virus - physiology
Hepatocellular carcinoma
Heterogeneity
High-Throughput Nucleotide Sequencing
Homology
Humans
Integration
Liver cancer
Liver Neoplasms - genetics
Liver Neoplasms - virology
Medicine and Health Sciences
Next-generation sequencing
Nucleotide sequence
Research and Analysis Methods
Sequence Analysis, DNA
Sequence Analysis, RNA
Software
Tumorigenesis
Virus Integration
Viruses
Workflow
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Summary:The integration of viruses into the human genome is known to be associated with tumorigenesis in many cancers, but the accurate detection of integration breakpoints from short read sequencing data is made difficult by human-viral homologies, viral genome heterogeneity, coverage limitations, and other factors. To address this, we present Exogene, a sensitive and efficient workflow for detecting viral integrations from paired-end next generation sequencing data. Exogene's read filtering and breakpoint detection strategies yield integration coordinates that are highly concordant with long read validation. We demonstrate this concordance across 6 TCGA Hepatocellular carcinoma (HCC) tumor samples, identifying integrations of hepatitis B virus that are also supported by long reads. Additionally, we applied Exogene to targeted capture data from 426 previously studied HCC samples, achieving 98.9% concordance with existing methods and identifying 238 high-confidence integrations that were not previously reported. Exogene is applicable to multiple types of paired-end sequence data, including genome, exome, RNA-Seq and targeted capture.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0250915