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Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites
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HGT-ID: an efficient and sensitive workflow to detect human-viral insertion sites using next-generation sequencing
Saurabh Baheti1, Xiaojia Tang1, Daniel R O'Brien1
1Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
BMC Bioinformatics
|July 19, 2018
Summary
A new tool, HGT-ID, identifies viral DNA integrated into human genomes using sequencing data. This method accurately detects viral integrations linked to cancers like liver cancer, aiding in diagnostics.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Horizontal gene transfer (HGT) involves integrating microbial or viral genetic material into a host genome.
- Viral integration into the human genome is implicated in various cancers.
- Next-generation sequencing (NGS) methods like whole genome sequencing (WGS) and RNA-sequencing enable detection of these integrations.
Purpose of the Study:
- To develop and validate a novel computational workflow, HGT-ID, for identifying viral integration into the human genome using sequencing data.
- To assess the accuracy, sensitivity, and specificity of HGT-ID in detecting known and novel viral integration events.
- To demonstrate the workflow's applicability across different sequencing data types (WGS and RNA-Seq) and cancer types.
Main Methods:
- HGT-ID workflow involves pre-processing reads, virus detection via subtraction, integration site identification using discordant and soft-clipped reads, and prioritization via a scoring function.
- The workflow includes annotation, visualization, and primer design for experimental validation.
- Performance was evaluated using cervical cancer samples with known human papillomavirus (HPV) integrations and liver cancer samples from The Cancer Genome Atlas (TCGA).
Main Results:
- HGT-ID accurately detected known HPV integration sites in cervical cancer samples with high sensitivity and specificity.
- Multiple hepatitis B virus (HBV) integration sites were identified in TCGA liver tumor samples using WGS data.
- These HBV integrations were confirmed using matched RNA-Seq data, demonstrating the method's utility across data types and cross-validation capabilities.
Conclusions:
- HGT-ID is a novel, fast, and accurate computational workflow for detecting viral integrations in the human genome using sequencing data.
- The workflow provides essential functions including prioritization, annotation, visualization, and primer design for validating horizontal gene transfer events.
- HGT-ID is available under the MIT License, facilitating broader research in viral-host interactions and cancer genomics.
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