ArticleBMC bioinformatics2018
HGT-ID: an efficient and sensitive workflow to detect human-viral insertion sites using next-generation sequencing data.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 34 citations in OpenAlex.
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- Genome Instability Precedes Viral Integration in Human Papillomavirus-Transformed Tonsillar Keratinocytes.Molecular cancer research : MCR · 2025Article
- Virusplot: a web server for viral integration analysis and visualization.Frontiers in oncology · 2025Article
- hgtseq: A Standard Pipeline to Study Horizontal Gene Transfer.International journal of molecular sciences · 2022Article
- Causes and Consequences of HPV Integration in Head and Neck Squamous Cell Carcinomas: State of the Art.Cancers · 2021Review
- 2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies.Genome biology · 2021Article
- ViR: a tool to solve intrasample variability in the prediction of viral integration sites using whole genome sequencing data.BMC bioinformatics · 2021Article
- Mutations inFrontiers in oncology · 2021Article
- Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data.PloS one · 2021Article
- A comprehensive analysis of RHOA mutation positive and negative angioimmunoblastic T-cell lymphomas by targeted deep sequencing, expression profiling and single cell digital image analysis.International journal of molecular medicine · 2020Article
- Expansion and persistence of antibiotic-specific resistance genes following antibiotic treatment.Gut microbesArticle
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundTransfer of genetic material from microbes or viruses into the host genome is known as horizontal gene transfer (HGT). The integration of viruses into the human genome is associated with multiple cancers, and these can now be detected using next-generation sequencing methods such as whole genome sequencing and RNA-sequencing.
resultsWe designed a novel computational workflow, HGT-ID, to identify the integration of viruses into the human genome using the sequencing data. The HGT-ID workflow primarily follows a four-step procedure: i) pre-processing of unaligned reads, ii) virus detection using subtraction approach, iii) identification of virus integration site using discordant and soft-clipped reads and iv) HGT candidates prioritization through a scoring function. Annotation and visualization of the events, as well as primer design for experimental validation, are also provided in the final report. We evaluated the tool performance with the well-understood cervical cancer samples. The HGT-ID workflow accurately detected known human papillomavirus (HPV) integration sites with high sensitivity and specificity compared to previous HGT methods. We applied HGT-ID to The Cancer Genome Atlas (TCGA) whole-genome sequencing data (WGS) from liver tumor-normal pairs. Multiple hepatitis B virus (HBV) integration sites were identified in TCGA liver samples and confirmed by HGT-ID using the RNA-Seq data from the matched liver pairs. This shows the applicability of the method in both the data types and cross-validation of the HGT events in liver samples. We also processed 220 breast tumor WGS data through the workflow; however, there were no HGT events detected in those samples.
conclusionsHGT-ID is a novel computational workflow to detect the integration of viruses in the human genome using the sequencing data. It is fast and accurate with functions such as prioritization, annotation, visualization and primer design for future validation of HGTs. The HGT-ID workflow is released under the MIT License and available at http://kalarikrlab.org/Software/HGT-ID.html .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.