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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8457494/ https://www.ncbi.nlm.nih.gov/pubmed/34550971 http://dx.doi.org/10.1371/journal.pone.0250915 |
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author | Stephens, Zachary O’Brien, Daniel Dehankar, Mrunal Roberts, Lewis R. Iyer, Ravishankar K. Kocher, Jean-Pierre |
author_facet | Stephens, Zachary O’Brien, Daniel Dehankar, Mrunal Roberts, Lewis R. Iyer, Ravishankar K. Kocher, Jean-Pierre |
author_sort | Stephens, Zachary |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8457494 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-84574942021-09-23 Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data Stephens, Zachary O’Brien, Daniel Dehankar, Mrunal Roberts, Lewis R. Iyer, Ravishankar K. Kocher, Jean-Pierre PLoS One Research Article 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. Public Library of Science 2021-09-22 /pmc/articles/PMC8457494/ /pubmed/34550971 http://dx.doi.org/10.1371/journal.pone.0250915 Text en © 2021 Stephens et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Stephens, Zachary O’Brien, Daniel Dehankar, Mrunal Roberts, Lewis R. Iyer, Ravishankar K. Kocher, Jean-Pierre Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data |
title | Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data |
title_full | Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data |
title_fullStr | Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data |
title_full_unstemmed | Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data |
title_short | Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data |
title_sort | exogene: a performant workflow for detecting viral integrations from paired-end next-generation sequencing data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8457494/ https://www.ncbi.nlm.nih.gov/pubmed/34550971 http://dx.doi.org/10.1371/journal.pone.0250915 |
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