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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Autores principales:	Zachary Stephens, Daniel O'Brien, Mrunal Dehankar, Lewis R Roberts, Ravishankar K Iyer, Jean-Pierre Kocher
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/be96bb101e274e2d9e876b69d9b85acf
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spelling	oai:doaj.org-article:be96bb101e274e2d9e876b69d9b85acf2021-12-02T20:08:08ZExogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data.1932-620310.1371/journal.pone.0250915https://doaj.org/article/be96bb101e274e2d9e876b69d9b85acf2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0250915https://doaj.org/toc/1932-6203The 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.Zachary StephensDaniel O'BrienMrunal DehankarLewis R RobertsRavishankar K IyerJean-Pierre KocherPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0250915 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zachary Stephens Daniel O'Brien Mrunal Dehankar Lewis R Roberts Ravishankar K Iyer Jean-Pierre Kocher Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data.
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	article
author	Zachary Stephens Daniel O'Brien Mrunal Dehankar Lewis R Roberts Ravishankar K Iyer Jean-Pierre Kocher
author_facet	Zachary Stephens Daniel O'Brien Mrunal Dehankar Lewis R Roberts Ravishankar K Iyer Jean-Pierre Kocher
author_sort	Zachary Stephens
title	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_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_sort	exogene: a performant workflow for detecting viral integrations from paired-end next-generation sequencing data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/be96bb101e274e2d9e876b69d9b85acf
work_keys_str_mv	AT zacharystephens exogeneaperformantworkflowfordetectingviralintegrationsfrompairedendnextgenerationsequencingdata AT danielobrien exogeneaperformantworkflowfordetectingviralintegrationsfrompairedendnextgenerationsequencingdata AT mrunaldehankar exogeneaperformantworkflowfordetectingviralintegrationsfrompairedendnextgenerationsequencingdata AT lewisrroberts exogeneaperformantworkflowfordetectingviralintegrationsfrompairedendnextgenerationsequencingdata AT ravishankarkiyer exogeneaperformantworkflowfordetectingviralintegrationsfrompairedendnextgenerationsequencingdata AT jeanpierrekocher exogeneaperformantworkflowfordetectingviralintegrationsfrompairedendnextgenerationsequencingdata
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Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data.

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