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dc.contributor.author
Posada Cespedes, Susana
dc.contributor.author
Seifert, David
dc.contributor.author
Topolsky, Ivan
dc.contributor.author
Metzner, Karin J.
dc.contributor.author
Beerenwinkel, Niko
dc.date.accessioned
2021-03-10T14:34:27Z
dc.date.available
2021-01-21T10:09:14Z
dc.date.available
2021-03-10T14:34:27Z
dc.date.issued
2020-06-11
dc.identifier.other
10.1101/2020.06.09.142919
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/464438
dc.identifier.doi
10.3929/ethz-b-000464438
dc.description.abstract
High-throughput sequencing technologies are used increasingly, not only in viral genomics research but also in clinical surveillance and diagnostics. These technologies facilitate the assessment of the genetic diversity in intra-host virus populations, which affects transmission, virulence, and pathogenesis of viral infections. However, there are two major challenges in analysing viral diversity. First, amplification and sequencing errors confound the identification of true biological variants, and second, the large data volumes represent computational limitations. To support viral high-throughput sequencing studies, we developed V-pipe, a bioinformatics pipeline combining various state-of-the-art statistical models and computational tools for automated end-to-end analyses of raw sequencing reads. V-pipe supports quality control, read mapping and alignment, low-frequency mutation calling, and inference of viral haplotypes. For generating high-quality read alignments, we developed a novel method, called ngshmmalign, based on profile hidden Markov models and tailored to small and highly diverse viral genomes. V-pipe also includes benchmarking functionality providing a standardized environment for comparative evaluations of different pipeline configurations. We demonstrate this capability by assessing the impact of three different read aligners (Bowtie 2, BWA MEM, ngshmmalign) and two different variant callers (LoFreq, ShoRAH) on the performance of calling single-nucleotide variants in intra-host virus populations. V-pipe supports various pipeline configurations and is implemented in a modular fashion to facilitate adaptations to the continuously changing technology landscape. V-pipe is freely available at https://github.com/cbg-ethz/V-pipe.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Cold Spring Harbor Laboratory
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.title
V-pipe: a computational pipeline for assessing viral genetic diversity from high-throughput sequencing data
en_US
dc.type
Working Paper
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
ethz.journal.title
bioRxiv
ethz.size
16 p.
en_US
ethz.publication.place
Cold Spring Harbor, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03790 - Beerenwinkel, Niko / Beerenwinkel, Niko
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03790 - Beerenwinkel, Niko / Beerenwinkel, Niko
en_US
ethz.relation.isPreviousVersionOf
10.3929/ethz-b-000473888
ethz.date.deposited
2021-01-21T10:09:22Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-03-10T14:34:38Z
ethz.rosetta.lastUpdated
2022-03-29T05:42:30Z
ethz.rosetta.versionExported
true
ethz.COinS
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