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Interpreting Viral Deep Sequencing Data with GLUE

1
MRC-University of Glasgow Centre for Virus Research, Glasgow G61 1QH, UK
2
Virus Reference Department, National Infection Service, Public Health England, Colindale, London NW9 5EQ, UK
3
Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine, University of Oxford, Oxford OX1 3SY, UK
*
Author to whom correspondence should be addressed.
Viruses 2019, 11(4), 323; https://doi.org/10.3390/v11040323
Received: 28 February 2019 / Revised: 13 March 2019 / Accepted: 14 March 2019 / Published: 3 April 2019
(This article belongs to the Special Issue Virus Bioinformatics)
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Abstract

Using deep sequencing technologies such as Illumina’s platform, it is possible to obtain reads from the viral RNA population revealing the viral genome diversity within a single host. A range of software tools and pipelines can transform raw deep sequencing reads into Sequence Alignment Mapping (SAM) files. We propose that interpretation tools should process these SAM files, directly translating individual reads to amino acids in order to extract statistics of interest such as the proportion of different amino acid residues at specific sites. This preserves per-read linkage between nucleotide variants at different positions within a codon location. The samReporter is a subsystem of the GLUE software toolkit which follows this direct read translation approach in its processing of SAM files. We test samReporter on a deep sequencing dataset obtained from a cohort of 241 UK HCV patients for whom prior treatment with direct-acting antivirals has failed; deep sequencing and resistance testing have been suggested to be of clinical use in this context. We compared the polymorphism interpretation results of the samReporter against an approach that does not preserve per-read linkage. We found that the samReporter was able to properly interpret the sequence data at resistance-associated locations in nine patients where the alternative approach was equivocal. In three cases, the samReporter confirmed that resistance or an atypical substitution was present at NS5A position 30. In three further cases, it confirmed that the sofosbuvir-resistant NS5B substitution S282T was absent. This suggests the direct read translation approach implemented is of value for interpreting viral deep sequencing data. View Full-Text
Keywords: deep sequencing; virus genomics; hepatitis C virus; variant calling; sequence interpretation; drug resistance; bioinformatics deep sequencing; virus genomics; hepatitis C virus; variant calling; sequence interpretation; drug resistance; bioinformatics
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Singer, J.B.; Thomson, E.C.; Hughes, J.; Aranday-Cortes, E.; McLauchlan, J.; da Silva Filipe, A.; Tong, L.; Manso, C.F.; Gifford, R.J.; Robertson, D.L.; Barnes, E.; Ansari, M.A.; Mbisa, J.L.; Bibby, D.F.; Bradshaw, D.; Smith, D. Interpreting Viral Deep Sequencing Data with GLUE. Viruses 2019, 11, 323.

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