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Influenza classification from short reads with VAPOR facilitates robust mapping pipelines and zoonotic strain detection for routine surveillance applications

Southgate, J.A., Bull, M.J., Brown, C.M., Watkins, J., Corden, S., Southgate, B., Moore, C. and Connor, T.R. 2019. Influenza classification from short reads with VAPOR facilitates robust mapping pipelines and zoonotic strain detection for routine surveillance applications. Bioinformatics 10.1093/bioinformatics/btz814

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Abstract

Motivation: Influenza viruses represent a global public health burden due to annual epidemics and pandemic potential. Due to a rapidly evolving RNA genome, inter-species transmission, intra-host variation, and noise in short-read data, reads can be lost during mapping, and de novo assembly can be time consuming and result in misassembly. We assessed read loss during mapping, and designed a graph-based classifier, VAPOR, for selecting mapping references, assembly validation, and detection of strains of non-human origin. Results: Standard human reference viruses were insufficient for mapping diverse influenza samples in simulation. VAPOR retrieved references for 257 real whole genome sequencing (WGS) samples with a mean of >99.8% identity to assemblies, and increased the proportion of mapped reads by up to 13.3% compared to standard references. VAPOR has the potential to improve the robustness of bioinformatics pipelines for surveillance and could be adapted to other RNA viruses.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Biosciences
Publisher: Oxford University Press (OUP)
ISSN: 1367-4803
Funders: MRC, BBSRC
Date of First Compliant Deposit: 30 October 2019
Date of Acceptance: 27 October 2019
Last Modified: 31 Jan 2020 03:36
URI: http://orca-mwe.cf.ac.uk/id/eprint/126410

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