DisCVR: rapid viral diagnosis from high-throughput sequencing data

Maabar, M., Davison, A. J. , Vučak, M., Thorburn, F., Murcia, P. , Gunson, R., Palmarini, M. and Hughes, J. (2019) DisCVR: rapid viral diagnosis from high-throughput sequencing data. Virus Evolution, 5(2), vez033. (doi: 10.1093/ve/vez033) (PMID:31528358) (PMCID:PMC6735924)

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High-throughput sequencing (HTS) enables most pathogens in a clinical sample to be detected from a single analysis, thereby providing novel opportunities for diagnosis, surveillance, and epidemiology. However, this powerful technology is difficult to apply in diagnostic laboratories because of its computational and bioinformatic demands. We have developed DisCVR, which detects known human viruses in clinical samples by matching sample k-mers (twenty-two nucleotide sequences) to k-mers from taxonomically labeled viral genomes. DisCVR was validated using published HTS data for eighty-nine clinical samples from adults with upper respiratory tract infections. These samples had been tested for viruses metagenomically and also by real-time polymerase chain reaction assay, which is the standard diagnostic method. DisCVR detected human viruses with high sensitivity (79%) and specificity (100%), and was able to detect mixed infections. Moreover, it produced results comparable to those in a published metagenomic analysis of 177 blood samples from patients in Nigeria. DisCVR has been designed as a user-friendly tool for detecting human viruses from HTS data using computers with limited RAM and processing power, and includes a graphical user interface to help users interpret and validate the output. It is written in Java and is publicly available from http://bioinformatics.cvr.ac.uk/discvr.php.

Item Type:Articles
Additional Information:This work was funded by the Medical Research Council (MC_UU_12014/12).
Glasgow Author(s) Enlighten ID:Palmarini, Professor Massimo and Gunson, Dr Rory and Maabar, Mrs Maha and Hughes, Dr Joseph and Vucak, Mr Matej and Davison, Professor Andrew and Thorburn, Dr Fiona and Murcia, Professor Pablo
Authors: Maabar, M., Davison, A. J., Vučak, M., Thorburn, F., Murcia, P., Gunson, R., Palmarini, M., and Hughes, J.
College/School:College of Medical Veterinary and Life Sciences > School of Infection & Immunity
College of Medical Veterinary and Life Sciences > School of Medicine, Dentistry & Nursing
College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Virus Research
Journal Name:Virus Evolution
Publisher:Oxford University Press
ISSN (Online):2057-1577
Copyright Holders:Copyright © The Authors 2019
First Published:First published in Virus Evolution 5(2):vez033
Publisher Policy:Reproduced under a Creative Commons license

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172630Quinquennial Core FundsMassimo PalmariniMedical Research Council (MRC)MC_UU_12014/9III-MRC-GU CVR Support Services