On the effective depth of viral sequence data

Illingworth, C. J.R. , Roy, S., Beale, M. A., Tutill, H., Williams, R. and Breuer, J. (2017) On the effective depth of viral sequence data. Virus Evolution, 3(2), vex030. (doi: 10.1093/ve/vex030) (PMID:29250429) (PMCID:PMC5724399)

Abstract

Genome sequence data are of great value in describing evolutionary processes in viral populations. However, in such studies, the extent to which data accurately describes the viral population is a matter of importance. Multiple factors may influence the accuracy of a dataset, including the quantity and nature of the sample collected, and the subsequent steps in viral processing. To investigate this phenomenon, we sequenced replica datasets spanning a range of viruses, and in which the point at which samples were split was different in each case, from a dataset in which independent samples were collected from a single patient to another in which all processing steps up to sequencing were applied to a single sample before splitting the sample and sequencing each replicate. We conclude that neither a high read depth nor a high template number in a sample guarantee the precision of a dataset. Measures of consistency calculated from within a single biological sample may also be insufficient; distortion of the composition of a population by the experimental procedure or genuine within-host diversity between samples may each affect the results. Where it is possible, data from replicate samples should be collected to validate the consistency of short-read sequence data.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Illingworth, Dr Chris
• Authors: Illingworth, C. J.R., Roy, S., Beale, M. A., Tutill, H., Williams, R., and Breuer, J.
• College/School: College of Medical Veterinary and Life Sciences > School of Infection & Immunity; College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Virus Research
• Journal Name: Virus Evolution
• Publisher: Oxford University Press
• ISSN: 2057-1577
• ISSN (Online): 2057-1577
• Published Online: 14 November 2017
• Copyright Holders: Copyright © 2017 The Authors
• First Published: First published in Virus Evolution 3(2):vex030
• Publisher Policy: Reproduced under a Creative Commons License