Inferring the clonal structure of viral populations from time series sequencing

Chedom, D. F., Murcia, P. R. and Greenman, C. D. (2015) Inferring the clonal structure of viral populations from time series sequencing. PLoS Computational Biology, 11(11), e1004344. (doi: 10.1371/journal.pcbi.1004344) (PMID:26571026) (PMCID:PMC4646700)

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Abstract

RNA virus populations will undergo processes of mutation and selection resulting in a mixed population of viral particles. High throughput sequencing of a viral population subsequently contains a mixed signal of the underlying clones. We would like to identify the underlying evolutionary structures. We utilize two sources of information to attempt this; within segment linkage information, and mutation prevalence. We demonstrate that clone haplotypes, their prevalence, and maximum parsimony reticulate evolutionary structures can be identified, although the solutions may not be unique, even for complete sets of information. This is applied to a chain of influenza infection, where we infer evolutionary structures, including reassortment, and demonstrate some of the difficulties of interpretation that arise from deep sequencing due to artifacts such as template switching during PCR amplification.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murcia, Professor Pablo
Authors: Chedom, D. F., Murcia, P. R., and Greenman, C. D.
College/School:UNSPECIFIED
Journal Name:PLoS Computational Biology
Publisher:Public Library of Science
ISSN:1553-734X
ISSN (Online):1553-7358
Copyright Holders:Copyright © 2015 2015 Chedom et al.
First Published:First published in PLoS Computational Biology 11(11): e1004344
Publisher Policy:Reproduced under a Creative Commons License

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