Inferring the clonal structure of viral populations from time series sequencing

Chedom, Donatien F., Murcia, Pablo R. and Greenman, Chris D. (2015) Inferring the clonal structure of viral populations from time series sequencing. PLoS Computational Biology, 11 (11). ISSN 1553-7358

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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:	Article
Additional Information:	The raw data is available from the NCBI (project accession number SRP044631). © 2015 Chedom et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Science > School of Natural Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology
Related URLs:	http://arxiv.org/abs/1407.7997
Depositing User:	Pure Connector
Date Deposited:	01 Dec 2015 07:23
Last Modified:	19 Apr 2023 00:43
URI:	https://ueaeprints.uea.ac.uk/id/eprint/55522
DOI:	10.1371/journal.pcbi.1004344

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