Jun 7, 2015

Coalescent inference using serially sampled, high-throughput sequencing data from intra-host HIV infection

BioRxiv : the Preprint Server for Biology
Kevin DialdestoroPaul Jenkins

Abstract

Human immunodeficiency virus (HIV) is a rapidly evolving pathogen that causes chronic infections, so genetic diversity within a single infection can be very high. High-throughput “deep” sequencing can now measure this diversity in unprecedented detail, particularly since it can be performed at different timepoints during an infection, and this offers a potentially powerful way to infer the evolutionary dynamics of the intra-host viral population. However, population genomic inference from HIV sequence data is challenging because of high rates of mutation and recombination, rapid demographic changes, and ongoing selective pressures. In this paper we develop a new method for inference using HIV deep sequencing data using an approach based on importance sampling of ancestral recombination graphs under a multi-locus coalescent model. The approach further extends recent progress in the approximation of so-called conditional sampling distributions, a quantity of key interest when approximating coalescent likelihoods. The chief novelties of our method are that it is able to infer rates of recombination and mutation, as well as the effective population size, while handling sampling over different timepoints and missing data without ext...Continue Reading

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Mentioned in this Paper

Computer Software
Size
Genome
Pathogenic Organism
Human Immunodeficiency Virus Test
Recombination, Genetic
Nucleic Acid Sequencing
HIV Infections
Genomics
Sequencing

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