title: Estimating Fitness of Viral Quasispecies from Next-Generation Sequencing Data.
abstract: The quasispecies model is ubiquitous in the study of viruses. While having lead to a number of insights that have stood the test of time, the quasispecies model has mostly been discussed in a theoretical fashion with little support of data. With next-generation sequencing (NGS), this situation is changing and a wealth of data can now be produced in a time- and cost-efficient manner. NGS can, after removal of technical errors, yield an exceedingly detailed picture of the viral population structure. The widespread availability of cross-sectional data can be used to study fitness landscapes of viral populations in the quasispecies model. This chapter highlights methods that estimate the strength of selection in selective sweeps, assesses marginal fitness effects of quasispecies, and finally infers the fitness landscape of a viral quasispecies, all on the basis of NGS data.
authors: ETH Zurich, Basel, Switzerland.
authors: David
authors: D
authors: Seifert
authors: ETH Zurich, Basel, Switzerland. niko.beerenwinkel@bsse.ethz.ch.
authors: Niko
authors: N
authors: Beerenwinkel
mesh_terms: D019143:Evolution, Molecular; D014644:Genetic Variation; D016679:Genome, Viral; D059014:High-Throughput Nucleotide Sequencing; D059645:Mutation Rate; D014780:Viruses / Q000145:classification / Q000235:genetics*