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Estimating Fitness of Viral Quasispecies from Next-Generation Sequencing Data

机译:从下一代测序数据估算病毒准种的适合度

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摘要

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.
机译:准物种模型在病毒研究中无处不在。虽然已经产生了许多经受时间考验的见解,但准种模型主要是在理论上没有数据支持的情况下进行讨论的。借助下一代测序(NGS),这种情况正在改变,现在可以以节省时间和成本的方式生成大量数据。在消除技术错误后,NGS可以得出病毒种群结构的极其详细的图像。横截面数据的广泛可用性可用于研究准种模型中病毒种群的适应状况。本章重点介绍了在NGS数据的基础上估算选择性扫描的选择强度,评估准种的边际适应性效果并最终推断出病毒性准种的适应性状况的方法。

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