Título:
DISSEQT—DIStribution-based modeling of SEQuence space Time dynamics
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Compilador:
Autor:
Henningsson, R.
Moratorio, Gonzalo
Bordería, A.V.
Vignuzzi, Marco
Fontes, Magnus
Moratorio, Gonzalo
Bordería, A.V.
Vignuzzi, Marco
Fontes, Magnus
Tutor:
Tipo de documento:
Artículo
Editor:
Palabras clave:
Multidimensional scaling
Quasispecies
NGS
Applied mathematics
Quasispecies
NGS
Applied mathematics
Descriptores:
Año de publicación:
2019
Contenido:
Resumen:
Rapidly evolving microbes are a challenge to model because of the volatile, complex, and dynamic nature of their populations. We developed the DISSEQT pipeline (DIStribution-based SEQuence space Time dynamics) for analyzing, visualizing, and predicting the evolution of heterogeneous biological populations in multidimensional genetic space, suited for population-based modeling of deep sequencing and high-throughput data. The pipeline is openly available on GitHub (https://github.com/rasmushenningsson/DISSEQT.jl, accessed 23 June 2019) and Synapse (https://www.synapse.org/#!Synapse: syn11425758, accessed 23 June 2019), covering the entire workflow from read alignment to visualization of results. Our pipeline is centered around robust dimension and model reduction algorithms for analysis of genotypic data with additional capabilities for including phenotypic features to explore dynamic genotype–phenotype maps. We illustrate its utility and capacity with examples from evolving RNA virus populations, which present one of the highest degrees of genetic heterogeneity within a given population found in nature. Using our pipeline, we empirically reconstruct the evolutionary trajectories of evolving populations in sequence space and genotype–phenotype fitness landscapes. We show that while sequence space is vastly multidimensional, the relevant genetic space of evolving microbial populations is of intrinsically low dimension. In addition, evolutionary trajectories of these populations can be faithfully monitored to identify the key minority genotypes contributing most to evolution. Finally, we show that empirical fitness landscapes, when reconstructed to include minority variants, can predict phenotype from genotype with high accuracy
Descripción:
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Editorial:
Oxford University Press
EN:
Virus Evolution, 2019, 5(2): vez028
Financiadores:
Citación:
Henningsson, R, Moratorio Linares, G, Bordería, A., y otros "DISSEQT—DIStribution-based modeling of SEQuence space Time dynamics". Virus Evolution. [en línea] 2019, 5(2): vez028. 14 h. DOI: 10.1093/ve/vez028
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ISSN:
2057-1577
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Licencia:
Licencia Creative Commons Atribución (CC - By 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| 10.1093vevez028.pdf | — | 1.27 MB | Adobe PDF |