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Computational identification of adaptive mutants using the VERT system

BACKGROUND: Evolutionary dynamics of microbial organisms can now be visualized using the Visualizing Evolution in Real Time (VERT) system, in which several isogenic strains expressing different fluorescent proteins compete during adaptive evolution and are tracked using fluorescent cell sorting to c...

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Detalles Bibliográficos
Autores principales: Winkler, James, Kao, Katy C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3351376/
https://www.ncbi.nlm.nih.gov/pubmed/22472487
http://dx.doi.org/10.1186/1754-1611-6-3
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author Winkler, James
Kao, Katy C
author_facet Winkler, James
Kao, Katy C
author_sort Winkler, James
collection PubMed
description BACKGROUND: Evolutionary dynamics of microbial organisms can now be visualized using the Visualizing Evolution in Real Time (VERT) system, in which several isogenic strains expressing different fluorescent proteins compete during adaptive evolution and are tracked using fluorescent cell sorting to construct a population history over time. Mutations conferring enhanced growth rates can be detected by observing changes in the fluorescent population proportions. RESULTS: Using data obtained from several VERT experiments, we construct a hidden Markov-derived model to detect these adaptive events in VERT experiments without external intervention beyond initial training. Analysis of annotated data revealed that the model achieves consensus with human annotation for 85-93% of the data points when detecting adaptive events. A method to determine the optimal time point to isolate adaptive mutants is also introduced. CONCLUSIONS: The developed model offers a new way to monitor adaptive evolution experiments without the need for external intervention, thereby simplifying adaptive evolution efforts relying on population tracking. Future efforts to construct a fully automated system to isolate adaptive mutants may find the algorithm a useful tool.
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spelling pubmed-33513762012-05-15 Computational identification of adaptive mutants using the VERT system Winkler, James Kao, Katy C J Biol Eng Methodology BACKGROUND: Evolutionary dynamics of microbial organisms can now be visualized using the Visualizing Evolution in Real Time (VERT) system, in which several isogenic strains expressing different fluorescent proteins compete during adaptive evolution and are tracked using fluorescent cell sorting to construct a population history over time. Mutations conferring enhanced growth rates can be detected by observing changes in the fluorescent population proportions. RESULTS: Using data obtained from several VERT experiments, we construct a hidden Markov-derived model to detect these adaptive events in VERT experiments without external intervention beyond initial training. Analysis of annotated data revealed that the model achieves consensus with human annotation for 85-93% of the data points when detecting adaptive events. A method to determine the optimal time point to isolate adaptive mutants is also introduced. CONCLUSIONS: The developed model offers a new way to monitor adaptive evolution experiments without the need for external intervention, thereby simplifying adaptive evolution efforts relying on population tracking. Future efforts to construct a fully automated system to isolate adaptive mutants may find the algorithm a useful tool. BioMed Central 2012-04-02 /pmc/articles/PMC3351376/ /pubmed/22472487 http://dx.doi.org/10.1186/1754-1611-6-3 Text en Copyright ©2012 Winkler and Kao; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology
Winkler, James
Kao, Katy C
Computational identification of adaptive mutants using the VERT system
title Computational identification of adaptive mutants using the VERT system
title_full Computational identification of adaptive mutants using the VERT system
title_fullStr Computational identification of adaptive mutants using the VERT system
title_full_unstemmed Computational identification of adaptive mutants using the VERT system
title_short Computational identification of adaptive mutants using the VERT system
title_sort computational identification of adaptive mutants using the vert system
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3351376/
https://www.ncbi.nlm.nih.gov/pubmed/22472487
http://dx.doi.org/10.1186/1754-1611-6-3
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