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Maximizing ecological and evolutionary insight in bisulfite sequencing data sets

Genome-scale bisulfite sequencing approaches have opened the door to ecological and evolutionary studies of DNA methylation in many organisms. These approaches can be powerful. However, they introduce new methodological and statistical considerations, some of which are particularly relevant to non-m...

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Detalles Bibliográficos
Autores principales: Lea, Amanda J., Vilgalys, Tauras P., Durst, Paul A.P., Tung, Jenny
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5656403/
https://www.ncbi.nlm.nih.gov/pubmed/29046582
http://dx.doi.org/10.1038/s41559-017-0229-0
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author Lea, Amanda J.
Vilgalys, Tauras P.
Durst, Paul A.P.
Tung, Jenny
author_facet Lea, Amanda J.
Vilgalys, Tauras P.
Durst, Paul A.P.
Tung, Jenny
author_sort Lea, Amanda J.
collection PubMed
description Genome-scale bisulfite sequencing approaches have opened the door to ecological and evolutionary studies of DNA methylation in many organisms. These approaches can be powerful. However, they introduce new methodological and statistical considerations, some of which are particularly relevant to non-model systems. Here, we highlight how these considerations influence a study’s power to link methylation variation with a predictor variable of interest. Relative to current practice, we argue that sample sizes will need to increase to provide robust insights. We also provide recommendations for overcoming common challenges and an R Shiny app to aid in study design.
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spelling pubmed-56564032018-01-21 Maximizing ecological and evolutionary insight in bisulfite sequencing data sets Lea, Amanda J. Vilgalys, Tauras P. Durst, Paul A.P. Tung, Jenny Nat Ecol Evol Article Genome-scale bisulfite sequencing approaches have opened the door to ecological and evolutionary studies of DNA methylation in many organisms. These approaches can be powerful. However, they introduce new methodological and statistical considerations, some of which are particularly relevant to non-model systems. Here, we highlight how these considerations influence a study’s power to link methylation variation with a predictor variable of interest. Relative to current practice, we argue that sample sizes will need to increase to provide robust insights. We also provide recommendations for overcoming common challenges and an R Shiny app to aid in study design. 2017-07-21 2017-08 /pmc/articles/PMC5656403/ /pubmed/29046582 http://dx.doi.org/10.1038/s41559-017-0229-0 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Lea, Amanda J.
Vilgalys, Tauras P.
Durst, Paul A.P.
Tung, Jenny
Maximizing ecological and evolutionary insight in bisulfite sequencing data sets
title Maximizing ecological and evolutionary insight in bisulfite sequencing data sets
title_full Maximizing ecological and evolutionary insight in bisulfite sequencing data sets
title_fullStr Maximizing ecological and evolutionary insight in bisulfite sequencing data sets
title_full_unstemmed Maximizing ecological and evolutionary insight in bisulfite sequencing data sets
title_short Maximizing ecological and evolutionary insight in bisulfite sequencing data sets
title_sort maximizing ecological and evolutionary insight in bisulfite sequencing data sets
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5656403/
https://www.ncbi.nlm.nih.gov/pubmed/29046582
http://dx.doi.org/10.1038/s41559-017-0229-0
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