Maximizing ecological and evolutionary insight in bisulfite sequencing data sets.

Published

Journal Article (Review)

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.

Full Text

Duke Authors

Cited Authors

  • Lea, AJ; Vilgalys, TP; Durst, PAP; Tung, J

Published Date

  • August 2017

Published In

Volume / Issue

  • 1 / 8

Start / End Page

  • 1074 - 1083

PubMed ID

  • 29046582

Pubmed Central ID

  • 29046582

Electronic International Standard Serial Number (EISSN)

  • 2397-334X

International Standard Serial Number (ISSN)

  • 2397-334X

Digital Object Identifier (DOI)

  • 10.1038/s41559-017-0229-0

Language

  • eng