Abstract
DNA methylation is an epigenetic modification known to affect gene expression, cellular differentiation, as well as phenotypes. Recent advancements in next-generation sequencing technologies have provided unparalleled insight into the location and function of DNA methylation in a variety of organisms. These data require vastly different statistical procedures than data from previous genomic-based technologies. We outline the biological and chemical processes involved in several approaches for gaining DNA methylation data. The implications of the differences between the approaches are discussed relative to the statistical methodology, and the use of genome annotation is explored for the purpose of improving the statistical power when testing for differential methylation.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Baumann, Douglas and Doerge, R. W.
(2011).
"ISSUES IN TESTING DNA METHYLATION USING NEXT-GENERATION SEQUENCING,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1043
ISSUES IN TESTING DNA METHYLATION USING NEXT-GENERATION SEQUENCING
DNA methylation is an epigenetic modification known to affect gene expression, cellular differentiation, as well as phenotypes. Recent advancements in next-generation sequencing technologies have provided unparalleled insight into the location and function of DNA methylation in a variety of organisms. These data require vastly different statistical procedures than data from previous genomic-based technologies. We outline the biological and chemical processes involved in several approaches for gaining DNA methylation data. The implications of the differences between the approaches are discussed relative to the statistical methodology, and the use of genome annotation is explored for the purpose of improving the statistical power when testing for differential methylation.
