Computational approaches for understanding the evolution of DNA methylation in animals

Abstract

DNA methylation is implicated in modulating gene transcription in a wide array of taxa. However, a complete understanding of the specific function of DNA methylation requires study of the evolutionary patterns of genome methylation in diverse organisms. Unfortunately, investigating DNA methylation through empirical means remains challenging due to the transient nature of DNA methylation, the paucity of model systems in which to study genome methylation, and the costs associated with experimental methodology. Here we review how computational methods can be used to complement experimental approaches to further our understanding of DNA methylation in animals. For instance, comparative analyses of the molecular machinery involved in DNA methylation have been informative in revealing the dynamics of genome methylation in many organisms. In addition, analyses of specific genomic signatures of DNA methylation have furthered our understanding of the patterns of methylation within species. Finally, insight into the role of DNA methylation has resulted from computational methods used to identify specific sets of methylated genes. We suggest that the evolution of genomic DNA methylation can be most readily understood by integrating computational and empirical methods.