Bayesian Networks in the Study of Genome-wide DNA Methylation

Abstract

This chapter explores the use of Bayesian networks in the study of genome-scale deoxyribonucleic acid (DNA) methylation. It begins by describing different experimental methods for the genome-scale annotation of DNA methylation. The Methyl-seq protocol is detailed and the biases induced by this technique are depicted, which constitute as many challenges for further analysis. These challenges are addressed introducing a Bayesian network framework for the analysis of Methyl-seq data. This previous model is extended to incorporate more information from the genomic sequence. Genomic structure is used as a prior on methylation status. A recurring theme is the interplay between the model used to glean information from the technology, and the view of methylation that drives the model specification. Finally, a study is described, in which such models were used, leading to both interesting biological conclusions and to insights about the nature of methylation.

Additional details