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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics$
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Christine Sinoquet and Raphaël Mourad

Print publication date: 2014

Print ISBN-13: 9780198709022

Published to Oxford Scholarship Online: December 2014

DOI: 10.1093/acprof:oso/9780198709022.001.0001

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Bayesian Networks in the Study of Genome-wide DNA Methylation

Bayesian Networks in the Study of Genome-wide DNA Methylation

Chapter:
(p.363) Chapter 14 Bayesian Networks in the Study of Genome-wide DNA Methylation
Source:
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Author(s):

Meromit Singer

Lior Pachter

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198709022.003.0014

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.

Keywords:   DNA methylation, Methyl-seq, Bayesian networks

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