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Bayesian Networks in the Study of Genome-wide DNA Methylation

Singer, Meromit and Pachter, Lior (2014) Bayesian Networks in the Study of Genome-wide DNA Methylation. In: Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics. Oxford University Press , New York, NY, pp. 363-386. ISBN 9780198709022. https://resolver.caltech.edu/CaltechAUTHORS:20170303-140414225

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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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1093/acprof:oso/9780198709022.003.0014DOIArticle
http://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780198709022.001.0001/acprof-9780198709022-chapter-14PublisherArticle
Additional Information:© 2014 Oxford University Press.
Subject Keywords:DNA methylation, Methyl-seq, Bayesian networks
Record Number:CaltechAUTHORS:20170303-140414225
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170303-140414225
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:74711
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:03 Mar 2017 23:57
Last Modified:03 Oct 2019 16:42

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