title: BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach.
abstract: Affinity capture of DNA methylation combined with high-throughput sequencing strikes a good balance between the high cost of whole genome bisulfite sequencing and the low coverage of methylation arrays. We present BayMeth, an empirical Bayes approach that uses a fully methylated control sample to transform observed read counts into regional methylation levels. In our model, inefficient capture can readily be distinguished from low methylation levels. BayMeth improves on existing methods, allows explicit modeling of copy number variation, and offers computationally efficient analytical mean and variance estimators. BayMeth is available in the Repitools Bioconductor package.
authors: Andrea
authors: A
authors: Riebler
authors: Mirco
authors: M
authors: Menigatti
authors: Jenny Z
authors: JZ
authors: Song
authors: Aaron L
authors: AL
authors: Statham
authors: Clare
authors: C
authors: Stirzaker
authors: Nadiya
authors: N
authors: Mahmud
authors: Charles A
authors: CA
authors: Mein
authors: Susan J
authors: SJ
authors: Clark
authors: Mark D
authors: MD
authors: Robinson
mesh_terms: D001499:Bayes Theorem; D018899:CpG Islands / Q000235:genetics; D056915:DNA Copy Number Variations; D019175:DNA Methylation / Q000235:genetics*; D005069:Evaluation Studies as Topic; D015894:Genome, Human; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans