TY - JOUR AU - Riebler, Andrea AU - Menigatti, Mirco AU - Song, Jenny Z. AU - Statham, Aaron L. AU - Stirzaker, Clare AU - Mahmud, Nadiya AU - Mein, Charles A. AU - Clark, Susan J. AU - Robinson, Mark D. PY - 2014 DA - 2014/02/11 TI - BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach JO - Genome Biology SP - R35 VL - 15 IS - 2 AB - 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. SN - 1474-760X UR - https://doi.org/10.1186/gb-2014-15-2-r35 DO - 10.1186/gb-2014-15-2-r35 ID - Riebler2014 ER -