BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach
Andrea Riebler1,2,3,*, Mirco Menigatti4, Jenny Z. Song5, Aaron L. Statham5, Clare Stirzaker5,6, Nadiya Mahmud7, Charles, A. Mein7, Susan J. Clark5,6, Mark D. Robinson1,8,*
1 Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190 CH-8057 Zurich, Switzerland
2 Institute of Social- and Preventive Medicine, University of Zurich, Hirschengraben 84, CH-8001 Zurich, Switzerland
3 Department of Mathematical Sciences, Norwegian University of Science and Technology, N-7491 Trondheim, Norway
4 Epigenetics Laboratory, Cancer Research Program, Garvan Institute of Medical Research, Sydney 2010, New South Wales, Australia
5 St Vincent's Clinical School, University of NSW, Sydney, NSW, Australia
6 Institute of Molecular Cancer Research, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
7 Genome Centre, Barts and the London, Queen Mary, University of London, Charterhouse Square, London EC1M 6BQ, United Kingdom
8 SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
* Corresponding authors: firstname.lastname@example.org, email@example.com.
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 an
empirical Bayes approach (BayMeth) 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, while allowing explicit modeling of
copy number variation and offers computationally-efficient analytical mean and variance estimators.
BayMeth is available in the Repitools Bioconductor package.
Supplementary Data (semi-processed), R Code for all Figures and analyses: