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BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach

Andrea Riebler123*, Mirco Menigatti4, Jenny Z Song5, Aaron L Statham5, Clare Stirzaker56, Nadiya Mahmud7, Charles A Mein7, Susan J Clark56 and Mark D Robinson18*

Author Affiliations

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 Institute of Molecular Cancer Research, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland

5 Epigenetics Laboratory, Cancer Research Program, Garvan Institute of Medical Research, Sydney 2010, New South Wales, Australia

6 St Vincent’s Clinical School, University of NSW, Sydney 2052, New South Wales, Australia

7 Genome Centre, Barts and the London, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK

8 SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland

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Genome Biology 2014, 15:R35  doi:10.1186/gb-2014-15-2-r35

Published: 11 February 2014

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.