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


Riebler, Andrea; Menigatti, Mirco; Song, Jenny Z; Statham, Aaron L; Stirzaker, Clare; Mahmud, Nadiya; Mein, Charles A; Clark, Susan J; Robinson, Mark D (2014). BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach. Genome Biology, 15(2):R35.

Abstract

Affinity capture of DNA methylation combined with high-throughput sequencing strikes a good balancebetween the high cost of whole genome bisulfite sequencing and the low coverage of methylationarrays. We present BayMeth, an empirical Bayes approach that uses a fully methylated control sampleto transform observed read counts into regional methylation levels. In our model, inefficient capturecan readily be distinguished from low methylation levels. BayMeth improves on existing methods,allows explicit modeling of copy number variation, and offers computationally-efficient analyticalmean and variance estimators. BayMeth is available in the Repitools Bioconductor package.

Abstract

Affinity capture of DNA methylation combined with high-throughput sequencing strikes a good balancebetween the high cost of whole genome bisulfite sequencing and the low coverage of methylationarrays. We present BayMeth, an empirical Bayes approach that uses a fully methylated control sampleto transform observed read counts into regional methylation levels. In our model, inefficient capturecan readily be distinguished from low methylation levels. BayMeth improves on existing methods,allows explicit modeling of copy number variation, and offers computationally-efficient analyticalmean and variance estimators. BayMeth is available in the Repitools Bioconductor package.

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Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Molecular Cancer Research
07 Faculty of Science > Institute of Molecular Cancer Research

07 Faculty of Science > Institute of Molecular Life Sciences
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2014
Deposited On:24 Mar 2014 15:03
Last Modified:06 Aug 2017 03:16
Publisher:BioMed Central
ISSN:1465-6906
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1186/gb-2014-15-2-r35
PubMed ID:24517713

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