Title |
BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach
|
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Published in |
Genome Biology, February 2014
|
DOI | 10.1186/gb-2014-15-2-r35 |
Pubmed ID | |
Authors |
Andrea Riebler, Mirco Menigatti, Jenny Z Song, Aaron L Statham, Clare Stirzaker, Nadiya Mahmud, Charles A Mein, Susan J Clark, Mark D Robinson |
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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Geographical breakdown
Country | Count | As % |
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United Kingdom | 3 | 19% |
United States | 3 | 19% |
Australia | 2 | 13% |
Switzerland | 2 | 13% |
Italy | 1 | 6% |
India | 1 | 6% |
Unknown | 4 | 25% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 9 | 56% |
Scientists | 6 | 38% |
Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 3% |
China | 1 | 1% |
Germany | 1 | 1% |
Luxembourg | 1 | 1% |
Unknown | 65 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 22 | 31% |
Researcher | 18 | 26% |
Student > Bachelor | 5 | 7% |
Student > Doctoral Student | 4 | 6% |
Professor > Associate Professor | 4 | 6% |
Other | 14 | 20% |
Unknown | 3 | 4% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 26 | 37% |
Biochemistry, Genetics and Molecular Biology | 14 | 20% |
Computer Science | 10 | 14% |
Medicine and Dentistry | 3 | 4% |
Engineering | 3 | 4% |
Other | 9 | 13% |
Unknown | 5 | 7% |