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Series GSE54375 Query DataSets for GSE54375
Status Public on Jan 25, 2014
Title BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach
Organism Homo sapiens
Experiment type Methylation profiling by genome tiling array
Summary 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.
Overall design Benchmarking samples to compare MBD- and MeDIP-seq [GSE38679, GSE24546; PMID 21045081] datasets against 450k measurements
Contributor(s) Robinson MD
Citation(s) 24517713
Submission date Jan 24, 2014
Last update date Mar 22, 2019
Contact name Mark Robinson
Organization name University of Zurich
Street address Winterthurerstrasse 190
City Zurich
State/province ZH
ZIP/Postal code 8057
Country Switzerland
Platforms (1)
GPL13534 Illumina HumanMethylation450 BeadChip (HumanMethylation450_15017482)
Samples (2)
GSM1314099 IMR-90
GSM1314100 SssI
BioProject PRJNA236357

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE54375_RAW.tar 227.0 Mb (http)(custom) TAR (of IDAT, TXT)
Processed data included within Sample table
Processed data provided as supplementary file

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