English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Information recovery from low coverage whole-genome bisulfite sequencing

MPS-Authors
/persons/resource/persons203770

Meissner,  Alexander
Dept. of Genome Regulation (Head: Alexander Meissner), Max Planck Institute for Molecular Genetics, Max Planck Society;
Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA;

External Resource
No external resources are shared
Fulltext (public)

Libertini.pdf
(Publisher version), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Libertini, E., Heath, S. C., Hamoudi, R. A., Gut, M., Ziller, M. J., Czyz, A., et al. (2016). Information recovery from low coverage whole-genome bisulfite sequencing. Nature Communications, 2016: 7:11306. doi:10.1038/ncomms11306.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002C-DEB9-E
Abstract
The cost of whole-genome bisulfite sequencing (WGBS) remains a bottleneck for many studies and it is therefore imperative to extract as much information as possible from a given dataset. This is particularly important because even at the recommend 30X coverage for reference methylomes, up to 50% of high-resolution features such as differentially methylated positions (DMPs) cannot be called with current methods as determined by saturation analysis. To address this limitation, we have developed a tool that dynamically segments WGBS methylomes into blocks of comethylation (COMETs) from which lost information can be recovered in the form of differentially methylated COMETs (DMCs). Using this tool, we demonstrate recovery of ∼30% of the lost DMP information content as DMCs even at very low (5X) coverage. This constitutes twice the amount that can be recovered using an existing method based on differentially methylated regions (DMRs). In addition, we explored the relationship between COMETs and haplotypes in lymphoblastoid cell lines of African and European origin. Using best fit analysis, we show COMETs to be correlated in a population-specific manner, suggesting that this type of dynamic segmentation may be useful for integrated (epi)genome-wide association studies in the future.