Information recovery from low coverage whole-genome bisulfite sequencing

Emanuele Libertini (), Simon C. Heath, Rifat A. Hamoudi, Marta Gut, Michael J. Ziller, Agata Czyz, Victor Ruotti, Hendrik G. Stunnenberg, Mattia Frontini, Willem H. Ouwehand, Alexander Meissner, Ivo G. Gut and Stephan Beck ()
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Emanuele Libertini: Medical Genomics, UCL Cancer Institute, University College London
Simon C. Heath: Centro Nacional de Análisis Genómico (CNAG), Parc Científic de Barcelona, Torre I
Rifat A. Hamoudi: University College London
Marta Gut: Centro Nacional de Análisis Genómico (CNAG), Parc Científic de Barcelona, Torre I
Michael J. Ziller: Broad Institute of MIT and Harvard
Agata Czyz: Illumina Inc.
Victor Ruotti: Illumina Inc.
Hendrik G. Stunnenberg: Radboud University Nijmegen
Mattia Frontini: University of Cambridge
Willem H. Ouwehand: University of Cambridge
Alexander Meissner: Broad Institute of MIT and Harvard
Ivo G. Gut: Centro Nacional de Análisis Genómico (CNAG), Parc Científic de Barcelona, Torre I
Stephan Beck: Medical Genomics, UCL Cancer Institute, University College London

Nature Communications, 2016, vol. 7, issue 1, 1-7

Abstract: 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.

Date: 2016
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DOI: 10.1038/ncomms11306

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