A full Bayesian partition model for identifying hypo- and hyper-methylated loci from single nucleotide resolution sequencing data
Date
2016-01-11
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Publisher
BioMed Central Ltd
Abstract
BACKGROUND: DNA methylation is an epigenetic modification that plays important roles on gene regulation. Study of
whole-genome bisulfite sequencing and reduced representation bisulfite sequencing brings the availability of DNA
methylation at single CpG resolution. The main interest of study on DNA methylation data is to test the methylation
difference under two conditions of biological samples. However, the high cost and complexity of this sequencing
experiment limits the number of biological replicates, which brings challenges to the development of statistical
methods.
RESULTS: Bayesian modeling is well known to be able to borrow strength across the genome, and hence is a powerful
tool for high-dimensional- low-sample- size data. In order to provide accurate identification of methylation loci,
especially for low coverage data, we propose a full Bayesian partition model to detect differentially methylated loci
under two conditions of scientific study. Since hypo-methylation and hyper-methylation have distinct biological
implication, it is desirable to differentiate these two types of differential methylation. The advantage of our Bayesian
model is that it can produce one-step output of each locus being either equal-, hypo- or hyper-methylated locus
without further post-hoc analysis. An R package named as MethyBayes implementing the proposed full Bayesian
partition model will be submitted to the bioconductor website upon publication of the manuscript.
CONCLUSIONS: The proposed full Bayesian partition model outperforms existing methods in terms of power while
maintaining a low false discovery rate based on simulation studies and real data analysis including bioinformatics
analysis.
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Citation
Wang, Henan, et al. "A full Bayesian partition model for identifying hypo-and hyper-methylated loci from single nucleotide resolution sequencing data." BMC Bioinformatics 17.1 (2016): 71.