Abstract
DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Recent developments in whole genome bisulfite sequencing (WGBS) technology have enabled genome-wide measurements of DNA methylation at single base pair resolution. Many experiments have been conducted to compare DNA methylation profiles under different biological contexts, with the goal of identifying differentially methylated regions (DMRs). Due to the high cost of WGBS experiments, many studies are still conducted without biological replicates. Methods and tools available for analyzing such data are very limited.We develop a statistical method, DSS-single, for detecting DMRs from WGBS data without replicates. We characterize the count data using a rigorous model that accounts for the spatial correlation of methylation levels, sequence depth and biological variation. We demonstrate that using information from neighboring CG sites, biological variation can be estimated accurately even without replicates. DMR detection is then carried out via a Wald test procedure. Simulations demonstrate that DSS-single has greater sensitivity and accuracy than existing methods, and an analysis of H1 versus IMR90 cell lines suggests that it also yields the most biologically meaningful results. DSS-single is implemented in the Bioconductor package DSS.
Highlights
DNA methylation is a covalent epigenetic modification on the 5-carbon position of cytosine that plays important roles in regulating gene expression
An important goal in whole genome bisulfite sequencing (WGBS) data analysis is the detection of differentially methylated regions (DMRs)
Due to the high cost, many WGBS experiments are performed without biological replicates, which makes DMR calling via existing methods difficult or impossible
Summary
DNA methylation is a covalent epigenetic modification on the 5-carbon position of cytosine that plays important roles in regulating gene expression. Because of its regulatory potential, studies of DNA methylation are of great interest Results from these studies enhance the understanding of epigenetic mechanisms of many basic biological processes and disease etiologies. Recent developments in high-throughput technologies such as second-generation sequencing have revolutionized the field by enabling genome-wide profiling of DNA methylation. Identification of DMRs between patients and unaffected individuals could lead to development of putative epigenetic biomarkers for early detection and diagnosis. For these reasons, there is great demand for methods for optimal detection of DMRs from WGBS data, where the goal is to define genomic regions that show statistically significant differences in methylation levels between biological conditions. A number of statistical methods and computational tools have been developed recently, including BSmooth [7], MethylKit [8], GBSA [9], BiSeq [10], DSS
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