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.

Understanding the link between non-coding sequence variants, identified in genome-wide association studies, and the pathophysiology of complex diseases remains challenging due to a lack of annotations in non-coding regions. To overcome this, we developed DIVAN, a novel feature selection and ensemble learning framework, which identifies disease-specific risk variants by leveraging a comprehensive collection of genome-wide epigenomic profiles across cell types and factors, along with other static genomic features. DIVAN accurately and robustly recognizes non-coding disease-specific risk variants under multiple testing scenarios; among all the features, histone marks, especially those marks associated with repressed chromatin, are often more informative than others.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1112-z) contains supplementary material, which is available to authorized users.

Epigenetic modifications such as cytosine methylation and histone modification are linked to the pathology of ischemic brain injury. Recent research has implicated 5-hydroxymethylcytosine (5hmC), a DNA base derived from 5-methylcytosine (5mC) via oxidation by ten-eleven translocation (Tet) enzymes, in DNA methylation-related plasticity. Here we show that 5hmC abundance was increased after ischemic injury, and Tet2 was responsible for this increase; furthermore, inhibiting Tet2 expression abolished the increase of 5hmC caused by ischemic injury. The decrease in 5hmC modifications from inhibiting Tet2 activity was accompanied by increased infarct volume after ischemic injury. Genome-wide profiling of 5hmC revealed differentially hydroxymethylated regions (DhMRs) associated with ischemic injury, and DhMRs were enriched among the genes involved in cell junction, neuronal morphogenesis and neurodevelopment. In particular, we found that 5hmC modifications at the promoter region of brain-derived neurotrophic factor (BDNF) increased, which was accompanied by increased BDNF mRNA, whereas the inhibition of Tet2 reduced BDNF mRNA and protein expression. Finally, we show that the abundance of 5hmC in blood samples from patients with acute ischemic stroke was also significantly increased. Together, these data suggest that 5hmC modification could serve as both a potential biomarker and a therapeutic target for the treatment of ischemic stroke.

An R software package ChIPComp is freely available at http://web1.sph.emory.edu/users/hwu30/software/ChIPComp.html.