A novel nonparametric computational strategy for identifying differential methylation regions

Background DNA methylation has long been known as an epigenetic gene silencing mechanism. For a motivating example, the methylomes of cancer and non-cancer cells show a number of methylation differences, indicating that certain features characteristics of cancer cells may be related to methylation characteristics. Robust methods for detecting differentially methylated regions (DMRs) could help scientists narrow down genome regions and even find biologically important regions. Although some statistical methods were developed for detecting DMR, there is no default or strongest method. Fisher’s exact test is direct, but not suitable for data with multiple replications, while regression-based methods usually come with a large number of assumptions. More complicated methods have been proposed, but those methods are often difficult to interpret. Results In this paper, we propose a three-step nonparametric kernel smoothing method that is both flexible and straightforward to implement and interpret. The proposed method relies on local quadratic fitting to find the set of equilibrium points (points at which the first derivative is 0) and the corresponding set of confidence windows. Potential regions are further refined using biological criteria, and finally selected based on a Bonferroni adjusted t-test cutoff. Using a comparison of three senescent and three proliferating cell lines to illustrate our method, we were able to identify a total of 1077 DMRs on chromosome 21. Conclusions We proposed a completely nonparametric, statistically straightforward, and interpretable method for detecting differentially methylated regions. Compared with existing methods, the non-reliance on model assumptions and the straightforward nature of our method makes it one competitive alternative to the existing statistical methods for defining DMRs.

MethylScore, a pipeline for accurate and context-aware identification of differentially methylated regions from population-scale plant WGBS data

Whole-genome bisulfite sequencing (WGBS) is the standard method for profiling DNA methylation at single-nucleotide resolution. Many WGBS-based studies aim to identify biologically relevant loci that display differential methylation between genotypes, treatment groups, tissues, or developmental stages. Over the years, different tools have been developed to extract differentially methylated regions (DMRs) from whole-genome data. Often, such tools are built upon assumptions from mammalian data and do not consider the substantially more complex and variable nature of plant DNA methylation. Here, we present MethylScore, a pipeline to analyze WGBS data and to account for plant-specific DNA methylation properties. MethylScore processes data from genomic alignments to DMR output and is designed to be usable by novice and expert users alike. It uses an unsupervised machine learning approach to segment the genome by classification into states of high and low methylation, substantially reducing the number of necessary statistical tests while increasing the signal-to-noise ratio and the statistical power. We show how MethylScore can identify DMRs from hundreds of samples and how its data-driven approach can stratify associated samples without prior information. We identify DMRs in the A. thaliana 1001 Genomes dataset to unveil known and unknown genotype-epigenotype associations. MethylScore is an accessible pipeline for plant WGBS data, with unprecedented features for DMR calling in small- and large-scale datasets; it is built as a Nextflow pipeline and its source code is available at https://github.com/Computomics/MethylScore.

Leukocyte methylomic imprints of exposure to the genocide against the Tutsi in Rwanda: a pilot epigenome-wide analysis

We conducted a pilot epigenome-wide association study of women from Tutsi ethnicity exposed to the genocide while pregnant and their resulting offspring, and a comparison group of women who were pregnant at the time of the genocide but living outside of Rwanda. Fifty-nine leukocyte-derived DNA samples survived quality control: 33 mothers (20 exposed, 13 unexposed) and 26 offspring (16 exposed, 10 unexposed). Twenty-four significant differentially methylated regions (DMRs) were identified in mothers and 16 in children. In utero genocide exposure was associated with CpGs in three of the 24 DMRs: BCOR, PRDM8 and VWDE, with higher DNA methylation in exposed versus unexposed offspring. Of note, BCOR and VWDE show significant correlation between brain and blood DNA methylation within individuals, suggesting these peripherally derived signals of genocide exposure may have relevance to the brain.

Use of Two Complementary Bioinformatic Approaches to Identify Differentially Methylated Regions in Neonatal Sepsis

Background: Neonatal sepsis is a heterogeneous condition affecting preterm infants whose underlying mechanisms remain unknown. The analysis of changes in the DNA methylation pattern can contribute to improving the understanding of molecular pathways underlying disease pathophysiology. Methylation EPIC 850K BeadChip technology is an excellent tool for genome-wide methylation analyses and the detection of differentially methylated regions (DMRs). Objective: The aim is to identify DNA methylation traits in complex diseases, such as neonatal sepsis, using data from Methylation EPIC 850K BeadChip arrays. Methods: Two different bioinformatic methods, DMRcate (a supervised approach) and mCSEA (an unsupervised approach), were used to identify DMRs using EPIC data from leukocytes of neonatal septic patients. Here, we describe with detail the implementation of both methods as well as their applicability, briefly discussing the results obtained for neonatal sepsis. Results: Differences in methylation levels were observed in neonatal sepsis patients. Moreover, differences were identified between the two subsets of the disease: Early-Onset neonatal Sepsis (EOS) and Late-Onset Neonatal Sepsis (LOS). Conclusion: This approach by using DMRcate and mCSA helped us to gain insight into the intricate mechanisms that may drive EOS and LOS development and progression in newborns.

A Parallel Tool for the Identification of Differentially Methylated Regions in Genomic Analyses

Methylation is a chemical process that modifies DNA through the addition of a methyl group to one or several nucleotides. Discovering differentially methylated regions is an important research field in genomics, as it can help to anticipate the risk of suffering from certain diseases. RADMeth is one of the most accurate tools in this field, but it has high computational complexity. In this work, we present a hybrid MPI-OpenMP parallel implementation of RADMeth to accelerate its execution on distributed-memory systems, reaching speedups of up to 189 when running on 256 cores and allowing for its application to large-scale datasets.

Role of hypermethylated-lncRNAs in the prognosis of bladder cancer patients

Objective To explore the hypermethylated long non-coding (lnc)RNAs involved in bladder carcinogenesis and prognosis. Methods Reduced representation bisulfite sequencing and RNA sequencing were performed on five paired tumor and adjacent normal tissue samples from bladder cancer patients. The differentially methylated regions around transcription start sites and differentially expressed genes, including lncRNAs, were analyzed. Correlations between DNA methylation modifications and the expression of lncRNAs were examined. Survival analysis was surveyed on the GEPIA web server. Results We identified 19,560 hypomethylated and 68,781 hypermethylated differentially methylated regions around transcription start sites in bladder cancer tissues. In total, 2321 differentially expressed genes were found in bladder tumors, among which, 367 were upregulated and 1954 were downregulated. There were 141 downregulated genes involving eight lncRNAs that were consistently hypermethylated, while 24 upregulated genes were consistently hypomethylated. Survival analysis demonstrated that hypermethylation of lncRNAs LINC00683 and MSC-AS1 were associated with poor overall survival in bladder cancer patients. Conclusion Some lncRNAs are controlled by DNA methylation in bladder cancer and they might be important factors in bladder carcinogenesis. Hypermethylated lncRNAs including LINC00683 and MSC-AS1 have the potential to be prognostic biomarkers for bladder cancer.

Simulated Wildfire Smoke Significantly Alters Sperm DNA Methylation Patterns in a Murine Model

Wildfires are now a common feature of the western US, increasing in both intensity and number of acres burned over the last three decades. The effects of this changing wildfire and smoke landscape are a critical public and occupational health issue. While respiratory morbidity due to smoke exposure is a priority, evaluating the molecular underpinnings that explain recent extrapulmonary observations is necessary. Here, we use an Apoe−/− mouse model to investigate the epigenetic impact of paternal exposure to simulated wildfire smoke. We demonstrate that 40 days of exposure to smoke from Douglas fir needles induces sperm DNA methylation changes in adult mice. DNA methylation was measured by reduced representation bisulfite sequencing and varied significantly in 3353 differentially methylated regions, which were subsequently annotated to 2117 genes. The differentially methylated regions were broadly distributed across the mouse genome, but the vast majority (nearly 80%) were hypermethylated. Pathway analyses, using gene-derived and differentially methylated region-derived gene ontology terms, point to a number of developmental processes that may warrant future investigation. Overall, this study of simulated wildfire smoke exposure suggests paternal reproductive risks are possible with prolonged exposure.