A survey of the approaches for identifying differential methylation using bisulfite sequencing data

Evaluation of Illumina and Oxford Nanopore Sequencing for the Study of DNA Methylation in Alzheimer's Disease and Frontotemporal Dementia

DNA methylation is a critical epigenetic mechanism involved in numerous physiological processes. Alterations in DNA methylation patterns are associated with various brain disorders, including dementias such as Alzheimer's disease (AD) and frontotemporal dementia (FTD). Investigating these alterations is essential for understanding the pathogenesis and progression of these disorders. Among the various methods for detecting DNA methylation, DNA sequencing is one of the most widely employed. Specifically, two main sequencing approaches are commonly used for DNA methylation analysis: bisulfite sequencing and single-molecule long-read sequencing. In this review, we compared the performances of CpG methylation detection obtained using two popular sequencing platforms, Illumina for bisulfite sequencing and Oxford Nanopore (ON) for long-read sequencing. Our comparison considers several factors, including accuracy, efficiency, genomic regions, costs, wet-lab protocols, and bioinformatics pipelines. We provide insights into the strengths and limitations of both methods with a particular focus on their application in research on AD and FTD.

Epigenomic Changes in Ostrinia Moths Under Elevated Pupal and Adult Temperature

Epigenetic changes in the methylation of DNA may occur in response to environmental stressors, including warming climates. DNA methylation may also play an important role in regulating gene expression during both male and female reproduction in many insect species. However, it is currently unknown how DNA methylation shifts when individuals are reproducing under warmer temperatures. We exposed European corn borer moths (Ostrinia nubilalis) to heat during the pupal and adult life stages then investigated changes in DNA methylation across the genome using enzymatic methyl-seq (EM-seq). We compared methylation patterns in reproductive males and females exposed to heat (28°C) to those that experienced an ambient temperature (23°C). We found that heat exposure led to a small but significant increase in the percentage of methylated CpG sites throughout the genome in both sexes. However, DNA methylation rates were higher in females and differential methylation following heat exposure localised to unique regions in each sex. In males, methylation shifted within genes belonging to pathways including Hippo signalling, ubiquitin-mediated proteolysis, DNA damage repair and spermatogenesis. In females, differential methylation occurred in genes related to histone modification and oogenesis. Our results suggest that DNA methylation patterns respond to moderate heat exposure in Lepidoptera and provide insight into epigenetic responses to heatwaves, suggesting novel pathways that may be involved in responding to heat stress during metamorphosis and reproduction.

Data-Driven Identification of Early Cancer-Associated Genes via Penalized Trans-Dimensional Hidden Markov Models

Colorectal cancer (CRC) is a significant worldwide health problem due to its high prevalence, mortality rates, and frequent diagnosis at advanced stages. While diagnostic and therapeutic approaches have evolved, the underlying mechanisms driving CRC initiation and progression are not yet fully understood. Early detection is critical for improving patient survival, as initial cancer stages often exhibit epigenetic changes-such as DNA methylation-that regulate gene expression and tumor progression. Identifying DNA methylation patterns and key survival-related genes in CRC could thus enhance diagnostic accuracy and extend patient lifespans. In this study, we apply two of our recently developed methods for identifying differential methylation and analyzing survival using a sparse, finite mixture of accelerated failure time regression models, focusing on key genes and pathways in CRC datasets. Our approach outperforms two other leading methods, yielding robust findings and identifying novel differentially methylated cytosines. We found that CRC patient survival time follows a two-component mixture regression model, where genes CDH11, EPB41L3, and DOCK2 are active in the more aggressive form of CRC, whereas TMEM215, PPP1R14A, GPR158, and NAPSB are active in the less aggressive form.

MethyLasso: a segmentation approach to analyze DNA methylation patterns and identify differentially methylated regions from whole-genome datasets

DNA methylation is an epigenetic mark involved in the regulation of gene expression, and patterns of DNA methylation anticorrelate with chromatin accessibility and transcription factor binding. DNA methylation can be profiled at the single cytosine resolution in the whole genome and has been performed in many cell types and conditions. Computational approaches are then essential to study DNA methylation patterns in a single condition or capture dynamic changes of DNA methylation levels across conditions. Toward this goal, we developed MethyLasso, a new approach to segment DNA methylation data. We use it as an all-in-one tool to perform the identification of low-methylated regions, unmethylated regions, DNA methylation valleys and partially methylated domains in a single condition as well as differentially methylated regions between two conditions. We performed a rigorous benchmarking comparing existing approaches by evaluating the agreement of the regions across tools, their number, size, level of DNA methylation, boundaries, cytosine-guanine content and coverage using several real datasets as well as the sensitivity and precision of the approaches using simulated data and show that MethyLasso performs best overall. MethyLasso is freely available at https://github.com/bardetlab/methylasso.

A varying-coefficient model for the analysis of methylation sequencing data

DNA methylation is an important epigenetic modification involved in gene regulation. Advances in the next generation sequencing technology have enabled the retrieval of DNA methylation information at single-base-resolution. However, due to the sequencing process and the limited amount of isolated DNA, DNA-methylation-data are often noisy and sparse, which complicates the identification of differentially methylated regions (DMRs), especially when few replicates are available. We present a varying-coefficient model for detecting DMRs by using single-base-resolved methylation information. The model simultaneously smooths the methylation profiles and allows detection of DMRs, while accounting for additional covariates. The proposed model takes into account possible overdispersion by using a beta-binomial distribution. The overdispersion itself can be modeled as a function of the genomic region and explanatory variables. We illustrate the properties of the proposed model by applying it to two real-life case studies.

Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model

DNA methylation plays an essential role in regulating gene activity, modulating disease risk, and determining treatment response. We can obtain insight into methylation patterns at a single-nucleotide level via next-generation sequencing technologies. However, complex features inherent in the data obtained via these technologies pose challenges beyond the typical big data problems. Identifying differentially methylated cytosines (dmc) or regions is one such challenge. We have developed DMCFB, an efficient dmc identification method based on Bayesian functional regression, to tackle these challenges. Using simulations, we establish that DMCFB outperforms current methods and results in better smoothing and efficient imputation. We analyzed a dataset of patients with acute promyelocytic leukemia and control samples. With DMCFB, we discovered many new dmcs and, more importantly, exhibited enhanced consistency of differential methylation within islands and their adjacent shores. Additionally, we detected differential methylation at more of the binding sites of the fused gene involved in this cancer.

Molecular Regulation of Fetal Brain Development in Inbred and Congenic Mouse Strains Differing in Longevity

The objective of this study was to investigate gene regulation of the developing fetal brain from congenic or inbred mice strains that differed in longevity. Gene expression and alternative splice variants were analyzed in a genome-wide manner in the fetal brain of C57BL/6J mice (long-lived) in comparison to B6.Cg-Cav1tm1Mls/J (congenic, short-lived) and AKR/J (inbred, short-lived) mice on day(d) 12, 15, and 17 of gestation. The analysis showed a contrasting gene expression pattern during fetal brain development in these mice. Genes related to brain development, aging, and the regulation of alternative splicing were significantly differentially regulated in the fetal brain of the short-lived compared to long-lived mice during development from d15 and d17. A significantly reduced number of splice variants was observed on d15 compared to d12 or d17 in a strain-dependent manner. An epigenetic clock analysis of d15 fetal brain identified DNA methylations that were significantly associated with single-nucleotide polymorphic sites between AKR/J and C57BL/6J strains. These methylations were associated with genes that show epigenetic changes in an age-correlated manner in mice. Together, the finding of this study suggest that fetal brain development and longevity are epigenetically linked, supporting the emerging concept of the early-life origin of longevity.

Understanding the role of DNA methylation in colorectal cancer: Mechanisms, detection, and clinical significance

Colorectal cancer (CRC) is one of the deadliest malignancies worldwide, ranking third in incidence and second in mortality. Remarkably, early stage localized CRC has a 5-year survival rate of over 90%; in stark contrast, the corresponding 5-year survival rate for metastatic CRC (mCRC) is only 14%. Compounding this problem is the staggering lack of effective therapeutic strategies. Beyond genetic mutations, which have been identified as critical instigators of CRC initiation and progression, the importance of epigenetic modifications, particularly DNA methylation (DNAm), cannot be underestimated, given that DNAm can be used for diagnosis, treatment monitoring and prognostic evaluation. This review addresses the intricate mechanisms governing aberrant DNAm in CRC and its profound impact on critical oncogenic pathways. In addition, a comprehensive review of the various techniques used to detect DNAm alterations in CRC is provided, along with an exploration of the clinical utility of cancer-specific DNAm alterations.

HBCR_DMR: A Hybrid Method Based on Beta-Binomial Bayesian Hierarchical Model and Combination of Ranking Method to Detect Differential Methylation Regions in Bisulfite Sequencing Data

DNA methylation is a key epigenetic modification involved in gene regulation, contributing to both physiological and pathological conditions. For a more profound comprehension, it is essential to conduct a precise comparison of DNA methylation patterns between sample groups that represent distinct statuses. Analysis of differentially methylated regions (DMRs) using computational approaches can help uncover the precise relationships between these phenomena. This paper describes a hybrid model that combines the beta-binomial Bayesian hierarchical model with a combination of ranking methods known as HBCR_DMR. During the initial phase, we model the actual methylation proportions of the CpG sites (CpGs) within the replicates. This modeling is achieved through beta-binomial distribution, with parameters set by a group mean and a dispersion parameter. During the second stage, we establish the selection of distinguishing CpG sites based on their methylation status, employing multiple ranking techniques. Finally, we combine the ranking lists of differentially methylated CpG sites through a voting system. Our analyses, encompassing simulations and real data, reveal outstanding performance metrics, including a sensitivity of 0.72, specificity of 0.89, and an F1 score of 0.76, yielding an overall accuracy of 0.82 and an AUC of 0.94. These findings underscore HBCR_DMR's robust capacity to distinguish methylated regions, confirming its utility as a valuable tool for DNA methylation analysis.

An ecologist's guide for studying DNA methylation variation in wild vertebrates

The field of molecular biology is advancing fast with new powerful technologies, sequencing methods and analysis software being developed constantly. Commonly used tools originally developed for research on humans and model species are now regularly used in ecological and evolutionary research. There is also a growing interest in the causes and consequences of epigenetic variation in natural populations. Studying ecological epigenetics is currently challenging, especially for vertebrate systems, because of the required technical expertise, complications with analyses and interpretation, and limitations in acquiring sufficiently high sample sizes. Importantly, neglecting the limitations of the experimental setup, technology and analyses may affect the reliability and reproducibility, and the extent to which unbiased conclusions can be drawn from these studies. Here, we provide a practical guide for researchers aiming to study DNA methylation variation in wild vertebrates. We review the technical aspects of epigenetic research, concentrating on DNA methylation using bisulfite sequencing, discuss the limitations and possible pitfalls, and how to overcome them through rigid and reproducible data analysis. This review provides a solid foundation for the proper design of epigenetic studies, a clear roadmap on the best practices for correct data analysis and a realistic view on the limitations for studying ecological epigenetics in vertebrates. This review will help researchers studying the ecological and evolutionary implications of epigenetic variation in wild populations.

Isogenic Human-Induced Pluripotent Stem-Cell-Derived Cardiomyocytes Reveal Activation of Wnt Signaling Pathways Underlying Intrinsic Cardiac Abnormalities in Rett Syndrome

Rett syndrome (RTT) is a severe neurodevelopmental disorder caused by MeCP2 mutations. Nonetheless, the pathophysiological roles of MeCP2 mutations in the etiology of intrinsic cardiac abnormality and sudden death remain unclear. In this study, we performed a detailed functional studies (calcium and electrophysiological analysis) and RNA-sequencing-based transcriptome analysis of a pair of isogenic RTT female patient-specific induced pluripotent stem-cell-derived cardiomyocytes (iPSC-CMs) that expressed either MeCP2^wildtype or MeCP2^mutant allele and iPSC-CMs from a non-affected female control. The observations were further confirmed by additional experiments, including Wnt signaling inhibitor treatment, siRNA-based gene silencing, and ion channel blockade. Compared with MeCP2^wildtype and control iPSC-CMs, MeCP2^mutant iPSC-CMs exhibited prolonged action potential and increased frequency of spontaneous early after polarization. RNA sequencing analysis revealed up-regulation of various Wnt family genes in MeCP2^mutant iPSC-CMs. Treatment of MeCP2^mutant iPSC-CMs with a Wnt inhibitor XAV939 significantly decreased the β-catenin protein level and CACN1AC expression and ameliorated their abnormal electrophysiological properties. In summary, our data provide novel insight into the contribution of activation of the Wnt/β-catenin signaling cascade to the cardiac abnormalities associated with MeCP2 mutations in RTT.

Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis

Developing cancer prognostic models using multiomics data is a major goal of precision oncology. DNA methylation provides promising prognostic biomarkers, which have been used to predict survival and treatment response in solid tumor or plasma samples. This review article presents an overview of recently published computational analyses on DNA methylation for cancer prognosis. To address the challenges of survival analysis with high-dimensional methylation data, various feature selection methods have been applied to screen a subset of informative markers. Using candidate markers associated with survival, prognostic models either predict risk scores or stratify patients into subtypes. The model's discriminatory power can be assessed by multiple evaluation metrics. Finally, we discuss the limitations of existing studies and present the prospects of applying machine learning algorithms to fully exploit the prognostic value of DNA methylation.

DNA methylation-based predictors of health: applications and statistical considerations

DNA methylation data have become a valuable source of information for biomarker development, because, unlike static genetic risk estimates, DNA methylation varies dynamically in relation to diverse exogenous and endogenous factors, including environmental risk factors and complex disease pathology. Reliable methods for genome-wide measurement at scale have led to the proliferation of epigenome-wide association studies and subsequently to the development of DNA methylation-based predictors across a wide range of health-related applications, from the identification of risk factors or exposures, such as age and smoking, to early detection of disease or progression in cancer, cardiovascular and neurological disease. This Review evaluates the progress of existing DNA methylation-based predictors, including the contribution of machine learning techniques, and assesses the uptake of key statistical best practices needed to ensure their reliable performance, such as data-driven feature selection, elimination of data leakage in performance estimates and use of generalizable, adequately powered training samples.

Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate

Whole genome bisulphite sequencing (WGBS) permits the genome-wide study of single molecule methylation patterns. One of the key goals of mammalian cell-type identity studies, in both normal differentiation and disease, is to locate differential methylation patterns across the genome. We discuss the most desirable characteristics for DML (differentially methylated locus) and DMR (differentially methylated region) detection tools in a genome-wide context and choose a set of statistical methods that fully or partially satisfy these considerations to compare for benchmarking. Our data simulation strategy is both biologically informed-employing distribution parameters derived from large-scale consortium datasets-and thorough. We report DML detection ability with respect to coverage, group methylation difference, sample size, variability and covariate size, both marginally and jointly, and exhaustively with respect to parameter combination. We also benchmark these methods on FDR control and computational time. We use this result to backend and introduce an expanded version of DMRcate: an existing DMR detection tool for microarray data that we have extended to now call DMRs from WGBS data. We compare DMRcate to a set of alternative DMR callers using a similarly realistic simulation strategy. We find DMRcate and RADmeth are the best predictors of DMRs, and conclusively find DMRcate the fastest.

ARPEGGIO: Automated Reproducible Polyploid EpiGenetic GuIdance workflOw

BACKGROUND

Whole genome duplication (WGD) events are common in the evolutionary history of many living organisms. For decades, researchers have been trying to understand the genetic and epigenetic impact of WGD and its underlying molecular mechanisms. Particular attention was given to allopolyploid study systems, species resulting from an hybridization event accompanied by WGD. Investigating the mechanisms behind the survival of a newly formed allopolyploid highlighted the key role of DNA methylation. With the improvement of high-throughput methods, such as whole genome bisulfite sequencing (WGBS), an opportunity opened to further understand the role of DNA methylation at a larger scale and higher resolution. However, only a few studies have applied WGBS to allopolyploids, which might be due to lack of genomic resources combined with a burdensome data analysis process. To overcome these problems, we developed the Automated Reproducible Polyploid EpiGenetic GuIdance workflOw (ARPEGGIO): the first workflow for the analysis of epigenetic data in polyploids. This workflow analyzes WGBS data from allopolyploid species via the genome assemblies of the allopolyploid's parent species. ARPEGGIO utilizes an updated read classification algorithm (EAGLE-RC), to tackle the challenge of sequence similarity amongst parental genomes. ARPEGGIO offers automation, but more importantly, a complete set of analyses including spot checks starting from raw WGBS data: quality checks, trimming, alignment, methylation extraction, statistical analyses and downstream analyses. A full run of ARPEGGIO outputs a list of genes showing differential methylation. ARPEGGIO was made simple to set up, run and interpret, and its implementation ensures reproducibility by including both package management and containerization.

RESULTS

We evaluated ARPEGGIO in two ways. First, we tested EAGLE-RC's performance with publicly available datasets given a ground truth, and we show that EAGLE-RC decreases the error rate by 3 to 4 times compared to standard approaches. Second, using the same initial dataset, we show agreement between ARPEGGIO's output and published results. Compared to other similar workflows, ARPEGGIO is the only one supporting polyploid data.

CONCLUSIONS

The goal of ARPEGGIO is to promote, support and improve polyploid research with a reproducible and automated set of analyses in a convenient implementation. ARPEGGIO is available at https://github.com/supermaxiste/ARPEGGIO .

Abnormal expression and methylation of PRR34-AS1 are associated with adverse outcomes in acute myeloid leukemia

It was previously reported that PRR34‐AS1 was overexpressed in some solid tumors. PRR34‐AS1 promoter was shown to have a differential methylation region (DMR), and was hypomethylated in acute myeloid leukemia (AML). Therefore, the present study used real‐time quantitative PCR (RQ‐PCR) to explore the expression characteristics of PRR34‐AS1 in AML. In addition, the correlation between the expression of PRR34‐AS1 and clinical prognosis of AML was determined. The findings of this study indicated that high PRR34‐AS1 expression was bound up with shorter overall survival (OS) in AML patients (p = 0.002). Moreover, patients with high expression of PRR34‐AS1 had significantly lower complete remission (CR) rate compared with those with low expression of PRR34‐AS1 after induction chemotherapy. Furthermore, multivariate analysis confirmed that PRR34‐AS1 expression was an independent factor affecting CR in whole‐AML, non‐APL‐AML, and CN‐AML patients (p = 0.032, 0.039, and 0.036, respectively). Methylation‐specific PCR (MSP) and bisulfite sequencing PCR (BSP) were used to explore the methylation status of PRR34‐AS1. PRR34‐AS1 promoter showed a pattern of hypomethylation in AML patients compared with normal controls (p = 0.122). Notably, of whole‐AML and non‐APL‐AML patients, PRR34‐AS1 hypomethylated patients presented a significantly shorter OS than those with a hypermethylated PRR34‐AS1 (p = 0.010 and 0.037, respectively). Multivariate analysis confirmed that the hypomethylation of PRR34‐AS1 served as an independent prognostic indicator in both whole‐cohort AML and non‐APL‐AML categories (p = 0.057 and 0.018, respectively). In summary, the findings of this study showed that abnormalities in PRR34‐AS1 are associated with poor prognosis in AML. Therefore, monitoring this index may be important in the prognosis of AML and can provide information on effective chemotherapy against the disease.

Clinical epigenomics for cardiovascular disease: Diagnostics and therapies

The study of epigenomics has advanced in recent years to span the regulation of a single genetic locus to the structure and orientation of entire chromosomes within the nucleus. In this review, we focus on the challenges and opportunities of clinical epigenomics in cardiovascular disease. As an integrator of genetic and environmental inputs, and because of advances in measurement techniques that are highly reproducible and provide sequence information, the epigenome is a rich source of potential biosignatures of cardiovascular health and disease. Most of the studies to date have focused on the latter, and herein we discuss observations on epigenomic changes in human cardiovascular disease, examining the role of protein modifiers of chromatin, noncoding RNAs and DNA modification. We provide an overview of cardiovascular epigenomics, discussing the challenges of data sovereignty, data analysis, doctor-patient ethics and innovations necessary to implement precision health.

A comprehensive evaluation of computational tools to identify differential methylation regions using RRBS data

DNA methylation plays a vital role in transcription regulation. Reduced representation bisulfite sequencing (RRBS) is becoming common for analyzing genome-wide methylation profiles at the single nucleotide level. A major goal of RRBS studies is to detect differentially methylated regions (DMRs) between different biological conditions. The previous tools to predict DMRs lack consistency. Here, we simulated RRBS datasets with significant attributes of real sequencing data under a wide range of scenarios, and systematically evaluated seven DMR detection tools in terms of type I error rate, precision/recall (PR), and area under ROC curve (AUC) using different methylation levels, sequencing coverage depth, length of DMRs, read length, and sample sizes. DMRfinder, methylSig, and methylKit were our preferred tools for RRBS data analysis, in terms of their AUC and PR curves. Our comparison highlights the different applicability of DMR detection tools and provides information to guide researchers towards the advancement of sequence-based DMR analysis.

Cell type-specific genome scans of DNA methylation divergence indicate an important role for transposable elements

In population genomics, genetic diversity measures play an important role in genome scans for divergent sites. In population epigenomics, comparable tools are rare although the epigenome can vary at several levels of organization. We propose a model-free, information-theoretic approach, the Jensen-Shannon divergence (JSD), as a flexible diversity index for epigenomic diversity. Here, we demonstrate how JSD uncovers the relationship between genomic features and cell type-specific methylome diversity in Arabidopsis thaliana. However, JSD is applicable to any epigenetic mark and any collection of individuals, tissues, or cells, for example to assess the heterogeneity in healthy organs and tumors.

HPG-DHunter: an ultrafast, friendly tool for DMR detection and visualization

BACKGROUND

Software tools for analyzing DNA methylation do not provide graphical results which can be easily identified, but huge text files containing the alignment of the samples and their methylation status at a resolution of base pairs. There have been proposed different tools and methods for finding Differentially Methylated Regions (DMRs) among different samples, but the execution time required by these tools is large, and the visualization of their results is far from being interactive. Additionally, these methods show more accurate results when identifying simulated DM regions that are long and have small within-group variation, but they have low concordance when used with real datasets, probably due to the different approaches they use for DMR identification. Thus, a tool which automatically detects DMRs among different samples and interactively visualizes DMRs at different scales (from a bunch to ten of millions of DNA locations) can be the key for shortening the DNA methylation analysis process in many studies.

RESULTS

In this paper, we propose a software tool based on the wavelet transform. This mathematical tool allows the fast automatic DMR detection by simple comparison of different signals at different resolution levels. Also, it allows an interactive visualization of the DMRs found at different resolution levels. The tool is publicly available at https://grev-uv.github.io/ , and it is part of a complete suite of tools which allow to carry out the complete process of DNA alignment and methylation analysis, creation of methylation maps of the whole genome, and the detection and visualization of DMRs between different samples.

CONCLUSIONS

The validation of the developed software tool shows similar concordance with other well-known and extended tools when used with real and synthetic data. The batch mode of the tool is capable of automatically detecting the existing DMRs for half (twelve) of the human chromosomes between two sets of six samples (whose.csv files after the alignment and mapping procedures have an aggregated size of 108 Gigabytes) in around three hours and a half. When compared to other well-known tools, HPG-DHunter only requires around 15% of the execution time required by other tools for detecting the DMRs.

A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation

Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. We propose a regression-based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.

DAMEfinder: a method to detect differential allele-specific methylation

Background

DNA methylation is a highly studied epigenetic signature that is associated with regulation of gene expression, whereby genes with high levels of promoter methylation are generally repressed. Genomic imprinting occurs when one of the parental alleles is methylated, i.e., when there is inherited allele-specific methylation (ASM). A special case of imprinting occurs during X chromosome inactivation in females, where one of the two X chromosomes is silenced, to achieve dosage compensation between the sexes. Another more widespread form of ASM is sequence dependent (SD-ASM), where ASM is linked to a nearby heterozygous single nucleotide polymorphism (SNP).

Results

We developed a method to screen for genomic regions that exhibit loss or gain of ASM in samples from two conditions (treatments, diseases, etc.). The method relies on the availability of bisulfite sequencing data from multiple samples of the two conditions. We leverage other established computational methods to screen for these regions within a new R package called DAMEfinder. It calculates an ASM score for all CpG sites or pairs in the genome of each sample, and then quantifies the change in ASM between conditions. It then clusters nearby CpG sites with consistent change into regions. In the absence of SNP information, our method relies only on reads to quantify ASM. This novel ASM score compares favorably to current methods that also screen for ASM. Not only does it easily discern between imprinted and non-imprinted regions, but also females from males based on X chromosome inactivation. We also applied DAMEfinder to a colorectal cancer dataset and observed that colorectal cancer subtypes are distinguishable according to their ASM signature. We also re-discover known cases of loss of imprinting.

Conclusion

We have designed DAMEfinder to detect regions of differential ASM (DAMEs), which is a more refined definition of differential methylation, and can therefore help in breaking down the complexity of DNA methylation and its influence in development and disease.

DNA methylation analysis in plants: review of computational tools and future perspectives

Genome-wide DNA methylation studies have quickly expanded due to advances in next-generation sequencing techniques along with a wealth of computational tools to analyze the data. Most of our knowledge about DNA methylation profiles, epigenetic heritability and the function of DNA methylation in plants derives from the model species Arabidopsis thaliana. There are increasingly many studies on DNA methylation in plants-uncovering methylation profiles and explaining variations in different plant tissues. Additionally, DNA methylation comparisons of different plant tissue types and dynamics during development processes are only slowly emerging but are crucial for understanding developmental and regulatory decisions. Translating this knowledge from plant model species to commercial crops could allow the establishment of new varieties with increased stress resilience and improved yield. In this review, we provide an overview of the most commonly applied bioinformatics tools for the analysis of DNA methylation data (particularly bisulfite sequencing data). The performances of a selection of the tools are analyzed for computational time and agreement in predicted methylated sites for A. thaliana, which has a smaller genome compared to the hexaploid bread wheat. The performance of the tools was benchmarked on five plant genomes. We give examples of applications of DNA methylation data analysis in crops (with a focus on cereals) and an outlook for future developments for DNA methylation status manipulations and data integration.

MethCP: Differentially Methylated Region Detection with Change Point Models

Whole-genome bisulfite sequencing (WGBS) provides a precise measure of methylation across the genome, yet presents a challenge in identifying differentially methylated regions (DMRs) between different conditions. Many methods have been developed, which focus primarily on the setting of two-group comparison. We develop a DMR detecting method MethCP for WGBS data, which is applicable for a wide range of experimental designs beyond the two-group comparisons, such as time-course data. MethCP identifies DMRs based on change point detection, which naturally segments the genome and provides region-level differential analysis. For simple two-group comparison, we show that our method outperforms developed methods in accurately detecting the complete DMR on a simulated data set and an Arabidopsis data set. Moreover, we show that MethCP is capable of detecting wide regions with small effect sizes, which can be common in some settings, but existing techniques are poor in detecting such DMRs. We also demonstrate the use of MethCP for time-course data on another data set after methylation throughout seed germination in Arabidopsis.

CpG-island-based annotation and analysis of human housekeeping genes

By reviewing previous CpG-related studies, we consider that the transcription regulation of about half of the human genes, mostly housekeeping (HK) genes, involves CpG islands (CGIs), their methylation states, CpG spacing and other chromosomal parameters. However, the precise CGI definition and positioning of CGIs within gene structures, as well as specific CGI-associated regulatory mechanisms, all remain to be explained at individual gene and gene-family levels, together with consideration of species and lineage specificity. Although previous studies have already classified CGIs into high-CpG (HCGI), intermediate-CpG (ICGI) and low-CpG (LCGI) densities based on CpG density variation, the correlation between CGI density and gene expression regulation, such as co-regulation of CGIs and TATA box on HK genes, remains to be elucidated. First, this study introduces such a problem-solving protocol for human-genome annotation, which is based on a combination of GTEx, JBLA and Gene Ontology (GO) analysis. Next, we discuss why CGI-associated genes are most likely regulated by HCGI and tend to be HK genes; the HCGI/TATA± and LCGI/TATA± combinations show different GO enrichment, whereas the ICGI/TATA± combination is less characteristic based on GO enrichment analysis. Finally, we demonstrate that Hadoop MapReduce-based MR-JBLA algorithm is more efficient than the original JBLA in k-mer counting and CGI-associated gene analysis.

Preliminary Analysis of Within-Sample Co-methylation Patterns in Normal and Cancerous Breast Samples

DNA methylation plays a significant role in regulating the expression of certain genes in both cancerous and normal breast tissues. It is therefore important to study within-sample co-methylation, ie, methylation patterns between consecutive sites in a chromosome. In this article, we develop 2 new methods to compare co-methylation patterns between normal and cancerous breast samples. In particular, we investigate the co-methylation patterns of 4 different methylation states/levels separately. Using these 2 methods, we focus on addressing the following questions: How often does 1 methylation state change to other methylation states and how is this change dependent on chromosome distance? What co-methylation patterns do normal and cancerous breast samples have? Do genomic sites with different methylation states/levels have different co-methylation patterns? Our results show that cancerous and normal co-methylation patterns are significantly different. We find that this difference exists even when the physical distance of 2 sites are less than 50 bases. Breast cancer cell lines tend to remain in the same methylation state more often than normal samples, especially for the no/low or high/full methylation states. We also find that the co-methylation region lengths for various methylation states (no/low, partial, and high/full methylation states) are very different. For example, the co-methylation region lengths for partial methylation regions are shorter than the unmethylated or fully methylated regions. Our research may provide a deep understanding of co-methylation patterns. These co-methylation patterns will aid in discovering and understanding new methylation events that may be related to novel biomarkers.

DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts

DNA methylation has been associated with transcriptional repression and detection of differential methylation is important in understanding the underlying causes of differential gene expression. Bisulfite-converted genomic DNA sequencing is the current gold standard in the field for building genome-wide maps at a base pair resolution of DNA methylation. Here we systematically investigate the underlying features of detecting differential DNA methylation in CpG and non-CpG contexts, considering both the case of mammalian systems and plants. In particular, we introduce DMRcaller, a highly efficient R/Bioconductor package, which implements several methods to detect differentially methylated regions (DMRs) between two samples. Most importantly, we show that different algorithms are required to compute DMRs and the most appropriate algorithm in each case depends on the sequence context and levels of methylation. Furthermore, we show that DMRcaller outperforms other available packages and we propose a new method to select the parameters for this tool and for other available tools. DMRcaller is a comprehensive tool for differential methylation analysis which displays high sensitivity and specificity for the detection of DMRs and performs entire genome wide analysis within a few hours.

Detecting differential DNA methylation from sequencing of bisulfite converted DNA of diverse species

DNA methylation is one of the most extensively studied epigenetic modifications of genomic DNA. In recent years, sequencing of bisulfite-converted DNA, particularly via next-generation sequencing technologies, has become a widely popular method to study DNA methylation. This method can be readily applied to a variety of species, dramatically expanding the scope of DNA methylation studies beyond the traditionally studied human and mouse systems. In parallel to the increasing wealth of genomic methylation profiles, many statistical tools have been developed to detect differentially methylated loci (DMLs) or differentially methylated regions (DMRs) between biological conditions. We discuss and summarize several key properties of currently available tools to detect DMLs and DMRs from sequencing of bisulfite-converted DNA. However, the majority of the statistical tools developed for DML/DMR analyses have been validated using only mammalian data sets, and less priority has been placed on the analyses of invertebrate or plant DNA methylation data. We demonstrate that genomic methylation profiles of non-mammalian species are often highly distinct from those of mammalian species using examples of honey bees and humans. We then discuss how such differences in data properties may affect statistical analyses. Based on these differences, we provide three specific recommendations to improve the power and accuracy of DML and DMR analyses of invertebrate data when using currently available statistical tools. These considerations should facilitate systematic and robust analyses of DNA methylation from diverse species, thus advancing our understanding of DNA methylation.

Detection and accurate false discovery rate control of differentially methylated regions from whole genome bisulfite sequencing

With recent advances in sequencing technology, it is now feasible to measure DNA methylation at tens of millions of sites across the entire genome. In most applications, biologists are interested in detecting differentially methylated regions, composed of multiple sites with differing methylation levels among populations. However, current computational approaches for detecting such regions do not provide accurate statistical inference. A major challenge in reporting uncertainty is that a genome-wide scan is involved in detecting these regions, which needs to be accounted for. A further challenge is that sample sizes are limited due to the costs associated with the technology. We have developed a new approach that overcomes these challenges and assesses uncertainty for differentially methylated regions in a rigorous manner. Region-level statistics are obtained by fitting a generalized least squares regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. We develop an inferential approach, based on a pooled null distribution, that can be implemented even when as few as two samples per population are available. Here, we demonstrate the advantages of our method using both experimental data and Monte Carlo simulation. We find that the new method improves the specificity and sensitivity of lists of regions and accurately controls the false discovery rate.

Contrasting DNA methylation responses of inbred fish lines to different rearing environments

Epigenetic mechanisms generate plastic phenotypes that can become locally adapted across environments. Disentangling genomic from epigenomic variation is challenging in sexual species due to genetic variation among individuals, but it is easier in self-fertilizing species. We analysed DNA methylation patterns of two highly inbred strains of a naturally self-fertilizing fish reared in two contrasting environments to investigate the obligatory (genotype-dependent), facilitated (partially depend on the genotype) or pure (genotype-independent) nature of the epigenetic variation. We found higher methylation differentiation between genotypes than between environments. Most methylation differences between environments common to both strains followed a pattern where the two genotypes (inbred lines) responded to the same environmental context with contrasting DNA methylation levels (facilitated epialleles). Our findings suggest that, at least in part, DNA methylation could depend on the dynamic interaction between the genotype and the environment, which could explain the plasticity of epigenetically mediated phenotypes.

HOME: a histogram based machine learning approach for effective identification of differentially methylated regions

Background

The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate.

Results

We present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organism’s dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME.

Conclusion

HOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2845-y) contains supplementary material, which is available to authorized users.

Within-sample co-methylation patterns in normal tissues

Background

DNA methylation is an epigenetic event that may regulate gene expression. Because of this regulation role, aberrant DNA methylation is often associated with many diseases. Within-sample DNA co-methylation is the similarity of methylation in nearby cytosine sites of a chromosome. It is important to study co-methylation patterns. However, it is not well studied yet, and it is unclear to us what co-methylation patterns normal DNA samples have. Are the co-methylation patterns of the same tissue across several samples different? Are the co-methylation patterns of various tissues of the same sample different? To answer these questions, we conduct analyses using two sets of data: 3-sample-1-tissue (3S1T) and 1-sample-8-tissue (1S8T).

Results

To study the co-methylation patterns of the two datasets, 3S1T and 1S8T, we investigate the following questions: How often does one methylation state change to other methylation states and how is this change associated with chromosome distance? Based on the 3S1T data, we find there is not significant co-methylation difference among the same spleen tissues of three different samples. However, the analysis results of 1S8T data show that there were significant differences among eight tissues of one sample. For both 3S1T and 1S8T data, we find that the no/low methylation state A and high/full methylation state D tend to remain the same along a chromosome region. We also find that the low/partial methylation state B and partial/high methylation state C tend to change to higher methylation states along a chromosome. Finally, we find that lengths of most co-methylation regions are very short with only a few hundred base pairs. In fact, only a small proportion of methylated regions are longer than 1000 base pairs.

Conclusions

In this paper, we have addressed a few questions regarding within-sample co-methylation patterns in normal tissues. Our statistical analysis results and answers may help researchers to better understand the biological process of DNA methylation. This may pave the way to develop better analysis methods for future methylation research.

Electronic supplementary material

The online version of this article (10.1186/s13040-019-0198-8) contains supplementary material, which is available to authorized users.

A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures

Although massive amounts of condition-specific molecular profiles are being accumulated in public repositories every day, meaningful interpretation of these data remains a major challenge. In an effort to identify the biomarkers that describe the key biological phenomena for a given condition, several approaches have been developed over the past few years. However, the majority of these approaches either (i) do not consider the known intermolecular interactions, or (ii) do not integrate molecular data of multiple types (e.g., genomics, transcriptomics, proteomics, epigenomics, etc.), and thus potentially fail to capture the true biological changes responsible for complex diseases (e.g., cancer). In addition, these approaches often ignore the heterogeneity and study bias present in independent molecular cohorts. In this manuscript, we propose a novel multi-cohort and multi-omics meta-analysis framework that overcomes all three limitations mentioned above in order to identify robust molecular subnetworks that capture the key dynamic nature of a given biological condition. Our framework integrates multiple independent gene expression studies, unmatched DNA methylation studies, and protein-protein interactions to identify methylation-driven subnetworks. We demonstrate the proposed framework by constructing subnetworks related to two complex diseases: glioblastoma and low-grade gliomas. We validate the identified subnetworks by showing their ability to predict patients' clinical outcome on multiple independent validation cohorts.

How to Design a Whole-Genome Bisulfite Sequencing Experiment

Aside from post-translational histone modifications and small RNA populations, the epigenome of an organism is defined by the level and spectrum of DNA methylation. Methyl groups can be covalently bound to the carbon-5 of cytosines or the carbon-6 of adenine bases. DNA methylation can be found in both prokaryotes and eukaryotes. In the latter, dynamic variation is shown across species, along development, and by cell type. DNA methylation usually leads to a lower binding affinity of DNA-interacting proteins and often results in a lower expression rate of the subsequent genome region, a process also referred to as transcriptional gene silencing. We give an overview of the current state of research facilitating the planning and implementation of whole-genome bisulfite-sequencing (WGBS) experiments. We refrain from discussing alternative methods for DNA methylation analysis, such as reduced representation bisulfite sequencing (rrBS) and methylated DNA immunoprecipitation sequencing (MeDIPSeq), which have value in specific experimental contexts but are generally disadvantageous compared to WGBS.

Methylation of TP53BP2 and Apaf-1 genes in embryonic lung cells and their impact on gene expression

Background

During embryonic development, epigenetics plays an irreplaceable role in maintaining the normal life activities of mammals. The study of methylation during embryonic lung development will gain a better understanding of the pathogenesis of lung disease. This study aimed to investigate the methylation of promoter-related CpG islands of TP53BP2 and Apaf-1 genes in human embryonic lung cells and their effects on the regulation of gene expression.

Methods

The analyses of the methylation-prone region and the relationship with transcription factor binding sites were done by bioinformatic prediction. The bisulfite sequencing PCR was conducted aiming to the target areas. The methylation in promoter area and its impact on transcription factor binding as well as gene expression regulation effect were investigated by methylation inhibitor treatment and real-time PCR detection.

Results

Bisulfite sequencing results showed that the CpG methylation predicted by bioinformatic prediction were in part agree with the bisulfite sequencing results, some of the CpG methylation were appeared in the important transcription factor binding sites. After treating with methylation inhibitors, the transcription of Apaf-1 was significantly increased compared with TP53BP2, indicating that partial methylation in proximal promoter of Apaf-1 had a certain effect on transcription Inhibition.

Conclusions

The methylation of genes had effect on the growth and development of the embryo in the embryonic lung development, which may be influenced by the combination of key transcription factors, thereby inhibiting the transcriptional expression, ultimately affected the expression and regulation of key genes. These results will help to further understand the epigenetic regulation and its impact on the embryonic development.

Analysis of DNA modifications in aging research

As geroscience research extends into the role of epigenetics in aging and age-related disease, researchers are being confronted with unfamiliar molecular techniques and data analysis methods that can be difficult to integrate into their work. In this review, we focus on the analysis of DNA modifications, namely cytosine methylation and hydroxymethylation, through next-generation sequencing methods. While older techniques for modification analysis performed relative quantitation across regions of the genome or examined average genome levels, these analyses lack the desired specificity, rigor, and genomic coverage to firmly establish the nature of genomic methylation patterns and their response to aging. With recent methodological advances, such as whole genome bisulfite sequencing (WGBS), bisulfite oligonucleotide capture sequencing (BOCS), and bisulfite amplicon sequencing (BSAS), cytosine modifications can now be readily analyzed with base-specific, absolute quantitation at both cytosine-guanine dinucleotide (CG) and non-CG sites throughout the genome or within specific regions of interest by next-generation sequencing. Additional advances, such as oxidative bisulfite conversion to differentiate methylation from hydroxymethylation and analysis of limited input/single-cells, have great promise for continuing to expand epigenomic capabilities. This review provides a background on DNA modifications, the current state-of-the-art for sequencing methods, bioinformatics tools for converting these large data sets into biological insights, and perspectives on future directions for the field.