Detect Differentially Methylated Regions Research Articles

Abstract P390: E-value Over P-value And Its Adjustments: Improving Dna Methylation Analysis With An Application To Hypertension Studies

DNA methylation plays a crucial role in transcriptional regulation. Reduced representation bisulfite sequencing (RRBS) is a technique of increasing use for analyzing genome-wide methylation profiles. Many computational tools such as Metilene, MethylKit, BiSeq and DMRfinder have been developed to use RRBS data to detect differentially methylated regions (DMRs) that may be involved in epigenetic regulations of gene expression. For DMR detection tools, as for countless other medical literature applications, P-values and their adjustments are among the most widely reported statistics used to assess the statistical significance of biological findings. However, P-values are coming under increasing criticism related to their questionable accuracy and relatively high levels of false positive or negative indications. Here, we proposed a method to calculate E-value for DMR detection in RRBS data, which is defined as likelihood ratios falling into the null hypothesis over the entire parameter space. We also provided the corresponding R package ‘ metevalue ’ as a user-friendly interface to implement E-value calculations into various DMR detection tools. To evaluate the performance of E-value, we generated various RRBS benchmarking datasets using our simulator ‘ RRBSsim ’ with 8 samples in each experimental group. Our comprehensive benchmarking analyses showed that using E-value not only significantly improved accuracy, AUC and power, over that of P-value or adjusted P-value, but also reduced false discovery rates and Type I errors. To illustrate the utility of E-value, we applied it to identify DMRs in two real RRBS datasets. One was genome-wide DNA methylome and transcriptome of renal T lymphocytes from Dahl Salt Sensitive rats treated with high- or low-salt diets. The other was genome-wide DNA methylation and gene expression data of human arterioles from our clinical trial in which 10 subjects were placed on 2-week low-salt diet (1200-mg sodium/day). Compared to the classical adjusted P-value, the use of E-value detected biologically more relevant DMRs and improved the negative association between DNA methylation and gene expression.

Methylation Genome-Wide Profiling in Lowly and Highly Efficient Somatic Cell Nuclear Transfer in Pigs

Swine is a common model organism for biomedical research. Epigenetic reprogramming in somatic cell nuclear transfer (SCNT) embryos does not fully recapitulate the natural DNA demethylation events at fertilisation. This study aimed to conduct genome-wide methylation profiling to detect differentially methylated regions (DMRs) responsible for epigenetic differences in stem cells that displayed high and low efficiency of SCNT and to elucidate the low efficiency of cloning rate in pigs. Adipose tissue mesenchymal stem cells (AMSC)s lines were isolated from adipose tissue of adult male pigs (n = 20; high-efficiency cells = 10; and low-efficiency cells = 10). Reduced representation bisulfite sequencing (RRBS) was performed on an Illumina HiSeq1500. Paired-end reads were filtered to remove the adapter contamination, and low-quality reads using TrimGalore! Filtered reads were mapped to the reference genome using Bismark. MethylKit was used to identify differentially methylated regions (DMRs) (bases and tiles), showing statistically significant differential methylation between high and low-efficiency AMSCs. Hierarchical cluster analysis according to methylation patterns clearly defined groups with low and high cloning efficiency. We report 3704 bases with statistically significant differences in methylation and 10062 tiles with statistically significant differences in methylation. Most differentially methylated sites are intergenic 62%, 31% are intronic, 4% are in exons, and 4% in promoters. Moreover, 37% of differentially methylated sites are located in known CpG islands (CGIs), and 4% in CpG island shores (CGSs).

Genome-wide DNA methylation analysis of pulmonary function in middle and old-aged Chinese monozygotic twins

BackgroundPrevious studies have determined the epigenetic association between DNA methylation and pulmonary function among various ethnics, whereas this association is largely unknown in Chinese adults. Thus, we aimed to explore epigenetic relationships between genome-wide DNA methylation levels and pulmonary function among middle-aged Chinese monozygotic twins.MethodsThe monozygotic twin sample was drawn from the Qingdao Twin Registry. Pulmonary function was measured by three parameters including forced expiratory volume the first second (FEV1), forced vital capacity (FVC), and FEV1/FVC ratio. Linear mixed effect model was used to regress the methylation level of CpG sites on pulmonary function. After that, we applied Genomic Regions Enrichment of Annotations Tool (GREAT) to predict the genomic regions enrichment, and used comb-p python library to detect differentially methylated regions (DMRs). Gene expression analysis was conducted to validate the results of differentially methylated analyses.ResultsWe identified 112 CpG sites with the level of P < 1 × 10–4 which were annotated to 40 genes. We identified 12 common enriched pathways of three pulmonary function parameters. We detected 39 DMRs located at 23 genes, of which PRDM1 was related to decreased pulmonary function, and MPL, LTB4R2, and EPHB3 were related to increased pulmonary function. The gene expression analyses validated DIP2C, ASB2, SLC6A5, and GAS6 related to decreased pulmonary function.ConclusionOur DNA methylation sequencing analysis on identical twins provides new references for the epigenetic regulation on pulmonary function. Several CpG sites, genes, biological pathways and DMRs are considered as possible crucial to pulmonary function.

Sex-specific longitudinal association of DNA methylation with lung function.

Investigating whether DNA methylation (DNA-M) at an earlier age is associated with lung function at a later age and whether this relationship differs by sex could enable prediction of future lung function deficit.A training/testing-based technique was used to screen 402 714 cytosine-phosphate-guanine dinucleotide sites (CpGs) to assess the longitudinal association of blood-based DNA-M at ages 10 and 18 years with lung function at 18 and 26 years, respectively, in the Isle of Wight birth cohort (IOWBC). Multivariable linear mixed models were applied to the CpGs that passed screening. To detect differentially methylated regions (DMRs), DMR enrichment analysis was conducted. Findings were further examined in the Avon Longitudinal Study of Parents and Children (ALSPAC). Biological relevance of the identified CpGs was assessed using gene expression data.DNA-M at eight CpGs (five CpGs with forced expiratory volume in 1 s (FEV1) and three CpGs with FEV1/forced vital capacity (FVC)) at an earlier age was associated with lung function at a later age regardless of sex, while at 13 CpGs (five CpGs with FVC, three with FEV1 and five with FEV1/FVC), the associations were sex-specific (pFDR<0.05) in IOWBC, with consistent directions of association in ALSPAC (IOWBC-ALSPAC consistent CpGs). cg16582803 (WNT10A) and cg14083603 (ZGPAT) were replicated in ALSPAC for main and sex-specific effects, respectively. Among IOWBC-ALSPAC consistent CpGs, DNA-M at cg01376079 (SSH3) and cg07557690 (TGFBR3) was associated with gene expression both longitudinally and cross-sectionally. In total, 57 and 170 DMRs were linked to lung function longitudinally in males and females, respectively.CpGs showing longitudinal associations with lung function have the potential to serve as candidate markers in future studies on lung function deficit prediction.

Wavelet Screening identifies regions highly enriched for differentially methylated loci for orofacial clefts.

DNA methylation is the most widely studied epigenetic mark in humans and plays an essential role in normal biological processes as well as in disease development. More focus has recently been placed on understanding functional aspects of methylation, prompting the development of methods to investigate the relationship between heterogeneity in methylation patterns and disease risk. However, most of these methods are limited in that they use simplified models that may rely on arbitrarily chosen parameters, they can only detect differentially methylated regions (DMRs) one at a time, or they are computationally intensive. To address these shortcomings, we present a wavelet-based method called ‘Wavelet Screening’ (WS) that can perform an epigenome-wide association study (EWAS) of thousands of individuals on a single CPU in only a matter of hours. By detecting multiple DMRs located near each other, WS identifies more complex patterns that can differentiate between different methylation profiles. We performed an extensive set of simulations to demonstrate the robustness and high power of WS, before applying it to a previously published EWAS dataset of orofacial clefts (OFCs). WS identified 82 associated regions containing several known genes and loci for OFCs, while other findings are novel and warrant replication in other OFCs cohorts.

Pre-adolescence DNA methylation is associated with lung function trajectories from pre-adolescence to adulthood

BackgroundThe pattern of lung function development from pre-adolescence to adulthood plays a significant role in the pathogenesis of respiratory diseases. Inconsistent findings in genetic studies on lung function trajectories, the importance of DNA methylation (DNA-M), and the critical role of adolescence in lung function development motivated the present study of pre-adolescent DNA-M with lung function trajectories. This study investigated epigenome-wide associations of DNA-M at cytosine-phosphate-guanine dinucleotide sites (CpGs) at childhood with lung function trajectories from childhood to young adulthood.MethodsDNA-M was measured in peripheral blood at age 10 years in the Isle of Wight (IOW) birth cohort. Spirometry was conducted at ages 10, 18, and 26 years. A training/testing-based method was used to screen CpGs. Multivariable logistic regressions were applied to assess the association of DNA-M with lung function trajectories from pre-adolescence to adulthood. To detect differentially methylated regions (DMRs) among CpGs, DMR enrichment analysis was conducted. Findings were further tested in the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Pathway analyses were performed on the mapped genes of the identified CpGs and DMRs. Biological relevance of the identified CpGs was assessed with gene expression. All analyses were stratified by sex.ResultsHigh and low trajectories of FVC, FEV1, and FEV1/FVC in each sex were identified. At PBonferroni < 0.05, DNA-M at 96 distinct CpGs (41 in males) showed associations with FVC, FEV1, and FEV1/FVC trajectories in IOW cohort. These 95 CpGs (cg24000797 was disqualified) were further tested in ALSPAC; 44 CpGs (19 in males) of these 95 showed the same directions of association as in the IOW cohort; and three CpGs (two in males) were replicated. DNA-M at two and four CpGs showed significant associations with the corresponding gene expression in males and females, respectively. At PFDR < 0.05, 23 and 10 DMRs were identified in males and females, respectively. Pathways were identified; some of those were linked to lung function and chronic obstructive lung diseases.ConclusionThe identified CpGs at pre-adolescence have the potential to serve as candidate markers for lung function trajectory prediction and chronic lung diseases.

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.

HMST-Seq-Analyzer: A new python tool for differential methylation and hydroxymethylation analysis in various DNA methylation sequencing data

DNA methylation (5mC) and hydroxymethylation (5hmC) are chemical modifications of cytosine bases which play a crucial role in epigenetic gene regulation. However, cost, data complexity and unavailability of comprehensive analytical tools is one of the major challenges in exploring these epigenetic marks. Hydroxymethylation-and Methylation-Sensitive Tag sequencing (HMST-seq) is one of the most cost-effective techniques that enables simultaneous detection of 5mC and 5hmC at single base pair resolution. We present HMST-Seq-Analyzer as a comprehensive and robust method for performing simultaneous differential methylation analysis on 5mC and 5hmC data sets. HMST-Seq-Analyzer can detect Differentially Methylated Regions (DMRs), annotate them, give a visual overview of methylation status and also perform preliminary quality check on the data. In addition to HMST-Seq, our tool can be used on whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) data sets as well. The tool is written in Python with capacity to process data in parallel and is available at (https://hmst-seq.github.io/hmst/).

Detection of Differentially Methylated Regions Using Bayes Factor for Ordinal Group Responses.

Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation, because they play an important role in regulating gene expression without changes in the DNA sequence. There have been significant advances in developing statistical methods to detect differentially methylated regions (DMRs) associated with binary disease status. Most of these methods are being developed for detecting differential methylation rates between cases and controls. We consider multiple severity levels of disease, and develop a Bayesian statistical method to detect the region with increasing (or decreasing) methylation rates as the disease severity increases. Patients are classified into more than two groups, based on the disease severity (e.g., stages of cancer), and DMRs are detected by using moving windows along the genome. Within each window, the Bayes factor is calculated to test the hypothesis of monotonic increase in methylation rates corresponding to severity of the disease versus no difference. A mixed-effect model is used to incorporate the correlation of methylation rates of nearby CpG sites in the region. Results from extensive simulation indicate that our proposed method is statistically valid and reasonably powerful. We demonstrate our approach on a bisulfite sequencing dataset from a chronic lymphocytic leukemia (CLL) study.

Epigenetic association analysis of clinical sub-phenotypes in patients with polycystic ovary syndrome (PCOS)

Polycystic ovarian syndrome (PCOS) is a complex disorder affecting up to 15–20% of reproductive women. PCOS has recently been investigated using genome-wide association studies revealing important mutations and DNA methylation sites associated with the syndrome. As a clinically highly heterogenous condition, studying the molecular basis of the differential manifestation of PCOS is both meaningful concerning individualized management and important for understanding the biology of PCOS. Using genome-wide DNA methylation data collected from PCOS patients, we performed a powerful region-based analysis to detect differentially methylated regions (DMR) by correlating DNA methylation pattern in a genomic region with the level of each PCOS clinical sub-phenotype. We identified seven significant DMRs on chromosome 19 (12877188-12876846 bp) and chromosome 6 (MHC region) associated with prolactin level, as well as chromosomes 11 and 2 associated with metabolic attributes. Functional annotation linked significant DNA methylation patterns to functional genes (HOOK2, BDNFl, HLA-G, HLA-H, HLA-J, RNF39, etc) of metabolic disorders and immunity or novel associations to serve as targets for validation and replication.

Comparison of novel and existing methods for detecting differentially methylated regions

BackgroundSingle-probe analyses in epigenome-wide association studies (EWAS) have identified associations between DNA methylation and many phenotypes, but do not take into account information from neighboring probes. Methods to detect differentially methylated regions (DMRs) (clusters of neighboring probes associated with a phenotype) may provide more power to detect associations between DNA methylation and diseases or phenotypes of interest.ResultsWe proposed a novel approach, GlobalP, and perform comparisons with 3 methods—DMRcate, Bumphunter, and comb-p—to identify DMRs associated with log triglycerides (TGs) in real GAW20 data before and after fenofibrate treatment. We applied these methods to the summary statistics from an EWAS performed on the methylation data. Comb-p, DMRcate, and GlobalP detected very similar DMRs near the gene CPT1A on chromosome 11 in both the pre- and posttreatment data. In addition, GlobalP detected 2 DMRs before fenofibrate treatment in the genes ETV6 and ABCG1. Bumphunter identified several DMRs on chromosomes 1 and 20, which did not overlap with DMRs detected by other methods.ConclusionsOur novel method detected the same DMR identified by two existing methods and detected two additional DMRs not identified by any of the existing methods we compared.

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.

Abstract 3309: Circulating epigenetic biomarkers for the early detection of ovarian cancer

Abstract Background Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy. Greater than 75% of cases are diagnosed with advanced stage, which is associated with 5-year survival rates of less than 20% compared with 92% if caught early. Previous attempts at discovering early detection biomarkers for EOC have largely failed because of reliance on sub-optimal discovery approaches and the lack of comprehensive patient cohorts. DNA methylation changes are among the earliest alterations in cancer and methylation represents a stable and robust modification on DNA. In addition, several groups including ours have reported the detection of tumor-associated methylation changes in cfDNA extracted from plasma. Accordingly, in this study, we performed comprehensive DNA methylation analysis by reduced representation bisulfite sequencing (RRBS) to identify DNA methylation biomarkers that can be detected in circulation and used for the early detection of ovarian cancer. Methods For the biomarker discovery phase, a cohort of 23 stage 1 serous EOC tissues, and 10 unmatched, adjacent normal tissues from fallopian tubes were analyzed by RRBS. We used Metilene, a binary segmentation algorithm combined with a two-dimensional statistical test, to detect differentially methylated regions (DMRs). For validation, we performed targeted bisulfite amplicon sequencing. Briefly, bisulfite primers were designed to regions of interest, optimized, multiplexed and sequenced to depths ranging from 500-25,000X on the Illumina MiSeq platform. The validation cohort consisted of cfDNA isolated from the plasma of 51 independent EOCs and 10 normal individuals as well as the genomic DNA from tissue used in the discovery cohort. Results We identified 10972 statistically significant (q-value &amp;lt;0.05) DMRs between stage 1 EOC and normal samples. Of these, 227 were hypermethylated and had an average differential methylation value of greater than 30% in tumors compared to normal. Unsupervised clustering analysis of these DMRs demonstrated clear separation of tumor from normal, with the possibility of three distinct molecular subtypes of serous EOC. Six DMRs were prioritized and further validated in both tissue and plasma. Targeted bisulfite amplicon sequencing performed revealed hypermethylation in greater than 50% of tumor samples across the DMRs. Additionally, the area under the curve (AUC) for the DMRs interrogated ranged from 0.91 and 0.94. Conclusion In conclusion, our RRBS based comprehensive biomarker discovery identified a six-DMR panel that demonstrated preliminary robust diagnostic accuracy in EOC samples. Further evaluation of these DMRs in multicenter retrospective and prospective cohorts might lead to the development of a non-invasive diagnostic, as well as a population-screening assay for OC patients. Citation Format: Gerald C. Gooden, Christophe Legendre, Monique Spillman, Ajay Goel, Bodour Salhia. Circulating epigenetic biomarkers for the early detection of ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3309.

Accounting for differential variability in detecting differentially methylated regions.

DNA methylation plays an essential role in cancer. Differential variability (DV) in cancer was recently observed that contributes to cancer heterogeneity and has been shown to be crucial in detecting epigenetic field defects, DNA methylation alterations happening early in carcinogenesis. As neighboring CpG sites are highly correlated, here, we present a new method to detect differentially methylated regions (DMRs) that uses combined signals from differential methylation and DV between sample groups. We demonstrated in simulation studies the superior performance of the new method than existing methods that use only one type of signals when true DMRs have both. Applications to DNA methylation data of breast invasive carcinoma (BRCA) and kidney renal clear cell carcinoma (KIRC) from The Cancer Genome Atlas (TCGA) and BRCA from Gene Expression Omnibus (GEO) suggest that the new method identified additional cancer-related DMRs that were missed by methods using one type of signals. Replication analyses using two independent BRCA data sets suggest that DMRs detected based on DV are reproducible. Only the new method identified epigenetic field defects when comparing normal tissues adjacent to tumors and normal tissues from age-matched cancer-free women from the GEO BRCA data and confirmed their enrichment in the progression to breast cancer.

Comparative analysis on genome-wide DNA methylation in longissimus dorsi muscle between Small Tailed Han and Dorper×Small Tailed Han crossbred sheep.

ObjectiveThe objective of this study was to compare the DNA methylation profile in the longissimus dorsi muscle between Small Tailed Han and Dorper×Small Tailed Han crossbred sheep which were known to exhibit significant difference in meat-production.MethodsSix samples (three in each group) were subjected to the methylated DNA immunoprecipitation sequencing (MeDIP-seq) and subsequent bioinformatics analyses to detect differentially methylated regions (DMRs) between the two groups.Results23.08 Gb clean data from six samples were generated and 808 DMRs were identified in gene body or their neighboring up/downstream regions. Compared with Small Tailed Han sheep, we observed a tendency toward a global loss of DNA methylation in these DMRs in the crossbred group. Gene ontology enrichment analysis found several gene sets which were hypo-methylated in gene-body region, including nucleoside binding, motor activity, phospholipid binding and cell junction. Numerous genes were found to be differentially methylated between the two groups with several genes significantly differentially methylated, including transforming growth factor beta 3 (TGFB3), acyl-CoA synthetase long chain family member 1 (ACSL1), ryanodine receptor 1 (RYR1), acyl-CoA oxidase 2 (ACOX2), peroxisome proliferator activated receptor-gamma2 (PPARG2), netrin 1 (NTN1), ras and rab interactor 2 (RIN2), microtubule associated protein RP/EB family member 1 (MAPRE1), ADAM metallopeptidase with thrombospondin type 1 motif 2 (ADAMTS2), myomesin 1 (MYOM1), zinc finger, DHHC type containing 13 (ZDHHC13), and SH3 and PX domains 2B (SH3PXD2B). The real-time quantitative polymerase chain reaction validation showed that the 12 genes are differentially expressed between the two groups.ConclusionIn the current study, a tendency to a global loss of DNA methylation in these DMRs in the crossbred group was found. Twelve genes, TGFB3, ACSL1, RYR1, ACOX2, PPARG2, NTN1, RIN2, MAPRE1, ADAMTS2, MYOM1, ZDHHC13, and SH3PXD2B, were found to be differentially methylated between the two groups by gene ontology enrichment analysis. There are differences in the expression of 12 genes, of which ACSL1, RIN2, and ADAMTS2 have a negative correlation with methylation levels and the data suggest that DNA methylation levels in DMRs of the 3 genes may have an influence on the expression. These results will serve as a valuable resource for DNA methylation investigations on screening candidate genes which might be related to meat production in sheep.

A Bayesian Approach for Analysis of Whole-Genome Bisulfite Sequencing Data Identifies Disease-Associated Changes in DNA Methylation.

DNA methylation is a key epigenetic modification involved in gene regulation whose contribution to disease susceptibility remains to be fully understood. Here, we present a novel Bayesian smoothing approach (called ABBA) to detect differentially methylated regions (DMRs) from whole-genome bisulfite sequencing (WGBS). We also show how this approach can be leveraged to identify disease-associated changes in DNA methylation, suggesting mechanisms through which these alterations might affect disease. From a data modeling perspective, ABBA has the distinctive feature of automatically adapting to different correlation structures in CpG methylation levels across the genome while taking into account the distance between CpG sites as a covariate. Our simulation study shows that ABBA has greater power to detect DMRs than existing methods, providing an accurate identification of DMRs in the large majority of simulated cases. To empirically demonstrate the method's efficacy in generating biological hypotheses, we performed WGBS of primary macrophages derived from an experimental rat system of glomerulonephritis and used ABBA to identify >1000 disease-associated DMRs. Investigation of these DMRs revealed differential DNA methylation localized to a 600bp region in the promoter of the Ifitm3 gene. This was confirmed by ChIP-seq and RNA-seq analyses, showing differential transcription factor binding at the Ifitm3 promoter by JunD (an established determinant of glomerulonephritis), and a consistent change in Ifitm3 expression. Our ABBA analysis allowed us to propose a new role for Ifitm3 in the pathogenesis of glomerulonephritis via a mechanism involving promoter hypermethylation that is associated with Ifitm3 repression in the rat strain susceptible to glomerulonephritis.

Detection of Differentially Methylated Regions Using Bayesian Curve Credible Bands

DNA methylation is one of the most crucial epigenetic modifications involved in regulating gene transcription, cellular differentiation, development, and disease. In recent years, aided by fast parallel sequencing technology, a number of genome-wide bisulfite sequencing platforms have been developed to provide high-throughput DNA methylation data. These are essentially short reads that can be classified as methylated or unmethylated for a particular CpG site. Numerous sophisticated statistical methods have been developed to analyze such a massive amount of correlated data, but they are mainly for detecting differentially methylated loci (DMLs). To detect differentially methylated regions (DMRs), which are often more relevant biologically, a post-processing step from the identified DMLs is needed. In this paper, we address this shortcoming and other issues by proposing a latent variable Bayesian smoothing Curve (BCurve) method for detecting DMRs directly by means of constructing Bayesian credible intervals and bands. In addition to direct detection of DMRs, BCurve differs from existing methods in several other aspects, including its ability to accommodate between-sample variability, taking correlation of methylation levels among nearby loci into account when detecting DMRs, and the construction of credible bands (not just point estimates). We carried out an extensive simulation study to evaluate the performance of BCurve and to compare it with an existing method, BSmooth. The results show that BCurve outperforms BSmooth in all scenarios considered. Finally, we applied BCurve to a dataset to illustrate its utility in real data applications.

Detection of differentially methylated regions in whole genome bisulfite sequencing data using local Getis-Ord statistics.

DNA methylation is an important epigenetic modification that has essential role in gene regulation, cell differentiation and cancer development. Bisulfite sequencing is a widely used technique to obtain genome-wide DNA methylation profiles, and one of the key tasks of analyzing bisulfite sequencing data is to detect differentially methylated regions (DMRs) among samples under different treatment conditions. Although numerous tools have been proposed to detect differentially methylated single CpG site (DMC) between samples, methods for direct DMR detection, especially for complex study designs, are largely limited. We present a new software, GetisDMR, for direct DMR detection. We use beta-binomial regression to model the whole-genome bisulfite sequencing data, where variations in methylation levels and confounding effects have been accounted for. We employ a region-wise test statistic, which is derived from local Getis-Ord statistics and considers the spatial correlation between nearby CpG sites, to detect DMRs. Unlike existing methods, that attempt to infer DMRs from DMCs based on empirical criteria, we provide statistical inference for direct DMR detection. Through extensive simulations and an application to two mouse datasets, we demonstrate that GetisDMR achieves better sensitivities, positive predictive values, more exact locations and better agreement of DMRs with current biological knowledge. It is available at https://github.com/DMU-lilab/GetisDMR CONTACTS: y.wen@auckland.ac.nz or zhiguangli@dlmedu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

A novel method for detecting association between DNA methylation and diseases using spatial information.

DNA methylation may represent an important contributor to the missing heritability described in complex trait genetics. However, technology to measure DNA methylation has outpaced statistical methods for analysis. Taking advantage of the recent finding that methylated sites cluster together, we propose a Spatial Clustering Method (SCM) to detect differentially methylated regions (DMRs) in the genome in case and control studies using spatial location information. This new method compares the distribution of distances in cases and controls between DNA methylation marks in the genomic region of interest. A statistic is computed based on these distances. Proper type I error rate is maintained and statistical significance is evaluated using permutation test. The effectiveness of the SCM we propose is evaluated by a simulation study. By simulating a simple disease model, we demonstrate that SCM has good power to detect DMRs associated with the disease. Finally, we applied the SCM to an exploratory analysis of chromosome 14 from a colorectal cancer data set and identified statistically significant genomic regions. Identification of these regions should lead to a better understanding of methylated sites and their contribution to disease. The SCM can be used as a reliable statistical method for the identification of DMRs associated with disease states in exploratory epigenetic analyses.

Bisulfighter: accurate detection of methylated cytosines and differentially methylated regions

Analysis of bisulfite sequencing data usually requires two tasks: to call methylated cytosines (mCs) in a sample, and to detect differentially methylated regions (DMRs) between paired samples. Although numerous tools have been proposed for mC calling, methods for DMR detection have been largely limited. Here, we present Bisulfighter, a new software package for detecting mCs and DMRs from bisulfite sequencing data. Bisulfighter combines the LAST alignment tool for mC calling, and a novel framework for DMR detection based on hidden Markov models (HMMs). Unlike previous attempts that depend on empirical parameters, Bisulfighter can use the expectation-maximization algorithm for HMMs to adjust parameters for each data set. We conduct extensive experiments in which accuracy of mC calling and DMR detection is evaluated on simulated data with various mC contexts, read qualities, sequencing depths and DMR lengths, as well as on real data from a wide range of biological processes. We demonstrate that Bisulfighter consistently achieves better accuracy than other published tools, providing greater sensitivity for mCs with fewer false positives, more precise estimates of mC levels, more exact locations of DMRs and better agreement of DMRs with gene expression and DNase I hypersensitivity. The source code is available at http://epigenome.cbrc.jp/bisulfighter.