Reduced Representation Bisulfite Sequencing Dataset 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.

A novel computational strategy for DNA methylation imputation using mixture regression model (MRM)

BackgroundDNA methylation is an important heritable epigenetic mark that plays a crucial role in transcriptional regulation and the pathogenesis of various human disorders. The commonly used DNA methylation measurement approaches, e.g., Illumina Infinium HumanMethylation-27 and -450 BeadChip arrays (27 K and 450 K arrays) and reduced representation bisulfite sequencing (RRBS), only cover a small proportion of the total CpG sites in the human genome, which considerably limited the scope of the DNA methylation analysis in those studies.ResultsWe proposed a new computational strategy to impute the methylation value at the unmeasured CpG sites using the mixture of regression model (MRM) of radial basis functions, integrating information of neighboring CpGs and the similarities in local methylation patterns across subjects and across multiple genomic regions. Our method achieved a better imputation accuracy over a set of competing methods on both simulated and empirical data, particularly when the missing rate is high. By applying MRM to an RRBS dataset from subjects with low versus high bone mineral density (BMD), we recovered methylation values of ~ 300 K CpGs in the promoter regions of chromosome 17 and identified some novel differentially methylated CpGs that are significantly associated with BMD.ConclusionsOur method is well applicable to the numerous methylation studies. By expanding the coverage of the methylation dataset to unmeasured sites, it can significantly enhance the discovery of novel differential methylation signals and thus reveal the mechanisms underlying various human disorders/traits.

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.

Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA.

Adjacent CpG sites in mammalian genomes can be co-methylated due to the processivity of methyltransferases or demethylases. Yet discordant methylation patterns have also been observed, and found related to stochastic or uncoordinated molecular processes. We focused on a systematic search and investigation of regions in the full human genome that exhibit highly coordinated methylation. We defined 147,888 blocks of tightly coupled CpG sites, called methylation haplotype blocks (MHBs) with 61 sets of whole genome bisulfite sequencing (WGBS) data, and further validated with 101 sets of reduced representation bisulfite sequencing (RRBS) data and 637 sets of methylation array data. Using a metric called methylation haplotype load (MHL), we performed tissue-specific methylation analysis at the block level. Subsets of informative blocks were further identified for deconvolution of heterogeneous samples. Finally, we demonstrated quantitative estimation of tumor load and tissue-of-origin mapping in the circulating cell-free DNA of 59 cancer patients using methylation haplotypes.

A Flexible, Efficient Binomial Mixed Model for Identifying Differential DNA Methylation in Bisulfite Sequencing Data.

Identifying sources of variation in DNA methylation levels is important for understanding gene regulation. Recently, bisulfite sequencing has become a popular tool for investigating DNA methylation levels. However, modeling bisulfite sequencing data is complicated by dramatic variation in coverage across sites and individual samples, and because of the computational challenges of controlling for genetic covariance in count data. To address these challenges, we present a binomial mixed model and an efficient, sampling-based algorithm (MACAU: Mixed model association for count data via data augmentation) for approximate parameter estimation and p-value computation. This framework allows us to simultaneously account for both the over-dispersed, count-based nature of bisulfite sequencing data, as well as genetic relatedness among individuals. Using simulations and two real data sets (whole genome bisulfite sequencing (WGBS) data from Arabidopsis thaliana and reduced representation bisulfite sequencing (RRBS) data from baboons), we show that our method provides well-calibrated test statistics in the presence of population structure. Further, it improves power to detect differentially methylated sites: in the RRBS data set, MACAU detected 1.6-fold more age-associated CpG sites than a beta-binomial model (the next best approach). Changes in these sites are consistent with known age-related shifts in DNA methylation levels, and are enriched near genes that are differentially expressed with age in the same population. Taken together, our results indicate that MACAU is an efficient, effective tool for analyzing bisulfite sequencing data, with particular salience to analyses of structured populations. MACAU is freely available at www.xzlab.org/software.html.

Base-resolution DNA methylation landscape of zebrafish brain and liver.

Zebrafish (Danio rerio) is a vertebrate model organism that is widely used for studying a plethora of biological questions, including developmental processes, effects of external cues on phenotype, and human disease modeling. DNA methylation is an important epigenetic mechanism that contributes to gene regulation, and is prevalent in all vertebrates. Reduced representation bisulfite sequencing (RRBS) is a cost-effective technique to generate genome-wide DNA methylation maps and has been used in mammalian genomes (e.g., human, mouse and rat) but not in zebrafish. High-resolution DNA methylation data in zebrafish are limited: increased availability of such data will enable us to model and better understand the roles, causes and consequences of changes in DNA methylation.Here we present five high-resolution DNA methylation maps for wild-type zebrafish brain (two pooled male and two pooled female methylomes) and liver. These data were generated using the RRBS technique (includes 1.43 million CpG sites of zebrafish genome) on the Illumina HiSeq platform. Alignment to the reference genome was performed using the Zv9 genome assembly.To our knowledge, these datasets are the only RRBS datasets and base-resolution DNA methylation data available at this time for zebrafish brain and liver. These datasets could serve as a resource for future studies to document the functional role of DNA methylation in zebrafish. In addition, these datasets could be used as controls while performing analysis on treated samples.

Modeling, simulation and analysis of methylation profiles from reduced representation bisulfite sequencing experiments

The ENCODE project has funded the generation of a diverse collection of methylation profiles using reduced representation bisulfite sequencing (RRBS) technology, enabling the analysis of epigenetic variation on a genomic scale at single-site resolution. A standard application of RRBS experiments is in the location of differentially methylated regions (DMRs) between two sets of samples. Despite numerous publications reporting DMRs identified from RRBS datasets, there have been no formal analyses of the effects of experimental and biological factors on the performance of existing or newly developed analytical methods. These factors include variable read coverage, differing group sample sizes across genomic regions, uneven spacing between CpG dinucleotide sites, and correlation in methylation levels among sites in close proximity. To better understand the interplay among technical and biological variables in the analysis of RRBS methylation profiles, we have developed an algorithm for the generation of experimentally realistic RRBS datasets. Applying insights derived from our simulation studies, we present a novel procedure that can identify DMRs spanning as few as three CpG sites with both high sensitivity and specificity. Using RRBS data from muscle vs. non-muscle cell cultures as an example, we demonstrate that our method reveals many more DMRs that are likely to be of biological significance than previous methods.