Digital Restriction Enzyme Analysis of Methylation (DREAM).

Digital Restriction Enzyme Analysis of Methylation (DREAM) is a method for quantitative mapping of DNA methylation across genomes using next-generation sequencing (NGS) technology. The method is based on sequential cuts of genomic DNA with a pair of restriction enzymes (SmaI and XmaI) at CCCGGG target sites. Unmethylated sites are first digested with SmaI. This enzyme cuts the sites in the middle at CCC^GGG, leaving behind blunt ended fragments. CpG methylation completely blocks SmaI; therefore, only unmethylated sites are cleaved. The remaining methylated sites are digested with XmaI in the next step. This enzyme is not blocked by CpG methylation. It cuts the recognition site sideways at C^CCGGG forming 5'-CCGG overhangs. The sequential cuts thus create distinct methylation-specific signatures at the ends of restriction fragments: 5'-GGG for unmethylated CpG sites and 5'-CCGGG for methylated sites. The DNA fragments resulting from the digestions are ligated to NGS adapters. Sequencing libraries are prepared using hexanucleotide barcodes for sample identification. Individual libraries with distinct barcodes are pooled and sequenced using a paired ends protocol. The sequencing reads are aligned to the genome and mapped to unique CCCGGG target sites. Methylation at individual CpG sites is calculated as the ratio of sequencing reads with the methylated signature to the total number of reads mapping to the site. Sequencing of 25 million reads per sample typically yields 50,000 unique CpG sites covered with hundreds of reads enabling accurate determination of DNA methylation levels. DREAM does not require bisulfite conversion, has a very low background, and has high sensitivity to low levels of methylation. The method is simple, cost-effective, quantitative, highly reproducible, and can be applied to any species.

Caloric restriction delays age-related methylation drift.

In mammals, caloric restriction consistently results in extended lifespan. Epigenetic information encoded by DNA methylation is tightly regulated, but shows a striking drift associated with age that includes both gains and losses of DNA methylation at various sites. Here, we report that epigenetic drift is conserved across species and the rate of drift correlates with lifespan when comparing mice, rhesus monkeys, and humans. Twenty-two to 30-year-old rhesus monkeys exposed to 30% caloric restriction since 7-14 years of age showed attenuation of age-related methylation drift compared to ad libitum-fed controls such that their blood methylation age appeared 7 years younger than their chronologic age. Even more pronounced effects were seen in 2.7-3.2-year-old mice exposed to 40% caloric restriction starting at 0.3 years of age. The effects of caloric restriction on DNA methylation were detectable across different tissues and correlated with gene expression. We propose that epigenetic drift is a determinant of lifespan in mammals.Caloric restriction has been shown to increase lifespan in mammals. Here, the authors provide evidence that age-related methylation drift correlates with lifespan and that caloric restriction in mice and rhesus monkeys results in attenuation of age-related methylation drift.

Local sequence and sequencing depth dependent accuracy of RNA-seq reads.

BACKGROUND: Many biases and spurious effects are inherent in RNA-seq technology, resulting in a non-uniform distribution of sequencing read counts for each base position in a gene. Therefore, a base-level strategy is required to model the non-uniformity. Also, the properties of sequencing read counts can be leveraged to achieve a more precise estimation of the mean and variance of measurement. RESULTS: In this study, we aimed to unveil the effects on RNA-seq accuracy from multiple factors and develop accurate modeling of RNA-seq reads in comparison. We found that the overdispersion rate decreased when sequencing depth increased on the base level. Moreover, the influence of local sequence(s) on the overdispersion rate was notable but no longer significant after adjusting the effect from sequencing depth. Based on these findings, we propose a desirable beta-binomial model with a dynamic overdispersion rate on the base-level proportion of sequencing read counts from two samples. CONCLUSIONS: The current study provides thorough insights into the impact of overdispersion at the position level and especially into its relationship with sequencing depth, local sequence, and preparation protocol. These properties of RNA-seq will aid in improvement of the quality control procedure and development of statistical methods for RNA-seq downstream analyses.

Bayesian Hierarchical Model for Differential Gene Expression Using RNA-seq Data.

We introduce model-based Bayesian inference to screen for differentially expressed genes based on RNA-seq data. RNA-seq is a high-throughput next-generation sequencing application that can be used to measure the expression of messenger RNA. We propose a Bayesian hierarchical model to implement coherent, fast and robust inference, focusing on differential gene expression experiments, i.e., experiments carried out to learn about differences in gene expression under two biologic conditions. The proposed model exploits available position-specific read counts, minimizing required data pre-processing and making maximum use of available information. Moreover, it includes mechanisms to automatically discount outliers at the level of positions within genes. The method combines gene-level information across replicates, and reports coherent posterior probabilities of differential expression at the gene level. An implementation as a public domain R package is available.

PAND: A Distribution to Identify Functional Linkage from Networks with Preferential Attachment Property.

Technology advances have immensely accelerated large-scale mapping of biological networks, which necessitates the development of accurate and powerful network-based algorithms to make functional inferences. A prevailing approach is to leverage functions of neighboring nodes to predict unknown molecular function. However, existing neighbor-based algorithms have ignored the scale-free property hidden in many biological networks. By assuming that neighbor sharing is constrained by the preferential attachment property, we developed a Preferential Attachment based common Neighbor Distribution (PAND) to calculate the probability of the neighbor-sharing event between any two nodes in scale-free networks, which nearly perfectly matched the observed probability in simulations. By applying PAND to a human protein-protein interaction (PPI) network, we showed that smaller probabilities represented closer functional linkages between proteins. With the PAND-derive linkages, we were able to build new networks where the links are more functionally reliable than those of the human PPI network. We then applied simple annotation schemes to a PAND-derived network to make reliable functional predictions for proteins. We also developed an R package called PANDA (PAND-derived functional Associations) to implement the methods proposed in this study. In conclusion, PAND is a useful distribution to calculate the probability of the neighbor-sharing events in scale-free networks. With PAND, we are able to extract reliable functional linkages from real biological networks and builds new networks that are better bases for further functional inference.

TET2 Mutations Affect Non-CpG Island DNA Methylation at Enhancers and Transcription Factor-Binding Sites in Chronic Myelomonocytic Leukemia.

TET2 enzymatically converts 5-methylcytosine to 5-hydroxymethylcytosine as well as other covalently modified cytosines and its mutations are common in myeloid leukemia. However, the exact mechanism and the extent to which TET2 mutations affect DNA methylation remain in question. Here, we report on DNA methylomes in TET2 wild-type (TET2-WT) and mutant (TET2-MT) cases of chronic myelomonocytic leukemia (CMML). We analyzed 85,134 CpG sites [28,114 sites in CpG islands (CGI) and 57,020 in non-CpG islands (NCGI)]. TET2 mutations do not explain genome-wide differences in DNA methylation in CMML, and we found few and inconsistent differences at CGIs between TET2-WT and TET2-MT cases. In contrast, we identified 409 (0.71%) TET2-specific differentially methylated CpGs (tet2-DMCs) in NCGIs, 86% of which were hypermethylated in TET2-MT cases, suggesting a strikingly different biology of the effects of TET2 mutations at CGIs and NCGIs. DNA methylation of tet2-DMCs at promoters and nonpromoters repressed gene expression. Tet2-DMCs showed significant enrichment at hematopoietic-specific enhancers marked by H3K4me1 and at binding sites for the transcription factor p300. Tet2-DMCs showed significantly lower 5-hydroxymethylcytosine in TET2-MT cases. We conclude that leukemia-associated TET2 mutations affect DNA methylation at NCGI regions containing hematopoietic-specific enhancers and transcription factor-binding sites.

A role for BMP-induced homeobox gene MIXL1 in acute myelogenous leukemia and identification of type I BMP receptor as a potential target for therapy.

Mesoderm Inducer in Xenopus Like1 (MIXL1), a paired-type homeobox transcription factor induced by TGF-ß family of ligands is required for early embryonic specification of mesoderm and endoderm. Retrovirally transduced Mixl1 is reported to induce acute myelogenous leukemia (AML) with a high penetrance. But the mechanistic underpinnings of MIXL1 mediated leukemogenesis are unknown. Here, we establish the protooncogene c-REL to be a transcriptional target of MIXL1 by genome wide chromatin immune precipitation. Accordingly, expression of c-REL and its downstream targets BCL2L1 and BCL2A2 are elevated in MIXL1 expressing cells. Notably, MIXL1 regulates c-REL through a zinc finger binding motif, potentially by a MIXL1-Zinc finger protein transcriptional complex. Furthermore, MIXL1 expression is detected in the cancer genome atlas (TCGA) AML samples in a pattern mutually exclusive from that of HOXA9, CDX2 and HLX suggesting the existence of a core, yet distinct HOX transcriptional program. Finally, we demonstrate MIXL1 to be induced by BMP4 and not TGF-ß in primary human hematopoietic stem and progenitor cells. Consequently, MIXL1 expressing AML cells are preferentially sensitive to the BMPR1 kinase inhibitor LDN-193189. These findings support the existence of a novel MIXL1-c REL mediated survival axis in AML that can be targeted by BMPR1 inhibitors. (MIXL1- human gene, Mixl1- mouse ortholog, MIXL1- protein).

SOX9 regulates multiple genes in chondrocytes, including genes encoding ECM proteins, ECM modification enzymes, receptors, and transporters.

The transcription factor SOX9 plays an essential role in determining the fate of several cell types and is a master factor in regulation of chondrocyte development. Our aim was to determine which genes in the genome of chondrocytes are either directly or indirectly controlled by SOX9. We used RNA-Seq to identify genes whose expression levels were affected by SOX9 and used SOX9 ChIP-Seq to identify those genes that harbor SOX9-interaction sites. For RNA-Seq, the RNA expression profile of primary Sox9flox/flox mouse chondrocytes infected with Ad-CMV-Cre was compared with that of the same cells infected with a control adenovirus. Analysis of RNA-Seq data indicated that, when the levels of Sox9 mRNA were decreased more than 8-fold by infection with Ad-CMV-Cre, 196 genes showed a decrease in expression of at least 4-fold. These included many cartilage extracellular matrix (ECM) genes and a number of genes for ECM modification enzymes (transferases), membrane receptors, transporters, and others. In ChIP-Seq, 75% of the SOX9-interaction sites had a canonical inverted repeat motif within 100 bp of the top of the peak. SOX9-interaction sites were found in 55% of the genes whose expression was decreased more than 8-fold in SOX9-depleted cells and in somewhat fewer of the genes whose expression was reduced more than 4-fold, suggesting that these are direct targets of SOX9. The combination of RNA-Seq and ChIP-Seq has provided a fuller understanding of the SOX9-controlled genetic program of chondrocytes.

TET1 is a maintenance DNA demethylase that prevents methylation spreading in differentiated cells.

TET1 is a 5-methylcytosine dioxygenase and its DNA demethylating activity has been implicated in pluripotency and reprogramming. However, the precise role of TET1 in DNA methylation regulation outside of developmental reprogramming is still unclear. Here, we show that overexpression of the TET1 catalytic domain but not full length TET1 (TET1-FL) induces massive global DNA demethylation in differentiated cells. Genome-wide mapping reveals that 5-hydroxymethylcytosine production by TET1-FL is inhibited as DNA methylation increases, which can be explained by the preferential binding of TET1-FL to unmethylated CpG islands (CGIs) through its CXXC domain. TET1-FL specifically accumulates 5-hydroxymethylcytosine at the edges of hypomethylated CGIs, while knockdown of endogenous TET1 induces methylation spreading from methylated edges into hypomethylated CGIs. We also found that gene expression changes after TET1-FL overexpression are relatively small and independent of its dioxygenase function. Thus, our results identify TET1 as a maintenance DNA demethylase that does not purposely decrease methylation levels, but specifically prevents aberrant methylation spreading into CGIs in differentiated cells.

Potentiation of colon cancer susceptibility in mice by colonic epithelial PPAR-δ/ß overexpression.

BACKGROUND: The nuclear receptor peroxisome proliferator-activated receptor-δ/ß (PPAR-d) is upregulated in human colorectal cancers, but its role in colonic tumorigenesis remains controversial. METHODS: We generated a novel mouse model of intestinally targeted PPAR-d overexpression to simulate PPAR-d upregulation in human colon carcinogenesis. Colon-specific PPAR-d overexpression was confirmed by real-time reverse transcription polymerase chain reaction, immunoblotting, and activity assays. Mice with and without targeted PPAR-d overexpression were tested for azoxymethane (AOM)-induced colonic tumorigenesis. Mouse whole-genome transcriptome microarray analyses were performed to identify PPAR-d target genes to promote tumorigenesis. We used linear models to test for PPAR-d overexpression trend effects on tumor multiplicity. All statistical tests were two-sided. RESULTS: Targeted PPAR-d overexpression markedly increased colonic tumor incidence (from 0 of 10 wild-type [WT] littermate mice to 9 of 10 mice [P < .001] in 2 FVB/N background mouse lines [villin-PPAR-d-1 and villin-PPAR-d-2] at a 5-mg/kg AOM dose) and multiplicity (number of tumors per mouse per mg/kg dose of AOM increased from 0.47 [95% confidence interval [CI] = 0.22 to 0.72] for the WT littermates to 2.15 [95% CI = 1.90 to 2.40] [P < .001] for the villin-PPAR-d-1 mice and from 0.44 [95% CI = 0.09 to 0.79] for the WT littermates to 1.91 [95% CI = 1.57 to 2.25] [P < .001] for the villin-PPAR-d-2 mice). PPAR-d overexpression reversed resistance to AOM-induced colonic tumorigenesis in C57BL/6 mice. PPAR-d overexpression modulated expression of several novel PPAR-d target genes in normal-appearing colonic epithelial cells of mice with PPAR-d overexpression in a pattern that matched the changes in colonic tumors. CONCLUSIONS: Our finding that PPAR-d upregulation profoundly enhances susceptibility to colonic tumorigenesis should impact the development of strategies of molecularly targeting PPAR-d in cancer and noncancerous diseases.

Age-related epigenetic drift in the pathogenesis of MDS and AML.

The myelodysplastic syndrome (MDS) is a clonal hematologic disorder that frequently evolves to acute myeloid leukemia (AML). Its pathogenesis remains unclear, but mutations in epigenetic modifiers are common and the disease often responds to DNA methylation inhibitors. We analyzed DNA methylation in the bone marrow and spleen in two mouse models of MDS/AML, the NUP98-HOXD13 (NHD13) mouse and the RUNX1 mutant mouse model. Methylation array analysis showed an average of 512/3445 (14.9%) genes hypermethylated in NHD13 MDS, and 331 (9.6%) genes hypermethylated in RUNX1 MDS. Thirty-two percent of genes in common between the two models (2/3 NHD13 mice and 2/3 RUNX1 mice) were also hypermethylated in at least two of 19 human MDS samples. Detailed analysis of 41 genes in mice showed progressive drift in DNA methylation from young to old normal bone marrow and spleen; to MDS, where we detected accelerated age-related methylation; and finally to AML, which markedly extends DNA methylation abnormalities. Most of these genes showed similar patterns in human MDS and AML. Repeat element hypomethylation was rare in MDS but marked the transition to AML in some cases. Our data show consistency in patterns of aberrant DNA methylation in human and mouse MDS and suggest that epigenetically, MDS displays an accelerated aging phenotype.

BM-SNP: A Bayesian Model for SNP Calling Using High Throughput Sequencing Data.

A single-nucleotide polymorphism (SNP) is a sole base change in the DNA sequence and is the most common polymorphism. Detection and annotation of SNPs are among the central topics in biomedical research as SNPs are believed to play important roles on the manifestation of phenotypic events, such as disease susceptibility. To take full advantage of the next-generation sequencing (NGS) technology, we propose a Bayesian approach, BM-SNP, to identify SNPs based on the posterior inference using NGS data. In particular, BM-SNP computes the posterior probability of nucleotide variation at each covered genomic position using the contents and frequency of the mapped short reads. The position with a high posterior probability of nucleotide variation is flagged as a potential SNP. We apply BM-SNP to two cell-line NGS data, and the results show a high ratio of overlap ( >95 percent) with the dbSNP database. Compared with MAQ, BM-SNP identifies more SNPs that are in dbSNP, with higher quality. The SNPs that are called only by BM-SNP but not in dbSNP may serve as new discoveries. The proposed BM-SNP method integrates information from multiple aspects of NGS data, and therefore achieves high detection power. BM-SNP is fast, capable of processing whole genome data at 20-fold average coverage in a short amount of time.

Architecture of epigenetic reprogramming following Twist1-mediated epithelial-mesenchymal transition.

BACKGROUND: Epithelial-mesenchymal transition (EMT) is known to impart metastasis and stemness characteristics in breast cancer. To characterize the epigenetic reprogramming following Twist1-induced EMT, we characterized the epigenetic and transcriptome landscapes using whole-genome transcriptome analysis by RNA-seq, DNA methylation by digital restriction enzyme analysis of methylation (DREAM) and histone modifications by CHIP-seq of H3K4me3 and H3K27me3 in immortalized human mammary epithelial cells relative to cells induced to undergo EMT by Twist1. RESULTS: EMT is accompanied by focal hypermethylation and widespread global DNA hypomethylation, predominantly within transcriptionally repressed gene bodies. At the chromatin level, the number of gene promoters marked by H3K4me3 increases by more than one fifth; H3K27me3 undergoes dynamic genomic redistribution characterized by loss at half of gene promoters and overall reduction of peak size by almost half. This is paralleled by increased phosphorylation of EZH2 at serine 21. Among genes with highly altered mRNA expression, 23.1% switch between H3K4me3 and H3K27me3 marks, and those point to the master EMT targets and regulators CDH1, PDGFRα and ESRP1. Strikingly, Twist1 increases the number of bivalent genes by more than two fold. Inhibition of the H3K27 methyltransferases EZH2 and EZH1, which form part of the Polycomb repressive complex 2 (PRC2), blocks EMT and stemness properties. CONCLUSIONS: Our findings demonstrate that the EMT program requires epigenetic remodeling by the Polycomb and Trithorax complexes leading to increased cellular plasticity. This suggests that inhibiting epigenetic remodeling and thus decrease plasticity will prevent EMT, and the associated breast cancer metastasis.

Toward breaking the histone code: bayesian graphical models for histone modifications.

BACKGROUND: Histones are proteins that wrap DNA around in small spherical structures called nucleosomes. Histone modifications (HMs) refer to the post-translational modifications to the histone tails. At a particular genomic locus, each of these HMs can either be present or absent, and the combinatory patterns of the presence or absence of multiple HMs, or the histone codes, are believed to coregulate important biological processes. We aim to use raw data on HM markers at different genomic loci to (1) decode the complex biological network of HMs in a single region, and (2) demonstrate how the HM networks differ in different regulatory regions. We suggest that these differences in network attributes form a significant link between histones and genomic functions. METHODS AND RESULTS: We develop a powerful graphical model under the Bayesian paradigm. Posterior inference is fully probabilistic, allowing us to compute the probabilities of distinct dependence patterns of the HMs using graphs. Furthermore, our model-based framework allows for easy but important extensions for inference on differential networks under various conditions, such as the different annotations of the genomic locations (eg, promoters versus insulators). We applied these models to ChIP-Seq data based on CD4+ T lymphocytes. The results confirmed many existing findings and provided a unified tool to generate various promising hypotheses. Differential network analyses revealed new insights on coregulation of HMs of transcriptional activities in different genomic regions. CONCLUSIONS: The use of Bayesian graphical models and borrowing strength across different conditions provide high power to infer histone networks and their differences.

Stabilization of the promoter nucleosomes in nucleosome-free regions by the yeast Cyc8-Tup1 corepressor.

The yeast Cyc8 (also known as Ssn6)-Tup1 complex regulates gene expression through a variety of mechanisms, including positioning of nucleosomes over promoters of some target genes to limit accessibility to the transcription machinery. To further define the functions of Cyc8-Tup1 in gene regulation and chromatin remodeling, we performed genome-wide profiling of changes in nucleosome organization and gene expression that occur upon loss of CYC8 or TUP1 and observed extensive nucleosome alterations in both promoters and gene bodies of derepressed genes. Our improved nucleosome profiling and analysis approaches revealed low-occupancy promoter nucleosomes (P nucleosomes) at locations previously defined as nucleosome-free regions. In the absence of CYC8 or TUP1, this P nucleosome is frequently lost, whereas nucleosomes are gained at -1 and +1 positions, accompanying up-regulation of downstream genes. Our analysis of public ChIP-seq data revealed that Cyc8 and Tup1 preferentially bind TATA-containing promoters, which are also enriched in genes derepressed upon loss of CYC8 or TUP1. These results suggest that stabilization of the P nucleosome on TATA-containing promoters may be a central feature of the repressive chromatin architecture created by the Cyc8-Tup1 corepressor, and that releasing the P nucleosome contributes to gene activation.

The epigenome of AML stem and progenitor cells.

Acute myeloid leukemia (AML) is sustained by a population of cancer stem cells (CSCs or cancer-initiating cell). The mechanisms underlying switches from CSCs to non-CSCs in vivo remain to be understood. We address this issue in AML from the aspect of epigenetics using genome-wide screening for DNA methylation and selected histone modifications. We found no major differences in DNA methylation, especially in promoter CpG islands, between CSCs and non-CSCs. By contrast, we found thousands of genes that change H3K4me3 and/or H3K27me3 status between stem and progenitor cells as well as between progenitor and mature cells. Stem cell related pathways and proliferation or metabolism related pathways characterize genes differentially enriched for H3K4me3/H3K27me3 in stem and progenitor populations. Bivalent genes in stem cells are more plastic during differentiation and are more likely to lose H3K4me3 than to lose H3K27me3, consistent with increasingly closed chromatin state with differentiation. Our data indicates that histone modifications but not promoter DNA methylation are involved in switches from CSCs to non-CSCs in AML.

Access to the nucleus and functional association with c-Myc is required for the full oncogenic potential of ΔEGFR/EGFRvIII.

ΔEGFR is a potent glioblastoma oncogene which has been studied primarily as a plasma membrane kinase. Using intracranial xenograft studies in mice, we show that blocking ΔEGFR access to the nucleus attenuates its tumorigenicity and, conversely, that promoting nuclear accumulation enhances this, providing the first in vivo evidence that the nuclear actions of ΔEGFR contribute strongly to its oncogenic function. Nuclear actions of ΔEGFR include regulation of gene expression by participation in chromatin-bound complexes, and genome-wide mapping of these sequences by chromatin immunoprecipitation and massively parallel sequencing identified 2294 peaks. Bioinformatic analysis showed enrichment of the E-box motif in the dataset, and c-Myc and ΔEGFR were corecruited to the promoters of and transcriptionally activated a subset of nuclear ΔEGFR chromatin targets. Knockdown of c-Myc decreased the expression of these targets and diminished ΔEGFR-stimulated anchorage-independent colony formation. We conclude that transcriptional regulation of target genes by association with gene regulatory chromatin in cooperation with c-Myc by nuclear ΔEGFR makes a unique contribution to its oncogenicity and propose that this venue provides new targets for therapeutic intervention.

Nonparametric Bayesian Bi-Clustering for Next Generation Sequencing Count Data.

Histone modifications (HMs) play important roles in transcription through post-translational modifications. Combinations of HMs, known as chromatin signatures, encode specific messages for gene regulation. We therefore expect that inference on possible clustering of HMs and an annotation of genomic locations on the basis of such clustering can contribute new insights about the functions of regulatory elements and their relationships to combinations of HMs. We propose a nonparametric Bayesian local clustering Poisson model (NoB-LCP) to facilitate posterior inference on two-dimensional clustering of HMs and genomic locations. The NoB-LCP clusters HMs into HM sets and lets each HM set define its own clustering of genomic locations. Furthermore, it probabilistically excludes HMs and genomic locations that are irrelevant to clustering. By doing so, the proposed model effectively identifies important sets of HMs and groups regulatory elements with similar functionality based on HM patterns.

Conserved DNA methylation patterns in healthy blood cells and extensive changes in leukemia measured by a new quantitative technique.

Genome wide analysis of DNA methylation provides important information in a variety of diseases, including cancer. Here, we describe a simple method, Digital Restriction Enzyme Analysis of Methylation (DREAM), based on next generation sequencing analysis of methylation-specific signatures created by sequential digestion of genomic DNA with SmaI and XmaI enzymes. DREAM provides information on 150,000 unique CpG sites, of which 39,000 are in CpG islands and 30,000 are at transcription start sites of 13,000 RefSeq genes. We analyzed DNA methylation in healthy white blood cells and found methylation patterns to be remarkably uniform. Inter individual differences > 30% were observed only at 227 of 28,331 (0.8%) of autosomal CpG sites. Similarly, > 30% differences were observed at only 59 sites when we comparing the cord and adult blood. These conserved methylation patterns contrasted with extensive changes affecting 18-40% of CpG sites in a patient with acute myeloid leukemia and in two leukemia cell lines. The method is cost effective, quantitative (r ( 2) = 0.93 when compared with bisulfite pyrosequencing) and reproducible (r ( 2) = 0.997). Using 100-fold coverage, DREAM can detect differences in methylation greater than 10% or 30% with a false positive rate below 0.05 or 0.001, respectively. DREAM can be useful in quantifying epigenetic effects of environment and nutrition, correlating developmental epigenetic variation with phenotypes, understanding epigenetics of cancer and chronic diseases, measuring the effects of drugs on DNA methylation or deriving new biological insights into mammalian genomes.