Differential methylation analysis for bisulfite sequencing using DSS

Bisulfite sequencing (BS-seq) technology measures DNA methylation at single nucleotide resolution. A key task in BS-seq data analysis is to identify differentially methylation (DM) under different conditions. Here we provide a tutorial for BS-seq DM analysis using Bioconductor package DSS. DSS uses a beta-binomial model to characterize the sequence counts from BS-seq, and implements rigorous statistical method for hypothesis testing. It provides flexible functionalities for a variety of DM analyses.     

高通量DNA甲基化数据的处理和分析方法

DNA甲基化作为一种表观遗传学修饰,在调控基因表达、X染色体失活、印记基因等方面都发挥着重要的作用.不同的DNA甲基化的预处理方法结合二代测序产生了大量的高通量甲基化数据,这些数据的存储、处理和分析是当前亟需解决的问题.在本文中,总结了目前存在的三种高通量DNA甲基化检测技术（限制性内切酶法,亲和纯化法,重亚硫酸盐转换法）,以及针对这些技术产生的高通量数据开发的存储、处理和分析工具.另外,还注重介绍了单碱基水平的DNA甲基化检测技术,BS-Seq的测序原理、数据处理流程以及后续的分析工具.     

Assessment of RainDrop BS-seq as a method for large-scale,targeted bisulfite sequencing

We present a systematic assessment of RainDrop BS-seq, a novel method for large-scale, targeted bisulfite sequencing using microdroplet-based PCR amplification coupled with next-generation sequencing. We compared DNA methylation levels at 498 target loci (1001 PCR amplicons) in human whole blood, osteosarcoma cells and an archived tumor tissue sample. We assessed the ability of RainDrop BS-seq to accurately measure DNA methylation over a range of DNA quantities (from 10 to 1500 ng), both with and without whole-genome amplification (WGA) following bisulfite conversion. DNA methylation profiles generated using at least 100 ng correlated well (median R = 0.92) with those generated on Illumina Infinium HumanMethylation450 BeadChips, currently the platform of choice for epigenome-wide association studies (EWAS). WGA allowed for testing of samples with a starting DNA amount of 10 and 50 ng, although a reduced correlation was observed (median R = 0.79). We conclude that RainDrop BS-seq is suitable for measuring DNA methylation levels using nanogram quantities of DNA, and can be used to study candidate epigenetic biomarker loci in an accurate and high-throughput manner, paving the way for its application to routine clinical diagnostics.     

Assessment of RainDrop BS-seq as a method for large-scale,targeted bisulfite sequencing

We present a systematic assessment of RainDrop BS-seq, a novel method for large-scale, targeted bisulfite sequencing using microdroplet-based PCR amplification coupled with next-generation sequencing. We compared DNA methylation levels at 498 target loci (1001 PCR amplicons) in human whole blood, osteosarcoma cells and an archived tumor tissue sample. We assessed the ability of RainDrop BS-seq to accurately measure DNA methylation over a range of DNA quantities (from 10 to 1500 ng), both with and without whole-genome amplification (WGA) following bisulfite conversion. DNA methylation profiles generated using at least 100 ng correlated well (median R = 0.92) with those generated on Illumina Infinium HumanMethylation450 BeadChips, currently the platform of choice for epigenome-wide association studies (EWAS). WGA allowed for testing of samples with a starting DNA amount of 10 and 50 ng, although a reduced correlation was observed (median R = 0.79). We conclude that RainDrop BS-seq is suitable for measuring DNA methylation levels using nanogram quantities of DNA, and can be used to study candidate epigenetic biomarker loci in an accurate and high-throughput manner, paving the way for its application to routine clinical diagnostics.     

MOABS: model based analysis of bisulfite sequencing data

Bisulfite sequencing (BS-seq) is the gold standard for studying genome-wide DNA methylation. We developed MOABS to increase the speed, accuracy, statistical power and biological relevance of BS-seq data analysis. MOABS detects differential methylation with 10-fold coverage at single-CpG resolution based on a Beta-Binomial hierarchical model and is capable of processing two billion reads in 24 CPU hours. Here, using simulated and real BS-seq data, we demonstrate that MOABS outperforms other leading algorithms, such as Fisher’s exact test and BSmooth. Furthermore, MOABS analysis can be easily extended to differential 5hmC analysis using RRBS and oxBS-seq. MOABS is available at http://code.google.com/p/moabs/.     

An Integrated Workflow for DNA Methylation Analysis

The analysis of cytosine methylation provides a new way to assess and describe epigenetic regulation at a whole-genome level in many eukaryotes. DNA methylation has a demonstrated role in the genome stability and protection, regulation of gene expression and many other aspects of genome function and maintenance. BS-seq is a relatively unbiased method for profiling the DNA methylation, with a resolution capable of measuring methylation at individual cytosines. Here we describe, as an example, a workflow to handle DNA methylation analysis, from BS-seq library preparation to the data visualization. We describe some applications for the analysis and interpretation of these data. Our laboratory provides public access to plant DNA methylation data via visualization tools available at our “Next-Gen Sequence” websites (http://mpss.udel.edu), along with small RNA, RNA-seq and other data types.     

HeteroMeth: A Database of Cell-to-cell Heterogeneity in DNA Methylation

DNA methylation is an important epigenetic mark that plays a vital role in gene expression and cell differentiation. The average DNA methylation level among a group of cells has been extensively documented. However, the cell-to-cell heterogeneity in DNA methylation, which reflects the differentiation of epigenetic status among cells, remains less investigated. Here we established a gold standard of the cell-to-cell heterogeneity in DNA methylation based on single-cell bisulfite sequencing (BS-seq) data. With that, we optimized a computational pipeline for estimating the heterogeneity in DNA methylation from bulk BS-seq data. We further built HeteroMeth, a database for searching, browsing, visualizing, and downloading the data for heterogeneity in DNA methylation for a total of 141 samples in humans, mice, Arabidopsis, and rice. Three genes are used as examples to illustrate the power of HeteroMeth in the identification of unique features in DNA methylation. The optimization of the computational strategy and the construction of the database in this study complement the recent experimental attempts on single-cell DNA methylomes and will facilitate the understanding of epigenetic mechanisms underlying cell differentiation and embryonic development. HeteroMeth is publicly available at http://qianlab.genetics.ac.cn/HeteroMeth.     

Loss of 5-hydroxymethylcytosine is linked to gene body hypermethylation in kidney cancer

Both 5-methylcytosine (5mC) and its oxidized form 5-hydroxymethylcytosine (5hmC) have been proposed to be involved in tumorigenesis. Because the readout of the broadly used 5mC mapping method, bisulfite sequencing (BS-seq), is the sum of 5mC and 5hmC levels, the 5mC/5hmC patterns and relationship of these two modifications remain poorly understood. By profiling real 5mC (BS-seq corrected by Tet-assisted BS-seq, TAB-seq) and 5hmC (TAB-seq) levels simultaneously at single-nucleotide resolution, we here demonstrate that there is no global loss of 5mC in kidney tumors compared with matched normal tissues. Conversely, 5hmC was globally lost in virtually all kidney tumor tissues. The 5hmC level in tumor tissues is an independent prognostic marker for kidney cancer, with lower levels of 5hmC associated with shorter overall survival. Furthermore, we demonstrated that loss of 5hmC is linked to hypermethylation in tumors compared with matched normal tissues, particularly in gene body regions. Strikingly, gene body hypermethylation was significantly associated with silencing of the tumor-related genes. Downregulation of IDH1 was identified as a mechanism underlying 5hmC loss in kidney cancer. Restoring 5hmC levels attenuated the invasion capacity of tumor cells and suppressed tumor growth in a xenograft model. Collectively, our results demonstrate that loss of 5hmC is both a prognostic marker and an oncogenic event in kidney cancer by remodeling the DNA methylation pattern.     

铜胁迫对不同抗性种群海州香薷酸性转化酶基因启动子甲基化的影响

采用亚硫酸氢盐测序技术,检测了Cu胁迫对海州香薷（Elsholtzia haichowensis Sun） Cu抗性和非抗性种群酸性转化酶基因启动子甲基化的影响。结果表明,海州香薷液泡转化酶基因（vINV）和细胞壁转化酶基因（cwINV）的启动子在CG位点分别表现出超甲基化和低甲基化的现象。酸性转化酶基因启动子CHG和CHH位点的甲基化状态受Cu胁迫影响较大。Cu胁迫下,vINV和cwINV启动子分别有1个CHG和6个CHH位点的甲基化状态在Cu抗性种群和非抗性种群之间表现出较大差异。抗Cu种群中这些甲基化差异位点对Cu胁迫不敏感,但在非抗性种群中这些位点的甲基化水平在Cu胁迫后出现大幅上升或下降。有些甲基化差异位点位于或者临近预测的启动子顺式作用元件区域,可能参与Cu胁迫下酸性转化酶基因的表达调控。     

Single-base resolution analysis of DNA epigenome via high-throughput sequencing

Epigenetic changes caused by DNA methylation and histone modifications play important roles in the regulation of various cellular processes and development. Recent discoveries of 5-methylcytosine (5mC) oxidation derivatives including 5-hydroxymethylcytosine (5hmC), 5-formylcytsine (5fC) and 5-carboxycytosine (5caC) in mammalian genome further expand our understanding of the epigenetic regulation. Analysis of DNA modification patterns relies increasingly on sequencing-based profiling methods. A number of different approaches have been established to map the DNA epigenomes with single-base resolution, as represented by the bisulfite-based methods, such as classical bisulfite sequencing (BS-seq), TAB-seq (TET-assisted bisulfite sequencing), oxBS-seq (oxidative bisulfite sequencing) and etc. These methods have been used to generate base-resolution maps of 5mC and its oxidation derivatives in genomic samples. The focus of this review will be to discuss the chemical methodologies that have been developed to detect the cytosine derivatives in the genomic DNA.     

Genome-wide and single-base resolution DNA methylomes of the Pacific oyster Crassostrea gigas provide insight into the evolution of invertebrate CpG methylation

Background

Studies of DNA methylomes in a wide range of eukaryotes have revealed both conserved and divergent characteristics of DNA methylation among phylogenetic groups. However, data on invertebrates particularly molluscs are limited, which hinders our understanding of the evolution of DNA methylation in metazoa. The sequencing of the Pacific oyster Crassostrea gigas genome provides an opportunity for genome-wide profiling of DNA methylation in this model mollusc.

Results

Homologous searches against the C. gigas genome identified functional orthologs for key genes involved in DNA methylation: DNMT1, DNMT2, DNMT3, MBD2/3 and UHRF1. Whole-genome bisulfite sequencing (BS-seq) of the oyster’s mantle tissues revealed that more than 99% methylation modification was restricted to cytosines in CpG context and methylated CpGs accumulated in the bodies of genes that were moderately expressed. Young repeat elements were another major targets of CpG methylation in oysters. Comparison with other invertebrate methylomes suggested that the 5’-end bias of gene body methylation and the negative correlation between gene body methylation and gene length were the derived features probably limited to the insect lineage. Interestingly, phylostratigraphic analysis showed that CpG methylation preferentially targeted genes originating in the common ancestor of eukaryotes rather than the oldest genes originating in the common ancestor of cellular organisms.

Conclusions

Comparative analysis of the oyster DNA methylomes and that of other animal species revealed that the characteristics of DNA methylation were generally conserved during invertebrate evolution, while some unique features were derived in the insect lineage. The preference of methylation modification on genes originating in the eukaryotic ancestor rather than the oldest genes is unexpected, probably implying that the emergence of methylation regulation in these ''relatively young’ genes was critical for the origin and radiation of eukaryotes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1119) contains supplementary material, which is available to authorized users.     

Epigenomic profiling indicates a role for DNA methylation in the postnatal liver and pancreas development of giant pandas

Giant pandas are unique within Carnivora with a strict bamboo diet. Here, the epigenomic profiles of giant panda liver and pancreas tissues collected from three important feeding stages were investigated using BS-seq. Few differences in DNA methylation profiles were exhibited between no feeding and suckling groups in both tissues. However, we observed a tendency toward a global loss of DNA methylation in the gene-body and promoter region of metabolism-related genes from newborn to adult. Correlation analysis revealed a significant negative correlation between the changes in methylation levels within gene promoters and gene expression. The majority of genes related to nutrition metabolism had lost DNA methylation with increased mRNA expression in adult giant pandas. The few galactose metabolism and unsaturated fatty acid metabolism related genes that were hypomethylated and highly-expressed at early stages of giant panda development may meet the nutritional requirement of this species' highly altricial neonates.     

CpG_MPs: identification of CpG methylation patterns of genomic regions from high-throughput bisulfite sequencing data

High-throughput bisulfite sequencing is widely used to measure cytosine methylation at single-base resolution in eukaryotes. It permits systems-level analysis of genomic methylation patterns associated with gene expression and chromatin structure. However, methods for large-scale identification of methylation patterns from bisulfite sequencing are lacking. We developed a comprehensive tool, CpG_MPs, for identification and analysis of the methylation patterns of genomic regions from bisulfite sequencing data. CpG_MPs first normalizes bisulfite sequencing reads into methylation level of CpGs. Then it identifies unmethylated and methylated regions using the methylation status of neighboring CpGs by hotspot extension algorithm without knowledge of pre-defined regions. Furthermore, the conservatively and differentially methylated regions across paired or multiple samples (cells or tissues) are identified by combining a combinatorial algorithm with Shannon entropy. CpG_MPs identified large amounts of genomic regions with different methylation patterns across five human bisulfite sequencing data during cellular differentiation. Different sequence features and significantly cell-specific methylation patterns were observed. These potentially functional regions form candidate regions for functional analysis of DNA methylation during cellular differentiation. CpG_MPs is the first user-friendly tool for identifying methylation patterns of genomic regions from bisulfite sequencing data, permitting further investigation of the biological functions of genome-scale methylation patterns.     

Heterogeneity and randomness of DNA methylation patterns in human embryonic stem cells

DNA methylation has been proposed to be important in many biological processes and is the subject of intense study. Traditional bisulfite genomic sequencing allows detailed high-resolution methylation pattern analysis of each molecule with haplotype information across a few hundred bases at each locus, but lacks the capacity to gather voluminous data. Although recent technological developments are aimed at assessing DNA methylation patterns in a high-throughput manner across the genome, the haplotype information cannot be accurately assembled when the sequencing reads are short or when each hybridization target only includes one or two cytosine-phosphate-guanine (CpG) sites. Whether a distinct and nonrandom DNA methylation pattern is present at a given locus is difficult to discern without the haplotype information, and the DNA methylation patterns are much less apparent because the data are often obtained only as methylation frequencies at each CpG site with some of these methods. It would facilitate the interpretation of data obtained from high-throughput bisulfite sequencing if the loci with nonrandom DNA methylation patterns could be distinguished from those that are randomly methylated. In this study, we carried out traditional genomic bisulfite sequencing using the normal diploid human embryonic stem (hES) cell lines, and utilized Hamming distance analysis to evaluate the existence of a distinct and nonrandom DNA methylation pattern at each locus studied. Our findings suggest that Hamming distance is a simple, quick, and useful tool to identify loci with nonrandom DNA methylation patterns and may be utilized to discern links between biological changes and DNA methylation patterns in the high-throughput bisulfite sequencing data sets.     

A Bayesian Framework to Identify Methylcytosines from High-Throughput Bisulfite Sequencing Data

High-throughput bisulfite sequencing technologies have provided a comprehensive and well-fitted way to investigate DNA methylation at single-base resolution. However, there are substantial bioinformatic challenges to distinguish precisely methylcytosines from unconverted cytosines based on bisulfite sequencing data. The challenges arise, at least in part, from cell heterozygosis caused by multicellular sequencing and the still limited number of statistical methods that are available for methylcytosine calling based on bisulfite sequencing data. Here, we present an algorithm, termed Bycom, a new Bayesian model that can perform methylcytosine calling with high accuracy. Bycom considers cell heterozygosis along with sequencing errors and bisulfite conversion efficiency to improve calling accuracy. Bycom performance was compared with the performance of Lister, the method most widely used to identify methylcytosines from bisulfite sequencing data. The results showed that the performance of Bycom was better than that of Lister for data with high methylation levels. Bycom also showed higher sensitivity and specificity for low methylation level samples (<1%) than Lister. A validation experiment based on reduced representation bisulfite sequencing data suggested that Bycom had a false positive rate of about 4% while maintaining an accuracy of close to 94%. This study demonstrated that Bycom had a low false calling rate at any methylation level and accurate methylcytosine calling at high methylation levels. Bycom will contribute significantly to studies aimed at recalibrating the methylation level of genomic regions based on the presence of methylcytosines.     

High density DNA methylation array with single CpG site resolution

We have developed a new generation of genome-wide DNA methylation BeadChip which allows high-throughput methylation profiling of the human genome. The new high density BeadChip can assay over 480K CpG sites and analyze twelve samples in parallel. The innovative content includes coverage of 99% of RefSeq genes with multiple probes per gene, 96% of CpG islands from the UCSC database, CpG island shores and additional content selected from whole-genome bisulfite sequencing data and input from DNA methylation experts. The well-characterized Infinium® Assay is used for analysis of CpG methylation using bisulfite-converted genomic DNA. We applied this technology to analyze DNA methylation in normal and tumor DNA samples and compared results with whole-genome bisulfite sequencing (WGBS) data obtained for the same samples. Highly comparable DNA methylation profiles were generated by the array and sequencing methods (average R² of 0.95). The ability to determine genome-wide methylation patterns will rapidly advance methylation research.     

MetMap Enables Genome-Scale Methyltyping for Determining Methylation States in Populations

The ability to assay genome-scale methylation patterns using high-throughput sequencing makes it possible to carry out association studies to determine the relationship between epigenetic variation and phenotype. While bisulfite sequencing can determine a methylome at high resolution, cost inhibits its use in comparative and population studies. MethylSeq, based on sequencing of fragment ends produced by a methylation-sensitive restriction enzyme, is a method for methyltyping (survey of methylation states) and is a site-specific and cost-effective alternative to whole-genome bisulfite sequencing. Despite its advantages, the use of MethylSeq has been restricted by biases in MethylSeq data that complicate the determination of methyltypes. Here we introduce a statistical method, MetMap, that produces corrected site-specific methylation states from MethylSeq experiments and annotates unmethylated islands across the genome. MetMap integrates genome sequence information with experimental data, in a statistically sound and cohesive Bayesian Network. It infers the extent of methylation at individual CGs and across regions, and serves as a framework for comparative methylation analysis within and among species. We validated MetMap''s inferences with direct bisulfite sequencing, showing that the methylation status of sites and islands is accurately inferred. We used MetMap to analyze MethylSeq data from four human neutrophil samples, identifying novel, highly unmethylated islands that are invisible to sequence-based annotation strategies. The combination of MethylSeq and MetMap is a powerful and cost-effective tool for determining genome-scale methyltypes suitable for comparative and association studies.