MAGI

By incorporating annotation information into the analysis of next-generation sequencing DNA methylation data, we provide an improvement in performance over current testing procedures. Methylation analysis using genome information (MAGI) is applicable for both unreplicated and replicated data, and provides an effective analysis for studies with low sequencing depth. When compared with current tests, the annotation-informed tests provide an increase in statistical power and offer a significance-based interpretation of differential methylation.     

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.     

DMRFusion: A differentially methylated region detection tool based on the ranked fusion method

DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Computational analysis of differentially methylated regions (DMRs) could explore the underlying reasons of methylation. DMRFusion is presented as a useful tool for comprehensive DNA methylation analysis of DMRs on methylation sequencing data. This tool is designed base on the integration of several ranking methods; Information gain, Between versus within Class scatter ratio, Fisher ratio, Z-score and Welch's t-test. In this study, DMRFusion on reduced representation bisulfite sequencing (RRBS) data in chronic lymphocytic leukemia cancer displayed 30 nominated regions and CpG sites with a maximum methylation difference detected in the hypermethylation DMRs. We realized that DMRFusion is able to process methylation sequencing data in an efficient and accurate manner and to provide annotation and visualization for DMRs with high fold difference score (p-value and FDR < 0.05 and type I error: 0.04).     

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.     

二代测序应用于检测GT1-7细胞中KISS1和GnRH启动子的DNA甲基化状态的研究

目的:利用二代测序技术检测GT1-7细胞中KISS1和GnRH基因启动子范围内的甲基化状态,并用金标准的亚硫酸氢盐修饰后的克隆测序作为对照,比较二代测序与金标准克隆测序在研究DNA甲基化检测中的差别。方法:提取GT1-7细胞基因组DNA并进行亚硫酸氢盐处理。进行巢式PCR,将PCR产物进行二代测序。同时采用金标准的亚硫酸氢盐修饰后克隆测序的方法作为对照,对相同批次的PCR产物进行克隆测序。结果:PCR产物二代测序结果表明KISS1和GnRH两个基因的27个CpG甲基化位点信息完整,结果准确。挑取10个克隆进行一代测序结果表明序列无丢失,KISS1和GnRH两个基因的27个CpG甲基化位点信息完整。结论:利用高通量的二代测序技术能够有效的对DNA甲基化的PCR产物进行检测,二代测序和克隆测序都是研究DNA甲基化的有效方法,但前者与克隆测序相比每一个读取序列(reads)都相当于一个单克隆,且二代测序每个区段得到成百上千个reads,因此二代测序结果更加精确。     

Detection method for methylation density on microarray using methyl-CpG-binding domain protein

A method for determining methylation density of target CpG islands has been established. In the method, DNA microarray was prepared by spotting a set of PCR products amplified from bisulfite-converted sample DNAs. The PCR products on the microarray were treated by SssI methyltransferase and labeled with TAMRA fluorescence. A recombinant, antibody-like methyl-CpG-binding protein labeled with Cy5 fluorescence was used to identify symmetrical methyl-CpG dinucleotide of the PCR products on the microarray. By use of a standard curve with control mixtures, the ratio of two fluorescence signals can be converted into percentage values to assess methylation density of targeted fragments. We obtained the methylation density of six CpG islands on the two tumor suppressor genes of CDK2A and CDK2B from seven cancer cell line samples and two normal blood samples. The validity of this method was tested by bisulfite sequencing. This method not only allows the quantitative analysis of regional methylation density of a set of given genes but also could provide information of methylation density for a large amount of clinical samples.     

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.     

Identification of DNA methylation changes at cis-regulatory elements during early steps of HSC differentiation using tagmentation-based whole genome bisulfite sequencing

Epigenetic alterations during cellular differentiation are a key molecular mechanism which both instructs and reinforces the process of lineage commitment. Within the haematopoietic system, progressive changes in the DNA methylome of haematopoietic stem cells (HSCs) are essential for the effective production of mature blood cells. Inhibition or loss of function of the cellular DNA methylation machinery has been shown to lead to a severe perturbation in blood production and is also an important driver of malignant transformation. HSCs constitute a very rare cell population in the bone marrow, capable of life-long self-renewal and multi-lineage differentiation. The low abundance of HSCs has been a major technological barrier to the global analysis of the CpG methylation status within both HSCs and their immediate progeny, the multipotent progenitors (MPPs). Within this Extra View article, we review the current understanding of how the DNA methylome regulates normal and malignant hematopoiesis. We also discuss the current methodologies that are available for interrogating the DNA methylation status of HSCs and MPPs and describe a new data set that was generated using tagmentation-based whole genome bisulfite sequencing (TWGBS) in order to comprehensively map methylated cytosines using the limited amount of genomic DNA that can be harvested from rare cell populations. Extended analysis of this data set clearly demonstrates the added value of genome-wide sequencing of methylated cytosines and identifies novel important cis-acting regulatory regions that are dynamically remodeled during the first steps of haematopoietic differentiation.     

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

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

De novo quantitative bisulfite sequencing using the pyrosequencing technology

Current protocols for DNA methylation analysis are either labor intensive or limited to the measurement of only one or two CpG positions. Pyrosequencing is a real-time sequencing technology that can overcome these limitations and be used as an epigenotype-mapping tool. Initial experiments demonstrated reliable quantification of the degree of DNA methylation when 2-6 CpGs were analyzed. We sought to improve the sequencing protocol so as to analyze as many CpGs as possible in a single sequencing run. By using an improved enzyme mix and adding single-stranded DNA-binding protein to the reaction, we obtained reproducible results for as many as 10 successive CpGs in a single sequencing reaction spanning up to 75 nucleotides. A minimum amount of 10 ng of bisulfite-treated DNA is necessary to obtain good reproducibility and avoid preferential amplification. We applied the assay to the analysis of DNA methylation patterns in four CpG islands in the vicinity of IGF2 and H19 genes. This allowed accurate and quantitative de novo sequencing of the methylation state of each CpG, showing reproducible variations of methylation state in contiguous CpGs, and proved to be a useful adjunct to current technologies.     

Role of CpG context and content in evolutionary signatures of brain DNA methylation

DNA methylation is essential in brain function and behavior; therefore, understanding the role of DNA methylation in brain-based disorders begins with the study of DNA methylation profiles in normal brain. Determining the patterns and scale of methylation conservation and alteration in an evolutionary context enables the design of focused but effective methylation studies of disease states. We applied an enzymatic-based approach, Methylation Mapping Analysis by Paired-end Sequencing (Methyl-MAPS), which utilizes second-generation sequencing technology to provide an unbiased representation of genome-wide DNA methylation profiles of human and mouse brains. In this large-scale study, we assayed CpG methylation in cerebral cortex of neurologically and psychiatrically normal human postmortem specimens, as well as mouse forebrain specimens. Cross-species human-mouse DNA methylation conservation analysis shows that DNA methylation is not correlated with sequence conservation. Instead, greater DNA methylation conservation is correlated with increasing CpG density. In addition to CpG density, these data show that genomic context is a critical factor in DNA methylation conservation and alteration signatures throughout mammalian brain evolution. We identify key genomic features that can be targeted for identification of epigenetic loci that may be developmentally and evolutionarily conserved and wherein aberrations in DNA methylation patterns can confer risk for disease.     

DNA methylation biomarkers for head and neck squamous cell carcinoma

DNA methylation plays an important role in the etiology and pathogenesis of head and neck squamous cell carcinoma (HNSCC). The current study aimed to identify aberrantly methylated-differentially expressed genes (DEGs) by a comprehensive bioinformatics analysis. In addition, we screened for DEGs affected by DNA methylation modification and further investigated their prognostic values for HNSCC. We included microarray data of DNA methylation (GSE25093 and GSE33202) and gene expression (GSE23036 and GSE58911) from Gene Expression Omnibus. Aberrantly methylated-DEGs were analyzed with R software. The Cancer Genome Atlas (TCGA) RNA sequencing and DNA methylation (Illumina HumanMethylation450) databases were utilized for validation. In total, 27 aberrantly methylated genes accompanied by altered expression were identified. After confirmation by The Cancer Genome Atlas (TCGA) database, 2 hypermethylated-low-expression genes (FAM135B and ZNF610) and 2 hypomethylated-high-expression genes (HOXA9 and DCC) were identified. A receiver operating characteristic (ROC) curve confirmed the diagnostic value of these four methylated genes for HNSCC. Multivariate Cox proportional hazards analysis showed that FAM135B methylation was a favorable independent prognostic biomarker for overall survival of HNSCC patients.     

A tag-based approach for high-throughput analysis of CCWGG methylation

Non-CpG methylation occurring in the context of CNG sequences is found in plants at a large number of genomic loci. However, there is still little information available about non-CpG methylation in mammals. Efficient methods that would allow detection of scarcely localized methylated sites in small quantities of DNA are required to elucidate the biological role of non-CpG methylation in both plants and animals. In this study, we tested a new whole genome approach to identify sites of CCWGG methylation (W is A or T), a particular case of CNG methylation, in genomic DNA. This technique is based on digestion of DNAs with methylation-sensitive restriction endonucleases EcoRII-C and AjnI. Short DNAs flanking methylated CCWGG sites (tags) are selectively purified and assembled in tandem arrays of up to nine tags. This allows high-throughput sequencing of tags, identification of flanking regions, and their exact positions in the genome. In this study, we tested specificity and efficiency of the approach.     

Bis-class: a new classification tool of methylation status using bayes classifier and local methylation information

Background

Whole genome sequencing of bisulfite converted DNA (‘methylC-seq’) method provides comprehensive information of DNA methylation. An important application of these whole genome methylation maps is classifying each position as a methylated versus non-methylated nucleotide. A widely used current method for this purpose, the so-called binomial method, is intuitive and straightforward, but lacks power when the sequence coverage and the genome-wide methylation level are low. These problems present a particular challenge when analyzing sparsely methylated genomes, such as those of many invertebrates and plants.

Results

We demonstrate that the number of sequence reads per position from methylC-seq data displays a large variance and can be modeled as a shifted negative binomial distribution. We also show that DNA methylation levels of adjacent CpG sites are correlated, and this similarity in local DNA methylation levels extends several kilobases. Taking these observations into account, we propose a new method based on Bayesian classification to infer DNA methylation status while considering the neighborhood DNA methylation levels of a specific site. We show that our approach has higher sensitivity and better classification performance than the binomial method via multiple analyses, including computational simulations, Area Under Curve (AUC) analyses, and improved consistencies across biological replicates. This method is especially advantageous in the analyses of sparsely methylated genomes with low coverage.

Conclusions

Our method improves the existing binomial method for binary methylation calls by utilizing a posterior odds framework and incorporating local methylation information. This method should be widely applicable to the analyses of methylC-seq data from diverse sparsely methylated genomes. Bis-Class and example data are provided at a dedicated website (http://bibs.snu.ac.kr/software/Bisclass).

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-608) contains supplementary material, which is available to authorized users.     

Semiconductor-based sequencing of genome-wide DNA methylation states

Methylated DNA immunoprecipitation sequencing (MeDIP-Seq) is a widely used approach to study DNA methylation genome-wide. Here, we developed a MeDIP-Seq protocol compatible with the Ion Torrent semiconductor-based sequencing platform that is low cost, rapid, and scalable. We applied this protocol to demonstrate MeDIP-Seq on the Ion Torrent platform provides adequate coverage of CpG cytosines, the methylation states of which we validated at single-base resolution on the Infinium HumanMethylation450 BeadChip array, and accurately identifies sites of differential DNA methylation. Furthermore, we applied an integrative approach to further investigate and confirm the role of DNA methylation in alternative splicing and to profile 5mC and 5hmC variants of DNA methylation in normal human brain tissue that is localized over distinct genomic regions. These applications of MeDIP-Seq on the Ion Torrent platform have broad utility and add to the current methodologies for profiling genome-wide DNA methylation states in normal and disease conditions.     

Genome-wide high throughput analysis of DNA methylation in eukaryotes

Cytosine methylation is the quintessential epigenetic mark. Two well-established methods, bisulfite sequencing and methyl-DNA immunoprecipitation (MeDIP) lend themselves to the genome-wide analysis of DNA methylation by high throughput sequencing. Here we provide an overview and brief review of these methods. We summarize our experience with MeDIP followed by high throughput Illumina/Solexa sequencing, exemplified by the analysis of the methylated fraction of the Neurospora crassa genome ("methylome"). We provide detailed methods for DNA isolation, processing and the generation of in vitro libraries for Illumina/Solexa sequencing. We discuss potential problems in the generation of sequencing libraries. Finally, we provide an overview of software that is appropriate for the analysis of high throughput sequencing data generated by Illumina/Solexa-type sequencing by synthesis, with a special emphasis on approaches and applications that can generate more accurate depictions of sequence reads that fall in repeated regions of a chosen reference genome.