A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies

DNA methylation plays a fundamental role in the regulation of the genome, but the optimal strategy for analysis of genome-wide DNA methylation data remains to be determined. We developed a comprehensive analysis pipeline for epigenome-wide association studies (EWAS) using the Illumina Infinium HumanMethylation450 BeadChip, based on 2,687 individuals, with 36 samples measured in duplicate. We propose new approaches to quality control, data normalisation and batch correction through control-probe adjustment and establish a null hypothesis for EWAS using permutation testing. Our analysis pipeline outperforms existing approaches, enabling accurate identification of methylation quantitative trait loci for hypothesis driven follow-up experiments.

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The online version of this article (doi:10.1186/s13059-015-0600-x) contains supplementary material, which is available to authorized users.     

DNA methylation profiling of the human major histocompatibility complex: a pilot study for the human epigenome project

The Human Epigenome Project aims to identify, catalogue, and interpret genome-wide DNA methylation phenomena. Occurring naturally on cytosine bases at cytosine–guanine dinucleotides, DNA methylation is intimately involved in diverse biological processes and the aetiology of many diseases. Differentially methylated cytosines give rise to distinct profiles, thought to be specific for gene activity, tissue type, and disease state. The identification of such methylation variable positions will significantly improve our understanding of genome biology and our ability to diagnose disease. Here, we report the results of the pilot study for the Human Epigenome Project entailing the methylation analysis of the human major histocompatibility complex. This study involved the development of an integrated pipeline for high-throughput methylation analysis using bisulphite DNA sequencing, discovery of methylation variable positions, epigenotyping by matrix-assisted laser desorption/ionisation mass spectrometry, and development of an integrated public database available at http://www.epigenome.org. Our analysis of DNA methylation levels within the major histocompatibility complex, including regulatory exonic and intronic regions associated with 90 genes in multiple tissues and individuals, reveals a bimodal distribution of methylation profiles (i.e., the vast majority of the analysed regions were either hypo- or hypermethylated), tissue specificity, inter-individual variation, and correlation with independent gene expression data.     

Quantitative DNA methylation analysis based on four-dye trace data from direct sequencing of PCR amplificates

MOTIVATION: Methylation of cytosines in DNA plays an important role in the regulation of gene expression, and the analysis of methylation patterns is fundamental for the understanding of cell differentiation, aging processes, diseases and cancer development. Such analysis has been limited, because technologies for detailed and efficient high-throughput studies have not been available. We have developed a novel quantitative methylation analysis algorithm and workflow based on direct DNA sequencing of PCR products from bisulfite-treated DNA with high-throughput sequencing machines. This technology is a prerequisite for success of the Human Epigenome Project, the first large genome-wide sequencing study for DNA methylation in many different tissues. Methylation in tissue samples which are compositions of different cells is a quantitative information represented by cytosine/thymine proportions after bisulfite conversion of unmethylated cytosines to uracil and PCR. Calculation of quantitative methylation information from base proportions represented by different dye signals in four-dye sequencing trace files needs a specific algorithm handling imbalanced and overscaled signals, incomplete conversion, quality problems and basecaller artifacts. RESULTS: The algorithm we developed has several key properties: it analyzes trace files from PCR products of bisulfite-treated DNA sequenced directly on ABI machines; it yields quantitative methylation measurements for individual cytosine positions after alignment with genomic reference sequences, signal normalization and estimation of effectiveness of bisulfite treatment; it works in a fully automated pipeline including data quality monitoring; it is efficient and avoids the usual cost of multiple sequencing runs on subclones to estimate DNA methylation. The power of our new algorithm is demonstrated with data from two test systems based on mixtures with known base compositions and defined methylation. In addition, the applicability is proven by identifying CpGs that are differentially methylated in real tissue samples.     

An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform

The proper identification of differentially methylated CpGs is central in most epigenetic studies. The Illumina HumanMethylation450 BeadChip is widely used to quantify DNA methylation; nevertheless, the design of an appropriate analysis pipeline faces severe challenges due to the convolution of biological and technical variability and the presence of a signal bias between Infinium I and II probe design types. Despite recent attempts to investigate how to analyze DNA methylation data with such an array design, it has not been possible to perform a comprehensive comparison between different bioinformatics pipelines due to the lack of appropriate data sets having both large sample size and sufficient number of technical replicates. Here we perform such a comparative analysis, targeting the problems of reducing the technical variability, eliminating the probe design bias and reducing the batch effect by exploiting two unpublished data sets, which included technical replicates and were profiled for DNA methylation either on peripheral blood, monocytes or muscle biopsies. We evaluated the performance of different analysis pipelines and demonstrated that: (1) it is critical to correct for the probe design type, since the amplitude of the measured methylation change depends on the underlying chemistry; (2) the effect of different normalization schemes is mixed, and the most effective method in our hands were quantile normalization and Beta Mixture Quantile dilation (BMIQ); (3) it is beneficial to correct for batch effects. In conclusion, our comparative analysis using a comprehensive data set suggests an efficient pipeline for proper identification of differentially methylated CpGs using the Illumina 450K arrays.     

A Reference Methylome Database and Analysis Pipeline to Facilitate Integrative and Comparative Epigenomics

DNA methylation is implicated in a surprising diversity of regulatory, evolutionary processes and diseases in eukaryotes. The introduction of whole-genome bisulfite sequencing has enabled the study of DNA methylation at a single-base resolution, revealing many new aspects of DNA methylation and highlighting the usefulness of methylome data in understanding a variety of genomic phenomena. As the number of publicly available whole-genome bisulfite sequencing studies reaches into the hundreds, reliable and convenient tools for comparing and analyzing methylomes become increasingly important. We present MethPipe, a pipeline for both low and high-level methylome analysis, and MethBase, an accompanying database of annotated methylomes from the public domain. Together these resources enable researchers to extract interesting features from methylomes and compare them with those identified in public methylomes in our database.     

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.     

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.     

Improved reduced representation bisulfite sequencing for epigenomic profiling of clinical samples

Background

DNA methylation plays crucial roles in epigenetic gene regulation in normal development and disease pathogenesis. Efficient and accurate quantification of DNA methylation at single base resolution can greatly advance the knowledge of disease mechanisms and be used to identify potential biomarkers. We developed an improved pipeline based on reduced representation bisulfite sequencing (RRBS) for cost-effective genome-wide quantification of DNA methylation at single base resolution. A selection of two restriction enzymes (Taq^αI and MspI) enables a more unbiased coverage of genomic regions of different CpG densities. We further developed a highly automated software package to analyze bisulfite sequencing results from the Solexa GAIIx system.

Results

With two sequencing lanes, we were able to quantify ~1.8 million individual CpG sites at a minimum sequencing depth of 10. Overall, about 76.7% of CpG islands, 54.9% of CpG island shores and 52.2% of core promoters in the human genome were covered with at least 3 CpG sites per region.

Conclusions

With this new pipeline, it is now possible to perform whole-genome DNA methylation analysis at single base resolution for a large number of samples for understanding how DNA methylation and its changes are involved in development, differentiation, and disease pathogenesis.     

Long-read whole-genome methylation patterning using enzymatic base conversion and nanopore sequencing

Long-read whole-genome sequencing analysis of DNA methylation would provide useful information on the chromosomal context of gene expression regulation. Here we describe the development of a method that improves the read length generated by using the bisulfite-sequencing-based approach. In this method, we combined recently developed enzymatic base conversion, where an unmethylated cytosine (C) should be converted to thymine (T), with nanopore sequencing. After methylation-sensitive base conversion, the sequencing library was constructed using long-range polymerase chain reaction. This type of analysis is possible using a minimum of 1 ng genomic DNA, and an N50 read length of 3.4–7.6 kb is achieved. To analyze the produced data, which contained a substantial number of base mismatches due to sequence conversion and an inaccurate base read of the nanopore sequencing, a new analytical pipeline was constructed. To demonstrate the performance of long-read methylation sequencing, breast cancer cell lines and clinical specimens were subjected to analysis, which revealed the chromosomal methylation context of key cancer-related genes, allele-specific methylated genes, and repetitive or deletion regions. This method should convert the intractable specimens for which the amount of available genomic DNA is limited to the tractable targets.     

Prediction of methylated CpGs in DNA sequences using a support vector machine

DNA methylation plays a key role in the regulation of gene expression. The most common type of DNA modification consists of the methylation of cytosine in the CpG dinucleotide. At the present time, there is no method available for the prediction of DNA methylation sites. Therefore, in this study we have developed a support vector machine (SVM)-based method for the prediction of cytosine methylation in CpG dinucleotides. Initially a SVM module was developed from human data for the prediction of human-specific methylation sites. This module achieved a MCC and AUC of 0.501 and 0.814, respectively, when evaluated using a 5-fold cross-validation. The performance of this SVM-based module was better than the classifiers built using alternative machine learning and statistical algorithms including artificial neural networks, Bayesian statistics, and decision trees. Additional SVM modules were also developed based on mammalian- and vertebrate-specific methylation patterns. The SVM module based on human methylation patterns was used for genome-wide analysis of methylation sites. This analysis demonstrated that the percentage of methylated CpGs is higher in UTRs as compared to exonic and intronic regions of human genes. This method is available on line for public use under the name of Methylator at http://bio.dfci.harvard.edu/Methylator/.     

Non-referenced genome assembly from epigenomic short-read data

Current computational methods used to analyze changes in DNA methylation and chromatin modification rely on sequenced genomes. Here we describe a pipeline for the detection of these changes from short-read sequence data that does not require a reference genome. Open source software packages were used for sequence assembly, alignment, and measurement of differential enrichment. The method was evaluated by comparing results with reference-based results showing a strong correlation between chromatin modification and gene expression. We then used our de novo sequence assembly to build the DNA methylation profile for the non-referenced Psammomys obesus genome. The pipeline described uses open source software for fast annotation and visualization of unreferenced genomic regions from short-read data.     

Non-referenced genome assembly from epigenomic short-read data

Current computational methods used to analyze changes in DNA methylation and chromatin modification rely on sequenced genomes. Here we describe a pipeline for the detection of these changes from short-read sequence data that does not require a reference genome. Open source software packages were used for sequence assembly, alignment, and measurement of differential enrichment. The method was evaluated by comparing results with reference-based results showing a strong correlation between chromatin modification and gene expression. We then used our de novo sequence assembly to build the DNA methylation profile for the non-referenced Psammomys obesus genome. The pipeline described uses open source software for fast annotation and visualization of unreferenced genomic regions from short-read data.     

Recommendations for a nomenclature system for reporting methylation aberrations in imprinted domains

The analysis of DNA methylation has become routine in the pipeline for diagnosis of imprinting disorders, with many publications reporting aberrant methylation associated with imprinted differentially methylated regions (DMRs). However, comparisons between these studies are routinely hampered by the lack of consistency in reporting sites of methylation evaluated. To avoid confusion surrounding nomenclature, special care is needed to communicate results accurately, especially between scientists and other health care professionals. Within the European Network for Human Congenital Imprinting Disorders we have discussed these issues and designed a nomenclature for naming imprinted DMRs as well as for reporting methylation values. We apply these recommendations for imprinted DMRs that are commonly assayed in clinical laboratories and show how they support standardized database submission. The recommendations are in line with existing recommendations, most importantly the Human Genome Variation Society nomenclature, and should facilitate accurate reporting and data exchange among laboratories and thereby help to avoid future confusion.     

A comparison of cluster analysis methods using DNA methylation data

MOTIVATION: Aberrant DNA methylation is common in cancer. DNA methylation profiles differ between tumor types and subtypes and provide a powerful diagnostic tool for identifying clusters of samples and/or genes. DNA methylation data obtained with the quantitative, highly sensitive MethyLight technology is not normally distributed; it frequently contains an excess of zeros. Established tools to analyze this type of data do not exist. Here, we evaluate a variety of methods for cluster analysis to determine which is most reliable. RESULTS: We introduce a Bernoulli-lognormal mixture model for clustering DNA methylation data obtained using MethyLight. We model the outcomes using a two-part distribution having discrete and continuous components. It is compared with standard cluster analysis approaches for continuous data and for discrete data. In a simulation study, we find that the two-part model has the lowest classification error rate for mixture outcome data compared with other approaches. The methods are illustrated using DNA methylation data from a study of lung cancer cell lines. Compared with competing hierarchical clustering methods, the mixture model approaches have the lowest cross-validation error for detecting lung cancer subtype (non-small versus small cell). The Bernoulli-lognormal mixture assigns observations to subgroups with the lowest uncertainty. AVAILABILITY: Software is available upon request from the authors. SUPPLEMENTARY INFORMATION: http://www-rcf.usc.edu/~kims/SupplementaryInfo.html     

Gel-free multiplexed reduced representation bisulfite sequencing for large-scale DNA methylation profiling

DNA methylation is an important epigenetic modification involved in gene regulation, which can now be measured using whole-genome bisulfite sequencing. However, cost, complexity of the data, and lack of comprehensive analytical tools are major challenges that keep this technology from becoming widely applied. Here we present BSmooth, an alignment, quality control and analysis pipeline that provides accurate and precise results even with low coverage data, appropriately handling biological replicates. BSmooth is open source software, and can be downloaded from http://rafalab.jhsph.edu/bsmooth.     

CNAmet: an R package for integrating copy number, methylation and expression data

SUMMARY: Gene copy number and DNA methylation alterations are key regulators of gene expression in cancer. Accordingly, genes that show simultaneous methylation, copy number and expression alterations are likely to have a key role in tumor progression. We have implemented a novel software package (CNAmet) for integrative analysis of high-throughput copy number, DNA methylation and gene expression data. To demonstrate the utility of CNAmet, we use copy number, DNA methylation and gene expression data from 50 glioblastoma multiforme and 188 ovarian cancer primary tumor samples. Our results reveal a synergistic effect of DNA methylation and copy number alterations on gene expression for several known oncogenes as well as novel candidate oncogenes. AVAILABILITY: CNAmet R-package and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/CNAmet.     

Genetic diversity analysis of Jatropha curcas L. (Euphorbiaceae) based on methylation-sensitive amplification polymorphism

Genetic analysis of 56 samples of Jatropha curcas L. collected from Thailand and other countries was performed using the methylation-sensitive amplification polymorphism (MSAP) technique. Nine primer combinations were used to generate MSAP fingerprints. When the data were interpreted as amplified fragment length polymorphism (AFLP) markers, 471 markers were scored. All 56 samples were classified into three major groups: γ-irradiated, non-toxic and toxic accessions. Genetic similarity among the samples was extremely high, ranging from 0.95 to 1.00, which indicated very low genetic diversity in this species. The MSAP fingerprint was further analyzed for DNA methylation polymorphisms. The results revealed differences in the DNA methylation level among the samples. However, the samples collected from saline areas and some species hybrids showed specific DNA methylation patterns. AFLP data were used, together with methylation-sensitive AFLP (MS-AFLP) data, to construct a phylogenetic tree, resulting in higher efficiency to distinguish the samples. This combined analysis separated samples previously grouped in the AFLP analysis. This analysis also distinguished some hybrids. Principal component analysis was also performed; the results confirmed the separation in the phylogenetic tree. Some polymorphic bands, involving both nucleotide and DNA methylation polymorphism, that differed between toxic and non-toxic samples were identified, cloned and sequenced. BLAST analysis of these fragments revealed differences in DNA methylation in some known genes and nucleotide polymorphism in chloroplast DNA. We conclude that MSAP is a powerful technique for the study of genetic diversity for organisms that have a narrow genetic base.     

DNA Methylation: Bisulphite Modification and Analysis

Epigenetics describes the heritable changes in gene function that occur independently to the DNA sequence. The molecular basis of epigenetic gene regulation is complex, but essentially involves modifications to the DNA itself or the proteins with which DNA associates. The predominant epigenetic modification of DNA in mammalian genomes is methylation of cytosine nucleotides (5-MeC). DNA methylation provides instruction to gene expression machinery as to where and when the gene should be expressed. The primary target sequence for DNA methylation in mammals is 5''-CpG-3'' dinucleotides (Figure 1). CpG dinucleotides are not uniformly distributed throughout the genome, but are concentrated in regions of repetitive genomic sequences and CpG "islands" commonly associated with gene promoters (Figure 1). DNA methylation patterns are established early in development, modulated during tissue specific differentiation and disrupted in many disease states including cancer. To understand the biological role of DNA methylation and its role in human disease, precise, efficient and reproducible methods are required to detect and quantify individual 5-MeCs.This protocol for bisulphite conversion is the "gold standard" for DNA methylation analysis and facilitates identification and quantification of DNA methylation at single nucleotide resolution. The chemistry of cytosine deamination by sodium bisulphite involves three steps (Figure 2). (1) Sulphonation: The addition of bisulphite to the 5-6 double bond of cytosine (2) Hydrolic Deamination: hydrolytic deamination of the resulting cytosine-bisulphite derivative to give a uracil-bisulphite derivative (3) Alkali Desulphonation: Removal of the sulphonate group by an alkali treatment, to give uracil. Bisulphite preferentially deaminates cytosine to uracil in single stranded DNA, whereas 5-MeC, is refractory to bisulphite-mediated deamination. Upon PCR amplification, uracil is amplified as thymine while 5-MeC residues remain as cytosines, allowing methylated CpGs to be distinguished from unmethylated CpGs by presence of a cytosine "C" versus thymine "T" residue during sequencing.DNA modification by bisulphite conversion is a well-established protocol that can be exploited for many methods of DNA methylation analysis. Since the detection of 5-MeC by bisulphite conversion was first demonstrated by Frommer et al.¹ and Clark et al.², methods based around bisulphite conversion of genomic DNA account for the majority of new data on DNA methylation. Different methods of post PCR analysis may be utilized, depending on the degree of specificity and resolution of methylation required. Cloning and sequencing is still the most readily available method that can give single nucleotide resolution for methylation across the DNA molecule.