Recent technological advances make it possible to map DNA methylation in essentially any cell type, tissue or organism.
Computational methods and software tools are essential for processing, analysing and interpreting large-scale DNA methylation data sets.
Tailored software tools are now available for processing data obtained with all common methods for genome-wide DNA methylation mapping (including bisulphite sequencing and the Infinium assay).
Bioinformatic methods for visualization of DNA methylation data facilitate quality assessment and help to pinpoint global trends in the data.
By combining stringent statistical methods with computational and experimental validation, researchers can establish accurate lists of differentially methylated regions for a phenotype of interest.
Biological interpretation of differential DNA methylation is aided by computational tools for data exploration and enrichment analysis.
Large community projects are currently generating reference epigenome maps for many different cell types; the interpretation of these maps will require a comprehensive effort in functional epigenomics.
DNA methylation is an epigenetic mark that has suspected regulatory roles in a broad range of biological processes and diseases. The technology is now available for studying DNA methylation genome-wide, at a high resolution and in a large number of samples. This Review discusses relevant concepts, computational methods and software tools for analysing and interpreting DNA methylation data. It focuses not only on the bioinformatic challenges of large epigenome-mapping projects and epigenome-wide association studies but also highlights software tools that make genome-wide DNA methylation mapping more accessible for laboratories with limited bioinformatics experience.
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The author would like to thank S. Beck, M. Esteller, T. Lengauer, A. Meissner, H. Stunnenberg and J. Walter for helpful discussions and all past and present collaborators for sharing their ideas, data and insights.
The author declares no competing financial interests.
Molecular assays that predict a clinical phenotype, such as disease status or response to a drug.
- Reference epigenomes
Publicly available epigenome maps that comprise multiple epigenetic marks for the same cell type (for example, DNA methylation, several histone modifications and non-coding RNA expression).
- Epigenome-wide association study
(EWAS). A study design that involves measuring an epigenetic mark in cases and controls to identify disease-associated differences.
- Differentially methylated regions
(DMRs). Genomic regions that exhibit statistically significant differences in DNA methylation between sample groups.
Bisulphite ions (HSO3−) selectively deaminate unmethylated but not methylated Cs, giving rise to Us, which are replaced by Ts during subsequent PCR amplification.
- Absolute DNA methylation levels
Percentage of methylated alleles for a given C; this value is always binary (0% or 100%) for single alleles but can take any value between 0% and 100% when averaging over many cells.
- Sequence complexity
The diversity of the DNA sequence; it can be measured by the information content of the base composition.
- Genotype calling
The determination of SNPs and other genetic variants in a given individual.
- Genome Analysis Toolkit
(GATK). A widely used software tool for genotype calling based on next-generation sequencing data.
- M bias plot
A quality-control diagram that plots mean DNA methylation levels for each position of the bisulphite-sequencing reads. Deviations from a horizontal line indicate biases.
A powerful command-line tool for data processing, statistical analysis and visualization of biological data sets.
An alternative term for the absolute DNA methylation levels, which stems from the observation that the distribution of DNA methylation levels across the genome resembles a β-distribution.
- M values
Logistically transformed β-values. The transformation mitigates some statistical problems of the β-value (namely, limited value range and strongly bimodal distribution) at the cost of reduced biological interpretability.
- Batch effects
Systematic biases in the data that are unrelated to the research question but that arise from undesirable (and often unrecognized) differences in sample handling.
A nonrandom relationship between the phenotype of interest and external factors (for example, batch effects or population structure) that can give rise to spurious associations.
- Enrichment scores
The relative enrichment of DNA fragments from a given genomic region compared to a control experiment (such as sequencing of unenriched DNA).
- Tiling map
Segmentation of the genome into tiling windows of a fixed and typically small size (for example, 100 bases).
- Logistic regression model
A type of regression model used for modelling the relationship between a binary outcome variable and one or more predictor variables.
- CpG methylation table
A data table that contains DNA methylation levels (and, optionally, confidence scores) for each assayed CpG in each sample after normalization and quality control.
- False discovery rate
(FDR). A measure of significance that corrects for a large number of statistical tests being carried out on the same data set.
- Effect size
A measure for the strength of association between two variables that provides important complementary information to P values and false discovery rates.
- Bisulphite pyrosequencing
A locus-specific method for accurate quantification of DNA methylation levels at a small number of CpGs in many samples.
- Combined bisulphite restriction analysis
(COBRA). A method that combines bisulphite treatment with sequence-specific restriction enzymes for locus-specific analysis of DNA methylation.
- Methylation-specific PCR
(MSP). A method for highly sensitive detection of locus-specific DNA methylation using PCR amplification of bisulphite-converted DNA.
A variant of methylation-specific PCR that is highly quantitative and practical for measuring locus-specific DNA methylation levels in many samples.
An assay for measuring locus-specific DNA methylation in many samples on the basis of a combination of bisulphite treatment and mass spectrometry.
A method for estimating the predictive power of a differentially methylated region or biomarker by carrying out training and validation on different portions of the same data set.
- Quantitative trait loci
(QTLs). Genomic regions that control a phenotype of interest, such as the DNA methylation levels of another genomic region.
- Mendelian randomization
Epidemiological method for assessing the causal role of an exposure for a phenotype of interest, using genetic variants that are affected neither by the exposure nor by the phenotype.
About this article
The EMBO Journal (2020)
Nursing Research (2020)
Genome Biology (2019)
Frontiers in Genetics (2019)