DNA methylation is a key epigenetic mechanism that shapes gene activity and cell identity by modifying regulatory elements across the genome. It plays a central role in development, disease progression, and evolutionary adaptation.

With the rise of high-throughput sequencing, the volume of methylation data has exploded—paving the way for a new generation of specialized databases. These platforms do more than just store data. They help standardize formats, link information across tissues and species, and offer tools that bridge basic research with clinical applications.

Methylation databases have evolved significantly. What once served as static repositories for single-species data now support single-cell resolution and AI-assisted analytics. This transformation is helping researchers unlock deeper insights into complex biological processes—from tumor suppressor gene silencing in cancer to stress-induced methylation reprogramming in plants.

Today's methylation databases have moved well beyond archiving. They now offer differential methylation analysis, biological age prediction, and multi-omics integration—all essential for advancing precision medicine and drug discovery.

In this article, we break down the strengths and use cases of the most widely used DNA methylation databases. You'll learn how:

• MethBank 4.0 enables comparative studies across species
• MethDB provides historical depth and foundational datasets
• EWAS Atlas supports disease mechanism research with epidemiological depth

We also offer a practical framework for choosing the right database based on your research goals, whether you're investigating developmental biology, cancer epigenetics, or translational medicine.

Methylation Databases for Epigenetic Drug Discovery

DNA methylation—one of the most fundamental mechanisms of epigenetic regulation—plays a pivotal role in shaping gene activity and cellular identity by modifying genomic regulatory elements. As next-generation sequencing technologies become more accessible, researchers have amassed enormous volumes of methylation data, fueling the need for specialized, centralized databases.

DNA methylation and tumorigenesis (Klutstein et al., 2016)

Today's DNA methylation databases do more than simply store information. They offer standardized, cross-species, and cross-tissue datasets while integrating complex metadata such as disease associations and regulatory networks. Their growing role in translational research means they are now essential not only for basic biology but also for diagnostics, biomarker discovery, and precision medicine applications. In essence, these databases have become the digital backbone of modern epigenetic research.

What Are DNA Methylation Databases?

DNA methylation involves the addition of a methyl group (–CH₃) to the 5' carbon of cytosine residues within CpG dinucleotides, a reaction catalyzed by DNA methyltransferases. This chemical tag helps silence transposable elements, regulate gene expression, and preserve genomic integrity.

Modern methylation databases serve three key functions:

• Uncovering Epigenetic Mechanisms

High-resolution datasets—such as those from whole-genome bisulfite sequencing (WGBS)—allow researchers to track methylation changes during embryonic development, cell differentiation, and disease progression. For instance, tumor suppressor genes often show excessive methylation at their promoters in cancer, silencing their expression.

• Cross-Species Comparisons

By aggregating data from model organisms like mice, zebrafish, and even plants, these databases enable evolutionary studies. In Arabidopsis, methylation reprogramming during salt stress mirrors epigenetic responses in mammals—highlighting shared adaptation strategies across kingdoms.

• Clinical Applications

Methylation signatures can be tied to disease phenotypes. For example, methylation levels of immune-related genes in the tumor microenvironment have been used to predict patient response to immunotherapies.

Leading platforms like MethBank and TCGA now offer end-to-end functionality—from raw data storage to advanced tools like differential methylation analysis and epigenetic age prediction—making it easier for researchers to prioritize regulatory targets efficiently.

Integrating Cross-Species and Tissue Data

Building a reliable methylation database requires combining multiple experimental approaches:

• Complementary Data Generation Methods:
• WGBS: Delivers base-level resolution by converting unmethylated cytosines to uracil, but requires microgram-level input and has higher costs.
• Methylation arrays (e.g., Illumina EPIC): Target over 850,000 CpG sites, offering cost-effective solutions for large cohorts—though they're limited to known loci.
• Targeted enrichment sequencing (e.g., TEEM-Seq): Uses hybridization probes to capture specific regions such as promoters and enhancers, providing a cost-resolution balance well-suited for clinical samples.
• Strategic Coverage Across Biological Systems:

MethBank, for example, includes methylation profiles from 34 human and 336 plant samples, with an emphasis on dynamic early developmental stages. Meanwhile, the pan-tissue methylation atlas developed by Andrew Teschendorff's group leverages EpiSCORE to infer methylation patterns from single-cell RNA-seq data—addressing the common gap in rare cell types.

• Data Standardisation for Consistency:

Uniform metadata frameworks (e.g., tissue type, disease stage) and standard pipelines (e.g., Bismark for alignment, MethylKit for differential analysis) are crucial for making data comparable across studies. Quality control metrics like alignment rates and read depth help filter out low-quality samples.

As single-cell techniques like scBS-seq become mainstream, future databases must overcome challenges such as data sparsity and batch effects—paving the way for more refined epigenetic insights at the cellular level.

Top Public DNA Methylation Databases to Know

Public methylation databases are now essential tools bridging basic epigenetics research and clinical applications. With next-generation sequencing technologies becoming mainstream, massive volumes of methylation data have been compiled into multidimensional, cross-species platforms. These databases not only offer standardized access to curated data but also enhance interpretation through disease associations, regulatory networks, and evolutionary conservation insights.

From the early, single-species MethDB to today's comprehensive resources like MethBank 4.0 and EWAS Atlas, public epigenetic databases have evolved rapidly—pushing the frontiers of methylation research across life sciences.

MethBank 4.0: A Cross-Species Methylation Data Powerhouse

Developed by China's National Genomics Data Center (NGDC), MethBank 4.0 represents the most advanced and integrated methylation database available. Updated in September 2023, it now contains high-quality data from 26 species and 3,552 samples—including humans, cattle, and Arabidopsis—marking a 69% increase over its previous version.

Screenshots of the methylome browser in MethBank (Zou et al., 2015)

Here's what sets MethBank 4.0 apart:

• Comprehensive Species and Tissue Coverage:

It offers base-resolution methylation maps across 236 tissues and cell lines from 23 species. This includes CG, CHG, and CHH sequence contexts—making it ideal for comparative epigenetics. For instance, CHH methylation shifts under salt stress in Arabidopsis offer new clues into how plants adapt to harsh environments.

• Cancer-Focused Modules:

MethBank 4.0 now includes differentially methylated regions (DMRs) for 12 cancers, such as prostate and breast cancer. These are annotated with regulatory elements like enhancers and silencers—supporting nuanced tumor heterogeneity studies.

• Integrated Analysis Ecosystem:

With 604 built-in tools—including DMR Toolkit for differential analysis and Age Predictor for epigenetic age estimation—users can go from raw data to actionable insights without leaving the platform.

To deepen its utility, MethBank 4.0 is integrating single-cell methylation data (like scBS-seq) and AI-driven annotation pipelines. Standardized data interfaces (e.g., Bismark workflows) also ensure compatibility across platforms, enabling seamless integration into diverse research workflows.

MethDB & EWAS Atlas: Historical Depth Meets Disease-Specific Insight

As one of the earliest methylation databases, MethDB—launched in 2001 by the Grunau team—pioneered epigenetic data sharing. It remains a valuable archive with 6,667 experiments covering 46 species and 160 tissue types. Data range from global 5mC levels to single-nucleotide resolution. A key feature is its phenotype search, which links methylation patterns to cancer traits, such as global hypomethylation in tumor suppressor genes.

The structure of MethDB (Grunau et al., 2001)

The 2003 update added grayscale heatmaps for intuitive data exploration. Though smaller in scale than newer platforms, MethDB offers crucial historical context for tracing the evolution of epigenetic research.

EWAS Atlas zeroes in on epigenome-wide association studies (EWAS), compiling 752,193 methylation associations from 1,121 published papers. It builds a disease–methylation–environment interaction network, with several standout features:

• Causal Inference Tools:

Using Mendelian randomization, it confirms causal links—such as between IL-6 methylation and cardiovascular disease.

• Multi-Omics Integration:

Through its EWAS Open Platform, users can run standardized workflows like GMQN for batch effect correction. This supports joint analysis of methylation, transcriptome, and proteome data. For example, in non-alcoholic fatty liver disease, global DNA hypomethylation has been repeatedly linked to disrupted lipid metabolism.

Schematic overview of EWAS Open Platform data processing workflow (Xiong et al., 2022)

Together, MethBank 4.0, MethDB, and EWAS Atlas form a complementary ecosystem for epigenetic research. MethBank leads in cross-species integration, MethDB preserves historical data lineage, and EWAS Atlas reveals how methylation drives disease mechanisms.

As single-cell and AI technologies become more integrated, these resources will further refine our understanding of cellular diversity and accelerate precision medicine. Their continued evolution ensures that the field of epigenetics keeps advancing—at greater scale and resolution than ever before.

Choosing the Best Database for Your Research

As epigenetics research accelerates, the number and complexity of DNA methylation databases have surged. But with differences in species coverage, data depth, and technical features, selecting the right resource can be a major hurdle.

Whether you're exploring cross-species comparisons or digging into disease mechanisms, your choice of database directly impacts the insights you can uncover. It all comes down to aligning your selection with your research goals—be it multi-species analysis, single-cell resolution, or clinical data mining. Researchers must also evaluate data quality, annotation depth, and usability to truly benefit from these resources.

In recent years, databases have evolved beyond static archives. Today, intelligent integration and interactive visualization are reshaping how scientists explore methylation data.

Key Factors: Scale, Annotation, and Species Coverage

The true value of a methylation database lies in the breadth and depth of its content.

• Data volume drives statistical power. For instance, MethBank 4.0 features over 1,400 WGBS datasets across 23 species—offering richer sample diversity than early-stage databases like MethDB. This makes it ideal for constructing cross-tissue or developmental regulatory networks.
• Species coverage matters too. MethDB spans 83 species, including both plants and animals—making it a top pick for evolutionary studies. In contrast, human-focused resources like DiseaseMeth and SurvivalMeth zoom in on disease-specific patterns, such as the hypermethylation of tumor suppressor genes in cancer.
• Annotation depth is another key differentiator. MethBank 4.0 integrates not just single-base data but also DMRs, methylation age predictors, and over 500 analysis tools. It provides a full pipeline from raw data to functional insight. Meanwhile, broader resources like ENCODE or Roadmap Epigenomics offer high-resolution methylation profiles but lack disease-specific annotations—making them more suitable for basic research on regulatory elements like enhancers or silencers.

For cancer-focused work, MethyCancer stands out. It links CpG island methylation with gene mutations—helping identify epigenetic drivers such as BRCA1 promoter methylation tied to chemo-resistance.

Looking ahead, standardized metadata and automated pre-processing tools (e.g., GMQN for batch correction) are helping bridge cross-database inconsistencies, enabling more robust multi-cohort analyses.

User Interface and Visualization Experience

Even the best data won't yield results if the platform is difficult to use. Today's researchers expect intuitive design and built-in tools that streamline analysis.

• User interfaces play a critical role. MethBank 4.0 features an interactive genome browser (JBrowse) that lets users view methylation levels alongside gene expression and SNPs. DNMIVD offers heatmaps and survival plots to explore methylation-expression links and clinical outcomes with minimal technical expertise.
• For single-cell studies, scMethBank's t-SNE clustering plots make it easier to detect methylation variability across cell types—lowering the barrier to entry for complex analyses.
• visualization capabilities are equally important. DiseaseMeth allows users to pinpoint disease-related methylation changes across chromosomes. In contrast, ENCODE requires external tools like the UCSC Genome Browser for methylation track display.
• API access is essential for bulk downloads and automated workflows. Currently, only a few databases—such as NGSmethDB—offer RESTful interfaces, while most still rely on web-based access, limiting high-throughput data mining.

The next leap? AI-powered tools. Imagine natural language searches ("Show me methylation changes in breast cancer") or auto-generated reports on differential methylation—especially valuable for teams without a bioinformatics background.

Matching Tools to Research Goals

Choosing a methylation database isn't one-size-fits-all. It's about balancing:

• Data size and species range
• Annotation depth and disease specificity
• Tool availability and user interface

For multi-species work, MethBank or MethDB leads the pack. For cancer studies, MethyCancer and DNMIVD offer sharper disease insights. And for single-cell research, scMethBank provides the resolution you need.

As metadata standards and smart analytics tools continue to evolve, DNA methylation databases are transforming from static repositories into dynamic analysis platforms—powering the next generation of epigenetics research.

References

1. Klutstein, Michael et al. "DNA Methylation in Cancer and Aging." Cancer research vol. 76,12 (2016): 3446-50. doi:10.1158/0008-5472.CAN-15-3278
2. Zou, Dong et al. "MethBank: a database integrating next-generation sequencing single-base-resolution DNA methylation programming data." Nucleic acids research vol. 43,Database issue (2015): D54-8. doi:10.1093/nar/gku920
3. Grunau, C et al. "MethDB--a public database for DNA methylation data." Nucleic acids research vol. 29,1 (2001): 270-4. doi:10.1093/nar/29.1.270
4. Xiong, Zhuang et al. "EWAS Open Platform: integrated data, knowledge and toolkit for epigenome-wide association study." Nucleic acids research vol. 50,D1 (2022): D1004-D1009. doi:10.1093/nar/gkab972