ONT RRMS Service | High-Resolution CpG Island Methylation Analysis
CD Genomics provides DNA-reduced representation methylation multiplex sequencing (RRMS) using Oxford Nanopore technology to deliver accurate, cost-efficient methylation profiling. Our ONT-gDNA-RRMS service directly detects native DNA modifications, eliminating the need for chemical bisulfite conversion used in reduced representation bisulfite sequencing (RRBS). This helps clients overcome common challenges such as incomplete CpG coverage, chemical bias, and high sequencing cost.
Through adaptive sampling, we enrich CpG-rich regions of the genome—including CpG islands, shores, shelves, and more than 90% of human promoters—covering ~310 Mb of targeted DNA. The platform enables single-base resolution detection of 5mC, 5hmC, 6mA, and 4mC, while providing information on CNVs, SNPs, and structural variants. By integrating methylation and variant analysis in one workflow, our ONT-RRMS service supports researchers, biotech teams, and pharma clients in advancing epigenetics studies, tumour methylation profiling, and drug development.
- Direct methylation detection with Oxford Nanopore (no bisulfite conversion)
- Comprehensive CpG island, promoter, and enhancer coverage (~310 Mb human genome)
- Simultaneous detection of 5mC, 5hmC, 6mA, and 4mC modifications
- Additional variant calling: CNV, SV, and SNPs
- Cost-effective, high-resolution alternative to RRBS and WGBS
At a glance:
- Introduction to ONT-gDNA-RRMS
- Why Choose ONT-gDNA-RRMS Over Traditional RRBS?
- Key Technical Advantages of ONT-gDNA-RRMS
- Step-by-Step Workflow
- Applications of ONT-gDNA-RRMS
- Sample Requirements
- FAQs
- Case Study: DeepMod2 applied to RRMS adaptive sampling data
Introduction to ONT-gDNA-RRMS
DNA methylation is a central epigenetic mechanism influencing gene expression, genome stability, and disease progression. In particular, CpG islands—dense clusters of cytosine–guanine dinucleotides found near promoters—are critical regulators of transcriptional activity. Aberrant methylation of these regions is strongly linked to cancer development, developmental disorders, and imprinting defects.
Traditional methods, such as reduced representation bisulfite sequencing (RRBS), rely on bisulfite conversion and complex library preparation. While effective, RRBS captures only a small fraction of CpG sites, often introduces chemical bias, and demands significant sequencing depth.
ONT-gDNA-RRMS (DNA-reduced representation methylation multiplex sequencing) overcomes these limitations by using Oxford Nanopore adaptive sampling. This approach directly detects methylated cytosines without chemical conversion, while selectively enriching CpG-rich regions of the genome. With ~310 Mb of human target coverage—including CpG islands, shores, shelves, and more than 90% of annotated promoters—ONT-gDNA-RRMS provides a more accurate and cost-efficient solution for genome-wide methylation studies.
By combining direct methylation detection with targeted enrichment, RRMS with Nanopore sequencing delivers high-resolution insights into 5mC, 5hmC, 6mA, and 4mC modifications at single-base resolution. This makes it a valuable platform for epigenetics research in oncology, developmental biology, and pharmaceutical discovery.
Recommended Reading: For a broader overview of how long-read sequencing transforms epigenetics, see our page on Epigenetics and Methylation Analysis Using Long-Read Sequencing.
Why Choose ONT-gDNA-RRMS Over Traditional RRBS?
Comparison of RRBS and ONT-gDNA-RRMS
| Feature / Challenge | Traditional RRBS (Reduced Representation Bisulfite Sequencing) | ONT-gDNA-RRMS (Reduced Representation Methylation Multiplex Sequencing with Nanopore) |
|---|---|---|
| Workflow | Requires bisulfite conversion and enzyme digestion | Direct sequencing of native DNA, no chemical conversion |
| CpG Coverage | Covers only 1–5% of CpGs; limited promoter/enhancer resolution | Targets ~310 Mb, including 100% CpG islands, shores, shelves, >90% of promoters |
| Data Bias | Chemical treatment can degrade DNA and introduce conversion errors | Native base detection ensures accurate methylation calling |
| Methylation Types | Detects only 5mC | Detects 5mC, 5hmC, 6mA, 4mC simultaneously |
| Additional Insights | Limited to methylation status | Provides CNV, SNP, and structural variant information in parallel |
| Resolution | High resolution, but only in enriched regions | Single-base resolution across all targeted CpG-rich regions |
| Cost & Efficiency | High sequencing depth required for adequate coverage | More cost-efficient, higher on-target yield with adaptive sampling |
| Reproducibility | Variable due to library prep complexity | High reproducibility, uniform CpG island coverage |
Key Takeaway:
ONT-gDNA-RRMS combines the strengths of reduced representation sequencing with the unique capabilities of Nanopore adaptive sampling. It delivers broader CpG coverage, captures multiple DNA modifications, and integrates methylation with genomic variation analysis—all in one streamlined workflow.
Key Technical Advantages of ONT-gDNA-RRMS
Direct Native DNA Methylation Detection
No bisulfite treatment or enzyme digestion is required. This avoids chemical damage to DNA and preserves true methylation patterns.
Comprehensive CpG Coverage
Targets ~310 Mb of the human genome, including 100% of CpG islands, shores, shelves, and over 90% of annotated promoters. Mouse coverage extends to ~308 Mb.
Multi-Modifications in a Single Run
Detects 5mC, 5hmC, 6mA, and 4mC simultaneously, providing a more complete view of the epigenetic landscape.
Additional Variant Information
Beyond methylation, the same data can reveal copy number variations (CNV), structural variants (SV), and single nucleotide polymorphisms (SNP).
Single-Base Resolution
Offers precise methylation calling at the level of individual cytosines, enabling accurate CpG island profiling.
High Sensitivity with Adaptive Sampling
Enriches CpG-rich regions in real time, achieving higher on-target yields and reducing sequencing waste.
Cost-Efficient and Scalable
Requires less sequencing depth compared to RRBS or whole-genome bisulfite sequencing, making it suitable for large-scale or multi-sample studies.
Step-by-Step Workflow
Our ONT-gDNA-RRMS service delivers an end-to-end solution, from sample preparation to advanced bioinformatics. Each step is optimised to ensure accuracy, reproducibility, and publication-ready results.
Genomic DNA Extraction
High-quality genomic DNA is isolated from cells or tissue. Intact, high molecular weight DNA is preferred to maximise sequencing efficiency.
DNA Fragmentation
DNA is sheared into fragments (~6 kb on average). Fragment size is optimised for Oxford Nanopore sequencing.
End Repair and Adapter Ligation
DNA ends are polished and sequencing adapters are ligated to enable compatibility with Nanopore flow cells.
Adaptive Sampling (RRMS Enrichment)
During sequencing, adaptive sampling prioritises CpG-rich regions—including CpG islands, shores, shelves, and promoters—while reducing off-target reads.
Nanopore Sequencing
DNA fragments are passed through nanopore channels. Real-time current changes allow direct detection of DNA bases and methylation marks without chemical conversion.
Bioinformatics Analysis
- Basecalling (FAST5/FASTQ generation)
- Methylation calling (5mC, 5hmC, 6mA, 4mC) using tools such as DeepMod2 or Remora
- CpG island annotation and DMR detection
- Variant analysis for CNV, SNP, and SV
- Data visualisation with genome browser tracks, methylation heatmaps, and summary reports
Applications of ONT-gDNA-RRMS
The ability to capture direct DNA methylation profiles at single-base resolution makes ONT-gDNA-RRMS a versatile tool for multiple research areas. By integrating methylation, variant, and structural insights in one workflow, this service supports a wide range of biological and translational studies.
Cancer Epigenetics
- Detects tumour-specific CpG island hypermethylation in promoter regions, which silences tumour suppressor genes.
- Identifies thousands of differentially methylated regions (DMRs) between tumour and normal tissues.
- Case studies in melanoma (COLO829) and triple-negative breast cancer (HCC1395) have shown strong overlap between tumour-specific DMRs and known cancer census genes.
Developmental Biology and Imprinting
- Profiles dynamic methylation changes across developmental stages.
- Monitors imprinting regions and parent-of-origin–specific methylation patterns.
- Captures both 5mC and 5hmC to reveal epigenetic reprogramming in embryonic and stem cell systems.
Biomarker Discovery
- Supports identification of epigenetic biomarkers for disease stratification and progression monitoring.
- Quantifies global methylation shifts that may serve as predictive markers in oncology and neurology.
Comparative Methylome Analysis
- Enables direct comparison between species, tissues, or experimental conditions.
- Adaptive sampling ensures high CpG island coverage across both human (~310 Mb) and mouse (~308 Mb) genomes.
- Useful for drug discovery studies, toxicology screening, and model organism research.
Multi-Omics Integration
- RRMS data can be combined with RNA-seq, ATAC-seq, or proteomics to establish direct links between methylation, gene expression, and chromatin accessibility.
- Recent work (Ahsan et al., Nat Commun, 2024) demonstrated how Nanopore methylation sequencing integrates with deep learning models (DeepMod2) for accurate and scalable methylome analysis.
Sample Requirements
To ensure high-quality sequencing data, please prepare and submit samples according to the following guidelines. If you have non-standard materials, contact our team for consultation before submission.
| Sample Type | Recommended Input | Minimum Input | Quality Requirements |
|---|---|---|---|
| Genomic DNA | ≥ 5 µg | ≥ 1 µg | OD260/280: 1.8–2.0; RIN ≥ 7; high molecular weight DNA preferred |
| Animal Cells | ≥ 1 × 10⁶ cells | — | Fresh/frozen; free of contamination |
| Animal Tissue | ≥ 1 g | 100 mg | Fresh/frozen; quick-frozen in liquid nitrogen |
| Plant Tissue | ≥ 3 g | 500 mg | Fresh/frozen; DEPC-treated water rinse recommended |
| Eukaryotic Microbes | ≥ 1 × 10⁶ cells or ≥ 300 mg wet weight | — | Ensure viability; ship on dry ice |
FAQs
Q: What is the difference between RRMS nanopore sequencing and traditional RRBS?
RRMS nanopore sequencing directly detects native DNA methylation (5mC, 5hmC, 6mA, 4mC) without bisulfite conversion, enriches CpG-rich regions via adaptive sampling, and also yields CNV/SNP/SV data. In contrast, reduced representation bisulfite sequencing (RRBS) relies on chemical conversion of unmethylated cytosines to uracil, covers only a fraction of CpG sites, and is limited to 5mC detection.
Q: How many CpG sites or genomic regions does ONT-RRMS cover?
In human samples, RRMS targets ~310 Mb of the genome including ~28,000 CpG islands, ~50,600 shores, ~42,700 shelves, and >90% of annotated promoters (∼7.1 million CpGs).
Q: Can ONT-RRMS detect modifications beyond 5mC?
Yes. The platform supports simultaneous detection of 5mC, 5hmC, 6mA, and 4mC modifications using nanopore signal–level methylation callers.
Q: Do I get variant information (CNV, SV, SNP) from RRMS data?
Yes. Reads that are rejected during adaptive sampling can also be leveraged for copy number variation (CNV) calling, and the on-target data enable SNP and structural variant (SV) detection as supplementary outputs.
Q: How does adaptive sampling work in RRMS?
Adaptive sampling monitors in real time whether a DNA strand maps to a region of interest (ROI). If not, the strand is ejected to free the pore for new strands. This enriches for CpG-dense genomic regions without prior sample manipulation.
Q: Is RRMS data reproducible and comparable with RRBS or WGBS?
Yes. In benchmark studies, methylation frequencies at shared CpG sites show high correlation (R > 0.96) between RRMS and RRBS. Also, RRMS recovers more CpGs, offers more uniform coverage, and improves reproducibility versus RRBS.
Q: What sample quality and input are required for ONT-RRMS?
High molecular weight genomic DNA is preferred, with minimal fragmentation, free from contaminants, and provided at sufficient quantity. If samples do not meet standards, low-quality input may reduce enrichment efficiency or yield.
Q: Can RRMS be used for nonhuman species (e.g. mouse, plant)?
Yes. RRMS protocols and target files exist for mouse (~308 Mb target), and the method can be adapted or custom-designed for other species on request.
Case Study: DeepMod2 applied to RRMS adaptive sampling data
Ahsan, M.U., Gouru, A., Chan, J. et al. A signal processing and deep learning framework for methylation detection using Oxford Nanopore sequencing. Nat Commun 15, 1448 (2024). https://doi.org/10.1038/s41467-024-45778-y
1. Background
Ahsan et al. developed DeepMod2, a hybrid deep learning framework combining BiLSTM and Transformer architectures, to detect DNA methylation from Nanopore current signals. They applied this tool not just to whole-genome data but also to reduced-representation methylation sequencing (RRMS) via adaptive sampling, demonstrating strong agreement between RRMS and full-genome methylation profiling.
2. Methods
- The authors used Oxford Nanopore data (FAST5/POD5) and applied their DeepMod2 model to call methylation (5mC) on both whole-genome and RRMS-enriched datasets.
- They designed RRMS target regions (~310 Mb, focusing on CpG islands, promoters, shores, shelves) and performed adaptive sampling to enrich those regions.
- Methylation calls from RRMS and WGS were compared via correlation metrics (Pearson's r), and concordance across CpG sites was evaluated.
3. Results
- DeepMod2 yielded a high correlation (r ≈ 0.96) between methylation frequencies in RRMS vs. full-genome nanopore data.
- RRMS data recovered more CpG site calls in targeted regions than expected.
- The authors observed that methylation patterns from RRMS and WGS were highly consistent across CpG islands.
Comparison of methylation correlation between ONT RRMS and whole-genome nanopore sequencing (DeepMod2) in CpG-rich target regions.
4. Conclusions
This study validates ONT-based RRMS as a reliable method for targeted methylation profiling. DeepMod2's performance demonstrates that RRMS can deliver methylation calls both accurately and reproducibly, nearly matching full-genome nanopore outputs. For clients, this means cost-effective, high-fidelity methylation sequencing on CpG islands and regulatory regions using adaptive sampling.
For research purposes only, not intended for personal diagnosis, clinical testing, or health assessment