FragMethyl-seq Service: One Sequencing Workflow for Five-Dimensional cfDNA Genetic and Epigenetic Profiling
When limited cfDNA must answer more than one biomarker question, splitting material across separate assays can weaken cross-modal comparisons. FragMethyl-seq uses one coordinated 5-base sequencing workflow to profile DNA methylation, fragment size, end motifs, nucleosome-associated patterns, and genomic variation from the same sample-level dataset.
Key Highlights of Our FragMethyl-seq Service:
- Five Evidence Layers: Connect methylation, fragment size, end motifs, nucleosome footprints, and supported variant signals from one coordinated library.
- Low-Input Planning: Supplier-supported planning begins at 1 ng cfDNA, subject to sample quality and project endpoints.
- Fragment-Preserving Workflow: Bisulfite-free enzymatic processing retains fragment characteristics while encoding CpG 5mC information.
- Integrated Bioinformatics: Branch-specific QC feeds a traceable sample-by-feature matrix for cohort comparison and optional model development.
What FragMethyl-seq Can Tell You
FragMethyl-seq connects the epigenetic state of cfDNA with how the molecules were released, cut, and represented across the genome. Five coordinated evidence layers address complementary questions from one sample-level sequencing dataset.
Identify global, regional, or CpG-level methylation changes and annotate differential signals to promoters, gene bodies, CpG features, and regulatory regions.
Compare fragment-size distributions, project-defined short-to-long ratios, and regional fragmentation profiles across samples or cohorts.
Quantify terminal k-mer and supported breakpoint-related features as a matrix for group comparison, clustering, or biomarker research.
Use coverage and fragment-end profiles around regulatory regions to support computational tissue and chromatin hypotheses, without treating them as direct nucleosome measurements.
Add SNV, small-variant, and copy-number analyses when sequencing depth, reference quality, and expected allele fraction support the endpoint.
For projects focused only on methylation, review our cell-free DNA methylation sequencing service. For broader cohort interpretation, outputs can also support the cfDNA epigenetic subtyping solution.
How One Workflow Preserves Five Evidence Layers
The value of FragMethyl-seq is not simply that several analyses appear in one report. Library and sequencing design preserve sequence diversity, paired-end fragment coordinates, and methylation encoding so that each analysis branch remains interpretable.
- Preserve Fragment Structure: A bisulfite-free enzymatic workflow retains cfDNA boundaries needed for fragment-size and end-motif analysis.
- Encode CpG 5mC: The 5-base workflow distinguishes A, T, G, C, and CpG 5mC at single-base resolution.
- Sequence Paired Ends: Coordinates, fragment length, terminal sequence, methylation calls, and supported variants share one coordinate system.
- Review Each Modality: Branch-specific QC prevents a weak feature family from being hidden inside a combined score.
- Integrate by Sample: A traceable feature matrix supports cohort comparison, tissue-contribution research, and optional model development.
A single coordinated library reduces sample splitting and assay-to-assay batch effects; fragment-preserving processing keeps information that bisulfite treatment may damage; and branch-specific QC shows which evidence supports each conclusion.
Learn how the underlying platform differs from methylation-only workflows in our 5-base sequencing service.
Principle of fragment-preserving five-dimensional cfDNA profiling.
FragMethyl-seq Service Workflow
The service workflow protects pre-analytical cfDNA quality, generates one coordinated library, and reserves most project complexity for transparent feature extraction and interpretation.
FragMethyl-seq service workflow from study design to integrated interpretation.
- Study Design: Define cohort structure, primary endpoint, covariates, evidence layers, and validation strategy.
- Sample QC: Assess cfDNA quantity and fragment profile; review plasma handling and genomic DNA contamination.
- 5-Base Library Preparation: Build one fragment-preserving, CpG 5mC-aware library.
- Paired-End Sequencing: Select depth according to methylation, fragmentomic, and supported variant endpoints.
- Parallel Feature Extraction: Process five technology-specific branches with separate QC.
- Integrated Delivery: Build the feature matrix and deliver data, figures, methods, parameters, and interpretation notes.
Sample Requirements and Key Considerations
Input quantity is only one part of cfDNA feasibility. Fragment profile, plasma processing, contamination, cohort balance, sequencing depth, and the intended downstream model can have equal or greater influence on what the dataset can support.
| Sample Type | Planning Input | Quality and Handling Considerations | Project Notes |
|---|---|---|---|
| Extracted cfDNA | ≥1 ng per sample | Quantify by high-sensitivity fluorescence; confirm the expected cfDNA fragment profile and minimal high-molecular-weight gDNA contamination. | Higher input may be recommended for broad variant endpoints or deeper sequencing. |
| Plasma | Project-dependent volume | Document tube type, collection-to-processing interval, storage, freeze-thaw history, and hemolysis. | Feasibility is evaluated from expected cfDNA yield, cohort size, and planned endpoints. |
| Genomic DNA | ≥50 ng per sample | Intact, clean DNA suitable for enzymatic library preparation. | Useful for reference tissue or matched genomic comparison; not a substitute for plasma-derived cfDNA. |
| Species | Human, mouse, or rat | A suitable reference genome and annotation must be available. | Other species require project-specific evaluation. |
Key considerations before submission:
- Plasma Handling: Delayed processing or leukocyte lysis can add long genomic DNA fragments and distort fragment-size metrics.
- Cohort Balance: Record collection site, tube type, processing time, storage, and major biological covariates before model development.
- Endpoint-Dependent Depth: Methylation, fragmentomics, tissue contribution, variant detection, and model training require different depths.
- Replication and Sample Size: Optional modeling requires adequate power, control of confounding, and an independent validation strategy.
These values are planning references based on the supplied service information. Final input, sequencing configuration, and supported outputs depend on sample condition, reference resources, cohort design, and selected analysis modules.
Bioinformatics and Integrated Interpretation
The bioinformatics workflow keeps five feature families separate long enough to evaluate their quality, then brings them together in a traceable matrix. This shows which modality contributes information instead of concealing all evidence inside one score.
Branch-specific QC and integrated interpretation of five cfDNA feature families.
Core processing and QC: Read QC and trimming; reference alignment; paired-end fragment reconstruction; methylation-control review; coverage, duplication, library complexity, and cross-sample consistency checks.
Five Analysis Branches:
- Methylation: CpG quantification, regional summaries, differential methylation, annotation, and functional enrichment.
- Fragment Size: Size distributions, project-defined ratios, regional profiles, and group comparisons.
- End Motifs: Terminal k-mer frequencies, supported breakpoint-related features, and comparative motif matrices.
- Nucleosome-Associated Signals: Coverage and fragment-end profiles around transcription start sites or selected regulatory regions.
- Genomic Variation: SNV, small-variant, and CNV analysis when depth and design support interpretation.
Integrated and Optional Analyses:
- Sample-by-feature matrix, correlation analysis, dimensionality reduction, and unsupervised clustering.
- Covariate-aware association testing across experimental, clinical-research, or longitudinal groups.
- Tissue-contribution deconvolution when a compatible reference panel is available.
- Optional supervised research modeling for adequately sized cohorts with prespecified training and validation.
Tissue deconvolution and model development are conditional modules. Reference compatibility, cohort size, endpoint prevalence, batch structure, and independent validation determine whether these analyses are defensible.
Deliverables and Representative Results
Deliverables are organized so experimental teams can inspect data quality, bioinformaticians can reproduce the analysis path, and project leads can connect each conclusion to its supporting feature layer.
Illustrative output types for coordinated cfDNA methylation and fragmentomics analysis.
Data, QC, and Modality Results:
- Raw FASTQ files, sequencing QC, alignments, coverage metrics, fragment-size QC, parameters, and reference-build information.
- CpG calls, methylation summaries, differential tables, annotations, heatmaps, and browser-compatible tracks.
- Fragment-size distributions, regional profiles, end-motif matrices, and nucleosome-associated profiles.
Genetic and Integrated Outputs:
- Supported SNV, small-variant, and CNV files when included in the design.
- Optional tissue-deconvolution or research-model outputs with documented assumptions and limitations.
- Integrated feature matrix, cohort visualizations, and final methods, QC, results, and interpretation report.
The representative figure is an original illustrative layout, not a customer dataset or fixed performance claim. Exact plots and files depend on study design and selected modules.
Research Applications
FragMethyl-seq is most useful when a research question depends on complementary cfDNA signals rather than methylation alone.
Compare methylation, fragmentation, end-motif, copy-number, and supported variant features when mutation evidence alone does not define a useful feature space.
Compare methylation signatures and nucleosome-associated patterns with compatible reference atlases to test tissue-contribution hypotheses.
Track whether methylation and fragmentation features move together or diverge across time points, treatment conditions, or disease-model stages.
Use tissue-associated methylation and fragmentation profiles as complementary indicators when tissue damage may alter the amount or origin of circulating DNA.
Use the integrated feature matrix for clustering and covariate-aware comparison when a heterogeneous cohort cannot be explained by one biomarker class.
Test whether interventions affecting cell death, chromatin organization, or epigenetic state produce coordinated changes in circulating DNA.
Case Study: Complementary cfDNA Methylation and Fragmentomics in Pancreatic Cancer Research
Source: Lapin M, Tjensvoll K, Edland KH, et al. "Tumor-agnostic detection of circulating tumor DNA in patients with advanced pancreatic cancer using targeted DNA methylation sequencing and cell-free DNA fragmentomics." Molecular Oncology, 2025, 19(12):3535-3547.
Method Selection Guide
Choose the assay according to the primary research endpoint. FragMethyl-seq is differentiated by coordinated extraction of methylation, fragmentomic, and supported genomic features, while a narrower method can be more efficient when only one evidence type is needed.
| Approach | Primary Answer | Best Fit | Main Boundary |
|---|---|---|---|
| FragMethyl-seq | How do five coordinated cfDNA evidence layers relate in one cohort? | Multimodal cfDNA discovery and integrated cohort analysis | Requires control of pre-analytics, depth, cohort structure, and model validation |
| cfDNA Methylation Sequencing | Where does cfDNA methylation differ? | Methylation-focused research | Does not automatically provide the same fragment-preserving multimodal feature set |
| General 5-Base Sequencing | What methylation and genomic-variant information can one workflow provide? | Dual genome-methylome projects | Fragmentomics and cfDNA-specific interpretation must be deliberately designed |
| EM-seq | Where is DNA methylated using low-damage enzymatic conversion? | Low-input or fragile DNA when methylation is the main question | Multimodal fragmentomic integration is not inherently included |
| 5hmC Profiling | Where is hydroxymethylcytosine enriched or located? | Projects centered on 5hmC | FragMethyl-seq does not separately resolve 5hmC as a sixth base |
Best for: Use FragMethyl-seq when limited cfDNA must support methylation, fragmentomics, and genomic feature evaluation within one coordinated research design.
Not the best fit: If methylation alone is the endpoint, EM-seq or another focused workflow may be simpler. If 5hmC is central, select a 5hmC-specific sequencing service. A targeted panel is usually more efficient when only predefined loci require validation.
Compare Options for Your StudyFrequently Asked Questions
References
- Lapin M, Tjensvoll K, Edland KH, et al. "Tumor-agnostic detection of circulating tumor DNA in patients with advanced pancreatic cancer using targeted DNA methylation sequencing and cell-free DNA fragmentomics." Molecular Oncology. 2025;19(12):3535-3547.
- Bie F, Wang Z, Li Y, et al. "Multimodal analysis of cell-free DNA whole-methylome sequencing for cancer detection and localization." Nature Communications. 2023;14:6042.
- Vaisvila R, Ponnaluri VKC, Sun Z, et al. "Enzymatic methyl sequencing detects DNA methylation at single-base resolution from picograms of DNA." Genome Research. 2021;31(7):1280-1289.
- Cristiano S, Leal A, Phallen J, et al. "Genome-wide cell-free DNA fragmentation in patients with cancer." Nature. 2019;570(7761):385-389.
- Sun K, Jiang P, Cheng SH, et al. "Orientation-aware plasma cell-free DNA fragmentation analysis in open chromatin regions informs tissue of origin." Genome Research. 2019;29(3):418-427.
- Lo YMD, Han DSC, Jiang P, Chiu RWK. "Epigenetics, fragmentomics, and topology of cell-free DNA in liquid biopsies." Science. 2021;372(6538):eaaw3616.
For Research Use Only. Not for use in diagnostic or clinical procedures. Service specifications, input requirements, sequencing configurations, and analysis deliverables are finalized according to sample quality, reference resources, cohort design, and project objectives.