Epigenomics Services for Mechanism Research, Biomarker Discovery, and Cohort Studies
Select epigenomics services around the biological question you need to answer—from regulatory mechanism discovery and biomarker development to drug target prioritization and longitudinal cohort analysis.
Not sure which combination of DNA methylation, chromatin, RNA, or single-cell assays fits your samples—or whether a single assay can support your intended conclusion? We help define the evidence gap before limited material, time, and budget are committed.
Find a Solution for Your Research Goal:
- Mechanisms & Regulation: Connect epigenetic changes with transcription, direct TF targets, and cell-state transitions.
- Biomarkers & Therapeutics: Discover cfDNA subtypes, prioritize cancer biomarkers, and identify regulatory drug targets.
- Cohorts & Exposures: Study biological aging, environmental exposure, and within-person methylation change.
Integrated Epigenomics Study Design, Not Just Assay Selection
Choosing among epigenomics services is not the same as designing an epigenomics study. A methylation change, binding peak, accessibility shift, or expression difference usually answers only one part of the biological question. CD Genomics connects study design, epigenomic profiling, and bioinformatics to build an evidence path around the conclusion your project needs to support.
Our customized epigenomics solutions cover regulatory mechanisms, transcription factor targets, cell-state transitions, cfDNA subtyping, cancer biomarkers, drug targets, epigenetic clocks, environmental exposure, and longitudinal cohorts. Each project can be configured around sample type, input amount, cohort structure, comparison groups, existing datasets, and required outputs—helping researchers select appropriate assays before committing limited samples, time, and budget.
Epigenomics Research Solutions by Study Goal
Explore epigenomics research solutions organized around the question your project needs to answer. Each solution combines the experimental and analytical evidence needed to move from broad molecular signals to interpretable research outcomes.
Mechanisms & Regulation
Resolve how epigenetic changes influence gene regulation, transcriptional networks, and cell-state decisions.
Connect DNA methylation, chromatin regulation, and RNA expression across developmental stages, disease models, treatments, or environmental challenges. Choose this integrated epigenomics solution when a single molecular layer cannot explain the observed phenotype.
Best for: Mechanistic multi-omics studies
Outputs: DMR–DEG relationships, pathways, and regulatory networks
Distinguish transcription factor binding from functional gene regulation by combining occupancy profiling, motif evidence, expression changes, and focused validation. The resulting evidence chain supports prioritization of direct TF target genes.
Best for: Direct TF–target relationships
Outputs: Ranked targets with binding and functional evidence
Investigate differentiation, immune activation, senescence, EMT, drug resistance, and cellular reprogramming with time-course or cell-resolved epigenomic analysis. Identify transient states and candidate regulators that endpoint comparisons may overlook.
Best for: Dynamic and single-cell studies
Outputs: State trajectories, regulatory transitions, and candidate drivers
Biomarkers & Therapeutics
Reduce complex disease-associated signals to molecular subtypes, biomarker candidates, and prioritized therapeutic targets.
Use cfDNA epigenetic profiling to investigate molecular heterogeneity when tissue access is limited or repeated non-invasive sampling is required. Integrated 5mC and 5hmC analysis supports subtype discovery across disease groups and cohorts.
Best for: Non-invasive molecular stratification
Outputs: Subtype clusters and cfDNA epigenetic signatures
Move from thousands of cancer-associated methylation or chromatin features to a reviewable biomarker shortlist. Evaluate candidates by effect size, tumor heterogeneity, specimen compatibility, reproducibility, biological relevance, and confirmation feasibility.
Best for: Discovery-to-confirmation projects
Outputs: Ranked CpGs, regions, or multi-feature signatures
Integrate DNA methylation, chromatin accessibility, factor occupancy, gene expression, and regulatory-network evidence to identify disease-driving nodes. Prioritize transcription factors, enhancers, chromatin regulators, and epigenetic enzymes for downstream validation.
Best for: Early-stage epigenomic drug target discovery
Outputs: Ranked targets and supporting regulatory evidence
Cohorts & Exposures
Separate biological change from population variation, exposure effects, longitudinal structure, and technical noise.
Estimate DNA methylation-based biological age and age acceleration across cohorts, tissues, disease groups, or intervention studies. Select epigenetic clock models according to sample type, population, study endpoint, and methylation platform.
Best for: Aging, disease, and intervention research
Outputs: DNAmAge, age acceleration, and cohort comparisons
Identify epigenetic signatures associated with pollutants, metals, occupational agents, lifestyle factors, or exposure mixtures. Connect exposure definition, biospecimen selection, confounder control, epigenomic profiling, and candidate confirmation.
Best for: Environmental and occupational cohorts
Outputs: Exposure-associated DMPs, DMRs, and regulatory regions
Track within-person DNA methylation changes across repeated visits while accounting for participant identity, sampling intervals, cell composition, missing follow-up, and batch allocation. Suitable for prospective and discovery-to-confirmation designs.
Best for: Repeated-measures and prospective research
Outputs: Methylation trajectories and time-associated biomarkers
Choose an Epigenomics Solution by Research Question
The right solution depends on the conclusion you need to support, the samples available, and the evidence gaps in your current study—not simply on the newest assay.
| Your Main Question | Recommended Solution | Likely Evidence Layers | Main Output |
|---|---|---|---|
| How does an epigenetic change affect transcription? | Mechanism Research | DNA methylation, chromatin, RNA expression | DMR–DEG relationships and regulatory networks |
| Which genes are directly regulated by my transcription factor? | TF Target Genes | Occupancy, motifs, RNA expression, validation | Prioritized direct target genes |
| Which regulatory events drive a cell-state transition? | Cell-State Dynamics | Single-cell RNA/ATAC, methylation, chromatin | Trajectories and candidate regulators |
| Can disease subtypes be identified from plasma? | cfDNA Subtyping | cfDNA 5mC and 5hmC | Subtype-associated signatures |
| Which epigenetic signals could advance as cancer biomarkers? | Cancer Biomarkers | Methylation, 5hmC, accessibility, histone marks | Reviewable biomarker shortlist |
| Which regulatory nodes should enter functional testing? | Drug Target Discovery | Multi-omics and regulatory-network evidence | Ranked candidate targets |
| How does biological age differ between groups or visits? | Epigenetic Clocks | DNA methylation and clock models | DNAmAge and age acceleration |
| Which epigenetic changes are associated with exposure? | Exposure Epigenomics | Exposure data, methylation, chromatin, covariates | Exposure-associated DMPs and DMRs |
| How does methylation change within the same participants? | Longitudinal Cohorts | Repeated methylation profiles and covariates | Within-person methylation trajectories |
Match Epigenomic Profiling to Your Sample Type and Input
Sample availability often determines which epigenomic evidence can be generated reliably. Before assay selection, we evaluate sample type, input amount, preservation condition, biological replicates, cohort structure, and the need for matched measurements. Low-input, degraded, or irreplaceable samples may require a focused design rather than applying every available assay.
Suitable for methylation, chromatin accessibility, protein–DNA binding, RNA expression, and matched multi-omics designs when material and biological replicates are available.
Requires review of DNA or RNA integrity, block age, fixation conditions, input quantity, and assay-specific feasibility before selecting a profiling route.
Supports low-input methylation and hydroxymethylation studies for molecular subtyping, biomarker discovery, and longitudinal liquid-biopsy research.
Blood, saliva, tissue, and extracted DNA can support epigenetic clock, exposure, EWAS, and longitudinal methylation designs when metadata and batch allocation are planned together.
Reference-genome quality, genome complexity, tissue composition, developmental stage, and species-specific methylation contexts are considered during method selection.
Integrated Epigenomics Methods and Evidence Layers
Depending on the research objective, a solution may incorporate genome-wide DNA methylation analysis, DNA hydroxymethylation profiling, chromatin analysis, cfDNA methylation analysis, RNA expression, single-cell assays, or integrated epigenomic data analysis. Methods are selected to close a defined evidence gap rather than to maximize the number of assays.
Experimental Evidence:
- DNA methylation and hydroxymethylation profiles
- Chromatin accessibility and histone modification maps
- Transcription factor or chromatin-protein occupancy
- RNA expression and matched transcriptomic measurements
- Targeted confirmation for prioritized loci or candidates
Analytical Evidence:
- DMP, DMR, peak, and differential accessibility analysis
- Gene, enhancer, motif, and pathway annotation
- Multi-omics correlation and regulatory-network analysis
- Cell-state trajectory or cohort-aware statistical analysis
- Biomarker, subtype, or target prioritization frameworks
Epigenomics Study Design and Analysis Workflow
Each project begins with the biological decision the data should support. Sample feasibility, experimental design, assay selection, data generation, and analysis are then aligned around that objective.
- Research Question: Define the biological contrast, intended conclusion, and evidence already available.
- Sample Feasibility: Review sample type, input, quality, replicates, collection time, cohort structure, and metadata.
- Assay Selection: Select DNA methylation, chromatin, RNA, single-cell, or multi-omics methods according to the evidence gap.
- Data Generation: Perform sample QC, library preparation, sequencing, and assay-specific quality assessment.
- Integrated Analysis: Connect methylation, peaks, accessibility, expression, trajectories, or cohort variables.
- Research Outputs: Deliver processed data, visualizations, candidate rankings, and a project-specific report.
Epigenomics Data Analysis and Research Deliverables
Deliverables are configured around the selected solution and analysis scope. Projects may include:
- Study-design summary and assay-selection rationale
- Sequencing and assay-specific quality-control metrics
- Normalized methylation, peak, accessibility, or expression datasets
- DMP, DMR, DEG, peak, and genomic-annotation tables
- Multi-omics correlations and regulatory-network results
- Cell-state trajectories or cohort-level statistical outputs
- Candidate biomarker, subtype, or target ranking tables
- Research-ready heatmaps, tracks, enrichment plots, and network figures
- Methods summary and project-specific interpretation report
Built Around Your Study
Assays are selected according to the biological conclusion and evidence gap rather than applying the same workflow to every project.
Study routes can be evaluated for cfDNA, FFPE, archival material, low-input tissue, cohort samples, plants, and non-model organisms.
Sampling, biological replicates, batch allocation, comparison groups, and covariates are aligned before matched datasets are generated.
Previously generated methylation, chromatin, RNA-seq, phenotype, exposure, or cohort data can be reviewed for integration with new results.
Epigenomics Research Solutions FAQ
Build an Epigenomics Solution Around Your Research Question
Share your study objective, available samples, comparison groups, and existing datasets. Our team can help identify the epigenomic evidence layers and analysis route needed to support your next research decision.
Discuss Your StudyFor research purposes only. Not intended for clinical diagnosis, treatment, or individual health assessments.