Chromatin Analysis Services for Epigenetics Research
Chromatin analysis encompasses a suite of technologies that interrogate how DNA is packaged, regulated, and organized within the nucleus. These methods enable researchers to map protein–DNA interactions, profile open chromatin regions, and reconstruct three-dimensional genome architecture — providing mechanistic insight into gene regulation, cellular differentiation, and disease pathogenesis. At CD Genomics, we offer an integrated chromatin analysis platform covering the full spectrum of epigenomic approaches, from classical ChIP-seq to single-cell ATAC-seq and Hi-C.
Key Highlights of Our Chromatin Analysis Portfolio:
- Multi-Technology Platform: ChIP-seq, ATAC-seq, CUT&Tag, Hi-C, CUT&RUN, DAP-seq, and more — all under one service umbrella.
- End-to-End Support: From experimental design and sample preparation through publication-ready bioinformatics and custom visualization.
- Challenging Sample Expertise: Optimized protocols for low-input material, FFPE tissues, and non-model organisms across plant and animal species.
- Rigorous Quality Control: Multi-point QC checkpoints including FRiP, TSS enrichment, library complexity, and peak reproducibility for every project.
Overview of Our Chromatin Analysis Portfolio
Chromatin analysis is central to modern epigenetics research. By mapping how DNA interacts with histones, transcription factors, and structural proteins, researchers can uncover the regulatory mechanisms that control gene expression in development, disease, and across species. Our integrated service platform delivers the full spectrum of chromatin analysis capabilities across six technology categories, with rigorous quality control at every stage and bioinformatics support designed for publication-ready results.
Core Technologies for Chromatin Analysis
Protein–DNA Interaction Mapping — ChIP-Seq, CUT&Tag, CUT&RUN
Chromatin immunoprecipitation sequencing (ChIP-seq) remains the most widely used method for genome-wide mapping of histone modifications, transcription factors, and other chromatin-associated proteins. The assay uses a specific antibody to enrich protein-bound DNA fragments, which are then sequenced to identify binding sites across the genome. Our ChIP-Seq service covers both narrow peak (transcription factors) and broad domain (repressive histone marks) analysis.
For researchers working with limited cell numbers, CUT&Tag (Cleavage Under Targets and Tagmentation) offers a streamlined alternative. By using a protein A–Tn5 fusion to directly tag target-bound DNA, CUT&Tag achieves higher signal-to-noise ratios than ChIP-seq while requiring significantly fewer cells. Our CUT&Tag service is particularly well suited for histone modification profiling of active chromatin marks. CUT&RUN (Cleavage Under Targets and Release Using Nuclease) provides an intermediate option with lower accessibility bias than CUT&Tag and reliable detection of both transcription factors and histone modifications. Our CUT&RUN service is recommended when a balanced profile across active and repressed chromatin is required.
ATAC-seq uses a hyperactive Tn5 transposase to fragment and tag DNA in open chromatin regions, identifying active promoters, enhancers, and regulatory elements. Single-cell ATAC-seq enables profiling at cellular resolution for heterogeneous tissues. See our ATAC-Seq and scATAC-Seq service pages.
Hi-C captures genome-wide chromatin conformation, revealing A/B compartments, TADs, and chromatin loops. We offer Hi-C, Capture Hi-C, Micro-C XL for nucleosome-resolution, and HiChIP for protein-centric interaction profiling.
ChIRP-Seq & ChIRP-MS identify genomic binding sites and protein interactors of lncRNAs. eCLIP-seq maps RNA-protein interactions at nucleotide resolution. Also available: ssDRIP-Seq (R-loop mapping), DRIPc-seq, and PIRCh-seq.
How to Choose the Right Chromatin Analysis Method
Selecting the appropriate chromatin analysis method depends on your biological question, sample availability, and the resolution needed. The table below summarizes key considerations across our technology portfolio.
| Method | Biological Question | Typical Sample Input | Antibody Required | Sequencing Depth | Signal-to-Noise | Best For |
|---|---|---|---|---|---|---|
| ChIP-Seq | Where does a specific protein bind to DNA? | Standard to high | Yes | 10–50 M reads | Moderate | Histone marks, TFs; repressive marks preferred |
| ATAC-Seq | Where is chromatin open/accessible? | Moderate to high | No | 50 M reads | High | Open chromatin, regulatory elements |
| CUT&Tag | Where does a protein bind (low input)? | Low | Yes | 5–8 M reads | Highest | Active marks, low cell numbers |
| CUT&RUN | Balanced protein–DNA profiling | Low to moderate | Yes | 8–15 M reads | High | TFs, histone marks, lower bias |
| Hi-C | How is chromatin organized in 3D? | High (live cells) | No | 200 M+ read pairs | N/A | Compartments, TADs, loops |
| DAP-Seq | Where does a TF bind (in vitro)? | In vitro | No | 20–30 M reads | High | Non-model organisms, plants |
Selection Strategy:
- Define your biological question first. Protein–DNA interaction mapping (ChIP-seq, CUT&Tag, or CUT&RUN), chromatin accessibility profiling (ATAC-seq), or three-dimensional conformation (Hi-C) address fundamentally different aspects of chromatin biology.
- Consider sample input. CUT&Tag and ATAC-seq are preferable when cell numbers are limited. A 2025 benchmark study confirmed that CUT&Tag achieves the highest signal-to-noise ratio among chromatin-protein interaction methods, though ChIP-seq remains more effective for repressive histone marks such as H3K27me3.
- For non-model organisms. DAP-seq enables transcription factor binding mapping without species-specific antibodies.
- For challenging samples. Specialized protocols exist for FFPE tissues (ATAC-seq, ChIP-seq) and low-input material. Contact us to discuss feasibility.
- For multi-omics integration. ATAC-seq and RNA-seq can be combined from the same sample to link chromatin accessibility with gene expression.
End-to-End Workflow and Quality Control
Our chromatin analysis service follows a standardized workflow with quality control checkpoints at every stage.
- Sample Receipt and Quality Assessment — QC Checkpoint: Sample integrity verification; viability assessment (ATAC-seq requires >80% viability); crosslinking confirmation (ChIP-seq, Hi-C); DNA quantification and fragmentation analysis.
- Library Preparation — QC Checkpoint: Fragment size distribution analysis; adapter dimer assessment; library yield quantification; qPCR-based enrichment validation (ChIP-seq, CUT&Tag).
- Sequencing — QC Checkpoint: Sequencing yield and quality scores; Q30 distribution; duplication rate assessment; PhiX alignment rate.
- Data Quality Control — QC Checkpoint: FRiP (Fraction of Reads in Peaks) for ChIP-seq and CUT&Tag; TSS enrichment score and fragment size periodicity for ATAC-seq; contact matrix quality metrics for Hi-C.
- Bioinformatics Analysis — QC Checkpoint: Peak reproducibility across replicates; differential analysis statistical rigor; motif enrichment significance; functional annotation coverage.
- Report Generation and Delivery — QC Checkpoint: Deliverable completeness check; custom visualization validation; final QC summary report.
Sample Requirements and Preparation Guidelines
Proper sample preparation is critical for high-quality chromatin analysis results. The table below outlines key requirements for each technology.
| Technology | Recommended Sample Type | Input Requirement | Key QC Checkpoints | Notes |
|---|---|---|---|---|
| ChIP-Seq | Crosslinked cells or fresh-frozen tissue | Project-specific — contact us | Chromatin shearing profile; IP efficiency by qPCR; library complexity | Antibody validation recommended; we provide antibody selection support |
| ATAC-Seq | Live cells (viability >80%) or fresh tissue nuclei | Project-specific — contact us | TSS enrichment score; fragment size distribution; library yield | Must use live cells; nucleosome-free fraction critical for data quality |
| CUT&Tag | Live cells (viability >80%) or fresh-frozen tissue | Lower than ChIP-seq — contact us | Signal-to-noise ratio; peak reproducibility; Tn5 insertion bias | No crosslinking required; compatible with low cell numbers |
| CUT&RUN | Live cells or fresh-frozen tissue | Low to moderate — contact us | Fragment size distribution; spike-in normalization; peak reproducibility | Lower accessibility bias than CUT&Tag |
| Hi-C | Live cells (fresh crosslinked) | Higher input typically needed — contact us | Crosslinking efficiency; restriction digestion; library complexity | Must be freshly crosslinked from live cells |
| DAP-Seq | In vitro expressed protein + genomic DNA | In vitro | Protein expression; DNA fragmentation; enrichment qPCR | No antibody required; ideal for non-model organisms |
Bioinformatics Analysis and Deliverables
All chromatin analysis services include standard bioinformatics processing with the option to add advanced analysis modules.
Standard Deliverables:
| Deliverable | Description |
|---|---|
| Raw sequencing data | Demultiplexed read files with quality scores |
| Aligned reads | Reads aligned to the reference genome |
| Signal tracks | Normalized coverage tracks for browser visualization |
| Peak calls | Enriched regions with statistical significance |
| QC report | Metrics including library complexity, alignment rates, FRiP or TSS enrichment scores |
| Differential analysis | Peak-level or signal-level comparison between conditions |
| Motif analysis | Enriched sequence motifs and transcription factor footprinting (ATAC-seq) |
| Peak annotation | Annotated peaks with genomic feature context and nearest gene |
Optional Advanced Analysis:
- Multi-omics integration — combined analysis of ATAC-seq and RNA-seq from matched samples
- Hi-C data processing — contact matrix generation, compartment and TAD calling, loop identification
- Single-cell analysis — dimensionality reduction, cell-type identification, trajectory inference for scATAC-seq
- Custom visualization — publication-ready figures tailored to your study
- Pathway analysis — functional enrichment of peak-associated genes
- Epigenomic data analysis — see our dedicated Epigenomic Data Analysis service for detailed options
Representative Data and Demo Results
Below are representative data types delivered with each chromatin analysis project. These demo results illustrate the quality and depth of our standard bioinformatics outputs, showcasing the key QC metrics, visualizations, and analytical results researchers can expect.
ChIP-seq and CUT&Tag Quality Metrics:
- FRiP (Fraction of Reads in Peaks) — quantifies signal-to-noise ratio by measuring the proportion of reads falling within called peak regions.
- Peak enrichment heatmap — visualizes normalized read density across called peaks, confirming strong, reproducible enrichment at target regions.
- Library complexity curve — assesses whether sequencing depth is sufficient to capture the full diversity of enriched fragments.
- Cross-correlation plot — evaluates strand-specific clustering of reads, indicating true binding events versus background noise (NSC > 1.05, RSC > 0.8).
ATAC-seq Quality and Structural Metrics:
- Fragment size distribution — the characteristic nucleosome periodicity pattern (~180, 360, 540 bp) confirms successful capture of both nucleosome-free and mono/di-nucleosome fragments.
- TSS enrichment score — measures read density around transcription start sites, with scores typically >8 indicating high-quality ATAC-seq libraries.
- Genomic feature distribution — pie chart or bar graph showing the proportion of peaks in promoters, introns, exons, and distal intergenic regions.
- Transcription factor footprinting — single-nucleotide resolution protection profiles identifying specific TF binding events in open chromatin.
Hi-C and 3D Genome Visualizations:
- Contact matrix heatmap — genome-wide interaction frequency map with visible chromosome territories and TAD structures along the diagonal.
- Compartment A/B eigenvector plot — identifies active (A) and inactive (B) chromatin compartments across the genome.
- TAD boundary calling — insulation score profiles identifying topologically associating domain boundaries.
- Virtual 4C / loop tracks — interaction frequency plots from a selected viewpoint, revealing local chromatin contacts.
Multi-Technology Comparative Analyses:
- Motif enrichment analysis — identifies transcription factor binding motifs enriched within differential peak regions (ChIP-seq / ATAC-seq).
- Differential peak volcano plot — statistically significant gained/lost chromatin accessibility or binding between conditions.
- Peak annotation summary — genomic context of identified peaks (promoter, intron, intergenic, etc.) for downstream functional interpretation.
- Integrative visualization — combined tracks showing ATAC-seq accessibility, ChIP-seq enrichment, and Hi-C interactions at key loci.
All demo results are generated from representative internal datasets and reflect the standard quality and analysis depth delivered with every project. Actual figures are customized to your specific experimental design, sample type, and research question.
Applications Across Research Areas
Chromatin analysis methods are widely applied in cancer research to identify tumor-specific regulatory alterations. A large-scale single-cell ATAC-seq study across eight cancer types (227,063 nuclei) demonstrated that chromatin accessibility landscapes are strongly shaped by copy number alterations and can distinguish malignant from normal cells (Sundaram et al., Science, 2024). In liquid biopsy, cfChIP-seq using H3K36me3 has been shown to accurately distinguish non-small cell from small cell lung cancer from 1 mL of plasma (Maansson et al., Molecular Oncology, 2023).
ATAC-seq and ChIP-seq are used to map dynamic changes in chromatin accessibility and histone modification landscapes during differentiation. The identification of transcription factors such as TFDP1 as global modulators of chromatin accessibility via CRISPR-based screening opens new avenues for understanding cell fate regulation (Ishii et al., Nature Genetics, 2024).
Hi-C and its derivatives have enabled 3D genome analysis in a wide range of plant species. A 2024 study in Nature Communications used Hi-C to discover that PDS5 proteins negatively regulate TAD-like domain formation in Arabidopsis thaliana, revealing conserved and divergent principles of genome organization across kingdoms.
Epigenomic profiling is increasingly integrated into precision medicine pipelines. Chromatin accessibility signatures derived from patient samples can identify active regulatory elements, nominate therapeutic targets, and classify disease subtypes. Integrating chromatin analysis with transcriptomic data provides a more complete picture of gene regulatory networks underlying disease.
Case Study: cfChIP-seq for Non-Invasive Lung Cancer Molecular Subtyping
Frequently Asked Questions
References
- Wang et al., Frontiers in Cell and Developmental Biology (2025). Benchmark of chromatin–protein interaction methods in haploid round spermatids. https://doi.org/10.3389/fcell.2025.1572405
- Maansson et al., Molecular Oncology (2023). Cell-free chromatin immunoprecipitation can determine tumor gene expression in lung cancer patients. https://doi.org/10.1002/1878-0261.13394
- Sundaram et al., Science (2024). Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers. https://doi.org/10.1126/science.adk9217
- Ishii et al., Nature Genetics (2024). Genome-wide ATAC-see screening identifies TFDP1 as a modulator of global chromatin accessibility. https://doi.org/10.1038/s41588-024-01658-1
- Nature Communications (2024). Mutations of PDS5 genes enhance TAD-like domain formation in Arabidopsis thaliana. https://doi.org/10.1038/s41467-024-53760-x