What is ATAC-seq and how is it different from RNA-seq?

ATAC-seq profiles chromatin accessibility to locate open promoters and enhancers, whereas RNA-seq measures transcript abundance; combining them links regulatory DNA activity to transcriptional output for mechanism discovery.

What does "ATAC-seq and RNA-seq correlation" mean in practice?

We compute Pearson correlation between accessibility features assigned to genes and their expression levels; strong positive trends support activation, while modest or mixed trends reflect additional regulation such as transcription-factor availability or chromatin context.

How do you link peaks to genes when integrating ATAC-seq and RNA-seq?

We prioritise promoter-anchored assignment, then use gene-body and nearest-gene rules; for advanced studies, peak-to-gene linkage strategies from correlation-based tooling across samples/conditions illustrate the same principle of correlating accessibility with expression.

Which ATAC-seq QC metrics matter most before integration?

TSS enrichment and FRiP summarise signal-to-noise and enrichment in regulatory regions; we report both alongside alignment and complexity metrics to ensure reliable downstream correlation and differential analyses.

What is FRiP and how is it interpreted?

FRiP (Fraction of Reads in Peaks) is the proportion of usable reads falling in called peaks; higher values generally indicate better enrichment but should be interpreted with TSS enrichment and context.

Why do you include IGV locus screenshots in the deliverables?

IGV enables interactive review of reads and signal tracks at specific genes, helping confirm that changes in accessibility co-occur with expression shifts and providing human-readable evidence for candidate mechanisms.

Which differential expression framework do you use for RNA-seq?

We rely on established count-based models (e.g., DESeq2) to estimate dispersion and shrunk fold-changes, producing stable gene-level statistics for integration with accessibility results.

What biological questions benefit most from integrating ATAC-seq and RNA-seq?

Use it to prioritise regulators behind DEGs, to connect enhancers/promoters to target genes, and to summarise pathway shifts when interpreting treatment response, disease comparisons, or genetic perturbations.

Can you work with data generated elsewhere (bioinformatics-only)?

Yes—provide raw counts/BAM/FASTQ plus a consistent genome build and metadata; we standardise processing and document parameters so your integrative results are reproducible across tools and releases.

What downstream biology do GO and KEGG enrichment explain?

GO summarises gene functions and processes, while KEGG maps genes to curated pathways; both help translate peak- and gene-level findings into mechanism-level hypotheses.

Can ATAC-seq be performed on frozen tissue if we also plan RNA-seq?

Modern ATAC-seq protocols (e.g., Omni-ATAC) improve signal-to-background and support archival or frozen tissue, enabling integrated studies when fresh material is limited.

Why integrate rather than analyse ATAC-seq or RNA-seq alone?

RNA-seq alone highlights outcome (expression), ATAC-seq alone highlights potential regulators (accessibility); integrating both resolves which regulatory elements plausibly drive the observed transcriptional changes.

ATAC-seq and RNA-seq Integration Service

Connect chromatin accessibility with transcriptional output to explain phenotype change.

We provide ATAC-seq and RNA-seq integration for matched samples, quantifying ATAC-seq and RNA-seq correlation and prioritising regulator candidates for mechanism discovery in research, drug development, and breeding.

What we solve

Which accessible regions drive my differentially expressed genes?
How do I rank peaks and genes linked to phenotype?
How do I visualise locus changes that match expression?

Why CD Genomics

End-to-end pipeline: Pearson correlation, Venn overlap, four/nine-quadrant maps, IGV reads tracks, GO/KEGG enrichment.
Matched-sample design, clear QC, transparent methods, PhD bioinformaticians.
Research-use-only delivery with reproducible figures and tables.

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ATAC-seq and RNA-seq integration: ATAC–RNA correlation, ranked regulator candidates, transparent QC, and publication-ready outputs (RUO).

Introduction

What This Service Reveals

Clear answers from integrating ATAC-seq and RNA-seq

Our ATAC-seq and RNA-seq integration links chromatin openness to expression change. We quantify ATAC-seq and RNA-seq correlation, then trace effects from peaks to genes to pathways.

Key insights you receive

Drivers of DEGs: promoter and enhancer accessibility tied to transcriptional output.
Concordant vs discordant loci: ↑access/↑expr, ↑/↓, ↓/↑, ↓/↓ classes highlight activation or restraint.
Regulatory context: prioritised genes with nearby differential peaks and documented peak-to-gene assignment.
Locus evidence: IGV reads tracks showing accessibility shifts co-occurring with RNA changes.
Pathway explanation: GO/KEGG terms that summarise mechanism and phenotype relevance.
Actionable shortlist: CSV tables rank candidates by quadrant class, effect size, and significance.

Quick answers

What does integrating ATAC-seq and RNA-seq reveal?

The link between chromatin accessibility and gene expression, with ranked regulator candidates.

How do I use it?

Select genes from concordant quadrants, review IGV images, and plan validation experiments.

Why not rely on RNA-seq alone?

Accessibility pinpoints regulatory regions and explains why expression changes occur.

Applications

When to Use It

Typical study designs

Treatment response or mechanism-of-action studies.
Disease vs normal, case vs control, or responder vs non-responder.
Developmental time courses and cell-state transitions.
Knockout/overexpression, CRISPR perturbations, or pathway inhibition.
Line, strain, or cultivar comparisons in breeding programs.

Signals this integration is the right choice

RNA-seq shows clear DEGs, but the upstream regulators are unclear.
Many ATAC-seq peaks change, but you need gene-level prioritisation.
You must link candidate enhancers or promoters to expression outcomes.
Reviewers or stakeholders ask for mechanism beyond expression alone.

Analysis Workflow

How the Integration Works

Multi-omics association operates at two levels: a global association that captures overall trends between ATAC-seq and RNA-seq, and a gene-level association that prioritises key genes and functions for mechanism discovery. We integrate ATAC-seq and RNA-seq to link chromatin accessibility with transcriptional output. The pipeline combines ATAC-seq and RNA-seq integration steps: correlation, overlap, quadrant classification, locus tracks, and enrichment.

Analytical Framework for Integrated ATAC-seq and Transcriptome Analysis

1) Data intake & QC

Verify metadata, replicates, and contrasts.
Normalise counts; assess library complexity and alignment metrics.
Assign peaks to genes using promoter-first rules, with gene-body and nearest-gene fallbacks.

2) Global association

Compute ATAC-seq and RNA-seq correlation (Pearson r) on matched genes.
Deliver density-scatter plots with r, confidence limits, and sample-level summaries.

3) Gene-level shortlisting

Intersect DEGs with genes linked to differential accessibility (Venn).
Classify genes into four or nine quadrants (↑access/↑expr, ↑/↓, ↓/↑, ↓/↓).
Rank by effect size, FDR, and consistency across replicates.

4) Locus evidence

Generate IGV reads tracks for exemplar genes and regulatory regions.
Document enhancer/promoter context and peak-to-gene linkage used.

5) Functional interpretation

Run GO and KEGG enrichment on prioritised gene sets.
Summarise pathways, upstream regulators, and biological themes.

Notes for robust results

Matched samples with ≥2 biological replicates per condition are recommended.
When global r is modest, we emphasise quadrant classes, locus evidence, and pathway signals.
RUO only; methods and assumptions are reported transparently.

Bioinformatics

Analyses Included

Our ATAC-seq and RNA-seq integration delivers end-to-end analyses that link accessibility to expression and pathways, with files ready for downstream use.

Global correlation

Pearson correlation of matched gene features to quantify ATAC-seq and RNA-seq correlation.
Density-scatter plots with r, p, n, and sample-level summaries.

Differential overlap

Venn analysis of DEGs and genes linked to differential peaks.
Candidate lists with peak-to-gene linkage and distance/region context.

Quadrant classification

Four- and nine-quadrant maps (↑access/↑expr, ↑/↓, ↓/↑, ↓/↓) to prioritise regulators.
CSV exports with effect sizes, FDR, and replicate consistency.

Locus evidence

IGV reads tracks for exemplar genes and regulatory elements.
Optional bigWig/BED tracks for genome browser review.

Functional interpretation

GO and KEGG enrichment on prioritised sets.
Bubble/bar plots and tables summarising pathways and biological themes.

Quality and documentation

Clear QC metrics (alignment, complexity, normalisation checks).
Methods, parameters, and assumptions reported for RUO reproducibility.

Deliverables

What You'll Receive

Report (PDF)

Key findings from ATAC-seq and RNA-seq integration, figures, and QC notes.

Figures (PNG/PDF)

ATAC-seq and RNA-seq correlation plots, Venn and quadrant maps, IGV loci, GO/KEGG charts.

Tables (CSV/TSV)

DEGs linked to differential accessibility, quadrant classes, enrichment results.

Optional files

Genome browser tracks (bigWig/BED); summary slide deck (PPTX).

Support

Email follow-ups for clarifications.

Sample Requirements

Sample & Data Requirements

Wet-Lab Samples (Sequencing Provided)

Sample Type	Recommended Input	Minimum	Quality Notes
RNA (for RNA-seq)	≥ 2 µg total RNA	≥ 1 µg	≥ 50 ng/µl; OD260/280 = 1.8–2.0; DNase-treated; intact RNA.
Cells (for ATAC-seq)	≥ 1 × 10⁶ live cells	5 × 10⁴	Live or cryopreserved; no fixation; intact nuclei.
Tissue (for ATAC-seq)	≥ 500 mg	200 mg	Rapid processing to preserve chromatin; avoid harsh homogenisation.
Species	Human, mouse, rat	—	Others by enquiry.
Replicates	≥ 2 biological per condition	—	Supports stable ATAC-seq and RNA-seq correlation.
Logistics	Dry-ice shipment	—	RNase/DNase-free tubes; clear labels and metadata.

Bioinformatics-Only (No Wet-Lab)

Item	RNA-seq	ATAC-seq	Notes
Primary input	Raw counts (CSV/TSV) or BAM+BAI or FASTQ	Peak set (BED/narrowPeak) + peak counts or BAM+BAI or FASTQ	Provide one option per assay; counts should be raw.
Reference	Genome build (e.g., GRCh38, mm39) + GTF	Genome build (e.g., GRCh38, mm39)	Use the same build across assays for integration.
Metadata	Sample sheet: ID, group, replicate, batch, library layout/strandedness	Sample sheet: ID, group, replicate, batch	File names must match IDs.
Replicates	≥ 2 per condition	≥ 2 per condition	Required for DE, overlap, and quadrant analyses.
Optional QC	FASTQC/MultiQC, aligner logs	TSS enrichment, FRiP, mitochondrial %	Improves troubleshooting and reporting.

Amounts are guidelines. Contact us for custom projects or edge cases.

Project Workflow

1) Scope & Design

Confirm organism, contrasts, replicates, genome build/annotation, and deliverable formats. Finalise a brief statement of work.

2) Intake & Verification

Receive samples or data; validate labels, metadata, and file integrity (checksums). Log QC requirements and acceptance criteria.

3) Processing & Integration

Run the agreed pipeline as described in "How the Integration Works" with documented parameters and versioning. Track progress in a shared checklist.

4) Reporting & Delivery

Provide the PDF report, figures, and tables; optional browser tracks. Include a methods/QC appendix and a reproducibility manifest.

5) Handover & Support

Answer follow-up questions by email, handle minor formatting updates, and retain data under our security policy for the agreed period.

Advantages

Why CD Genomics

Proven multi-omics expertise

We specialise in ATAC-seq and RNA-seq integration, delivering consistent results across research, drug discovery, and breeding projects.

Reproducible, transparent methods

Each step in integrating ATAC-seq and RNA-seq is version-controlled with clear parameters, references, and QC thresholds.

Decision-ready outputs

Figures and tables are formatted for manuscripts, slide decks, and grant reports. Correlation plots, quadrant maps, and enrichment tables are directly reusable.

Robust study design

We advise on contrasts, replicates, depth, and genome resources to stabilise ATAC-seq and RNA-seq correlation and downstream statistics.

Data security and compliance

Secure upload, encrypted storage, role-based access, and defined retention policies. RUO-only workflows.

Case

Case Study

HTLV-1–Infected CD4⁺ T Cells: ATAC-seq + RNA-seq Integration Reveals Altered Accessibility

Title: Integrative analysis of ATAC-seq and RNA-seq for cells infected by human T-cell leukemia virus type 1

Journal: PLOS Computational Biology, 2025

Authors: Azusa Tanaka, Yasuhiro Ishitsuka, Hiroki Ohta, et al.

Methods: Bulk ATAC-seq and RNA-seq integration linking chromatin accessibility to gene expression; differential accessibility, gene assignment, fold-change–based accessibility vs expression scatter, shortlisting of candidate regulators, locus evidence and validation.

Summary: The study compared HTLV-1–infected CD4⁺ T cells with healthy donors and found altered enhancer landscapes and distinct relationships between accessibility and mRNA levels in ATL cases. Integration highlighted genes with discordant or concordant behaviour and prioritised candidates; TLL1 emerged with ATL-specific expression and accessibility, supported by functional assays indicating isoform-dependent effects on TGF-β. These results show how integrating ATAC-seq and RNA-seq provides global trends and locus-level evidence to rank targets for validation—exactly the analysis delivered by this service.

Integrated ATAC-seq vs RNA-seq scatter for ATL vs healthy CD4⁺ T cells

FAQ

FAQs

! For research purposes only. Not intended for clinical diagnosis, treatment, or individual health assessments.