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?
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.
Amounts are guidelines. Contact us for custom projects or edge cases.
Project Workflow
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.
Analytical Framework for Integrated ATAC-seq and Transcriptome Analysis
