ATAC-Seq Analysis Services

Complete ATAC-Seq data analysis

ATAC-Seq (Assay for Transposase-Accessible Chromatin using sequencing) is a fast and sensitive next-generation sequencing method for mapping genome-wide chromatin accessibility. It uses a hyperactive Tn5 transposase to simultaneously cut and tag open regions of the genome, revealing where the regulatory machinery can engage the DNA. Because it requires very low input material and a simple library preparation, ATAC-Seq has become a method of choice for studying the regulatory landscape of cells and tissues.

Our team takes your raw or aligned ATAC-Seq data and delivers a complete, reproducible analysis — from quality control to biological interpretation — together with publication-ready figures and the code used to produce them.

Our Services

• Quality control, adapter trimming and read alignment
• Mitochondrial and duplicate read removal with fragment-size QC
• Peak calling and accessible-region annotation
• Differential accessibility analysis between conditions
• Transcription-factor footprinting and motif-enrichment analysis
• Annotation of peaks to genes, promoters and enhancers
• Integration with RNA-Seq and ChIP-Seq datasets
• Visualisation: coverage tracks, heatmaps and TSS-enrichment plots

ATAC-Seq insights help researchers understand gene regulation, enhancer activity and how chromatin state changes across conditions, developmental stages and disease.

What you receive

• Filtered, deduplicated alignment files and a consensus peak atlas in BED / narrowPeak format
• A differential accessibility table with fold changes, p-values and FDR-adjusted significance
• Motif-enrichment and transcription-factor footprinting results with ranked candidate regulators
• Publication-ready figures: TSS-enrichment profile, fragment-size distribution, PCA, accessibility heatmaps and genome-browser tracks
• A written report that explains what the results mean biologically, not just which numbers are significant
• All scripts, parameters and software versions, so the analysis can be rerun, extended and cited

How the analysis runs

ATAC-Seq is unusually sensitive to library quality, so the pipeline front-loads quality control rather than treating it as an afterthought.

• Read quality assessment and adapter trimming
• Alignment to the reference genome with parameters tuned for short, variable-length fragments
• Removal of mitochondrial reads, PCR duplicates and ENCODE blacklist regions
• Fragment-size distribution check for the expected nucleosomal periodicity
• Peak calling with MACS2 or MACS3, then a reproducible consensus peak set across replicates
• Differential accessibility testing with DESeq2 or edgeR on the consensus peak counts
• Motif enrichment (HOMER, MEME suite) and footprinting (TOBIAS, HINT-ATAC) over differential regions
• Peak annotation to promoters, enhancers and nearest genes, with pathway enrichment where useful

The quality metrics that actually decide whether a dataset is usable

Three numbers determine whether an ATAC-Seq experiment can support conclusions. TSS enrichment score measures signal concentration at transcription start sites and is the single best indicator of library quality. FRiP — the fraction of reads in peaks — indicates signal-to-noise. Fragment-size distribution should show clear nucleosome-free and mono-nucleosomal populations; losing that periodicity usually means over-digestion or degraded input.

We report these openly for every sample, including any that fall short. A dataset that cannot support a conclusion is worth knowing about before analysis, not after.

Data we accept

Paired-end FASTQ files are preferred because fragment size carries real biological information in ATAC-Seq. We also work from aligned BAM files, existing peak calls, or public accessions from SRA, GEO and ENCODE. Human, mouse and non-model organisms are all supported, provided a reference genome and annotation exist.

Questions this analysis answers

• Which regulatory regions open or close between your conditions, treatments or genotypes?
• Which transcription factors are driving those accessibility changes?
• Do chromatin changes correspond to changes in gene expression, or act independently?
• Which enhancers are associated with the genes you care about?