Step-by-step workflows for ATAC-seq peak calling, ChIP-seq analysis, differential accessibility, and methylation profiling.

You want to find the active regulatory regions around your genes — or how they change between conditions — from sequencing data; this walks you through four common workflows: calling ATAC-seq peaks, comparing chromatin accessibility, calling ChIP-seq peaks, and profiling DNA methylation, each starting from an aligned BAM file.

Epigenomics quickstart

This guide covers four common workflows: calling ATAC-seq peaks, comparing chromatin accessibility between conditions, calling ChIP-seq peaks, and profiling DNA methylation.

Workflow 1: Call ATAC-seq peaks

Upload your BAM file

Upload your aligned, sorted, and deduplicated ATAC-seq BAM file through the file upload interface or reference an existing file.

Run peak calling

Ask Ivy in chat (or submit the job directly with POST /api/v1/jobs; see the API reference):

Call ATAC-seq peaks from my BAM file

Or spell the options out in your prompt:

Option	Value
BAM file URI	Your uploaded ATAC-seq BAM
Genome	`hg38`
Peak caller	`macs2` (default)
Broad peaks	`false` (narrow)

Review peak results

Field	Example Value
Peaks called (`num_peaks`)	45,230
FRiP score	0.32
Median peak width	350 bp

The tool returns peak counts, quality metrics, and the top peaks by signal (top_peaks) with genomic coordinates and q-values — it doesn't return gene annotation directly; chain into a genomics tool for nearest-gene lookups.

Workflow 2: Differential accessibility analysis

Call peaks for each replicate

Run atacseq_peaks on each sample first (Workflow 1). You need at least two peak files per condition (biological replicates):

Condition	Peak files
Treatment	peaks from `treatment_rep1.bam`, `treatment_rep2.bam`
Control	peaks from `control_rep1.bam`, `control_rep2.bam`

Run differential analysis

Find regions with differential chromatin accessibility between treatment and control ATAC-seq samples

Or configure:

Parameter	Value
Peak files	the four per-sample peak files from Step 1
Metadata file URI	CSV with `sample`, `condition` columns
Contrast	`"treatment_vs_control"`
Method	`diffbind` (default)

Review differential results

Region	Log2 FC	P-value	Adjusted P-value	Direction
chr1:1000000-1001000	2.3	1.2e-8	3.5e-6	gained
chr17:7670000-7671000	-1.8	4.5e-6	8.2e-4	lost
chr12:25200000-25201000	1.5	2.1e-5	2.8e-3	gained

The result doesn't include gene annotation — chain into a genomics tool to map regions to nearest genes.

Workflow 3: Call ChIP-seq peaks

Prepare your files

You need a ChIP BAM file and optionally an input control BAM:

File	Description
ChIP BAM	Immunoprecipitated sample
Input BAM	Input control (recommended)

Run peak calling

Call H3K27ac ChIP-seq peaks with input control normalization

Or configure:

Parameter	Value
Treatment BAM URI	Your ChIP-seq BAM file
Control BAM URI	Your input control BAM
Histone mark	`H3K27ac`
Peak caller	`macs3` (default)
Genome	`hg38`

Review peaks

Field	Example Value
Peaks called (`num_peaks`)	28,500
FRiP score	0.34
Median peak width	500 bp

Use SICER2 for diffuse marks (optional)

For histone marks like H3K27me3, H3K36me3, or H3K9me3, set peak_caller: "sicer2" — it's better suited to wide, diffuse domains of enrichment than the default MACS3.

Workflow 4: Profile DNA methylation

Submit bisulfite-seq data

Call methylation levels from my bisulfite sequencing BAM file

Or configure:

Parameter	Value
BAM file URI	Your bisulfite-seq BAM
Genome	`hg38`
Method	`bismark` (default)
Min coverage	`5`

Review methylation summary

Field	Example Value
Total CpGs analyzed	24,800,000
Mean coverage	12.4x
Global methylation	0.62

The result is a genome-wide and per-chromosome summary — it doesn't include a per-site methylation table.

Detect DMRs (optional)

Run methylation_call on each replicate first, then:

Find differentially methylated regions between tumor and normal samples

This runs methylation_dmr on the per-sample methylation files plus a metadata CSV and a contrast string (e.g. "tumor_vs_normal") to identify regions where methylation levels differ significantly between conditions.

Next steps

See inputs for all parameters, genome options, and peak calling modes
See outputs for result field descriptions and quality metrics
Use Genomics for gene annotation of peaks
Use CRISPR for guide design at accessible chromatin regions

Epigenomics quickstart