---
title: Epigenomics on the platform
description: >-
  Chromatin accessibility, histone modification, and DNA methylation analysis
  tools for epigenomic profiling.
tags:
  - epigenomics
---

Epigenomics reveals the regulatory context around a target gene — which enhancers and promoters are active, and which regions are methylation-silenced — by calling peaks and differential regions from ATAC-seq, ChIP-seq, and bisulfite sequencing data. Reach for it once you have a candidate gene and want to understand how it is switched on or off before committing to a therapeutic strategy. It sits alongside [genomics](/docs/genomics) in the target-identification stage, adding the regulatory evidence that gene-information lookups alone can't provide. See [what you can achieve](/docs/capabilities#genomics-and-omics) for the full set.

## Epigenomics overview

The Epigenomics toolkit provides six tools spanning three major assay types for chromatin and methylation analysis. All tools run on Modal compute. The three calling tools (`atacseq_peaks`, `chipseq_peaks`, `methylation_call`) take an aligned BAM file and return peak or methylation summary statistics; the three differential/DMR tools take the calling tools' output files plus a sample metadata file and return differential regions with fold changes and significance.

## Architecture

```
User Query (BAM files, conditions, genome assembly)
  --> Intent Detection
       |- ATAC-seq peaks --> atacseq_peaks (1-5 min, Modal)
       |- ATAC-seq differential --> atacseq_differential (2-10 min, Modal)
       |- ChIP-seq peaks --> chipseq_peaks (1-5 min, Modal)
       |- ChIP-seq differential --> chipseq_differential (2-10 min, Modal)
       |- Methylation calling --> methylation_call (2-8 min, Modal)
       +- DMR detection --> methylation_dmr (2-10 min, Modal)
  --> Structured Results with Genomic Coordinates and Statistics
```

## Tool inventory

### ATAC-seq tools

| Tool | Name | Description | Compute |
|------|------|-------------|---------|
| ATAC-seq Peaks | `atacseq_peaks` | Calls open chromatin peaks from ATAC-seq BAM files using MACS2-style algorithms with nucleosome-free region detection | Modal |
| ATAC-seq Differential | `atacseq_differential` | Identifies regions with significantly different chromatin accessibility between two conditions using DESeq2-style statistics | Modal |

### ChIP-seq tools

| Tool | Name | Description | Compute |
|------|------|-------------|---------|
| ChIP-seq Peaks | `chipseq_peaks` | Calls enrichment peaks from ChIP-seq BAM files for histone marks or transcription factor binding sites | Modal |
| ChIP-seq Differential | `chipseq_differential` | Identifies regions with significantly different binding between two conditions using count-based differential testing | Modal |

### DNA methylation tools

| Tool | Name | Description | Compute |
|------|------|-------------|---------|
| Methylation Calling | `methylation_call` | Extracts per-CpG methylation levels from bisulfite sequencing BAM files with coverage and confidence metrics | Modal |
| DMR Detection | `methylation_dmr` | Detects differentially methylated regions between conditions using sliding window or predefined region approaches | Modal |

## Capabilities

### Chromatin accessibility (ATAC-seq)

ATAC-seq measures open chromatin using Tn5 transposase insertion. `atacseq_peaks` identifies nucleosome-free regions (NFRs) as peaks, with options for narrow peak calling (promoters, TF sites) or broad peak calling (enhancers, regulatory domains). The tool handles Tn5 insertion offset correction (+4/-5 bp) and filters mitochondrial reads automatically.

### Histone modifications and TF binding (ChIP-seq)

ChIP-seq measures protein-DNA interactions. `chipseq_peaks` supports two peak calling modes: narrow peaks for sharp signals (transcription factors, H3K4me3, H3K27ac) and broad peaks for diffuse marks (H3K27me3, H3K36me3, H3K9me3). Input control normalization removes background signal. Peak calling uses a local Poisson model for significance testing.

### DNA methylation (bisulfite-seq)

Bisulfite sequencing converts unmethylated cytosines to uracil, allowing measurement of CpG methylation. `methylation_call` extracts per-site methylation ratios from aligned bisulfite-seq BAM files. `methylation_dmr` identifies genomic regions where methylation differs significantly between conditions, which often correspond to regulatory changes.

### Differential analysis

Both ATAC-seq and ChIP-seq differential tools use count-based approaches with replicate-aware statistical modeling. Reads are counted in a union peak set across all samples, normalized for library size and composition, and tested for differential signal using negative binomial models. Results include log2 fold changes, p-values, and FDR-adjusted significance.

## When to use each tool

| I want to... | Use this tool |
|--------------|---------------|
| Find open chromatin regions | `atacseq_peaks` |
| Compare accessibility between conditions | `atacseq_differential` |
| Find histone mark or TF binding sites | `chipseq_peaks` |
| Compare binding between conditions | `chipseq_differential` |
| Measure CpG methylation levels | `methylation_call` |
| Find differentially methylated regions | `methylation_dmr` |

## When to use other tools instead

| I want to... | Use this instead |
|--------------|------------------|
| Look up gene coordinates | An Ensembl or NCBI Gene lookup ([Genomics](/docs/genomics)) |
| Analyze gene expression (RNA-seq) | RNA-seq tools |
| Design CRISPR guides at accessible regions | [CRISPR](/docs/crispr) guide design (after ATAC-seq peaks) |
| Search literature on epigenetic regulation | [PubMed](/docs/pubmed) |
| Get or predict protein structure | [AlphaFold DB](/docs/alphafold) or [ESMFold](/docs/esmfold) |

## Pipeline position

The Epigenomics toolkit sits in the **Target Identification** stage of the drug discovery pipeline:

```
Target Identification
  |- Genomics (gene information)
  |- CRISPR (functional validation)
  |- Epigenomics (regulatory context) <--
  +- Literature (evidence gathering)
```

Epigenomic data provides regulatory context for target genes, identifying active enhancers, promoter states, and methylation-silenced regions that inform target selection and therapeutic strategy.

## Citations

> Buenrostro JD, et al. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. *Nat Methods*. 2013;10(12):1213-1218. doi: [10.1038/nmeth.2688](https://doi.org/10.1038/nmeth.2688)

> Zhang Y, et al. Model-based analysis of ChIP-Seq (MACS). *Genome Biol*. 2008;9(9):R137. doi: [10.1186/gb-2008-9-9-r137](https://doi.org/10.1186/gb-2008-9-9-r137)

> Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. *Bioinformatics*. 2011;27(11):1571-1572. doi: [10.1093/bioinformatics/btr167](https://doi.org/10.1093/bioinformatics/btr167)

> Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. *Genome Biol*. 2014;15(12):550. doi: [10.1186/s13059-014-0550-8](https://doi.org/10.1186/s13059-014-0550-8)

## Chat Usage

### Example Prompts

- "Call ATAC-seq peaks from my treatment BAM file"
- "Find differentially accessible regions between drug-treated and DMSO control"
- "Call H3K27ac ChIP-seq peaks with input control normalization"
- "Identify regions where H3K4me3 binding changes between wild-type and mutant"
- "Profile CpG methylation levels across the genome from my bisulfite-seq data"
- "Detect DMRs between tumor and matched normal tissue"

### Workflow Chaining

- **Feeds into**: Genomics (gene annotation of peaks), CRISPR (guide design at open regions), Target Passport (epigenetic evidence), Literature (regulatory annotation)
- **Accepts from**: Genomics (gene coordinates), CRISPR screens (hit genes for epigenetic profiling), Literature (known regulatory elements)

## FAQ

### What assay types does the Epigenomics toolkit support?

  The toolkit supports three major epigenomic assay types -- ATAC-seq (chromatin accessibility), ChIP-seq (histone modifications and transcription factor binding), and bisulfite sequencing (DNA methylation). Each assay type has a peak/region calling tool and a differential analysis tool.

### What input file formats are required?

  The calling tools (`atacseq_peaks`, `chipseq_peaks`, `methylation_call`) accept an aligned BAM file as primary input; BAM files should be sorted and indexed, with duplicates marked. The differential and DMR tools instead take the peak or methylation files those calling tools produced for two or more conditions with biological replicates, plus a sample metadata file.

### Do I need to provide a reference genome?

  Yes, for the calling tools. `genome` defaults to `hg38` and also accepts `mm10` (mouse) — no other assemblies are currently supported.

### How long do epigenomics analyses take?

  Peak calling typically completes in 1-5 minutes depending on BAM file size and peak count. Differential analysis runs in 2-10 minutes depending on the number of samples and regions. All analyses run on Modal GPU compute.

## Read next