Epigenomics on the platform

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 in the target-identification stage, adding the regulatory evidence that gene-information lookups alone can't provide. See what you can achieve 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

ChIP-seq tools

DNA methylation tools

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

When to use other tools instead

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

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

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

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

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

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