# Bioconductor Epigrahmm

EpigraHMM provides a set of tools for the analysis of epigenomic data based on hidden...

- Repository: https://github.com/bioMate-AI/biomate-bioconductor-kb (793 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-06-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-epigrahmm -g -a claude-code -y`
Codex: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-epigrahmm -g -a codex -y`
Cursor: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-epigrahmm -g -a cursor -y`
Gemini CLI: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-epigrahmm -g -a gemini-cli -y`

## Description

epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# epigraHMM

## Workflows

### Differential Peak Calling

Detect differential enrichment regions (peaks) across multiple experimental conditions and classify peaks into combinatorial patterns of enrichment.

```r
library(epigraHMM)

# Define input BAM files and metadata
bamFiles <- system.file(
  package = "genomationData", 
  "extdata", 
  c("wgEncodeBroadHistoneH1hescCtcfStdAlnRep1.chr21.bam", 
    "wgEncodeBroadHistoneH1hescSuz12051317AlnRep1.chr21.bam")
)
colData <- data.frame(condition = c("CTCF", "SUZ12"), replicate = c(1, 1))

# Create dataset object
object_differential <- epigraHMMDataSetFromBam(
  bamFiles = bamFiles, 
  colData = colData, 
  genome = 'hg19', 
  windowSize = 500, 
  gapTrack = TRUE, 
  blackList = TRUE
)

# Set up EM control and run differential peak calling
control <- controlEM(fileName = 'control-differential', criterion = 'MACPE')
object_differential <- normalizeCounts(object_differential, control)
object_differential <- initializer(object_differential, control)
object_differential <- epigraHMM(object = object_differential, control = control, type = 'differential')

# Call peaks
peaks_differential <- callPeaks(object = object_differential)
```
*Input: BAM files and sample metadata; Output: A GRanges object containing called differential peak regions.*

### Standard Workflow

Detect consensus enrichment regions (peaks) across technical or biological replicates of a single experimental condition.

```r
library(epigraHMM)

# Define input BAM files and metadata
bamFiles <- system.file(
  package = "genomationData", 
  "extdata", 
  "wgEncodeBroadHistoneH1hescCtcfStdAlnRep1.chr21.bam"
)
colData <- data.frame(condition = "CTCF", replicate = 1)

# Create dataset object
object_consensus <- epigraHMMDataSetFromBam(
  bamFiles = bamFiles, 
  colData = colData, 
  genome = 'hg19', 
  windowSize = 500, 
  gapTrack = TRUE, 
  blackList = TRUE
)

The rest: https://github.com/bioMate-AI/biomate-bioconductor-kb/blob/HEAD/skills/epigenomics/epigrahmm/SKILL.md