# Bioconductor Macsr

The Model-based Analysis of ChIP-Seq (MACS) is a widely used toolkit for identifying...

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

## Description

The Model-based Analysis of ChIP-Seq (MACS) is a widely used toolkit for identifying transcript factor binding sites. This package is an R wrapper of the lastest MACS3.

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

# MACSr

## Workflows

### Standard Workflow

```r
library(MACSr)

# Call narrow peaks
cp1 <- callpeak(CHIP, CTRL, gsize = 5.2e7, store_bdg = TRUE,
                name = "run_callpeak_narrow0", outdir = tempdir(),
                cutoff_analysis = TRUE)

# Call broad peaks
cp2 <- callpeak(CHIP, CTRL, gsize = 5.2e7, store_bdg = TRUE,
                name = "run_callpeak_broad", outdir = tempdir(),
                broad = TRUE)
```
Input: ChIP-seq treatment and control alignment files (e.g., BED or BAM format).
Output: A `macsList` object containing paths to called peaks (narrow or broad), summits, and bedGraph files.

## When to Use
- Calling narrow or broad peaks from ChIP-seq alignment files using `callpeak()`.
- Performing ATAC-seq chromatin accessibility peak calling using the Hidden Markov Model-based `hmmratac()`.
- Comparing two signal tracks in bedGraph format using `bdgcmp()` or detecting differential peaks using `bdgdiff()`.
- Predicting fragment size or d from alignment results using `predictd()`.

## When NOT to Use
- For initial sequence alignment or quality control of raw fastq files, use aligners like `Rsubread` or external tools like Bowtie2.
- For downstream peak annotation and motif discovery, use packages like `ChIPseeker` or `motifStack`.

## Data Requirements
- Alignment files in BED, BAM, or other supported formats (can be gzipped).
- Effective genome size (`gsize`) corresponding to the organism of interest.

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