Skills

Bioconductor Macsr

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

Install

npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-macsr -g -a claude-code -y
Category
Engineering
Updated
5mo ago
License
See repository
Context cost
Lightabout 796 tokens
Runs scripts
No, instructions only

What it does

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.

MACSr

Workflows

Standard Workflow

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.
Read the whole SKILL.md on GitHub

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