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macsr

Skill file skills/epigenomics/macsr/SKILL.md in bioMate-AI/biomate-bioconductor-kb. Read the publisher’s upstream source.

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Use bioconductor-macsr to call [narrow/broad] peaks from ChIP-seq treatment and control alignment files using a genome size of [gsize]

Required inputs

  • [narrow/broad]
  • [gsize]

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SKILL.md

name: bioconductor-macsrdescription: 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.when_to_use: Use when: ChIP-seq peak analysis (MACSr); ATAC-seq chromatin accessibility analysis (MACSr). Not for: Requires R ≥ 4.1.0user-invocable: false

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.

Key Parameters

  • gsize (required): Effective genome size (e.g., 5.2e7 for a subset or standard values for organisms).
  • store_bdg (FALSE): Logical indicating whether to save pileup and lambda signal tracks in bedGraph format.
  • broad (FALSE): Logical indicating whether to call broad peaks instead of narrow peaks.
  • cutoff_analysis (FALSE): Logical indicating whether to perform cutoff vs peak count analysis.
  • name ("NA"): Character string prefix for output files.
  • outdir ("."): Directory path where output files will be written.

Best Practices

  • Always provide a control/input sample (CTRL) to increase peak-calling specificity and reduce false positives.
  • Enable store_bdg = TRUE to generate signal tracks for downstream visualization in genome browsers.
  • Inspect the execution log stored in the macsList object using cat(paste(cp1$log, collapse="\n")) to verify model building and fragment size prediction.

Common Pitfalls

  • Using an incorrect effective genome size (gsize): This will lead to inaccurate p-value and q-value calculations. Ensure gsize matches your target organism.
  • Forgetting that MACSr writes files directly to disk: Always specify a valid, writable outdir (e.g., tempdir()) to avoid permission issues.

Alternatives

  • BayesPeak: For Bayesian analysis of ChIP-seq data.
  • csaw: For window-based de novo detection of differentially bound regions.

Citations

  • Zhang et al. (2008) "Model-based Analysis of ChIP-Seq (MACS)" (implied by the package description and vignette introduction).

References