
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- Repository
- bioMate-AI/biomate-bioconductor-kb793
- 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 usingbdgdiff(). - 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
Rsubreador external tools like Bowtie2. - For downstream peak annotation and motif discovery, use packages like
ChIPseekerormotifStack.
Data Requirements
- Alignment files in BED, BAM, or other supported formats (can be gzipped).
- Effective genome size (
gsize) corresponding to the organism of interest.
