bio-atac-seq-differential-accessibility
Find differentially accessible chromatin regions between conditions using DiffBind or DESeq2. Use when comparing chromatin accessibility between treatment groups, cell types, or developmental stages in ATAC-seq experiments.
npx skills add majiayu000/claude-skill-registry --skill differential-accessibility-gptomics-bioskills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Differential Accessibility ## DiffBind Workflow ```r library(DiffBind) # 1. Create sample sheet samples <- data.frame( SampleID = c('ctrl_1', 'ctrl_2', 'treat_1', 'treat_2'), Condition = c('control', 'control', 'treated', 'treated'), Replicate = c(1, 2, 1, 2), bamReads = c('ctrl_1.bam', 'ctrl_2.bam', 'treat_1.bam', 'treat_2.bam'), Peaks = c('ctrl_1.narrowPeak', 'ctrl_2.narrowPeak', 'treat_1.narrowPeak', 'treat_2.narrowPeak') ) write.csv(samples, 'samples.csv', row.names=FALSE) # 2. Load data dba <- dba(sampleSheet='samples.csv') # 3. Count reads dba <- dba.count(dba) # 4. Normalize (required in DiffBind 3.0+) dba <- dba.normalize(dba) # 5. Set up contrasts dba <- dba.contrast(dba, contrast=c('Condition', 'treated', 'control')) # 6. Differential analysis dba <- dba.analyze(dba) # 7. Get results results <- dba.report(dba) ``` ## DiffBind with Consensus Peaks ```r library(DiffBind) # Load samples dba <- dba(sampleSheet='samples.csv') # Count with specific parameters dba <- dba.count(dba, summits=250, # Re-center peaks on summit minOverlap=2, # Peak in at least 2 samples score=DBA_SCORE_NORMALIZED) # Normalize dba <- dba.normalize(dba, normalize=DBA_NORM_NATIVE) # Analyze dba <- dba.
- DiffBind Workflow
- DiffBind with Consensus Peaks
- DiffBind Visualizations
- Using DESeq2 Directly
- Count Reads in Peaks
- Python Alternative
- Annotate Differential Peaks
- Filter Results
- Multi-factor Designs
- Related Skills
Using featureCounts
First convert peaks to SAF format
awk 'BEGIN{OFS="\t"; print "GeneID\tChr\tStart\tEnd\tStrand"}
featureCounts \What does the bio-atac-seq-differential-accessibility skill do?
Find differentially accessible chromatin regions between conditions using DiffBind or DESeq2. Use when comparing chromatin accessibility between treatment groups, cell types, or developmental stages in ATAC-seq experiments.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill differential-accessibility-gptomics-bioskills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.