differential-region-analysis
The differential-region-analysis pipeline identifies genomic regions exhibiting significant differences in signal intensity between experimental conditions using a count-based framework and DESeq2. It supports detection of both differentially accessible regions (DARs) from open-chromatin assays (e.g., ATAC-seq, DNase-seq) and differential transcription factor (TF) binding regions from TF-centric assays (e.g., ChIP-seq, CUT&RUN, CUT&Tag). The pipeline can start from aligned BAM files or a precomputed count matrix and is suitable whenever genomic signal can be summarized as read counts per regio
npx skills add majiayu000/claude-skill-registry --skill 8-differential-region-analysis --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 Region Analysis with DESeq2 ## Overview This skill performs differential region analysis between experimental conditions using DESeq2 in a count-based framework. Main steps include: - Initialize the project directory. - Refer to the **Inputs & Outputs** section to check inputs and build the output architecture. All the output file should located in `${proj_dir}` in Step 0. - **Always prompt user** if required files are missing. - **Always prompt user** for the threshold of `qvalues` and `log2foldchange` to define significant regions. - Merge peaks across replicates or samples to build a consensus peak set. - Generate read count matrix over peaks using featureCounts or bedtools. - Prepare sample metadata file describing conditions and replicates. - Perform differential analysis using DESeq2. - Visualize and interpret results (PCA, volcano plot). - Output significantly up and down accessible regions. --- ## When to use this skill Use the differential-region-analysis pipeline when your goal is to identify genomic regions with condition-dependent changes in signal intensity, provided the signal can be represented as raw read counts per region. Recommended scenarios inclu
- Overview
- When to use this skill
- Inputs & Outputs
- Inputs (choose one)
- Outputs
- Decision Tree
- Step 0: Initialize Project
- Step 1: Generate Consensus Peaks
- Step 2: Generate Count Matrix
- Step 3: Prepare Metadata
- Step 4: Differential Accessibility with pyDESeq2
- Step 5: Visualization and QC
- Step 6: Output significantly up and down accessible regions
- Advanced Usage
tables/ all_peaks.bed consensus_peaks.bed # Unified peak set atac_counts.txt # Count matrix of reads per peak samples.csv # Sample metadata DARs/ DAR_results.csv # DESeq2 results (log2FC, p-values) DAR_sig.bed # Significantly diffential accessible regions DAR_up.bed DAR_down.bed
What does the differential-region-analysis skill do?
The differential-region-analysis pipeline identifies genomic regions exhibiting significant differences in signal intensity between experimental conditions using a count-based framework and DESeq2. It supports detection of both differentially accessible regions (DARs) from open-chromatin assays (e.g., ATAC-seq, DNase-seq) and differential transcription factor (TF) binding regions from TF-centric assays (e.g., ChIP-seq, CUT&RUN, CUT&Tag). The pipeline can start from aligned BAM files or a precomputed count matrix and is suitable whenever genomic signal can be summarized as read counts per regio
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill 8-differential-region-analysis --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.