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differential-region-analysis

Identify condition-dependent genomic regions from count data using DESeq2.

12|3|Updated Nov 4, 2025
Search Tags:#genomics#deseq2#atac-seq#differential-analysis#chip-seq#counts
One-click install
npx skills add https://github.com/BIsnake2001/ChromSkills --skill differential-region-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: differential-region-analysis
Source: https://github.com/BIsnake2001/ChromSkills/tree/main/8.differential-region-analysis
Command: npx skills add https://github.com/BIsnake2001/ChromSkills --skill differential-region-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The differential-region-analysis pipeline identifies genomic regions exhibiting significant differences in signal intensity between conditions using a count-based framework and DESeq2. It supports detection of both differential accessible regions (DARs) from open-chromatin assays and differential transcription factor (TF) binding regions from TF-centric assays. 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 region.

Core Features & Use Cases

  • Consensus peak generation across replicates to define a common feature space.
  • Count-matrix construction from BAMs or peak inputs, metadata preparation, and DESeq2 differential testing.
  • Visualization and export of significant regions (PCA, volcano plots, DAR bed files) for downstream interpretation.

Quick Start

Provide inputs (BAMs and peak files or a count matrix) and set your qvalue and log2fc thresholds to run the analysis.

Frequently Asked Questions about differential-region-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find differential accessible regions from ATAC-seq count data?▼

Yes, DESeq2 handles batch-aware designs by incorporating batch variables into the differential testing model. This allows accurate detection of differential genomic regions while controlling for batch effects across replicates.

What is the best way to generate a consensus peak set across replicates for differential analysis?▼

Generating a consensus peak set across replicates defines a common feature space for differential analysis. The pipeline creates this consensus automatically from your peak inputs before constructing the count matrix for DESeq2 testing.

Can I start differential region analysis directly from a count matrix instead of BAM files?▼

The pipeline exports DAR BED files of significant regions for downstream interpretation. It also generates PCA and volcano plots to help visualize the differential testing results from your genomic signal data.

Do I need replicates to detect differential TF binding regions using DESeq2?▼

Yes, you need replicates to detect differential TF binding regions using DESeq2. The pipeline requires replicate samples to model biological variance and accurately identify significant differences in TF binding signal intensity.

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