Hi-C Compartment Shift Analysis

关于

This skill streamlines the identification of A/B compartment shifts between genomic conditions using Hi-C data and the HOMER framework. By processing PC1 eigenvector values across multiple replicates, it automates the complex workflow of file conversion, genomic binning, and differential analysis using limma statistics. It is particularly valuable for researchers looking to detect regions flipping between active (A) and inactive (B) compartments, providing clear insights into chromatin structural changes across different cell types or experimental conditions.

主要功能

• Replicate-aware differential PC1 analysis using limma statistics
• Guided prompts for genome assembly selection to ensure analytical accuracy
• Generation of unified genomic bin lists for consistent multi-sample comparison
• 2 GitHub stars
• Identification of significant compartment-shift domains and flipping regions
• Automatic conversion of TSV data to HOMER-compatible bedGraph formats

使用场景

• Integrating structural Hi-C changes with other epigenetic datasets for multi-omic analysis
• Identifying genomic regions that flip from active to inactive compartments during cellular development
• Comparing chromatin organization between two different cell types or disease states