regulatory-community-analysis-ChIA-PET

To identify regulatory communities from ChIA-PET data, provide BED and BEDPE files to construct a peak-centered chromatin interaction network. The pipeline builds an undirected igraph from loops and peaks, applies Louvain clustering, and outputs community memberships.

A peak-centered chromatin interaction network represents protein-binding peaks as nodes and ChIA-PET loops as edges in an undirected graph. This structure enables graph clustering to detect modular regulatory communities and rank hub anchors by centrality.

Yes, the community detection workflow supports standard BED and BEDPE inputs containing protein-binding peaks. It standardizes peak data to build the network and produce visualizable community reports for comparison across conditions.

Yes, you can prioritize hub anchors in chromatin networks by computing centralities within the constructed igraph. After Louvain clustering detects modular structures, hub anchors are ranked by centrality to identify key regulatory drivers.

The best way to compare chromatin interaction networks across conditions is to construct separate peak-centered graphs from each dataset and compare their Louvain community structures. This produces interpretable visualizations of the largest communities.

Louvain clustering for ChIA-PET network analysis depends on the quality of BEDPE loops and BED peaks provided. Sparse chromatin interaction data may yield poorly connected graphs, affecting community detection accuracy and hub-anchor prioritization.