hi-c-3d-genomics

To identify compartments, loops, and TADs from Hi-C data, provide processed contact matrices to a workflow that calls these 3D genome features. The workflow applies Python-based tooling to extract structural features and outputs reproducible artifacts in a structured layout.

Comparing 3D genome structure between treated and control samples requires applying a Hi-C analysis workflow across conditions and resolutions. This workflow identifies differential chromatin interactions and structural rearrangements, generating interpretable visualizations for publication or reporting.

Yes, this Hi-C analysis workflow enforces Python-based tooling to process contact maps and call 3D genome features. It also records software versions and key parameters to ensure the analysis is fully reproducible.

Yes, you can generate publication-ready figures from Hi-C contact matrices. The workflow produces interpretable visualizations of contact maps and derived structural features like loops and TADs directly suitable for publication or reporting.

To start analyzing 3D genome organization, you need to provide a processed Hi-C dataset, such as contacts or matrices. Supplying this input allows the workflow to generate compartments, loops, TADs, and corresponding visualizations.

Yes, you can apply this workflow to Hi-C contact matrices across different resolutions to compare structural differences. It processes the matrices across subjects and conditions to identify variations in 3D genome features.