pC-SAC: A method for high-resolution 3D genome reconstruction from low-resolution Hi-C data

Area of Science:

• Genomics
• Computational Biology
• Epigenetics

Background:

• The 3D genome organization is vital for gene regulation and its disruption is linked to diseases.
• High-throughput Chromosome Conformation Capture (Hi-C) technologies map genomic interactions but require high sequencing depth for enhancer-promoter analysis.
• Current methods struggle to achieve high-resolution mapping of enhancer-promoter interactions cost-effectively.

Purpose of the Study:

• Introduce pC-SAC (probabilistically Constrained Self-Avoiding Chromatin), a novel computational method.
• Enable accurate high-resolution Hi-C matrices from low-resolution data.
• Enhance the study of 3D genome organization, gene regulation, and disease.

Main Methods:

• Utilizes adaptive importance sampling with sequential Monte Carlo.
• Generates ensembles of 3D chromatin chains adhering to physical constraints from low-resolution Hi-C data.
• Reconstructs high-resolution chromatin maps.

Main Results:

• Achieves over 95% accuracy in reconstructing high-resolution chromatin maps.
• Identifies novel interactions enriched with candidate cis-regulatory elements (cCREs) and expression quantitative trait loci (eQTLs).
• Outperforms state-of-the-art deep learning models in reconstructing both short- and long-range interactions.

Conclusions:

• pC-SAC provides a cost-effective solution for increasing Hi-C data resolution.
• Facilitates deeper insights into 3D genome organization, gene regulation, and disease mechanisms.
• The pC-SAC tool is publicly available for research use.