PHi-C

PHi-C transforms Hi-C contact matrices into polymer-network models and polymer-dynamics simulations to infer spatial and temporal (4D) genome organization.

Key Features:

• Polymer Network Modeling: Employs a polymer network model that represents genomic loci as monomers with attractive and repulsive interactions to capture chromosomal spatial organization.
• Optimization of Interaction Parameters: Optimizes interaction parameters for the polymer network to reconstruct an optimized contact matrix that reflects underlying physical interactions.
• 4D Genome Simulation: Simulates four-dimensional genome features from 2D Hi-C contact matrices, enabling comparison with live-cell imaging observations.
• Dynamic Characterization: Produces polymer dynamics simulations that reveal dynamic characteristics of genomic loci and chromosomes over time.
• Input and Implementation: Accepts Hi-C contact matrices, including Juicer-formatted matrices, and is implemented in Python3.

Scientific Applications:

• Spatial genome modeling: Models and simulates the spatial organization of genomes within the cell nucleus using Hi-C-derived contact matrices.
• Dynamics analysis: Explores the dynamic behavior of genomic loci and chromosomes to study temporal aspects of genome organization.
• Integration with functional studies: Integrates static Hi-C data with simulated dynamic behavior to inform studies of gene regulation and chromosomal interactions.

Methodology:

Input of Hi-C contact matrix data; modeling the genomic region as a polymer network with attractive and repulsive monomer interactions; optimization of interaction parameters to reconstruct an optimized contact matrix; and execution of polymer dynamics simulations to generate 4D genome features.