PCHi-C

PCHi-C analyzes promoter capture Hi-C (PCHi-C) chromatin interaction networks to identify connected components, assign significance scores, and relate component topology to biological function.

Key Features:

• Automatic Identification of Connected Components: Employs a method to automatically identify connected components in chromatin interaction graphs derived from PCHi-C data.
• Significance Scoring: Assigns a significance score to each component and filters components using a specified threshold.
• Biological Assessment of Components: Enables assessment of the biological roles of identified components to determine biological relevance.
• Application to Large Datasets: Applied to a PCHi-C dataset spanning 17 hematopoietic cell types, demonstrating scalability to large-scale interaction data.
• Correlation with Gene Modules: Identified component structure often correlates with functionally related gene modules.
• Adaptability: Method can be applied to other chromatin interaction datasets with multiple cell types and to other cell type-specific networks.

Scientific Applications:

• Genome architecture analysis: Characterizes topological features of chromatin interaction networks to inform genome organization studies.
• Gene regulation studies: Links component topology to functionally related gene modules to support investigations of regulatory interactions.
• Cellular differentiation research: Facilitates comparison of chromatin interaction components across cell types, including hematopoietic lineages, to study differentiation-related changes.
• Disease mechanism investigation: Supports exploration of how altered chromatin interaction topology may associate with disease-related regulatory perturbations.

Methodology:

Computationally identifies connected components in chromatin interaction graphs, assigns significance scores above a specified threshold per component, and permits assessment of their biological roles.