dcHiC

dcHiC identifies differential chromatin compartments from Hi-C contact maps using a multivariate distance measure to detect biologically significant changes in genome compartmentalization across samples and timepoints.

Key Features:

• Multivariate Distance Measure: Employs a multivariate distance measure to compare multiple Hi-C contact maps and detect changes in compartmentalization.
• High Sensitivity and Effectiveness: Has been evaluated in in vitro mouse neural differentiation, mouse hematopoiesis, human lymphoblastoid cell lines (LCLs), and post-natal mouse brain development, demonstrating detection of biologically relevant compartment changes.
• Orthogonal Validation: Detected compartment changes have been validated by orthogonal methods and linked to dynamically regulated genes and cell identity.
• Integration of Multiple Genomic Features: Correlates compartment changes with chromatin states, subcompartments, replication timing, and lamin association.
• High-Resolution Analysis: Supports high-resolution analysis of genomic compartments.
• Differential Interaction Identification and Time-Series Clustering: Identifies differential interactions between genomic regions and supports clustering analyses over time-series Hi-C data.

Scientific Applications:

• Genome organization and chromatin dynamics: Characterizes dynamic organization of chromatin compartments in mammalian genomes.
• Cellular differentiation and development: Applied to studies of cellular differentiation and developmental processes, including mouse neural differentiation, hematopoiesis, and post-natal brain development.
• Gene regulation and cell identity: Links compartment changes to regulation of genes involved in cell identity and related epigenetic states.
• Comparative and longitudinal Hi-C analyses: Enables comparison across multiple conditions, samples, or timepoints in both bulk and single-cell Hi-C datasets.

Methodology:

Uses a multivariate distance measure to compare multiple Hi-C contact maps, identifies differential interactions, performs time-series clustering, and correlates compartment changes with chromatin states, subcompartments, replication timing, and lamin association.