Exploring chromatin hierarchical organization via Markov State Modelling.

Area of Science:

• Computational Biology
• Genomics
• Structural Biology

Background:

• Chromatin's three-dimensional structure is crucial for genome regulation and function.
• Understanding chromosome organization at different scales remains a challenge.

Purpose of the Study:

• To introduce a novel computational method for analyzing chromatin structural organization.
• To investigate the hierarchical structure and interactions within and between chromosomes.
• To link chromatin architecture to genome function through epigenetic data integration.

Main Methods:

• Markov State Modelling (MSM) of Hi-C interaction data.
• Analysis of Markov State Model metastability upon annealing to identify hierarchical partitions.
• Derivation of effective interactions between genomic partitions.
• Mapping epigenetic and gene expression data onto the derived interaction networks.

Main Results:

• Identified hierarchical structural partitions within chromosomes, analogous to topologically associating domains and epigenomic compartments.
• Delineated whole-genome architecture and inter-chromosomal associations.
• Revealed structure-function relationships by correlating chromatin organization with epigenetic factors and gene expression regulators.
• Developed a freely available Python package, ChromaWalker, to implement the method.

Conclusions:

• The proposed MSM-based method provides a robust framework for chromatin structural reconstruction and dynamic modeling.
• The analysis reveals key insights into genome architecture and its role in regulating gene function.
• This work lays the foundation for further exploration of chromatin dynamics and genome regulation.