Statistical relationships across epigenomes using large-scale hierarchical clustering.

Area of Science:

• Genomics and Epigenetics
• Computational Biology
• Immunology

Background:

• Genomic sequencing generates vast epigenomic data, challenging biological interpretation due to complex patterns.
• Machine learning offers a promising approach to analyze epigenomic data for insights into infectivity and susceptibility.

Purpose of the Study:

• To develop a framework for characterizing relationships among epigenetic modifiers, their regulators, genetic loci, and immune cell types.
• To apply hierarchical clustering to over 3000 epigenomes from uninfected individuals.

Main Methods:

• Utilized hierarchical clustering to analyze epigenomic data across all chromosomes.
• Performed Gene Ontology and KEGG pathway analyses to identify enriched biological functions.
• Employed co-occurrence analysis to identify sets of modifiers that function together.

Main Results:

• Identified consistent epigenetic patterns across chromosomes, with epigenetic modifier variation being greater than cell type variation.
• Found significant enrichment of genes involved in chromatin remodeling, immune responses, and RNA regulation.
• Observed biologically relevant clustering of epigenetic modifiers, including cohesin complex and PRC2 members, with consistent cross-chromosomal patterns.

Conclusions:

• The developed framework robustly characterizes epigenetic modifier relationships and their consistency across chromosomes.
• Findings highlight the importance of epigenetic regulation in immune responses and gene expression.
• The analysis pipeline is publicly available, promoting reproducibility and further research.