Epigenoomi

Epigenoomi is a term used to describe the integrated study of epigenetic information across multiple omics layers to understand how regulatory programs control gene expression and cellular phenotype. The concept emphasizes combining epigenomic data—DNA methylation, histone modifications, and chromatin accessibility—with other omics measurements, including transcriptomics, proteomics, and metabolomics, to build a unified view of regulation.

Methodologically, epigenoomi relies on high-throughput profiling techniques (WGBS, RRBS, ChIP-seq for histone marks, ATAC-seq, Hi-C) and on computational frameworks for multi-omics integration (matrix factorization, Bayesian networks, machine learning). Single-cell variants (scRNA-seq, scATAC-seq, single-cell methylation) enable analysis at cellular resolution. Key challenges include heterogeneity, data sparsity in single-cell data, and batch effects across platforms, requiring robust normalization and integration strategies.

Applications span basic and translational research. In cancer and development, epigenoomi aims to map regulatory networks, identify epigenetic biomarkers, and predict therapy responses. In aging and exposure science, it investigates how epigenetic landscapes influence phenotypes and disease risk. The field also supports interpreting noncoding variation and designing epigenetic interventions.

Status and terminology: epigenoomi is not yet a standardized discipline. Definitions vary, with some using it as an umbrella for epigenomics research that includes multi-omics data, while others reference specific analytic frameworks. Ongoing efforts seek to standardize data models, pipelines, and reporting practices to enable cross-study comparisons.