COCOA

COCOA quantifies and annotates inter-sample epigenetic variation by analyzing covariation of epigenetic signals across genomic coordinates and supports DNA methylation and other coordinate-based epigenetic data.

Key Features:

• Coordinate covariation analysis: Measures covariation of epigenetic signals across individuals at genomic coordinates to detect shared patterns of variation.
• DNA methylation specialization: Implemented as the first method specifically tailored for DNA methylation data.
• Region sets annotation: Annotates variation using a database of "region sets," i.e., collections of genomic regions annotated with transcription factor (TF) binding sites, histone modification regions, or open chromatin regions.
• Supervised and unsupervised quantification: Quantifies inter-sample variation using either supervised or unsupervised techniques to identify region sets associated with significant variation.
• Multi-data applicability: Applicable to DNA methylation, ATAC-seq, and multi-omic datasets for comparative epigenetic analyses.

Scientific Applications:

• Annotation of epigenetic heterogeneity: Assigns biological annotations to regions exhibiting inter-sample epigenetic variation.
• Identification of regulatory region involvement: Links variation to region sets such as TF binding sites, histone modification regions, and open chromatin regions.
• Analysis of DNA methylation studies: Detects and annotates methylation-based inter-individual differences.
• Integration with ATAC-seq and multi-omics: Applies to open chromatin and multi-omic datasets to reveal coordinated epigenetic variation.

Methodology:

Quantifies inter-sample variation by analyzing covariation of epigenetic signals across genomic coordinates using supervised or unsupervised techniques and annotates identified variation with a database of region sets (e.g., TF binding sites, histone modification regions, open chromatin regions).