scEnhancer

scEnhancer annotates enhancers, promoters, and enhancer-gene interactions from single-cell ATAC-seq (scATAC-seq) data across human (hg19), mouse (mm9), and fly (dm3) to provide cell-type-resolved regulatory annotations.

Key Features:

• Extensive Data Coverage: Contains 14,527,776 enhancers and 63,658,600 enhancer-gene interactions derived from 1,196,906 single cells across 775 distinct tissue or cell types.
• Single-cell chromatin accessibility profiling: Utilizes single-cell ATAC-seq (scATAC-seq) to profile open chromatin accessibility and annotate enhancers and promoters at individual-cell resolution.
• Unsupervised Learning Methodology: Aggregates and analyzes tens to hundreds of single-cell profiles within each tissue or cell type using an unsupervised learning approach to identify consensus enhancers.
• Cis-Regulatory Network Algorithm: Applies a cis-regulatory network algorithm to map connections between enhancers and their target genes at single-cell resolution.

Scientific Applications:

• Enhancer heterogeneity analysis: Investigate the heterogeneity of enhancer activities across different cell types and tissues.
• Single-cell enhancer-target regulation: Explore how specific enhancers regulate target genes within individual cells to reveal cell-type-specific regulatory dynamics.
• Development and disease regulatory element identification: Identify potential regulatory elements involved in developmental processes or disease states by examining enhancer-gene interactions at single-cell resolution.

Methodology:

Processes single-cell ATAC-seq (scATAC-seq) data, aggregates tens to hundreds of single-cell profiles per tissue or cell type via unsupervised learning, and applies a cis-regulatory network algorithm to infer enhancer-gene interactions.