dbscATAC

dbscATAC provides a specialized single-cell database for annotating super-enhancers, gene markers, and enhancer-gene interactions derived from scATAC-seq data across thirteen species.

Key Features:

• Super-enhancer annotation: Identifies 213,835 super-enhancers across 520 tissue/cell types from three species using improved machine learning algorithms.
• Enhancer and gene marker catalog: Contains 13,470,526 enhancers and 347,484 gene markers derived from 1,668,076 single cells spanning 1028 tissue/cell types.
• Enhancer-gene interaction mapping: Includes 10,402,346 enhancer-gene interactions to link cis-regulatory elements to target genes.
• Cross-species coverage: Integrates data from thirteen species for comparative analysis of cell-type-specific regulatory networks.
• Search and visualization: Enables exploration of enhancer landscapes and gene regulation through hierarchical query options and multiple analytic modules.

Scientific Applications:

• Cell-type-specific regulatory network analysis: Facilitates uncovering of cell-type-specific regulatory networks that complement scRNA-seq analysis.
• Single-cell epigenomics research: Supports exploration of enhancer landscapes, gene regulation, and cell-type-specific characteristics at single-cell resolution.
• Cis-regulatory element annotation: Enables annotation of dynamic enhancer-gene linkages and super-enhancer/typical enhancer identification in scATAC-seq datasets.

Methodology:

Improved machine learning algorithms were used to identify super-enhancers, enhancers, and gene markers from scATAC-seq data.