Developing a comprehensive database and search tool for single-cell ATAC-seq data

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) enables chromatin accessibility analysis at single-cell resolution.
• scATAC-seq is crucial for understanding gene regulation in cell development and disease.
• Existing scATAC-seq data is fragmented, lacking a unified, consistently formatted database.

Purpose of the Study:

• To create a comprehensive, accessible database of publicly available scATAC-seq datasets.
• To provide a search tool for efficient retrieval and analysis of scATAC-seq data.
• To facilitate multi-dataset analysis in R and Python environments.

Main Methods:

• Developed scATAC.Explorer, an R-accessible database and search tool.
• Curated a collection of 39 scATAC-seq datasets from diverse tissues and cell types.
• Integrated accessibility matrices, cell type annotations, and metadata for each dataset.

Main Results:

• scATAC.Explorer offers fast query and retrieval of scATAC-seq datasets.
• The database contains consistent formatting for 39 diverse scATAC-seq datasets.
• The tool is available via GitHub and as an R Bioconductor package.

Conclusions:

• scATAC.Explorer addresses the need for a unified scATAC-seq data repository.
• The database and search tool streamline analysis of single-cell chromatin accessibility data.
• Facilitates integration into existing R and Python workflows for broader research application.