AgeAnno

AgeAnno characterizes aging-related genes across diverse human tissue-cell types using single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) to link gene expression and chromatin accessibility changes to aging.

Key Features:

• Extensive Dataset: Contains 1,678,610 cells from 28 healthy tissue samples spanning ages 0 to 110 years.
• Integration of scRNA and scATAC Data: Integrates scRNA-seq and scATAC-seq to analyze gene expression and chromatin accessibility at single-cell resolution.
• Dynamic Functional Annotations: Provides functional annotations for 5,580 aging-related genes sourced from established databases and contextualized by cell type.
• scRNA-seq Analysis: Performs differential gene expression, computes gene variation coefficients, constructs cell–cell communication and transcription factor regulatory networks, and estimates immune cell proportions.
• scATAC-seq Analysis: Performs differential chromatin accessibility analysis, motif and transcription factor (TF) enrichment, footprint analysis, and co-accessibility peak analysis.

Scientific Applications:

• Biomarker Discovery: Identifies tissue- and cell type-specific biomarkers of aging.
• Mechanistic Insights: Elucidates molecular mechanisms of aging to inform development of targeted anti-aging therapeutics.
• Aging-Related Disease Research: Supports investigation of diseases associated with aging by linking gene- and chromatin-level changes to potential intervention pathways.

Methodology:

Collection of scRNA-seq and scATAC-seq data, integration and analysis to identify gene expression patterns and chromatin accessibility changes associated with aging, and application of bioinformatics tools to perform differential expression, network construction, and motif enrichment.