AgingBank

AgingBank aggregates experimentally supported multi-omics data and provides analysis modules to characterize molecular mechanisms of aging and relationships between aging and cancer.

Key Features:

• Manually Curated Database: Contains 3,771 experimentally verified multi-omics entries derived from studies on over 50 model organisms including human, mouse, worm, fly, and yeast.
• Genome: Includes single nucleotide polymorphisms (SNPs), copy number variations (CNVs), and somatic mutations.
• Transcriptome: Covers mRNA, long non-coding RNA (lncRNA), microRNA (miRNA), and circular RNA (circRNA).
• Epigenome: Features DNA methylation and histone modifications.
• Regulatory Elements: Encompasses transcription factors, enhancers, promoters, gene silencing mechanisms, alternative splicing events, and RNA editing.
• Aging Landscape: Visualizes aging-related data across different species.
• Differential Expression Analyzer: Identifies changes in gene expression associated with aging.
• Data Heat Mapper: Generates heatmaps for visual representation of complex datasets.
• Co-Expression Network: Analyzes co-expression patterns among genes, miRNAs, lncRNAs, circRNAs, and methylation sites.
• Functional Annotation Analyzer: Performs functional annotation to interpret biological implications of aging-related data.
• Cancer & Aging Module: Analyzes relationships between aging and cancer.
• Submit & Analysis Module: Accepts user-provided experimental datasets for integrated analysis with the curated database.

Scientific Applications:

• Molecular mechanism elucidation: Supports identification of molecular mechanisms underlying aging via integrated multi-omics analysis.
• Comparative multi-species analysis: Enables identification of conserved pathways and species-specific processes across humans, mice, worms, flies, and yeast.
• Biomarker discovery: Facilitates differential expression and functional annotation to identify potential biomarkers for aging-related diseases.
• Aging–cancer interaction analysis: Enables exploration of interactions between aging and cancer to reveal shared mechanisms or targets.
• Custom dataset integration: Allows incorporation and analysis of user experimental datasets alongside curated entries for comparative studies.

Methodology:

Manual curation of 3,771 experimentally verified multi-omics entries; visualization via Aging Landscape; differential expression analysis; heatmap generation; co-expression network analysis among genes, miRNAs, lncRNAs, circRNAs, and methylation sites; functional annotation; computational analysis of aging–cancer relationships; processing of user-submitted datasets for integrated analysis.