Aging Atlas

Aging Atlas integrates multi-omics datasets including transcriptomics, single-cell transcriptomics, epigenomics, proteomics, genomics, metabolomics, and pharmacogenomics to enable analysis of molecular mechanisms underlying organismal aging.

Key Features:

• Multi-Omics Integration: Integrates high-throughput datasets across genomics, transcriptomics (RNA-seq), single-cell transcriptomics (scRNA-seq), epigenomics (ChIP-seq), proteomics, metabolomics, and pharmacogenomics.
• Transcriptomics (RNA-seq): Provides age-associated gene expression profiles derived from RNA-seq datasets.
• Single-Cell Transcriptomics (scRNA-seq): Contains single-cell expression data to reveal cellular heterogeneity in aging tissues.
• Epigenomics (ChIP-seq): Includes ChIP-seq data on epigenetic modifications that influence gene regulation during aging.
• Proteomics (protein-protein interactions): Contains proteomic datasets and protein-protein interaction information to assess functional proteome changes with age.
• Pharmacogenomics (geroprotective compounds): Catalogues pharmacogenomic data on geroprotective compounds to support identification of potential anti-aging agents.
• Genomics: Includes genomic datasets relevant to aging studies.
• Metabolomics: Incorporates metabolomic datasets relevant to biochemical changes during aging.

Scientific Applications:

• Exploration of Molecular Profiles: Investigate molecular signatures associated with aging at both population and single-cell levels.
• Regulatory Status Analysis: Analyze gene expression regulation and epigenetic modifications during aging.
• Development of Aging Interventions: Identify candidate targets and geroprotective compounds for anti-aging therapies through integrated multi-omics analysis.

Methodology:

Collection and integration of high-throughput omics datasets (RNA-seq, scRNA-seq, ChIP-seq, proteomics, metabolomics, genomics, and pharmacogenomics) with modular curation for each data type.