finding-aging-specific

SOP: finding aging-specific data

Beyond the general molecular-biology databases, several resources are aging-specific. Use them when researching genes, pathways, or interventions that have been studied in the context of aging.

Curated gene/intervention databases

GenAge — https://genomics.senescence.info/genes/

Best for: Genes with experimentally demonstrated effects on aging or longevity. Two subsets:

• GenAge-human (~300 genes) — genes with evidence linking them to human aging.
• GenAge-models (~2000 genes) — genes that affect lifespan in model organisms (mouse, worm, fly, yeast).

What to extract: GenAge entry ID, the supporting reference list, the organism in which the effect was shown.

Caveat: Inclusion criteria are loose — a single paper showing a lifespan effect in worms qualifies. Always check the strength of the underlying evidence.

LongevityMap — https://genomics.senescence.info/longevity/

Best for: GWAS hits and candidate-gene studies for human longevity. ~3000 entries.

What to extract: SNPs and effect sizes for human longevity associations. Useful for distinguishing “longevity gene in mice” from “longevity-associated locus in humans.”

DrugAge — https://genomics.senescence.info/drugs/

Best for: Compounds shown to extend lifespan in any model organism. ~700 entries.

What to extract: Compound, organism tested, magnitude of lifespan extension, reference paper.

CellAge — https://genomics.senescence.info/cells/

Best for: Genes whose manipulation alters cellular senescence (induces, prevents, or reverses).

AnAge — https://genomics.senescence.info/species/

Best for: Maximum lifespan, body weight, and life-history traits across ~4000 species. Useful when designing extrapolation arguments (e.g., “species X lives Y× longer than expected for body size — what’s special about it?”).

Atlas-style resources

Aging Atlas — https://ngdc.cncb.ac.cn/aging/

Best for: Multi-omics aging data (transcriptomics, epigenomics, single-cell across tissues and ages). Curated by Beijing Institute of Genomics.

What to extract: Tissue- and cell-type-specific expression changes with age, with references.

Tabula Muris Senis — https://tabula-muris-senis.ds.czbiohub.org/

Best for: Single-cell transcriptomic atlas of mouse aging across many tissues and ages. Authoritative resource for “what changes in cell type X with age in mouse.”

GTEx — https://gtexportal.org/

Best for: Human tissue gene expression with age and sex annotations. Use to check whether mouse expression-with-age findings translate to humans.

Translation / drug-target resources

Open Targets — https://platform.opentargets.org/

Best for: Disease-target evidence aggregation. For aging, search by aging-related diseases (Alzheimer’s, sarcopenia, frailty, cardiovascular disease, type 2 diabetes) and look at the genetic and chemical evidence supporting each target.

What to extract: Target-disease association scores; the underlying evidence types (genetics, drugs, RNA expression, animal models, text mining).

Open Targets Genetics — https://genetics.opentargets.org/

Best for: Mapping GWAS hits to likely causal genes via L2G (locus-to-gene) scoring.

Trial / intervention tracking

ClinicalTrials.gov — https://clinicaltrials.gov/

Best for: Active and completed human trials. Search by intervention name, condition (use both “aging” and aging-related conditions like “frailty”), or sponsor.

What to extract: NCT number, phase, status, primary outcomes, sponsor, completion date.

For senolytic and geroprotector trials, this is the canonical source.

EU Clinical Trials Register — https://www.clinicaltrialsregister.eu/

Best for: EU trials not registered on ClinicalTrials.gov.

Workflow tips

• Start narrow, expand outward. For a specific gene → GenAge → then UniProt + STRING + pathways.
• For interventions → DrugAge first to see if it’s been tested in any organism, then ChEMBL + DrugBank for mechanism.
• For “is X a hallmark or driver of aging” questions → Aging Atlas + Open Targets for translational evidence.
• For “is this mouse finding likely to replicate in humans” → GTEx + LongevityMap for human-side support.

What NOT to trust

• “Top supplements for longevity” listicles (use DrugAge instead).
• “Anti-aging gene” press releases (check GenAge for the actual evidence).
• Old (pre-2015) age-effect bulk-tissue transcriptomics — single-cell data has revealed major cell-composition confounds.

See also

• finding-pathway-data
• finding-protein-data
• finding-compound-data
• _extrapolation-guide — for evaluating model→human translation