SenSASP: A Unified, Multi-Layer Database of Senescence and SASP Genes

1,250 unique human senescence / SASP genes unified from CellAge, GenAge, SenMayo, and Reactome, enriched with cross-species conservation, GTEx expression, and STRING interactions.

Xuan, H., Huang, Y., & Bian, J. (2026). SenSASP: A Unified, Multi-Layer Database of Senescence and SASP Genes. bioRxiv 2026.08.26.747427. https://doi.org/10.64898/2026.08.26.747427

---

Web database

Browse and search all genes interactively — no account or software required:

https://xuan13hao.github.io/sensasp

• Searchable/sortable/filterable gene table (Tabulator.js)
• Per-gene detail pages: sensasp/gene.html?gene=TP53
• Coverage and source statistics
• One-click citation export (APA + BibTeX)

---

MCP server (Claude AI integration)

SenSASP ships as an MCP server so Claude and other AI assistants can query the database in natural language.

Install

pip install sensasp-mcp

Connect to Claude Desktop

Add to your Claude Desktop config file:

• Linux: ~/.config/claude/claude_desktop_config.json
• Mac: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "sensasp": {
      "command": "python3",
      "args": ["-m", "sensasp_mcp.server"]
    }
  }
}

Restart Claude Desktop. Then ask in natural language:

"Which SenSASP genes are confirmed by all four sources?" "Show me the full annotation for CDKN2A." "What are the top hub genes by STRING interaction degree?"

Zero-install with uvx

uvx sensasp-mcp

Available tools

Tool	Description
search_genes	Free-text + filter search (source, conservation, expression, STRING degree)
get_gene	Full record for one gene: conservation, GTEx tissues, STRING partners
get_database_stats	Gene count, coverage percentages, build metadata
list_sources	The 4 seed databases with gene counts and URLs
get_top_hub_genes	Top-N genes by STRING intra-set interaction degree
get_multi_source_genes	All 173 genes confirmed by ≥2 sources

Remote / self-hosted

Run as an HTTP server that any MCP client can reach without a local install:

python3 -m sensasp_mcp.server --transport sse --port 8080

Users connect via:

{ "mcpServers": { "sensasp": { "url": "https://your-server.com/sse" } } }

---

Pipeline

A fully scripted pipeline that reproduces the database from scratch:

./main.sh            # install deps, run all steps, regenerate figures
./main.sh --no-figs  # data only

Everything is fetched from public APIs at run time — no manual downloads.

Steps

Step	Module	Output
1	step1_acquire.py	CellAge, GenAge, SenMayo, Reactome → data/raw/
2	step2_harmonize.py	Symbol → Ensembl/UniProt/Entrez via MyGene.info
3	step3_unify.py	Collapse to unique genes, record provenance
4	step4_conservation.py	Mouse + zebrafish orthologs (Ensembl BioMart)
5	step5_expression.py	GTEx v8 (54 tissues) + Human Protein Atlas
6	step6_interactions.py	STRING v12 high-confidence PPI (score ≥ 700)
7	step7_assemble.py	Merge all layers → senescence_sasp_database.{json,csv}
8	step8_metrics.py	Coverage metrics → build_metrics.json

PYTHONPATH=. python3 run_pipeline.py          # all steps
PYTHONPATH=. python3 run_pipeline.py 4 5 6   # specific steps only

Regenerate site data

After re-running the pipeline:

python3 generate_site_data.py

Rebuilds docs/data/genes.json, genes_full.json, metrics.json, sources.json, and the bundled data in sensasp_mcp/data/.

---

Repository layout

sensasp/
├── senescence_sasp_database.{json,csv}  # final database
├── generate_site_data.py                # regenerate docs/data/ + sensasp_mcp/data/
├── mcp_server.py                        # standalone MCP server (dev)
├── docs/                                # GitHub Pages site
│   ├── index.html                       # gene table
│   ├── gene.html                        # per-gene detail page
│   ├── cite.html                        # citation page
│   └── data/                            # JSON files served to the browser
├── sensasp_mcp/                         # PyPI package (pip install sensasp-mcp)
│   ├── server.py                        # MCP server with stdio + SSE transport
│   └── data/                            # bundled JSON for offline use
└── senescence_db_pipeline/              # reproducible pipeline
    ├── pipeline/                        # step1..step8 modules
    ├── notebooks/figures.ipynb
    └── results/                         # build metrics, summary report

---

Key results

1,460 summed source entries → 1,250 unique genes (210 redundant collapsed, 14.4%); 173 genes confirmed by ≥2 sources, 2 (IL6, JUN) by all four. Net-new annotation reaches 95.8% (conservation), 97.9% (expression), 93.0% (interactions), with 89.4% complete across all three layers.

---

Citation

@article{Xuan2026.08.26.747427,
  author       = {Xuan, Hao and Huang, Yu and Bian, Jiang},
  title        = {SenSASP: A Unified, Multi-Layer Database of Senescence and SASP Genes},
  elocation-id = {2026.08.26.747427},
  year         = {2026},
  doi          = {10.64898/2026.08.26.747427},
  publisher    = {Cold Spring Harbor Laboratory},
  URL          = {https://www.biorxiv.org/content/early/2026/09/11/2026.08.26.747427},
  journal      = {bioRxiv}
}