bio-db-tools

Bio DB Tools

Use this skill when the user asks to search or fetch data from public biology knowledge bases rather than analyze local files.

Typical triggers:

• protein function, accession, annotation, sequence metadata, domain architecture
• experimental PDB structures or AlphaFold models
• ClinVar pathogenicity or variant significance
• dbSNP rsIDs or gnomAD population-frequency / constraint lookups
• Ensembl gene coordinates, transcripts, or rsIDs
• GEO datasets, pathway databases, or interaction networks
• drug target and disease association evidence

Environment Check

The bundled template uses Python plus HTTP APIs. Check first.

which python3 || true
python3 - <<'PY'
mods = ["requests"]
for name in mods:
    try:
        __import__(name)
        print(f"{name}: ok")
    except Exception as exc:
        print(f"{name}: missing ({exc})")
PY

If outbound network access is blocked, say so explicitly before claiming the lookup ran.

Bundled Asset

Use the reusable template instead of rewriting API snippets every time:

• templates/bio_db_lookup.py

Supported sources:

• uniprot
• pdb
• alphafold
• clinvar
• dbsnp
• gnomad
• ensembl
• geo
• interpro
• kegg
• opentargets
• reactome
• stringdb

Quick Start

python3 templates/bio_db_lookup.py uniprot \
  --query TP53 \
  --organism-id 9606 \
  --output bio/uniprot_tp53.csv \
  --summary bio/uniprot_tp53.json

python3 templates/bio_db_lookup.py pdb \
  --query "EGFR kinase inhibitor" \
  --limit 5 \
  --output bio/pdb_egfr.csv \
  --summary bio/pdb_egfr.json

python3 templates/bio_db_lookup.py clinvar \
  --query 'BRCA1[gene] AND clinsig_pathogenic[prop]' \
  --output bio/clinvar_brca1.csv \
  --summary bio/clinvar_brca1.json

python3 templates/bio_db_lookup.py gnomad \
  --mode gene-constraint \
  --gene-symbol BRCA1 \
  --output bio/gnomad_brca1.csv \
  --summary bio/gnomad_brca1.json

python3 templates/bio_db_lookup.py reactome \
  --mode enrichment \
  --gene BRCA1 --gene BRCA2 --gene TP53 --gene ATM \
  --output bio/reactome_dna_repair.csv \
  --summary bio/reactome_dna_repair.json

Working Rules

1. Save both a machine-readable result file and a short summary JSON.
2. Report the exact database, query string, species, and any filters used.
3. Prefer exact identifiers when available: UniProt accession, PDB ID, Ensembl ID, rsID, ClinVar query, Reactome stable ID.
4. Return direct links for the user whenever the database exposes stable pages.
5. Distinguish clearly between:
   • search hits
   • record detail
   • enrichment or network evidence
6. If the remote API returns nothing, say that explicitly instead of inferring a biological conclusion.
7. Treat these sources as lookup surfaces, not experimental validation.

Common Patterns

Protein annotation

python3 templates/bio_db_lookup.py uniprot \
  --accession P04637 \
  --output bio/uniprot_p04637.csv \
  --summary bio/uniprot_p04637.json

Experimental structure lookup

python3 templates/bio_db_lookup.py pdb \
  --pdb-id 6LU7 \
  --output bio/pdb_6lu7.csv \
  --summary bio/pdb_6lu7.json

AlphaFold model metadata and download

python3 templates/bio_db_lookup.py alphafold \
  --uniprot-id P04637 \
  --download bio/AF-P04637-F1-model.pdb \
  --output bio/alphafold_tp53.csv \
  --summary bio/alphafold_tp53.json

dbSNP record lookup

python3 templates/bio_db_lookup.py dbsnp \
  --rsid rs429358 \
  --output bio/dbsnp_rs429358.csv \
  --summary bio/dbsnp_rs429358.json

Pathway and target evidence

python3 templates/bio_db_lookup.py opentargets \
  --mode disease-targets \
  --id EFO_0000305 \
  --limit 10 \
  --output bio/opentargets_breast_cancer.csv \
  --summary bio/opentargets_breast_cancer.json

python3 templates/bio_db_lookup.py stringdb \
  --mode network \
  --gene BRCA1 --gene BRCA2 --gene TP53 \
  --species 9606 \
  --output bio/string_brca_network.csv \
  --summary bio/string_brca_network.json

Output Expectations

Good answers should mention:

• which API or database was queried
• the exact identifier or text query
• how many hits were returned
• the key IDs, names, scores, frequencies, constraint metrics, or annotations
• the saved output paths
• any rate-limit, network, or schema caveats

Related Skills

For local sequence analysis, QC, plotting, or PubMed-style literature work, activate bio-tools. For single-cell, BAM or mzML dataset triage, activate omics-tools. For local VCF, SNV, indel, or SV summarization, activate variant-analysis-tools. For integrated target briefs across disease, drug, pathway, and interaction evidence, activate target-intelligence-tools. For public compound, regulatory, clinical-trial, or literature APIs such as PubChem, ChEMBL, openFDA, ClinicalTrials.gov, or OpenAlex, activate pharma-db-tools. For docking and structure preparation, activate docking-tools. For ligand properties, ADMET, DrugBank, or chemistry ML, activate chem-tools.