| name | bio-database-evidence |
| description | Unified biological database evidence owner. Use for gene annotation, variant clinical significance, cancer mutation evidence, GWAS trait associations, pathway mapping, target-disease evidence, protein structures, protein interaction networks, reference single-cell census queries, and cross-database biological ID mapping. Do not use for full single-cell analysis, bulk RNA-seq differential expression, BAM/VCF processing, protein embedding models, metabolic flux modeling, genomic interval ML, or flow-cytometry file parsing. |
Bio Database Evidence
Use This Skill For
Use this skill when the main task is biological database lookup, annotation, or evidence gathering across one or more biological sources:
- Gene annotation, identifiers, RefSeq, Ensembl IDs, orthologs, VEP, GO, and genomic coordinates.
- Variant clinical significance, VUS interpretation support, ClinVar review status, cancer mutations, and COSMIC evidence.
- GWAS Catalog trait associations, rs IDs, p-values, summary statistics, and genetic epidemiology evidence.
- Pathway mapping, ID conversion, KEGG pathways, Reactome enrichment, disease pathways, and pathway evidence.
- Target-disease association evidence, tractability, safety, known drugs, and Open Targets evidence.
- Protein structure evidence from AlphaFold DB or RCSB PDB, including UniProt IDs, mmCIF/PDB downloads, pLDDT, PAE, and structure metadata.
- Protein-protein interaction evidence, STRING networks, hub proteins, and enrichment evidence.
- Reference single-cell data lookup from CELLxGENE Census when the user asks for census metadata or expression data, not full downstream analysis.
- Cross-database biological ID mapping and evidence tables across multiple resources.
Do Not Use This Skill For
- Single-cell RNA-seq analysis, clustering, UMAP, marker genes, cell annotation, AnnData/h5ad container editing, or scVI/scANVI batch-correction planning. Use
scanpy.
- Bulk RNA-seq differential expression. Use
pydeseq2.
- BAM, SAM, CRAM, VCF, pileup, coverage, or region extraction as a primary file-processing task.
- deepTools signal-track processing and heatmaps.
- Protein language models, embeddings, inverse folding, or protein-design workflows.
- Constraint-based metabolic modeling, FBA, or metabolic-engineering simulation.
- BED/genomic interval embeddings, genomic-region ML, or gene regulatory network inference.
- FCS or flow-cytometry file parsing.
Workflow
- Identify the biological entity type: gene, transcript, variant, pathway, target, protein structure, protein interaction, trait association, or reference cell population.
- Pick the narrowest source that answers the evidence question.
- Preserve source names, query terms, access dates, identifiers, and API caveats in the result.
- Return evidence in a table when comparing multiple sources.
- State when authentication, license, rate limits, or non-public access restricts a source.
Source Guide
See references/database-evidence-sources.md for source-specific boundaries and query notes.
