domains / families / science / bio-database-evidence

bio-database-evidence

separate

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.

When to use

• 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.

When not to use

• 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.

Skill page

Source