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Run or interpret gene set enrichment (Enrichr/g:Profiler) and summarize results.
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Basé sur la classification professionnelle SOC
Manage the BioAPEX current-feature workflow from scoping through review and completion
Turn an analysis request into a Slurm-ready execution plan with commands, resource assumptions, and job structure.
Scale a buffer recipe to a target volume and compute component masses/volumes.
Save a fetched summary or document to the knowledge base for later retrieval (e.g. after PubMed/UniProt lookup).
Interpret scRNA clusters using marker genes and suggest cell type or state.
Critically evaluate a perturbation hypothesis — challenge assumptions, propose negative controls, and flag confounders.
| name | gene_set_enrichment |
| description | Run or interpret gene set enrichment (Enrichr/g:Profiler) and summarize results. |
| category | bio/single_cell_rna |
| version | 1 |
| requires_tools | ["fetch_url","python_repl"] |
| requires_network | true |
| user_invocable | true |
| species | any |
| modality | single_cell_rna |
| stage | interpretation |
| stability | experimental |
| safety_level | low |
User has a list of genes (e.g. DE genes) and wants enrichment against GO, KEGG, Reactome, or similar.
Normalize: Parse gene list into a clean list (max ~500 for API limits).
Enrichr: Use Enrichr API if available:
https://maayanlab.cloud/Enrichr/addList then GET results from https://maayanlab.cloud/Enrichr/enrich with ontology (e.g. GO_Biological_Process_2021, KEGG_2021_Human).Or g:Profiler: fetch_url to g:Profiler API with gene list and sources (GO, KEGG, REAC).
Parse: Extract top 10–15 terms: term name, p-value, adjusted p-value, genes overlapping.
Interpret: One paragraph summary: what biological processes/pathways are enriched and what that suggests.