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Gene regulatory network analysis — TF-target inference (JASPAR motifs, ChIP-seq), motif scanning, eQTL integration, perturbation evidence (knockout/overexpression).
Passed 14 of 20 test cases with the skill, and 8 of 20 without it.
and 7 more in the full report
7 fixed10 passed either way7 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
What tool and ontology abbreviation should be queried to retrieve Sequence Ontology terms for transcription factor binding sites?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies calling ols_search_terms. | Missed | Passed |
| Specifies passing so as the ontology filter parameter. | Missed | Passed |
The instructions your model reads when the skill loads.
GRN inference starts with: which TF regulates which gene? Direct evidence (ChIP-seq binding) is stronger than indirect (co-expression correlation). A TF binding near a gene doesn't prove regulation — check if expression changes when the TF is perturbed. JASPAR provides binding motifs but motif presence in a promoter is only computational evidence (T3); ENCODE ChIP-seq data that places the TF at the locus in the relevant cell type is stronger (T1). eQTLs from GTEx show which variants affect expression but don't identify the upstream regulator — combine with TF motif disruption analysis for mechanistic insight.
LOOK UP DON'T GUESS: never assume JASPAR matrix IDs, Enrichr library names, or GTEx tissue identifiers — always search JASPAR by TF name and verify library names before calling enrichr.
Activate this skill when the user asks about:
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
Determine:
Search JASPAR for the TF's position weight matrix (PWM) and binding motif profile.
Tool: jaspar_search_matrices
Parameters:
search string TF name to search (e.g., "TP53")
limit integer Max results (default 10)
collection string JASPAR collection filter (e.g., "CORE")
species string Taxonomy ID filter (e.g., "9606" for human)
test cases passed, vs 8 / 20 without
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