From tooluniverse
Interprets CRISPR-KO/CRISPRi/shRNA screen hits by integrating DepMap essentiality, gnomAD constraint, pathway context, druggability, and clinical evidence for hit prioritization and target shortlisting.
How this skill is triggered — by the user, by Claude, or both
Slash command
/tooluniverse:tooluniverse-functional-genomics-screensThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Pipeline for validating and prioritizing hits from genetic screens (CRISPR-KO, CRISPRi, shRNA) by integrating essentiality (DepMap), constraint (gnomAD), pathways (Reactome, STRING), druggability (DGIdb), and clinical evidence (CIViC, COSMIC).
Pipeline for validating and prioritizing hits from genetic screens (CRISPR-KO, CRISPRi, shRNA) by integrating essentiality (DepMap), constraint (gnomAD), pathways (Reactome, STRING), druggability (DGIdb), and clinical evidence (CIViC, COSMIC).
Guiding principles:
When uncertain about any scientific fact, SEARCH databases first.
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.
Phase 0: Input Processing → gene list, screen type, cell line, disease context
Phase 1: Hit Validation → DepMap dependency, gnomAD constraint, UniProt function
Phase 2: Pathway & Network → Reactome enrichment, STRING network, functional clusters
Phase 3: Druggability → DGIdb interactions, druggable categories, PharmacoDB
Phase 4: Clinical Evidence → CIViC, COSMIC mutations
Phase 5: Literature → PubMed for key hits
Phase 6: Prioritized Report → ranked target list with multi-dimensional scoring
Tools:
DepMap_get_gene_dependencies(gene_symbol=...) -- returns gene metadata only (NOT per-cell-line scores)DepMap_search_cell_lines(query=...) -- cell line metadatagnomad_get_gene_constraints(gene_symbol=...) -- pLI, LOEUF (may return "Service overloaded")UniProt_get_function_by_accession(accession=...) -- function summaryClassification: Pan-essential (>90% lines), Selectively essential (specific lineages), Context-specific (screen model only). Chronos < -0.5 = likely essential, < -1.0 = strongly essential.
DepMap limitation: Tool returns metadata only. For actual Chronos scores, download CRISPRGeneEffect.csv from depmap.org and analyze locally. Fallback: gnomAD constraint + PubMed_search_articles(query="[gene] CRISPR screen [cancer]").
ReactomeAnalysis_pathway_enrichment(identifiers="TP53 BRCA1 EGFR") -- space-separated stringSTRING_get_network(identifiers="GENE1\rGENE2\rGENE3", species=9606) -- carriage-return separatedSTRING_functional_enrichment(identifiers=..., species=9606) -- GO/KEGG enrichmentDGIdb_get_drug_gene_interactions(genes=["EGFR","BRAF"]) -- drug-gene interactionsDGIdb_get_gene_druggability(genes=[...]) -- categories (kinase, GPCR, etc.)search_clinical_trials and PubMed for novel inhibitors not yet in DGIdb.civic_search_evidence_items(molecular_profile=gene) -- NOT queryCOSMIC_get_mutations_by_gene(gene_name=...) -- somatic mutation frequencyScoring (0-18):
| Criterion | Score 3 | Score 0 |
|---|---|---|
| Selective essentiality | <-0.5 in disease AND >-0.2 elsewhere | >-0.2 (not essential) |
| Pathway convergence | 3+ hits same pathway | Isolated hit |
| Druggability | Approved drug exists | Not druggable |
| Clinical evidence | CIViC therapeutic | No clinical data |
| Constraint | pLI >0.9 | No data |
| Literature | Multiple validation studies | No publications |
Tiers: T1 (15-18) high-confidence, T2 (10-14) promising, T3 (5-9) speculative, T4 (<5) likely false positive.
npx claudepluginhub mims-harvard/tooluniverse --plugin tooluniverseAnalyze CRISPR-Cas9 genetic screens: MAGeCK gene scores, sgRNA count QC, replicate correlation, hit prioritization, and pathway GSEA for essentiality, synthetic lethality, and drug target discovery.
Ranks CRISPR screen gene hits from local guide-level count tables by combining depletion, essentiality, and druggability into a deterministic triage score.
Queries DepMap for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use to find cancer-specific genetic vulnerabilities and synthetic lethal interactions.