Tooluniverse Functional Genomics Screens

Functional Genomics Screen Interpretation

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: 1. Hits are hypotheses -- screen results contain false positives; validate through orthogonal evidence 2. Selectivity matters -- pan-essential genes are poor drug targets; context-specific essentiality is high-value 3. Pathway over gene -- enriched pathways are more robust than individual hits 4. Druggability is practical -- prioritize chemically modulable targets 5. English-first queries -- use English gene names in tool calls

LOOK UP, DON'T GUESS

When uncertain about any scientific fact, SEARCH databases first.

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COMPUTE, DON'T DESCRIBE

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.

Workflow

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

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Phase Details

Phase 1: Hit Validation

Tools:

• DepMap_get_gene_dependencies(gene_symbol=...) -- returns gene metadata only (NOT per-cell-line scores)
• DepMap_search_cell_lines(query=...) -- cell line metadata
• gnomad_get_gene_constraints(gene_symbol=...) -- pLI, LOEUF (may return "Service overloaded")
• UniProt_get_function_by_accession(accession=...) -- function summary

Classification: Pan-essential (>90% lines), Selectively essential (specific lineages), Context-specific (screen model only). Chronos < -0.5 = likely essential, < -1.0 = strongly essential.

DepMap per-cell-line Chronos scores: DepMap_get_gene_dependencies returns metadata only. For the actual per-cell-line scores, use the bundled script in the cell-line-profiling skill — tooluniverse-cell-line-profiling/scripts/depmap_gene_dependency.py (downloads the current DepMap Public CRISPRGeneEffect.csv once, cached; queries by gene or cell-line):

python depmap_gene_dependency.py gene KRAS --lineage Lung --top 20   # most-dependent lines
python depmap_gene_dependency.py cell-line A375 --top 25             # genes the line needs

Chronos < -0.5 ≈ dependency, < -1.0 strongly essential. Fallback if you can't run it: gnomAD constraint + PubMed_search_articles(query="[gene] CRISPR screen [cancer]").

Phase 2: Pathway & Network

• ReactomeAnalysis_pathway_enrichment(identifiers="TP53 BRCA1 EGFR") -- space-separated string
• STRING_get_network(identifiers="GENE1\rGENE2\rGENE3", species=9606) -- carriage-return separated
• STRING_functional_enrichment(identifiers=..., species=9606) -- GO/KEGG enrichment

Phase 3: Druggability

• DGIdb_get_drug_gene_interactions(genes=["EGFR","BRAF"]) -- drug-gene interactions
• DGIdb_get_gene_druggability(genes=[...]) -- categories (kinase, GPCR, etc.)
• For high-priority hits, also search search_clinical_trials and PubMed for novel inhibitors not yet in DGIdb.

Phase 4: Clinical Evidence

• civic_search_evidence_items(molecular_profile=gene) -- NOT query
• COSMIC_get_mutations_by_gene(gene_name=...) -- somatic mutation frequency

Phase 6: Prioritized Report

Scoring (0-18):

Tiers: T1 (15-18) high-confidence, T2 (10-14) promising, T3 (5-9) speculative, T4 (<5) likely false positive.

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Edge Cases

• gnomAD overloaded: Retry once, proceed without, note gap
• Gene not in DepMap: Fall back to gnomAD + UniProt
• Large hit lists (>500): Pathway enrichment on full list; per-gene analysis on top 50
• Non-cancer screens: DepMap less informative; weight constraint/pathway more
• shRNA vs CRISPR: Higher validation bar for shRNA (off-target effects)

Limitations

• DepMap is cancer-centric (~1000 cancer lines)
• No raw screen analysis (use MAGeCK/BAGEL upstream)
• STRING interactions are associations, not causal