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.

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

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.

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