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:

Hits are hypotheses -- screen results contain false positives; validate through orthogonal evidence
Selectivity matters -- pan-essential genes are poor drug targets; context-specific essentiality is high-value
Pathway over gene -- enriched pathways are more robust than individual hits
Druggability is practical -- prioritize chemically modulable targets
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 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]").

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):

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.

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

tooluniverse-functional-genomics-screens