mims-harvard 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 metadatagnomad_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 stringSTRING_get_network(identifiers="GENE1\rGENE2\rGENE3", species=9606)-- carriage-return separatedSTRING_functional_enrichment(identifiers=..., species=9606)-- GO/KEGG enrichment
Phase 3: Druggability
DGIdb_get_drug_gene_interactions(genes=["EGFR","BRAF"])-- drug-gene interactionsDGIdb_get_gene_druggability(genes=[...])-- categories (kinase, GPCR, etc.)- For high-priority hits, also search
search_clinical_trialsand PubMed for novel inhibitors not yet in DGIdb.
Phase 4: Clinical Evidence
civic_search_evidence_items(molecular_profile=gene)-- NOTqueryCOSMIC_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