Tooluniverse Functional Genomics Screens

---
name: tooluniverse-functional-genomics-screens
description: Interpret hits from CRISPR-KO/CRISPRi/shRNA screens by integrating DepMap essentiality, gnomAD constraint scores, pathway context (Reactome, STRING), druggability (DGIdb), and clinical evidence (CIViC, COSMIC). Use for screen-hit prioritization, essentiality ranking, and turning a list of screen hits into a prioritized target shortlist.
disable-model-invocation: true
---

# 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`):

```bash
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)**:

| 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