tooluniverse-crispr-screen-analysis - Claude MCP Skill

# ToolUniverse CRISPR Screen Analysis

Comprehensive skill for analyzing CRISPR-Cas9 genetic screens to identify essential genes, synthetic lethal interactions, and therapeutic targets through robust statistical analysis and pathway enrichment.

## Overview

CRISPR screens enable genome-wide functional genomics by systematically perturbing genes and measuring fitness effects. This skill provides an 8-phase workflow for:
- Processing sgRNA count matrices
- Quality control and normalization
- Gene-level essentiality scoring (MAGeCK-like and BAGEL-like approaches)
- Synthetic lethality detection
- Pathway enrichment analysis
- Drug target prioritization with DepMap integration
- Integration with expression and mutation data

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## Core Workflow

### Phase 1: Data Import & sgRNA Count Processing

Load sgRNA count matrix (MAGeCK format or generic TSV). Expected columns: `sgRNA`, `Gene`, plus sample columns. Create experimental design table linking samples to conditions (baseline/treatment) with replicate assignments.

### Phase 2: Quality Control & Filtering

Assess sgRNA distribution quality:
- **Library sizes** per sample (total reads)
- **Zero-count sgRNAs**: Count across samples
- **Low-count filtering**: Remove sgRNAs below threshold (default: <30 reads in >N-2 samples)
- **Gini coefficient**: Assess distribution skewness per sample
- Report filtering recommendations

### Phase 3: Normalization

Normalize sgRNA counts to account for library size differences:
- **Median ratio** (DESeq2-like): Calculate geometric mean reference, compute size factors as median of ratios
- **Total count** (CPM-like): Divide by library size in millions

Calculate log2 fold changes (LFC) between treatment and control conditions with pseudocount.

### Phase 4: Gene-Level Scoring

Two scoring approaches:
- **MAGeCK-like (RRA)**: Rank all sgRNAs by LFC, compute mean rank per gene. Lower mean rank = more essential. Includes sgRNA count and mean LFC per gene.
- **BAGEL-like (Bayes Factor)**: Use reference essential/non-essential gene sets to estimate LFC distributions. Calculate likelihood ratio (Bayes Factor) for each gene. Higher BF = more likely essential.

### Phase 5: Synthetic Lethality Detection

Compare essentiality scores between wildtype and mutant cell lines:
- Merge gene scores, calculate delta LFC and delta rank
- Filter for genes essential in mutant (LFC < threshold) but not wildtype (LFC > -0.5) with large rank change
- Sort by differential essentiality

Query DepMap/literature for known dependencies using PubMed search.

### Phase 6: Pathway Enrichment Analysis

Submit top essential genes to Enrichr for pathway enrichment:
- KEGG pathways
- GO Biological Process
- Retrieve enriched terms with p-values and gene lists

### Phase 7: Drug Target Prioritization

Composite scoring combining:
- **Essentiality** (50% weight): Normalized mean LFC from CRISPR screen
- **Expression** (30% weight): Log2 fold change from RNA-seq (if available)
- **Druggability** (20% weight): Number of drug interactions from DGIdb

Query DGIdb for each candidate gene to find existing drugs, interaction types, and sources.

### Phase 8: Report Generation

Generate markdown report with:
- Summary statistics (total genes, essential genes, non-essential genes)
- Top 20 essential genes table (rank, gene, mean LFC, sgRNAs, score)
- Pathway enrichment results (top 10 terms per database)
- Drug target candidates (rank, gene, essentiality, expression FC, druggability, priority score)
- Methods section

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## ToolUniverse Tool Integration

**Key Tools Used**:
- `PubMed_search` - Literature search for gene essentiality
- `Enrichr_submit_genelist` - Pathway enrichment submission
- `Enrichr_get_results` - Retrieve enrichment results
- `DGIdb_query_gene` - Drug-gene interactions and druggability
- `STRING_get_network` - Protein interaction networks
- `KEGG_get_pathway` - Pathway visualization

**Expression Integration**:
- `GEO_get_dataset` - Download expression data
- `ArrayExpress_get_experiment` - Alternative expression source

**Variant Integration**:
- `ClinVar_query_gene` - Known pathogenic variants
- `gnomAD_get_gene` - Population allele frequencies

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## Quick Start

```python
import pandas as pd
from tooluniverse import ToolUniverse

# 1. Load data
counts, meta = load_sgrna_counts("sgrna_counts.txt")
design = create_design_matrix(['T0_1', 'T0_2', 'T14_1', 'T14_2'],
                               ['baseline', 'baseline', 'treatment', 'treatment'])

# 2. Process
filtered_counts, filtered_mapping = filter_low_count_sgrnas(counts, meta['sgrna_to_gene'])
norm_counts, _ = normalize_counts(filtered_counts)
lfc, _, _ = calculate_lfc(norm_counts, design)

# 3. Score genes
gene_scores = mageck_gene_scoring(lfc, filtered_mapping)

# 4. Enrich pathways
enrichment = enrich_essential_genes(gene_scores, top_n=100)

# 5. Find drug targets
drug_targets = prioritize_drug_targets(gene_scores)

# 6. Generate report
report = generate_crispr_report(gene_scores, enrichment, drug_targets)
```

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## References

- Li W, et al. (2014) MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biology
- Hart T, et al. (2015) High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell
- Meyers RM, et al. (2017) Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens. Nature Genetics
- Tsherniak A, et al. (2017) Defining a Cancer Dependency Map. Cell (DepMap)

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## See Also

- `ANALYSIS_DETAILS.md` - Detailed code snippets for all 8 phases
- `USE_CASES.md` - Complete use cases (essentiality screen, synthetic lethality, drug target discovery, expression integration) and best practices
- `EXAMPLES.md` - Example usage and quick reference
- `QUICK_START.md` - Quick start guide
- `FALLBACK_PATCH.md` - Fallback patterns for API issues