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

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

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

Quick Start

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)

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)