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) 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