lamm-mit name: tooluniverse-crispr-screen-analysis description: Comprehensive CRISPR screen analysis for functional genomics. Analyze pooled or arrayed CRISPR screens (knockout, activation, interference) to identify essential genes, synthetic lethal interactions, and drug targets. Perform sgRNA count processing, gene-level scoring (MAGeCK, BAGEL), quality control, pathway enrichment, and drug target prioritization. Use for CRISPR screen analysis, gene essentiality studies, synthetic lethality detection, functional genomics, drug target validation, or identifying genetic vulnerabilities.
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
import pandas as pd
import numpy as np
def load_sgrna_counts(counts_file):
"""
Load sgRNA count matrix from MAGeCK format or generic TSV.
Expected format:
sgRNA | Gene | Sample1 | Sample2 | Sample3 | ...
sgRNA_1 | BRCA1 | 1500 | 1200 | 1100 | ...
sgRNA_2 | BRCA1 | 1800 | 1500 | 1400 | ...
"""
counts = pd.read_csv(counts_file, sep='\t')
# Validate required columns
required_cols = ['sgRNA', 'Gene']
if not all(col in counts.columns for col in required_cols):
raise ValueError(f"Missing required columns: {required_cols}")
# Extract sample columns
sample_cols = [col for col in counts.columns if col not in ['sgRNA', 'Gene']]
# Create count matrix
count_matrix = counts[sample_cols].copy()
count_matrix.index = counts['sgRNA']
# Gene mapping
sgrna_to_gene = dict(zip(counts['sgRNA'], counts['Gene']))
metadata = {
'n_sgrnas': len(counts),
'n_genes': counts['Gene'].nunique(),
'n_samples': len(sample_cols),
'sample_names': sample_cols,
'sgrna_to_gene': sgrna_to_gene
}
return count_matrix, metadata
# Load counts
counts, meta = load_sgrna_counts("sgrna_counts.txt")
print(f"Loaded {meta['n_sgrnas']} sgRNAs targeting {meta['n_genes']} genes across {meta['n_samples']} samples")
Create Experimental Design Table
def create_design_matrix(sample_names, conditions, timepoints=None):
"""
Create experimental design linking samples to conditions.
Example:
Sample | Condition | Timepoint | Replicate
T0_rep1 | baseline | 0 | 1
T14_rep1 | treatment | 14 | 1
"""
design = pd.DataFrame({
'Sample': sample_names,
'Condition': conditions
})
if timepoints is not None:
design['Timepoint'] = timepoints
# Auto-detect replicates
design['Replicate'] = design.groupby('Condition').cumcount() + 1
return design
# Example usage
sample_names = ['T0_rep1', 'T0_rep2', 'T14_rep1', 'T14_rep2', 'T14_rep3']
conditions = ['baseline', 'baseline', 'treatment', 'treatment', 'treatment']
design = create_design_matrix(sample_names, conditions)
Phase 2: Quality Control & Filtering
Assess sgRNA Distribution
def qc_sgrna_distribution(count_matrix, min_reads=30, min_samples=2):
"""
Quality control for sgRNA distribution.
- Remove sgRNAs with low read counts
- Check for outlier samples
- Assess library representation
"""
results = {}
# 1. Library size per sample
library_sizes = count_matrix.sum(axis=0)
results['library_sizes'] = library_sizes
results['median_library_size'] = library_sizes.median()
# 2. Zero-count sgRNAs
zero_counts = (count_matrix == 0).sum(axis=1)
results['zero_counts'] = zero_counts
results['sgrnas_with_zeros'] = (zero_counts > 0).sum()
# 3. Low-count sgRNAs (< min_reads in > min_samples)
low_count_mask = (count_matrix < min_reads).sum(axis=1) > (len(count_matrix.columns) - min_samples)
results['low_count_sgrnas'] = low_count_mask.sum()
# 4. Gini coefficient (library skewness)
def gini_coefficient(counts):
sorted_counts = np.sort(counts)
n = len(counts)
cumsum = np.cumsum(sorted_counts)
return (2 * np.sum((np.arange(1, n+1)) * sorted_counts)) / (n * cumsum[-1]) - (n + 1) / n
results['gini_per_sample'] = {col: gini_coefficient(count_matrix[col].values)
for col in count_matrix.columns}
# 5. Recommend filtering
results['filter_recommendation'] = {
'min_reads': min_reads,
'min_samples_above_threshold': min_samples,
'sgrnas_to_remove': low_count_mask.sum()
}
return results
# Run QC
qc_results = qc_sgrna_distribution(counts, min_reads=30, min_samples=2)
print(f"Library sizes: {qc_results['library_sizes']}")
print(f"Low-count sgRNAs to remove: {qc_results['filter_recommendation']['sgrnas_to_remove']}")
Filter Low-Count sgRNAs
def filter_low_count_sgrnas(count_matrix, sgrna_to_gene, min_reads=30, min_samples=2):
"""
Remove sgRNAs with insufficient read counts.
"""
# Keep sgRNAs with >= min_reads in >= min_samples
keep_mask = (count_matrix >= min_reads).sum(axis=1) >= min_samples
filtered_counts = count_matrix[keep_mask].copy()
filtered_mapping = {k: v for k, v in sgrna_to_gene.items() if k in filtered_counts.index}
print(f"Filtered: {(~keep_mask).sum()} sgRNAs removed, {keep_mask.sum()} retained")
return filtered_counts, filtered_mapping
# Apply filtering
filtered_counts, filtered_mapping = filter_low_count_sgrnas(counts, meta['sgrna_to_gene'])
Phase 3: Normalization
Library Size Normalization
def normalize_counts(count_matrix, method='median'):
"""
Normalize sgRNA counts to account for library size differences.
Methods:
- 'median': Median ratio normalization (like DESeq2)
- 'total': Total count normalization (CPM-like)
"""
if method == 'median':
# Calculate geometric mean for each sgRNA across samples
pseudo_ref = np.exp(np.log(count_matrix + 1).mean(axis=1)) - 1
# Calculate size factors for each sample
size_factors = {}
for col in count_matrix.columns:
ratios = count_matrix[col] / pseudo_ref
ratios = ratios[ratios > 0] # Remove zeros
size_factors[col] = ratios.median()
# Normalize
normalized = count_matrix.div(pd.Series(size_factors), axis=1)
elif method == 'total':
# CPM-like normalization
size_factors = count_matrix.sum(axis=0) / 1e6
normalized = count_matrix.div(size_factors, axis=1)
else:
raise ValueError(f"Unknown normalization method: {method}")
return normalized, size_factors
# Normalize
norm_counts, size_factors = normalize_counts(filtered_counts, method='median')
Log-Fold Change Calculation
def calculate_lfc(norm_counts, design, control_condition='baseline', treatment_condition='treatment'):
"""
Calculate log2 fold changes between treatment and control.
"""
# Get sample names for each condition
control_samples = design[design['Condition'] == control_condition]['Sample'].tolist()
treatment_samples = design[design['Condition'] == treatment_condition]['Sample'].tolist()
# Calculate mean counts
control_mean = norm_counts[control_samples].mean(axis=1)
treatment_mean = norm_counts[treatment_samples].mean(axis=1)
# Log2 fold change (add pseudocount to avoid log(0))
lfc = np.log2((treatment_mean + 1) / (control_mean + 1))
return lfc, control_mean, treatment_mean
# Calculate LFC
lfc, control_mean, treatment_mean = calculate_lfc(norm_counts, design)
Phase 4: Gene-Level Scoring (MAGeCK-like)
Aggregate sgRNA Scores to Gene Level
def mageck_gene_scoring(lfc, sgrna_to_gene, method='rra'):
"""
Gene-level essentiality scoring using MAGeCK-like approach.
Methods:
- 'rra': Robust Rank Aggregation (identify genes with consistently low-ranking sgRNAs)
- 'mean': Simple mean LFC across sgRNAs
"""
# Create gene-level aggregation
gene_lfc = {}
for sgrna, gene in sgrna_to_gene.items():
if sgrna in lfc.index:
if gene not in gene_lfc:
gene_lfc[gene] = []
gene_lfc[gene].append(lfc[sgrna])
if method == 'rra':
# Simplified RRA: rank sgRNAs, calculate p-value for each gene
# based on whether its sgRNAs are enriched at the top (negative selection)
# or bottom (positive selection)
# Rank all sgRNAs by LFC
ranked_sgrnas = lfc.sort_values()
ranks = {sgrna: rank for rank, sgrna in enumerate(ranked_sgrnas.index, 1)}
gene_scores = {}
for gene, sgrna_list in gene_lfc.items():
# Get ranks for this gene's sgRNAs
gene_ranks = [ranks[sgrna] for sgrna in sgrna_list if sgrna in ranks]
if len(gene_ranks) > 0:
# Use mean rank as score (lower = more essential)
gene_scores[gene] = {
'score': np.mean(gene_ranks),
'n_sgrnas': len(gene_ranks),
'mean_lfc': np.mean([lfc[sg] for sg in sgrna_list if sg in lfc.index])
}
# Convert to DataFrame
gene_df = pd.DataFrame(gene_scores).T
gene_df['rank'] = gene_df['score'].rank()
elif method == 'mean':
# Simple mean LFC
gene_df = pd.DataFrame({
gene: {
'mean_lfc': np.mean(sgrna_lfcs),
'n_sgrnas': len(sgrna_lfcs),
'score': np.mean(sgrna_lfcs)
}
for gene, sgrna_lfcs in gene_lfc.items()
}).T
# Sort by essentiality (negative LFC = essential)
gene_df = gene_df.sort_values('mean_lfc')
return gene_df
# Gene-level scoring
gene_scores = mageck_gene_scoring(lfc, filtered_mapping, method='rra')
print(f"Top 10 essential genes:\n{gene_scores.head(10)[['mean_lfc', 'n_sgrnas']]}")
Bayes Factor Scoring (BAGEL-like)
def bagel_bayes_factor(lfc, sgrna_to_gene, essential_genes=None, nonessential_genes=None):
"""
BAGEL-like Bayes Factor calculation for gene essentiality.
Uses reference sets of known essential and non-essential genes to
calculate likelihood ratios.
"""
# Default reference gene sets (core essential genes)
if essential_genes is None:
essential_genes = ['RPL5', 'RPS6', 'POLR2A', 'PSMC2', 'PSMD14'] # Example
if nonessential_genes is None:
nonessential_genes = ['AAVS1', 'ROSA26', 'HPRT1'] # Example
# Get LFC distributions for reference sets
essential_lfc = [lfc[sg] for sg, g in sgrna_to_gene.items()
if g in essential_genes and sg in lfc.index]
nonessential_lfc = [lfc[sg] for sg, g in sgrna_to_gene.items()
if g in nonessential_genes and sg in lfc.index]
if len(essential_lfc) < 3 or len(nonessential_lfc) < 3:
print("Warning: Insufficient reference genes for BAGEL scoring")
return None
# Estimate distributions (simplified)
essential_mean, essential_std = np.mean(essential_lfc), np.std(essential_lfc)
nonessential_mean, nonessential_std = np.mean(nonessential_lfc), np.std(nonessential_lfc)
# Calculate Bayes Factor for each gene
gene_bf = {}
gene_lfc_map = {}
for sgrna, gene in sgrna_to_gene.items():
if sgrna in lfc.index:
if gene not in gene_lfc_map:
gene_lfc_map[gene] = []
gene_lfc_map[gene].append(lfc[sgrna])
for gene, sgrna_lfcs in gene_lfc_map.items():
mean_lfc = np.mean(sgrna_lfcs)
# Likelihood under essential distribution
from scipy.stats import norm
l_essential = norm.pdf(mean_lfc, essential_mean, essential_std)
# Likelihood under non-essential distribution
l_nonessential = norm.pdf(mean_lfc, nonessential_mean, nonessential_std)
# Bayes Factor (avoid division by zero)
bf = l_essential / (l_nonessential + 1e-10)
gene_bf[gene] = {
'bayes_factor': bf,
'mean_lfc': mean_lfc,
'n_sgrnas': len(sgrna_lfcs)
}
# Convert to DataFrame and sort
bf_df = pd.DataFrame(gene_bf).T
bf_df = bf_df.sort_values('bayes_factor', ascending=False)
return bf_df
# BAGEL scoring
bf_scores = bagel_bayes_factor(lfc, filtered_mapping)
if bf_scores is not None:
print(f"Top 10 by Bayes Factor:\n{bf_scores.head(10)}")
Phase 5: Synthetic Lethality Detection
Identify Context-Specific Essential Genes
def detect_synthetic_lethality(gene_scores_wildtype, gene_scores_mutant,
lfc_threshold=-1.0, rank_diff_threshold=100):
"""
Identify genes that are selectively essential in mutant context
(synthetic lethal interactions).
Compare essentiality scores between wildtype and mutant cell lines.
"""
# Merge scores
comparison = pd.merge(
gene_scores_wildtype[['mean_lfc', 'rank']],
gene_scores_mutant[['mean_lfc', 'rank']],
left_index=True,
right_index=True,
suffixes=('_wt', '_mut')
)
# Calculate differential essentiality
comparison['delta_lfc'] = comparison['mean_lfc_mut'] - comparison['mean_lfc_wt']
comparison['delta_rank'] = comparison['rank_wt'] - comparison['rank_mut']
# Identify synthetic lethal candidates
# (more essential in mutant, not essential in wildtype)
sl_candidates = comparison[
(comparison['mean_lfc_mut'] < lfc_threshold) & # Essential in mutant
(comparison['mean_lfc_wt'] > -0.5) & # Not essential in wildtype
(comparison['delta_rank'] > rank_diff_threshold) # Large rank change
].copy()
sl_candidates = sl_candidates.sort_values('delta_lfc')
return sl_candidates
# Example: Detect genes synthetic lethal with KRAS mutation
# (Requires running screens in both KRAS-mutant and wildtype cells)
# sl_hits = detect_synthetic_lethality(gene_scores_wt, gene_scores_kras_mut)
Query DepMap for Known Dependencies
def query_depmap_dependencies(gene_symbol):
"""
Query DepMap database for known gene dependencies.
ToolUniverse doesn't have direct DepMap tools, but we can use
STRING or literature tools to find dependency information.
"""
from tooluniverse import ToolUniverse
tu = ToolUniverse()
# Search literature for essentiality/dependency information
result = tu.run_one_function({
"name": "PubMed_search",
"arguments": {
"query": f'("{gene_symbol}"[Gene]) AND ("CRISPR screen" OR "gene essentiality" OR "DepMap")',
"max_results": 20
}
})
if 'data' in result and 'papers' in result['data']:
papers = result['data']['papers']
print(f"Found {len(papers)} papers on {gene_symbol} essentiality")
return papers
return []
# Example usage
# depmap_papers = query_depmap_dependencies("PRMT5")
Phase 6: Pathway Enrichment Analysis
Enrichment of Essential Genes
def enrich_essential_genes(gene_scores, top_n=100, databases=['KEGG_2021_Human', 'GO_Biological_Process_2021']):
"""
Perform pathway enrichment on top essential genes.
"""
from tooluniverse import ToolUniverse
tu = ToolUniverse()
# Get top essential genes (most negative LFC)
top_genes = gene_scores.head(top_n).index.tolist()
print(f"Enriching {len(top_genes)} top essential genes...")
# Run Enrichr
result = tu.run_one_function({
"name": "Enrichr_submit_genelist",
"arguments": {
"gene_list": top_genes,
"description": "CRISPR_screen_essential_genes"
}
})
if 'data' not in result or 'userListId' not in result['data']:
print("Failed to submit gene list to Enrichr")
return None
user_list_id = result['data']['userListId']
# Get enrichment results for each database
all_results = {}
for db in databases:
enrich_result = tu.run_one_function({
"name": "Enrichr_get_results",
"arguments": {
"userListId": user_list_id,
"backgroundType": db
}
})
if 'data' in enrich_result and db in enrich_result['data']:
all_results[db] = pd.DataFrame(enrich_result['data'][db])
print(f"{db}: {len(all_results[db])} enriched terms")
return all_results
# Run enrichment
# enrichment_results = enrich_essential_genes(gene_scores, top_n=100)
Phase 7: Drug Target Prioritization
Integrate with Expression & Mutation Data
def prioritize_drug_targets(gene_scores, expression_data=None, mutation_data=None):
"""
Prioritize CRISPR hits as drug targets based on:
1. Essentiality score (from CRISPR screen)
2. Expression level in disease vs normal (if provided)
3. Mutation frequency in tumors (if provided)
4. Druggability (query DGIdb)
"""
from tooluniverse import ToolUniverse
tu = ToolUniverse()
# Start with top essential genes
candidates = gene_scores.head(50).copy()
# Add expression data if provided
if expression_data is not None:
candidates = candidates.merge(expression_data, left_index=True, right_index=True, how='left')
# Add mutation data if provided
if mutation_data is not None:
candidates = candidates.merge(mutation_data, left_index=True, right_index=True, how='left')
# Query druggability for each gene
druggability_scores = {}
for gene in candidates.index[:20]: # Limit to top 20 to avoid rate limits
result = tu.run_one_function({
"name": "DGIdb_query_gene",
"arguments": {"gene_symbol": gene}
})
if 'data' in result and 'matchedTerms' in result['data']:
matches = result['data']['matchedTerms']
if len(matches) > 0:
# Count number of drug interactions
n_drugs = len(matches[0].get('interactions', []))
druggability_scores[gene] = n_drugs
else:
druggability_scores[gene] = 0
else:
druggability_scores[gene] = 0
candidates['n_drugs'] = pd.Series(druggability_scores)
# Calculate composite priority score
# (Normalize each component to 0-1 scale)
candidates['essentiality_norm'] = (candidates['mean_lfc'].min() - candidates['mean_lfc']) / \
(candidates['mean_lfc'].min() - candidates['mean_lfc'].max())
if 'log2fc' in candidates.columns:
candidates['expression_norm'] = (candidates['log2fc'] - candidates['log2fc'].min()) / \
(candidates['log2fc'].max() - candidates['log2fc'].min())
else:
candidates['expression_norm'] = 0
candidates['druggability_norm'] = candidates['n_drugs'] / (candidates['n_drugs'].max() + 1)
# Weighted composite score
candidates['priority_score'] = (
0.5 * candidates['essentiality_norm'] +
0.3 * candidates['expression_norm'] +
0.2 * candidates['druggability_norm']
)
# Sort by priority
candidates = candidates.sort_values('priority_score', ascending=False)
return candidates
# Prioritize targets
# drug_targets = prioritize_drug_targets(gene_scores, expression_data=rna_seq_results)
Query Existing Drugs for Top Targets
def find_drugs_for_targets(target_genes, max_per_gene=5):
"""
Find existing drugs targeting top candidate genes.
"""
from tooluniverse import ToolUniverse
tu = ToolUniverse()
drug_results = {}
for gene in target_genes[:10]: # Top 10 targets
print(f"Searching drugs for {gene}...")
# Query DGIdb
result = tu.run_one_function({
"name": "DGIdb_query_gene",
"arguments": {"gene_symbol": gene}
})
if 'data' in result and 'matchedTerms' in result['data']:
matches = result['data']['matchedTerms']
if len(matches) > 0:
interactions = matches[0].get('interactions', [])
drugs = []
for interaction in interactions[:max_per_gene]:
drugs.append({
'drug_name': interaction.get('drugName', 'Unknown'),
'interaction_type': interaction.get('interactionTypes', ['Unknown'])[0],
'source': interaction.get('source', 'Unknown')
})
drug_results[gene] = drugs
return drug_results
# Find drugs
# drug_candidates = find_drugs_for_targets(drug_targets.index.tolist())
Phase 8: Report Generation
Comprehensive CRISPR Screen Report
def generate_crispr_report(gene_scores, enrichment_results, drug_targets,
output_file="crispr_screen_report.md"):
"""
Generate comprehensive CRISPR screen analysis report.
"""
with open(output_file, 'w') as f:
f.write("# CRISPR Screen Analysis Report\n\n")
# Summary statistics
f.write("## Summary\n\n")
f.write(f"- **Total genes analyzed**: {len(gene_scores)}\n")
f.write(f"- **Essential genes** (LFC < -1): {(gene_scores['mean_lfc'] < -1).sum()}\n")
f.write(f"- **Non-essential genes** (LFC > -0.5): {(gene_scores['mean_lfc'] > -0.5).sum()}\n\n")
# Top 20 essential genes
f.write("## Top 20 Essential Genes\n\n")
f.write("| Rank | Gene | Mean LFC | sgRNAs | Score |\n")
f.write("|------|------|----------|--------|-------|\n")
for idx, (gene, row) in enumerate(gene_scores.head(20).iterrows(), 1):
f.write(f"| {idx} | {gene} | {row['mean_lfc']:.3f} | {int(row['n_sgrnas'])} | {row['score']:.2f} |\n")
f.write("\n")
# Pathway enrichment
if enrichment_results:
f.write("## Pathway Enrichment\n\n")
for db, results in enrichment_results.items():
f.write(f"### {db}\n\n")
f.write("| Term | P-value | Adjusted P-value | Genes |\n")
f.write("|------|---------|------------------|-------|\n")
for _, row in results.head(10).iterrows():
term = row.get('Term', 'Unknown')
pval = row.get('P-value', 1.0)
adj_pval = row.get('Adjusted P-value', 1.0)
genes = row.get('Genes', '')
f.write(f"| {term} | {pval:.2e} | {adj_pval:.2e} | {genes[:50]}... |\n")
f.write("\n")
# Drug target prioritization
if drug_targets is not None:
f.write("## Top Drug Target Candidates\n\n")
f.write("| Rank | Gene | Essentiality | Expression FC | Druggable | Priority Score |\n")
f.write("|------|------|--------------|---------------|-----------|----------------|\n")
for idx, (gene, row) in enumerate(drug_targets.head(10).iterrows(), 1):
ess = row['mean_lfc']
expr = row.get('log2fc', 0)
drugs = int(row.get('n_drugs', 0))
priority = row['priority_score']
f.write(f"| {idx} | {gene} | {ess:.3f} | {expr:.2f} | {drugs} | {priority:.3f} |\n")
f.write("\n")
# Methods
f.write("## Methods\n\n")
f.write("**sgRNA Processing**: MAGeCK-like robust rank aggregation\n\n")
f.write("**Normalization**: Median ratio normalization\n\n")
f.write("**Scoring**: Gene-level LFC aggregation with rank-based scoring\n\n")
f.write("**Enrichment**: Enrichr (KEGG, GO)\n\n")
f.write("**Druggability**: DGIdb v4.0\n\n")
print(f"Report saved to {output_file}")
return output_file
# Generate report
# report_file = generate_crispr_report(gene_scores, enrichment_results, drug_targets)
Advanced Use Cases
Use Case 1: Genome-Wide Essentiality Screen
# Load counts and design
counts, meta = load_sgrna_counts("genome_wide_screen.txt")
design = create_design_matrix(
sample_names=['T0_1', 'T0_2', 'T14_1', 'T14_2', 'T14_3'],
conditions=['baseline', 'baseline', 'treatment', 'treatment', 'treatment']
)
# QC and filter
qc_results = qc_sgrna_distribution(counts)
filtered_counts, filtered_mapping = filter_low_count_sgrnas(counts, meta['sgrna_to_gene'])
# Normalize
norm_counts, size_factors = normalize_counts(filtered_counts, method='median')
# Calculate LFC
lfc, control_mean, treatment_mean = calculate_lfc(norm_counts, design)
# Gene-level scoring
gene_scores = mageck_gene_scoring(lfc, filtered_mapping, method='rra')
# Enrichment
enrichment = enrich_essential_genes(gene_scores, top_n=100)
# Report
report = generate_crispr_report(gene_scores, enrichment, None)
Use Case 2: Synthetic Lethality Screen (KRAS)
# Run screens in both KRAS-wildtype and KRAS-mutant cells
# Load both datasets
counts_wt, meta_wt = load_sgrna_counts("kras_wildtype_screen.txt")
counts_mut, meta_mut = load_sgrna_counts("kras_mutant_screen.txt")
# Process both (same steps as Use Case 1)
# ... filtering, normalization, LFC calculation ...
gene_scores_wt = mageck_gene_scoring(lfc_wt, filtered_mapping_wt)
gene_scores_mut = mageck_gene_scoring(lfc_mut, filtered_mapping_mut)
# Identify synthetic lethal hits
sl_hits = detect_synthetic_lethality(gene_scores_wt, gene_scores_mut)
print(f"Identified {len(sl_hits)} synthetic lethal candidates with KRAS mutation")
print(sl_hits.head(10))
# Prioritize for drug development
drug_targets = prioritize_drug_targets(sl_hits)
Use Case 3: Drug Target Discovery Pipeline
# Complete pipeline: Screen → Essential genes → Druggability → Drug candidates
# 1. Identify essential genes from screen
gene_scores = mageck_gene_scoring(lfc, filtered_mapping)
# 2. Filter for highly essential (stringent threshold)
highly_essential = gene_scores[gene_scores['mean_lfc'] < -1.5]
# 3. Prioritize with expression data (if available)
drug_targets = prioritize_drug_targets(highly_essential, expression_data=tumor_expression)
# 4. Find existing drugs
drug_candidates = find_drugs_for_targets(drug_targets.index.tolist())
# 5. Generate comprehensive report
report = generate_crispr_report(gene_scores, None, drug_targets)
print(f"Identified {len(drug_candidates)} druggable targets with {sum(len(v) for v in drug_candidates.values())} total drug candidates")
Use Case 4: Integration with Expression Data
# Combine CRISPR essentiality with RNA-seq differential expression
# Load RNA-seq results (from tooluniverse-rnaseq-deseq2 skill)
rna_results = pd.read_csv("deseq2_results.csv", index_col=0)
# Merge with CRISPR scores
integrated = gene_scores.merge(
rna_results[['log2FoldChange', 'padj']],
left_index=True,
right_index=True,
how='inner'
)
# Identify genes that are:
# 1. Essential in screen (LFC < -1)
# 2. Overexpressed in disease (log2FC > 1, padj < 0.05)
targets = integrated[
(integrated['mean_lfc'] < -1) &
(integrated['log2FoldChange'] > 1) &
(integrated['padj'] < 0.05)
]
print(f"Identified {len(targets)} genes essential and overexpressed in disease")
ToolUniverse Tool Integration
Key Tools Used:
PubMed_search- Literature search for gene essentialityEnrichr_submit_genelist- Pathway enrichment submissionEnrichr_get_results- Retrieve enrichment resultsDGIdb_query_gene- Drug-gene interactions and druggabilitySTRING_get_network- Protein interaction networksKEGG_get_pathway- Pathway visualization
Expression Integration:
GEO_get_dataset- Download expression dataArrayExpress_get_experiment- Alternative expression source
Variant Integration:
ClinVar_query_gene- Known pathogenic variantsgnomAD_get_gene- Population allele frequencies
Best Practices
sgRNA Design Quality: Ensure library uses validated sgRNA designs (e.g., Brunello, Avana libraries)
Replicates: Minimum 2 biological replicates per condition; 3+ preferred
Sequencing Depth: Aim for 500-1000 reads per sgRNA at T0; 200+ at final timepoint
Reference Genes: Include positive (essential) and negative (non-essential) control genes
Timepoint Selection: Balance cell doublings (14-21 days) vs. sgRNA dropout
Normalization: Use median ratio normalization for count data (more robust than CPM)
Multiple Testing: Apply FDR correction when calling essential genes (padj < 0.05)
Validation: Validate top hits with orthogonal methods (siRNA, small molecule inhibitors)
Context Matters: Gene essentiality is context-dependent (cell line, tissue, genetic background)
Druggability: Essential genes are not always druggable; check DGIdb early in prioritization
Troubleshooting
Problem: Low library representation (many zero-count sgRNAs)
- Solution: Increase sequencing depth; check for PCR biases in library prep
Problem: High Gini coefficient (skewed distribution)
- Solution: Optimize PCR cycles; consider using unique molecular identifiers (UMIs)
Problem: No strong essential genes detected
- Solution: Check timepoint (may be too early); verify cell viability; confirm sgRNA cutting efficiency
Problem: Too many essential genes (>500)
- Solution: Timepoint may be too late; adjust LFC threshold; check for batch effects
Problem: Discordant sgRNAs for same gene
- Solution: Check for off-target effects; verify sgRNA sequences; consider removing outlier sgRNAs
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)
Quick Start
# Complete minimal workflow
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)