bio-workflows-crispr-screen-pipeline

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End-to-end CRISPR screen analysis from FASTQ to hit genes. Orchestrates guide counting, QC, statistical analysis with MAGeCK, and hit calling with multiple methods. Use when analyzing pooled CRISPR screens from count data to hit calling.

mdbabumiamssm By mdbabumiamssm schedule Updated 2/4/2026
play_arrow Run Skill in Manus View GitHub

name: bio-workflows-crispr-screen-pipeline description: End-to-end CRISPR screen analysis from FASTQ to hit genes. Orchestrates guide counting, QC, statistical analysis with MAGeCK, and hit calling with multiple methods. Use when analyzing pooled CRISPR screens from count data to hit calling. tool_type: mixed primary_tool: MAGeCK workflow: true depends_on: - crispr-screens/screen-qc - crispr-screens/mageck-analysis - crispr-screens/hit-calling - crispr-screens/library-design - crispr-screens/batch-correction measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command

CRISPR Screen Pipeline

Pipeline Overview

FASTQ Files ──> Guide Counting ──> Count Matrix
                                        │
                                        ▼
              ┌─────────────────────────────────────────────┐
              │         crispr-screen-pipeline              │
              ├─────────────────────────────────────────────┤
              │  1. Guide Counting (MAGeCK count)           │
              │  2. QC: Library coverage, gini index        │
              │  3. Gene-level Analysis (MAGeCK RRA/MLE)    │
              │  4. Hit Calling (FDR, effect size)          │
              │  5. Visualization & Reporting               │
              └─────────────────────────────────────────────┘
                                        │
                                        ▼
                    Hit Genes + Volcano/Rank Plots

Complete Workflow

Step 1: Guide Counting

# From FASTQ files
mageck count \
    -l library.csv \
    -n experiment \
    --sample-label Day0,Day14_Rep1,Day14_Rep2,Day14_Rep3 \
    --fastq Day0.fastq.gz Day14_Rep1.fastq.gz Day14_Rep2.fastq.gz Day14_Rep3.fastq.gz \
    --trim-5 0 \
    --pdf-report

Step 2: Quality Control

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

counts = pd.read_csv('experiment.count.txt', sep='\t', index_col=0)
counts_numeric = counts.iloc[:, 1:]

qc_stats = {}
for col in counts_numeric.columns:
    total = counts_numeric[col].sum()
    zeros = (counts_numeric[col] == 0).sum()
    gini = calculate_gini(counts_numeric[col].values)
    qc_stats[col] = {'total_reads': total, 'zero_count_guides': zeros, 'gini': gini}

qc_df = pd.DataFrame(qc_stats).T
print('QC Summary:')
print(qc_df)

# Gini index function
def calculate_gini(x):
    x = np.sort(x[x > 0])
    n = len(x)
    cumsum = np.cumsum(x)
    return (2 * np.sum((np.arange(1, n+1) * x)) - (n + 1) * cumsum[-1]) / (n * cumsum[-1])

# QC thresholds
assert qc_df['zero_count_guides'].max() < len(counts) * 0.2, 'Too many zero-count guides'
assert qc_df['gini'].max() < 0.4, 'Gini index too high (uneven distribution)'
print('QC passed!')

Step 3: MAGeCK RRA Analysis (Negative Selection)

# For dropout/negative selection screens
mageck test \
    -k experiment.count.txt \
    -t Day14_Rep1,Day14_Rep2,Day14_Rep3 \
    -c Day0 \
    -n negative_screen \
    --pdf-report \
    --gene-lfc-method alphamedian

Step 4: MAGeCK MLE (Complex Designs)

# For screens with multiple conditions
# Design matrix: design.txt
# samplename,baseline,treatment
# Day0,1,0
# Day14_Ctrl,1,0
# Day14_Drug,1,1

mageck mle \
    -k experiment.count.txt \
    -d design.txt \
    -n mle_analysis \
    --threads 8

Step 5: Hit Calling

import pandas as pd

# Load MAGeCK results
gene_summary = pd.read_csv('negative_screen.gene_summary.txt', sep='\t')

# Define hits
gene_summary['neg_hit'] = (gene_summary['neg|fdr'] < 0.05) & (gene_summary['neg|lfc'] < -0.5)
gene_summary['pos_hit'] = (gene_summary['pos|fdr'] < 0.05) & (gene_summary['pos|lfc'] > 0.5)

neg_hits = gene_summary[gene_summary['neg_hit']].sort_values('neg|rank')
pos_hits = gene_summary[gene_summary['pos_hit']].sort_values('pos|rank')

print(f'Negative selection hits (dropout): {len(neg_hits)}')
print(f'Positive selection hits (enriched): {len(pos_hits)}')

# Save hit lists
neg_hits.to_csv('negative_hits.csv', index=False)
pos_hits.to_csv('positive_hits.csv', index=False)

Step 6: Visualization

import matplotlib.pyplot as plt
import numpy as np

# Volcano plot
fig, ax = plt.subplots(figsize=(10, 8))
x = gene_summary['neg|lfc']
y = -np.log10(gene_summary['neg|fdr'] + 1e-10)

colors = ['red' if h else 'blue' if p else 'gray'
          for h, p in zip(gene_summary['neg_hit'], gene_summary['pos_hit'])]
ax.scatter(x, y, c=colors, alpha=0.5, s=20)

ax.axhline(-np.log10(0.05), linestyle='--', color='black', alpha=0.5)
ax.axvline(-0.5, linestyle='--', color='black', alpha=0.5)
ax.axvline(0.5, linestyle='--', color='black', alpha=0.5)

ax.set_xlabel('Log2 Fold Change')
ax.set_ylabel('-Log10(FDR)')
ax.set_title('CRISPR Screen Volcano Plot')
plt.tight_layout()
plt.savefig('volcano_plot.png', dpi=150)

Complete R Workflow

library(MAGeCKFlute)
library(ggplot2)

# Load MAGeCK results
gene_summary <- read.delim('negative_screen.gene_summary.txt')
sgrna_summary <- read.delim('negative_screen.sgrna_summary.txt')

# QC with MAGeCKFlute
FluteMLE(mle_output = 'mle_analysis.gene_summary.txt',
         treatname = 'treatment',
         proj = 'crispr_screen',
         pathview.top = 10)

# Or for RRA results
FluteRRA(gene_summary = gene_summary,
         sgrna_summary = sgrna_summary,
         proj = 'rra_analysis')

# Custom rank plot
gene_summary$rank <- rank(gene_summary$`neg.score`)
gene_summary$is_hit <- gene_summary$`neg.fdr` < 0.05

ggplot(gene_summary, aes(x = rank, y = -log10(`neg.fdr` + 1e-10), color = is_hit)) +
    geom_point(alpha = 0.5) +
    geom_hline(yintercept = -log10(0.05), linetype = 'dashed') +
    scale_color_manual(values = c('gray', 'red')) +
    theme_bw() +
    labs(title = 'Gene Rank Plot', x = 'Rank', y = '-Log10(FDR)')
ggsave('rank_plot.png', width = 10, height = 6)

BAGEL2 Alternative (Essential Genes)

# Calculate Bayes Factor for essentiality
BAGEL.py bf \
    -i experiment.count.txt \
    -o bagel_output \
    -e CEGv2.txt \
    -n NEGv1.txt \
    -c Day0 \
    -s Day14_Rep1,Day14_Rep2,Day14_Rep3

# Precision-recall analysis
BAGEL.py pr \
    -i bagel_output.bf \
    -o bagel_pr \
    -e CEGv2.txt \
    -n NEGv1.txt

QC Checkpoints

Stage Check Action if Failed
Counting >70% mapping rate Check library/trimming
Zero guides <20% Check sequencing depth
Gini index <0.4 Check for amplification bias
Replicates r > 0.8 Check experimental consistency
Controls Separate in PCA Check screen worked

Workflow Variants

Positive Selection Screen

# For enrichment screens (e.g., drug resistance)
mageck test \
    -k counts.txt \
    -t Resistant_Rep1,Resistant_Rep2 \
    -c Sensitive \
    -n positive_screen \
    --gene-lfc-method alphamedian

CRISPRi/CRISPRa

# Same workflow, different interpretation
# CRISPRi: negative LFC = gene promotes phenotype
# CRISPRa: positive LFC = gene promotes phenotype
mageck test -k counts.txt -t Treated -c Control -n crispri_screen

Related Skills

  • crispr-screens/screen-qc - Detailed QC metrics
  • crispr-screens/mageck-analysis - MAGeCK parameters
  • crispr-screens/hit-calling - Hit calling methods
  • crispr-screens/crispresso-editing - Individual editing analysis
  • crispr-screens/library-design - sgRNA selection and library design
  • crispr-screens/batch-correction - Multi-batch normalization
  • pathway-analysis/go-enrichment - Pathway enrichment of hits
Install via CLI
npx skills add https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- --skill bio-workflows-crispr-screen-pipeline
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