--- 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 ```bash # 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 ```python 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) ```bash # 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) ```bash # 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 ```python 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 ```python 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 ```r 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) ```bash # 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 ```bash # 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 ```bash # 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 --- Source attribution: GPTomics Original project: https://github.com/GPTomics/bioSkills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 FridrichMethod Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --- NOTE ON BUNDLED CONTENT This repository aggregates skills from multiple upstream projects (see the README for the source list). Each `skills//` directory may carry its own license inherited from its upstream source. The MIT license above applies to this repository's curation and the scripts/workflows in `/`, `/scripts/`, and `/.github/` — not to the upstream skill content itself. Consult the upstream repository linked in the README and any LICENSE file inside a skill directory before reusing skill content commercially. # Mega AI Hub resource metadata Creator / publisher: GPTomics Original project: https://github.com/GPTomics/bioSkills Attribution basis: Exact package in biomedical source index and publisher source table Fields of work: Data & Analytics, Education & Research Roles: Business Intelligence Analyst, Data Scientist, Researcher Specialties: Backend, Design, Frontend, Playwright, Testing Tools: Mageck Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 3efaa9359a3dbf0fc727799155c4dab66bb142b0f8ffbb3c4f4b987d30d7980f Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.