MAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) for pooled CRISPR screen analysis. Covers count normalization, gene ranking, and pathway analysis. Use when identifying essential genes, drug targets, or resistance mechanisms from dropout or enrichment screens.
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MAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) for pooled CRISPR screen analysis. Covers count normalization, gene ranking, and pathway analysis. Use when identifying essential genes, drug targets, or resistance mechanisms from dropout or enrichment screens.
Before using code patterns, verify installed versions match. If versions differ:
Python: pip show <package> then help(module.function) to check signatures
CLI: <tool> --version then <tool> --help to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
MAGeCK CRISPR Screen Analysis
"Analyze my pooled CRISPR screen with MAGeCK" → Count sgRNA reads, normalize across samples, and rank genes by enrichment or depletion using the MAGeCK robust rank aggregation algorithm.
CLI: mageck count → mageck test for standard analysis
CLI: mageck mle for multi-condition designs
Count sgRNAs from FASTQ
Quantify sgRNA representation from raw sequencing data.
Goal:
Approach: Map FASTQ reads to the sgRNA library sequences with MAGeCK count, producing a normalized count matrix and QC summary across all samples.
Goal: Identify genes significantly enriched or depleted between treatment and control conditions.
Approach: Run MAGeCK test with robust rank aggregation, which ranks sgRNAs by fold change, tests whether per-gene sgRNA rankings deviate from uniform, and reports gene-level significance with FDR correction.
# Compare treatment vs control
mageck test \
-k experiment.count.txt \
-t Treated1,Treated2 \
-c Control1,Control2 \
-n results \
--norm-method median \
--gene-test-fdr-threshold 0.25
# Output files:# results.gene_summary.txt - gene-level results# results.sgrna_summary.txt - sgRNA-level results
MAGeCK MLE (Maximum Likelihood)
Goal: Estimate gene effects in complex experimental designs with multiple conditions or covariates.
Approach: Define a design matrix specifying sample-condition relationships, then run MAGeCK MLE which fits a generalized linear model to estimate per-gene beta scores (effect sizes) for each condition.
Goal: Extract significant essential and resistance genes from MAGeCK output.
Approach: Load the gene summary table, filter by negative-selection FDR for dropout/essential genes and positive-selection FDR for enriched/resistance genes, and rank by MAGeCK score.
# For CRISPRa, focus on positive selection
mageck test \
-k crispra.count.txt \
-t Activated1,Activated2 \
-c Control1,Control2 \
-n crispra_results
# Hits are genes where activation causes phenotype# Use pos|fdr and pos|score columns
