MAGeCK pooled-screen analysis

When to use

Use this skill to count existing sequencing reads against a supplied guide library or compare already-counted pooled screens. Deliver the count matrix, QC, guide and gene results, contrast provenance, and a short interpretation of enrichment/depletion. This workflow analyzes screens; it does not design guides or infer gene function from a hit alone.

Runtime

The tested source installation and external-runtime caveat are in references/runtime.md. Verify both mageck --version and mageck test --help before an analysis. The bundled Python helper is standard-library only. MAGeCK itself also needs NumPy, SciPy, and the RRA binary. PDF/R reporting is optional and not needed by the helper.

工作流程

1. Establish the library version, perturbation modality, sample names, selection direction, biological replicates, baseline material, time point, and batch. Separate sequencing lanes from independent biological replicates. A plasmid baseline and a cell day-zero baseline answer different questions. Require an explicit treatment/control contrast; the helper never silently assigns all unused samples to the control group.
2. Validate a headerless TSV library containing guide ID, DNA sequence, gene. IDs and sequences must be unique. The helper rejects ambiguous sequences and any count/library ID or gene mismatch; resolve intentional multi-target guides explicitly upstream.
3. For FASTQ, inspect read structure and known guide sequences to establish trimming and orientation. Use MAGeCK count, with one space-separated argument per biological sample; comma-join lanes only when they are technical replicates of that same sample. Preserve unmapped-read and count-summary evidence when mapping is poor. A zero-count guide remains in the library; do not drop it to improve QC.
4. Run QC before statistical testing. Review library representation, median reads per guide, zero fractions, Gini coefficients, and within-condition replicate correlations. The helper's 10% zero and 0.8 correlation flags are review prompts, not universal acceptance thresholds. High correlation can coexist with systematic artifacts. Read depth is not experimental cell coverage.
5. Choose normalization based on the screen. Median normalization assumes most guides are stable. For a strong global shift, supplied validated negative-control guides may support --normalization control. These must be guide IDs, one per line; a gene list is not interchangeable. Biological control samples and negative-control guides serve different roles. Record their origin and check their count distribution.
6. Use test for a two-group comparison. --paired requires both lists in corresponding biological order and equal length; matching lengths alone do not establish pairing. The helper reports genes at the requested FDR in both directions and retains full rankings. For a multi-factor design, see references/design.md; do not collapse batches or time courses into an unjustified two-group test.
7. Inspect guide concordance for leading genes, essential-gene recovery where appropriate, negative controls, replicate consistency, and copy-number artifacts in nuclease knockout screens. Report effect sizes alongside FDR. An enriched guide can indicate resistance, growth advantage, or a sampling artifact depending on the selection; depletion need not imply universal essentiality. Lack of replication or low-count guides weakens inference.

Commands

Run paths relative to the installed skill directory. Input and output paths refer to the user's analysis directory. The helper refuses to reuse an existing results directory.

# Single-end guide reads; trim and orientation must match the user's library preparation.
mageck count -l library.tsv --fastq c1.fastq.gz c2.fastq.gz t1.fastq.gz t2.fastq.gz \
  --sample-label c1,c2,t1,t2 --trim-5 0 --norm-method none -n counts

python scripts/screen_analysis.py qc --counts counts.count.txt --library library.tsv \
  --control c1 c2 --treatment t1 t2 --output qc.json

python scripts/screen_analysis.py test --counts counts.count.txt --library library.tsv \
  --control c1 c2 --treatment t1 t2 --normalization median --fdr 0.05 --output result

The FASTQ command structure was exercised with a synthetic two-guide library: counts of 30 and 12 were recovered exactly. The two-group helper was exercised with 500 guides and two replicates per condition; the known depleted and enriched genes ranked first in their respective directions and passed FDR 0.05. These synthetic tests establish execution and signal direction, not statistical calibration for a real screen. Real-file paths above are illustrative.

result/report.json records count/library/control-guide SHA-256, control-guide IDs, MAGeCK version, actual arguments, QC, hit direction, FDR, log fold change and rank. screen.gene_summary.txt, screen.sgrna_summary.txt, normalized counts and the execution log retain the complete evidence. Include the original library, sample sheet and negative-control list in the analysis handoff; the count checksum cannot reconstruct them.

Primary references

• MAGeCK usage and command semantics
• Official installation guidance
• Input formats