bio-crispr-screens-mageck-analysis
Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS
npx skills add BioTender-max/awesome-bio-agent-skills --skill mageck-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: MAGeCK 0.5.9+, MAGeCKFlute 2.0+ (R/Bioconductor), MAGeCK-VISPR 0.5.6+, pandas 2.2+, numpy 1.26+, matplotlib 3.8+. Before using code patterns, verify installed versions match. If versions differ: - CLI: `mageck --version`, `mageck count --help`, `mageck test --help`, `mageck mle --help` - R: `packageVersion('MAGeCKFlute')`, `?FluteRRA`, `?FluteMLE` 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 **"Run MAGeCK on my pooled CRISPR screen"** -> Count sgRNAs from FASTQ, normalize across samples, and rank genes by enrichment or depletion using either the robust rank aggregation (RRA) test for two-condition designs or the maximum-likelihood (MLE) model with explicit design matrix for multi-condition / time-course / drug screens. - CLI: `mageck count` -> `mageck test` for two-condition RRA - CLI: `mageck mle` for multi-condition / time-course / multi-cell-line MLE - R: `MAGeCKFlute::FluteRRA()` / `FluteMLE()` for downstream visualization and pathway analysis - Python: `mageck-vispr` for inter
- Version Compatibility
- MAGeCK CRISPR Screen Analysis
- RRA vs MLE Decision Tree
- The RRA Algorithm (under the hood)
- The MLE Model (under the hood)
- Count sgRNAs from FASTQ
- Normalization Decision
- MAGeCK Test (RRA for Two-Condition)
- MAGeCK MLE (Multi-Condition)
- Sample MAGeCK Test for Drug Screen with sgRNA Efficiency
- Time-Course Analysis
- Visualizing Results
- MAGeCKFlute Integration (R)
- MAGeCK-VISPR Interactive Dashboard
mageck count \ screen.count.txt raw counts screen.count_normalized.txt normalized counts (median-scaled) screen.countsummary.txt per-sample QC: Gini, reads, mapping rate, % zero-count screen.log per-FASTQ mapping stats mageck test \ Design matrix: design.txt (tab-separated) Samples must match sample-label in mageck count output baseline column must be present and all 1 cat > design.txt <<EOF
What does the bio-crispr-screens-mageck-analysis skill do?
Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill mageck-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.