bio-workflows-crispr-screen-pipeline
End-to-end pooled and single-cell CRISPR screen analysis from FASTQ to hit genes. Orchestrates library design QC, guide counting, six-stage screen QC (plasmid Gini, replicate Pearson, CEGv2 PR-AUC, copy-number artifact), method-appropriate hit calling across MAGeCK RRA/MLE, BAGEL2, drugZ, JACKS, and Chronos, cancer-cell-line copy-number correction (CRISPRcleanR / Chronos), batch correction for multi-batch screens, and the specialized branches for combinatorial paralog screens, single-cell Perturb-seq, base-editor variant-function screens, prime-editor screens, and in vivo bottleneck-aware scre
npx skills add BioTender-max/awesome-bio-agent-skills --skill crispr-screen-pipeline --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+, BAGEL2 1.0.5+, drugZ Aug 2019+, JACKS 0.2.0+, Chronos 2.0+, CRISPRcleanR 3.0+ (R), Pertpy 0.6+, PRIDICT2, CRISPResso2 2.2.14+, MAGeCKFlute 2.0+, pandas 2.2+, numpy 1.26+, matplotlib 3.8+. Before using code patterns, verify installed versions match. If versions differ: - CLI: `mageck --version`, `BAGEL.py fc --help`, `drugz -h`, `CRISPResso --version` - Python: `pip show pertpy mageck-vispr jacks chronos-cn` - R: `packageVersion('CRISPRcleanR')`, `packageVersion('MAGeCKFlute')` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. ## CRISPR Screen Pipeline **"Analyze my pooled or single-cell CRISPR screen end-to-end"** -> Pick the screen design branch, run guide counting, audit six QC stages, apply copy-number and batch correction as needed, run the design-matched hit-calling method, and consolidate across methods for high-confidence hits. ## Pipeline Branches by Screen Design ``` Library Design ([[library-design]]) | v FASTQ Files -> mageck count -> count matrix | v Six-Stage QC ([[screen-qc]]) | +----------
- Version Compatibility
- CRISPR Screen Pipeline
- Pipeline Branches by Screen Design
- Step 1: Library Design and Pre-Screen Validation
- Step 2: Guide Counting
- Step 3: Six-Stage Quality Control
- Step 4: Copy-Number Correction (Cancer Cell Lines Only)
- Step 5: Batch Correction (Multi-Batch Screens)
- Step 6: Method-Matched Hit Calling
- 6a. Two-condition essentiality (MAGeCK RRA or BAGEL2)
- 6b. Time-course / multi-condition (MAGeCK MLE)
- 6c. Drug-modifier (drugZ)
- 6d. Multi-screen joint analysis (JACKS)
- 6e. Cancer cell-line panels (Chronos)
mageck count \ mageck test \ BAGEL.py fc -i experiment.count.txt -o foldchange.txt -c Day0 --min-reads 30 BAGEL.py bf -i foldchange.txt -o bayes_factor.txt -e CEGv2.txt -n NEGv1.txt \ mageck mle --count-table experiment.count.txt --design-matrix design.txt \ python drugz.py \ python run_JACKS.py experiment.count.txt replicatemap.txt guidemap.txt \
What does the bio-workflows-crispr-screen-pipeline skill do?
End-to-end pooled and single-cell CRISPR screen analysis from FASTQ to hit genes. Orchestrates library design QC, guide counting, six-stage screen QC (plasmid Gini, replicate Pearson, CEGv2 PR-AUC, copy-number artifact), method-appropriate hit calling across MAGeCK RRA/MLE, BAGEL2, drugZ, JACKS, and Chronos, cancer-cell-line copy-number correction (CRISPRcleanR / Chronos), batch correction for multi-batch screens, and the specialized branches for combinatorial paralog screens, single-cell Perturb-seq, base-editor variant-function screens, prime-editor screens, and in vivo bottleneck-aware scre
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill crispr-screen-pipeline --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.