Agent skill · Design & Presentation

bio-crispr-screens-drugz-chemogenomic

Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Li & Hart 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving 2-3x sensitivity over MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-mo

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill drugz-chemogenomic --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 14 KB
Bundled scripts: yes
Path: skills/bioskills/drugz-chemogenomic/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Version Compatibility Reference examples tested with: drugZ Aug-2019+ (hart-lab/drugz; Python 3.6+), MAGeCK 0.5.9+, pandas 2.2+, numpy 1.26+, scipy 1.12+, statsmodels 0.14+, matplotlib 3.8+. Before using code patterns, verify installed versions match. If versions differ: - CLI: `drugz --version`; `python drugz.py --help` - GitHub: install via `git clone https://github.com/hart-lab/drugz` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. ## drugZ Chemogenomic Analysis **"Identify genes that sensitize or confer resistance to my drug in a CRISPR screen"** -> Compare drug-treated vs vehicle-treated arms (NOT Day-0 baseline) using bidirectional Z-scores per sgRNA, sum to per-gene normalized Z, and rank genes for sensitizer (synthetic lethal) vs suppressor (resistance) phenotype. - CLI: `python drugz.py -i counts.txt -o drugz.txt -c Vehicle_r1,Vehicle_r2 -x Drug_r1,Drug_r2` - Python: programmatic via `drugz.drugz_analysis()` (internal Python module) - Workflow: vehicle-anchored counts -> Z-scoring -> per-gene summation -> direction-specific FDR ## Why drugZ for Drug Screens (no

What's inside
Steps it walks through
  1. Version Compatibility
  2. drugZ Chemogenomic Analysis
  3. Why drugZ for Drug Screens (not MAGeCK)
  4. The drugZ Algorithm (under the hood)
  5. Run drugZ on a Drug-Modifier Screen
  6. Vehicle vs Day-0 Reference: Critical Decision
  7. Drug-Dose and Time-Course Designs
  8. Comparison: drugZ vs MAGeCK MLE for Drug Screen
  9. Removing Genes from Null Distribution
  10. Failure Modes
  11. drugZ shows no synthetic-lethal hits despite known sensitizing genes
  12. High false-positive rate among essential genes
  13. Inconsistent results between repeats of drugZ
  14. drugZ ignores dose information
Ships with 2 files
  • examples/run_drugz.py
  • usage-guide.md
Commands it runs
git clone https://github.com/hart-lab/drugz
cd drugz
Standard drug screen comparison:
Vehicle (DMSO or carrier) replicates: Veh_r1, Veh_r2, Veh_r3
Drug-treated replicates: Drug_r1, Drug_r2, Drug_r3
python drugz.py \
for DOSE in low mid high; do
done
Then aggregate: genes significant at high dose AND consistent direction at mid/low dose
Pass a file with one gene per line
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About this skill
What does the bio-crispr-screens-drugz-chemogenomic skill do?

Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Li & Hart 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving 2-3x sensitivity over MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-mo

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill drugz-chemogenomic --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.

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