bio-crispr-screens-jacks-analysis
JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens) for modeling sgRNA efficacy and gene essentiality. Use when analyzing multiple CRISPR screens simultaneously or when accounting for variable sgRNA efficiency across experiments.
npx skills add majiayu000/claude-skill-registry --skill jacks-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.
# JACKS CRISPR Screen Analysis JACKS jointly models sgRNA efficacy and gene essentiality across multiple experiments. It infers both gene-level fitness effects and sgRNA-specific efficiency. ## Installation ```bash pip install jacks # or git clone https://github.com/felicityallen/JACKS.git cd JACKS && pip install -e . ``` ## Input File Formats ### Count Data ``` # counts.txt (tab-separated) sgRNA Gene Sample1 Sample2 Sample3 Control1 Control2 sgRNA1 GENE_A 100 120 90 80 85 sgRNA2 GENE_A 200 180 210 150 160 sgRNA3 GENE_B 50 45 55 60 58 ... ``` ### Replicate Map ``` # replicatemap.txt Sample1 Experiment1 Day14 Sample2 Experiment1 Day14 Sample3 Experiment2 Day14 Control1 Experiment1 Day0 Control2 Experiment2 Day0 ``` ### Guide-Gene Map ``` # guidemap.txt sgRNA1 GENE_A sgRNA2 GENE_A sgRNA3 GENE_B sgRNA4 GENE_B ... ``` ## Basic JACKS Analysis ### Command Line ```bash # Run JACKS python -m jacks.run_JACKS \ counts.txt \ replicatemap.txt \ guidemap.txt \ output_prefix \ --ctrl_sample_pattern "Day0" \ --ctrl_sample_pattern_column "Condition" ``` ### Python API ```python from jacks import infer import pandas as pd # Load data counts = pd.read_csv('counts.txt', sep='\t', index_col=0) guide_g
- Installation
- Input File Formats
- Count Data
- Replicate Map
- Guide-Gene Map
- Basic JACKS Analysis
- Command Line
- Python API
- Output Files
- Interpret Gene Results
- sgRNA Efficacy Analysis
- Visualization
- Gene Effect Plot
- sgRNA Efficacy Distribution
pip install jacks or git clone https://github.com/felicityallen/JACKS.git cd JACKS && pip install -e . Run JACKS python -m jacks.run_JACKS \ counts.txt \ replicatemap.txt \ guidemap.txt \ output_prefix \
What does the bio-crispr-screens-jacks-analysis skill do?
JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens) for modeling sgRNA efficacy and gene essentiality. Use when analyzing multiple CRISPR screens simultaneously or when accounting for variable sgRNA efficiency across experiments.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill jacks-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 majiayu000/claude-skill-registry, a repository with 534 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.