bio-crispr-screens-jacks-analysis
Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essent
npx skills add BioTender-max/awesome-bio-agent-skills --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.
## Version Compatibility Reference examples tested with: JACKS 0.2.0+ (felicityallen/JACKS), pandas 2.2+, numpy 1.26+, scipy 1.12+, matplotlib 3.8+. Before using code patterns, verify installed versions match. If versions differ: - CLI: `python run_JACKS.py --help` (run_JACKS.py at the JACKS repo root after clone) - Python: from jacks.jacks_io import runJACKS; help(runJACKS) - GitHub: install via `git clone https://github.com/felicityallen/JACKS && cd JACKS && pip install .` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. ## JACKS CRISPR Screen Analysis **"Analyze CRISPR screens with guide-level efficacy modeling"** -> Jointly model per-sgRNA log-fold-change across one or more screens as the product of gene essentiality and guide efficacy, sharing efficacy across screens with the same library so that low-quality guides are down-weighted automatically. - CLI: `python run_JACKS.py countfile replicatefile guidemappingfile [options]` (script at JACKS repo root) - Python: `from jacks.jacks_io import runJACKS` for programmatic use; lower-level `from jacks.infer import inferJACKS
- Version Compatibility
- JACKS CRISPR Screen Analysis
- The JACKS Model (under the hood)
- When JACKS Outperforms MAGeCK and BAGEL2
- When JACKS Is Not the Right Tool
- Run JACKS Joint Analysis
- Output Interpretation
- Build Library-Wide Efficacy Prior from Reference Screens
- Per-sgRNA Efficacy Diagnostics
- Comparing JACKS, MAGeCK, BAGEL2
- Failure Modes
- Efficacy collapsed near zero for all guides
- Cross-cell-line efficacy disagreement
- MCMC / variational convergence failure
Equivalent CLI run (run_JACKS.py is at the JACKS repo root after clone) python run_JACKS.py \ counts.txt \ replicatemap.txt \ guidemap.txt \ jacks_out_gene_JACKS_results.txt gene-level: X1 (effect), X2 (std), p_neg, p_pos jacks_out_grna_JACKS_results.txt sgRNA-level: X1 (efficacy 0-1), X2 (std) jacks_out_JACKS_results_full.pickle full posterior for downstream
What does the bio-crispr-screens-jacks-analysis skill do?
Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essent
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --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 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.