skills.rest
mrsonord2240
mrsonord2240COMMUNITY
Explore all skills by this author →
GitHub

bio-crispr-screens-jacks-analysis

Runs JACKS Bayesian joint analysis of CRISPR screens to estimate gene essentiality and guide efficacy.

Updated Sep 15, 2026
Search Tags:#bioinformatics#bayesian#crispr#gene essentiality#screen analysis#jacks#sgrna efficacy
One-click install
npx skills add https://github.com/mrsonord2240/openscience-specialists --skill bio-crispr-screens-jacks-analysis-mrsonord2240
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-crispr-screens-jacks-analysis
Source: https://github.com/mrsonord2240/openscience-specialists/tree/main/specialists/crispr-screen-analyst/versions/1.0.0/package/skills/bio-crispr-screens-jacks-analysis
Command: npx skills add https://github.com/mrsonord2240/openscience-specialists --skill bio-crispr-screens-jacks-analysis-mrsonord2240

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, matplotlib.

What problem does it solve? CRISPR screen analysis is confounded by variable sgRNA efficacy, and single-screen tools like MAGeCK cannot share guide-quality information across screens. This Skill runs JACKS to jointly decompose per-sgRNA log-fold-changes into gene essentiality and guide efficacy, improving accuracy when multiple screens share the same library. ## Core Features & Use Cases - Multi-Screen Joint Analysis: Run JACKS via CLI or Python API across screens sharing a library and chemistry, producing per-gene effects, posterior standard deviations, and per-sgRNA efficacy estimates. - Efficacy Prior Transfer: Extract per-sgRNA efficacy posteriors from reference panels like DepMap or Project Score and reuse them via --reffile to enable roughly 2.5x smaller screens. - Library Diagnostics: Flag low-efficacy guides (X1 < 0.3) and genes where every guide is weak, generating candidates for library re-design. - Use Case: You have four Brunello screens across different cell lines. Use this Skill to run JACKS jointly, call essential genes with effect/std < -2, and compare results against MAGeCK to identify high-confidence hits. ## Quick Start Ask the agent to run JACKS jointly on your screens sharing the same library and output per-cell-line gene effects, posterior standard deviations, and shared sgRNA efficacy estimates.

Frequently Asked Questions about bio-crispr-screens-jacks-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run JACKS on multiple CRISPR screens?▼

Run python run_JACKS.py with a count matrix, replicate map, and sgRNA-to-gene map from the JACKS/jacks/ directory, or call runJACKS from jacks.jacks_io in Python. JACKS shares guide efficacy across all screens using the same library and outputs per-condition gene effects.

JACKS vs MAGeCK for CRISPR screen analysis?▼

JACKS outperforms MAGeCK when analyzing three or more screens with the same library, giving about 21% lower error per Allen 2019, because it models guide efficacy jointly. For a single screen with no efficacy prior, MAGeCK or BAGEL2 perform equivalently and are simpler.

Can JACKS efficacy priors transfer across CRISPR libraries?▼

No. Guide efficacy is sequence-specific, so priors must come from the exact same library matched by sequence, not just ID. Efficacy also cannot transfer across chemistries: Cas9 knockout efficacy differs fundamentally from CRISPRi or CRISPRa.

Why do JACKS p-values differ between runs?▼

Gene-level p-values are computed by resampling pseudo-genes from negative controls using Python's random module, so unseeded runs differ. Call random.seed before runJACKS for reproducible p-values; gene effects and efficacies themselves are deterministic.

When should I not use JACKS for screen analysis?▼

Avoid JACKS for single screens without a reference prior, heavy-selection drug screens (use drugZ), and multi-cell-line cancer panels with copy-number bias (use Chronos). JACKS also cannot share efficacy across different libraries or chemistries.

Related Skills

Explore More Education & Research Skills→
stellaromicsstellaromics

bio-crispr-screens-screen-qc

Assess library representation, read distribution, replicate correlation, and essential gene recovery in MAGeCK CRISPR screen outputs.

Official
Advanced
Lord1EgyptLord1Egypt
2

crispr-screen-analysis

Analyze pooled CRISPR screen data with MAGeCK and GSEA.

Community
Advanced
stellaromicsstellaromics

bio-workflows-crispr-screen-pipeline

Automate pooled CRISPR screen analysis from FASTQ reads to gene hits.

Official
Advanced
SciMate-AISciMate-AI
1

depmap

Query DepMap Chronos scores and gene effects across cancer cell lines.

Official
Advanced