bio-crispr-screens-hit-calling

Selects and reconciles statistical methods for calling hits in pooled CRISPR screens.

Updated Sep 15, 2026
One-click install
npx skills add https://github.com/mrsonord2240/openscience-specialists --skill bio-crispr-screens-hit-calling-mrsonord2240
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-crispr-screens-hit-calling
Source: https://github.com/mrsonord2240/openscience-specialists/tree/main/specialists/crispr-screen-analyst/versions/1.0.0/package/skills/bio-crispr-screens-hit-calling
Command: npx skills add https://github.com/mrsonord2240/openscience-specialists --skill bio-crispr-screens-hit-calling-mrsonord2240

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Choosing the wrong statistical method for a pooled CRISPR screen produces inflated or empty hit lists, and single-method results carry high false-discovery risk. This Skill matches the hit-calling method to the experimental design and reconciles disagreement across methods with tiered confidence scoring. ## Core Features & Use Cases - Design-Matched Method Selection: Decision tree mapping screen designs (two-condition, time course, drug screen, cancer-line panel, multi-screen) to MAGeCK RRA/MLE, BAGEL2, drugZ, JACKS, Chronos, or CERES. - Multi-Method Consensus: Builds Tier 1/2/3 confidence-stratified hit lists from 2-3 orthogonal methods, with overlap-enrichment checks to detect mismatched input comparisons. - Failure-Mode Reconciliation: Diagnoses disagreement patterns (copy-number bias, heavy selection breaking median normalization, unseeded BAGEL2 reruns) and prescribes fixes. - Use Case: A researcher with a vehicle-vs-drug chemogenomic screen runs drugZ as primary and MAGeCK MLE as confirmatory, then applies the second-best-sgRNA rule to flag single-guide-driven hits before arrayed validation. ## Quick Start Ask the AI to pick the right hit-calling method for your screen design and build a tiered consensus hit list from MAGeCK, BAGEL2, and drugZ outputs.

Frequently Asked Questions about bio-crispr-screens-hit-calling

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

FAQPage Schema
How do I choose between MAGeCK RRA and MAGeCK MLE for CRISPR screens?▼

Use MAGeCK RRA for standard two-condition essentiality screens; it is fast and robust to outliers. Use MAGeCK MLE for time-course, multi-condition, or drug screens, since it models a design matrix and supports sgRNA efficiency weighting.

What is the best hit-calling method for a drug modifier CRISPR screen?▼

drugZ is the primary method for drug-modifier screens because its bidirectional Z-score detects small-effect sensitizers and suppressors that MAGeCK RRA misses. Use MAGeCK MLE with a dose covariate as a confirmatory check.

How do I build a consensus hit list from MAGeCK and BAGEL2?▼

Merge gene-level results, call MAGeCK hits at FDR below 0.05 and BAGEL2 hits at Bayes factor above 6, then count supporting methods per gene. Genes called by all methods are Tier 1 high-confidence hits requiring minimal validation.

Why do MAGeCK and Chronos disagree on cancer cell line screens?▼

Chronos models copy-number bias and screen quality jointly, while MAGeCK does not, so amplified loci like ERBB2 appear as false MAGeCK hits. Apply copy-number correction before MAGeCK or trust Chronos for cancer-line panels.

Why does BAGEL2 give different Bayes factors on identical reruns?▼

BAGEL2's bf step uses a clock-derived random seed by default, so unseeded reruns can flip gene calls at the BF>6 threshold. Always pass a fixed -s seed and verify byte-identical reruns before trusting results.

When should I not use MAGeCK RRA for hit calling?▼

Avoid RRA when more than 40 percent of guides change, since heavy selection breaks median normalization, and for time-course or cancer-line screens with copy-number bias. Use BAGEL2, MAGeCK with control normalization, or Chronos instead.