Region-Level Design and Analysis of CRISPR Perturbation Screens with FRACTEL

Area of Science:

• Genomics
• Bioinformatics
• Statistical Genetics

Background:

• CRISPR screens are powerful for gene function discovery.
• Analyzing CRISPR screen data at a regional level is challenging.
• Existing methods may lack sensitivity for certain perturbation effects.

Purpose of the Study:

• Introduce FRACTEL, a novel statistical framework for region-level analysis of CRISPR screens.
• Improve statistical power and accuracy in identifying functional genetic elements.
• Provide insights into experimental design for CRISPR screening.

Main Methods:

• FRACTEL aggregates guide RNA (gRNA) p-values using bounded minimum across order statistics.
• Region-level null distributions are estimated via simulation for precise error control.
• The framework integrates with existing bioinformatics pipelines.

Main Results:

• FRACTEL demonstrates improved power and replication rates compared to gRNA-level analyses in simulations and real data.
• The method effectively handles sparse or diffuse perturbation effects.
• Identified trade-offs in experimental design, such as gRNA redundancy versus efficacy.

Conclusions:

• FRACTEL offers a robust statistical approach for region-level CRISPR screen analysis.
• The framework enhances the discovery of genetic perturbations and informs experimental optimization.
• FRACTEL supports diverse applications in CRISPR screening research.