JACKS

JACKS models guide RNA efficacy across genome-wide CRISPR/Cas9 knockout screens using a Bayesian framework to improve gene-effect estimation and hit identification in mammalian functional genomics.

Key Features:

• Bayesian framework: Implements a Bayesian model to jointly analyze data from multiple screens conducted with the same guide RNA library.
• Joint analysis of multiple screens: Combines information across screens using the same guide RNA library to increase statistical power for downstream inference.
• Modeling guide RNA variability: Explicitly models variable guide RNA efficacies to separate guide-level effects from gene-level effects.
• Improved hit identification: Enhances identification of genetic hits compared with methods that process each screen independently.
• Performance and efficiency: Reported to outperform existing methodologies in efficiency and accuracy for hit detection.
• Reduced experimental size: Enables reduction of required cell numbers by approximately 2.5-fold without compromising performance.

Scientific Applications:

• Genome-wide CRISPR/Cas9 knockout screens: Analysis and interpretation of genome-wide CRISPR/Cas9 knockout screening data to detect gene-level effects.
• Mammalian functional genomics: Application in mammalian systems to systematically investigate gene function across the genome.
• Hit discovery and gene-effect estimation: Identification of genetic hits and quantitative estimation of gene effects while accounting for guide RNA variability.
• Experimental design optimization: Informing screen design to reduce cell requirements and increase resource efficiency.

Methodology:

Uses a Bayesian model that jointly analyzes multiple screens conducted with the same guide RNA library and explicitly models variable guide RNA efficacies to estimate gene effects.