ScreenBEAM

ScreenBEAM performs gene-level meta-analysis of high-throughput functional genomics screening data to identify gene-level effects from RNA interference (RNAi) and CRISPR loss-of-function screens using Bayesian hierarchical modeling.

Key Features:

• R package: Implemented as an R package for computational analysis of functional genomics screening data.
• Support for Diverse Data Types: Supports microarray and next-generation sequencing (NGS) data formats.
• Bayesian Hierarchical Modeling: Employs Bayesian hierarchical modeling to integrate multiple short hairpin RNA (shRNA) or single-guide RNA (sgRNA) datasets targeting the same gene.
• Noise and Off-target Accounting: Accounts for off-target effects, variable reagent potency, and experimental noise across reagents and experiments.
• Robust Performance Across Data Quality: Maintains robustness relative to classical microarray algorithms and methods tailored for NGS, including on lower-quality datasets that constitute approximately 80–95% of publicly available functional genomics screens.
• Comprehensive Data Integration: Combines data from public RNAi and CRISPR repositories to produce gene-level meta-analyses.

Scientific Applications:

• Systematic loss-of-function studies: Enables meta-analysis of RNAi and CRISPR screens to identify consistent gene-level effects across experiments.
• Therapeutic target discovery: Supports identification and prioritization of candidate therapeutic targets from genome-wide loss-of-function screening data.

Methodology:

Uses Bayesian hierarchical modeling to integrate multiple shRNA and sgRNA datasets targeting the same gene, evaluates large publicly accessible RNAi and CRISPR repositories, and applies a meta-analysis approach to account for off-target effects, reagent potency variation, and experimental noise.