SIGNAL

SIGNAL integrates pathway enrichment and predicted network interaction analyses in an iterative framework to optimize gene prioritization from genome-scale assays and high-throughput omic-scale studies.

Key Features:

• Iterative Integration: Employs an iterative approach that combines pathway enrichment methods with predicted network interactions to refine results over multiple cycles.
• Pathway Enrichment Methods: Uses pathway enrichment analyses to identify biologically relevant gene sets from genome-scale data.
• Predicted Network Interactions: Incorporates predicted network interactions to contextualize and expand candidate gene lists beyond pathway membership.
• Optimization of Gene Prioritization: Iteratively refines gene rankings to improve the accuracy and reliability of hit selection.
• Reduction of Analytical Trade-offs: Provides a systematic mechanism to combine rankings from different analytical approaches, reducing reliance on arbitrary cutoffs.
• Benchmarking Against Independent Studies: Utilizes comparative analyses of parallel independent studies to inform and validate the integration framework.

Scientific Applications:

• Genomics: Prioritizes candidate genes from genome-wide screens for downstream functional validation.
• Proteomics: Integrates proteomic data with pathway and network contexts to identify prioritized protein candidates.
• Metabolomics: Applies pathway enrichment and network interaction information to interpret metabolomics-derived gene associations.
• Hit Selection and Secondary Validation: Improves hit selection accuracy to guide more efficient secondary validation efforts.

Methodology:

Iterative combination of pathway enrichment methods with predicted network interactions to iteratively refine gene rankings and to combine rankings from distinct analytical approaches, with benchmarking via comparative analyses of parallel independent studies.