TRIAGE

TRIAGE prioritizes candidate genes from genome-scale experiments by iteratively integrating pathway enrichment and network-based statistical analyses to improve hit selection from high-throughput genomic studies.

Key Features:

• Iterative Analysis: Employs an iterative approach that systematically refines gene rankings through repeated integration of complementary analyses.
• Pathway and Network Integration: Combines pathway enrichment analysis with network-based statistical methods to evaluate genes in both functional and interaction contexts.
• Use of Public Databases: Leverages publicly available databases to inform pathway and network annotations used during prioritization.
• Reduction of Analytical Trade-offs: Characterizes complementary contributions of commonly used analysis approaches to minimize trade-offs and enhance overlap of candidate genes across studies.

Scientific Applications:

• Genome-wide hit selection: Applied to high-throughput genomic studies requiring robust, reproducible selection of gene hits from genome-scale experiments.
• HIV host factor studies: Used to prioritize candidate host genes in studies investigating HIV host factors where variability across screens impedes consensus.
• Addressing cutoff limitations: Provides an alternative to arbitrary cutoffs and ad hoc prioritization strategies by using integrated statistical evidence.

Methodology:

Iteratively integrates pathway enrichment and network-based statistical methods, leverages publicly available databases, and characterizes the complementary contributions of commonly used analysis approaches to refine and rank candidate genes.