GenRank

GenRank prioritizes genes by integrating convergent evidence across multiple independent genomic data layers to rank candidate genes from high-throughput studies for phenotype and complex trait association.

Key Features:

• Convergent evidence integration: Integrates multiple independent data layers at the gene level to synthesize evidence supporting candidate genes.
• Convergent Evidence (CE) method: Aggregates evidence using a weighted vote counting strategy to prioritize genes based on support across data layers.
• Rank Product (RP) method: Performs meta-analysis of microarray-based gene expression data to identify genes consistently ranked across datasets.
• Traditional method: Combines p-values from multiple sources to provide a statistical basis for gene ranking.
• Implementation: Implemented in R/Bioconductor as a computational package for gene-level evidence integration.

Scientific Applications:

• Complex trait gene prioritization: Prioritizes candidate genes implicated in complex genetic traits and phenotype associations.
• Meta-analysis of expression studies: Identifies consistently differentially expressed genes across multiple microarray datasets.
• Biomarker and therapeutic target discovery: Integrates diverse evidence layers to highlight candidate biomarkers and therapeutic targets.

Methodology:

Implements three explicit ranking methods: the Convergent Evidence (CE) method using weighted vote counting, the Rank Product (RP) method for microarray meta-analysis to find consistently ranked genes, and a Traditional method that combines p-values from multiple sources.