ToppGene Suite for gene list enrichment analysis and candidate gene prioritization

TLDR

ToppGene Suite is a free, web‑based portal that performs gene‑list functional enrichment, candidate gene prioritization via functional annotations or network analysis, and identifies novel disease genes in the interactome. It prioritizes genes by computing fuzzy similarity scores from semantic annotations, aggregating them with meta‑analysis, assigning P‑values through genome‑wide random sampling, and applying extended PageRank, HITS, and K‑Step Markov algorithms to protein‑protein interaction networks. In a benchmark of 20 GWAS‑derived gene‑disease associations, ToppGene placed 19 of 20 (95 %) candidates in the top 20 % and ToppNet placed 12 of 16 (75 %) in the top 20 %.

Abstract

ToppGene Suite (http://toppgene.cchmc.org; this web site is free and open to all users and does not require a login to access) is a one-stop portal for (i) gene list functional enrichment, (ii) candidate gene prioritization using either functional annotations or network analysis and (iii) identification and prioritization of novel disease candidate genes in the interactome. Functional annotation-based disease candidate gene prioritization uses a fuzzy-based similarity measure to compute the similarity between any two genes based on semantic annotations. The similarity scores from individual features are combined into an overall score using statistical meta-analysis. A P-value of each annotation of a test gene is derived by random sampling of the whole genome. The protein–protein interaction network (PPIN)-based disease candidate gene prioritization uses social and Web networks analysis algorithms (extended versions of the PageRank and HITS algorithms, and the K-Step Markov method). We demonstrate the utility of ToppGene Suite using 20 recently reported GWAS-based gene–disease associations (including novel disease genes) representing five diseases. ToppGene ranked 19 of 20 (95%) candidate genes within the top 20%, while ToppNet ranked 12 of 16 (75%) candidate genes among the top 20%.