Confidence: a web app for cross-platform differential gene expression analysis, gene scoring, and enrichment analysis

RNA-seq quantifies the abundance of transcripts within a biological sample and performs differential analysis between different conditions to reveal regulated gene signatures. Three challenges exist: (1) different analytical packages can often report different expression patterns and false-discovery-rates and P-values; (2) the effective use of these analytical packages requires substantial knowledge of programming and bioinformatics; and (3) there are a lack of intuitive methods to prioritize target genes for further investigation. To address these challenges, we developed Confidence, a web-based application to perform simultaneous statistical analysis of RNA-seq count data. Confidence incorporates the Confidence Score (CS), ranging from 1 to 4 to aid in gene prioritization, where 1 represents low confidence and 4 represents high confidence. The Confidence web-based application was designed for rapid and intuitive analysis of standard experimental metadata and gene count inputs providing a web-based, 'wide-net' approach to RNA-seq analysis. Gene scoring allows for unbiased gene selection and identification of novel genes strongly associated with disease/treatment models across multiple species. Pathway analysis has been integrated so that highly confident genes can be placed into biological context. Confidence provides a new strategy for target prioritization in RNA-seq analysis and the generation of publication-quality figures, which we demonstrate here using a published database.