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<p>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: (<b>1</b>) different analytical packages can often report different expression patterns and false-discovery-rates and <i>P</i>-values; (<b>2</b>) the effective use of these analytical packages requires substantial knowledge of programming and bioinformatics; and (<b>3</b>) 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月28日

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  • Abhishek Shastry,
  • Benjamin P. Ott,
  • Tanvi Nandani,
  • Alex Paterson,
  • Matt Simpson,
  • Kimberly J. Dunham-Snary,
  • Charles C. T. Hindmarch

摘要

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.