The paper introduces a novel method for differential analysis of RNA-Seq data, called sleuth, which combines bootstrapping with response error linear modeling to decouple biological variance from inferential variance. This approach is implemented in an interactive Shiny app that utilizes kallisto quantifications and bootstrapping for fast and accurate analysis. The method addresses key challenges in RNA-Seq differential analysis, such as the complexity of transcriptome-wide data and the need to account for ambiguously mapped reads. Compared to other methods, sleuth provides more accurate estimates of total variance, better controls false discovery rates, and maintains its advantage across different filtering strategies. The paper also demonstrates the effectiveness of sleuth in isoform-level differential analysis and highlights its interactive exploratory data analysis capabilities through a Shiny app. Overall, sleuth offers a statistically rigorous, flexible, and efficient solution for RNA-Seq data analysis.The paper introduces a novel method for differential analysis of RNA-Seq data, called sleuth, which combines bootstrapping with response error linear modeling to decouple biological variance from inferential variance. This approach is implemented in an interactive Shiny app that utilizes kallisto quantifications and bootstrapping for fast and accurate analysis. The method addresses key challenges in RNA-Seq differential analysis, such as the complexity of transcriptome-wide data and the need to account for ambiguously mapped reads. Compared to other methods, sleuth provides more accurate estimates of total variance, better controls false discovery rates, and maintains its advantage across different filtering strategies. The paper also demonstrates the effectiveness of sleuth in isoform-level differential analysis and highlights its interactive exploratory data analysis capabilities through a Shiny app. Overall, sleuth offers a statistically rigorous, flexible, and efficient solution for RNA-Seq data analysis.