This paper introduces a novel method for differential analysis of RNA-Seq data called sleuth, which uses bootstrapping and response error linear modeling to separate biological variance from inferential variance. The method is implemented in an interactive Shiny app that uses kallisto quantifications and bootstraps for fast and accurate RNA-Seq analysis. RNA-Seq has largely replaced microarray technology for gene expression analysis, but there are key differences between the two technologies. While microarrays measure cDNA hybridization intensities, RNA-Seq provides a de novo sampling of the transcriptome, making it more powerful for detecting individual gene isoform transcription but more complex for differential analysis. Many methods have been developed for RNA-Seq differential analysis, with some translating microarray approaches to RNA-Seq and others using RNA-Seq-specific models. A key difference between RNA-Seq and microarrays is that RNA-Seq data consists of read counts rather than probe intensities, requiring appropriate distributions for count-based modeling. However, there is ongoing debate about the best way to utilize RNA-Seq data for differential analysis, including how to measure gene abundance, whether there is sufficient power to detect isoform differences, and how to best use biological replicates. The uncertainty in RNA-Seq analysis is partly due to a lack of agreed-upon standards for testing and benchmarking. Most accuracy claims are based on simulated read counts rather than real reads, and such simulations often fail to capture the complexities of isoform-specific differential analysis. Studies using real data often use questionable "ground truth" methods, leading to difficult-to-interpret benchmarks. The sleuth method improves upon traditional "count-based" methods by using improved estimates of transcript and gene abundances in a flexible and powerful statistical framework. It explicitly models biological and inferential variance using a response error model, allowing for the separation of these variances before shrinkage. This approach enables sleuth to more accurately identify differentially expressed genes and isoforms, as demonstrated in simulations and real data analysis. Sleuth also provides interactive visualization software for exploring results, which is crucial for transparency and exploratory data analysis. The method was tested against other widely used methods in simulated and real data, showing superior performance in terms of sensitivity and false discovery rate (FDR) control. It was also shown to perform well in isoform-level differential analysis, where traditional methods often struggle. The sleuth workflow is designed to be simple, interpretable, and fast, making it a versatile tool for RNA-Seq analysis. It uses kallisto for quantification, which has significantly reduced running times for quantification based on pseudoalignment. The method is fully reproducible and has been validated through simulations and real data experiments. Overall, sleuth provides a statistically rigorous, flexible, and efficient solution for RNA-Seq analysis.This paper introduces a novel method for differential analysis of RNA-Seq data called sleuth, which uses bootstrapping and response error linear modeling to separate biological variance from inferential variance. The method is implemented in an interactive Shiny app that uses kallisto quantifications and bootstraps for fast and accurate RNA-Seq analysis. RNA-Seq has largely replaced microarray technology for gene expression analysis, but there are key differences between the two technologies. While microarrays measure cDNA hybridization intensities, RNA-Seq provides a de novo sampling of the transcriptome, making it more powerful for detecting individual gene isoform transcription but more complex for differential analysis. Many methods have been developed for RNA-Seq differential analysis, with some translating microarray approaches to RNA-Seq and others using RNA-Seq-specific models. A key difference between RNA-Seq and microarrays is that RNA-Seq data consists of read counts rather than probe intensities, requiring appropriate distributions for count-based modeling. However, there is ongoing debate about the best way to utilize RNA-Seq data for differential analysis, including how to measure gene abundance, whether there is sufficient power to detect isoform differences, and how to best use biological replicates. The uncertainty in RNA-Seq analysis is partly due to a lack of agreed-upon standards for testing and benchmarking. Most accuracy claims are based on simulated read counts rather than real reads, and such simulations often fail to capture the complexities of isoform-specific differential analysis. Studies using real data often use questionable "ground truth" methods, leading to difficult-to-interpret benchmarks. The sleuth method improves upon traditional "count-based" methods by using improved estimates of transcript and gene abundances in a flexible and powerful statistical framework. It explicitly models biological and inferential variance using a response error model, allowing for the separation of these variances before shrinkage. This approach enables sleuth to more accurately identify differentially expressed genes and isoforms, as demonstrated in simulations and real data analysis. Sleuth also provides interactive visualization software for exploring results, which is crucial for transparency and exploratory data analysis. The method was tested against other widely used methods in simulated and real data, showing superior performance in terms of sensitivity and false discovery rate (FDR) control. It was also shown to perform well in isoform-level differential analysis, where traditional methods often struggle. The sleuth workflow is designed to be simple, interpretable, and fast, making it a versatile tool for RNA-Seq analysis. It uses kallisto for quantification, which has significantly reduced running times for quantification based on pseudoalignment. The method is fully reproducible and has been validated through simulations and real data experiments. Overall, sleuth provides a statistically rigorous, flexible, and efficient solution for RNA-Seq analysis.