In 2015, Soneson, Love, and Robinson published a study on differential analysis of RNA-seq data, highlighting that gene-level abundance estimates and statistical inference are more accurate and interpretable than transcript-level analyses. They demonstrated that differential isoform usage can inflate false discovery rates in gene expression analyses, but this issue can be mitigated by incorporating transcript-level abundance estimates. The study also showed that this problem is relatively minor in real data sets. The authors provided an R package, tximport, to help integrate transcript-level abundance estimates into statistical inference tools. The study emphasized the importance of defining the research question before selecting an analysis method and suggested that gene-level analysis is often more powerful and interpretable than transcript-level analysis. They also discussed the limitations of simple counting methods and the benefits of using transcript-level estimates for more accurate results. The study included simulations and real data sets to evaluate the performance of different methods and concluded that gene-level analysis is generally more accurate and robust. The authors also noted that while transcript-level analysis can be useful in specific cases, gene-level analysis is often preferred for most applications. The study provided a comprehensive overview of the challenges and benefits of different RNA-seq analysis approaches and offered practical solutions for improving the accuracy of gene-level inferences.In 2015, Soneson, Love, and Robinson published a study on differential analysis of RNA-seq data, highlighting that gene-level abundance estimates and statistical inference are more accurate and interpretable than transcript-level analyses. They demonstrated that differential isoform usage can inflate false discovery rates in gene expression analyses, but this issue can be mitigated by incorporating transcript-level abundance estimates. The study also showed that this problem is relatively minor in real data sets. The authors provided an R package, tximport, to help integrate transcript-level abundance estimates into statistical inference tools. The study emphasized the importance of defining the research question before selecting an analysis method and suggested that gene-level analysis is often more powerful and interpretable than transcript-level analysis. They also discussed the limitations of simple counting methods and the benefits of using transcript-level estimates for more accurate results. The study included simulations and real data sets to evaluate the performance of different methods and concluded that gene-level analysis is generally more accurate and robust. The authors also noted that while transcript-level analysis can be useful in specific cases, gene-level analysis is often preferred for most applications. The study provided a comprehensive overview of the challenges and benefits of different RNA-seq analysis approaches and offered practical solutions for improving the accuracy of gene-level inferences.