The article introduces the voom method, which estimates the mean-variance relationship of log-counts from RNA-seq experiments and generates precision weights for each observation. These weights are then used in the limma empirical Bayes analysis pipeline, allowing RNA-seq analysts to access a wide range of microarray-like statistical methods. The voom method performs as well or better than count-based RNA-seq methods, even under the assumptions of earlier methods, as demonstrated through simulation studies. Two case studies illustrate the use of linear modeling and gene set testing methods with voom. The voom method is particularly advantageous when sequencing depths vary between samples, and it provides accurate type I error rate control even with small sample sizes. The article also discusses the background of RNA-seq and microarray technologies, the limitations of count-based methods, and the advantages of using normal-based methods. The voom method is shown to be faster and more convenient, and it enables the analysis of complex experiments and pathway analysis.The article introduces the voom method, which estimates the mean-variance relationship of log-counts from RNA-seq experiments and generates precision weights for each observation. These weights are then used in the limma empirical Bayes analysis pipeline, allowing RNA-seq analysts to access a wide range of microarray-like statistical methods. The voom method performs as well or better than count-based RNA-seq methods, even under the assumptions of earlier methods, as demonstrated through simulation studies. Two case studies illustrate the use of linear modeling and gene set testing methods with voom. The voom method is particularly advantageous when sequencing depths vary between samples, and it provides accurate type I error rate control even with small sample sizes. The article also discusses the background of RNA-seq and microarray technologies, the limitations of count-based methods, and the advantages of using normal-based methods. The voom method is shown to be faster and more convenient, and it enables the analysis of complex experiments and pathway analysis.