This article presents a comprehensive guide to performing count-based differential expression analysis of RNA sequencing (RNA-seq) data using R and Bioconductor. The protocol covers the entire workflow from data preparation to statistical analysis, focusing on two widely-used tools: DESeq and edgeR. Key steps include read counting, quality control, alignment to a reference genome, and statistical modeling. The article emphasizes the importance of quality checks, appropriate statistical methods, and handling of biological variability. It also discusses the use of generalized linear models (GLMs) and the negative binomial distribution for modeling count data. The protocol is designed for small to moderate sample sizes and provides detailed instructions for both beginners and experienced users. Additionally, it highlights the modular nature of the workflow, allowing for flexibility in adapting it to different experimental designs and data types.This article presents a comprehensive guide to performing count-based differential expression analysis of RNA sequencing (RNA-seq) data using R and Bioconductor. The protocol covers the entire workflow from data preparation to statistical analysis, focusing on two widely-used tools: DESeq and edgeR. Key steps include read counting, quality control, alignment to a reference genome, and statistical modeling. The article emphasizes the importance of quality checks, appropriate statistical methods, and handling of biological variability. It also discusses the use of generalized linear models (GLMs) and the negative binomial distribution for modeling count data. The protocol is designed for small to moderate sample sizes and provides detailed instructions for both beginners and experienced users. Additionally, it highlights the modular nature of the workflow, allowing for flexibility in adapting it to different experimental designs and data types.