DESeq2 is one of the most widely used gene expression differential analysis toolkits in the R/Bioconductor ecosystem. It models RNA-Seq count data based on the negative binomial distribution model, effectively handling library size differences and biological variations. By shrinking the dispersion estimation through empirical Bayesian methods, it enhances the statistical power of experiments with small sample sizes. DESeq2 accepts count matrices from various quantitative tools (such as featureCounts, tximport, Salmon, etc.) as input, providing complete functions for differential expression gene screening, result visualization, and data export. This tool is widely adopted in medical, agricultural, and model organism research, and is one of the industry-standard tools for transcriptome differential expression analysis.