This course comprehensively covers the core steps of reference‑based transcriptome RNA‑Seq analysis. It begins with an introduction to RNA‑Seq technology, comparing Sanger, NGS, and third‑generation sequencing, and explains the FASTQ format along with the fastp quality‑control workflow (adapter trimming, low‑quality filtering, and polyG removal). In the quantification section, the statistical differences among RPKM, FPKM, and TPM are clarified, and the pipeline using Hisat2 for alignment, StringTie for assembly and quantification, is demonstrated to generate gene‑level count matrices. For differential expression analysis, three mainstream methods – Ballgown (based on FPKM), DESeq2, and edgeR (based on raw counts) – are taught in detail, including data preprocessing, modeling, hypothesis testing, and multiple‑testing correction (padj). The visualization module covers volcano plots with ggplot2 (highlighting significantly up‑ and down‑regulated genes), Venn diagrams via VennDiagram (showing set intersections), and heatmaps with pheatmap (two‑way clustering of samples and genes). The advanced section introduces WGCNA, explaining how to construct weighted gene co‑expression networks, identify modules, correlate modules with external traits, and screen hub genes within modules based on MM and GS values, which is especially suitable for large‑sample complex phenotype studies. Throughout the course, real data examples and command‑line/R scripts are provided, offering a reliable analytical framework for functional genomics exploration.