This chapter provides a comprehensive overview of the best practices for RNA-seq data analysis, covering all major steps from experimental design to advanced analysis. It highlights the challenges associated with each step and discusses the integration of RNA-seq with other functional genomics techniques. The chapter emphasizes the importance of quality control at different stages to ensure reproducibility and reliability. It also reviews the analysis of small RNAs and the integration of RNA-seq with other methods. Finally, it explores the potential of novel technologies such as single-cell RNA-seq and long-read sequencing, which are changing the landscape of transcriptomics. The chapter aims to provide a commented guideline for RNA-seq data analysis, focusing on current standards and resources in bioinformatics.This chapter provides a comprehensive overview of the best practices for RNA-seq data analysis, covering all major steps from experimental design to advanced analysis. It highlights the challenges associated with each step and discusses the integration of RNA-seq with other functional genomics techniques. The chapter emphasizes the importance of quality control at different stages to ensure reproducibility and reliability. It also reviews the analysis of small RNAs and the integration of RNA-seq with other methods. Finally, it explores the potential of novel technologies such as single-cell RNA-seq and long-read sequencing, which are changing the landscape of transcriptomics. The chapter aims to provide a commented guideline for RNA-seq data analysis, focusing on current standards and resources in bioinformatics.