This review summarizes best practices for RNA-seq data analysis. RNA-seq is a powerful tool for transcriptome profiling, but no single pipeline works for all applications. The review covers all major steps in RNA-seq analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection, and eQTL mapping. It highlights the challenges associated with each step and discusses the analysis of small RNAs and integration with other functional genomics techniques. The review also discusses the future of RNA-seq with novel technologies. RNA-seq has become a standard tool in life sciences research, but the variety of applications and analysis scenarios means there is no optimal pipeline. Scientists choose different analysis strategies based on the organism and research goals. For example, if a genome sequence is available, transcripts can be identified by mapping RNA-seq reads onto the genome. For organisms without sequenced genomes, reads are assembled into contigs and then mapped onto the transcriptome. For well-annotated genomes, researchers may use existing annotated reference transcriptomes or identify new transcripts. The experimental design and analysis procedures vary greatly depending on the research question. A crucial prerequisite for a successful RNA-seq study is that the data generated have the potential to answer the biological questions of interest. This is achieved by defining a good experimental design, choosing the appropriate library type, sequencing depth, and number of replicates, and planning an adequate execution of the sequencing experiment. RNA-seq can be used alone for transcriptome profiling or in combination with other functional genomics methods to enhance the analysis of gene expression. It can also be coupled with biochemical assays to analyze various aspects of RNA biology. The analysis of RNA-seq data has many variations depending on the application. The review addresses all major analysis steps for a typical RNA-seq experiment, including quality control, read alignment, obtaining metrics for gene and transcript expression, and approaches for detecting differential gene expression. It also discusses analysis options for applications involving alternative splicing, fusion transcripts, and small RNA expression. Useful packages for data visualization are also reviewed. Quality control checks are applied at different stages of the analysis to ensure reproducibility and reliability of the results. The review discusses the importance of quality control for raw reads, read alignment, and quantification. It also highlights the challenges of transcript identification and quantification, especially for novel transcripts. The review discusses the importance of experimental design, including the number of replicates, sequencing depth, and library size. It also discusses the challenges of transcript identification and quantification, especially for novel transcripts. The review highlights the importance of quality control for raw reads, read alignment, and quantification. It also discusses the challenges of transcript identification and quantification, especially for novel transcripts.This review summarizes best practices for RNA-seq data analysis. RNA-seq is a powerful tool for transcriptome profiling, but no single pipeline works for all applications. The review covers all major steps in RNA-seq analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection, and eQTL mapping. It highlights the challenges associated with each step and discusses the analysis of small RNAs and integration with other functional genomics techniques. The review also discusses the future of RNA-seq with novel technologies. RNA-seq has become a standard tool in life sciences research, but the variety of applications and analysis scenarios means there is no optimal pipeline. Scientists choose different analysis strategies based on the organism and research goals. For example, if a genome sequence is available, transcripts can be identified by mapping RNA-seq reads onto the genome. For organisms without sequenced genomes, reads are assembled into contigs and then mapped onto the transcriptome. For well-annotated genomes, researchers may use existing annotated reference transcriptomes or identify new transcripts. The experimental design and analysis procedures vary greatly depending on the research question. A crucial prerequisite for a successful RNA-seq study is that the data generated have the potential to answer the biological questions of interest. This is achieved by defining a good experimental design, choosing the appropriate library type, sequencing depth, and number of replicates, and planning an adequate execution of the sequencing experiment. RNA-seq can be used alone for transcriptome profiling or in combination with other functional genomics methods to enhance the analysis of gene expression. It can also be coupled with biochemical assays to analyze various aspects of RNA biology. The analysis of RNA-seq data has many variations depending on the application. The review addresses all major analysis steps for a typical RNA-seq experiment, including quality control, read alignment, obtaining metrics for gene and transcript expression, and approaches for detecting differential gene expression. It also discusses analysis options for applications involving alternative splicing, fusion transcripts, and small RNA expression. Useful packages for data visualization are also reviewed. Quality control checks are applied at different stages of the analysis to ensure reproducibility and reliability of the results. The review discusses the importance of quality control for raw reads, read alignment, and quantification. It also highlights the challenges of transcript identification and quantification, especially for novel transcripts. The review discusses the importance of experimental design, including the number of replicates, sequencing depth, and library size. It also discusses the challenges of transcript identification and quantification, especially for novel transcripts. The review highlights the importance of quality control for raw reads, read alignment, and quantification. It also discusses the challenges of transcript identification and quantification, especially for novel transcripts.