RNA-Seq is a cutting-edge method in next-generation sequencing that is utilized to uncover the genes and pathways associated with specific diseases or conditions. As the cost and accessibility of high-throughput sequencing continue to improve, the ability to analyze this data has become an increasingly valuable skill for researchers. In this course, we invite you to join us as we delve into the RNA-Seq workflow and explore how to identify the genes and biological processes that may be crucial for your area of interest. We will begin with a concise overview of the RNA-Seq workflow, focusing on the analysis of differential expression (DE). Throughout the course, we will guide you through the process of preparing data for DE analysis, evaluating the quality of count data, and identifying any outliers or significant sources of variation within the data. To accomplish this, we will utilize the DESeq2 R package, which employs a negative binomial model to effectively model the count data and test for genes that are differentially expressed. Furthermore, we will demonstrate how to visualize the results using heatmaps and volcano plots, enabling you to easily identify and save the genes that exhibit significant differential expression. By the end of this course, you will have gained a comprehensive understanding of the RNA-Seq workflow and the tools necessary to identify and analyze genes and biological processes that are relevant to your specific area of interest.