Gene Expression Analysis: From RNA-seq to Functional Insights

Understanding how genes change expression is central to modern biology and personalized medicine. This course provides a clear path for anyone looking to enter the field of bioinformatics through the lens of transcriptomics, focusing on the practical application of gene expression analysis. You will move from basic biological concepts to performing full-scale differential expression analyses. By reading through structured explanations and code examples, you will learn to transform raw sequencing data into meaningful biological discoveries and gain confidence in handling genomic datasets. What you'll learn: - Understand the core principles of transcriptomics and gene expression measurement. - Perform differential expression analysis using standard R-based bioinformatics workflows. - Apply modern data manipulation techniques using the tidyverse for cleaner genomic analysis. - Interpret RNA-seq results to identify significantly regulated genes and biological pathways. - Explore functional enrichment analysis to understand the biological context of your findings. - Practice writing reproducible R scripts for genomic data processing. The course begins with foundational terminology and the biology of the transcriptome before moving into practical R programming and the step-by-step logic of the RNA-seq pipeline. Each section is designed to build your skills through written explanations and code-based exercises. This course is designed for beginners in bioinformatics, life science students, or researchers with no prior experience in gene expression analysis. No previous programming knowledge is required to get started. Start your journey into bioinformatics and begin analyzing genomic data today.