Functional Genomics Data Analysis with R and Bioconductor

Understanding high-throughput genomic data is essential for modern biological research, yet navigating complex computational pipelines can feel overwhelming. This course guides you through the foundational concepts of functional genomics, translating raw biological data into meaningful scientific insights. By reading through structured explanations and code walkthroughs, you will develop a solid understanding of genomic data analysis. You will learn how to process, analyze, and interpret RNA-Seq, ChIP-Seq, and DNA methylation data using standard open-source tools. What you'll learn: - Understand the core biological and computational concepts behind functional genomics - Analyze RNA-Seq data to identify differentially expressed genes using R and Bioconductor - Process ChIP-Seq datasets to locate protein-DNA binding sites and genomic features - Explore DNA methylation patterns to analyze epigenetic regulation - Apply modern Bioconductor packages and tidyverse integration for cleaner genomic data science - Practice writing reproducible R scripts to document your bioinformatics workflows The course starts with essential terminology and the basic structure of genomic data before diving into step-by-step case studies. You will read clear explanations of the statistical methods and code patterns needed to handle high-dimensional biological data. This text-based course is designed for life scientists, researchers, and students who are new to bioinformatics. Start your journey into computational biology and unlock the power of genomic data today.