Data Analysis for Life Sciences: Functional Genomics in R

Modern biology generates massive datasets, making computational data analysis an essential skill for life science researchers. This text-based course guides you through the foundational concepts and practical steps of analyzing functional genomics data using R and Bioconductor. You will transition from understanding raw sequencing files to performing complete biological data analyses. By reading through structured explanations and studying real-world case studies, you will learn how to process, analyze, and interpret genomic data, gaining the confidence to apply these methods to your own research projects. What you'll learn: 1. Understand foundational molecular biology concepts and genomic data formats. 2. Analyze RNA-Seq data to identify differentially expressed genes. 3. Process ChIP-Seq datasets to detect protein-DNA binding sites. 4. Explore DNA methylation patterns to study epigenetic modifications. 5. Apply Bioconductor packages for high-throughput genomic data analysis. 6. Practice reproducible research workflows using modern R package management. The course starts with key biological terminology and essential R programming concepts before diving into hands-on case studies. You will progress step-by-step through RNA-Seq, ChIP-Seq, and DNA methylation workflows, learning how to interpret statistical outputs along the way. This course is designed for beginners in bioinformatics, life science students, and researchers with basic R knowledge who want to learn genomic data analysis. No advanced background in statistics or bioinformatics is required. Start reading today to unlock the power of genomic data analysis in your research.