Graph Algorithms for Genome Assembly and Sequencing

How do scientists read billions of tiny fragments of DNA and reconstruct a complete genome? The answer lies in graph theory, the mathematical backbone of modern bioinformatics. This text-based course guides you from foundational biology and computer science concepts to the algorithmic techniques used to assemble genomes and build the evolutionary Tree of Life. You will understand how to model biological data as graphs and solve real computational problems. What you'll learn: 1. Understand the foundational biological concepts of DNA sequencing and fragment assembly. 2. Map sequencing reads using Hamiltonian and Eulerian paths in de Bruijn graphs. 3. Apply overlap-layout-consensus graph approaches to assemble genomes. 4. Construct evolutionary trees using distance-based and character-based algorithms. 5. Explore modern graphical fragment assembly formats and assembly evaluation metrics. 6. Practice translating biological challenges into concrete graph-theory problems. You will start with core terminology before moving step-by-step through graph representations, path-finding algorithms, and evolutionary tree construction. Each concept is reinforced with clear written explanations and practical algorithmic breakdowns. This course is designed for beginners in bioinformatics, computer science, or biology, and no advanced programming or genetics experience is required. Start reading today to unlock the algorithms that power modern genetic science.