Genome Assembly Algorithms: De Bruijn and Overlap Graphs

Reconstructing a complete genome from millions of short DNA fragments is one of the most significant computational challenges in bioinformatics. Understanding the underlying graph theory is essential for anyone entering the field of computational biology. In this text-based course, you will learn how to model and solve genome assembly problems using two foundational graph-theoretic approaches. You will gain a clear, conceptual understanding of how sequence data is transformed into structured graphs to reconstruct DNA. What you'll learn: - Understand the fundamental terminology of genomics, reads, k-mers, and sequencing data. - Compare the structure, construction, and memory requirements of overlap graphs and De Bruijn graphs. - Analyze how Hamiltonian paths solve the assembly problem in overlap graphs. - Explore how Eulerian paths offer a more computationally efficient solution in De Bruijn graphs. - Evaluate the algorithmic trade-offs between classic short-read and modern long-read sequencing technologies. - Practice modeling basic graph representations using modern, clean Python programming patterns. The course begins with foundational biological and computational definitions before guiding you step-by-step through graph construction, path-finding algorithms, and modern assembly challenges. You will read through detailed explanations and analyze clear text-based code snippets to solidify your understanding. This course is designed for absolute beginners in bioinformatics, computer science students, or biology enthusiasts with no prior experience in graph theory. Start reading today to master the computational logic behind DNA sequencing.