Bioinformatics Algorithms: Graph Representations for Genome Assembly

Reconstructing DNA sequences from short fragments is one of the most critical challenges in modern bioinformatics. Understanding how to model these biological sequences as mathematical graphs is the first step toward solving complex genetic puzzles. This text-only course guides you through the foundational concepts of graph theory applied to genomics. You will transition from theoretical overlap graphs to practical, clean code representations, gaining the skills to model and manipulate biological data programmatically. What you'll learn: Understand the fundamental terminology of graph theory and how it applies to genome assembly; Represent overlap graphs using modern Python structures like dataclasses and type hints; Implement and compare adjacency matrices and adjacency lists for spatial and computational efficiency; Build string reconstruction algorithms to assemble genome sequences from overlapping reads; Apply basic testing principles to verify the correctness of your graph-based code. You will begin by learning core definitions and theoretical concepts before moving on to step-by-step code implementations. Through clear written explanations and structured code walk-throughs, you will build, traverse, and optimize your own graph structures. This course is designed for beginner programmers, computer science students, and aspiring bioinformaticians. No prior background in biology or advanced graph theory is required, though a basic familiarity with Python is helpful. Start reading today to master the algorithmic foundations of modern bioinformatics.