Genome Sequencing and Assembly with Graph Theory

Modern biology relies on the ability to read DNA, yet technology cannot yet process a whole genome in one single pass. This course explores the fascinating computational challenge of piecing together millions of small genetic fragments to reveal the complete picture of an organism's genome. You will discover how the intersection of biology and computer science allows us to solve complex puzzles that are essential for personalized medicine and evolutionary research. Through this text-based curriculum, you will transform from a beginner into someone who understands the algorithmic backbone of bioinformatics. You will learn how to represent biological data as mathematical structures and apply logical patterns to find the most likely sequence of nucleotides. What you'll learn: - Understand the fundamental biological constraints of DNA sequencing and the necessity of fragment assembly. - Apply graph theory concepts, including De Bruijn graphs, to organize and connect overlapping genetic sequences. - Explore the logic of brute force algorithms and their specific applications in sequencing small proteins and peptides. - Compare traditional short-read sequencing approaches with modern long-read computational paradigms. - Master the terminology of bioinformatics, from k-mers and contigs to Eulerian paths. - Practice solving assembly challenges through written algorithmic exercises and logic-based scenarios. The course begins with essential terminology and the biological foundations of DNA before moving into the specific graph-based methods used in modern labs. You will progress through written explanations of assembly logic, followed by step-by-step breakdowns of the algorithms that make genome reconstruction possible. This course is designed for beginners interested in the intersection of life sciences and programming. No prior experience in bioinformatics or advanced mathematics is required to start. Step into the world of computational biology and start decoding the language of life.