Genomic Mutation Analysis: Mapping DNA and Proteins

Identifying genetic mutations is at the heart of modern medicine and evolutionary biology, yet processing millions of DNA fragments requires specialized computational strategies. This text-based course introduces you to the core algorithmic concepts used to compare individual genomes against a reference standard. You will transition from understanding basic biological sequences to analyzing how mutations occur and how they are computationally detected. By studying the underlying combinatorial pattern matching algorithms, you will gain a clear mental model of how modern alignment tools map short reads to massive reference genomes. What you'll learn: - Understand the foundational biological and computational concepts of DNA, proteins, and genetic mutations. - Map short DNA reads to a reference genome using core pattern-matching and alignment algorithms. - Identify single nucleotide polymorphisms (SNPs) and structural insertions or deletions. - Analyze sequence data formats and modern variant calling standards used in genomic research. - Practice writing clean, modern Python code snippets to parse sequence data and simulate basic alignment workflows. The course begins with essential terminology and the biological foundations of genetic variation before guiding you through the step-by-step logic of sequence alignment algorithms. You will explore how these theoretical models translate into practical computational workflows for identifying mutations in both DNA and protein sequences. This course is designed for beginners in bioinformatics, computer science students curious about computational biology, or life science enthusiasts with basic programming exposure. No prior advanced genetics or complex algorithm experience is required. Start reading today to unlock the algorithmic secrets behind genomic discovery.