Algorithms for Biological Sequence Analysis: Theory and Practice

Understanding how to analyze DNA, RNA, and protein sequences is the foundation of modern bioinformatics. This course guides you through the core computational algorithms used to decipher biological data, translating complex biological questions into solvable algorithmic problems. You will transition from understanding basic genetic sequences to analyzing the actual algorithms that power sequence alignment, database searching, and pattern matching. Through structured written explanations and step-by-step Python code examples, you will build a solid foundation in computational biology. What you will learn: 1. Understand key biological sequence formats like FASTA and FASTQ and how to parse them. 2. Implement dynamic programming algorithms for pairwise global and local sequence alignment. 3. Analyze the mechanics of heuristic database search tools like BLAST. 4. Practice calculating substitution matrices and scoring schemes for proteins and nucleotides. 5. Apply Python-based string matching and pattern recognition techniques to genomic data. 6. Explore modern sequence analysis concepts, including basic probabilistic models. The course begins with fundamental biological terminology and sequence representations, then progresses systematically through exact alignment algorithms, heuristic search methods, and practical coding exercises. It is designed for beginners in bioinformatics, computer science students, or life science researchers wanting to understand the computational tools they use daily. No prior advanced biology or complex algorithm design experience is required. Start reading today and master the algorithmic foundations of bioinformatics.