Backtracking Algorithms for Sequence Alignment and LCS

Comparing DNA, RNA, or text sequences requires finding common patterns efficiently. Understanding how algorithms reconstruct these optimal alignments is a foundational skill in bioinformatics and computer science. This written course guides you through the core concepts of the Longest Common Subsequence (LCS) problem, alignment graphs, and backtracking. You will transition from understanding basic sequence comparison to reading and writing clean backtracking algorithms that solve real-world alignment challenges. What you'll learn: Understand the foundational mathematics behind the Longest Common Subsequence (LCS) problem; Represent sequence alignments structurally using directed acyclic graphs (DAGs); Apply dynamic programming matrices to compute alignment scores systematically; Implement backtracking algorithms to trace back and reconstruct optimal alignments; Analyze the time and space complexity of standard LCS and sequence alignment methods; Explore modern optimizations and memory-efficient variations of alignment algorithms. The course begins with essential terminology and the mathematical formulation of sequence alignment. You will then progress through step-by-step written walkthroughs of alignment graphs, dynamic programming tables, and the exact mechanics of backtracking. Designed for beginner programmers, computer science students, and aspiring bioinformaticians, this course requires only a basic understanding of programming logic. Start reading today to master the algorithmic logic behind sequence comparison.