Algorithms for Next-Generation DNA Sequencing

High-throughput DNA sequencing generates massive volumes of biological data with unique encoding schemes and error patterns, requiring specialized computational algorithms. This text-based course breaks down the core mathematical and algorithmic concepts behind genome analysis, giving you a clear mental model of how bioinformatics tools process raw sequencing data. What you'll learn: Understand fundamental genomics terminology, read representations, and biological data structures; Explore alignment algorithms for specialized read formats, including color-space data; Analyze computational strategies for handling high insertion and deletion error rates in single-molecule sequencing; Examine core sequence indexing structures such as the Burrows-Wheeler Transform and FM-index; Apply modern read-correction techniques and consensus-calling logic to noisy genomic datasets. The course begins with foundational vocabulary and computational models before progressing step by step through sequence alignment, error modeling, and modern long-read analysis workflows. This course is designed for beginners in bioinformatics, computer science students, and software engineers with no prior genetics background required. Start reading today to build a strong foundation in computational genomics algorithms.