Lesson 279
Bioinformatics &<br>Computational Biology
sequences · alignment · BLAST · assembly · trees
1:00How classic algorithms — sequence alignment, BLAST, genome assembly, and phylogenetic trees — make biological data computable, from DNA strings to protein 3-D structures.
By the end, you can
- Describe DNA, RNA, and protein as strings and explain the central dogma flow.
- Explain why sequence alignment is more appropriate than exact string matching for comparing biological sequences.
- Fill in a small Needleman-Wunsch DP table and perform a traceback to read off the optimal alignment.
- Distinguish global alignment (Needleman-Wunsch) from local alignment (Smith-Waterman) and choose the right one for a given problem.
- State why O(m·n) DP is impractical at genome scale and explain how BLAST's seed-and-extend heuristic addresses this.
- Interpret a BLAST E-value and explain why a low E-value indicates a significant hit.
- Explain how de Bruijn graphs reduce genome assembly to an Eulerian path problem, and contrast this with the OLC approach.
- Distinguish UPGMA from Neighbor-Joining as phylogenetic distance methods.
- Describe what AlphaFold2 achieved and why protein structure prediction was considered a grand challenge.
Up next in String Algorithms, Advanced DP & Competitive Techniques
