Bioinformatics AlgorithmsAn Active Learning Approach

Lessons in this chapter

  1. 5.1Cracking the Non-Ribosomal CodeWhy Compare Biological Sequences? Cracking the Non-Ribosomal Code
  2. 5.2Introduction to Sequence AlignmentIntroduction to Sequence Alignment and the Longest Common Subsequence
  3. 5.3The Manhattan Tourist ProblemThe Manhattan Tourist Problem and Longest Paths in a DAG
  4. 5.4Sequence Alignment is the Manhattan Tourist Problem in DisguiseSequence Alignment as the Manhattan Tourist Problem: The Alignment Graph
  5. 5.5An Introduction to Dynamic Programming: The Change ProblemAn Introduction to Dynamic Programming: The Change Problem
  6. 5.6The Manhattan Tourist Problem RevisitedSolving the Manhattan Tourist Problem with Dynamic Programming
  7. 5.7From Manhattan to an Arbitrary DAGDynamic Programming in a DAG and Topological Orderings
  8. 5.8Backtracking in the Alignment GraphBacktracking in the Alignment Graph: Finding an LCS
  9. 5.9Scoring AlignmentScoring Alignments: Scoring Matrices, Mismatches and Indels
  10. 5.10From Global to Local AlignmentGlobal Alignment vs. Local Alignment
  11. 5.11The Changing Faces of Sequence AlignmentEdit Distance, Fitting Alignment and Overlap Alignment
  12. 5.12Penalizing Insertions and Deletions in Sequence AlignmentAffine Gap Penalties in Sequence Alignment
  13. 5.13Space Efficient Sequence AlignmentSpace-Efficient Sequence Alignment in Linear Memory
  14. EpilogueMultiple Sequence AlignmentMultiple Sequence Alignment
Solve the code challengesEvery problem in this chapter, autograded, in the interactive text Lecture videosThe full Chapter 5 playlist

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