Bioinformatics Lectures

(b) indicates slides that contain primarily background information. (a) indicates "advanced" material. All slides (and errors) by Carl Kingsford unless noted. Often the material for a lecture was derived from some source material that is cited in each PDF file.

    Introduction to Computers and Biology

  1. Introduction and Computational Successes
  2. Quick Biology Introduction (b)

    3. Exact String Search

  3. Z-Algorithm
  4. Knuth-Morris-Pratt and Boyer-Moore (a)
  5. Seminumerical String Matching

    6. Dynamic Programming & Sequence Alignment

  6. Introduction to Dynamic Programming (b)
  7. More Dynamic Programming Examples: Subset Sum & Knapsack (b)
  8. Global Sequence Alignment
  9. Local Sequence Alignment
  10. General & Affine Gap Penalties [examples]
  11. Multiple Sequence Alignment
  12. Linear-space Sequence Alignment (a)

    13. Genome Sequencing & Assembly

  13. DNA (Sanger) Sequencing & Lander-Waterman Statistics
  14. Introduction to Graphs (b)
  15. Genome Assembly Paradigms
  16. Comparing Whole Genomes with MUMmer

    17. Data Structures for String Queries

  17. Introduction to Trees (b)
  18. (Optimal) Binary Search Trees (b)
  19. Suffix Tries & Trees [paper]
  20. Suffix Arrays (including Skew Algorithm) [examples]
  21. Burrows-Wheeler Transform [examples, paper]

    22. Structural Biology

  22. RNA Folding [examples]
  23. Protein Folding
  24. Linear Programming (a)
  25. Side-chain Positioning (a)

    26. Pattern Discovery

  26. Motif Finding with Gibbs Sampling [paper]
  27. Bacterial Gene Finding
  28. Hidden Markov Models and Eukaryotic Gene Finding [examples]
  29. Profile Hidden Markov Models

    30. Phylogenetics

  30. Phylogenetics [soon!]

    31. Clustering Biological Networks

  31. Yeast 2-hybrid
  32. Other Interaction Experiments
  33. Function Prediction
  34. Network Modularity (a)
  35. Clustering via Minimum Spanning Trees
  36. Kernighan-Lin Graph Partitioning & Clustering via Distances (a)
  37. MCL and Other Graph Clustering (MCODE, RNSC) (a)

    38. Aligning Biological Networks

  38. PathBLAST (a)
  39. Alignment via Color Coding (a)
  40. IsoRank (a)

    41. Random Models of Network Evolution

  41. Models of Network Evolution (a)
  42. Attack vs. Failure Response (a)
  43. Network Motifs & Symmetry Breaking (a)

Computer Science Background

All slides by Carl Kingsford unless noted. Often the material for a lecture was derived from some source material that is cited in each PDF file.

    Basic Data Structures

  1. Lists, Stacks, and Queues
  2. AVL Trees
  3. Splay Trees
  4. B-Trees
  5. Skip Lists
  6. Heaps

    7. Interval & Spatial Data Structures

  7. Quadtrees (PR, MX, Point)
  8. kd-Trees
  9. Searching in kd-Trees
  10. Range Trees
  11. Interval Trees
  12. Finding Closest Pair of Points

    13. Programming Languages

  13. Python [soon!]
  14. C++

    15. Network Flow Algorithms

  15. Network Flows
  16. Bipartite Matching
  17. Edge-disjoint paths
  18. Flows with Demands, etc.
  19. Shortest paths with negative weights

    20. The Theory of NP-Completeness

  20. The P and NP Complexity Classes
  21. NP-completeness
  22. SAT, Coloring, TSP Problems
  23. 3-dimensional Matching, Subset Sum Problems