| LEC # | TOPICS | KEY Dates |
|---|---|---|
| Introduction to Biology | ||
| 1 | The Central Dogma: Some Algorithms Introduction | |
| Enumerative Solutions: Partial Digest Problem and Median Strings | ||
| 2 | Partial Digest Problem | Problem set 1 out |
| 3 | Motifs and Median Strings | |
| Dynamic Programming: Sequence Alignments | ||
| 4 | Global Alignment | Problem set 2 out |
| 5 | Local Alignment | |
| 6 | Spliced Alignment | |
| 7 | More Efficient Alignment | |
| Graph Theory: Sequencing Genes and Proteins | ||
| 8 | Genomics and SBH Graphs | Problem set 3 out |
| 9 | Peptide Graphs | |
| Pattern Matching: Exact Matches, Gapless Alignments, and BLAST | ||
| 10 | Exact Pattern Matching | Problem set 4 out |
| 11 | Suffix Trees | |
| 12 | Suffix Arrays and BWTs | |
| 13 | BLAST | |
| Clustering: Microarrays and Phylogeny | ||
| 14 | Clustering (Guest Lecturer) | Problem set 5 out |
| 15 | Trees | |
| Neighbor Joining | ||
| 16 | Review of Phylogenetic Analysis Coalescent Theory in Biology |
|
| 17 | Application: Microarrays (Guest Lecturer) | |
| Probabilistic Models and Machine Learning: Gene Annotation and Evolution | ||
| 18 | Hidden Markov Models I | Problem set 6 out |
| 19 | Hidden Markov Models II | |
| 20 | Gibbs Sampling | |
| 21 | Random Projections | |
| 22 | MCMC and Bayesian Networks | |
| Horizons | ||
| 23 | The Future: Protein Structure (Guest Lecturer) | |
| 24 | The Future: Haplotype Mapping (Guest Lecturer) | |
| 25 | Presentations of Final Projects | |
| 26 | Presentations of Final Projects (cont.) | |