| SES # | TOPICS | READINGS |
|---|---|---|
| L1 | Algorithms; Machine Learning; Biology | J: 3.1-3.7, 2.8-2.10 D: 11 |
| R1 | Running Times, Molecular Biology and Probability | |
| L2 | Evolutionary Models; Seq Alignment; Dynamic Programming | D: 2.1-2.3 J: 6.4-6.9 |
| L3 | Local/Global Alignments; Variations on Dynamic Programming | D: 2.1-2.3 J: 6.4-6.9 |
| R2 | Python and Dynamic Programming and Alignments | |
| L4 | Linear Time String Searching; Suffix Trees; String Preprocessing | J: 9.1-9.8 |
| L5 | Database Search; Hashing; Random Projections | J: 9.1-9.8 |
| R3 | Randomization, Modulus, Hashing, Random Projections, BLAST, Suffix Trees | |
| L6 | Biological Signals; HMMs | D: 3.1-3.2 |
| L7 | CpG Islands/Simple ORFs; Learning with HMMs | D: 3.3 |
| R4 | Hidden Markov Models | |
| L8 | Expression Analysis; Clustering | J: 10.1-10.3 |
| L9 | Multi-dimensional Clustering; Feature Selection | J: 10.1-10.3 |
| L10 | Regulatory Motifs; Gibbs Sampling; Expectation Maximization | J: 4.4-4.9, 5.5, and 12.2 |
| R5 | Motif Finding Using EM and Gibbs Sampling | |
| L11 | Biological Networks; Graph Algorithms | J: 8.1-8.2 |
| L12 | Phylogenetic Trees; Greedy Algorithms; Parsimony; EM | D: 7.1-7.5 |
| R6 | UPGMA, Neighbor Joining, Parsimony | |
| L13 | Multiple Alignment; Profile Alignment; Iterative Alignment | J: 6.10 |
| L14 | Midterm | |
| L15 | RNA Folding; Context-free Grammars; Phylo-CFGs | D: 9 |
| L16 | Combine Alignment and Feature Finding; Pair HMM | D: 4 |
| R7 | RNA Folding, Context Free Grammars and Related Algorithms | |
| L17 | Gene Finding; Generalized HMMs | |
| L18 | Comparative Gene Finding; Phylogenetic HMMs | |
| L19 | microRNA Regulation; Target Prediction | |
| L20 | Regulatory Relationships; Bayesian Networks | |
| R8 | Sequencing by Hybridization | |
| L21 | Generative Models of Regulation; Bayesian Graphs | |
| L22 | Genome Assembly; Euler Graphs | J: 8.4 |
| L23 | Genome Duplication; Genome Rearrangements | J: 5.1-5.4 |
| L24 | Whole-genome Comparative Genomics | |
| L25-L26 | Final Presentations |
Table of Contents
Study Materials
Readings
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The reading assignments for the course are provided below. The abbreviations in the table refer to chapters and sections in the following textbooks:
D = Durbin, Richard, Sean R. Eddy, Anders Krogh, and Graeme Mitchison. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Reprint ed. Cambridge, UK: Cambridge University Press, 1999. ISBN: 0521629713.
J = Jones, Neil, and Pavel Pevzner. An Introduction to Bioinformatics Algorithms. Cambridge, MA: MIT Press, 2004. ISBN: 0262101068.