Assignment 5, due Sunday Feb 14
SYLLABUS & LECTURE SLIDES:
Math Notation
Nature paper on Avida
Avida web site
Nature paper on human genome sequence
Nature paper on mouse genome sequence
Siepel et al. paper on PhyloHMMs & sequence conservation
Rabiner tutorial on HMMs
HMM scaling tutorial (Tobias Mann)
Supervised learning tutorial
- Biological Review : Gene and genome structure in prokaryotes and eukaryotes; the genetic code & codon usage; "global" genome organization. Sources and characteristics of sequence data; Genbank and other sequence databases.
- Lecture 1: Finding exact matches in sequences using suffix arrays.
- Lecture 2: Algorithmic complexity. Directed graphs; depth structure of directed acyclic graphs (DAGs); trees and linked lists. Reading: Durbin et al. section 2.1, 2.2, 2.3.
- Discussion Section 1: HW1 and general programming tips.
- Lecture 3: Dynamic programming on weighted DAGs. Reading: Durbin et al. 2.4, 2.5, 2.6.
- Lecture 4: Maximal-scoring sequence segments. Edit graphs & sequence alignment. Reading: Durbin et al. 6.1, 6.2, 6.3; Ewens & Grant 1.1, 1.2, 1.12, 3.1, 3.2, 3.4, 3.6, 5.2, 9.1, 9.2
- Discussion Section 2: HW1 & 2, DAGs, more graph algorithms, dynamic programming, RNA folding.
- Lecture 5: Smith-Waterman algorithm. Needleman-Wunsch algorithm. Local vs. global. Multiple sequence alignment. Linear space algorithms. Reading: Ewens & Grant 5.3.1, 5.3.2, 12.1, 12.2, 12.3; Durbin et al. chapter 3
- Lecture 6: Linear space algorithms (cont'd). General & affine gap penalties. Profiles.
- Discussion Section 3: BLAST.
- Lecture 7: Smith-Waterman special cases. Word nucleation approaches/BLAST. Probability models on sequences; review of basic probability theory: probability spaces, conditional probabilities, independence. Reading: Ewens & Grant 12.2, 12.3, 1.14, Appendix B.10; Durbin et al. chapter 3
- Lecture 8: Probabilities on sequences. Failure of equal frequency assumption for DNA. Site models. Site model examples: 3' splice sites, 5' splice sites, protein motifs. Site probability models.
- Discussion Section 4: BOWTIE, MUSCLE.
- Lecture 9: Comparing alternative models. Neyman-Pearson lemma. Weight matrices for site models. Weight matrices for splice sites in C. elegans. Score distributions.
- Lecture 10: Limitations of site models (variable spacing, non-independence). Hidden Markov Models: introduction; formal definition. Reading: Siepel et al.
- Discussion Section 5: motif finding.
C/C++ PROGRAMMING GUIDES:
OTHER RELEVANT COURSES AT UW:
COMPUTATIONAL BIOLOGY COURSES AT OTHER SITES: