1. “Evolutionary speculation constitutes a kind of metascience, which has the same intellectual fascination for some biologists that metaphysical speculation possessed for some mediaeval scholastics. It can be considered a relatively harmless habit, like eating peanuts, unless it assumes the form of an obsession; then it becomes a vice” (Stanier, 1970)

2. Linnaean classification Two major characteristics KingdomAnimalia PhylumChordata ClassMammalia OrderPrimates FamilyHominidae Genus Homo Speciessapiens BIOL E-127 – 10/01/07

3. Tree of Life: primary divisions

5. Tree of Life: three “domains” Based on 16S rRNA (Woese, 1987):

6. Tree of Life: three “domains” Based on 16S rRNA (Pace, 1997):

7. Tree basics: meaning

8. Tree basics: rotation

9. Tree basics: shape

10. Tree basics: lengths, unrooted cladograms vs. phylograms

11. Tree basics: character change

12. Key phylogenetic terms

14. Phenetics vs. cladistics

15. Lysozyme amino acid changes in unrelated ruminants Phenetics vs. cladistics

16. Maximum Parsimony Parsimony – shortest tree (fewest homoplasies)

17. Microbial systematics Formerly Pseudomonas (partial list): Ralstonia, Burkholderia, Hydrogenophaga, Sphingomonas, Methylobacterium, Cellvibrio, Xanthomonas, Acidovorax, Hydrogenophillus, Brevundimonas, Pandoraea

18. Molecular phylogenetics Zuckerkandl & Pauling. 1965. Molecules as documents of evolutionary history. J Theor Biol. 8:357-366. Neutral theory (Motoo Kimura, 1968)

19. 16S rRNA as phylogenetic marker Why a good molecule?

20. Process to analyze sequence data

21. Ortholog vs. paralog?

22. Good Dataset [A1, A2, A3, A4] [A1, B2, A3, A4] Bad Dataset A B species 1 species 2 species 3 species 4 A1 B1 A2 B2 A4 B4 A3 B3 1. Collect Sequence Data Ortholog vs. paralog?

23. 2. Sequence Alignment CGGATAAAC CGGATAGAC CGCTGATAAAC CGGATAC taxa1 taxa2 taxa3 taxa4 Alignment

24. 3. Choose Models Ancestral Sequences Observed Sequences ? Model Choose “model”

25. Example: Neighbor Joining (NJ) 4. Choose Methods Taxa Characters Species A ATGGCTATTCTTATAGTACG Species B ATCGCTAGTCTTATATTACA Species C TTCACTAGACCTGTGGTCCA Species D TTGACCAGACCTGTGGTCCG Species E TTGACCAGTTCTCTAGTTCG A B C D E Choose methods: distance-based A B C D E Species A ---- 4 10 9 8 Species B ---- 8 11 10 Species C ---- 3 8 Species D ---- 5 Species E ---- A B C D E Species A ---- 4 10 9 8 Species B -19.3 ---- 8 11 10 Species C -10 -14.7 ---- 3 8 Species D -10.7 -11.3 -16 ---- 5 Species E -12.7 -13.3 -12 -14.7 ---- M(AB)=d(AB) -[(r(A) + r(B)]/(N-2)

26. 4. Choose Methods Maximum Parsimony (MP): Model: Evolution goes through the least number of changes Maximum Likelihood (ML): L (data| model) Bayesian Inference Markov chain Monte Carlo (MCMC) method for sampling from posterior probability distribution Discrete character methods

27. 5. Assess Reliability I. Bootstrap Re-sampling to produce pseudo-dataset (random weighting) II. Jacknife Sampling with replacement III. Permutation test Random deletion of sub-dataset Randomize dataset to build null likelihood distribution CGATCGTTA CAATGATAG CGCTGATAA CGCTGATCG taxa1 taxa2 taxa3 taxa4 123456789 Dataset1: 729338554 Dataset2: 631981282 … Dataset1: 1-3-56789 Dataset2: 12-45678- … 100 73 Assess reliability

28. 5. Assess Reliability Example analysis: ancestry of HIV-1 (Gao et al., 1999)

29. 5. Assess Reliability Further analysis: timing of HIV-1 (Korber et al., 2000. Nature 288:1789-1796)