“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)

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

Tree of Life: primary divisions

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

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

Tree basics: meaning

Tree basics: rotation

Tree basics: shape

Tree basics: lengths, unrooted cladograms vs. phylograms

Tree basics: character change

Key phylogenetic terms

Phenetics vs. cladistics

Lysozyme amino acid changes in unrelated ruminants Phenetics vs. cladistics

Maximum Parsimony Parsimony – shortest tree (fewest homoplasies)

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

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

16S rRNA as phylogenetic marker Why a good molecule?

Process to analyze sequence data

Ortholog vs. paralog?

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?

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

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

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 Species B Species C Species D Species E ---- A B C D E Species A Species B Species C Species D Species E M(AB)=d(AB) -[(r(A) + r(B)]/(N-2)

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

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 taxa Dataset1: Dataset2: … Dataset1: Dataset2: … Assess reliability

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

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