1. phylogeny GENE 3000

2. why is coalescent theory important for understanding phylogenetics (species trees)? coalescent theory lets us test our assumptions of how DNA sequences evolve before we use them to reconstruct phylogeny coalescent theory explains why recently- diverged populations may not yet have synapomorphies despite already being on different evolutionary paths this model gives us basis for estimating time to ancestor of any two sequences, within a population or between species

3. DNA characters are just like phenotypic characters 4 character states A,C,T,G plus information in insertion-deletion, gene copy number, etc. same concerns of homology and shared descent apply

4. human population isolated ~200kya “mitochondrial Eve” sets up misunderstanding every locus sampled now has a point in the past where all current alleles coalesce to a common ancestor in recently diverged species, diversity is often older than the species

5. understanding coalescence 1. larger effective size (Ne), more diversity 2. when time between branching events short relative to Ne, more likely that allelic diversity is older than branching event Ne isolation

6. "This coalescence does not mean that the population originally consisted of a single individual with that ancestral allele. It just means that particular individual’s allele was the one that, out of all the alleles present at that time, later became fixed in the population." National Geographic OCT. 2013

7. phylogeny inference 2 basic approaches: algorithm vs. criterion “neighbor joining” shown in book is an algorithm that generates a single tree by finding shortest “distances” (proportion of differences at nucleotide sites) algorithm approaches do not help identify our uncertainty: one answer comes out, whether well supported or not

8. criterion-based phylogeny 30 tips results in 8.7 x 10 36 possible trees computer search necessary

9. A phylogeny is a hypothesis.

10. 3 of >10,000 possible trees which fits data best? depends on the criterion

12. 3 of >10,000 possible trees which fits data best? depends on the criterion 11 changes 7 changes = most parsimonious of these 3

13. Take out paper... Quick. Draw a phylogeny with 7 tips. Without thinking add: baboon, hamster, mouse, chicken, human, cow, sheep as randomly as possible (Not this, draw your own. With 7 tips.)

14. Just scoring these Characters in the most parsimonious way What score do you get?

15. criteria used in phylogeny parsimony - the fewest # of changes indicates the most acceptable tree topology maximum likelihood - both topology (arrangement of branches) and branch lengths are iteratively searched for tree(s) that fit statistical model of molecular evolution (e.g. transitions > transversions) Bayesian - criterion is still maximum likelihood, search strategy is different (sums result over many similar-likelihood trees)

16. green fluorescent protein has evolved to be more than green the nucleotides are not what we are interested in. we are interested in how traits that affect fitness, ecology, speciation, performance, evolve along a phylogeny

17. why different criteria? 1. we are making our assumptions explicit for inference of the unknown 2. different scientists have different backgrounds that drive their assumptions 3. using multiple methods/criteria lets us test how safe our assumptions are

18. are your data sufficient? all of these methods will find a tree: whether algorithm or criterion-based search is it one you can believe is better than random? is it one you would put your name behind? bootstrapping, and consensus methods

19. bootstrapping text: “statistical method for estimating the strength of evidence that a particular node in a phylogeny exists” more general: resampling technique used to obtain estimates of... parameters and accuracy/variance around those parameters observed data: is a particular subset of the data driving the result of our analysis?

20. mean vs. median 1,2,2,3,3,3,4,4,4,4,5,5,5,6,6,15 mean is 4.5... median is 4. you can change 15 to 150 and mean goes up but median doesn’t change, more robust random resample of data with replacement (means same data entry can be used multiple times) can identify true tendency of data, help ignore ‘outliers’

21. higher bootstrap proportions better! this value is % of “pseudo” replicates that divide tree in same way (represents data tendency to support node)

22. consensus tree can also ask equally- supported trees (equally parsimonious, equal likelihood) how well they all support same nodes doesn’t have to involve subset of data like in bootstrap may summarize the stable parts of tree across 2+ trees bacde bacde bacde

23. support for the method do we believe phylogeny reconstruction works? need to test it against a known history (fish(salamander(bird(mouse,human)) we feel pretty strongly about experimental phylogenetics uses virus evolution to go one step further

24. experimental evolution growing T7 phage on E. coli plates; speed up mutation process by adding mutagen 40 generations 40 generations 40 generations

25. experimental evolution so phylogeny is known, and ancestral strains can be kept in freezer sequence part of DNA and use parsimony, likelihood, and other approaches consistently got the right (TRUE) answer! can also track “traits” on this tree, e.g. changes in growth rate and plaque size on E. coli plates (and check against actual ancestors)

26. # DNA mutations on this branch Text: “Because constructing phylogenies, and science more broadly, is often a process of evaluating evidence, scientists often test the effectiveness of the methodologies used to draw conclusions.”

27. well-supported phylogeny of rabies virus lineages, coded by host bat species

28. For RNA viruses, rapid viral evolution and the biological similarity of closely related host species have been proposed as key determinants of the occurrence and long-term outcome of cross-species transmission. Using a data set of hundreds of rabies viruses sampled from 23 North American bat species, we present a general framework to quantify per capita rates of cross-species transmission and reconstruct historical patterns of viral establishment in new host species using molecular sequence data. These estimates demonstrate diminishing frequencies of both cross-species transmission and host shifts with increasing phylogenetic distance between bat species. Evolutionary constraints on viral host range indicate that host species barriers may trump the intrinsic mutability of RNA viruses in determining the fate of emerging host-virus interactions. analysis indicates rate of virus jumping from one host to another

29. so this study requires TWO phylogenies (virus and bats) CST: cross-species transmission