Lecture 3: A Brief Overview of Molecular Phylogeny - MCD Biology

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16 A. Several other probabilistic methods are around, e.g. “Bayesian methods” that calculate the probability of a tree topology based on the data (http://mrbayes.csit.fsu.edu/). B. Phylogeneticists often argue about the ‘best” method for phylogenetic analysis, but they all work about equally well given the constraints of the particular method and with appropriate “corrections” for variable rates, base compositions, etc. The best approach, as usual in science, is all of the above. I don’t believe a tree that results from only one method and not others. 7. How to validate a particular tree “topology”? A. Construct tree with different taxa; since the tree is dependent on which seqs you include, use of a different suite of seqs. will test whether associations observed in one tree are consistent in the context of different taxa. B. Use all methods available to test specific associations of particular seqs of interest. C. “Bootstrap” analysis: resolve tree many times using random subsets of data set. E.g., compile data set for each analysis by drawing alignment columns from the data set at random, with replacement, to compile data
17 set for each tree calculation. Typical bootstraps use the same number of characters as in the alignment, selecting positions at random, “with replacement”. Question: If you do a bootstrap analysis where you (the computer) draws sequence positions at random, with replacement, to equal the number of nt in the alignment, what fraction of the sequences do you NOT sample? (A Poisson calculation and a great exam question.) 1. The bootstrap analysis tests anomalies in tree calculations (any particular tree calculation is a mathematical anecdote) and to some extent whether particular sequence blocks in an alignment cause weird behavior. 2. E.g. of “Bootstrap support” for Big Tree nodes for one tree: Support for nodes are marked: ML/parsimony; note different results with different methods. that is, a particular node is observed in ##% of bootstrap trees. (Note that many peripheral nodes have funky bootstrap values because too many “outgroups” included - below “Troubles”.)
Page 1 and 2: 1 Lecture 3: A Brief Overview of Mo
Page 3 and 4: 3 B. Note that seqs from different
Page 5 and 6: 5 alignment becomes” straightforw
Page 7 and 8: 7 Vignette: Poisson Statistics 1. P
Page 9 and 10: 1. One ml Eco at 5x10 8 /ml = 5x10
Page 11 and 12: 11 C. This can be estimated (from P
Page 13 and 14: 13 5. Even between the bacterial ph
Page 15: 15 In this “heuristic” method (
Page 20 and 21: 20 if you don’t correct for “fa
Page 22 and 23: 22 2. What causes variation in geno
Page 24 and 25: position in sequence* 24 All Arch

Lecture 3: A Brief Overview of Molecular Phylogeny - MCD Biology

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