Classification and Phylogeny 2

Tree searching with 2 species only 1 possible tree

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Unformatted text preview: GMA: clusters based on similarity Algorithmic: always produces the same result Take home message: Distance methods use a single measure of similarity (distance) to infer phylogeny. Be sure you can see the difference between this general approach and the next two options (parsimony and likelihood) Parsimony: Tree search: we desire the best tree (out of ALL the imaginable trees for your sample of taxa) Optimality criterion: the best tree is the tree that minimized the number of changes (minimizes homoplasies) We’re going to search tree space for a “best” tree Occam’s razor. Tree Searching: With 2 species, only 1 possible tree with three branches (including the branch at the bottom) Add a third species on any branch Three branches to choose from Produces a node plus two new branches Add the next species on any branch Now there are (3+2) five branches to choose from Produces one more node plus two new branches The nth species has 2n- 3 branches to choose from Therefore, with n species, there are 3x5x7x9x(2n- 3) possible trees The number of operational taxonomic units (species): trees 2:1 3:3 4:15 5:105 10:34,459,425 50: 2.75x(10^76) Comparing trees using the parsimony criterion Showing all three possible trees for three taxa and two outgroups. In the first tree: 1 and 2 have a G nucleotide in common because of common ancestry. In the second tree: species 1 and 2 evolve G in two separate events (homoplasies) Alternate, still with homoplasies: the G evolved at the common ancestor of 1,2, and 3, but then 3 evolved back to T. Parsimony: MO: propose a bunch of hypothetical trees and choose the one with the least homoplasy A non- informative character: a lone character state compared to other OTUs. Maximum likelihood Tree search: we desire the b...
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