Newer methods for tree building

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Presentation transcript:

Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria, ignore synapomorphy and symplesiomorphy, and pull root out on tree using outgroup All computationally intense

Newer methods for tree building Search Criteria Parsimony Analysis = build unrooted tree with the fewest character state changes Maximum likelihood = given rules about how DNA sequences change over time, a tree can be found that reflects the most likely sequence of events Bayesian inference = generates a posterior distribution for a parameter, composed of a phylogenetic tree and a model of evolution, based on the prior for that parameter and the likelihood of the data

Pretty hard to do by hand…

Rooted vs. unrooted trees A B C D e f A C e f g B D Basal node obvious vs. basal node unknown Rooted trees are way more fun! Just need to know which node is basal (oldest) Imagine pulling strings

Rooted vs. unrooted trees Basal node obvious, basal node unknown

Number of unrooted trees for 4 taxa = 3 C A B B D C D A C D B

Number of unrooted trees for 4 taxa = 3 Number of rooted trees for 4 taxa = 15 C A B B D Practice rooting C D A C D B

Unrooted Tree of Herpes Viruses

Number of trees increases with number of taxa 4 taxa  3 unrooted trees 8 taxa  10,395 unrooted trees 10 taxa  2,027,025 unrooted trees 22 taxa  3 x 1023 (almost a mole) 50 taxa  3 x 1074 (More trees than the number of atoms in the universe) “Exhaustive searches” of tree topologies are nearly impossible with modern data sets Algorithms attempt to find best tree anyway Barry Hall, Phylogenetic Trees Made Easy

Estimating confidence in trees: Character conflict is common in data. We want to know if it is overwhelming the signal in the data. We may not have collected enough data to estimate relationships confidently.

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Breaking the Bifurcation Rule: Hard polytomies and Soft polytomies Cichlids

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Non-venereal Trepanomatoses 1950 50-100 million people

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Endemic Trepanomatoses 1950 50-100 million 1970 1990 = 2.5 million