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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 Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

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Page 1: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

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

Page 2: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

Newer methods for tree buildingSearch 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

Page 3: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

Pretty hard to do by hand…

Page 4: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

Rooted vs. unrooted trees

Basal node obvious vs. basal node unknownRooted trees are way more fun!

Just need to know which node is basal (oldest) Imagine pulling strings

A B C D

e f

g

A

B

C

D

e f

Page 5: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

Rooted vs. unrooted trees

Basal node obvious, basal node unknown

Page 6: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

Number of unrooted trees for 4 taxa = 3A

B

C

D

A

C

B

D

A

D

C

B

Page 7: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

Number of unrooted trees for 4 taxa = 3Number of rooted trees for 4 taxa = 15

A

B

C

D

A

C

B

D

A

D

C

B

Practice rooting

Page 8: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,
Page 9: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

UnrootedTree of HerpesViruses

Page 10: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

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

Page 11: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,
Page 12: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

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.

Page 13: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

Estimating confidence in trees:

• Bootstrap support• If low, mentally collapse the node into a

polytomy

Page 14: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

Breaking the Bifurcation Rule:Hard polytomies and Soft polytomies

Cichlids

Page 15: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,
Page 16: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

Venereal syphilis

http://www.ncbi.nlm.nih.gov/pubmedhealth/PMH0002303

Page 17: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

195050-100 millionpeople

Non-venereal Trepanomatoses

Page 18: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

1950

Non-venereal Trepanomatoses

Page 19: Newer methods for tree building Parsimony analysis Maximum likelihood Bayesian inference For all 3: Build an unrooted tree on one of these three criteria,

195050-100million

1970

Endemic Trepanomatoses

1990 = 2.5 million