1. Parsimony and searching tree-space

2. The basic idea To infer trees we want to find clades (groups) that are supported by synapomorpies (shared derived traits). More simply put, we assume that if species are similar it is usually due to common descent rather than due to chance.

3. Sometimes the data agrees ACCGCTTA ACTGCTTA ACTGCTAAACTGCTTA ACCCCTTA Time ACCCCATA ACCCCTTA ACCCCATA ACTGCTTA ACTGCTAA

4. Sometimes not ACCGCTTA ACTGCTTA ACTGCTAAACTGCTTC ACCCCTTA ACCCCTTC Time ACCCCATA ACCCCTTC ACCCCATA ACTGCTTC ACTGCTAA

5. Homoplasy When we have two or more characters that can’t possibly fit on the same tree without requiring one character to undergo a parallel change or reversal it is called homoplasy. ACCGCTTA ACTGCTTA ACTGCTAAACTGCTTC ACCCCTTA ACCCCTTC Time ACCCCATA

6. How can we choose the best tree? To decide which tree is best we can use an optimality criterion. Parsimony is one such criterion. It chooses the tree which requires the fewest mutations to explain the data. The Principle of Parsimony is the general scientific principle that accepts the simplest of two explanations as preferable.

7. S1 ACCCCTTC S2 ACCCCATA S3 ACTGCTTC S4 ACTGCTAA (1,2),(3,4) (1,3),(2,4) 1 2 3 4 1 3 2 4

8. S1 ACCCCTTC S2 ACCCCATA S3 ACTGCTTC S4 ACTGCTAA (1,2),(3,4) 0 (1,3),(2,4) 0 A A A A A A A A

9. S1 ACCCCTTC S2 ACCCCATA S3 ACTGCTTC S4 ACTGCTAA (1,2),(3,4) 001 (1,3),(2,4) 002 C C T T C T C T CT CT T C

10. S1 ACCCCTTC S2 ACCCCATA S3 ACTGCTTC S4 ACTGCTAA (1,2),(3,4) 0011 (1,3),(2,4) 0022 C C G G C G C G CG CG G C

11. S1 ACCCCTTC S2 ACCCCATA S3 ACTGCTTC S4 ACTGCTAA (1,2),(3,4) 001101 (1,3),(2,4) 002201 T A T T T T A T T A A T

12. S1 ACCCCTTC S2 ACCCCATA S3 ACTGCTTC S4 ACTGCTAA (1,2),(3,4) 0011011 (1,3),(2,4) 0022011 T T T A T T T A T A T A

13. S1 ACCCCTTC S2 ACCCCATA S3 ACTGCTTC S4 ACTGCTAA (1,2),(3,4) 00110112 6 (1,3),(2,4) 00220111 7 C A C A C C A A A C C A CA According to the parsimony optimality criterion we should prefer the tree (1,2),(3,4) over the tree (1,3),(2,4) as it requires the fewest mutations.

14. Maximum Parsimony The parsimony criterion tries to minimise the number of mutations required to explain the data The “Small Parsimony Problem” is to compute the number of mutations required on a given tree. For small examples it is straightforward to see how many mutations are needed Cat Dog Rat Mouse A A G G G A Cat Dog Rat A G G A A A Mouse

15. The Fitch algorithm For larger examples we need an algorithm to solve the small parsimony problem a b c d e f g h Site a A bA cC dC eG fG gT hA

16. The Fitch algorithm  Label the tips of the tree with the observed sequence at the site A A C C G G T A

17. The Fitch algorithm Pick an arbitrary root to work towards A A C C G G T A

18. The Fitch algorithm Work from the tips of the tree towards the root. Label each node with the intersection of the states of its child nodes. If the intersection is empty label the node with the union and add one to the cost A A C C G G T A A {A,T} A {C,G} C {A,C,G} Cost 4

19. Fitch continued… The Fitch algorithm also has a second phase that allocates states to the internal nodes but it does not affect the cost. To find the Fitch cost of an alignment for a particular tree we just sum the Fitch costs of all the sites.

21. The “large parsimony problem” The small parsimony problem – to find the score of a given tree - can be solved in linear time in the size of the tree. The large parsimony problem is to find the tree with minimum score. It is known to be NP-Hard.

22. How many trees are there? #species#unrooted binary tip- labelled trees 43 53*5=15 63*5*7=105 73*5*7*9=945 102,027,025 202.2*10 20 n(2n-5)!! An exact search for the best tree, where each tree is evaluated according to some optimality criterion such as parsimony quickly becomes intractable as the number of species increases

23. Counting trees 1 2 3 1 1 1 1 2 22 52 4 3 3 3 3 4 4 4 132 4 5 512 4 3 125 4 3 142 5 3 1 1 x 3 = 3 1 x 3 x 5 = 15

24. Search strategies Exact search  possible for small n only Branch and Bound  up to ~20 taxa Local Search - Heuristics  pick a good starting tree and use moves within a “neighbourhood” to find a better tree. Meta-heuristics  Genetic algorithms  Simulated annealing  The ratchet

25. Exact searches for small number of taxa (n<=12) it is possible to compute the score of every tree Branch and Bound searches also guarantee to find the optimal solution but use some clever rules to avoid having to check all trees. They may be effective for up to 25 taxa.

26. http://evolution.gs.washington.edu/gs541/2005/lecture25.pdf

27. No need to evaluate this whole branch of the search tree, as no tree can have a score better than 9

30. The problem of local optima

31. Nearest Neighbor Interchange(NNI)

32. Subtree Pruning Regrafting (SPR)

33. Tree Bisection Reconnection(TBR)