1. BNFO 602 Phylogenetics Usman Roshan

2. Summary of last time Models of evolution Distance based tree reconstruction –Neighbor joining –UPGMA

3. Why phylogenetics? Study of evolution –Origin and migration of humans –Origin and spead of disease Many applications in comparative bioinformatics –Sequence alignment –Motif detection (phylogenetic motifs, evolutionary trace, phylogenetic footprinting) –Correlated mutation (useful for structural contact prediction) –Protein interaction –Gene networks –Vaccine devlopment –And many more…

4. Maximum Parsimony Character based method NP-hard (reduction to the Steiner tree problem) Widely-used in phylogenetics Slower than NJ but more accurate Faster than ML Assumes i.i.d.

5. Maximum Parsimony Input: Set S of n aligned sequences of length k Output: A phylogenetic tree T –leaf-labeled by sequences in S –additional sequences of length k labeling the internal nodes of T such that is minimized.

6. Maximum parsimony (example) Input: Four sequences –ACT –ACA –GTT –GTA Question: which of the three trees has the best MP scores?

7. Maximum Parsimony ACT GTTACA GTA ACA ACT GTA GTT ACT ACA GTT GTA

8. Maximum Parsimony ACT GTT GTA ACA GTA 1 2 2 MP score = 5 ACA ACT GTA GTT ACAACT 3 1 3 MP score = 7 ACT ACA GTT GTA ACAGTA 1 2 1 MP score = 4 Optimal MP tree

9. Maximum Parsimony: computational complexity ACT ACA GTT GTA ACAGTA 1 2 1 MP score = 4 Finding the optimal MP tree is NP-hard Optimal labeling can be computed in linear time O(nk)

10. Local search strategies Phylogenetic trees Cost Global optimum Local optimum

11. Local search for MP Determine a candidate solution s While s is not a local minimum –Find a neighbor s’ of s such that MP(s’)<MP(s) –If found set s=s’ –Else return s and exit Time complexity: unknown---could take forever or end quickly depending on starting tree and local move Need to specify how to construct starting tree and local move

12. Starting tree for MP Random phylogeny---O(n) time Greedy-MP

13. Greedy-MP takes O(n^2k^2) time

14. Local moves for MP: NNI For each edge we get two different topologies Neighborhood size is 2n-6

15. Local moves for MP: SPR Neighborhood size is quadratic in number of taxa Computing the minimum number of SPR moves between two rooted phylogenies is NP-hard

16. Local moves for MP: TBR Neighborhood size is cubic in number of taxa Computing the minimum number of TBR moves between two rooted phylogenies is NP-hard

17. Local optima is a problem

18. Iterated local search: escape local optima by perturbation Local optimum Local search

19. Iterated local search: escape local optima by perturbation Local optimum Output of perturbation Perturbation Local search

20. Iterated local search: escape local optima by perturbation Local optimum Output of perturbation Perturbation Local search

21. ILS for MP Ratchet Iterative-DCM3 TNT