1. Christian Arnold Bioinformatics Group, University of Leipzig Bioinformatics Herbstseminar October 23th, 2009 Three Weeks of Experience at the formatics Institute

2. Content 1.The 10kTrees Project 2.Phylogenetic Targeting 3.Acknowledgements

3. 1. The 10kTrees Project

4. Goals Updated primate phylogeny that includes phylogenetic uncertainty –Use newest available sequence data, include as much primate species as possible, and update regularly –Produce a set of >=10,000 primate-wide trees (with branch lengths) that are appropriate for taxonomically broad comparative research on primate behavior, ecology and morphology using Bayesian methods Make it accessible to other researchers

5. Methodology

6. Version 1 vs. Version 2 Version 1Version 2 Species187231 Genes 4 mitochondrial (COI, COII, CYTB and ND1) and 1 autosomal gene (SRY) 6 mitochondrial (12S rRNA, 16S rRNA, COI, COII, CYTB, cluster of other mitochondrial genes) and 3 autosomal genes (SRY, CCR5, MC1R) Genetic loci24 Total No. of Sites5134~9000 Collected sequences 413 out of 935 total (55.8% missing data) 1007 out of 2079 total (51.6% missing data) No. of constraints291 Generations8 millions60 millions Computing time ~ 48 days (16 processors in parallel, ~ 3 days each) ~ 2 years (32 processors in parallel, ~ 3 weeks each)

7. Preliminary consensus tree Green: Cercopithecines Blue: Hominoids Red: Platyrrhines Yellow: Tarsiers Brown: Strepsirrhines Rooted with Galeopterus variegatus

8. The 10kTrees Website http://10ktrees.fas.harvard.edu/

9. Current Progress Submitted to Evolutionary Anthropology, in press. Will be presented at the AAPA conference (April 2010) in Albuquerque, New Mexico Version 2 is almost finished Available at http://10kTrees.fas.harvard.edu

10. Summary Bayesian approach is time-consuming, but works well, even though data matrix is very sparse Increased number of sequences in Version 2 dramatically reduces need for constraints and improves quality of tree and branch lengths estimates Ongoing project Total number of downloaded trees since June 2009: 95800

11. 2. Phylogenetic Targeting

12. Which species should we study?

13. For which species should we collect data in order to increase the size of comparative data sets ? Goals ?

14. Example 1/2 Hypothesis: Two characters (x and y) show correlated evolution Goal: Test this hypothesis comparatively (e.g. by using phylogenetically independent contrasts and correlation tests) Problem 1: Data has been only collected for x, but not for y Solution 1: Collect data for y and test hypothesis Problem 2: From which species should we collect data for y? Solution 2: Phylogenetic targeting!?

15. Example 2/2 Brain sizeCognitive data 4 ? 9 7 10 ? 3 ? 2 ? Collecting new data is time-consuming and expensive…

16. Methods Systematically generate all possible pairwise comparisons For every pairwise comparison, calculate character differences for the two species that form the pair and assign a score Determine set of phylogenetically independent pairs that maximizes the sum of all selected pair scores (maximal pairing)

17. Maximal pairing: Example

18. Time complexity:, for balanced trees: Decomposition of the maximal pairing

19. Simulation results 1/2 Random (Rnd) selection of species –Type 1 errors close to nominal level –Power: ~40%, independent of number of taxa –Uses 67% of available variation Phylogenetic targeting (PT) induced selection of species –Type 1 errors close to nominal level –Power: 67-81%, increases with number of taxa –Uses 89% of available variation Detecting correlated character evolution, based on selection of 12 species

20. Simulation results 2/2 PT Rnd 12 18 24 Number of selected species Fraction of available variation after sampling PT Rnd PT Rnd PT Rnd

21. Current Progress A revised version will be resubmitted to American Naturalist in the not too distant future TODO: Extend simulations and clarify some issues Available at http://phylotargeting.fas.harvard.edu

22. Summary A focused selection of species can save valuable time and money Phylogenetic targeting provides a very flexible approach and can address different questions in the context of limited resources Dynamic programming algorithms are everywhere

23. 3. Acknowledgements

24. Harvard University Max-Planck Institute for Evolutionary Anthropology University of Leipzig Charlie Nunn Luke Matthews Peter F. Stadler Thanks!

25. Thank you for your attention! Questions? If not: Cheers (it’s early, but not too early…) Any Questions?