1. CIPRES: Enabling Tree of Life Projects Tandy Warnow The University of Texas at Austin

2. Reconstructing the “Tree” of Life Handling large datasets: millions of species The “Tree of Life” is not really a tree: reticulate evolution

3. Cyber Infrastructure for Phylogenetic Research Purpose: to create a national infrastructure of hardware, open source software, database technology, etc., necessary to infer the Tree of Life. Group: 40 biologists, computer scientists, and mathematicians from 13 institutions. Funding: $11.6 M (large ITR grant from NSF). URL: http://www.phylo.org

4. CIPRes Members University of New Mexico Bernard Moret David Bader UCSD/SDSC Fran Berman Alex Borchers Phil Bourne John Huelsenbeck Terri Liebowitz Mark Miller University of Connecticut Paul O Lewis University of Pennsylvania Junhyong Kim Susan Davidson Sampath Kannan Val Tannen Texas A&M Tiffani Williams UT Austin Tandy Warnow David M. Hillis Warren Hunt Robert Jansen Randy Linder Lauren Meyers Daniel Miranker University of Arizona David R. Maddison University of British Columbia Wayne Maddison North Carolina State University Spencer Muse American Museum of Natural History Ward C. Wheeler NJIT Usman Roshan UC Berkeley Satish Rao Steve Evans Richard M Karp Brent Mishler Elchanan Mossel Eugene W. Myers Christos M. Papadimitriou Stuart J. Russell Rice Luay Nakhleh SUNY Buffalo William Piel Florida State University David L. Swofford Mark Holder Yale Michael Donoghue Paul Turner

5. CIPRES activity Databases - e.g. TreeBase II (Bill Piel and others) Simulations of large-scale complex genome-scale evolution (Junhyong Kim) Outreach (Michael Donoghue and Brent Mishler) Algorithms (Tandy Warnow) Open source software (Wayne Maddison, Dave Swofford, Mark Holder, and Bernard Moret) Computer cluster at SDSC (Fran Berman and Mark Miller) - available to ATOL projects and other groups with datasets above 1000 taxa

6. DNA Sequence Evolution AAGACTT TGGACTTAAGGCCT -3 mil yrs -2 mil yrs -1 mil yrs today AGGGCATTAGCCCTAGCACTT AAGGCCTTGGACTT TAGCCCATAGACTTAGCGCTTAGCACAAAGGGCAT TAGCCCTAGCACTT AAGACTT TGGACTTAAGGCCT AGGGCATTAGCCCTAGCACTT AAGGCCTTGGACTT AGCGCTTAGCACAATAGACTTTAGCCCAAGGGCAT

7. Phylogeny Problem TAGCCCATAGACTTTGCACAATGCGCTTAGGGCAT UVWXY U VW X Y

8. Complex Evolutionary Processes Gap events “Heterotachy” (violations of the rates- across-sites assumption) New types of data (e.g., whole genomes) Reticulate evolution (e.g., hybrid speciation and horizontal gene transfer)

9. Challenges in reconstructing large and/or complex evolutionary histories Previous simulation studies don’t necessarily help us understand phylogenetic reconstruction on large or complex datasets We need new statistical models, new theory, and probably new methods. Reticulate evolution and whole genome evolution in particular present many interesting challenges for reconstruction.

10. CIPRES research in algorithms Multiple sequence alignment Genomic alignment Heuristics for Maximum Parsimony and Maximum Likelihood Bayesian MCMC methods Supertree methods Whole genome phylogeny reconstruction Reticulate evolution detection and reconstruction Data mining on sets of trees, and compact representations of these sets

11. 1.Heuristics for hard optimization criteria (Maximum Parsimony and Maximum Likelihood) - hard to solve on large datasets Phylogenetic reconstruction methods Phylogenetic trees Cost Global optimum Local optimum 2.Polynomial time distance-based methods: Neighbor Joining, FastME, Weighbor, etc. - poor accuracy on datasets with large evolutionary distances

12. DCMs: Divide-and-conquer for improving phylogeny reconstruction

13. “Boosting” phylogeny reconstruction methods DCMs “boost” the performance of phylogeny reconstruction methods. DCM Base method MDCM-M

14. DCMs (Disk-Covering Methods) DCMs for polynomial time methods improve topological accuracy (empirical observation), and have provable theoretical guarantees under Markov models of evolution DCMs for hard optimization problems reduce running time needed to achieve good levels of accuracy (empirically observation)

15. DCM1-boosting distance-based methods [Nakhleh et al. ISMB 2001] DCM1-boosting makes distance- based methods more accurate Theoretical guarantees that DCM1-NJ converges to the true tree from polynomial length sequences NJ DCM1-NJ 0 40080016001200 No. Taxa 0 0.2 0.4 0.6 0.8 Error Rate

16. Major challenge: MP and ML Maximum Parsimony (MP) and Maximum Likelihood (ML) remain the methods of choice for most systematists The main challenge here is to make it possible to obtain good solutions to MP or ML in reasonable time periods on large datasets

17. Solving NP-hard problems exactly is … unlikely Number of (unrooted) binary trees on n leaves is (2n-5)!! If each tree on 1000 taxa could be analyzed in 0.001 seconds, we would find the best tree in 2890 millennia #leaves#trees 43 515 6105 7945 810395 9135135 102027025 202.2 x 10 20 1004.5 x 10 190 10002.7 x 10 2900

18. How good an MP analysis do we need? Our research shows that we need to get within 0.01% of optimal (or better even, on large datasets) to return reasonable estimates of the true tree’s “topology”

19. Problems with current techniques for MP Shown here is the performance of a heuristic maximum parsimony analysis on a real dataset of almost 14,000 sequences. (“Optimal” here means best score to date, using any method for any amount of time.) Acceptable error is below 0.01%. Performance of TNT with time

20. Observations The best MP heuristics cannot get acceptably good solutions within 24 hours on most of these large datasets. Datasets of these sizes may need months (or years) of further analysis to reach reasonable solutions. Apparent convergence can be misleading.

21. Our objective: speed up the best MP heuristics Time MP score of best trees Performance of hill-climbing heuristic Desired Performance Fake study

22. DCM3 decomposition Input: Set S of sequences, and guide-tree T 1. Compute short subtree graph G(S,T), based upon T 2. Find clique separator in the graph G(S,T) and form subproblems DCM3 decompositions (1) can be obtained in O(n) time (2) yield small subproblems (3) can be used iteratively (4) can be applied recursively

23. Iterative-DCM3 T T’ Base method DCM3

24. New DCMs DCM3 1.Compute subproblems using DCM3 decomposition 2.Apply base method to each subproblem to yield subtrees 3.Merge subtrees using the Strict Consensus Merger technique 4.Randomly refine to make it binary Recursive-DCM3 Iterative DCM3 1.Compute a DCM3 tree 2.Perform local search and go to step 1 Recursive-Iterative DCM3

25. Rec-I-DCM3 significantly improves performance Comparison of TNT to Rec-I-DCM3(TNT) on one large dataset Current best techniques DCM boosted version of best techniques

26. Datasets 1322 lsu rRNA of all organisms 2000 Eukaryotic rRNA 2594 rbcL DNA 4583 Actinobacteria 16s rRNA 6590 ssu rRNA of all Eukaryotes 7180 three-domain rRNA 7322 Firmicutes bacteria 16s rRNA 8506 three-domain+2org rRNA 11361 ssu rRNA of all Bacteria 13921 Proteobacteria 16s rRNA Obtained from various researchers and online databases

27. Rec-I-DCM3(TNT) vs. TNT (Comparison of scores at 24 hours) Base method is the default TNT technique, the current best method for MP. Rec-I-DCM3 significantly improves upon the unboosted TNT by returning trees which are at most 0.01% above optimal on most datasets.

28. Observations Rec-I-DCM3 improves upon the best performing heuristics for MP. The improvement increases with the difficulty of the dataset.

29. DCMs DCM for NJ and other distance methods produces absolute fast converging (afc) methods DCMs for MP heuristics DCMs for use with the GRAPPA software for whole genome phylogenetic analysis; these have been shown to let GRAPPA scale from its maximum of about 15-20 genomes to 1000 genomes. Current projects: DCM development for maximum likelihood and multiple sequence alignment.

30. Part II: Whole-Genome Phylogenetics A B C D E F X Y Z W A B C D E F

32. Genomes Evolve by Rearrangements Inverted Transposition 1 2 3 9 -8 –7 –6 –5 –4 10 1 2 3 4 5 6 7 8 9 10 Inversion (Reversal) 1 2 3 –8 –7 –6 –5 -4 9 10 Transposition 1 2 3 9 4 5 6 7 8 10

33. Genome Rearrangement Has A Huge State Space DNA sequences : 4 states per site Signed circular genomes with n genes: states, 1 site Circular genomes (1 site) –with 37 genes: states –with 120 genes: states

34. Why use gene orders? “Rare genomic changes”: huge state space and relative infrequency of events (compared to site substitutions) could make the inference of deep evolution easier, or more accurate. Our research shows this is true, but accurate analysis of gene order data is computationally very intensive!

35. The Generalized Nadeau-Taylor model Wang and Warnow, 2001 Three types of events: inversions, transpositions, and inverted transpositions Each event of each type is equiprobable The relative probabilities of the three events are parameters that the user can specify

36. Phylogeny reconstruction in 1998 Distance-based –Breakpoint (BP) distances [Blanchette, Kunisawa, Sankoff 1998] Minimum length trees (NP-hard, even for three taxa) –BPAnalysis: [Sankoff & Blanchette 1998]: exhaustive search through treespace to find the minimum breakpoint length (the number of breakpoints on the tree)

37. 40 taxa, 120 genes, Inv.:Transp.:InvTrans p=2:1:1 birth-death trees, expected deviation from ultrametricity=2 NJ(BP) NJ(BP): seconds BPanalysis: will not finish (will take 200 years for a 13 genome dataset) Error in inferred tree Amount of evolution

38. Progress Statistically-defined distance estimators: EDE and IEBP, highly robust to model violations FastME(EDE) yields very accurate trees, except when the datasets are close to “saturated”

39. BP=breakpoint distance INV=inversion distance EDE: statistically-based estimator [Wang et al. ‘ 01] - highly robust. All these methods are polynomial time. 40 taxa, 120 genes Inv.:Transp.:InvTransp =2:1:1 Birth-death trees, expected deviation from ultrametricity=2 Amount of evolution

40. Progress for equal gene content EDE and IEBP: Statistically-based evolutionary distance estimators, which enable fast and accurate tree reconstruction and are robust to model violations (Wang and W) GRAPPA (software package) for finding trees of minimum inversion or breakpoint length (Moret et al.). Computationally intensive but robust. MPME: heuristic for the breakpoint phylogeny DCM4-MPME: can handle larger datasets than MPME or GRAPPA (Wang and W). All methods are fairly robust to model violations

41. Minimum length trees (“parsimony”) Breakpoint length and inversion length: both NP-hard to solve even on three-leaf trees. Exact solutions exponential in both number of taxa and number of genes. Inversion-phylogeny has better topological accuracy than breakpoint phylogeny, but is harder to solve. Highly robust to model violations.

42. “Solving” the inversion and breakpoint phylogeny problems Phylogenetic trees MP score Global optimum Local optimum Usual issue of getting stuck in local optima, since the optimization problems are NP-hard Additional problem: finding the best trees is enormously hard, since even the “point estimation” problem is hard (worse than estimating branch lengths in ML).

43. Minimum length trees (“parsimony”) Breakpoint phylogeny –BPAnalysis: [Sankoff & Blanchette 1998] –GRAPPA [Moret et al. 2001] –MPME [Wang et al. PSB 2002]: represents gene orders as multi- state strings, and solves parsimony on this modified dataset. This problem is exponential in the number of taxa, but polynomial in the number of genes). Because of MP software, it cannot handle large datasets. –DCM4-MPME: uses a divide-and-conquer strategy (similar to DCM3) to decompose a large dataset into smaller datasets, on the basis of a guide tree. It can handle larger datasets than any of the other methods. Inversion phylogeny: –GRAPPA: highly accurate, robust to model violations, but cannot analyze trees with large edge lengths in reasonable time periods.

44. Benchmark gene order dataset: Campanulaceae 12 genomes + 1 outgroup (Tobacco), 105 gene segments NP-hard optimization problems: breakpoint and inversion phylogenies (techniques score every tree) Joint work with Bob Jansen, Linda Raubeson, Jijun Tang, and Li-San Wang 1997: BPAnalysis (Blanchette and Sankoff): 200 years (est.) 2000: Using GRAPPA v1.1 on the 512-processor Los Lobos Supercluster machine: 2 minutes (200,000-fold speedup per processor) 2003: Using latest version of GRAPPA: 2 minutes on a single processor (1-billion-fold speedup per processor)

45. Analyzing Large Datasets Problem size / divergence Topological Error NJ(EDE) poor accuracy for highly diverged datasets GRAPPA cannot handle datasets of moderate size, or trees with long branches MPME Cannot handle datasets of large size NJ(EDE)+MPME in a Divide-and- Conquer approach NJ(BP) NJ(EDE) MPME GRAPPA

46. DCM4-MPME: Guide tree=NJ(EDE) GRAPPA & MPME won’t finish (long branch lengths; too many taxa) 120 genes, 200 taxa, Inversion/Transposition/Inverted Transposition=2:1:1 Birth-Death Trees with deviation from ultrametricity NJ(EDE) DCM4-MPME

47. Summary True evolutionary distance estimators improve accuracy of NJ Sequence-based heuristic (MPME) Divide-and-conquer, integrated approach for large-scale data

48. Future Directions for Whole Genome Phylogeny Scale maximum parsimony to large datasets Analyze nuclear genomes –Multiple chromosomes –Unequal gene content: insertions, deletions, duplications (Moret’s group) –Nonuniform model: hotspots/short inversions

49. Limitations and ongoing research Current methods are mostly limited to single chromosomes with equal gene content (or very small amounts of deletions and duplications). Moret et al. have made some progress on developing a reliable distance-based method for chromosomes with unequal gene content (tests on real and simulated data show high accuracy) Handling the multiple chromosome case is harder

50. GRAPPA (Genome Rearrangement Analysis under Parsimony and other Phylogenetic Algorithms) http://www.cs.unm.edu/~moret/GRAPPA/ Heuristics for NP-hard optimization problems Fast polynomial time distance-based methods Contributors: U. New Mexico, U. Texas at Austin, Universitá di Bologna, Italy Freely available in source code at this site. Project leader: Bernard Moret (UNM) (moret@cs.unm.edu)

51. CIPRES software distributions Software group leaders: Wayne Maddison and Dave Swofford The first distribution (in the next months) will focus on Rec-I-DCM3(PAUP*): fast heuristic searches for maximum parsimony on large datasets for PAUP* users All software will be open source Community contributions to software will be enabled

52. Acknowledgements NSF The David and Lucile Packard Foundation The Program in Evolutionary Dynamics at Harvard The Institute for Cellular and Molecular Biology at UT- Austin See http://www.phylo.org and http://www.cs.utexas.edu/~tandy and http://www.cs.unm.edu/~moret/GRAPPAhttp://www.phylo.org http://www.cs.utexas.edu/~tandy http://www.cs.unm.edu/~moret/GRAPPA