1. Algorithms research Tandy Warnow UT-Austin

2. “Algorithms group” UT-Austin: Warnow, Hunt UCB: Rao, Karp, Papadimitriou, Russell, Myers UCSD: Huelsenbeck UNM: Moret, Bader, Williams External participants: Mossel (UCB), Huson (Germany), Steel (NZ), and others

3. Main research foci Solving maximum parsimony and maximum likelihood more effectively “Fast converging methods” Gene order and content phylogeny Reticulate evolution Multiple sequence alignment at the genomic level

4. 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 Poster: Jijun Tang

5. Maximum Parsimony on Rearranged Genomes (MPRG) The leaves are rearranged genomes. Find the tree that minimizes the total number of rearrangement events A B C D 3 6 2 3 4 A B C D E F Total length = 18

6. Benchmark gene order dataset: Campanulaceae 12 genomes + 1 outgroup (Tobacco), 105 gene segments NP-hard optimization problems: breakpoint and inversion phylogenies 1997: BPAnalysis (Blanchette and Sankoff): 200 years (est.)

7. Benchmark gene order dataset: Campanulaceae 12 genomes + 1 outgroup (Tobacco), 105 gene segments NP-hard optimization problems: breakpoint and inversion phylogenies 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)

8. Benchmark gene order dataset: Campanulaceae 12 genomes + 1 outgroup (Tobacco), 105 gene segments NP-hard optimization problems: breakpoint and inversion phylogenies 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)

9. Reticulate Evolution Group leader: Randy Linder Software: (1) producing random networks, (2) simulating sequences down networks, (3) performance evaluation of methods (4) inferring reticulate networks Current reconstruction methods limited to one reticulation event Poster: Luay Nakhleh

10. 20-taxon 1-hybrid network. 0.1 scaling factor.

11. MP/ML heuristics Disk-Covering Methods (DCMs): Divide- and-conquer strategies that boosting the performance of base methods for MP/ML (Warnow) Mr Bayes (Huelsenbeck) New I-DCM3 technique improves upon the Ratchet and TBR Poster: Usman Roshan (DCM-MP)

12. Gutell dataset: 854 rRNA sequences Iterative-DCM3 trials find trees of MP score 103210 in 30 hours, whereas ratchet500 trials take 45 hours to find trees of same score

13. Other planned projects (partial list) Multiple Sequence Alignment (Myers and Williams) Steiner Tree algorithms - error bounds and new heuristics (Rao) MCMC methods (Russell and Huelsenbeck) Symbolic representation of data (Hunt) Parallel algorithms (Bader and Williams)

14. Questions for group How should we measure performance? How should we use simulated data? How should we use real datasets? How can we study criteria (MP, ML, etc.) as opposed to methods? Should we sponsor DIMACS-style challenges? Others? (please bring questions, comments, answers, to the break-out session)