Algorithms research

Tandy WarnowUT-Austin

“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

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

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

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

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.)

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)

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)

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

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

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)

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

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)

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)