Iterative-DCM3: A Fast Algorithmic Technique for Reconstructing Large Phylogenetic Trees Usman Roshan and Tandy Warnow U. of Texas at Austin Bernard Moret.

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Presentation transcript:

Iterative-DCM3: A Fast Algorithmic Technique for Reconstructing Large Phylogenetic Trees Usman Roshan and Tandy Warnow U. of Texas at Austin Bernard Moret and Tiffani Williams U. of New Mexico

Large-Scale Phylogeny Reconstruction Challenge: New techniques are required which can find optimal MP/ML trees quickly (especially on large datasets)

Speeding up MP/ML heuristics Time MP score of best trees Performance of current heuristics Desired Performance Fake study

This talk New technique, based upon a particular divide-and-conquer strategy (DCM3), for speeding up heuristics for MP Comparison against current MP heuristics on real datasets Future research

DCM3 Decompositions 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

New technique: Iterative DCM3 Repeat: 1. Apply TBR-based local search till a local optimum is reached. 2. Obtain a DCM3-decomposition based upon the local optimum (the “guide tree” ). 3. Apply base method to subproblems, and merge subtrees using the Strict Consensus Merger. 4. Randomly refine the tree. Variants we have examined: I-DCM3(TBR) and I-DCM3(Ratchet).

Comparison of MP heuristics Datasets: All datasets have uninformative sites removed 429 Eukaryotes rDNA (Lipscomb et. al.) 576 Metazoa DNA (Goloboff) 500 rbcL DNA (Rice et. al.) 567 rbcL, atpb, and 18s DNA (three-gene; Soltis et. al.) 854 rbcL DNA (Goloboff) 921 Avian Cytochrome DNA (birds; Johnson) 2000 Eukaryotes sRNA (Gutell et. al.) 2594 rbcL DNA (Kallersjo et. al.)

Comparison of MP heuristics Methods: TBR search, Ratchet, I-DCM3(TBR), I-DCM3(Ratchet) Datasets: Biological data Experimental Methodology: –On each dataset we ran 10 trials of each method (each trial for 24 hours). –We then plotted avg. best MP scores after fixed time intervals. Implementation: Ratchet was implemented using PAUP*4.0 and I-DCM3 was implemented by us using C++. We used Linux Pentium machines for our experiments.

2000 Eukaryotes sRNA (Gutell et. al.)

2594 rbcL DNA (Kallersjo et. al.)

Conclusions I-DCM3(Ratchet) finds best known trees faster than Ratchet. On larger trees the improvement of I-DCM3 (Ratchet) over Ratchet is more pronounced. Out of 10 trials, on the two largest datasets, best I-DCM3(Ratchet) tree is 9 and 7 steps better then best Ratchet tree

Future work Use recursive I-DCM3 for analyzing very large datasets Biological analysis of real datasets Use I-DCM3 for boosting ML heuristics