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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. This talk.
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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
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 • 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.
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