1 / 9

Iterative-DCM3: A Fast Algorithmic Technique for Reconstructing Large Phylogenetic Trees

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.

nasia
Download Presentation

Iterative-DCM3: A Fast Algorithmic Technique for Reconstructing Large Phylogenetic Trees

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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

  3. 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

  4. 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).

  5. 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.

  6. 2000 Eukaryotes sRNA (Gutell et. al.)

  7. 2594 rbcL DNA (Kallersjo et. al.)

  8. 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

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

More Related