Abstract
In a recent paper (Zhang, Rao, and Warnow, Algorithms for Molecular Biology 2019), the INC (incremental tree building) algorithm was presented and proven to be absolute fast converging under standard sequence evolution models. A variant of INC which allows a set of disjoint constraint trees to be provided and then uses INC to merge the constraint trees was also presented (i.e., Constrained INC). We report on a study evaluating INC on a range of simulated datasets, and show that it has very poor accuracy in comparison to standard methods. We also explore the design space for divide-and-conquer strategies for phylogeny estimation that use Constrained INC, and show modifications that provide improved accuracy. In particular, we present INC-ML, a divide-and-conquer approach to maximum likelihood (ML) estimation that comes close to the leading ML heuristics in terms of accuracy, and is more accurate than the current best distance-based methods.
Supported by the University of Illinois at Urbana-Champaign and NSF grants DGE-1144245, CCF-1535977, and CCF-1535989. Computational experiments were performed on Blue Waters, supported by NSF grants OCI-0725070 and ACI-1238993 and by the State of Illinois.
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Le, T., Sy, A., Molloy, E.K., Zhang, Q.(., Rao, S., Warnow, T. (2019). Using INC Within Divide-and-Conquer Phylogeny Estimation. In: Holmes, I., MartÃn-Vide, C., Vega-RodrÃguez, M. (eds) Algorithms for Computational Biology. AlCoB 2019. Lecture Notes in Computer Science(), vol 11488. Springer, Cham. https://doi.org/10.1007/978-3-030-18174-1_12
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