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Advancing Divide-And-Conquer Phylogeny Estimation Using Robinson-Foulds Supertrees

Authors Xilin Yu , Thien Le , Sarah Christensen , Erin K. Molloy , Tandy Warnow

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Author Details

Xilin Yu
  • Amazon AWS, Seattle, WA, USA
Thien Le
  • Department of EECS, Massachusetts Institute of Technology, Cambridge, MA, USA.
Sarah Christensen
  • Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Erin K. Molloy
  • Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Tandy Warnow
  • Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

Funding

  • Christensen, Sarah: Ira and Debra Cohen Fellowship.
  • Molloy, Erin K.: NSF Graduate Research Fellowship DGE-1144245 and the Ira and Debra Cohen Fellowship.
  • Warnow, Tandy: US National Science Foundation grants 1458652 1513629, and 1535977.

Acknowledgements

This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.

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Xilin Yu, Thien Le, Sarah Christensen, Erin K. Molloy, and Tandy Warnow. Advancing Divide-And-Conquer Phylogeny Estimation Using Robinson-Foulds Supertrees. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 15:1-15:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.WABI.2020.15

Abstract

One of the Grand Challenges in Science is the construction of the Tree of Life, an evolutionary tree containing several million species, spanning all life on earth. However, the construction of the Tree of Life is enormously computationally challenging, as all the current most accurate methods are either heuristics for NP-hard optimization problems or Bayesian MCMC methods that sample from tree space. One of the most promising approaches for improving scalability and accuracy for phylogeny estimation uses divide-and-conquer: a set of species is divided into overlapping subsets, trees are constructed on the subsets, and then merged together using a "supertree method". Here, we present Exact-RFS-2, the first polynomial-time algorithm to find an optimal supertree of two trees, using the Robinson-Foulds Supertree (RFS) criterion (a major approach in supertree estimation that is related to maximum likelihood supertrees), and we prove that finding the RFS of three input trees is NP-hard. We also present GreedyRFS (a greedy heuristic that operates by repeatedly using Exact-RFS-2 on pairs of trees, until all the trees are merged into a single supertree). We evaluate Exact-RFS-2 and GreedyRFS, and show that they have better accuracy than the current leading heuristic for RFS.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Graph algorithms
Keywords
  • supertrees
  • divide-and-conquer
  • phylogeny estimation

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