A Faster Construction of Greedy Consensus Trees

Authors Pawel Gawrychowski, Gad M. Landau, Wing-Kin Sung, Oren Weimann



PDF
Thumbnail PDF

File

LIPIcs.ICALP.2018.63.pdf
  • Filesize: 496 kB
  • 14 pages

Document Identifiers

Author Details

Pawel Gawrychowski
  • Institute of Computer Science, University of Wrocław, Poland
Gad M. Landau
  • University of Haifa, Israel
Wing-Kin Sung
  • National University of Singapore, Singapore
Oren Weimann
  • University of Haifa, Israel

Cite AsGet BibTex

Pawel Gawrychowski, Gad M. Landau, Wing-Kin Sung, and Oren Weimann. A Faster Construction of Greedy Consensus Trees. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 63:1-63:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.ICALP.2018.63

Abstract

A consensus tree is a phylogenetic tree that captures the similarity between a set of conflicting phylogenetic trees. The problem of computing a consensus tree is a major step in phylogenetic tree reconstruction. It is also central for predicting a species tree from a set of gene trees, as indicated recently in [Nature 2013]. This paper focuses on two of the most well-known and widely used consensus tree methods: the greedy consensus tree and the frequency difference consensus tree. Given k conflicting trees each with n leaves, the previous fastest algorithms for these problems were O(k n^2) for the greedy consensus tree [J. ACM 2016] and O~(min{k n^2, k^2n}) for the frequency difference consensus tree [ACM TCBB 2016]. We improve these running times to O~(k n^{1.5}) and O~(k n) respectively.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
Keywords
  • phylogenetic trees
  • greedy consensus trees
  • dynamic trees

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. E. N. Adams III. Consensus techniques and the comparison of taxonomic trees. Systematic Zoology, 21(4):390-397, 1972. Google Scholar
  2. Md. Shamsuzzoha Bayzid and Tandy J. Warnow. Naive binning improves phylogenomic analyses. Bioinformatics, 29(18):2277-2284, 2013. Google Scholar
  3. M.S. Bayzid, S. Mirarab, B. Boussau, and T. Warnow. Weighted statistical binning: enabling statistically consistent genome-scale phylogenetic analyses. PLOS One, page e0129183, 2015. Google Scholar
  4. K. Bremer. Combinable component consensus. Cladistics, 6(4):369-372, 1990. Google Scholar
  5. D. Bryant. A classification of consensus methods for phylogenetics. In Bioconsensuss, volume 61 of DIMACS Series in Discrete Mathematics and Theoretical Computer Science, pages 163-184. American Mathematical Society, 2003. Google Scholar
  6. J. H. Degnan, M. DeGiorgio, D. Bryant, and N. A. Rosenberg. Properties of consensus methods for inferring species trees from gene trees. Systematic Biology, 58(1):35-54, 2009. Google Scholar
  7. J. Dong, D. Fernández-Baca, F. R. McMorris, and R. C. Powers. Majority-rule (+) consensus trees. Mathematical Biosciences, 228(1):10-15, 2010. Google Scholar
  8. J. Felsenstein. Inferring Phylogenies. Sinauer Associates, Inc., Sunderland, Massachusetts, 2004. Google Scholar
  9. J. Felsenstein. PHYLIP, version 3.6. Software package, Department of Genome Sciences, University of Washington, Seattle, U.S.A., 2005. Google Scholar
  10. P. A. Goloboff, J. S. Farris, and K. C. Nixon. TNT, a free program for phylogenetic analysis. Cladistics, 24(5):774-786, 2008. Google Scholar
  11. Jesper Jansson, Ramesh Rajaby, Chuanqi Shen, and Wing-Kin Sung. Algorithms for the majority rule (+) consensus tree and the frequency difference consensus tree. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2016. Google Scholar
  12. Jesper Jansson, Chuanqi Shen, and Wing-Kin Sung. Fast Algorithms for Consensus Trees (FACT). http://compbio.ddns.comp.nus.edu.sg/~consensus.tree, 2013.
  13. Jesper Jansson, Chuanqi Shen, and Wing-Kin Sung. Improved algorithms for constructing consensus trees. Journal of the ACM, 63(3):1-24, 2016. Google Scholar
  14. E. D. Jarvis et al. Whole-genome analyses resolve early branches in the tree of life of modern birds. Science, 346(6215):1320-1331, 2014. Google Scholar
  15. Liang Liu, Lili Yu, and Scott Edwards. A maximum pseudo-likelihood approach for estimating species trees under the coalescent model. BMC Evolutionary Biology, 10:302, 2010. Google Scholar
  16. Liang Liu, Lili Yu, Laura Kubatko, Dennis K. Pearl, and Scott V. Edwards. Coalescent methods for estimating phylogenetic trees. Molecular Phylogenetics and Evolution, 53(1):320-328, 2009. Google Scholar
  17. T. Margush and F. R. McMorris. Consensus n-trees. Bulletin of Mathematical Biology, 43(2):239-244, 1981. Google Scholar
  18. S. Mirarab, S. Bayzid, and T. Warnow. Evaluating summary methods for multilocus species tree estimation in the presence of incomplete lineage sorting. Systematic Biology, 65(3):366-380, 2016. Google Scholar
  19. James B. Pease, David C. Haak, Matthew W. Hahn, and Leonie C. Moyle. Phylogenomics reveals three sources of adaptive variation during a rapid radiation. PLOS Biology, 14(2):1-24, 2016. Google Scholar
  20. Cynthia Phillips and Tandy J. Warnow. The asymmetric median tree emdash a new model for building consensus trees. Discrete Applied Mathematics, 71(1-3):311-335, 1996. Google Scholar
  21. Ramesh Rajaby and Wing-Kin Sung. Computing asymmetric median tree of two trees via better bipartite matching algorithm. In IWOCA, 2017. Google Scholar
  22. F. Ronquist and J. P. Huelsenbeck. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics, 19(12):1572-1574, 2003. Google Scholar
  23. L. Salichos and A. Rokas. Inferring ancient divergences requires genes with strong phylogenetic signals. Nature, 497:327-331, 2013. Google Scholar
  24. Leonidas Salichos, Alexandros Stamatakis, and Antonis Rokas. Novel information theory-based measures for quantifying incongruence among phylogenetic trees. Molecular Biology and Evolution, 31(5):1261-1271, 2014. Google Scholar
  25. Daniel Dominic Sleator and Robert Endre Tarjan. A data structure for dynamic trees. J. Comput. Syst. Sci., 26(3):362-391, 1983. Google Scholar
  26. Jordan V. Smith, Edward L. Braun, and Rebecca T. Kimball. Ratite nonmonophyly: independent evidence from 40 novel loci. Systematic Biology, 62(1):35-49, 2013. Google Scholar
  27. R. R. Sokal and F. J. Rohlf. Taxonomic congruence in the Leptopodomorpha re-examined. Systematic Zoology, 30(3):309-325, 1981. Google Scholar
  28. A. Stamatakis. Raxml version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics, 30(9):1312-1313, 2014. Google Scholar
  29. Alexandros Stamatakis, Paul Hoover, Jacques Rougemont, and Susanne Renner. A rapid bootstrap algorithm for the raxml web servers. Systematic Biology, 57(5):758, 2008. Google Scholar
  30. M. Steel and J. D. Velasco. Axiomatic opportunities and obstacles for inferring a species tree from gene trees. Systematic Biology, 63(5):772-778, 2014. Google Scholar
  31. Wing-Kin Sung. Algorithms in Bioinformatics: A Practical Introduction. Chapman &Hall/CRC, Boca Raton, Florida, 2010. Google Scholar
  32. D. L. Swofford. PAUP*, version 4.0. Software package, Sinauer Associates, Inc., Sunderland, Massachusetts, 2003. Google Scholar
  33. Jimmy Yang and Tandy J. Warnow. Fast and accurate methods for phylogenomic analyses. BMC Bioinformatics, 12(S-9):S4, 2011. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail