An experimental study of quartets MaxCut and other supertree methods

M. Shel Swenson, Rahul Suri, C. Randal Linder, Tandy Warnow

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Although many supertree methods have been developed in the last few decades, none has been shown to produce more accurate trees than the popular Matrix Representation with Parsimony (MRP) method. In this paper, we evaluate the performance of several supertree methods based upon the Quartets MaxCut method of Snir and Rao. We show that two of these methods usually outperform MRP and all other supertree methods we studied under many realistic model conditions. In addition, we show that the popular criterion of minimizing the total topological distance to the source trees is only weakly correlated with topological accuracy, and therefore that evaluating supertree methods on biological datasets is problematic.

Original languageEnglish (US)
Title of host publicationAlgorithms in Bioinformatics - 10th International Workshop, WABI 2010, Proceedings
Pages288-299
Number of pages12
DOIs
StatePublished - Nov 10 2010
Externally publishedYes
Event10th International Workshop on Algorithms in Bioinformatics, WABI 2010 - Liverpool, United Kingdom
Duration: Sep 6 2010Sep 8 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6293 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Workshop on Algorithms in Bioinformatics, WABI 2010
CountryUnited Kingdom
CityLiverpool
Period9/6/109/8/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Swenson, M. S., Suri, R., Linder, C. R., & Warnow, T. (2010). An experimental study of quartets MaxCut and other supertree methods. In Algorithms in Bioinformatics - 10th International Workshop, WABI 2010, Proceedings (pp. 288-299). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6293 LNBI). https://doi.org/10.1007/978-3-642-15294-8_24