Reconstructing phylogenies from noisy quartets in polynomial time with a high success probability

Gang Wu*, Ming-Yang Kao, Guohui Lin, Jia Huai You

*Corresponding author for this work

Research output: Contribution to journalArticle

5 Scopus citations


Background: In recent years, quartet-based phylogeny reconstruction methods have received considerable attentions in the computational biology community. Traditionally, the accuracy of a phylogeny reconstruction method is measured by simulations on synthetic datasets with known "true" phylogenies, while little theoretical analysis has been done. In this paper, we present a new model-based approach to measuring the accuracy of a quartet-based phylogeny reconstruction method. Under this model, we propose three efficient algorithms to reconstruct the "true" phylogeny with a high success probability. Results: The first algorithm can reconstruct the "true" phylogeny from the input quartet topology set without quartet errors in O(n2) time by querying at most (n - 4) log(n - 1) quartet topologies, where n is the number of the taxa. When the input quartet topology set contains errors, the second algorithm can reconstruct the "true" phylogeny with a probability approximately 1 - p in O(n4log n) time, where p is the probability for a quartet topology being an error. This probability is improved by the third algorithm to approximately 1/1+q2+1/2q4 +1/16q5, where q=p/1-p with running time of O(n5), which is at least 0.984 when p < 0.05. Conclusion: The three proposed algorithms are mathematically guaranteed to reconstruct the "true " phylogeny with a high success probability. The experimental results showed that the third algorithm produced phylogenies with a higher probability than its aforementioned theoretical lower bound and outperformed some existing phylogeny reconstruction methods in both speed and accuracy.

Original languageEnglish (US)
Article number1
JournalAlgorithms for Molecular Biology
Issue number1
StatePublished - Jan 24 2008


ASJC Scopus subject areas

  • Structural Biology
  • Molecular Biology
  • Computational Theory and Mathematics
  • Applied Mathematics

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