title: Reconstructing phylogenies from noisy quartets in polynomial time with a high success probability.
abstract: 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(n4 log n) time, where p is the probability for a quartet topology being an error. This probability is improved by the third algorithm to approximately [equation; see text], where [equation, see text], with running time of O(n5), which is at least 0.984 when p < 0.05.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. wgang@cs.ualberta.ca
authors.forename: Gang
authors.initials: G
authors.lastname: Wu
authors.forename: Ming-Yang
authors.initials: MY
authors.lastname: Kao
authors.forename: Guohui
authors.initials: G
authors.lastname: Lin
authors.forename: Jia-Huai
authors.initials: JH
authors.lastname: You