Snir Sagi, Warnow Tandy, Rao Satish
Mathematics Department, University of California, Berkeley, California 94720, USA.
J Comput Biol. 2008 Jan-Feb;15(1):91-103. doi: 10.1089/cmb.2007.0103.
Quartet-based phylogeny reconstruction methods, such as Quartet Puzzling, were introduced in the hope that they might be competitive with maximum likelihood methods, without being as computationally intensive. However, despite the numerous quartet-based methods that have been developed, their performance in simulation has been disappointing. In particular, Ranwez and Gascuel, the developers of one of the best quartet methods, conjecture that quartet-based methods have inherent limitations that make them unable to produce trees as accurate as neighbor joining or maximum parsimony. In this paper, we present Short Quartet Puzzling, a new quartet-based phylogeny reconstruction algorithm, and we demonstrate the improved topological accuracy of the new method over maximum parsimony and neighbor joining, disproving the conjecture of Ranwez and Gascuel. We also show a dramatic improvement over Quartet Puzzling. Thus, while our new method is not compared to any ML method (as it is not expected to be as accurate as the best of these), this study shows that quartet methods are not as limited in performance as was previously conjectured, and opens the possibility to further improvements through new algorithmic designs.
基于四重奏的系统发育重建方法，如四重奏迷惑法，被引入是希望它们能与最大似然法竞争，同时计算量又没那么大。然而，尽管已经开发了众多基于四重奏的方法，但其在模拟中的表现却令人失望。特别是，最好的四重奏方法之一的开发者兰韦兹和加斯屈尔推测，基于四重奏的方法存在固有局限性，使其无法生成像邻接法或最大简约法那样准确的树。在本文中，我们提出了短四重奏迷惑法，这是一种新的基于四重奏的系统发育重建算法，并且我们证明了新方法在拓扑准确性上优于最大简约法和邻接法，从而反驳了兰韦兹和加斯屈尔的推测。我们还展示了相对于四重奏迷惑法的显著改进。因此，虽然我们的新方法没有与任何最大似然法进行比较（因为预计它不如其中最好的方法准确），但这项研究表明，四重奏方法在性能上并不像之前推测的那样受限，并且为通过新的算法设计进一步改进开辟了可能性。
