Bogusz Marcin, Whelan Simon
Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, 752 36 Uppsala, Sweden.
Syst Biol. 2017 Mar 1;66(2):218-231. doi: 10.1093/sysbio/syw074.
Phylogenetic tree inference is a critical component of many systematic and evolutionary studies. The majority of these studies are based on the two-step process of multiple sequence alignment followed by tree inference, despite persistent evidence that the alignment step can lead to biased results. Here we present a two-part study that first presents PaHMM-Tree, a novel neighbor joining-based method that estimates pairwise distances without assuming a single alignment. We then use simulations to benchmark its performance against a wide-range of other phylogenetic tree inference methods, including the first comparison of alignment-free distance-based methods against more conventional tree estimation methods. Our new method for calculating pairwise distances based on statistical alignment provides distance estimates that are as accurate as those obtained using standard methods based on the true alignment. Pairwise distance estimates based on the two-step process tend to be substantially less accurate. This improved performance carries through to tree inference, where PaHMM-Tree provides more accurate tree estimates than all of the pairwise distance methods assessed. For close to moderately divergent sequence data we find that the two-step methods using statistical inference, where information from all sequences is included in the estimation procedure, tend to perform better than PaHMM-Tree, particularly full statistical alignment, which simultaneously estimates both the tree and the alignment. For deep divergences we find the alignment step becomes so prone to error that our distance-based PaHMM-Tree outperforms all other methods of tree inference. Finally, we find that the accuracy of alignment-free methods tends to decline faster than standard two-step methods in the presence of alignment uncertainty, and identify no conditions where alignment-free methods are equal to or more accurate than standard phylogenetic methods even in the presence of substantial alignment error. [Alignment-free; distance-based phylogenetics; pair Hidden Markov Models; phylogenetic inference; statistical alignment.].
系统发育树推断是许多系统学和进化研究的关键组成部分。尽管有持续的证据表明比对步骤可能导致有偏差的结果，但这些研究中的大多数都基于多序列比对后进行树推断的两步过程。在这里，我们提出了一项分为两部分的研究，首先介绍了PaHMM-Tree，这是一种基于邻居连接的新方法，它在不假设单一比对的情况下估计成对距离。然后，我们使用模拟将其性能与其他多种系统发育树推断方法进行基准测试，包括首次将基于无比对距离的方法与更传统的树估计方法进行比较。我们基于统计比对计算成对距离的新方法提供的距离估计与使用基于真实比对的标准方法获得的估计一样准确。基于两步过程的成对距离估计往往准确性要低得多。这种改进的性能在树推断中也有所体现，PaHMM-Tree提供的树估计比所有评估的成对距离方法都更准确。对于接近中度分歧的序列数据，我们发现使用统计推断的两步方法（其中所有序列的信息都包含在估计过程中）往往比PaHMM-Tree表现更好，特别是完全统计比对，它同时估计树和比对。对于深度分歧，我们发现比对步骤变得非常容易出错，以至于我们基于距离的PaHMM-Tree优于所有其他树推断方法。最后，我们发现在存在比对不确定性的情况下，无比对方法的准确性往往比标准两步方法下降得更快，并且即使在存在大量比对错误的情况下，也没有发现无比对方法等于或比标准系统发育方法更准确的情况。[无比对；基于距离的系统发育学；成对隐马尔可夫模型；系统发育推断；统计比对。]
