Statistically Consistent k-mer Methods for Phylogenetic Tree Reconstruction
Statistically Consistent k-mer Methods for Phylogenetic Tree Reconstruction
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DOI：
10.1089/cmb.2015.0216
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发表时间：
2017-02-01
影响因子：
1.7
通讯作者：
Sullivant, Seth
中科院分区：
文献类型：
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作者：
Allman, Elizabeth S.;Rhodes, John A.;Sullivant, Seth
序列中 k-mers 的频率有时被用作推断系统发育树的基础，而无需首先获得多序列比对。我们表明，使用 k-mer 向量之间的平方欧几里得距离来近似树度量的标准方法可能在统计上不一致。为了解决这个问题，我们对无间隙的直系同源序列导出基于模型的距离校正，从而实现一致的树推理。还研究了 k 聚体频率的模型参数的可识别性。最后，我们报告的模拟表明，即使序列是通过插入和删除过程生成的，校正后的距离也优于许多其他 k 聚体方法。这些结果对多序列比对也有影响，因为 k-mer 方法通常是为此类算法构建引导树的第一步。
Frequencies of k-mers in sequences are sometimes used as a basis for inferring phylogenetic trees without first obtaining a multiple sequence alignment. We show that a standard approach of using the squared Euclidean distance between k-mer vectors to approximate a tree metric can be statistically inconsistent. To remedy this, we derive model-based distance corrections for orthologous sequences without gaps, which lead to consistent tree inference. The identifiability of model parameters from k-mer frequencies is also studied. Finally, we report simulations showing that the corrected distance outperforms many other k-mer methods, even when sequences are generated with an insertion and deletion process. These results have implications for multiple sequence alignment as well since k-mer methods are usually the first step in constructing a guide tree for such algorithms.