An Alignment Confidence Score Capturing Robustness to Guide Tree Uncertainty
An Alignment Confidence Score Capturing Robustness to Guide Tree Uncertainty
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DOI：
10.1093/molbev/msq066
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发表时间：
2010-08-01
影响因子：
10.7
通讯作者：
Pupko, Tal
中科院分区：
文献类型：
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作者：
Penn, Osnat;Privman, Eyal;Pupko, Tal
多序列比对（MSA）是从分子遗传学到三维结构预测的广泛比较序列分析的基础。已经开发了复杂的算法用于序列比对，但在实践中，可以预期许多错误，并且MSA的大部分是不可靠的。因此，必须了解和表征MSA中的各种误差来源，并量化位点特异性比对置信度。在本文中，我们表明，在逐步对准方法所使用的指导树的不确定性是对准不确定性的主要来源。我们利用这一见解，开发一种新的方法来量化每个对齐列的鲁棒性，以指导树的不确定性。我们建立在广泛使用的自举方法扰动系统发育树。具体地说，我们生成一个树的集合，并将每个树用作对齐算法中的指导树，从而产生一组MSA。接下来，我们测试从未扰动的指导树获得的MSA的每一列相对于MSA的集合的一致性。我们将此度量命名为“基于GUIDe树的对齐置信度”（GUIDANCE）评分。使用基准对齐数据库基准以及模拟研究，我们表明，GUIDANCE分数准确地识别错误的MSA。此外，我们将我们的结果与以前发表的Heads or Tails评分进行了比较，并表明GUIDANCE评分是不可靠对齐区域的更好预测因子。
Multiple sequence alignment (MSA) is the basis for a wide range of comparative sequence analyses from molecular phylogenetics to 3D structure prediction. Sophisticated algorithms have been developed for sequence alignment, but in practice, many errors can be expected and extensive portions of the MSA are unreliable. Hence, it is imperative to understand and characterize the various sources of errors in MSAs and to quantify site-specific alignment confidence. In this paper, we show that uncertainties in the guide tree used by progressive alignment methods are a major source of alignment uncertainty. We use this insight to develop a novel method for quantifying the robustness of each alignment column to guide tree uncertainty. We build on the widely used bootstrap method for perturbing the phylogenetic tree. Specifically, we generate a collection of trees and use each as a guide tree in the alignment algorithm, thus producing a set of MSAs. We next test the consistency of every column of the MSA obtained from the unperturbed guide tree with respect to the set of MSAs. We name this measure the "GUIDe tree based AligNment ConfidencE" (GUIDANCE) score. Using the Benchmark Alignment data BASE benchmark as well as simulation studies, we show that GUIDANCE scores accurately identify errors in MSAs. Additionally, we compare our results with the previously published Heads-or-Tails score and show that the GUIDANCE score is a better predictor of unreliably aligned regions.