Local Reliability Measures from Sets of Co-Optimal Multiple Sequence Alignments
Local Reliability Measures from Sets of Co-Optimal Multiple Sequence Alignments
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DOI：
10.1142/9789812776136_0003
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发表时间：
2007-12
影响因子：
--
通讯作者：
Giddy Landan;D. Graur
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文献类型：
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作者：
Giddy Landan;D. Graur
多序列比对质量问题受到了比对方法开发者的广泛关注。然而，在现实生活中量化对准可靠性的实际措施却鲜为人知。在这里，我们提出了一种识别和量化多序列比对中不确定性的方法。所提出的方法基于以下观察：在任何目标函数或进化模型下，重建比对的某些部分是唯一最优的，而其他部分则构成从一组共优替代方案中的任意选择。因此，重建对齐的共优部分的可靠性至多是唯一最优部分的一半。对于成对比对，这种不可约的不确定性可以通过比较高路和低路比对来量化，这形成了两个序列的共优包络。我们通过形成一大组同样可能的共优比对来扩展这种方法，用于渐进式多序列比对的情况，这些共优比对包围了共优空间。然后，该集合可用于导出任何候选对齐的一系列局部可靠性度量。由此产生的可靠性测量可以用作对准误差的预测器和分类器。我们报告了一项模拟研究，证明了所提出的本地可靠性措施的优越性。
The question of multiple sequence alignment quality has received much attention from developers of alignment methods. Less forthcoming, however, are practical measures for quantifying alignment reliability in real life settings. Here, we present a method to identify and quantify uncertainties in multiple sequence alignments. The proposed method is based upon the observation that under any objective function or evolutionary model, some portions of reconstructed alignments are uniquely optimal, while other parts constitute an arbitrary choice from a set of co-optimal alternatives. The co-optimal portions of reconstructed alignments are, thus, at most half as reliable as the uniquely optimal portions. For pairwise alignments, this irreducible uncertainty can be quantified by the comparison of the high-road and low-road alignments, which form the cooptimality envelope for the two sequences. We extend this approach for the case of progressive multiple sequence alignment by forming a large set of equally likely co-optimal alignments that bracket the co-optimality space. This set can, then, be used to derive a series of local reliability measures for any candidate alignment. The resulting reliability measures can be used as predictors and classifiers of alignment errors. We report a simulation study that demonstrates the superior power of the proposed local reliability measures.