Class of Multiple Sequence Alignment Algorithm Affects Genomic Analysis
Class of Multiple Sequence Alignment Algorithm Affects Genomic Analysis
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DOI：
10.1093/molbev/mss256
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发表时间：
2013-03-01
影响因子：
10.7
通讯作者：
Whelan, Simon
中科院分区：
文献类型：
--
作者：
Blackburne, Benjamin P.;Whelan, Simon
多序列比对（MSA）是比较序列分析的核心。最近的研究表明，MSA算法在分析基因组时可以产生不同的结果，包括系统发育树推断和自适应进化检测。这些研究还表明，MSA算法之间的差异与算法内的不确定性具有相似的顺序，并建议对这种不确定性进行整合。在本研究中，我们进一步研究了MSA算法之间的分歧问题以及它们如何影响下游分析。我们还研究了整合跨对齐不确定性是否会影响下游分析。我们通过分析200个脊索动物基因家族来解决这些问题，这些基因家族具有反映大规模基因组分析中使用的特性。我们发现新开发的距离度量揭示了两类显著不同的MSA方法（msam）。基于相似性的类包括渐进式比对和一致性比对，代表了许多序列比对的方法创新，而基于进化的类包括系统发育意识比对和统计比对。我们继续表明MSAM的类别对下游分析有实质性的影响。对于系统发育推断，树的估计和它们的分支长度似乎高度依赖于所使用的对准器的类别。据推测，经历了适应性进化的科的数量和这些科内的地点取决于所使用的定位器的类别。基于相似性的比对者倾向于识别更多的适应性进化。我们还开发和测试了在检测适应性进化时纳入MSA不确定性的方法，但发现尽管考虑MSA不确定性确实会影响下游分析，但它似乎不如所选择的对准器类别重要。我们的结果证明了MSA方法在下游分析中的关键作用，强调了分析中选择的校准器的类别对其结果有明显的影响。
Multiple sequence alignment (MSA) is the heart of comparative sequence analysis. Recent studies demonstrate that MSA algorithms can produce different outcomes when analyzing genomes, including phylogenetic tree inference and the detection of adaptive evolution. These studies also suggest that the difference between MSA algorithms is of a similar order to the uncertainty within an algorithm and suggest integrating across this uncertainty. In this study, we examine further the problem of disagreements between MSA algorithms and how they affect downstream analyses. We also investigate whether integrating across alignment uncertainty affects downstream analyses. We address these questions by analyzing 200 chordate gene families, with properties reflecting those used in large-scale genomic analyses. We find that newly developed distance metrics reveal two significantly different classes of MSA methods (MSAMs). The similarity-based class includes progressive aligners and consistency aligners, representing many methodological innovations for sequence alignment, whereas the evolution-based class includes phylogenetically aware alignment and statistical alignment. We proceed to show that the class of an MSAM has a substantial impact on downstream analyses. For phylogenetic inference, tree estimates and their branch lengths appear highly dependent on the class of aligner used. The number of families, and the sites within those families, inferred to have undergone adaptive evolution depend on the class of aligner used. Similarity-based aligners tend to identify more adaptive evolution. We also develop and test methods for incorporating MSA uncertainty when detecting adaptive evolution but find that although accounting for MSA uncertainty does affect downstream analyses, it appears less important than the class of aligner chosen. Our results demonstrate the critical role that MSA methodology has on downstream analysis, highlighting that the class of aligner chosen in an analysis has a demonstrable effect on its outcome.