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隐含比对:一种基于共衍征的多序列比对方法及其在分支图搜索中的应用。

Implied alignment: a synapomorphy-based multiple-sequence alignment method and its use in cladogram search.

作者信息

Wheeler Ward C

机构信息

Division of Invertebrate Zoology, American Museum of Natural History, New York, NY 10024-5192, USA.

出版信息

Cladistics. 2003 Jun;19(3):261-8.

PMID:12901383
Abstract

A method to align sequence data based on parsimonious synapomorphy schemes generated by direct optimization (DO; earlier termed optimization alignment) is proposed. DO directly diagnoses sequence data on cladograms without an intervening multiple-alignment step, thereby creating topology-specific, dynamic homology statements. Hence, no multiple-alignment is required to generate cladograms. Unlike general and globally optimal multiple-alignment procedures, the method described here, implied alignment (IA), takes these dynamic homologies and traces them back through a single cladogram, linking the unaligned sequence positions in the terminal taxa via DO transformation series. These "lines of correspondence" link ancestor-descendent states and, when displayed as linearly arrayed columns without hypothetical ancestors, are largely indistinguishable from standard multiple alignment. Since this method is based on synapomorphy, the treatment of certain classes of insertion-deletion (indel) events may be different from that of other alignment procedures. As with all alignment methods, results are dependent on parameter assumptions such as indel cost and transversion:transition ratios. Such an IA could be used as a basis for phylogenetic search, but this would be questionable since the homologies derived from the implied alignment depend on its natal cladogram and any variance, between DO and IA + Search, due to heuristic approach. The utility of this procedure in heuristic cladogram searches using DO and the improvement of heuristic cladogram cost calculations are discussed.

摘要

提出了一种基于直接优化(DO;早期称为优化比对)生成的简约共衍征方案来比对序列数据的方法。DO直接在分支图上诊断序列数据,无需中间的多重比对步骤,从而创建特定拓扑结构的动态同源性声明。因此,生成分支图不需要多重比对。与一般的和全局最优的多重比对程序不同,这里描述的方法,即隐含比对(IA),采用这些动态同源性,并通过单个分支图追溯它们,通过DO变换系列连接终端分类群中未比对的序列位置。这些“对应线”连接祖先-后代状态,并且当显示为没有假设祖先的线性排列列时,与标准多重比对在很大程度上难以区分。由于该方法基于共衍征,对某些类型的插入-缺失(indel)事件的处理可能与其他比对程序不同。与所有比对方法一样,结果取决于诸如indel成本和颠换:转换比率等参数假设。这样的IA可以用作系统发育搜索的基础,但这可能存在问题,因为从隐含比对中得出的同源性取决于其原始分支图以及由于启发式方法导致的DO和IA +搜索之间的任何差异。讨论了该程序在使用DO的启发式分支图搜索中的效用以及启发式分支图成本计算的改进。

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