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通过系统发育信号与噪声比过滤核苷酸位点,提高了基于超保守元件的新鸟类系统发育树的可信度。

Filtering nucleotide sites by phylogenetic signal to noise ratio increases confidence in the Neoaves phylogeny generated from ultraconserved elements.

机构信息

Department of Ecology & Evolutionary Biology, University of California, Los Angeles, CA, USA.

Henry Samueli School of Engineering and Applied Science, Department of Computer Science, University of California, Los Angeles, CA, USA.

出版信息

Mol Phylogenet Evol. 2018 Sep;126:116-128. doi: 10.1016/j.ympev.2018.03.033. Epub 2018 Apr 4.

Abstract

Despite genome scale analyses, high-level relationships among Neoaves birds remain contentious. The placements of the Neoaves superorders are notoriously difficult to resolve because they involve deep splits followed by short internodes. Using our approach, we investigate whether filtering UCE loci on their phylogenetic signal to noise ratio helps to resolve key nodes in the Neoaves tree of life. We find that our analysis of data sets filtered for high signal to noise ratio results in topologies that are inconsistent with unfiltered results but that are congruent with whole-genome analyses. These relationships include the Columbea + Passerea sister relationship and the Phaethontimorphae + Aequornithia sister relationship. We also find increased statistical support for more recent nodes (i.e. the Pelecanidae + Ardeidae sister relationship, the Eucavitaves clade, and the Otidiformes + Musophagiformes sister relationship). We also find instances where support is reduced for well-established clades, possibly due to the removal of sites with moderate signal-to-noise ratio. Our results suggest that filtering on the basis of signal to noise ratio is a useful tool for resolving problematic splits in phylogenomic data sets.

摘要

尽管进行了基因组规模的分析,但新鸟类鸟类之间的高级关系仍然存在争议。新鸟超目的位置很难确定,因为它们涉及到深度分裂,然后是短的节点。我们采用这种方法,研究了对 UCE 位点进行过滤,以提高其系统发育信号与噪声比,是否有助于解决新鸟类生命之树中的关键节点。我们发现,我们对高信噪比数据集的分析结果导致的拓扑结构与未过滤结果不一致,但与全基因组分析结果一致。这些关系包括鸽形目+雀形目姐妹关系和佛法僧目+反嘴鹬目姐妹关系。我们还发现,更近的节点(即鹈形目+鹳形目姐妹关系、真骨小目鸟类、佛法僧目+犀鸟目姐妹关系)的统计支持度增加。我们还发现,一些确立的类群的支持度降低,可能是由于去除了具有中等信号与噪声比的位点。我们的结果表明,基于信号与噪声比的过滤是解决系统发育数据集问题分裂的有用工具。

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