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INSTRAL:基于四重分位得分的分歧意识系统发育定位。

INSTRAL: Discordance-Aware Phylogenetic Placement Using Quartet Scores.

机构信息

Department of Computer Science and Engineering, UC San Diego, La Jolla, CA 92093, USA.

Department of Electrical and Computer Engineering, UC, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.

出版信息

Syst Biol. 2020 Mar 1;69(2):384-391. doi: 10.1093/sysbio/syz045.

Abstract

Phylogenomic analyses have increasingly adopted species tree reconstruction using methods that account for gene tree discordance using pipelines that require both human effort and computational resources. As the number of available genomes continues to increase, a new problem is facing researchers. Once more species become available, they have to repeat the whole process from the beginning because updating species trees is currently not possible. However, the de novo inference can be prohibitively costly in human effort or machine time. In this article, we introduce INSTRAL, a method that extends ASTRAL to enable phylogenetic placement. INSTRAL is designed to place a new species on an existing species tree after sequences from the new species have already been added to gene trees; thus, INSTRAL is complementary to existing placement methods that update gene trees. [ASTRAL; ILS; phylogenetic placement; species tree reconstruction.].

摘要

系统发生基因组分析越来越多地采用物种树重建方法,这些方法使用需要人力和计算资源的管道来解释基因树分歧。随着可用基因组数量的不断增加,研究人员面临着一个新的问题。一旦有更多的物种可用,他们就必须从头开始重复整个过程,因为目前不可能更新物种树。然而,从头推断在人力或机器时间方面可能代价过高。在本文中,我们介绍了 INSTRAL,这是一种扩展 ASTRAL 以实现系统发生定位的方法。INSTRAL 旨在在将新物种的序列添加到基因树后,将新物种置于现有物种树上;因此,INSTRAL 是对现有更新基因树的定位方法的补充。[ASTRAL;ILS;系统发生定位;物种树重建。]

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