使用 log-det 距离从基因组序列推断物种树。

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED
SIAM Journal on Applied Algebra and Geometry Pub Date : 2019-01-01 Epub Date: 2019-03-14 DOI:10.1137/18m1194134
Elizabeth S Allman, Colby Long, John A Rhodes
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引用次数: 0

摘要

两个对齐的 DNA 序列之间的对数距离(log-det distance)被引入作为一种工具,用于在简单的非混合物序列进化模型下对基因树进行统计上一致的推断。在这里,我们证明对数-det 距离与基于距离的树构建方法相结合,也能在适合基因组尺度序列数据的混合模型下一致地推断物种树。数据可能包括来自许多基因位点的位点,这些位点在不同的基因树上进化,这是因为超对称物种树上的世系排序不完全,具有不同的时间可逆替换过程。基于距离推断的简单性和速度表明,基于 log-det 的方法应作为判断更复杂和计算密集型物种树推断方法的基准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SPECIES TREE INFERENCE FROM GENOMIC SEQUENCES USING THE LOG-DET DISTANCE.

SPECIES TREE INFERENCE FROM GENOMIC SEQUENCES USING THE LOG-DET DISTANCE.

SPECIES TREE INFERENCE FROM GENOMIC SEQUENCES USING THE LOG-DET DISTANCE.

The log-det distance between two aligned DNA sequences was introduced as a tool for statistically consistent inference of a gene tree under simple non-mixture models of sequence evolution. Here we prove that the log-det distance, coupled with a distance-based tree construction method, also permits consistent inference of species trees under mixture models appropriate to aligned genomic-scale sequences data. Data may include sites from many genetic loci, which evolved on different gene trees due to incomplete lineage sorting on an ultrametric species tree, with different time-reversible substitution processes. The simplicity and speed of distance-based inference suggests log-det based methods should serve as benchmarks for judging more elaborate and computationally-intensive species trees inference methods.

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CiteScore
2.20
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