Bioinformatics and Systems Biology, University of California San Diego, San Diego, CA.
ISEM, Université de Montpellier, CNRS, IRD, EPHE, Montpellier, France.
Mol Biol Evol. 2020 Nov 1;37(11):3292-3307. doi: 10.1093/molbev/msaa139.
Phylogenetic inference from genome-wide data (phylogenomics) has revolutionized the study of evolution because it enables accounting for discordance among evolutionary histories across the genome. To this end, summary methods have been developed to allow accurate and scalable inference of species trees from gene trees. However, most of these methods, including the widely used ASTRAL, can only handle single-copy gene trees and do not attempt to model gene duplication and gene loss. As a result, most phylogenomic studies have focused on single-copy genes and have discarded large parts of the data. Here, we first propose a measure of quartet similarity between single-copy and multicopy trees that accounts for orthology and paralogy. We then introduce a method called ASTRAL-Pro (ASTRAL for PaRalogs and Orthologs) to find the species tree that optimizes our quartet similarity measure using dynamic programing. By studying its performance on an extensive collection of simulated data sets and on real data sets, we show that ASTRAL-Pro is more accurate than alternative methods.
基于全基因组数据的系统发育推断（系统基因组学）彻底改变了进化研究，因为它能够解释整个基因组中进化历史的不一致性。为此，已经开发了汇总方法，以允许从基因树准确且可扩展地推断物种树。然而，这些方法中的大多数，包括广泛使用的 ASTRAL，只能处理单拷贝基因树，并且不尝试对基因复制和基因丢失进行建模。结果，大多数系统基因组学研究都集中在单拷贝基因上，并丢弃了大部分数据。在这里，我们首先提出了一种在单拷贝和多拷贝树之间测量四联体相似性的方法，该方法考虑了直系同源物和旁系同源物。然后，我们引入了一种称为 ASTRAL-Pro（针对旁系同源物和直系同源物的 ASTRAL）的方法，该方法使用动态编程找到优化我们四联体相似性度量的物种树。通过在大量模拟数据集和真实数据集上研究其性能，我们表明 ASTRAL-Pro 比替代方法更准确。
