ASTRAL-Pro: Quartet-based species-tree inference despite paralogy

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 programming. 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（系统发育基因组学，phylogenomics）已彻底革新了演化生物学研究，因其能够考量基因组内不同演化历史之间的不一致性。为此，学界已开发出汇总法，以实现基于基因树对物种树进行准确且可扩展的推断。然而，此类方法中的绝大多数（包括广泛使用的ASTRAL）仅能处理单拷贝基因树，且未对基因重复与基因丢失进行建模。因此，大多数系统发育基因组学研究均聚焦于单拷贝基因，而丢弃了大量可用数据。本研究首先提出了一种兼顾直系同源与旁系同源的单拷贝与多拷贝树之间的四分体相似性度量。随后，我们介绍了一种名为ASTRAL-Pro（针对旁系同源与直系同源的ASTRAL）的方法，该方法借助动态规划算法寻找可最大化我们提出的四分体相似性度量的物种树。通过在海量模拟数据集与真实数据集上评估其性能，我们证实ASTRAL-Pro的准确性优于各类替代方法。