欧洲生物信息学研究所Nick Goldman课题组的最新研究提出了疫情规模系统发生学研究中的速率变异与重复序列错误。2026年2月9日出版的《自然—方法学》杂志发表了这项成果。

研究小组提出了算法和模型，显著提升了疫情规模系统发生学的计算性能与准确性。特别地，该课题组研究人员对突变率变异和重复序列错误进行了量化与识别。该课题组重建了一个可靠的、公开的200万个严重急性呼吸综合征冠状病毒2号基因组序列比对和系统发育树，涵盖了截至2023年2月该病毒的进化史和全球传播。

据了解，传染性病原体基因组序列的系统发育分析揭示了其进化和传播的重要信息，正如2019年冠状病毒病大流行期间所看到的那样。近年来开发的疫情规模系统发生推断方法，大幅降低了从基因组流行病学数据集中重建系统发生的计算需求，使得分析数百万个高度关联的基因组成为可能。然而，由重复突变与序列错误导致的广泛同型突变，常引发系统发生推断的不确定性与偏差。

附：英文原文

Title: Rate variation and recurrent sequence errors in pandemic-scale phylogenetics

Author: De Maio, Nicola, Willemsen, Myrthe, Martin, Samuel, Guo, Zihao, Saha, Abhratanu, Hunt, Martin, Ly-Trong, Nhan, Minh, Bui Quang, Iqbal, Zamin, Goldman, Nick

Issue&Volume: 2026-02-09

Abstract: Phylogenetic analyses of genome sequences from infectious pathogens reveal essential information regarding their evolution and transmission, as seen during the coronavirus disease 2019 pandemic. Recently developed pandemic-scale phylogenetic inference methods reduce the computational demand of phylogenetic reconstruction from genomic epidemiological datasets, allowing the analysis of millions of closely related genomes. However, widespread homoplasies, due to recurrent mutations and sequence errors, cause phylogenetic uncertainty and biases. We present algorithms and models to substantially improve the computational performance and accuracy of pandemic-scale phylogenetics. In particular, we account for, and identify, mutation rate variation and recurrent sequence errors. We reconstruct a reliable and public sequence alignment and phylogenetic tree of >2 million severe acute respiratory syndrome coronavirus 2 genomes encapsulating the evolutionary history and global spread of the virus up to February 2023.

DOI: 10.1038/s41592-025-02932-8

Source: https://www.nature.com/articles/s41592-025-02932-8