tetano
Editor, Senior Moderator
Nat Methods
. 2026 Feb 9.
doi: 10.1038/s41592-025-02932-8. Online ahead of print.
Rate variation and recurrent sequence errors in pandemic-scale phylogenetics
Nicola De Maio[SUP] 1 [/SUP], Myrthe Willemsen[SUP] 2 3 [/SUP], Samuel Martin[SUP] 2 [/SUP], Zihao Guo[SUP] 2 4 [/SUP], Abhratanu Saha[SUP] 2 5 [/SUP], Martin Hunt[SUP] 2 6 7 8 [/SUP], Nhan Ly-Trong[SUP] 9 [/SUP], Bui Quang Minh[SUP] 9 [/SUP], Zamin Iqbal[SUP] 2 10 [/SUP], Nick Goldman[SUP] 2 [/SUP]
Affiliations
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.
. 2026 Feb 9.
doi: 10.1038/s41592-025-02932-8. Online ahead of print.
Rate variation and recurrent sequence errors in pandemic-scale phylogenetics
Nicola De Maio[SUP] 1 [/SUP], Myrthe Willemsen[SUP] 2 3 [/SUP], Samuel Martin[SUP] 2 [/SUP], Zihao Guo[SUP] 2 4 [/SUP], Abhratanu Saha[SUP] 2 5 [/SUP], Martin Hunt[SUP] 2 6 7 8 [/SUP], Nhan Ly-Trong[SUP] 9 [/SUP], Bui Quang Minh[SUP] 9 [/SUP], Zamin Iqbal[SUP] 2 10 [/SUP], Nick Goldman[SUP] 2 [/SUP]
Affiliations
- PMID: 41663576
- DOI: 10.1038/s41592-025-02932-8
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.