Extended Data Fig. 2 | Addition of two perfectly correlated errors significantly reduces UShER accuracy. As in Fig. 2, the Robinson-Foulds distances, proportion of sister nodes identical to the reference tree, distance from true placement and equally parsimonious placements, respecitvely, are shown for UShER experiments in placing 10 lineages, with two perfectly correlated errors added to 1, 2 … 10 of the lineages to be placed. To the far right in the left-most panel, labeled 'Null', the distribution of scores across 100 replicates in which 10 lineages were added randomly to the phylogeny is shown as a null model for comparison. N = 100 independent replicates for each experiment. The whiskers in the boxplot on the left are centered on the median of the data and extend to the first and third quartiles. In the error bars panel (second from the left), the data points are centered on the mean of the data and extend to the bounds of the 95% confidence interval, calculated by 1,000 iterations of bootstrapping. NATURE GENETICS | www.nature.com/naturegeneticsArticles NATURE GENETICS Extended Data Fig. 3 | UShER can output multiple trees to accommodate phylogenetic uncertainty. (A): Composite of 239 trees with 424 samples, representing all possible parsimony-optimal placements of two samples on a starting tree having 422 samples, computed using DensiTree 52 and plotted using the phangorn package (https://cran.r-project.org/web/packages/phangorn). All trees were scaled to be the same height. (B): Two of the trees from (A) compared in a tanglegram, colored according to COG-UK lineage assignments, with linker lines shown only for the two placed samples whose placements differ between topologies. As in Fig. 4, both trees in this tanglegram are ultrametric and branch lengths are arbitrary.Extended Data Fig. 6 | A demonstration of our distance metric for placements. To evaluate the accuracy of each placement in a new phylogeny, we compute the distance for each newly placed sample in the UShER tree (Tree 1) with the reference tree (Tree 2). The clade sets in the two trees are shown for each N1 and N2 value, representing the number of generations from the Sample D in Tree 1 and Tree 2, respectively. We compute the values of N1+N2-2 such that the descendant clades for both trees are identical. In case of newly placed Sample D, clades are identical when N1=2 and N2=2 and when N1=3 and N2=3, which are highlighted in bold. Hence the distance (smallest N1+N2-2) from the true placement is equal to 2.

The SARS-CoV-2 pandemic has led to unprecedented, nearly real-time genetic tracing due to the rapid community sequencing response. Researchers immediately leveraged these data to infer the evolutionary relationships among viral samples and to study key biological questions, including whether host viral genome editing and recombination are features of SARS-CoV-2 evolution. This global sequencing effort is inherently decentralized and must rely on data collected by many labs using a wide variety of molecular and bioinformatic techniques. There is thus a strong possibility that systematic errors associated with lab—or protocol—specific practices affect some sequences in the repositories. We find that some recurrent mutations in reported SARS-CoV-2 genome sequences have been observed predominantly or exclusively by single labs, co-localize with commonly used primer binding sites and are more likely to affect the protein-coding sequences than other similarly recurrent mutations. We show that their inclusion can affect phylogenetic inference on scales relevant to local lineage tracing, and make it appear as though there has been an excess of recurrent mutation or recombination among viral lineages. We suggest how samples can be screened and problematic variants removed, and we plan to regularly inform the scientific community with our updated results as more SARS-CoV-2 genome sequences are shared (https://virological.org/t/issues-with-sars-cov-2-sequencing-data/473 and https://virological.org/t/masking-strategies-for-sars-cov-2-alignments/480). We also develop tools for comparing and visualizing differences among very large phylogenies and we show that consistent clade- and tree-based comparisons can be made between phylogenies produced by different groups. These will facilitate evolutionary inferences and comparisons among phylogenies produced for a wide array of purposes. Building on the SARS-CoV-2 Genome Browser at UCSC, we present a toolkit to compare, analyze and combine SARS-CoV-2 phylogenies, find and remove potential sequencing errors and establish a widely shared, stable clade structure for a more accurate scientific inference and discourse.

The vast scale of SARS-CoV-2 sequencing data has made it increasingly challenging to comprehensively analyze all available data using existing tools and file formats. To address this, we present a database of SARS-CoV-2 phylogenetic trees inferred with unrestricted public sequences, which we update daily to incorporate new sequences. Our database uses the recently-proposed mutation-annotated tree (MAT) format to efficiently encode the tree with branches labeled with parsimony-inferred mutations, as well as Nextstrain clade and Pango lineage labels at clade roots. As of June 9, 2021, our SARS-CoV-2 MAT consists of 834,521 sequences and provides a comprehensive view of the virus' evolutionary history using public data. We also present matUtils—a command-line utility for rapidly querying, interpreting and manipulating the MATs. Our daily-updated SARS-CoV-2 MAT database and matUtils software are available at http://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/UShER_SARS-CoV-2/ and https://github.com/yatisht/usher, respectively.

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