TreeKnit: Inferring ancestral reassortment graphs of influenza viruses

Barrat-Charlaix, Pierre; Vaughan, Timothy G.; Neher, Richard A.

doi:10.1371/journal.pcbi.1010394

Cited by 8 publications

(18 citation statements)

References 31 publications

(57 reference statements)

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“…We demonstrated that finding the maximum parsimony number of remove-and-rejoin moves between sampled segment trees is not a consistent way to count the correct number of visible reassortment events, unless the first-infection tree is known. Existing topology-based methods (like those implemented in TreeKnit [22]) could be adapted to search the space of remove-and-rejoin moves for a set of segment trees given a first-infection tree. It is unlikely that the first-infection tree would be known, so a more reasonable way to deal with conditioning on the first-infection tree is to treat it as a nuisance variable that we would need to integrate out in the context of some prior distribution over the space of first-infection trees.…”

Section: Discussionmentioning

confidence: 99%

“…We first simulated the full population history such that the expected population size is ( I = 100), with rates λ = 5, µ = 4.75, Ψ = 0.25, ρ = ρ A + ρ B + ρ AB = 0.2, until 50 samples were obtained. These parameters are chosen to align with simulations in previous literature [22, 27], meanwhile making sure the frequencies of aforementioned situations are significant enough. For each of the number of visible reassortments, ranging from 5 to 14, we produced 100 replicates.…”

Section: Methodsmentioning

confidence: 99%

“…In this context, the remove-and-rejoin approach is called omniscient because we base it on the simulated population history rather than on inference, removing the issue of errors. In the case where reassortments only occur in one segment, the omniscient approach is always correct, by definition, but both the coalescent-based [27] and topology-based [22] approaches tend to get the right number of reassortment events about 15%-45% of the time. However, when both segments are allowed to reassort, the 'omniscient' method also tends to get the number of reassortment events wrong because one of the segment trees is no longer guaranteed to have the same structure as the first-infection tree.…”

Section: Four Situations That Make Inference Of Reassortment Dynamics...mentioning

confidence: 99%

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Modeling the evolution of segment trees reveals deficiencies in current inferential methods for genomic reassortment

Lin,

Goldberg,

Leitner

et al. 2023

Preprint

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Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to new viral variants. Large-scale genome rearrangements, such as reassortment, have the potential to quickly generate new phenotypes, making the understanding of viral reassortment important to both evolutionary biology and public health research. In this paper, we argue that reassortment cannot be reliably inferred from incongruities between segment phylogenies using the established remove-and-rejoin or coalescent approaches. We instead show that reassortment must be considered in the context of a broader population process that includes the dynamics of the infected hosts. Using illustrative examples and simulation we identify four types of evolutionary events that are difficult or impossible to reconstruct with incongruence-based methods. Further, we show that these specific situations are very common and will likely occur even in small samples. Finally, we argue that existing methods can be augmented or modified to account for all the problematic situations that we identify in this paper. Robust assessment of the role of reassortment in viral evolution is difficult, and we hope to provide conceptual clarity on some important methodological issues that can arise in the development of the next generation of tools for studying reassortment.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Four Situations That Make Inference Of Reassortment Dynamics...mentioning

confidence: 99%

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Modeling the evolution of segment trees reveals deficiencies in current inferential methods for genomic reassortment

Lin,

Goldberg,

Leitner

et al. 2023

Preprint

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“…Therefore, we focused our reassortment analysis on HA and NA sequences, sampling 1,607 viruses collected between January 2016 and January 2018 with sequences for both genes. We inferred HA and NA phylogenies from these sequences and applied TreeKnit to both trees to identify maximally compatible clades (MCCs) that represent reassortment events [11]. Of the 208 reassortment events identified by TreeKnit, 15 (7%) contained at least 10 samples representing 1,049 samples (65%).…”

Section: Joint Embeddings Of Hemagglutinin and Neuraminidase Genomes ...mentioning

confidence: 99%

“…When the sets are maximally different, VI is log N where N is the total number of samples. To make VI values comparable across datasets, we normalized each value by dividing by log N , following the pattern used to validate TreeKnit's MCCs [11]. Unlike other standard metrics like accuracy, sensitivity, or specificity, VI distances do not favor methods that tend to produce more, smaller clusters.…”

Section: Clustering Of Samples In Embeddingsmentioning

confidence: 99%

Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2

Nanduri,

Black,

Bedford

et al. 2024

Preprint

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Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic assumptions and require more sophisticated methods. Even when phylogenies are appropriate, they can be unnecessary or difficult to interpret without specialty knowledge. For example, pairwise distances between sequences can be enough to identify clusters of related samples or assign new samples to existing phylogenetic clusters. In this work, we tested whether dimensionality reduction methods could capture known genetic groups within two human pathogenic viruses that cause substantial human morbidity and mortality and frequently reassort or recombine, respectively: seasonal influenza A/H3N2 and SARS-CoV-2. We applied principal component analysis (PCA), multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to sequences with well-defined phylogenetic clades and either reassortment (H3N2) or recombination (SARS-CoV-2). For each low-dimensional embedding of sequences, we calculated the correlation between pairwise genetic and Euclidean distances in the embedding and applied a hierarchical clustering method to identify clusters in the embedding. We measured the accuracy of clusters compared to previously defined phylogenetic clades, reassortment clusters, or recombinant lineages. We found that MDS maintained the strongest correlation between pairwise genetic and Euclidean distances between sequences and best captured the intermediate placement of recombinant lineages between parental lineages. Clusters from t-SNE most accurately recapitulated known phylogenetic clades and recombinant lineages. Both MDS and t-SNE accurately identified reassortment groups. We show that simple statistical methods without a biological model can accurately represent known genetic relationships for relevant human pathogenic viruses. Our open source implementation of these methods for analysis of viral genome sequences can be easily applied when phylogenetic methods are either unnecessary or inappropriate.Author summaryTo track the progress of viral epidemics, public health researchers often need to identify groups of genetically-related samples. A common approach to find these groups involves inferring the complete evolutionary history of virus samples using phylogenetic methods. However, these methods assume that new viruses descend from a single parent, while many viruses including seasonal influenza and SARS-CoV-2 produce offspring through a form of sexual reproduction that violates this assumption. Additionally, phylogenies may be unnecessarily complex or unintuitive when researchers only need to find and visualize clusters of related samples. We tested an alternative approach by applying widely-used statistical methods (PCA, MDS, t-SNE, and UMAP) to create 2- or 3-dimensional maps of virus samples from their pairwise genetic distances and identify clusters of samples that place close together in these maps. We found that these statistical methods without an underlying biological model could accurately capture known genetic relationships in populations of seasonal influenza and SARS-CoV-2 even in the presence of sexual reproduction. The conceptual and practical simplicity of our open source implementation of these methods enables researchers to visualize and compare human pathogenic virus samples when phylogenetic methods are unnecessary or inappropriate.

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