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Machine learning enables scalable and systematic hierarchical virus taxonomy

Nature Biotechnology (2025)Cite this article

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Abstract

Although virus ecogenomics has expanded access to and understanding of the virosphere, existing classification tools lack taxonomic resolution and are unable to scale to modern discovery-based datasets or classify previously unknown sequence space. Here we develop vConTACT3—a machine learning-based tool that improves scalability and accuracy of virus taxonomy. By optimizing gene-sharing thresholds and leveraging adaptive, realm-specific cut-offs, vConTACT3 expands classification to both eukaryote and prokaryote viruses for four of the six officially recognized realms, and establishes accurate hierarchical taxonomy from genus to order. Specifically, vConTACT3 achieves >95% agreement with official taxonomy for 35,545 and 13,524 public prokaryotic and eukaryotic virus genomes, respectively, to surpass vConTACT2 across most realms, while still uniquely classifying previously uncharacterized taxa, and doing so even faster. vConTACT3 application provides taxonomy assignments for tens of thousands of unclassified taxa rapidly, automatically and systematically; evaluates virus sequence space to reveal support for fewer taxonomic ranks than currently available and identifies taxonomically challenging areas across the virosphere.

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Fig. 1: vConTACT3 new features overview.
Fig. 2: Benchmarking vConTACT3 with 60 million combinations.
Fig. 3: Comparing vConTACT3 versus ICTV taxonomic assignments.
Fig. 4: Assessment of the impact of genome fragments on taxonomic predictions.

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Data availability

Data used for benchmarks (parameter optimizations), construction of databases and fine-tuning the pipeline are available from NCBI Virus RefSeq (v.218). Data used to test scalability, assess fragmentation and evaluate labeling stability are available from IMG/VR v.4.1 (December 2022 release). Databases used by vConTACT3 (as well as source files) are available via Zenodo at https://doi.org/10.5281/zenodo.10035619 and https://doi.org/10.5281/zenodo.10935513 (refs. 67,68).

Code availability

vConTACT3 is available via Bitbucket at https://bitbucket.org/MAVERICLab/vcontact3 (ref. 69) as an installable Python package, as well as through Python package managers Anaconda (https://anaconda.org/) and Mamba (https://mamba.readthedocs.io/). Instructions for building an Apptainer container of vConTACT3 is available on Bitbucket, along with a definitions file. A comprehensive documentation site is available through https://vcontact3.readthedocs.io. Optimization benchmarks were performed using v.3.0.0b36 (‘beta’ v.36), fragmentation analyses using v.3.0.0b63 and label stability, v3.1.4. All other results (including tool comparisons) should be assumed as v.3.0.0.b36. Through all analyses, Python 3.10 was used. Data processing and analyses were conducted using numpy v.1.23.5, pandas v.2.1.1 and scipy v.1.10.1. Taxonomic parsing was handled by the ETE3 Toolkit, v.3.1.3. Statistical analyses were performed using scikit-learn v.1.2.2 and scikit-bio v.0.5.8. Data visualizations were created using matplotlib v.3.7.1, seaborn v.0.12.1 and UpSetPlot v.0.7.0. Networks were rendered through a combination of Cytoscape v.3.10.1, networkx v.3.1 and Python-igraph v.0.10.4. Gene predictions were done through pyprodigal v.2.3.0 and pyprodigal-gv v.0.3.1 and all sequence processing through biopython v.1.81. Protein clustering was done using MMSeqs2 v.14-7e284.

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Acknowledgements

This work was supported by the National Science Foundation under Grants No. DBI-2149505 (iVirus2) and DBI-2022070 (BII-Implementation: the EMERGE Institute). This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under Award Number DE-SC0023307. High-performance computating was provided by the Ohio Supercomputer Center. Additional support was provided by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy (EXC 2051) Project-ID 390713860, the European Research Council (ERC) Consolidator grant 865694: DiversiPHI, the Alexander von Humboldt Foundation in the context of an Alexander von Humboldt-Professorship founded by German Federal Ministry of Education and Research, and the European Union’s Horizon 2020 research and innovation program, under the Marie Skłodowska-Curie Actions Innovative Training Networks grant agreement no. 955974 (VIROINF). EMA gratefully acknowledges the support of the Biotechnology and Biological Sciences Research Council (BBSRC); this research was funded by the BBSRC Institute Strategic Programme Food Microbiome and Health BB/X011054/1 and its constituent projects BBS/E/QU/230001B and BBS/E/QU/230001D, as well as the BBSRC Institute Strategic Programme Microbes and Food Safety BB/X011011/1 and its constituent projects BBS/E/QU/230002A, BBS/E/QU/230002B and BBS/E/QU/230002C.

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Authors and Affiliations

  1. Department of Microbiology, Ohio State University, Columbus, OH, USA

    Benjamin Bolduc, Olivier Zablocki, Jiarong Guo & Matthew B. Sullivan

  2. Center of Microbiome Science, Ohio State University, Columbus, OH, USA

    Benjamin Bolduc, Olivier Zablocki, Jiarong Guo & Matthew B. Sullivan

  3. EMERGE Biology Integration Institute, Columbus, OH, USA

    Benjamin Bolduc & Matthew B. Sullivan

  4. School of Applied Sciences, College of Health, Science and Society, University of the West of England, Bristol, UK

    Dann Turner

  5. Center for Study of Emerging and Re-emerging Viruses, Korea Virus Research Institute, Institute for Basic Science (IBS), Daejeon, Republic of Korea

    Ho Bin Jang

  6. Quadram Institute Bioscience, Norwich Research Park, Norwich, UK

    Evelien M. Adriaenssens

  7. Centre for Microbial Interactions, Norwich Research Park, Norwich, UK

    Evelien M. Adriaenssens

  8. Institute of Biodiversity, Ecology, and Evolution, Faculty of Biological Sciences, Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany

    Bas E. Dutilh

  9. Theoretical Biology and Bioinformatics, Science4Life, Utrecht University, Utrecht, the Netherlands

    Bas E. Dutilh

  10. Department of Civil, Environmental and Geodetic Engineering, Ohio State University, Columbus, OH, USA

    Matthew B. Sullivan

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Contributions

B.B. and M.B.S. designed the study. B.B., O.Z. and M.B.S. wrote the manuscript with substantial contributions from all authors. D.T., H.B.J. and B.B. performed the phylogenetic analyses and B.E.D., D.T., H.B.J. and B.B. performed the statistical and network analyses. J.G., E.M.A. and B.B. evaluated the distance metrics. B.B. developed the code with contributions from J.G.

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Correspondence to Benjamin Bolduc or Matthew B. Sullivan.

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Nature Biotechnology thanks Alexander Gorbalenya, Arthur Gruber and Guanxiang Liang for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Detailed vConTACT3 workflow and outputs.

User genomes are provided to vConTACT3. ORFs are predicted with prodigal, and then sent to MMSeqs2 to be clustered at 5 identities (30%, 40%, 50%, 60%, 70%). These 5 clustering identities are used to build 5 separate protein cluster (PC) profiles, corresponding to each clustering identity. These 5 PC profiles are sent to the “Resolver,” which constructs a distance matrix (per profile) based on the selected distance metric (default “SqRoot”, see Methods). This distance matrix is subsequently converted into a network, which is annotated with any available genome information, such as realm-unique genes and proximity to reference sequences. The network is then filtered by the minimum number of shared genes allowed between genomes (see Main Text for details on recommended values), and then “repaired” if they are within the same connected component. Additionally, users can select a high-accuracy repair, which more carefully reviews dropped edges at the expense of computation time. The final stage of the resolver predicts virus realms associated with each genome using network edge-connected references and/or the presence of PCs exclusively identified and co-shared with references. Entirely novel genomes are assigned a default, user-selectable realm. The 5 filtered and repaired networks are then sent to the guilt-by-genome-association (GBGA) assigner. The GBGA aggregates realm predictions per-genome and per-network component (combining both genome and network information) and assigns a final realm for each genome. This realm prediction is used to select the optimal distance cutoff for each virus rank (genus, subfamily, family and order) determined by benchmarks (see Methods) and hierarchically cluster all genomes of that realm at that cutoff. These clusters are then matched against references (if co-clustered and/or available) or used as novel ranks to assign each virus rank order and below. (The upper ranks of phylum and class inherit reference-based assignments within the predicted realm). After assignments, GBGA output is then compared with reference sequences (if available) and performance metrics are calculated. Finally, PC profiles, performance metrics, and GBGA outputs are integrated into the exports/results component, which provides user-controlled outputs in Cytoscape format, a d3js interactive HTML network, UpSet plots, profiles, and Newick-formatted dendrograms.

Extended Data Fig. 2 vConTACT3-Extended-Data-Fig2-prokaryotes-duplo.jpg.

A 5×7 grid of line plots representing accuracies of Duplornaviria viruses infecting prokaryotes (Bacteria and Archaea). From left-to-right, plots increase in the minimum number of shared genes required to establish an edge/relationship between genomes and be considered as related. The top left plot is 1 minimum gene shared, the top right plot has 5 minimum genes shared, and moving from left to right increases minimum genes shared by 1. From the top-to-bottom direction, plots increase in minimum clustering identity used during MMSeqs2 clustering to establish protein clusters (PCs). Since PCs are used to determine the number of shared genes between genomes, increasing clustering identity increases the stringency required for two genes between two separate genomes to be considered shared, and thus, related. The top left plot is 30% clustering identity, the bottom left plot is 90% clustering identity, with each plot moving downward an increase in 10% clustering identity. Within each plot, accuracy (Y-axis) is a measure of agreement between NCBI taxonomy and vConTACT3 predictions. Pairwise distance cutoff (X-axis) represents the cutoff threshold used during hierarchical clustering to define clusters. The cutoff ranges between 0 - 0.99, with 0 representing completely identical PC profiles, and 0.99 representing nearly no shared genes. Line colors represent taxonomic rank, and dashed lines represent the type of distance metric (Jaccard, “SqRoot”, ‘VirClust” and “Shorter”) employed. See Methods for description of each distance metric.

Extended Data Fig. 3 Eukaryotic-infecting viruses of the Duplornaviria realm.

A 5×7 grid of line plots representing accuracies of Duplornaviria viruses infecting Eukaryota. Details are as Extended Data Fig. 2.

Extended Data Fig. 4 Prokaryotic-infecting viruses of the Adnaviria and Varidnaviria realms.

A 5×7 grid of line plots representing accuracies of Adnaviria and Varidnaviria viruses infecting prokaryotes (Bacteria and Archaea). Details are as Extended Data Fig. 2.

Extended Data Fig. 5 Eukaryotic-infecting viruses of the Adnaviria and Varidnaviria realms.

A 5×7 grid of line plots representing accuracies of Adnaviria and Varidnaviria viruses infecting Eukaryota. Details are as Extended Data Fig. 2.

Extended Data Fig. 6 Comparison in distance cutoffs of Duplodnaviria and Adnaviria & Varidnaviria between domains.

Accuracy plots showing the similarity in optimal cutoffs between realms, with prokaryote-infecting viruses appearing downshifted relative to Eukaryote-infecting viruses.

Extended Data Fig. 7 Prokaryotic-infecting viruses of the Monodnaviria realm.

A 5×5 grid of line plots representing accuracies of Monodnaviria viruses infecting prokaryotes (Bacteria and Archaea). Details are as Extended Data Fig. 2.

Extended Data Fig. 8 Eukaryotic-infecting viruses of the Monodnaviria realm.

A 5×5 grid of line plots representing accuracies of Monodnaviria viruses infecting Eukaryota. Details are as Extended Data Fig. 2.

Extended Data Fig. 9 Prediction labeling stability.

Line plots of adjusted rand index (ARI) and normalized mutual information (NMI) of a set of prediction labels. Colors indicate rank, dashed lines exclude singleton groups from the analysis, whereas solid lines include singletons. “Successive” plots are when ARI/NMI is calculated between fractions, “cumulative” is ARI/NMI calculated against the final (100%) fraction. High ARI and NMI indicate high agreement between genome predictions (that is labels) as more data is added, where an ARI and NMI of 1.00 indicate perfect agreement between labels between datasets.

Extended Data Table 1 vConTACT3 accuracies of Eukaryote-infecting viruses
Full size table

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Bolduc, B., Zablocki, O., Turner, D. et al. Machine learning enables scalable and systematic hierarchical virus taxonomy. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02946-9

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