Roux, S. et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537, 689–693 (2016).
Paez-Espino, D. et al. Uncovering Earth’s virome. Nature 536, 425–430 (2016).
Gregory, A. C. et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell 177, 1109–1123.e14 (2019).
ter Horst, A. M. et al. Minnesota peat viromes reveal terrestrial and aquatic niche partitioning for local and global viral populations. Microbiome 9, 233 (2021).
Gregory, A. C. et al. The gut virome database reveals age-dependent patterns of virome diversity in the human gut. Cell Host Microbe 28, 724–740.e8 (2020).
Camarillo-Guerrero, L. F., Almeida, A., Rangel-Pineros, G., Finn, R. D. & Lawley, T. D. Massive expansion of human gut bacteriophage diversity. Cell 184, 1098–1109.e9 (2021).
Nayfach, S. et al. Metagenomic compendium of 189,680 DNA viruses from the human gut microbiome. Nat. Microbiol. 6, 960–970 (2021).
Roux, S., Enault, F., Hurwitz, B. L. & Sullivan, M. B. VirSorter: mining viral signal from microbial genomic data. PeerJ 3, e985 (2015).
Ren, J., Ahlgren, N. A., Lu, Y. Y., Fuhrman, J. A. & Sun, F. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome 5, 69 (2017).
Ren, J. et al. Identifying viruses from metagenomic data using deep learning. Quant. Biol. 8, 64–77 (2020).
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).
Guo, J. et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 9, 37 (2021).
Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 8, 90 (2020).
Tisza, M. J., Belford, A. K., Domínguez-Huerta, G., Bolduc, B. & Buck, C. B. Cenote-Taker 2 democratizes virus discovery and sequence annotation. Virus Evol. 7, veaa100 (2021).
Glickman, C., Hendrix, J. & Strong, M. Simulation study and comparative evaluation of viral contiguous sequence identification tools. BMC Bioinformatics 22, 329 (2021).
Camargo, A.P., et al. Identification of mobile genetic elements with geNomad. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01953-y (2023).
Meier-Kolthoff, J. P. & Göker, M. VICTOR: genome-based phylogeny and classification of prokaryotic viruses. Bioinformatics 33, 3396–3404 (2017).
Bin Jang, H. et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat. Biotechnol. 37, 632–639 (2019).
Moraru, C. Virclust-a tool for hierarchical clustering, core gene detection and annotation of (prokaryotic) viruses. Viruses 15, 1007 (2023).
Pons, J. C. et al. VPF-Class: taxonomic assignment and host prediction of uncultivated viruses based on viral protein families. Bioinformatics 37, 1805–1813 (2021).
Terzian, P. et al. PHROG: families of prokaryotic virus proteins clustered using remote homology. NAR Genom. Bioinform. 3, lqab067 (2021).
Zayed, A. A. et al. efam: an expanded, metaproteome-supported HMM profile database of viral protein families. Bioinformatics 37, 4202–4208 (2021).
Abdelkareem, A. O., Khalil, M. I., Elaraby, M., Abbas, H. & Elbehery, A. H. A. VirNet: deep attention model for viral reads identification. In 2018 13th Int. Conf. Computer Engineering and Systems (ICCES) 623–626 (IEEE, 2018).
Tynecki, P. et al. PhageAI—bacteriophage life cycle recognition with machine learning and natural language processing. Preprint at bioRxiv https://www.biorxiv.org/content/early/2020/07/12/2020.07.11.198606 (2020).
Asgari, E. & Mofrad, M. R. K. Continuous distributed representation of biological sequences for deep proteomics and genomics. PLoS ONE 10, e0141287 (2015).
Heinzinger, M. et al. Modeling aspects of the language of life through transfer-learning protein sequences. BMC Bioinformatics 20, 723 (2019).
Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl Acad. Sci. USA 118, e2016239118 (2021).
Elnaggar, A. et al. ProtTrans: towards cracking the language of lifes code through self-supervised deep learning and high performance computing. IEEE Trans. Pattern Anal. Mach. Intell. 44, 7112–7127 (2021).
Brandes, N., Ofer, D., Peleg, Y., Rappoport, N. & Linial, M. ProteinBERT: a universal deep-learning model of protein sequence and function. Bioinformatics 38, 2102–2110 (2022).
Bepler, T. & Berger, B. Learning the protein language: evolution, structure, and function. Cell Syst. 12, 654–669.e3 (2021).
Dohan, D., Gane, A., Bileschi, M. L., Belanger, D. & Colwell, L. Improving protein function annotation via unsupervised pre-training: robustness, efficiency, and insights. In Proc. 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, KDD ’21 2782–2791 (Association for Computing Machinery, 2021); https://doi.org/10.1145/3447548.3467163
Gane, A. et al. ProtNLM: model-based natural language protein annotation. Preprint at https://storage.googleapis.com/brain-genomics-public/research/proteins/protnlm/uniprot_2022_04/protnlm_preprint_draft.pdf (2022).
Nasir, A. & Caetano-Anollés, G. A phylogenomic data-driven exploration of viral origins and evolution. Sci. Adv. 1, e1500527 (2015).
Balaji, S. & Srinivasan, N. Comparison of sequence-based and structure-based phylogenetic trees of homologous proteins: inferences on protein evolution. J. Biosci. 32, 83–96 (2007).
Meng, C., Zhang, J., Ye, X., Guo, F. & Zou, Q. Review and comparative analysis of machine learning-based phage virion protein identification methods. Biochim. Biophys. Acta Proteins Proteom. 1868, 140406 (2020).
Fang, Z., Feng, T., Zhou, H. & Chen, M. DeePVP: identification and classification of phage virion proteins using deep learning. GigaScience 11, giac076 (2022).
Cantu, V. A. et al. PhANNs, a fast and accurate tool and web server to classify phage structural proteins. PLoS Comput. Biol. 16, e1007845 (2020).
Mizuno, C. M., Ghai, R., Saghaï, A., López-García, P. & Rodriguez-Valera, F. Genomes of abundant and widespread viruses from the deep ocean. mBio 7, e00805–16 (2016).
Hackl, T. et al. Novel integrative elements and genomic plasticity in ocean ecosystems. Cell 186, 47–62.e16 (2023).
Eppley, J. M., Biller, S. J., Luo, E., Burger, A. & DeLong, E. F. Marine viral particles reveal an expansive repertoire of phage-parasitizing mobile elements. Proc. Natl Acad. Sci. USA 119, e2212722119 (2022).
Smyshlyaev, G., Bateman, A. & Barabas, O. Sequence analysis of tyrosine recombinases allows annotation of mobile genetic elements in prokaryotic genomes. Mol. Syst. Biol. 17, e9880 (2021).
Gibb, B. et al. Requirements for catalysis in the Cre recombinase active site. Nucleic Acids Res. 38, 5817–5832 (2010).
Williams, K. P. Integration sites for genetic elements in prokaryotic tRNA and tmRNA genes: sublocation preference of integrase subfamilies. Nucleic Acids Res. 30, 866–875 (2002).
Hatfull, G. F. & Hendrix, R. W. Bacteriophages and their genomes. Curr. Opin. Virol. 1, 298–303 (2011).
Koonin, E. V., Krupovic, M. & Dolja, V. V. The global virome: how much diversity and how many independent origins? Environ. Microbiol. 25, 40–44 (2023).
Shen, A. & Millard, A. Phage genome annotation: where to begin and end. Phage 2, 183–193 (2021).
Borodovich, T., Shkoporov, A. N., Ross, R. P. & Hill, C. Phage-mediated horizontal gene transfer and its implications for the human gut microbiome. Gastroenterol. Rep. 10, goac012 (2022).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Nicolas, E. et al. The tn3-family of replicative transposons. Microbiol. Spectr. 3, 3.4.14 (2015).
Mavrich, T. N. & Hatfull, G. F. Bacteriophage evolution differs by host, lifestyle and genome. Nat. Microbiol. 2, 1–9, 17112 (2017).
Mohssen, M., et al. efam. CyVerse Data Commons https://datacommons.cyverse.org/browse/iplant/home/shared/iVirus/Zayed_efam_2020.1 (2021).
Steinegger, M. et al. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinformatics 20, 473 (2019).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous systems https://www.tensorflow.org/ (2015).
Sievers, F. & Higgins, D. G. Clustal Omega for making accurate alignments of many protein sequences. Protein Sci. 27, 135–145 (2018).
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).
Charlier, F. et al. Statannotations. Zenodo (2022); https://doi.org/10.5281/zenodo.7213391
Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using NetworkX. In Proc. 7th Python in Science Conference (eds Varoquaux, G. et al.) 11–15 (2008).
Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).
Paysan-Lafosse, T. et al. InterPro in 2022. Nucleic Acids Res. 51, D418–D427 (2022).
Gabler, F. et al. Protein sequence analysis using the MPI Bioinformatics Toolkit. Curr. Protoc. Bioinformatics 72, e108 (2020).
Zimmermann, L. et al. A completely reimplemented MPI Bioinformatics Toolkit with a new HHpred server at its core. J. Mol. Biol. 430, 2237–2243 (2018).
Potter, S. C. et al. HMMER web server: 2018 update. Nucleic Acids Res. 46, W200–W204 (2018).
Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10, 845–858 (2015).
Mitchell, A. L. et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 48, D570–D578 (2019).
Roux, S. et al. IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses. Nucleic Acids Res. 49, D764–D775 (2020).
Paez-Espino, D. et al. IMG/VR v2.0: an integrated data management and analysis system for cultivated and environmental viral genomes. Nucleic Acids Res. 47, D678–D686 (2018).
Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).
van Kempen, M. et al. Fast and accurate protein structure search with Foldseek. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01773-0 (2023).
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).
Camargo, A. geNomad Database. Zenodo (2023); https://doi.org/10.5281/zenodo.7793532
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Kauffman, K. M. et al. Viruses of the Nahant Collection, characterization of 251 marine Vibrionaceae viruses. Sci. Data 5, 180114 (2018).
Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303 (2018).
Biswas, T. et al. A structural basis for allosteric control of DNA recombination by lambda integrase. Nature 435, 1059–1066 (2005).
Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D. 66, 12–21 (2010).
Flamholz, Z. kellylab/viral-protein-function-plm: v1.0. Zenodo (2023); https://doi.org/10.5281/zenodo.10182747
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
pandas development team pandas-dev/pandas: Pandas. Zenodo (2020); https://doi.org/10.5281/zenodo.3509134
Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Waskom, M. L. seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).
Granger, B. E. & Pérez, F. Jupyter: thinking and storytelling with code and data. Comput. Sci. Eng. 23, 7–14 (2021).
