{"refrec":{"BRefID":323148,"RR":"<b>Jang, H.B.; Bolduc, B.; Zablocki, O.; Kuhn, J.H.; Roux, S.; Adriaenssens, E.M.; Brister, J.R.; Kropinski, A.M.; Krupovic, M.; Lavigne, R.; Turner, D.; Sullivan, M.B.</b> (2019). Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. <i>Nature Biotechnology 37(6)</i>: 632-639. <a href=\"https://dx.doi.org/10.1038/s41587-019-0100-8\" target=\"_blank\">https://dx.doi.org/10.1038/s41587-019-0100-8</a>","BEntID":316619,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Nature Biotechnology 37(6)</i>: 632-639. <a href=\"https://dx.doi.org/10.1038/s41587-019-0100-8\" target=\"_blank\">https://dx.doi.org/10.1038/s41587-019-0100-8</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Jang, H.B.; Bolduc, B.; Zablocki, O.; Kuhn, J.H.; Roux, S.; Adriaenssens, E.M.; Brister, J.R.; Kropinski, A.M.; Krupovic, M.; Lavigne, R.; Turner, D.; Sullivan, M.B.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Jang, H.B. <i>et al.</i>","Englishabstract":"Microbiomes from every environment contain a myriad of uncultivated archaeal and bacterial viruses, but studying these viruses is hampered by the lack of a universal, scalable taxonomic framework. We present vConTACT v.2.0, a network-based application utilizing whole genome gene-sharing profiles for virus taxonomy that integrates distance-based hierarchical clustering and confidence scores for all taxonomic predictions. We report near-identical (96%) replication of existing genus-level viral taxonomy assignments from the International Committee on Taxonomy of Viruses for National Center for Biotechnology Information virus RefSeq. Application of vConTACT v.2.0 to 1,364 previously unclassified viruses deposited in virus RefSeq as reference genomes produced automatic, high-confidence genus assignments for 820 of the 1,364. We applied vConTACT v.2.0 to analyze 15,280 Global Ocean Virome genome fragments and were able to provide taxonomic assignments for 31% of these data, which shows that our algorithm is scalable to very large metagenomic datasets. 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