Taxonomy of prokaryotic viruses: 2018-2019
update from the ICTV Bacterial and Archaeal Viruses Subcommittee

Adriaenssens, Evelien M.; Sullivan, Matthew B.; Knežević, Petar; Zyl, Leonardo Joaquim van; Sarkar, BL; Dutilh, Bas E.; Alfenas‐Zerbini, Poliane; Łobocka, Małgorzata; Tong, Yigang; Brister, J. Rodney; Switt, Andrea I. Moreno; Klumpp, Jochen; Aziz, Ramy K.; Barylski, Jakub; Uchiyama, Jumpei; Edwards, Robert A.; Kropinski, Andrew M.; Petty, Nicola K.; Clokie, Martha R. J.; Kushkina, Alla; Morozova, Vera; Duffy, Siobain; Gillis, Annika; Rumnieks, J.; Kurtböke, D. İpek; Chanishvili, Nina; Goodridge, Lawrence; Wittmann, Johannes; Lavigne, Rob; Jang, Ho Bin; Prangishvili, David; Enault, François; Turner, Dann; Poranen, Minna M.; Oksanen, Hanna M.; Krupovìč, Mart

doi:10.1007/s00705-020-04577-8

Cited by 157 publications

(131 citation statements)

References 50 publications

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“…Researchers can easily utilize any of these avenues to process their metagenomic datasets and rapidly discover previously unknown phages. Given its ability to detect a wide diversity of bacteriophages, we expect that widespread application of Seeker leads to the discovery of numerous phages, some of which would represent distinct families or even higher taxa in the forthcoming new phage taxonomy (Adriaenssens et al, 2020). This work demonstrates that LSTM neural networks can learn long-term dependencies within DNA sequences and thus can efficiently tackle tasks that are not easily amenable to standard techniques based on explicit sequence similarity.…”

Section: Discussionmentioning

confidence: 85%

Seeker: Alignment-free identification of bacteriophage genomes by deep learning

Auslander

Gussow

Wolf³

et al. 2020

Preprint

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Advances in metagenomics enable massive discovery of diverse, distinct microbes and viruses. Bacteriophages, the most abundant biological entity on Earth, evolve rapidly, and therefore, detection of unknown bacteriophages in sequence datasets is a challenge. The existing methods rely on sequence similarity to known bacteriophage sequences, impeding the identification and characterization of distinct bacteriophage families. We present Seeker, a deep-learning tool for reference-free identification of phage sequences. Seeker allows rapid detection of phages in sequence datasets and clean differentiation of phage sequences from bacterial ones, even for phages with little sequence similarity to established phage families. We comprehensively validate Seeker's ability to identify unknown phages and employ Seeker to detect unknown phages, some of which are highly divergent from known phage families. We provide a web portal (seeker.pythonanywhere.com) and a user-friendly python package (https://github.com/gussow/seeker) allowing researchers to easily apply Seeker in metagenomic studies, for the detection of diverse unknown bacteriophages.

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Section: Discussionmentioning

confidence: 85%

Seeker: Alignment-free identification of bacteriophage genomes by deep learning

Auslander

Gussow

Wolf³

et al. 2020

Preprint

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“…Results of an all-by-all pairwise nucleotide identity analysis or intergenomic similarity analysis with VIRIDIC gave strong evidence for the proposal of eight new subfamilies and 30 genera which were confirmed by phylogenetic analysis of the terminase large subunit and vRNA polymerase genes, i.e., all proposed taxa are monophyletic in these marker gene trees ( Supplementary Figures S1 and S2 ). In line with previously established taxa, we used 95% and 70% nucleotide sequence identity over the length of the genome as species and genus demarcation criteria, respectively [ 11 , 12 , 68 , 69 ]. At the subfamily level, members of the same subfamily share at least 40% intergenomic distance as calculated with VIRIDIC, with members of different subfamilies sharing little to no nucleotide identity [ 70 ].…”

Section: Resultsmentioning

confidence: 99%

“…Since 2015, following a comprehensive analysis of at that time 33 N4-like genomes [ 9 ], the number of publicly available N4-like phage genomes has nearly tripled [ 10 ]. The last report of the Bacterial and Archaeal Viruses Subcommittee [ 11 ] presented the new taxonomic classifications and reassessments that were achieved in 2018 and 2019 and listed a new order ( Tubulavirales ), ten new families, 22 new sub-families, 424 new genera and 964 new species, which still represent only a fraction of the genomes currently available. However, it has to be taken into account that ICTV does not classify viral strains or variants, i.e., those phage isolates with genomes that show ≥95% DNA sequence identity with an exemplar isolate of a species [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

From Orphan Phage to a Proposed New Family–The Diversity of N4-Like Viruses

et al. 2020

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Escherichia phage N4 was isolated in 1966 in Italy and has remained a genomic orphan for a long time. It encodes an extremely large virion-associated RNA polymerase unique for bacterial viruses that became characteristic for this group. In recent years, due to new and relatively inexpensive sequencing techniques the number of publicly available phage genome sequences expanded rapidly. This revealed new members of the N4-like phage group, from 33 members in 2015 to 115 N4-like viruses in 2020. Using new technologies and methods for classification, the Bacterial and Archaeal Viruses Subcommittee of the International Committee on Taxonomy of Viruses (ICTV) has moved the classification and taxonomy of bacterial viruses from mere morphological approaches to genomic and proteomic methods. The analysis of 115 N4-like genomes resulted in a huge reassessment of this group and the proposal of a new family “Schitoviridae”, including eight subfamilies and numerous new genera.

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“…A specific family of phages, the Autographiviridae , encode their own transcriptional machinery for viral replication 90 . The archetype of this class is the well-known T7 phage, which has a small, single-subunit RNA polymerase (RNAP) and strong T7 promoters, RBSs and terminators, which have made their mark on the SynBio field 2 .…”

Section: Phages Are Unexplored Troves For Synthetic Biology Tools Andmentioning

confidence: 99%

Exploring the synthetic biology potential of bacteriophages for engineering non-model bacteria

2020

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Non-model bacteria like Pseudomonas putida, Lactococcus lactis and other species have unique and versatile metabolisms, offering unique opportunities for Synthetic Biology (SynBio). However, key genome editing and recombineering tools require optimization and large-scale multiplexing to unlock the full SynBio potential of these bacteria. In addition, the limited availability of a set of characterized, species-specific biological parts hampers the construction of reliable genetic circuitry. Mining of currently available, diverse bacteriophages could complete the SynBio toolbox, as they constitute an unexplored treasure trove for fully adapted metabolic modulators and orthogonally-functioning parts, driven by the longstanding co-evolution between phage and host.

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Taxonomy of prokaryotic viruses: 2018-2019 update from the ICTV Bacterial and Archaeal Viruses Subcommittee

Cited by 157 publications

References 50 publications

Seeker: Alignment-free identification of bacteriophage genomes by deep learning

Seeker: Alignment-free identification of bacteriophage genomes by deep learning

From Orphan Phage to a Proposed New Family–The Diversity of N4-Like Viruses

Exploring the synthetic biology potential of bacteriophages for engineering non-model bacteria

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