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      VICTOR: genome-based phylogeny and classification of prokaryotic viruses

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      1 , 1
      Bioinformatics
      Oxford University Press

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          Abstract

          Motivation

          Bacterial and archaeal viruses are crucial for global biogeochemical cycles and might well be game-changing therapeutic agents in the fight against multi-resistant pathogens. Nevertheless, it is still unclear how to best use genome sequence data for a fast, universal and accurate taxonomic classification of such viruses.

          Results

          We here present a novel in silico framework for phylogeny and classification of prokaryotic viruses, in line with the principles of phylogenetic systematics, and using a large reference dataset of officially classified viruses. The resulting trees revealed a high agreement with the classification. Except for low resolution at the family level, the majority of taxa was well supported as monophyletic. Clusters obtained with distance thresholds chosen for maximizing taxonomic agreement appeared phylogenetically reasonable, too. Analysis of an expanded dataset, containing >4000 genomes from public databases, revealed a large number of novel species, genera, subfamilies and families.

          Availability and implementation

          The selected methods are available as the easy-to-use web service ‘VICTOR’ at https://victor.dsmz.de.

          Supplementary information

          Supplementary data are available at Bioinformatics online.

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          Most cited references36

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          Digital DNA-DNA hybridization for microbial species delineation by means of genome-to-genome sequence comparison

          The pragmatic species concept for Bacteria and Archaea is ultimately based on DNA-DNA hybridization (DDH). While enabling the taxonomist, in principle, to obtain an estimate of the overall similarity between the genomes of two strains, this technique is tedious and error-prone and cannot be used to incrementally build up a comparative database. Recent technological progress in the area of genome sequencing calls for bioinformatics methods to replace the wet-lab DDH by in-silico genome-to-genome comparison. Here we investigate state-of-the-art methods for inferring whole-genome distances in their ability to mimic DDH. Algorithms to efficiently determine high-scoring segment pairs or maximally unique matches perform well as a basis of inferring intergenomic distances. The examined distance functions, which are able to cope with heavily reduced genomes and repetitive sequence regions, outperform previously described ones regarding the correlation with and error ratios in emulating DDH. Simulation of incompletely sequenced genomes indicates that some distance formulas are very robust against missing fractions of genomic information. Digitally derived genome-to-genome distances show a better correlation with 16S rRNA gene sequence distances than DDH values. The future perspectives of genome-informed taxonomy are discussed, and the investigated methods are made available as a web service for genome-based species delineation.
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            Virioplankton: Viruses in Aquatic Ecosystems

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              Standard operating procedure for calculating genome-to-genome distances based on high-scoring segment pairs

              DNA-DNA hybridization (DDH) is a widely applied wet-lab technique to obtain an estimate of the overall similarity between the genomes of two organisms. To base the species concept for prokaryotes ultimately on DDH was chosen by microbiologists as a pragmatic approach for deciding about the recognition of novel species, but also allowed a relatively high degree of standardization compared to other areas of taxonomy. However, DDH is tedious and error-prone and first and foremost cannot be used to incrementally establish a comparative database. Recent studies have shown that in-silico methods for the comparison of genome sequences can be used to replace DDH. Considering the ongoing rapid technological progress of sequencing methods, genome-based prokaryote taxonomy is coming into reach. However, calculating distances between genomes is dependent on multiple choices for software and program settings. We here provide an overview over the modifications that can be applied to distance methods based in high-scoring segment pairs (HSPs) or maximally unique matches (MUMs) and that need to be documented. General recommendations on determining HSPs using BLAST or other algorithms are also provided. As a reference implementation, we introduce the GGDC web server (http://ggdc.gbdp.org).
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                Author and article information

                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                01 November 2017
                07 July 2017
                07 July 2017
                : 33
                : 21
                : 3396-3404
                Affiliations
                [1 ]Department of Bioinformatics, Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures, 38124 Braunschweig, Germany
                Author notes
                [* ]To whom correspondence should be addressed.
                [*]

                Associate Editor: Janet Kelso

                Author information
                http://orcid.org/0000-0001-9105-9814
                Article
                btx440
                10.1093/bioinformatics/btx440
                5860169
                29036289
                0c536d90-45dd-4f6f-9eed-e4cb6dc239a7
                © The Author 2017. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 March 2017
                : 14 June 2017
                : 05 July 2017
                Page count
                Pages: 9
                Funding
                Funded by: Deutsche Forschungsgemeinschaft 10.13039/501100001659
                Award ID: TRR 51
                Categories
                Original Papers
                Phylogenetics

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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