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      vConTACT: an iVirus tool to classify double-stranded DNA viruses that infect Archaea and Bacteria

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          Abstract

          Taxonomic classification of archaeal and bacterial viruses is challenging, yet also fundamental for developing a predictive understanding of microbial ecosystems. Recent identification of hundreds of thousands of new viral genomes and genome fragments, whose hosts remain unknown, requires a paradigm shift away from traditional classification approaches and towards the use of genomes for taxonomy. Here we revisited the use of genomes and their protein content as a means for developing a viral taxonomy for bacterial and archaeal viruses. A network-based analytic was evaluated and benchmarked against authority-accepted taxonomic assignments and found to be largely concordant. Exceptions were manually examined and found to represent areas of viral genome ‘sequence space’ that are under-sampled or prone to excessive genetic exchange. While both cases are poorly resolved by genome-based taxonomic approaches, the former will improve as viral sequence space is better sampled and the latter are uncommon. Finally, given the largely robust taxonomic capabilities of this approach, we sought to enable researchers to easily and systematically classify new viruses. Thus, we established a tool, vConTACT, as an app at iVirus, where it operates as a fast, highly scalable, user-friendly app within the free and powerful CyVerse cyberinfrastructure.

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          Most cited references40

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          Community detection in graphs

          The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
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            Towards a natural system of organisms: proposal for the domains Archaea, Bacteria, and Eucarya.

            Molecular structures and sequences are generally more revealing of evolutionary relationships than are classical phenotypes (particularly so among microorganisms). Consequently, the basis for the definition of taxa has progressively shifted from the organismal to the cellular to the molecular level. Molecular comparisons show that life on this planet divides into three primary groupings, commonly known as the eubacteria, the archaebacteria, and the eukaryotes. The three are very dissimilar, the differences that separate them being of a more profound nature than the differences that separate typical kingdoms, such as animals and plants. Unfortunately, neither of the conventionally accepted views of the natural relationships among living systems--i.e., the five-kingdom taxonomy or the eukaryote-prokaryote dichotomy--reflects this primary tripartite division of the living world. To remedy this situation we propose that a formal system of organisms be established in which above the level of kingdom there exists a new taxon called a "domain." Life on this planet would then be seen as comprising three domains, the Bacteria, the Archaea, and the Eucarya, each containing two or more kingdoms. (The Eucarya, for example, contain Animalia, Plantae, Fungi, and a number of others yet to be defined). Although taxonomic structure within the Bacteria and Eucarya is not treated herein, Archaea is formally subdivided into the two kingdoms Euryarchaeota (encompassing the methanogens and their phenotypically diverse relatives) and Crenarchaeota (comprising the relatively tight clustering of extremely thermophilic archaebacteria, whose general phenotype appears to resemble most the ancestral phenotype of the Archaea.
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              Detecting overlapping protein complexes in protein-protein interaction networks.

              We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a high extent of functional homogeneity.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                3 May 2017
                2017
                : 5
                : e3243
                Affiliations
                [1 ]Department of Microbiology, Ohio State University , Columbus, OH, United States
                [2 ]Institut de Biologie de l’ENS (IBENS), École normale supérieure, PSL Research University , Paris, France
                [3 ]ESPCI, PSL Research University , Paris, France
                [4 ]Department of Chemistry and Biochemistry, Ohio State University , Columbus, OH, United States
                [5 ]Department of Civil, Environmental and Geodetic Engineering, Ohio State University , Columbus, OH, United States
                Article
                3243
                10.7717/peerj.3243
                5419219
                28480138
                2ff6e794-1649-4bb2-b115-fdca0c3276ca
                ©2017 Bolduc et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 3 January 2017
                : 28 March 2017
                Funding
                Funded by: Genomic Science program
                Award ID: DE-SC0010580
                Funded by: National Science Foundation
                Award ID: OCE-1536989
                Funded by: Gordon and Betty Moore Foundation
                Award ID: #3790, 3305
                This research was supported by awards to MBS from the US Department of Energy, Office of Science, Office of Biological and Environmental Research under the Genomic Science program (DE-SC0010580), the National Science Foundation (OCE-1536989), and the Gordon and Betty Moore Foundation (#3790, 3305) and computational resources provided by the Ohio Supercomputer Center (1987, http://osc.edu/ark:/19495/f5s1ph73). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Bioinformatics
                Genomics
                Taxonomy
                Virology

                virus,bacteriophage,archaeal viruses,taxonomy
                virus, bacteriophage, archaeal viruses, taxonomy

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