Host Taxon Predictor - A Tool for Predicting Taxon of the Host of a Newly Discovered Virus View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Wojciech Gałan, Maciej Bąk, Małgorzata Jakubowska

ABSTRACT

Recent advances in metagenomics provided a valuable alternative to culture-based approaches for better sampling viral diversity. However, some of newly identified viruses lack sequence similarity to any of previously sequenced ones, and cannot be easily assigned to their hosts. Here we present a bioinformatic approach to this problem. We developed classifiers capable of distinguishing eukaryotic viruses from the phages achieving almost 95% prediction accuracy. The classifiers are wrapped in Host Taxon Predictor (HTP) software written in Python which is freely available at https://github.com/wojciech-galan/viruses_classifier . HTP's performance was later demonstrated on a collection of newly identified viral genomes and genome fragments. In summary, HTP is a culture- and alignment-free approach for distinction between phages and eukaryotic viruses. We have also shown that it is possible to further extend our method to go up the evolutionary tree and predict whether a virus can infect narrower taxa. More... »

PAGES

3436

References to SciGraph publications

  • 2017-12. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data in MICROBIOME
  • 2015-03. Rising to the challenge: accelerated pace of discovery transforms marine virology in NATURE REVIEWS MICROBIOLOGY
  • 2017-12. Highly diverse population of Picornaviridae and other members of the Picornavirales, in Cameroonian fruit bats in BMC GENOMICS
  • 2016-06. Coming of age: ten years of next-generation sequencing technologies in NATURE REVIEWS GENETICS
  • 2017-12. Amyloidogenic motifs revealed by n-gram analysis in SCIENTIFIC REPORTS
  • 2018-12. Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences in SCIENTIFIC REPORTS
  • 2017-03. Prediction of virus-host infectious association by supervised learning methods in BMC BIOINFORMATICS
  • 2017-08. Highly divergent cyclo-like virus in a great roundleaf bat (Hipposideros armiger) in Vietnam in ARCHIVES OF VIROLOGY
  • 2006-05. Evolutionary Basis of Codon Usage and Nucleotide Composition Bias in Vertebrate DNA Viruses in JOURNAL OF MOLECULAR EVOLUTION
  • 1995-09. Support-vector networks in MACHINE LEARNING
  • 2016-12. Novel approach for identification of influenza virus host range and zoonotic transmissible sequences by determination of host-related associative positions in viral genome segments in BMC GENOMICS
  • 2018-12. A human gut phage catalog correlates the gut phageome with type 2 diabetes in MICROBIOME
  • 2006-12. Evidence of host-virus co-evolution in tetranucleotide usage patterns of bacteriophages and eukaryotic viruses in BMC GENOMICS
  • 2015-12. Inferring the hosts of coronavirus using dual statistical models based on nucleotide composition in SCIENTIFIC REPORTS
  • 2013-12. External validation of two prediction models identifying employees at risk of high sickness absence: cohort study with 1-year follow-up in BMC PUBLIC HEALTH
  • 2002-01. Gene Selection for Cancer Classification using Support Vector Machines in MACHINE LEARNING
  • 2017-12. Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data in BMC BIOINFORMATICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-019-39847-2

    DOI

    http://dx.doi.org/10.1038/s41598-019-39847-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112544009

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30837511


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Genetics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Jagiellonian University", 
              "id": "https://www.grid.ac/institutes/grid.5522.0", 
              "name": [
                "Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University in Krak\u00f3w, ul. Gronostajowa 7, 30-387, Krak\u00f3w, Poland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ga\u0142an", 
            "givenName": "Wojciech", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Jagiellonian University", 
              "id": "https://www.grid.ac/institutes/grid.5522.0", 
              "name": [
                "Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University in Krak\u00f3w, ul. Gronostajowa 7, 30-387, Krak\u00f3w, Poland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "B\u0105k", 
            "givenName": "Maciej", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "AGH University of Science and Technology", 
              "id": "https://www.grid.ac/institutes/grid.9922.0", 
              "name": [
                "AGH University of Science and Technology, Faculty of Materials Science and Ceramics, al. Mickiewicza 30, 30-059, Krak\u00f3w, Poland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Jakubowska", 
            "givenName": "Ma\u0142gorzata", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1073/pnas.89.4.1358", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000319208"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0074109", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002766703"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-7-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004290296", 
              "https://doi.org/10.1186/1471-2164-7-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2458-13-105", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004669605", 
              "https://doi.org/10.1186/1471-2458-13-105"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.meegid.2015.04.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006175460"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/1462-2920.12100", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008276612"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0006282", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008368053"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btn043", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009743745"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkl732", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012441274"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bib/bbw068", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013174655"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0022-2836(05)80360-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013618994"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0029145", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014592901"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jvi.00869-12", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018016617"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-016-3250-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020579969", 
              "https://doi.org/10.1186/s12864-016-3250-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-016-3250-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020579969", 
              "https://doi.org/10.1186/s12864-016-3250-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gks406", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021314472"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1239181", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025066872"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00994018", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025150743", 
              "https://doi.org/10.1007/bf00994018"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkw1002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025919209"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkw387", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028379982"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jvi.00601-10", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028961576"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0041882", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032363911"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/mbio.00373-12", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032802904"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.7554/elife.08490", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033116807"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg.2016.49", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033123702", 
              "https://doi.org/10.1038/nrg.2016.49"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0040598", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035552848"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12859-016-1415-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036034287", 
              "https://doi.org/10.1186/s12859-016-1415-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12859-016-1415-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036034287", 
              "https://doi.org/10.1186/s12859-016-1415-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pbio.1002409", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036652634"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/femsre/fuv048", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037111258"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep17155", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037865088", 
              "https://doi.org/10.1038/srep17155"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1117/12.2032817", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039239359"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pcbi.1003254", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041845662"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0005-2795(75)90109-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042105629"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0005-2795(75)90109-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042105629"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pgen.1003987", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044628356"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00239-005-0221-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046185848", 
              "https://doi.org/10.1007/s00239-005-0221-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00239-005-0221-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046185848", 
              "https://doi.org/10.1007/s00239-005-0221-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1012487302797", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048573168", 
              "https://doi.org/10.1023/a:1012487302797"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.celrep.2015.12.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048668044"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.7717/peerj.985", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049829801"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrmicro3404", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052276978", 
              "https://doi.org/10.1038/nrmicro3404"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/biomet/54.1-2.167", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1059417604"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1099/vir.0.058172-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060399419"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tit.1967.1053964", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061646286"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/0907087", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062855872"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1198/016214501753382273", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1064197908"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1075224397", 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1148/radiology.143.1.7063747", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1082130998"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jvi.02381-16", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083403677"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.virusres.2017.02.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083757050"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12859-017-1473-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084249905", 
              "https://doi.org/10.1186/s12859-017-1473-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12859-017-1473-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084249905", 
              "https://doi.org/10.1186/s12859-017-1473-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-017-3632-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084250096", 
              "https://doi.org/10.1186/s12864-017-3632-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-017-3632-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084250096", 
              "https://doi.org/10.1186/s12864-017-3632-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00705-017-3377-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085072370", 
              "https://doi.org/10.1007/s00705-017-3377-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00705-017-3377-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085072370", 
              "https://doi.org/10.1007/s00705-017-3377-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s40168-017-0283-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090371935", 
              "https://doi.org/10.1186/s40168-017-0283-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s40168-017-0283-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090371935", 
              "https://doi.org/10.1186/s40168-017-0283-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/apt.14280", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091438455"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-017-13210-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092102120", 
              "https://doi.org/10.1038/s41598-017-13210-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s40168-018-0410-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100772831", 
              "https://doi.org/10.1186/s40168-018-0410-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/bty351", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103720641"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-018-28308-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105180762", 
              "https://doi.org/10.1038/s41598-018-28308-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fgene.2018.00304", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1106017342"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pbio.3000003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1106141799"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-12", 
        "datePublishedReg": "2019-12-01", 
        "description": "Recent advances in metagenomics provided a valuable alternative to culture-based approaches for better sampling viral diversity. However, some of newly identified viruses lack sequence similarity to any of previously sequenced ones, and cannot be easily assigned to their hosts. Here we present a bioinformatic approach to this problem. We developed classifiers capable of distinguishing eukaryotic viruses from the phages achieving almost 95% prediction accuracy. The classifiers are wrapped in Host Taxon Predictor (HTP) software written in Python which is freely available at https://github.com/wojciech-galan/viruses_classifier . HTP's performance was later demonstrated on a collection of newly identified viral genomes and genome fragments. In summary, HTP is a culture- and alignment-free approach for distinction between phages and eukaryotic viruses. We have also shown that it is possible to further extend our method to go up the evolutionary tree and predict whether a virus can infect narrower taxa.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1038/s41598-019-39847-2", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.4713447", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1045337", 
            "issn": [
              "2045-2322"
            ], 
            "name": "Scientific Reports", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "9"
          }
        ], 
        "name": "Host Taxon Predictor - A Tool for Predicting Taxon of the Host of a Newly Discovered Virus", 
        "pagination": "3436", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "1375e97376a2b08d0e6abc715626291f11b37a4c985f7b8697f26d6b46bef169"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "30837511"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "101563288"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/s41598-019-39847-2"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1112544009"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/s41598-019-39847-2", 
          "https://app.dimensions.ai/details/publication/pub.1112544009"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T11:16", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000354_0000000354/records_11691_00000002.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://www.nature.com/articles/s41598-019-39847-2"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-39847-2'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-39847-2'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-39847-2'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-39847-2'


     

    This table displays all metadata directly associated to this object as RDF triples.

    274 TRIPLES      21 PREDICATES      87 URIs      21 LITERALS      9 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/s41598-019-39847-2 schema:about anzsrc-for:06
    2 anzsrc-for:0604
    3 schema:author N638d455fa04143ad9b231b3666a9165a
    4 schema:citation sg:pub.10.1007/bf00994018
    5 sg:pub.10.1007/s00239-005-0221-1
    6 sg:pub.10.1007/s00705-017-3377-2
    7 sg:pub.10.1023/a:1012487302797
    8 sg:pub.10.1038/nrg.2016.49
    9 sg:pub.10.1038/nrmicro3404
    10 sg:pub.10.1038/s41598-017-13210-9
    11 sg:pub.10.1038/s41598-018-28308-x
    12 sg:pub.10.1038/srep17155
    13 sg:pub.10.1186/1471-2164-7-8
    14 sg:pub.10.1186/1471-2458-13-105
    15 sg:pub.10.1186/s12859-016-1415-9
    16 sg:pub.10.1186/s12859-017-1473-7
    17 sg:pub.10.1186/s12864-016-3250-9
    18 sg:pub.10.1186/s12864-017-3632-7
    19 sg:pub.10.1186/s40168-017-0283-5
    20 sg:pub.10.1186/s40168-018-0410-y
    21 https://app.dimensions.ai/details/publication/pub.1075224397
    22 https://doi.org/10.1016/0005-2795(75)90109-9
    23 https://doi.org/10.1016/j.celrep.2015.12.011
    24 https://doi.org/10.1016/j.meegid.2015.04.005
    25 https://doi.org/10.1016/j.virusres.2017.02.002
    26 https://doi.org/10.1016/s0022-2836(05)80360-2
    27 https://doi.org/10.1073/pnas.89.4.1358
    28 https://doi.org/10.1093/bib/bbw068
    29 https://doi.org/10.1093/bioinformatics/btn043
    30 https://doi.org/10.1093/bioinformatics/bty351
    31 https://doi.org/10.1093/biomet/54.1-2.167
    32 https://doi.org/10.1093/femsre/fuv048
    33 https://doi.org/10.1093/nar/gkl732
    34 https://doi.org/10.1093/nar/gks406
    35 https://doi.org/10.1093/nar/gkw1002
    36 https://doi.org/10.1093/nar/gkw387
    37 https://doi.org/10.1099/vir.0.058172-0
    38 https://doi.org/10.1109/tit.1967.1053964
    39 https://doi.org/10.1111/1462-2920.12100
    40 https://doi.org/10.1111/apt.14280
    41 https://doi.org/10.1117/12.2032817
    42 https://doi.org/10.1126/science.1239181
    43 https://doi.org/10.1128/jvi.00601-10
    44 https://doi.org/10.1128/jvi.00869-12
    45 https://doi.org/10.1128/jvi.02381-16
    46 https://doi.org/10.1128/mbio.00373-12
    47 https://doi.org/10.1137/0907087
    48 https://doi.org/10.1148/radiology.143.1.7063747
    49 https://doi.org/10.1198/016214501753382273
    50 https://doi.org/10.1371/journal.pbio.1002409
    51 https://doi.org/10.1371/journal.pbio.3000003
    52 https://doi.org/10.1371/journal.pcbi.1003254
    53 https://doi.org/10.1371/journal.pgen.1003987
    54 https://doi.org/10.1371/journal.pone.0006282
    55 https://doi.org/10.1371/journal.pone.0029145
    56 https://doi.org/10.1371/journal.pone.0040598
    57 https://doi.org/10.1371/journal.pone.0041882
    58 https://doi.org/10.1371/journal.pone.0074109
    59 https://doi.org/10.3389/fgene.2018.00304
    60 https://doi.org/10.7554/elife.08490
    61 https://doi.org/10.7717/peerj.985
    62 schema:datePublished 2019-12
    63 schema:datePublishedReg 2019-12-01
    64 schema:description Recent advances in metagenomics provided a valuable alternative to culture-based approaches for better sampling viral diversity. However, some of newly identified viruses lack sequence similarity to any of previously sequenced ones, and cannot be easily assigned to their hosts. Here we present a bioinformatic approach to this problem. We developed classifiers capable of distinguishing eukaryotic viruses from the phages achieving almost 95% prediction accuracy. The classifiers are wrapped in Host Taxon Predictor (HTP) software written in Python which is freely available at https://github.com/wojciech-galan/viruses_classifier . HTP's performance was later demonstrated on a collection of newly identified viral genomes and genome fragments. In summary, HTP is a culture- and alignment-free approach for distinction between phages and eukaryotic viruses. We have also shown that it is possible to further extend our method to go up the evolutionary tree and predict whether a virus can infect narrower taxa.
    65 schema:genre research_article
    66 schema:inLanguage en
    67 schema:isAccessibleForFree true
    68 schema:isPartOf Nb8259e25baf84ad38951714d648fc908
    69 Ndabee48c968f4792a920f2a51779b5b5
    70 sg:journal.1045337
    71 schema:name Host Taxon Predictor - A Tool for Predicting Taxon of the Host of a Newly Discovered Virus
    72 schema:pagination 3436
    73 schema:productId N0cf82835362e400bb402d741baefdebc
    74 N3ca89f165acd43a8a1684707d4a2ecd3
    75 Ncf75723da0f044c6881b8148e0ca5a78
    76 Nd023947954bb427194aca62b17abf4df
    77 Nd687a36690654b089a8f8e3b98047e1a
    78 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112544009
    79 https://doi.org/10.1038/s41598-019-39847-2
    80 schema:sdDatePublished 2019-04-11T11:16
    81 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    82 schema:sdPublisher N3b657603bbd0487d97772122fb2f9dcc
    83 schema:url https://www.nature.com/articles/s41598-019-39847-2
    84 sgo:license sg:explorer/license/
    85 sgo:sdDataset articles
    86 rdf:type schema:ScholarlyArticle
    87 N0cf82835362e400bb402d741baefdebc schema:name pubmed_id
    88 schema:value 30837511
    89 rdf:type schema:PropertyValue
    90 N22780555566d481eaf3b41227321ce2d schema:affiliation https://www.grid.ac/institutes/grid.5522.0
    91 schema:familyName Gałan
    92 schema:givenName Wojciech
    93 rdf:type schema:Person
    94 N3b657603bbd0487d97772122fb2f9dcc schema:name Springer Nature - SN SciGraph project
    95 rdf:type schema:Organization
    96 N3ca89f165acd43a8a1684707d4a2ecd3 schema:name doi
    97 schema:value 10.1038/s41598-019-39847-2
    98 rdf:type schema:PropertyValue
    99 N55b74ab9d43d43ca8ae5c9e6c230ec9b rdf:first Nd59f7e8ac06f428d842b5123cbfbbfc9
    100 rdf:rest rdf:nil
    101 N638d455fa04143ad9b231b3666a9165a rdf:first N22780555566d481eaf3b41227321ce2d
    102 rdf:rest N9f09c71710154cc6a07e8fc8865c93e8
    103 N9f09c71710154cc6a07e8fc8865c93e8 rdf:first Ne3b0cb96544a483dad0d0f245ab0ee32
    104 rdf:rest N55b74ab9d43d43ca8ae5c9e6c230ec9b
    105 Nb8259e25baf84ad38951714d648fc908 schema:volumeNumber 9
    106 rdf:type schema:PublicationVolume
    107 Ncf75723da0f044c6881b8148e0ca5a78 schema:name dimensions_id
    108 schema:value pub.1112544009
    109 rdf:type schema:PropertyValue
    110 Nd023947954bb427194aca62b17abf4df schema:name nlm_unique_id
    111 schema:value 101563288
    112 rdf:type schema:PropertyValue
    113 Nd59f7e8ac06f428d842b5123cbfbbfc9 schema:affiliation https://www.grid.ac/institutes/grid.9922.0
    114 schema:familyName Jakubowska
    115 schema:givenName Małgorzata
    116 rdf:type schema:Person
    117 Nd687a36690654b089a8f8e3b98047e1a schema:name readcube_id
    118 schema:value 1375e97376a2b08d0e6abc715626291f11b37a4c985f7b8697f26d6b46bef169
    119 rdf:type schema:PropertyValue
    120 Ndabee48c968f4792a920f2a51779b5b5 schema:issueNumber 1
    121 rdf:type schema:PublicationIssue
    122 Ne3b0cb96544a483dad0d0f245ab0ee32 schema:affiliation https://www.grid.ac/institutes/grid.5522.0
    123 schema:familyName Bąk
    124 schema:givenName Maciej
    125 rdf:type schema:Person
    126 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    127 schema:name Biological Sciences
    128 rdf:type schema:DefinedTerm
    129 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    130 schema:name Genetics
    131 rdf:type schema:DefinedTerm
    132 sg:grant.4713447 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-019-39847-2
    133 rdf:type schema:MonetaryGrant
    134 sg:journal.1045337 schema:issn 2045-2322
    135 schema:name Scientific Reports
    136 rdf:type schema:Periodical
    137 sg:pub.10.1007/bf00994018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025150743
    138 https://doi.org/10.1007/bf00994018
    139 rdf:type schema:CreativeWork
    140 sg:pub.10.1007/s00239-005-0221-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046185848
    141 https://doi.org/10.1007/s00239-005-0221-1
    142 rdf:type schema:CreativeWork
    143 sg:pub.10.1007/s00705-017-3377-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085072370
    144 https://doi.org/10.1007/s00705-017-3377-2
    145 rdf:type schema:CreativeWork
    146 sg:pub.10.1023/a:1012487302797 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048573168
    147 https://doi.org/10.1023/a:1012487302797
    148 rdf:type schema:CreativeWork
    149 sg:pub.10.1038/nrg.2016.49 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033123702
    150 https://doi.org/10.1038/nrg.2016.49
    151 rdf:type schema:CreativeWork
    152 sg:pub.10.1038/nrmicro3404 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052276978
    153 https://doi.org/10.1038/nrmicro3404
    154 rdf:type schema:CreativeWork
    155 sg:pub.10.1038/s41598-017-13210-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092102120
    156 https://doi.org/10.1038/s41598-017-13210-9
    157 rdf:type schema:CreativeWork
    158 sg:pub.10.1038/s41598-018-28308-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1105180762
    159 https://doi.org/10.1038/s41598-018-28308-x
    160 rdf:type schema:CreativeWork
    161 sg:pub.10.1038/srep17155 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037865088
    162 https://doi.org/10.1038/srep17155
    163 rdf:type schema:CreativeWork
    164 sg:pub.10.1186/1471-2164-7-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004290296
    165 https://doi.org/10.1186/1471-2164-7-8
    166 rdf:type schema:CreativeWork
    167 sg:pub.10.1186/1471-2458-13-105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004669605
    168 https://doi.org/10.1186/1471-2458-13-105
    169 rdf:type schema:CreativeWork
    170 sg:pub.10.1186/s12859-016-1415-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036034287
    171 https://doi.org/10.1186/s12859-016-1415-9
    172 rdf:type schema:CreativeWork
    173 sg:pub.10.1186/s12859-017-1473-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084249905
    174 https://doi.org/10.1186/s12859-017-1473-7
    175 rdf:type schema:CreativeWork
    176 sg:pub.10.1186/s12864-016-3250-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020579969
    177 https://doi.org/10.1186/s12864-016-3250-9
    178 rdf:type schema:CreativeWork
    179 sg:pub.10.1186/s12864-017-3632-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084250096
    180 https://doi.org/10.1186/s12864-017-3632-7
    181 rdf:type schema:CreativeWork
    182 sg:pub.10.1186/s40168-017-0283-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090371935
    183 https://doi.org/10.1186/s40168-017-0283-5
    184 rdf:type schema:CreativeWork
    185 sg:pub.10.1186/s40168-018-0410-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1100772831
    186 https://doi.org/10.1186/s40168-018-0410-y
    187 rdf:type schema:CreativeWork
    188 https://app.dimensions.ai/details/publication/pub.1075224397 schema:CreativeWork
    189 https://doi.org/10.1016/0005-2795(75)90109-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042105629
    190 rdf:type schema:CreativeWork
    191 https://doi.org/10.1016/j.celrep.2015.12.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048668044
    192 rdf:type schema:CreativeWork
    193 https://doi.org/10.1016/j.meegid.2015.04.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006175460
    194 rdf:type schema:CreativeWork
    195 https://doi.org/10.1016/j.virusres.2017.02.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083757050
    196 rdf:type schema:CreativeWork
    197 https://doi.org/10.1016/s0022-2836(05)80360-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013618994
    198 rdf:type schema:CreativeWork
    199 https://doi.org/10.1073/pnas.89.4.1358 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000319208
    200 rdf:type schema:CreativeWork
    201 https://doi.org/10.1093/bib/bbw068 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013174655
    202 rdf:type schema:CreativeWork
    203 https://doi.org/10.1093/bioinformatics/btn043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009743745
    204 rdf:type schema:CreativeWork
    205 https://doi.org/10.1093/bioinformatics/bty351 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103720641
    206 rdf:type schema:CreativeWork
    207 https://doi.org/10.1093/biomet/54.1-2.167 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059417604
    208 rdf:type schema:CreativeWork
    209 https://doi.org/10.1093/femsre/fuv048 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037111258
    210 rdf:type schema:CreativeWork
    211 https://doi.org/10.1093/nar/gkl732 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012441274
    212 rdf:type schema:CreativeWork
    213 https://doi.org/10.1093/nar/gks406 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021314472
    214 rdf:type schema:CreativeWork
    215 https://doi.org/10.1093/nar/gkw1002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025919209
    216 rdf:type schema:CreativeWork
    217 https://doi.org/10.1093/nar/gkw387 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028379982
    218 rdf:type schema:CreativeWork
    219 https://doi.org/10.1099/vir.0.058172-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060399419
    220 rdf:type schema:CreativeWork
    221 https://doi.org/10.1109/tit.1967.1053964 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061646286
    222 rdf:type schema:CreativeWork
    223 https://doi.org/10.1111/1462-2920.12100 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008276612
    224 rdf:type schema:CreativeWork
    225 https://doi.org/10.1111/apt.14280 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091438455
    226 rdf:type schema:CreativeWork
    227 https://doi.org/10.1117/12.2032817 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039239359
    228 rdf:type schema:CreativeWork
    229 https://doi.org/10.1126/science.1239181 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025066872
    230 rdf:type schema:CreativeWork
    231 https://doi.org/10.1128/jvi.00601-10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028961576
    232 rdf:type schema:CreativeWork
    233 https://doi.org/10.1128/jvi.00869-12 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018016617
    234 rdf:type schema:CreativeWork
    235 https://doi.org/10.1128/jvi.02381-16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083403677
    236 rdf:type schema:CreativeWork
    237 https://doi.org/10.1128/mbio.00373-12 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032802904
    238 rdf:type schema:CreativeWork
    239 https://doi.org/10.1137/0907087 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062855872
    240 rdf:type schema:CreativeWork
    241 https://doi.org/10.1148/radiology.143.1.7063747 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082130998
    242 rdf:type schema:CreativeWork
    243 https://doi.org/10.1198/016214501753382273 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064197908
    244 rdf:type schema:CreativeWork
    245 https://doi.org/10.1371/journal.pbio.1002409 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036652634
    246 rdf:type schema:CreativeWork
    247 https://doi.org/10.1371/journal.pbio.3000003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106141799
    248 rdf:type schema:CreativeWork
    249 https://doi.org/10.1371/journal.pcbi.1003254 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041845662
    250 rdf:type schema:CreativeWork
    251 https://doi.org/10.1371/journal.pgen.1003987 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044628356
    252 rdf:type schema:CreativeWork
    253 https://doi.org/10.1371/journal.pone.0006282 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008368053
    254 rdf:type schema:CreativeWork
    255 https://doi.org/10.1371/journal.pone.0029145 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014592901
    256 rdf:type schema:CreativeWork
    257 https://doi.org/10.1371/journal.pone.0040598 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035552848
    258 rdf:type schema:CreativeWork
    259 https://doi.org/10.1371/journal.pone.0041882 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032363911
    260 rdf:type schema:CreativeWork
    261 https://doi.org/10.1371/journal.pone.0074109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002766703
    262 rdf:type schema:CreativeWork
    263 https://doi.org/10.3389/fgene.2018.00304 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106017342
    264 rdf:type schema:CreativeWork
    265 https://doi.org/10.7554/elife.08490 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033116807
    266 rdf:type schema:CreativeWork
    267 https://doi.org/10.7717/peerj.985 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049829801
    268 rdf:type schema:CreativeWork
    269 https://www.grid.ac/institutes/grid.5522.0 schema:alternateName Jagiellonian University
    270 schema:name Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University in Kraków, ul. Gronostajowa 7, 30-387, Kraków, Poland
    271 rdf:type schema:Organization
    272 https://www.grid.ac/institutes/grid.9922.0 schema:alternateName AGH University of Science and Technology
    273 schema:name AGH University of Science and Technology, Faculty of Materials Science and Ceramics, al. Mickiewicza 30, 30-059, Kraków, Poland
    274 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...