Variant site strain typer (VaST): efficient strain typing using a minimal number of variant genomic sites View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Tara N. Furstenau, Jill H. Cocking, Jason W. Sahl, Viacheslav Y. Fofanov

ABSTRACT

BACKGROUND: Targeted PCR amplicon sequencing (TAS) techniques provide a sensitive, scalable, and cost-effective way to query and identify closely related bacterial species and strains. Typically, this is accomplished by targeting housekeeping genes that provide resolution down to the family, genera, and sometimes species level. Unfortunately, this level of resolution is not sufficient in many applications where strain-level identification of bacteria is required (biodefense, forensics, clinical diagnostics, and outbreak investigations). Adding more genomic targets will increase the resolution, but the challenge is identifying the appropriate targets. VaST was developed to address this challenge by finding the minimum number of targets that, in combination, achieve maximum strain-level resolution for any strain complex. The final combination of target regions identified by the algorithm produce a unique haplotype for each strain which can be used as a fingerprint for identifying unknown samples in a TAS assay. VaST ensures that the targets have conserved primer regions so that the targets can be amplified in all of the known strains and it also favors the inclusion of targets with basal variants which makes the set more robust when identifying previously unseen strains. RESULTS: We analyzed VaST's performance using a number of different pathogenic species that are relevant to human disease outbreaks and biodefense. The number of targets required to achieve full resolution ranged from 20 to 88% fewer sites than what would be required in the worst case and most of the resolution is achieved within the first 20 targets. We computationally and experimentally validated one of the VaST panels and found that the targets led to accurate phylogenetic placement of strains, even when the strains were not a part of the original panel design. CONCLUSIONS: VaST is an open source software that, when provided a set of variant sites, can find the minimum number of sites that will provide maximum resolution of a strain complex, and it has many different run-time options that can accommodate a wide range of applications. VaST can be an effective tool in the design of strain identification panels that, when combined with TAS technologies, offer an efficient and inexpensive strain typing protocol. More... »

PAGES

222

References to SciGraph publications

  • 2009-12. "PolyMin": software for identification of the minimum number of polymorphisms required for haplotype and genotype differentiation in BMC BIOINFORMATICS
  • 2010-12. Pan-genome sequence analysis using Panseq: an online tool for the rapid analysis of core and accessory genomic regions in BMC BIOINFORMATICS
  • 2007. Variability of the Protein Sequences of LcrV Between Epidemic and Atypical Rhamnose-Positive Strains of Yersinia pestis in THE GENUS YERSINIA
  • 2005-12. htSNPer1.0: software for haplotype block partition and htSNPs selection in BMC BIOINFORMATICS
  • 2014-10. Clinical detection and characterization of bacterial pathogens in the genomics era in GENOME MEDICINE
  • 2010-12. Yersinia pestis genome sequencing identifies patterns of global phylogenetic diversity in NATURE GENETICS
  • 2011-12. Genome sequence analyses of two isolates from the recent Escherichia coli outbreak in Germany reveal the emergence of a new pathotype: Entero-Aggregative-Haemorrhagic Escherichia coli (EAHEC) in ARCHIVES OF MICROBIOLOGY
  • 2010-02. Target-enrichment strategies for next-generation sequencing in NATURE METHODS
  • 2011-12. Phylogeography of Francisella tularensis subspecies holarctica from the country of Georgia in BMC MICROBIOLOGY
  • 2009-12. Bacillus anthracis in China and its relationship to worldwide lineages in BMC MICROBIOLOGY
  • 2015-12. Phylogenetically typing bacterial strains from partial SNP genotypes observed from direct sequencing of clinical specimen metagenomic data in GENOME MEDICINE
  • 2009-12. Development of a multi-locus sequence typing scheme for Laribacter hongkongensis, a novel bacterium associated with freshwater fish-borne gastroenteritis and traveler's diarrhea in BMC MICROBIOLOGY
  • 2012-04. Fast gapped-read alignment with Bowtie 2 in NATURE METHODS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12859-018-2225-z

    DOI

    http://dx.doi.org/10.1186/s12859-018-2225-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1104515518

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29890941


    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/2008/1108", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical Microbiology", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical and Health Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Northern Arizona University", 
              "id": "https://www.grid.ac/institutes/grid.261120.6", 
              "name": [
                "The School of Informatics, Computing, and Cyber Systems, Northern Arizona University, 1295 S Knoles Dr., 86001, Flagstaff, Arizona, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Furstenau", 
            "givenName": "Tara N.", 
            "id": "sg:person.0750266526.51", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0750266526.51"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Northern Arizona University", 
              "id": "https://www.grid.ac/institutes/grid.261120.6", 
              "name": [
                "The School of Informatics, Computing, and Cyber Systems, Northern Arizona University, 1295 S Knoles Dr., 86001, Flagstaff, Arizona, USA", 
                "Pathogen and Microbiome Institute, Northern Arizona University, 1395 S Knoles Dr., 86001, Flagstaff, Arizona, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Cocking", 
            "givenName": "Jill H.", 
            "id": "sg:person.01226673233.50", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01226673233.50"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Northern Arizona University", 
              "id": "https://www.grid.ac/institutes/grid.261120.6", 
              "name": [
                "Pathogen and Microbiome Institute, Northern Arizona University, 1395 S Knoles Dr., 86001, Flagstaff, Arizona, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sahl", 
            "givenName": "Jason W.", 
            "id": "sg:person.0636364415.27", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0636364415.27"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Northern Arizona University", 
              "id": "https://www.grid.ac/institutes/grid.261120.6", 
              "name": [
                "The School of Informatics, Computing, and Cyber Systems, Northern Arizona University, 1295 S Knoles Dr., 86001, Flagstaff, Arizona, USA", 
                "Pathogen and Microbiome Institute, Northern Arizona University, 1395 S Knoles Dr., 86001, Flagstaff, Arizona, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Fofanov", 
            "givenName": "Viacheslav Y.", 
            "id": "sg:person.01171532274.28", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01171532274.28"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1371/journal.pone.0066567", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003534437"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3201/eid1802.111305", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003550984"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jcm.02671-12", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004232697"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2180-9-21", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004377564", 
              "https://doi.org/10.1186/1471-2180-9-21"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/13.9.3021", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004878828"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/19.2.287", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005611482"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0000461", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005915272"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.1923", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006541515", 
              "https://doi.org/10.1038/nmeth.1923"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-10-176", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006635084", 
              "https://doi.org/10.1186/1471-2105-10-176"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jb.00124-06", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009493125"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13073-014-0114-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009583237", 
              "https://doi.org/10.1186/s13073-014-0114-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13073-014-0114-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009583237", 
              "https://doi.org/10.1186/s13073-014-0114-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/gbe/evr106", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010093653"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2180-11-139", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010309847", 
              "https://doi.org/10.1186/1471-2180-11-139"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0039630", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010357894"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-0-387-72124-8_3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010860589", 
              "https://doi.org/10.1007/978-0-387-72124-8_3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-0-387-72124-8_3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010860589", 
              "https://doi.org/10.1007/978-0-387-72124-8_3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0031604", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012674582"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1099/mic.0.071605-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013440171"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fmicb.2016.01599", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013637066"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.1016657108", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014872808"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.705", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015236659", 
              "https://doi.org/10.1038/ng.705"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.705", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015236659", 
              "https://doi.org/10.1038/ng.705"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0085417", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016800940"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-6-38", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018010862", 
              "https://doi.org/10.1186/1471-2105-6-38"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jcm.42.12.5644-5649.2004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018151590"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btr330", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018404011"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jcm.01233-06", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020815978"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0131967", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021665039"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jcm.40.10.3671-3680.2002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021802519"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3201/eid2005.131559", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028488024"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pntd.0001954", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028512061"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.1633613100", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029593215"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/gepi.20095", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029989293"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/gepi.20095", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029989293"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jcm.43.9.4382-4390.2005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031418744"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-11-461", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031672096", 
              "https://doi.org/10.1186/1471-2105-11-461"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3201/eid1404.070984", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032215404"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13073-015-0176-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032716746", 
              "https://doi.org/10.1186/s13073-015-0176-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13073-015-0176-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032716746", 
              "https://doi.org/10.1186/s13073-015-0176-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/1462-2920.12052", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033308606"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jb.01786-08", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036973755"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0026201", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040344890"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.1419", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042265948", 
              "https://doi.org/10.1038/nmeth.1419"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.1419", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042265948", 
              "https://doi.org/10.1038/nmeth.1419"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0408026101", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042383197"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pntd.0001319", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042419592"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2180-9-71", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042741495", 
              "https://doi.org/10.1186/1471-2180-9-71"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/genomea.00148-15", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043679260"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jcm.00046-16", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044114099"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1146/annurev-food-041715-033259", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045186257"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00203-011-0725-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046599734", 
              "https://doi.org/10.1007/s00203-011-0725-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0008360", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047268220"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jb.184.16.4601-4611.2002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048537777"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0710834105", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051587751"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jb.186.17.5808-5818.2004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052697628"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.95.6.3140", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053351767"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1099/mgen.0.000074", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060393884"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/genomea.01024-15", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062714261"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/jb.173.2.697-703.1991", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062719841"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/mbio.01501-16", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062727430"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2144/000112815", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1069095813"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.4172/2157-2526.s3-007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1072329858"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fmicb.2018.00551", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101701460"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2018-12", 
        "datePublishedReg": "2018-12-01", 
        "description": "BACKGROUND: Targeted PCR amplicon sequencing (TAS) techniques provide a sensitive, scalable, and cost-effective way to query and identify closely related bacterial species and strains. Typically, this is accomplished by targeting housekeeping genes that provide resolution down to the family, genera, and sometimes species level. Unfortunately, this level of resolution is not sufficient in many applications where strain-level identification of bacteria is required (biodefense, forensics, clinical diagnostics, and outbreak investigations). Adding more genomic targets will increase the resolution, but the challenge is identifying the appropriate targets. VaST was developed to address this challenge by finding the minimum number of targets that, in combination, achieve maximum strain-level resolution for any strain complex. The final combination of target regions identified by the algorithm produce a unique haplotype for each strain which can be used as a fingerprint for identifying unknown samples in a TAS assay. VaST ensures that the targets have conserved primer regions so that the targets can be amplified in all of the known strains and it also favors the inclusion of targets with basal variants which makes the set more robust when identifying previously unseen strains.\nRESULTS: We analyzed VaST's performance using a number of different pathogenic species that are relevant to human disease outbreaks and biodefense. The number of targets required to achieve full resolution ranged from 20 to 88% fewer sites than what would be required in the worst case and most of the resolution is achieved within the first 20 targets. We computationally and experimentally validated one of the VaST panels and found that the targets led to accurate phylogenetic placement of strains, even when the strains were not a part of the original panel design.\nCONCLUSIONS: VaST is an open source software that, when provided a set of variant sites, can find the minimum number of sites that will provide maximum resolution of a strain complex, and it has many different run-time options that can accommodate a wide range of applications. VaST can be an effective tool in the design of strain identification panels that, when combined with TAS technologies, offer an efficient and inexpensive strain typing protocol.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1186/s12859-018-2225-z", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1023786", 
            "issn": [
              "1471-2105"
            ], 
            "name": "BMC Bioinformatics", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "19"
          }
        ], 
        "name": "Variant site strain typer (VaST): efficient strain typing using a minimal number of variant genomic sites", 
        "pagination": "222", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "458ad47cf04815962aebc0f4da9d620abbd18dd73ba6449bc311619eb95cccb2"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "29890941"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "100965194"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s12859-018-2225-z"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1104515518"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s12859-018-2225-z", 
          "https://app.dimensions.ai/details/publication/pub.1104515518"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T16:51", 
        "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/v90/0000000001_0000000264/records_8669_00000570.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1186%2Fs12859-018-2225-z"
      }
    ]
     

    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.10.1186/s12859-018-2225-z'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12859-018-2225-z'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12859-018-2225-z'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12859-018-2225-z'


     

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

    277 TRIPLES      21 PREDICATES      87 URIs      21 LITERALS      9 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s12859-018-2225-z schema:about anzsrc-for:11
    2 anzsrc-for:1108
    3 schema:author Ne07656c3f37c4ca494dbc343e52a06f0
    4 schema:citation sg:pub.10.1007/978-0-387-72124-8_3
    5 sg:pub.10.1007/s00203-011-0725-6
    6 sg:pub.10.1038/ng.705
    7 sg:pub.10.1038/nmeth.1419
    8 sg:pub.10.1038/nmeth.1923
    9 sg:pub.10.1186/1471-2105-10-176
    10 sg:pub.10.1186/1471-2105-11-461
    11 sg:pub.10.1186/1471-2105-6-38
    12 sg:pub.10.1186/1471-2180-11-139
    13 sg:pub.10.1186/1471-2180-9-21
    14 sg:pub.10.1186/1471-2180-9-71
    15 sg:pub.10.1186/s13073-014-0114-2
    16 sg:pub.10.1186/s13073-015-0176-9
    17 https://doi.org/10.1002/gepi.20095
    18 https://doi.org/10.1073/pnas.0408026101
    19 https://doi.org/10.1073/pnas.0710834105
    20 https://doi.org/10.1073/pnas.1016657108
    21 https://doi.org/10.1073/pnas.1633613100
    22 https://doi.org/10.1073/pnas.95.6.3140
    23 https://doi.org/10.1093/bioinformatics/19.2.287
    24 https://doi.org/10.1093/bioinformatics/btr330
    25 https://doi.org/10.1093/gbe/evr106
    26 https://doi.org/10.1093/nar/13.9.3021
    27 https://doi.org/10.1099/mgen.0.000074
    28 https://doi.org/10.1099/mic.0.071605-0
    29 https://doi.org/10.1111/1462-2920.12052
    30 https://doi.org/10.1128/genomea.00148-15
    31 https://doi.org/10.1128/genomea.01024-15
    32 https://doi.org/10.1128/jb.00124-06
    33 https://doi.org/10.1128/jb.01786-08
    34 https://doi.org/10.1128/jb.173.2.697-703.1991
    35 https://doi.org/10.1128/jb.184.16.4601-4611.2002
    36 https://doi.org/10.1128/jb.186.17.5808-5818.2004
    37 https://doi.org/10.1128/jcm.00046-16
    38 https://doi.org/10.1128/jcm.01233-06
    39 https://doi.org/10.1128/jcm.02671-12
    40 https://doi.org/10.1128/jcm.40.10.3671-3680.2002
    41 https://doi.org/10.1128/jcm.42.12.5644-5649.2004
    42 https://doi.org/10.1128/jcm.43.9.4382-4390.2005
    43 https://doi.org/10.1128/mbio.01501-16
    44 https://doi.org/10.1146/annurev-food-041715-033259
    45 https://doi.org/10.1371/journal.pntd.0001319
    46 https://doi.org/10.1371/journal.pntd.0001954
    47 https://doi.org/10.1371/journal.pone.0000461
    48 https://doi.org/10.1371/journal.pone.0008360
    49 https://doi.org/10.1371/journal.pone.0026201
    50 https://doi.org/10.1371/journal.pone.0031604
    51 https://doi.org/10.1371/journal.pone.0039630
    52 https://doi.org/10.1371/journal.pone.0066567
    53 https://doi.org/10.1371/journal.pone.0085417
    54 https://doi.org/10.1371/journal.pone.0131967
    55 https://doi.org/10.2144/000112815
    56 https://doi.org/10.3201/eid1404.070984
    57 https://doi.org/10.3201/eid1802.111305
    58 https://doi.org/10.3201/eid2005.131559
    59 https://doi.org/10.3389/fmicb.2016.01599
    60 https://doi.org/10.3389/fmicb.2018.00551
    61 https://doi.org/10.4172/2157-2526.s3-007
    62 schema:datePublished 2018-12
    63 schema:datePublishedReg 2018-12-01
    64 schema:description BACKGROUND: Targeted PCR amplicon sequencing (TAS) techniques provide a sensitive, scalable, and cost-effective way to query and identify closely related bacterial species and strains. Typically, this is accomplished by targeting housekeeping genes that provide resolution down to the family, genera, and sometimes species level. Unfortunately, this level of resolution is not sufficient in many applications where strain-level identification of bacteria is required (biodefense, forensics, clinical diagnostics, and outbreak investigations). Adding more genomic targets will increase the resolution, but the challenge is identifying the appropriate targets. VaST was developed to address this challenge by finding the minimum number of targets that, in combination, achieve maximum strain-level resolution for any strain complex. The final combination of target regions identified by the algorithm produce a unique haplotype for each strain which can be used as a fingerprint for identifying unknown samples in a TAS assay. VaST ensures that the targets have conserved primer regions so that the targets can be amplified in all of the known strains and it also favors the inclusion of targets with basal variants which makes the set more robust when identifying previously unseen strains. RESULTS: We analyzed VaST's performance using a number of different pathogenic species that are relevant to human disease outbreaks and biodefense. The number of targets required to achieve full resolution ranged from 20 to 88% fewer sites than what would be required in the worst case and most of the resolution is achieved within the first 20 targets. We computationally and experimentally validated one of the VaST panels and found that the targets led to accurate phylogenetic placement of strains, even when the strains were not a part of the original panel design. CONCLUSIONS: VaST is an open source software that, when provided a set of variant sites, can find the minimum number of sites that will provide maximum resolution of a strain complex, and it has many different run-time options that can accommodate a wide range of applications. VaST can be an effective tool in the design of strain identification panels that, when combined with TAS technologies, offer an efficient and inexpensive strain typing protocol.
    65 schema:genre research_article
    66 schema:inLanguage en
    67 schema:isAccessibleForFree true
    68 schema:isPartOf N54884299ee314cef80ca8bda42b66cb7
    69 Ne19a5166989d406999621f83f080d1d5
    70 sg:journal.1023786
    71 schema:name Variant site strain typer (VaST): efficient strain typing using a minimal number of variant genomic sites
    72 schema:pagination 222
    73 schema:productId N494a14ec9f084bfc9e199b555945a1c3
    74 N5d12c6332106402bb9c30316a96c52d2
    75 N6394c1918be444379b25cfe7927a9362
    76 N6dd155c426bf4376b83bf5947a9c1ea4
    77 N8fae1b39a08641faa37fab97acffd6b7
    78 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104515518
    79 https://doi.org/10.1186/s12859-018-2225-z
    80 schema:sdDatePublished 2019-04-10T16:51
    81 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    82 schema:sdPublisher Nb65ba83e407d4e32a16f23456f0e7000
    83 schema:url https://link.springer.com/10.1186%2Fs12859-018-2225-z
    84 sgo:license sg:explorer/license/
    85 sgo:sdDataset articles
    86 rdf:type schema:ScholarlyArticle
    87 N00ea11b145c64da8aee20921539bab87 rdf:first sg:person.01171532274.28
    88 rdf:rest rdf:nil
    89 N494a14ec9f084bfc9e199b555945a1c3 schema:name pubmed_id
    90 schema:value 29890941
    91 rdf:type schema:PropertyValue
    92 N51a23396745f431fb38160931d263a1f rdf:first sg:person.0636364415.27
    93 rdf:rest N00ea11b145c64da8aee20921539bab87
    94 N54884299ee314cef80ca8bda42b66cb7 schema:volumeNumber 19
    95 rdf:type schema:PublicationVolume
    96 N5d12c6332106402bb9c30316a96c52d2 schema:name doi
    97 schema:value 10.1186/s12859-018-2225-z
    98 rdf:type schema:PropertyValue
    99 N6394c1918be444379b25cfe7927a9362 schema:name readcube_id
    100 schema:value 458ad47cf04815962aebc0f4da9d620abbd18dd73ba6449bc311619eb95cccb2
    101 rdf:type schema:PropertyValue
    102 N6dd155c426bf4376b83bf5947a9c1ea4 schema:name dimensions_id
    103 schema:value pub.1104515518
    104 rdf:type schema:PropertyValue
    105 N8fae1b39a08641faa37fab97acffd6b7 schema:name nlm_unique_id
    106 schema:value 100965194
    107 rdf:type schema:PropertyValue
    108 Nb65ba83e407d4e32a16f23456f0e7000 schema:name Springer Nature - SN SciGraph project
    109 rdf:type schema:Organization
    110 Nc9a64c7df8a341549932cae9f94de486 rdf:first sg:person.01226673233.50
    111 rdf:rest N51a23396745f431fb38160931d263a1f
    112 Ne07656c3f37c4ca494dbc343e52a06f0 rdf:first sg:person.0750266526.51
    113 rdf:rest Nc9a64c7df8a341549932cae9f94de486
    114 Ne19a5166989d406999621f83f080d1d5 schema:issueNumber 1
    115 rdf:type schema:PublicationIssue
    116 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    117 schema:name Medical and Health Sciences
    118 rdf:type schema:DefinedTerm
    119 anzsrc-for:1108 schema:inDefinedTermSet anzsrc-for:
    120 schema:name Medical Microbiology
    121 rdf:type schema:DefinedTerm
    122 sg:journal.1023786 schema:issn 1471-2105
    123 schema:name BMC Bioinformatics
    124 rdf:type schema:Periodical
    125 sg:person.01171532274.28 schema:affiliation https://www.grid.ac/institutes/grid.261120.6
    126 schema:familyName Fofanov
    127 schema:givenName Viacheslav Y.
    128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01171532274.28
    129 rdf:type schema:Person
    130 sg:person.01226673233.50 schema:affiliation https://www.grid.ac/institutes/grid.261120.6
    131 schema:familyName Cocking
    132 schema:givenName Jill H.
    133 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01226673233.50
    134 rdf:type schema:Person
    135 sg:person.0636364415.27 schema:affiliation https://www.grid.ac/institutes/grid.261120.6
    136 schema:familyName Sahl
    137 schema:givenName Jason W.
    138 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0636364415.27
    139 rdf:type schema:Person
    140 sg:person.0750266526.51 schema:affiliation https://www.grid.ac/institutes/grid.261120.6
    141 schema:familyName Furstenau
    142 schema:givenName Tara N.
    143 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0750266526.51
    144 rdf:type schema:Person
    145 sg:pub.10.1007/978-0-387-72124-8_3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010860589
    146 https://doi.org/10.1007/978-0-387-72124-8_3
    147 rdf:type schema:CreativeWork
    148 sg:pub.10.1007/s00203-011-0725-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046599734
    149 https://doi.org/10.1007/s00203-011-0725-6
    150 rdf:type schema:CreativeWork
    151 sg:pub.10.1038/ng.705 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015236659
    152 https://doi.org/10.1038/ng.705
    153 rdf:type schema:CreativeWork
    154 sg:pub.10.1038/nmeth.1419 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042265948
    155 https://doi.org/10.1038/nmeth.1419
    156 rdf:type schema:CreativeWork
    157 sg:pub.10.1038/nmeth.1923 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006541515
    158 https://doi.org/10.1038/nmeth.1923
    159 rdf:type schema:CreativeWork
    160 sg:pub.10.1186/1471-2105-10-176 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006635084
    161 https://doi.org/10.1186/1471-2105-10-176
    162 rdf:type schema:CreativeWork
    163 sg:pub.10.1186/1471-2105-11-461 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031672096
    164 https://doi.org/10.1186/1471-2105-11-461
    165 rdf:type schema:CreativeWork
    166 sg:pub.10.1186/1471-2105-6-38 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018010862
    167 https://doi.org/10.1186/1471-2105-6-38
    168 rdf:type schema:CreativeWork
    169 sg:pub.10.1186/1471-2180-11-139 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010309847
    170 https://doi.org/10.1186/1471-2180-11-139
    171 rdf:type schema:CreativeWork
    172 sg:pub.10.1186/1471-2180-9-21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004377564
    173 https://doi.org/10.1186/1471-2180-9-21
    174 rdf:type schema:CreativeWork
    175 sg:pub.10.1186/1471-2180-9-71 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042741495
    176 https://doi.org/10.1186/1471-2180-9-71
    177 rdf:type schema:CreativeWork
    178 sg:pub.10.1186/s13073-014-0114-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009583237
    179 https://doi.org/10.1186/s13073-014-0114-2
    180 rdf:type schema:CreativeWork
    181 sg:pub.10.1186/s13073-015-0176-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032716746
    182 https://doi.org/10.1186/s13073-015-0176-9
    183 rdf:type schema:CreativeWork
    184 https://doi.org/10.1002/gepi.20095 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029989293
    185 rdf:type schema:CreativeWork
    186 https://doi.org/10.1073/pnas.0408026101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042383197
    187 rdf:type schema:CreativeWork
    188 https://doi.org/10.1073/pnas.0710834105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051587751
    189 rdf:type schema:CreativeWork
    190 https://doi.org/10.1073/pnas.1016657108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014872808
    191 rdf:type schema:CreativeWork
    192 https://doi.org/10.1073/pnas.1633613100 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029593215
    193 rdf:type schema:CreativeWork
    194 https://doi.org/10.1073/pnas.95.6.3140 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053351767
    195 rdf:type schema:CreativeWork
    196 https://doi.org/10.1093/bioinformatics/19.2.287 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005611482
    197 rdf:type schema:CreativeWork
    198 https://doi.org/10.1093/bioinformatics/btr330 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018404011
    199 rdf:type schema:CreativeWork
    200 https://doi.org/10.1093/gbe/evr106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010093653
    201 rdf:type schema:CreativeWork
    202 https://doi.org/10.1093/nar/13.9.3021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004878828
    203 rdf:type schema:CreativeWork
    204 https://doi.org/10.1099/mgen.0.000074 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060393884
    205 rdf:type schema:CreativeWork
    206 https://doi.org/10.1099/mic.0.071605-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013440171
    207 rdf:type schema:CreativeWork
    208 https://doi.org/10.1111/1462-2920.12052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033308606
    209 rdf:type schema:CreativeWork
    210 https://doi.org/10.1128/genomea.00148-15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043679260
    211 rdf:type schema:CreativeWork
    212 https://doi.org/10.1128/genomea.01024-15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062714261
    213 rdf:type schema:CreativeWork
    214 https://doi.org/10.1128/jb.00124-06 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009493125
    215 rdf:type schema:CreativeWork
    216 https://doi.org/10.1128/jb.01786-08 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036973755
    217 rdf:type schema:CreativeWork
    218 https://doi.org/10.1128/jb.173.2.697-703.1991 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062719841
    219 rdf:type schema:CreativeWork
    220 https://doi.org/10.1128/jb.184.16.4601-4611.2002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048537777
    221 rdf:type schema:CreativeWork
    222 https://doi.org/10.1128/jb.186.17.5808-5818.2004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052697628
    223 rdf:type schema:CreativeWork
    224 https://doi.org/10.1128/jcm.00046-16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044114099
    225 rdf:type schema:CreativeWork
    226 https://doi.org/10.1128/jcm.01233-06 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020815978
    227 rdf:type schema:CreativeWork
    228 https://doi.org/10.1128/jcm.02671-12 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004232697
    229 rdf:type schema:CreativeWork
    230 https://doi.org/10.1128/jcm.40.10.3671-3680.2002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021802519
    231 rdf:type schema:CreativeWork
    232 https://doi.org/10.1128/jcm.42.12.5644-5649.2004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018151590
    233 rdf:type schema:CreativeWork
    234 https://doi.org/10.1128/jcm.43.9.4382-4390.2005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031418744
    235 rdf:type schema:CreativeWork
    236 https://doi.org/10.1128/mbio.01501-16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062727430
    237 rdf:type schema:CreativeWork
    238 https://doi.org/10.1146/annurev-food-041715-033259 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045186257
    239 rdf:type schema:CreativeWork
    240 https://doi.org/10.1371/journal.pntd.0001319 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042419592
    241 rdf:type schema:CreativeWork
    242 https://doi.org/10.1371/journal.pntd.0001954 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028512061
    243 rdf:type schema:CreativeWork
    244 https://doi.org/10.1371/journal.pone.0000461 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005915272
    245 rdf:type schema:CreativeWork
    246 https://doi.org/10.1371/journal.pone.0008360 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047268220
    247 rdf:type schema:CreativeWork
    248 https://doi.org/10.1371/journal.pone.0026201 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040344890
    249 rdf:type schema:CreativeWork
    250 https://doi.org/10.1371/journal.pone.0031604 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012674582
    251 rdf:type schema:CreativeWork
    252 https://doi.org/10.1371/journal.pone.0039630 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010357894
    253 rdf:type schema:CreativeWork
    254 https://doi.org/10.1371/journal.pone.0066567 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003534437
    255 rdf:type schema:CreativeWork
    256 https://doi.org/10.1371/journal.pone.0085417 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016800940
    257 rdf:type schema:CreativeWork
    258 https://doi.org/10.1371/journal.pone.0131967 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021665039
    259 rdf:type schema:CreativeWork
    260 https://doi.org/10.2144/000112815 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069095813
    261 rdf:type schema:CreativeWork
    262 https://doi.org/10.3201/eid1404.070984 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032215404
    263 rdf:type schema:CreativeWork
    264 https://doi.org/10.3201/eid1802.111305 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003550984
    265 rdf:type schema:CreativeWork
    266 https://doi.org/10.3201/eid2005.131559 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028488024
    267 rdf:type schema:CreativeWork
    268 https://doi.org/10.3389/fmicb.2016.01599 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013637066
    269 rdf:type schema:CreativeWork
    270 https://doi.org/10.3389/fmicb.2018.00551 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101701460
    271 rdf:type schema:CreativeWork
    272 https://doi.org/10.4172/2157-2526.s3-007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072329858
    273 rdf:type schema:CreativeWork
    274 https://www.grid.ac/institutes/grid.261120.6 schema:alternateName Northern Arizona University
    275 schema:name Pathogen and Microbiome Institute, Northern Arizona University, 1395 S Knoles Dr., 86001, Flagstaff, Arizona, USA
    276 The School of Informatics, Computing, and Cyber Systems, Northern Arizona University, 1295 S Knoles Dr., 86001, Flagstaff, Arizona, USA
    277 rdf:type schema:Organization
     




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


    ...