Selection of suitable reference genes for gene expression analysis in gills and liver of fish under field pollution conditions View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Noemí Rojas-Hernandez, David Véliz, Caren Vega-Retter

ABSTRACT

To understand the role of gene expression in adaptive variation, it is necessary to examine expression variation in an ecological context. Quantitative real-time PCR (qPCR) is considered the most accurate and reliable technique to measure gene expression and to validate the data obtained by RNA-seq; however, accurate normalization is crucial. In Chile, the freshwater silverside fish Basilichthys microlepidotus inhabits both polluted and nonpolluted areas, showing differential gene expression related to pollution. In this study, we infer the stability of six potential reference genes (tubulin alpha, hypoxanthine-guanine phosphoribosyltransferase, glyceraldehyde-3-phosphate dehydrogenase, beta-actin, 60S ribosomal protein L13, and 60S ribosomal protein L8) in the gills and liver of silverside individuals inhabiting polluted and nonpolluted areas. To validate the reference genes selected, the most and least stable reference genes were used to normalize two target transcripts, one for each organ. The RefFinder tool was used to analyze and identify the most stably expressed genes. The 60S ribosomal protein L8 gene was ranked as the most stable gene for both organs. Our results show that reference gene selection influences the detection of differences in the expression levels of target genes in different organs and, also highlighting candidate reference genes that could be used in field studies. More... »

PAGES

3459

References to SciGraph publications

  • 2008-02. Demographic response of Stratiodrilus aeglaphilus (Anelida, Histriobdellidae) to organic enrichment: experimental assessment in HYDROBIOLOGIA
  • 2012-12. ReadqPCR and NormqPCR: R packages for the reading, quality checking and normalisation of RT-qPCR quantification cycle (Cq) data in BMC GENOMICS
  • 2013-11. Reference genes in real-time PCR in JOURNAL OF APPLIED GENETICS
  • 2014-10. Genetic effects of living in a highly polluted environment: the case of the silverside Basilichthys microlepidotus (Jenyns) (Teleostei: atherinopsidae) in the Maipo River basin, central Chile in POPULATION ECOLOGY
  • 2012-09. miRDeepFinder: a miRNA analysis tool for deep sequencing of plant small RNAs in PLANT MOLECULAR BIOLOGY
  • 2007-02. qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data in GENOME BIOLOGY
  • 2017-12. Heavy Metal Content in Chilean Fish Related to Habitat Use, Tissue Type and River of Origin in BULLETIN OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY
  • 2002-10. Variation in gene expression within and among natural populations in NATURE GENETICS
  • 2015-06. Signatures of Directional and Balancing Selection in the Silverside Basilichthys microlepidotus (Teleostei: Atherinopsidae) Inhabiting a Polluted River in EVOLUTIONARY BIOLOGY
  • 2018-12. Differential gene expression revealed with RNA-Seq and parallel genotype selection of the ornithine decarboxylase gene in fish inhabiting polluted areas in SCIENTIFIC REPORTS
  • 2002-06. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes in GENOME BIOLOGY
  • 2017-12. Transcriptomic responses of the endangered freshwater mussel Margaritifera margaritifera to trace metal contamination in the Dronne River, France in ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
  • 2006-06. Comparative Effects of Direct Cadmium Contamination on Gene Expression in Gills, Liver, Skeletal Muscles and Brain of the Zebrafish (Danio rerio) in BIOMETALS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-019-40196-3

    DOI

    http://dx.doi.org/10.1038/s41598-019-40196-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112544082

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30837616


    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": "University of Chile", 
              "id": "https://www.grid.ac/institutes/grid.443909.3", 
              "name": [
                "Departamento de Ciencias Ecol\u00f3gicas, Instituto de Ecolog\u00eda y Biodiversidad (IEB), Facultad de Ciencias, Universidad de Chile, Santiago, Chile"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Rojas-Hernandez", 
            "givenName": "Noem\u00ed", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Catholic University of the North", 
              "id": "https://www.grid.ac/institutes/grid.8049.5", 
              "name": [
                "Departamento de Ciencias Ecol\u00f3gicas, Instituto de Ecolog\u00eda y Biodiversidad (IEB), Facultad de Ciencias, Universidad de Chile, Santiago, Chile", 
                "N\u00facleo Milenio de Ecolog\u00eda y Manejo Sustentable de Islas Oce\u00e1nicas (ESMOI), Departamento de Biolog\u00eda Marina, Universidad Cat\u00f3lica del Norte, Coquimbo, Chile"
              ], 
              "type": "Organization"
            }, 
            "familyName": "V\u00e9liz", 
            "givenName": "David", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Chile", 
              "id": "https://www.grid.ac/institutes/grid.443909.3", 
              "name": [
                "Departamento de Ciencias Ecol\u00f3gicas, Instituto de Ecolog\u00eda y Biodiversidad (IEB), Facultad de Ciencias, Universidad de Chile, Santiago, Chile"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Vega-Retter", 
            "givenName": "Caren", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1017/s1464793105006950", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002342850"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1242/jeb.058735", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003727240"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2007-8-2-r19", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007077528", 
              "https://doi.org/10.1186/gb-2007-8-2-r19"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10144-014-0444-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009289736", 
              "https://doi.org/10.1007/s10144-014-0444-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2478/jomb-2014-0001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010517356"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0507648103", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011584103"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11103-012-9885-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019238883", 
              "https://doi.org/10.1007/s11103-012-9885-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0068737", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019868042"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11692-015-9307-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020174262", 
              "https://doi.org/10.1007/s11692-015-9307-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0141853", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021807105"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10534-005-5670-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021994581", 
              "https://doi.org/10.1007/s10534-005-5670-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10534-005-5670-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021994581", 
              "https://doi.org/10.1007/s10534-005-5670-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/9781444312249.ch12", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023519953"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/0008-5472.can-04-0496", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025731697"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/meth.2001.1262", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027621591"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/meth.2001.1262", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027621591"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1046/j.1439-0426.2002.00337.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029117476"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10750-007-9136-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030314002", 
              "https://doi.org/10.1007/s10750-007-9136-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-13-296", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033208543", 
              "https://doi.org/10.1186/1471-2164-13-296"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.tree.2012.07.014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039533272"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2002-3-7-research0034", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039751959", 
              "https://doi.org/10.1186/gb-2002-3-7-research0034"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng983", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044334187", 
              "https://doi.org/10.1038/ng983"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng983", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044334187", 
              "https://doi.org/10.1038/ng983"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1197761", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047725788"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13353-013-0173-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049413029", 
              "https://doi.org/10.1007/s13353-013-0173-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.aquatox.2015.11.029", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052395429"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2144/000112776", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1069095784"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11356-017-0294-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091992095", 
              "https://doi.org/10.1007/s11356-017-0294-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00128-017-2200-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092415911", 
              "https://doi.org/10.1007/s00128-017-2200-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1017/s1751731117003111", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093031295"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1017/s1751731117003111", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093031295"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1017/s1751731117003111", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093031295"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.envpol.2017.11.054", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099740077"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.envpol.2017.11.054", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099740077"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-018-23182-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101558215", 
              "https://doi.org/10.1038/s41598-018-23182-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-018-23182-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101558215", 
              "https://doi.org/10.1038/s41598-018-23182-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-018-23182-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101558215", 
              "https://doi.org/10.1038/s41598-018-23182-z"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-12", 
        "datePublishedReg": "2019-12-01", 
        "description": "To understand the role of gene expression in adaptive variation, it is necessary to examine expression variation in an ecological context. Quantitative real-time PCR (qPCR) is considered the most accurate and reliable technique to measure gene expression and to validate the data obtained by RNA-seq; however, accurate normalization is crucial. In Chile, the freshwater silverside fish Basilichthys microlepidotus inhabits both polluted and nonpolluted areas, showing differential gene expression related to pollution. In this study, we infer the stability of six potential reference genes (tubulin alpha, hypoxanthine-guanine phosphoribosyltransferase, glyceraldehyde-3-phosphate dehydrogenase, beta-actin, 60S ribosomal protein L13, and 60S ribosomal protein L8) in the gills and liver of silverside individuals inhabiting polluted and nonpolluted areas. To validate the reference genes selected, the most and least stable reference genes were used to normalize two target transcripts, one for each organ. The RefFinder tool was used to analyze and identify the most stably expressed genes. The 60S ribosomal protein L8 gene was ranked as the most stable gene for both organs. Our results show that reference gene selection influences the detection of differences in the expression levels of target genes in different organs and, also highlighting candidate reference genes that could be used in field studies.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1038/s41598-019-40196-3", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1045337", 
            "issn": [
              "2045-2322"
            ], 
            "name": "Scientific Reports", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "9"
          }
        ], 
        "name": "Selection of suitable reference genes for gene expression analysis in gills and liver of fish under field pollution conditions", 
        "pagination": "3459", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "ca890626a212318ca1a8f7b7617246d1190708c7d1d1845669b7ae7a9bd851ee"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "30837616"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "101563288"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/s41598-019-40196-3"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1112544082"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/s41598-019-40196-3", 
          "https://app.dimensions.ai/details/publication/pub.1112544082"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T11:20", 
        "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_11716_00000002.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://www.nature.com/articles/s41598-019-40196-3"
      }
    ]
     

    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/s41598-019-40196-3'

    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/s41598-019-40196-3'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-40196-3'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-40196-3'


     

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

    183 TRIPLES      21 PREDICATES      58 URIs      21 LITERALS      9 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/s41598-019-40196-3 schema:about anzsrc-for:06
    2 anzsrc-for:0604
    3 schema:author Nf992b041a4d34c7c8ea80b03ef8ae2a7
    4 schema:citation sg:pub.10.1007/s00128-017-2200-9
    5 sg:pub.10.1007/s10144-014-0444-3
    6 sg:pub.10.1007/s10534-005-5670-x
    7 sg:pub.10.1007/s10750-007-9136-8
    8 sg:pub.10.1007/s11103-012-9885-2
    9 sg:pub.10.1007/s11356-017-0294-6
    10 sg:pub.10.1007/s11692-015-9307-x
    11 sg:pub.10.1007/s13353-013-0173-x
    12 sg:pub.10.1038/ng983
    13 sg:pub.10.1038/s41598-018-23182-z
    14 sg:pub.10.1186/1471-2164-13-296
    15 sg:pub.10.1186/gb-2002-3-7-research0034
    16 sg:pub.10.1186/gb-2007-8-2-r19
    17 https://doi.org/10.1002/9781444312249.ch12
    18 https://doi.org/10.1006/meth.2001.1262
    19 https://doi.org/10.1016/j.aquatox.2015.11.029
    20 https://doi.org/10.1016/j.envpol.2017.11.054
    21 https://doi.org/10.1016/j.tree.2012.07.014
    22 https://doi.org/10.1017/s1464793105006950
    23 https://doi.org/10.1017/s1751731117003111
    24 https://doi.org/10.1046/j.1439-0426.2002.00337.x
    25 https://doi.org/10.1073/pnas.0507648103
    26 https://doi.org/10.1126/science.1197761
    27 https://doi.org/10.1158/0008-5472.can-04-0496
    28 https://doi.org/10.1242/jeb.058735
    29 https://doi.org/10.1371/journal.pone.0068737
    30 https://doi.org/10.1371/journal.pone.0141853
    31 https://doi.org/10.2144/000112776
    32 https://doi.org/10.2478/jomb-2014-0001
    33 schema:datePublished 2019-12
    34 schema:datePublishedReg 2019-12-01
    35 schema:description To understand the role of gene expression in adaptive variation, it is necessary to examine expression variation in an ecological context. Quantitative real-time PCR (qPCR) is considered the most accurate and reliable technique to measure gene expression and to validate the data obtained by RNA-seq; however, accurate normalization is crucial. In Chile, the freshwater silverside fish Basilichthys microlepidotus inhabits both polluted and nonpolluted areas, showing differential gene expression related to pollution. In this study, we infer the stability of six potential reference genes (tubulin alpha, hypoxanthine-guanine phosphoribosyltransferase, glyceraldehyde-3-phosphate dehydrogenase, beta-actin, 60S ribosomal protein L13, and 60S ribosomal protein L8) in the gills and liver of silverside individuals inhabiting polluted and nonpolluted areas. To validate the reference genes selected, the most and least stable reference genes were used to normalize two target transcripts, one for each organ. The RefFinder tool was used to analyze and identify the most stably expressed genes. The 60S ribosomal protein L8 gene was ranked as the most stable gene for both organs. Our results show that reference gene selection influences the detection of differences in the expression levels of target genes in different organs and, also highlighting candidate reference genes that could be used in field studies.
    36 schema:genre research_article
    37 schema:inLanguage en
    38 schema:isAccessibleForFree true
    39 schema:isPartOf N3329ded730ae4930a9ab5a16e0cbd6b8
    40 Ne068707878cb4e33a4038ec4f38504eb
    41 sg:journal.1045337
    42 schema:name Selection of suitable reference genes for gene expression analysis in gills and liver of fish under field pollution conditions
    43 schema:pagination 3459
    44 schema:productId N3f7f89b3388c4b5f86762dab2f505d84
    45 N48c3d432abfe4073a1496e632990e07d
    46 N5bafc7a9543646879da6417e58f1a218
    47 N759ec85cafe044d7aeb29e5b2467dd09
    48 Nbfcdb492efc64e6cbbfd2c44c0300322
    49 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112544082
    50 https://doi.org/10.1038/s41598-019-40196-3
    51 schema:sdDatePublished 2019-04-11T11:20
    52 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    53 schema:sdPublisher N06239d22494341c181181336c5d8d1bc
    54 schema:url https://www.nature.com/articles/s41598-019-40196-3
    55 sgo:license sg:explorer/license/
    56 sgo:sdDataset articles
    57 rdf:type schema:ScholarlyArticle
    58 N06239d22494341c181181336c5d8d1bc schema:name Springer Nature - SN SciGraph project
    59 rdf:type schema:Organization
    60 N0979bbdee8cc42c3b297bcfc8e177743 rdf:first Nc0776609b8d040e1ba8a3b00c9e82f17
    61 rdf:rest rdf:nil
    62 N3329ded730ae4930a9ab5a16e0cbd6b8 schema:issueNumber 1
    63 rdf:type schema:PublicationIssue
    64 N368e8f799da64972bf8f9f1b08bb2a3c rdf:first Nb2938c862f944f4a858b2ee69cfef1d6
    65 rdf:rest N0979bbdee8cc42c3b297bcfc8e177743
    66 N3f7f89b3388c4b5f86762dab2f505d84 schema:name readcube_id
    67 schema:value ca890626a212318ca1a8f7b7617246d1190708c7d1d1845669b7ae7a9bd851ee
    68 rdf:type schema:PropertyValue
    69 N48c3d432abfe4073a1496e632990e07d schema:name pubmed_id
    70 schema:value 30837616
    71 rdf:type schema:PropertyValue
    72 N5bafc7a9543646879da6417e58f1a218 schema:name doi
    73 schema:value 10.1038/s41598-019-40196-3
    74 rdf:type schema:PropertyValue
    75 N759ec85cafe044d7aeb29e5b2467dd09 schema:name nlm_unique_id
    76 schema:value 101563288
    77 rdf:type schema:PropertyValue
    78 Nb087bf2d1bf94874804684ab0b7e1a4b schema:affiliation https://www.grid.ac/institutes/grid.443909.3
    79 schema:familyName Rojas-Hernandez
    80 schema:givenName Noemí
    81 rdf:type schema:Person
    82 Nb2938c862f944f4a858b2ee69cfef1d6 schema:affiliation https://www.grid.ac/institutes/grid.8049.5
    83 schema:familyName Véliz
    84 schema:givenName David
    85 rdf:type schema:Person
    86 Nbfcdb492efc64e6cbbfd2c44c0300322 schema:name dimensions_id
    87 schema:value pub.1112544082
    88 rdf:type schema:PropertyValue
    89 Nc0776609b8d040e1ba8a3b00c9e82f17 schema:affiliation https://www.grid.ac/institutes/grid.443909.3
    90 schema:familyName Vega-Retter
    91 schema:givenName Caren
    92 rdf:type schema:Person
    93 Ne068707878cb4e33a4038ec4f38504eb schema:volumeNumber 9
    94 rdf:type schema:PublicationVolume
    95 Nf992b041a4d34c7c8ea80b03ef8ae2a7 rdf:first Nb087bf2d1bf94874804684ab0b7e1a4b
    96 rdf:rest N368e8f799da64972bf8f9f1b08bb2a3c
    97 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    98 schema:name Biological Sciences
    99 rdf:type schema:DefinedTerm
    100 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    101 schema:name Genetics
    102 rdf:type schema:DefinedTerm
    103 sg:journal.1045337 schema:issn 2045-2322
    104 schema:name Scientific Reports
    105 rdf:type schema:Periodical
    106 sg:pub.10.1007/s00128-017-2200-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092415911
    107 https://doi.org/10.1007/s00128-017-2200-9
    108 rdf:type schema:CreativeWork
    109 sg:pub.10.1007/s10144-014-0444-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009289736
    110 https://doi.org/10.1007/s10144-014-0444-3
    111 rdf:type schema:CreativeWork
    112 sg:pub.10.1007/s10534-005-5670-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1021994581
    113 https://doi.org/10.1007/s10534-005-5670-x
    114 rdf:type schema:CreativeWork
    115 sg:pub.10.1007/s10750-007-9136-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030314002
    116 https://doi.org/10.1007/s10750-007-9136-8
    117 rdf:type schema:CreativeWork
    118 sg:pub.10.1007/s11103-012-9885-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019238883
    119 https://doi.org/10.1007/s11103-012-9885-2
    120 rdf:type schema:CreativeWork
    121 sg:pub.10.1007/s11356-017-0294-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091992095
    122 https://doi.org/10.1007/s11356-017-0294-6
    123 rdf:type schema:CreativeWork
    124 sg:pub.10.1007/s11692-015-9307-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1020174262
    125 https://doi.org/10.1007/s11692-015-9307-x
    126 rdf:type schema:CreativeWork
    127 sg:pub.10.1007/s13353-013-0173-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1049413029
    128 https://doi.org/10.1007/s13353-013-0173-x
    129 rdf:type schema:CreativeWork
    130 sg:pub.10.1038/ng983 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044334187
    131 https://doi.org/10.1038/ng983
    132 rdf:type schema:CreativeWork
    133 sg:pub.10.1038/s41598-018-23182-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1101558215
    134 https://doi.org/10.1038/s41598-018-23182-z
    135 rdf:type schema:CreativeWork
    136 sg:pub.10.1186/1471-2164-13-296 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033208543
    137 https://doi.org/10.1186/1471-2164-13-296
    138 rdf:type schema:CreativeWork
    139 sg:pub.10.1186/gb-2002-3-7-research0034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039751959
    140 https://doi.org/10.1186/gb-2002-3-7-research0034
    141 rdf:type schema:CreativeWork
    142 sg:pub.10.1186/gb-2007-8-2-r19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007077528
    143 https://doi.org/10.1186/gb-2007-8-2-r19
    144 rdf:type schema:CreativeWork
    145 https://doi.org/10.1002/9781444312249.ch12 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023519953
    146 rdf:type schema:CreativeWork
    147 https://doi.org/10.1006/meth.2001.1262 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027621591
    148 rdf:type schema:CreativeWork
    149 https://doi.org/10.1016/j.aquatox.2015.11.029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052395429
    150 rdf:type schema:CreativeWork
    151 https://doi.org/10.1016/j.envpol.2017.11.054 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099740077
    152 rdf:type schema:CreativeWork
    153 https://doi.org/10.1016/j.tree.2012.07.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039533272
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1017/s1464793105006950 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002342850
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1017/s1751731117003111 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093031295
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1046/j.1439-0426.2002.00337.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1029117476
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1073/pnas.0507648103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011584103
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1126/science.1197761 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047725788
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.1158/0008-5472.can-04-0496 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025731697
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1242/jeb.058735 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003727240
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1371/journal.pone.0068737 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019868042
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1371/journal.pone.0141853 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021807105
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.2144/000112776 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069095784
    174 rdf:type schema:CreativeWork
    175 https://doi.org/10.2478/jomb-2014-0001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010517356
    176 rdf:type schema:CreativeWork
    177 https://www.grid.ac/institutes/grid.443909.3 schema:alternateName University of Chile
    178 schema:name Departamento de Ciencias Ecológicas, Instituto de Ecología y Biodiversidad (IEB), Facultad de Ciencias, Universidad de Chile, Santiago, Chile
    179 rdf:type schema:Organization
    180 https://www.grid.ac/institutes/grid.8049.5 schema:alternateName Catholic University of the North
    181 schema:name Departamento de Ciencias Ecológicas, Instituto de Ecología y Biodiversidad (IEB), Facultad de Ciencias, Universidad de Chile, Santiago, Chile
    182 Núcleo Milenio de Ecología y Manejo Sustentable de Islas Oceánicas (ESMOI), Departamento de Biología Marina, Universidad Católica del Norte, Coquimbo, Chile
    183 rdf:type schema:Organization
     




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


    ...