Genome-wide DNA methylation profiles in Tibetan and Yorkshire pigs under high-altitude hypoxia View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Bo Zhang, Dongmei Ban, Xiao Gou, Yawen Zhang, Lin Yang, Yangzom Chamba, Hao Zhang

ABSTRACT

Background: Tibetan pigs, which inhabit the Tibetan Plateau, exhibit distinct phenotypic and physiological characteristics from those of lowland pigs and have adapted well to the extreme conditions at high altitude. However, the genetic and epigenetic mechanisms of hypoxic adaptation in animals remain unclear. Methods: Whole-genome DNA methylation data were generated for heart tissues of Tibetan pigs grown in the highland (TH, n = 4) and lowland (TL, n = 4), as well as Yorkshire pigs grown in the highland (YH, n = 4) and lowland (YL, n = 4), using methylated DNA immunoprecipitation sequencing. Results: We obtained 480 million reads and detected 280679, 287224, 259066, and 332078 methylation enrichment peaks in TH, YH, TL, and YL, respectively. Pairwise TH vs. YH, TL vs. YL, TH vs. TL, and YH vs. YL comparisons revealed 6829, 11997, 2828, and 1286 differentially methylated regions (DMRs), respectively. These DMRs contained 384, 619, 192, and 92 differentially methylated genes (DMGs), respectively. DMGs that were enriched in the hypoxia-inducible factor 1 signaling pathway and pathways involved in cancer and hypoxia-related processes were considered to be important candidate genes for high-altitude adaptation in Tibetan pigs. Conclusions: This study elucidates the molecular and epigenetic mechanisms involved in hypoxic adaptation in pigs and may help further understand human hypoxia-related diseases. More... »

PAGES

25

References to SciGraph publications

  • 2014-12. Population history and genomic signatures for high-altitude adaptation in Tibetan pigs in BMC GENOMICS
  • 2015-12. Flotillin-2 promotes metastasis of nasopharyngeal carcinoma by activating NF-κB and PI3K/Akt3 signaling pathways in SCIENTIFIC REPORTS
  • 2008-11. Model-based Analysis of ChIP-Seq (MACS) in GENOME BIOLOGY
  • 2008-06. DNA methylation landscapes: provocative insights from epigenomics in NATURE REVIEWS GENETICS
  • 2009-01. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources in NATURE PROTOCOLS
  • 2007-05-24. Phenotypic plasticity and the epigenetics of human disease in NATURE
  • 2013-12. Genomic analyses identify distinct patterns of selection in domesticated pigs and Tibetan wild boars in NATURE GENETICS
  • 2008-07. A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis in NATURE BIOTECHNOLOGY
  • 2007-05-24. Perceptions of epigenetics in NATURE
  • 2010-12. Genome-wide analysis of aberrant methylation in human breast cancer cells using methyl-DNA immunoprecipitation combined with high-throughput sequencing in BMC GENOMICS
  • 2008-12. Insulin and insulin-like growth factor signalling in neoplasia in NATURE REVIEWS CANCER
  • 2002-08. DNA methylation and gene silencing in cancer: which is the guilty party? in ONCOGENE
  • 2017-12. Comparative transcriptomic and proteomic analyses provide insights into the key genes involved in high-altitude adaptation in the Tibetan pig in SCIENTIFIC REPORTS
  • 2002-01. Hypoxia — a key regulatory factor in tumour growth in NATURE REVIEWS CANCER
  • 2012-02. Epigenetics and the environment: emerging patterns and implications in NATURE REVIEWS GENETICS
  • 2016-09. Tumour hypoxia causes DNA hypermethylation by reducing TET activity in NATURE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s40104-019-0316-y

    DOI

    http://dx.doi.org/10.1186/s40104-019-0316-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112474397

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30867905


    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": "China Agricultural University", 
              "id": "https://www.grid.ac/institutes/grid.22935.3f", 
              "name": [
                "National Engineering Laboratory for Animal Breeding, Beijing Key Laboratory for Animal Genetic Improvement, China Agricultural University, 100193, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhang", 
            "givenName": "Bo", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "China Agricultural University", 
              "id": "https://www.grid.ac/institutes/grid.22935.3f", 
              "name": [
                "National Engineering Laboratory for Animal Breeding, Beijing Key Laboratory for Animal Genetic Improvement, China Agricultural University, 100193, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ban", 
            "givenName": "Dongmei", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Yunnan Agricultural University", 
              "id": "https://www.grid.ac/institutes/grid.410696.c", 
              "name": [
                "College of Animal Science and Technology, Yunnan Agricultural University, 650201, Kunming, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Gou", 
            "givenName": "Xiao", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "China Agricultural University", 
              "id": "https://www.grid.ac/institutes/grid.22935.3f", 
              "name": [
                "National Engineering Laboratory for Animal Breeding, Beijing Key Laboratory for Animal Genetic Improvement, China Agricultural University, 100193, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhang", 
            "givenName": "Yawen", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "China Agricultural University", 
              "id": "https://www.grid.ac/institutes/grid.22935.3f", 
              "name": [
                "National Engineering Laboratory for Animal Breeding, Beijing Key Laboratory for Animal Genetic Improvement, China Agricultural University, 100193, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Yang", 
            "givenName": "Lin", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Tibet University", 
              "id": "https://www.grid.ac/institutes/grid.440680.e", 
              "name": [
                "College of Animal Science, Tibet Agriculture and Animal Husbandry University, 860000, Linzhi, Tibet, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Chamba", 
            "givenName": "Yangzom", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "China Agricultural University", 
              "id": "https://www.grid.ac/institutes/grid.22935.3f", 
              "name": [
                "National Engineering Laboratory for Animal Breeding, Beijing Key Laboratory for Animal Genetic Improvement, China Agricultural University, 100193, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhang", 
            "givenName": "Hao", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/s0092-8674(00)81656-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000139913"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/age.12436", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002543758"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep11614", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003147911", 
              "https://doi.org/10.1038/srep11614"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep11614", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003147911", 
              "https://doi.org/10.1038/srep11614"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0168161", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003285747"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/ijc.26010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004157598"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.apsb.2015.05.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004691663"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.anireprosci.2012.08.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005298127"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.109678.110", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006200761"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/molbev/msq290", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006428402"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-11-137", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007994490", 
              "https://doi.org/10.1186/1471-2164-11-137"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1189406", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009519251"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1189406", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009519251"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1161/hypertensionaha.112.198242", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011279616"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1161/hypertensionaha.112.198242", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011279616"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.074609.107", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013642908"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.vph.2015.03.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013704235"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0143260", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016213698"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ejmech.2012.01.033", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016459436"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature05913", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017539696", 
              "https://doi.org/10.1038/nature05913"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ymeth.2010.04.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021229907"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ymeth.2010.04.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021229907"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg3142", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027336196", 
              "https://doi.org/10.1038/nrg3142"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2008-9-9-r137", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027608848", 
              "https://doi.org/10.1186/gb-2008-9-9-r137"
            ], 
            "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/10.1371/journal.pone.0077859", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028326602"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrc704", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028740665", 
              "https://doi.org/10.1038/nrc704"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrc704", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028740665", 
              "https://doi.org/10.1038/nrc704"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkn294", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028765445"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2217/epi.09.6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028775982"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1535-7163.mct-11-0294", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029071616"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1535-7163.mct-11-0294", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029071616"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.2811", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029722149", 
              "https://doi.org/10.1038/ng.2811"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1128/mcb.01121-10", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029731557"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/sj.onc.1205598", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030129251", 
              "https://doi.org/10.1038/sj.onc.1205598"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/sj.onc.1205598", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030129251", 
              "https://doi.org/10.1038/sj.onc.1205598"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1159/000366358", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030704415"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt1414", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030992136", 
              "https://doi.org/10.1038/nbt1414"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0086459", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032338015"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1190371", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032676222"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1190371", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032676222"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pgen.1003110", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033299171"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature05919", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033770262", 
              "https://doi.org/10.1038/nature05919"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.7301508", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034459629"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.18632/oncotarget.4811", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035969713"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg2341", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036150552", 
              "https://doi.org/10.1038/nrg2341"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nprot.2008.211", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039987283", 
              "https://doi.org/10.1038/nprot.2008.211"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.18632/oncotarget.4553", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040353249"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3109/03008207.2014.947369", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042866706"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/0008-5472.can-08-3153", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044412845"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-15-834", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045347720", 
              "https://doi.org/10.1186/1471-2164-15-834"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pgen.1001116", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046201570"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1161/circulationaha.106.624544", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046283244"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1161/hypertensionaha.109.148767", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047149922"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1161/hypertensionaha.109.148767", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047149922"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.1002443107", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047614495"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/ppul.22919", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047664267"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/0008-5472.can-10-3059", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048109744"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.110114.110", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048693216"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature19081", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050002188", 
              "https://doi.org/10.1038/nature19081"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrc2536", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050986935", 
              "https://doi.org/10.1038/nrc2536"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/molbev/msq277", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052857463"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ymeth.2008.09.022", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053542420"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ymeth.2008.09.022", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053542420"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.1120600109", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053621804"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1089/ham.2014.1047", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1059270606"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/abbs/gmw065", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1059352894"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2144/000112708", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1069095725"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.4161/epi.5.4.11684", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1072303012"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.4238/2015.september.28.10", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1072395099"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-017-03976-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1086000828", 
              "https://doi.org/10.1038/s41598-017-03976-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.7717/peerj.3891", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092114043"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/gigascience/gix105", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092697680"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.gene.2017.11.074", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093080940"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-12", 
        "datePublishedReg": "2019-12-01", 
        "description": "Background: Tibetan pigs, which inhabit the Tibetan Plateau, exhibit distinct phenotypic and physiological characteristics from those of lowland pigs and have adapted well to the extreme conditions at high altitude. However, the genetic and epigenetic mechanisms of hypoxic adaptation in animals remain unclear.\nMethods: Whole-genome DNA methylation data were generated for heart tissues of Tibetan pigs grown in the highland (TH, n\u2009=\u20094) and lowland (TL, n\u2009=\u20094), as well as Yorkshire pigs grown in the highland (YH, n\u2009=\u20094) and lowland (YL, n\u2009=\u20094), using methylated DNA immunoprecipitation sequencing.\nResults: We obtained 480 million reads and detected 280679, 287224, 259066, and 332078 methylation enrichment peaks in TH, YH, TL, and YL, respectively. Pairwise TH vs. YH, TL vs. YL, TH vs. TL, and YH vs. YL comparisons revealed 6829, 11997, 2828, and 1286 differentially methylated regions (DMRs), respectively. These DMRs contained 384, 619, 192, and 92 differentially methylated genes (DMGs), respectively. DMGs that were enriched in the hypoxia-inducible factor 1 signaling pathway and pathways involved in cancer and hypoxia-related processes were considered to be important candidate genes for high-altitude adaptation in Tibetan pigs.\nConclusions: This study elucidates the molecular and epigenetic mechanisms involved in hypoxic adaptation in pigs and may help further understand human hypoxia-related diseases.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1186/s40104-019-0316-y", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1046697", 
            "issn": [
              "1674-9782", 
              "2049-1891"
            ], 
            "name": "Journal of Animal Science and Biotechnology", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "10"
          }
        ], 
        "name": "Genome-wide DNA methylation profiles in Tibetan and Yorkshire pigs under high-altitude hypoxia", 
        "pagination": "25", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "d5ae75ad11b3dd2f2052562c39d5ed3211d8dd2cd1abf7e10d8a6c7f5933b7ac"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "30867905"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "101581293"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s40104-019-0316-y"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1112474397"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s40104-019-0316-y", 
          "https://app.dimensions.ai/details/publication/pub.1112474397"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T13:20", 
        "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/v90/0000000368_0000000368/records_78968_00000001.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1186%2Fs40104-019-0316-y"
      }
    ]
     

    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/s40104-019-0316-y'

    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/s40104-019-0316-y'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s40104-019-0316-y'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s40104-019-0316-y'


     

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

    318 TRIPLES      21 PREDICATES      93 URIs      21 LITERALS      9 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s40104-019-0316-y schema:about anzsrc-for:06
    2 anzsrc-for:0604
    3 schema:author N95f5976e7c614a9a99cd731d12d66759
    4 schema:citation sg:pub.10.1038/nature05913
    5 sg:pub.10.1038/nature05919
    6 sg:pub.10.1038/nature19081
    7 sg:pub.10.1038/nbt1414
    8 sg:pub.10.1038/ng.2811
    9 sg:pub.10.1038/nprot.2008.211
    10 sg:pub.10.1038/nrc2536
    11 sg:pub.10.1038/nrc704
    12 sg:pub.10.1038/nrg2341
    13 sg:pub.10.1038/nrg3142
    14 sg:pub.10.1038/s41598-017-03976-3
    15 sg:pub.10.1038/sj.onc.1205598
    16 sg:pub.10.1038/srep11614
    17 sg:pub.10.1186/1471-2164-11-137
    18 sg:pub.10.1186/1471-2164-15-834
    19 sg:pub.10.1186/gb-2008-9-9-r137
    20 https://doi.org/10.1002/ijc.26010
    21 https://doi.org/10.1002/ppul.22919
    22 https://doi.org/10.1006/meth.2001.1262
    23 https://doi.org/10.1016/j.anireprosci.2012.08.010
    24 https://doi.org/10.1016/j.apsb.2015.05.007
    25 https://doi.org/10.1016/j.ejmech.2012.01.033
    26 https://doi.org/10.1016/j.gene.2017.11.074
    27 https://doi.org/10.1016/j.vph.2015.03.003
    28 https://doi.org/10.1016/j.ymeth.2008.09.022
    29 https://doi.org/10.1016/j.ymeth.2010.04.009
    30 https://doi.org/10.1016/s0092-8674(00)81656-6
    31 https://doi.org/10.1073/pnas.1002443107
    32 https://doi.org/10.1073/pnas.1120600109
    33 https://doi.org/10.1089/ham.2014.1047
    34 https://doi.org/10.1093/abbs/gmw065
    35 https://doi.org/10.1093/gigascience/gix105
    36 https://doi.org/10.1093/molbev/msq277
    37 https://doi.org/10.1093/molbev/msq290
    38 https://doi.org/10.1093/nar/gkn294
    39 https://doi.org/10.1101/gr.074609.107
    40 https://doi.org/10.1101/gr.109678.110
    41 https://doi.org/10.1101/gr.110114.110
    42 https://doi.org/10.1101/gr.7301508
    43 https://doi.org/10.1111/age.12436
    44 https://doi.org/10.1126/science.1189406
    45 https://doi.org/10.1126/science.1190371
    46 https://doi.org/10.1128/mcb.01121-10
    47 https://doi.org/10.1158/0008-5472.can-08-3153
    48 https://doi.org/10.1158/0008-5472.can-10-3059
    49 https://doi.org/10.1158/1535-7163.mct-11-0294
    50 https://doi.org/10.1159/000366358
    51 https://doi.org/10.1161/circulationaha.106.624544
    52 https://doi.org/10.1161/hypertensionaha.109.148767
    53 https://doi.org/10.1161/hypertensionaha.112.198242
    54 https://doi.org/10.1371/journal.pgen.1001116
    55 https://doi.org/10.1371/journal.pgen.1003110
    56 https://doi.org/10.1371/journal.pone.0077859
    57 https://doi.org/10.1371/journal.pone.0086459
    58 https://doi.org/10.1371/journal.pone.0143260
    59 https://doi.org/10.1371/journal.pone.0168161
    60 https://doi.org/10.18632/oncotarget.4553
    61 https://doi.org/10.18632/oncotarget.4811
    62 https://doi.org/10.2144/000112708
    63 https://doi.org/10.2217/epi.09.6
    64 https://doi.org/10.3109/03008207.2014.947369
    65 https://doi.org/10.4161/epi.5.4.11684
    66 https://doi.org/10.4238/2015.september.28.10
    67 https://doi.org/10.7717/peerj.3891
    68 schema:datePublished 2019-12
    69 schema:datePublishedReg 2019-12-01
    70 schema:description Background: Tibetan pigs, which inhabit the Tibetan Plateau, exhibit distinct phenotypic and physiological characteristics from those of lowland pigs and have adapted well to the extreme conditions at high altitude. However, the genetic and epigenetic mechanisms of hypoxic adaptation in animals remain unclear. Methods: Whole-genome DNA methylation data were generated for heart tissues of Tibetan pigs grown in the highland (TH, n = 4) and lowland (TL, n = 4), as well as Yorkshire pigs grown in the highland (YH, n = 4) and lowland (YL, n = 4), using methylated DNA immunoprecipitation sequencing. Results: We obtained 480 million reads and detected 280679, 287224, 259066, and 332078 methylation enrichment peaks in TH, YH, TL, and YL, respectively. Pairwise TH vs. YH, TL vs. YL, TH vs. TL, and YH vs. YL comparisons revealed 6829, 11997, 2828, and 1286 differentially methylated regions (DMRs), respectively. These DMRs contained 384, 619, 192, and 92 differentially methylated genes (DMGs), respectively. DMGs that were enriched in the hypoxia-inducible factor 1 signaling pathway and pathways involved in cancer and hypoxia-related processes were considered to be important candidate genes for high-altitude adaptation in Tibetan pigs. Conclusions: This study elucidates the molecular and epigenetic mechanisms involved in hypoxic adaptation in pigs and may help further understand human hypoxia-related diseases.
    71 schema:genre research_article
    72 schema:inLanguage en
    73 schema:isAccessibleForFree true
    74 schema:isPartOf N9a2a063639dc4104ae99bfac5da00f89
    75 Ncb5031d9840147fe81ffe47070c28289
    76 sg:journal.1046697
    77 schema:name Genome-wide DNA methylation profiles in Tibetan and Yorkshire pigs under high-altitude hypoxia
    78 schema:pagination 25
    79 schema:productId N0331e5d2f9bf4433aee8f46b13d85d72
    80 N61eee1b84ef7475e8477eb55bf3d9db7
    81 Ncaaad25867c44027944fae368da2908c
    82 Ned241e5dfbbb4557bbdb313658e6a7cf
    83 Nfeeb091419bd477280cfd84f7da3a6db
    84 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112474397
    85 https://doi.org/10.1186/s40104-019-0316-y
    86 schema:sdDatePublished 2019-04-11T13:20
    87 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    88 schema:sdPublisher Nb7d2885693594501ae72fc6f58deae5d
    89 schema:url https://link.springer.com/10.1186%2Fs40104-019-0316-y
    90 sgo:license sg:explorer/license/
    91 sgo:sdDataset articles
    92 rdf:type schema:ScholarlyArticle
    93 N0331e5d2f9bf4433aee8f46b13d85d72 schema:name nlm_unique_id
    94 schema:value 101581293
    95 rdf:type schema:PropertyValue
    96 N0b45b8f101714f519b59a23ba0a87ff2 rdf:first N7f29f5b8142148ba9faaaae77bdb36ad
    97 rdf:rest rdf:nil
    98 N2844cfadc53d41b1b5227e9b79bb71b0 schema:affiliation https://www.grid.ac/institutes/grid.22935.3f
    99 schema:familyName Ban
    100 schema:givenName Dongmei
    101 rdf:type schema:Person
    102 N508eb9c6fb3248f384363eeac3142158 schema:affiliation https://www.grid.ac/institutes/grid.22935.3f
    103 schema:familyName Yang
    104 schema:givenName Lin
    105 rdf:type schema:Person
    106 N5767beaf41794808a93684cab6f8a56b schema:affiliation https://www.grid.ac/institutes/grid.410696.c
    107 schema:familyName Gou
    108 schema:givenName Xiao
    109 rdf:type schema:Person
    110 N5cda3877763a4094a7efabb1fe626dd0 schema:affiliation https://www.grid.ac/institutes/grid.22935.3f
    111 schema:familyName Zhang
    112 schema:givenName Bo
    113 rdf:type schema:Person
    114 N61eee1b84ef7475e8477eb55bf3d9db7 schema:name dimensions_id
    115 schema:value pub.1112474397
    116 rdf:type schema:PropertyValue
    117 N6ff8373b511f4866aaf505d07487fd89 rdf:first N5767beaf41794808a93684cab6f8a56b
    118 rdf:rest Nffad941e53434665bb8b059aa67a8fd5
    119 N7f29f5b8142148ba9faaaae77bdb36ad schema:affiliation https://www.grid.ac/institutes/grid.22935.3f
    120 schema:familyName Zhang
    121 schema:givenName Hao
    122 rdf:type schema:Person
    123 N9256180a1d264c11ac6e9dd71525f434 schema:affiliation https://www.grid.ac/institutes/grid.22935.3f
    124 schema:familyName Zhang
    125 schema:givenName Yawen
    126 rdf:type schema:Person
    127 N95f5976e7c614a9a99cd731d12d66759 rdf:first N5cda3877763a4094a7efabb1fe626dd0
    128 rdf:rest Neac4089a247b44b79fef634dab5ef2bc
    129 N9a2a063639dc4104ae99bfac5da00f89 schema:volumeNumber 10
    130 rdf:type schema:PublicationVolume
    131 Na24a2db739044ac094e7d0d0de696e0c schema:affiliation https://www.grid.ac/institutes/grid.440680.e
    132 schema:familyName Chamba
    133 schema:givenName Yangzom
    134 rdf:type schema:Person
    135 Nb7d2885693594501ae72fc6f58deae5d schema:name Springer Nature - SN SciGraph project
    136 rdf:type schema:Organization
    137 Ncaaad25867c44027944fae368da2908c schema:name pubmed_id
    138 schema:value 30867905
    139 rdf:type schema:PropertyValue
    140 Ncb5031d9840147fe81ffe47070c28289 schema:issueNumber 1
    141 rdf:type schema:PublicationIssue
    142 Nd1b23e2f512c4bf4acc39499c981a622 rdf:first Na24a2db739044ac094e7d0d0de696e0c
    143 rdf:rest N0b45b8f101714f519b59a23ba0a87ff2
    144 Neac4089a247b44b79fef634dab5ef2bc rdf:first N2844cfadc53d41b1b5227e9b79bb71b0
    145 rdf:rest N6ff8373b511f4866aaf505d07487fd89
    146 Ned241e5dfbbb4557bbdb313658e6a7cf schema:name readcube_id
    147 schema:value d5ae75ad11b3dd2f2052562c39d5ed3211d8dd2cd1abf7e10d8a6c7f5933b7ac
    148 rdf:type schema:PropertyValue
    149 Nfea1c9228a004b8bafea8e782e25af8f rdf:first N508eb9c6fb3248f384363eeac3142158
    150 rdf:rest Nd1b23e2f512c4bf4acc39499c981a622
    151 Nfeeb091419bd477280cfd84f7da3a6db schema:name doi
    152 schema:value 10.1186/s40104-019-0316-y
    153 rdf:type schema:PropertyValue
    154 Nffad941e53434665bb8b059aa67a8fd5 rdf:first N9256180a1d264c11ac6e9dd71525f434
    155 rdf:rest Nfea1c9228a004b8bafea8e782e25af8f
    156 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    157 schema:name Biological Sciences
    158 rdf:type schema:DefinedTerm
    159 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    160 schema:name Genetics
    161 rdf:type schema:DefinedTerm
    162 sg:journal.1046697 schema:issn 1674-9782
    163 2049-1891
    164 schema:name Journal of Animal Science and Biotechnology
    165 rdf:type schema:Periodical
    166 sg:pub.10.1038/nature05913 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017539696
    167 https://doi.org/10.1038/nature05913
    168 rdf:type schema:CreativeWork
    169 sg:pub.10.1038/nature05919 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033770262
    170 https://doi.org/10.1038/nature05919
    171 rdf:type schema:CreativeWork
    172 sg:pub.10.1038/nature19081 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050002188
    173 https://doi.org/10.1038/nature19081
    174 rdf:type schema:CreativeWork
    175 sg:pub.10.1038/nbt1414 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030992136
    176 https://doi.org/10.1038/nbt1414
    177 rdf:type schema:CreativeWork
    178 sg:pub.10.1038/ng.2811 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029722149
    179 https://doi.org/10.1038/ng.2811
    180 rdf:type schema:CreativeWork
    181 sg:pub.10.1038/nprot.2008.211 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039987283
    182 https://doi.org/10.1038/nprot.2008.211
    183 rdf:type schema:CreativeWork
    184 sg:pub.10.1038/nrc2536 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050986935
    185 https://doi.org/10.1038/nrc2536
    186 rdf:type schema:CreativeWork
    187 sg:pub.10.1038/nrc704 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028740665
    188 https://doi.org/10.1038/nrc704
    189 rdf:type schema:CreativeWork
    190 sg:pub.10.1038/nrg2341 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036150552
    191 https://doi.org/10.1038/nrg2341
    192 rdf:type schema:CreativeWork
    193 sg:pub.10.1038/nrg3142 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027336196
    194 https://doi.org/10.1038/nrg3142
    195 rdf:type schema:CreativeWork
    196 sg:pub.10.1038/s41598-017-03976-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086000828
    197 https://doi.org/10.1038/s41598-017-03976-3
    198 rdf:type schema:CreativeWork
    199 sg:pub.10.1038/sj.onc.1205598 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030129251
    200 https://doi.org/10.1038/sj.onc.1205598
    201 rdf:type schema:CreativeWork
    202 sg:pub.10.1038/srep11614 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003147911
    203 https://doi.org/10.1038/srep11614
    204 rdf:type schema:CreativeWork
    205 sg:pub.10.1186/1471-2164-11-137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007994490
    206 https://doi.org/10.1186/1471-2164-11-137
    207 rdf:type schema:CreativeWork
    208 sg:pub.10.1186/1471-2164-15-834 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045347720
    209 https://doi.org/10.1186/1471-2164-15-834
    210 rdf:type schema:CreativeWork
    211 sg:pub.10.1186/gb-2008-9-9-r137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027608848
    212 https://doi.org/10.1186/gb-2008-9-9-r137
    213 rdf:type schema:CreativeWork
    214 https://doi.org/10.1002/ijc.26010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004157598
    215 rdf:type schema:CreativeWork
    216 https://doi.org/10.1002/ppul.22919 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047664267
    217 rdf:type schema:CreativeWork
    218 https://doi.org/10.1006/meth.2001.1262 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027621591
    219 rdf:type schema:CreativeWork
    220 https://doi.org/10.1016/j.anireprosci.2012.08.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005298127
    221 rdf:type schema:CreativeWork
    222 https://doi.org/10.1016/j.apsb.2015.05.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004691663
    223 rdf:type schema:CreativeWork
    224 https://doi.org/10.1016/j.ejmech.2012.01.033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016459436
    225 rdf:type schema:CreativeWork
    226 https://doi.org/10.1016/j.gene.2017.11.074 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093080940
    227 rdf:type schema:CreativeWork
    228 https://doi.org/10.1016/j.vph.2015.03.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013704235
    229 rdf:type schema:CreativeWork
    230 https://doi.org/10.1016/j.ymeth.2008.09.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053542420
    231 rdf:type schema:CreativeWork
    232 https://doi.org/10.1016/j.ymeth.2010.04.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021229907
    233 rdf:type schema:CreativeWork
    234 https://doi.org/10.1016/s0092-8674(00)81656-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000139913
    235 rdf:type schema:CreativeWork
    236 https://doi.org/10.1073/pnas.1002443107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047614495
    237 rdf:type schema:CreativeWork
    238 https://doi.org/10.1073/pnas.1120600109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053621804
    239 rdf:type schema:CreativeWork
    240 https://doi.org/10.1089/ham.2014.1047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059270606
    241 rdf:type schema:CreativeWork
    242 https://doi.org/10.1093/abbs/gmw065 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059352894
    243 rdf:type schema:CreativeWork
    244 https://doi.org/10.1093/gigascience/gix105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092697680
    245 rdf:type schema:CreativeWork
    246 https://doi.org/10.1093/molbev/msq277 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052857463
    247 rdf:type schema:CreativeWork
    248 https://doi.org/10.1093/molbev/msq290 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006428402
    249 rdf:type schema:CreativeWork
    250 https://doi.org/10.1093/nar/gkn294 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028765445
    251 rdf:type schema:CreativeWork
    252 https://doi.org/10.1101/gr.074609.107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013642908
    253 rdf:type schema:CreativeWork
    254 https://doi.org/10.1101/gr.109678.110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006200761
    255 rdf:type schema:CreativeWork
    256 https://doi.org/10.1101/gr.110114.110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048693216
    257 rdf:type schema:CreativeWork
    258 https://doi.org/10.1101/gr.7301508 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034459629
    259 rdf:type schema:CreativeWork
    260 https://doi.org/10.1111/age.12436 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002543758
    261 rdf:type schema:CreativeWork
    262 https://doi.org/10.1126/science.1189406 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009519251
    263 rdf:type schema:CreativeWork
    264 https://doi.org/10.1126/science.1190371 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032676222
    265 rdf:type schema:CreativeWork
    266 https://doi.org/10.1128/mcb.01121-10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029731557
    267 rdf:type schema:CreativeWork
    268 https://doi.org/10.1158/0008-5472.can-08-3153 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044412845
    269 rdf:type schema:CreativeWork
    270 https://doi.org/10.1158/0008-5472.can-10-3059 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048109744
    271 rdf:type schema:CreativeWork
    272 https://doi.org/10.1158/1535-7163.mct-11-0294 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029071616
    273 rdf:type schema:CreativeWork
    274 https://doi.org/10.1159/000366358 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030704415
    275 rdf:type schema:CreativeWork
    276 https://doi.org/10.1161/circulationaha.106.624544 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046283244
    277 rdf:type schema:CreativeWork
    278 https://doi.org/10.1161/hypertensionaha.109.148767 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047149922
    279 rdf:type schema:CreativeWork
    280 https://doi.org/10.1161/hypertensionaha.112.198242 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011279616
    281 rdf:type schema:CreativeWork
    282 https://doi.org/10.1371/journal.pgen.1001116 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046201570
    283 rdf:type schema:CreativeWork
    284 https://doi.org/10.1371/journal.pgen.1003110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033299171
    285 rdf:type schema:CreativeWork
    286 https://doi.org/10.1371/journal.pone.0077859 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028326602
    287 rdf:type schema:CreativeWork
    288 https://doi.org/10.1371/journal.pone.0086459 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032338015
    289 rdf:type schema:CreativeWork
    290 https://doi.org/10.1371/journal.pone.0143260 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016213698
    291 rdf:type schema:CreativeWork
    292 https://doi.org/10.1371/journal.pone.0168161 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003285747
    293 rdf:type schema:CreativeWork
    294 https://doi.org/10.18632/oncotarget.4553 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040353249
    295 rdf:type schema:CreativeWork
    296 https://doi.org/10.18632/oncotarget.4811 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035969713
    297 rdf:type schema:CreativeWork
    298 https://doi.org/10.2144/000112708 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069095725
    299 rdf:type schema:CreativeWork
    300 https://doi.org/10.2217/epi.09.6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028775982
    301 rdf:type schema:CreativeWork
    302 https://doi.org/10.3109/03008207.2014.947369 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042866706
    303 rdf:type schema:CreativeWork
    304 https://doi.org/10.4161/epi.5.4.11684 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072303012
    305 rdf:type schema:CreativeWork
    306 https://doi.org/10.4238/2015.september.28.10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072395099
    307 rdf:type schema:CreativeWork
    308 https://doi.org/10.7717/peerj.3891 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092114043
    309 rdf:type schema:CreativeWork
    310 https://www.grid.ac/institutes/grid.22935.3f schema:alternateName China Agricultural University
    311 schema:name National Engineering Laboratory for Animal Breeding, Beijing Key Laboratory for Animal Genetic Improvement, China Agricultural University, 100193, Beijing, China
    312 rdf:type schema:Organization
    313 https://www.grid.ac/institutes/grid.410696.c schema:alternateName Yunnan Agricultural University
    314 schema:name College of Animal Science and Technology, Yunnan Agricultural University, 650201, Kunming, China
    315 rdf:type schema:Organization
    316 https://www.grid.ac/institutes/grid.440680.e schema:alternateName Tibet University
    317 schema:name College of Animal Science, Tibet Agriculture and Animal Husbandry University, 860000, Linzhi, Tibet, China
    318 rdf:type schema:Organization
     




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


    ...