Integration of multi-omics data for prediction of phenotypic traits using random forest View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-06

AUTHORS

Animesh Acharjee, Bjorn Kloosterman, Richard G. F. Visser, Chris Maliepaard

ABSTRACT

BACKGROUND: In order to find genetic and metabolic pathways related to phenotypic traits of interest, we analyzed gene expression data, metabolite data obtained with GC-MS and LC-MS, proteomics data and a selected set of tuber quality phenotypic data from a diploid segregating mapping population of potato. In this study we present an approach to integrate these ~ omics data sets for the purpose of predicting phenotypic traits. This gives us networks of relatively small sets of interrelated ~ omics variables that can predict, with higher accuracy, a quality trait of interest. RESULTS: We used Random Forest regression for integrating multiple ~ omics data for prediction of four quality traits of potato: tuber flesh colour, DSC onset, tuber shape and enzymatic discoloration. For tuber flesh colour beta-carotene hydroxylase and zeaxanthin epoxidase were ranked first and forty-fourth respectively both of which have previously been associated with flesh colour in potato tubers. Combining all the significant genes, LC-peaks, GC-peaks and proteins, the variation explained was 75 %, only slightly more than what gene expression or LC-MS data explain by themselves which indicates that there are correlations among the variables across data sets. For tuber shape regressed on the gene expression, LC-MS, GC-MS and proteomics data sets separately, only gene expression data was found to explain significant variation. For DSC onset, we found 12 significant gene expression, 5 metabolite levels (GC) and 2 proteins that are associated with the trait. Using those 19 significant variables, the variation explained was 45 %. Expression QTL (eQTL) analyses showed many associations with genomic regions in chromosome 2 with also the highest explained variation compared to other chromosomes. Transcriptomics and metabolomics analysis on enzymatic discoloration after 5 min resulted in 420 significant genes and 8 significant LC metabolites, among which two were putatively identified as caffeoylquinic acid methyl ester and tyrosine. CONCLUSIONS: In this study, we made a strategy for selecting and integrating multiple ~ omics data using random forest method and selected representative individual peaks for networks based on eQTL, mQTL or pQTL information. Network analysis was done to interpret how a particular trait is associated with gene expression, metabolite and protein data. More... »

PAGES

180

References to SciGraph publications

  • 2003-03. Mass spectrometry-based proteomics in NATURE
  • 2000-03. Making the most of microarray data in NATURE GENETICS
  • 2011-07-10. Genome sequence and analysis of the tuber crop potato in NATURE
  • 2006-07. The genetics of plant metabolism in NATURE GENETICS
  • 2001. The Elements of Statistical Learning, Data Mining, Inference, and Prediction in NONE
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2010-12. From QTL to candidate gene: Genetical genomics of simple and complex traits in potato using a pooling strategy in BMC GENOMICS
  • 2010-08. Identification of alleles of carotenoid pathway genes important for zeaxanthin accumulation in potato tubers in PLANT MOLECULAR BIOLOGY
  • 2006-09. Segregation of total carotenoid in high level potato germplasm and its relationship to beta-carotene hydroxylase polymorphism in AMERICAN JOURNAL OF POTATO RESEARCH
  • 2012-08. MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data in METABOLOMICS
  • 1995-07. A genetic map of potato (Solanum tuberosum) integrating molecular markers, including transposons, and classical markers in THEORETICAL AND APPLIED GENETICS
  • 2002-01. Metabolomics – the link between genotypes and phenotypes in PLANT MOLECULAR BIOLOGY
  • 2007-03. MetaNetwork: a computational protocol for the genetic study of metabolic networks in NATURE PROTOCOLS
  • 2012-12. Organ specificity and transcriptional control of metabolic routes revealed by expression QTL profiling of source-sink tissues in a segregating potato population in BMC PLANT BIOLOGY
  • 2008-09. Phenotypic diversity and relationships of fruit quality traits in apricot (Prunus armeniaca L.) germplasm in EUPHYTICA
  • 2009-12. Regularized estimation of large-scale gene association networks using graphical Gaussian models in BMC BIOINFORMATICS
  • 2008-11. Genes driving potato tuber initiation and growth: identification based on transcriptional changes using the POCI array in FUNCTIONAL & INTEGRATIVE GENOMICS
  • 2007-06-26. Unravelling enzymatic discoloration in potato through a combined approach of candidate genes, QTL, and expression analysis in THEORETICAL AND APPLIED GENETICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12859-016-1043-4

    DOI

    http://dx.doi.org/10.1186/s12859-016-1043-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1025872318

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27295212


    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"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Chromatography, High Pressure Liquid", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Chromosomes, Plant", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gas Chromatography-Mass Spectrometry", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gene Expression Regulation, Plant", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genomics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Mass Spectrometry", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Metabolomics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Phenotype", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Plant Proteins", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Plant Tubers", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Proteomics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Quantitative Trait Loci", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Solanum tuberosum", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "MRC Human Nutrition Research", 
              "id": "https://www.grid.ac/institutes/grid.415055.0", 
              "name": [
                "Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands", 
                "MRC Human Nutrition Research, 120 Fulbourn Road, CB1 9NL, Cambridge, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Acharjee", 
            "givenName": "Animesh", 
            "id": "sg:person.01201366413.30", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01201366413.30"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "KeyGene (Netherlands)", 
              "id": "https://www.grid.ac/institutes/grid.425600.5", 
              "name": [
                "Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands", 
                "Keygene NV, PO Box 216, 6700 AE, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kloosterman", 
            "givenName": "Bjorn", 
            "id": "sg:person.0750643451.31", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0750643451.31"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Wageningen University & Research", 
              "id": "https://www.grid.ac/institutes/grid.4818.5", 
              "name": [
                "Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Visser", 
            "givenName": "Richard G. F.", 
            "id": "sg:person.07374343732.28", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07374343732.28"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Wageningen University & Research", 
              "id": "https://www.grid.ac/institutes/grid.4818.5", 
              "name": [
                "Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Maliepaard", 
            "givenName": "Chris", 
            "id": "sg:person.01247501613.36", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247501613.36"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/s0014-5793(00)01772-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001776486"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature10158", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002525491", 
              "https://doi.org/10.1038/nature10158"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pcbi.1003168", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007788160"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2229-12-17", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009026174", 
              "https://doi.org/10.1186/1471-2229-12-17"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature01511", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012180132", 
              "https://doi.org/10.1038/nature01511"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature01511", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012180132", 
              "https://doi.org/10.1038/nature01511"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.copbio.2010.01.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015304182"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.carbpol.2005.08.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016178610"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.aca.2011.03.050", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018135175"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1146/annurev.biochem.72.121801.161511", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018341141"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1022356842", 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-0-387-21606-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022356842", 
              "https://doi.org/10.1007/978-0-387-21606-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-0-387-21606-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022356842", 
              "https://doi.org/10.1007/978-0-387-21606-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1744-7348.2003.tb00284.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022546414"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1039/b418288j", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022837755"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ymeth.2014.06.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022904282"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ymeth.2014.06.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022904282"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11103-010-9647-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023255330", 
              "https://doi.org/10.1007/s11103-010-9647-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11103-010-9647-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023255330", 
              "https://doi.org/10.1007/s11103-010-9647-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1046/j.1365-313x.1996.9050745.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024275878"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1010933404324", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024739340", 
              "https://doi.org/10.1023/a:1010933404324"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10142-008-0083-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024797241", 
              "https://doi.org/10.1007/s10142-008-0083-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1467-7652.2010.00516.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025081307"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1467-7652.2010.00516.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025081307"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0168-9525(01)02310-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026477807"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1046/j.1365-313x.2001.00993.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026494245"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1013713905833", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027843923", 
              "https://doi.org/10.1023/a:1013713905833"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00220891", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031677694", 
              "https://doi.org/10.1007/bf00220891"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng1815", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033471111", 
              "https://doi.org/10.1038/ng1815"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng1815", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033471111", 
              "https://doi.org/10.1038/ng1815"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-007-0560-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033526515", 
              "https://doi.org/10.1007/s00122-007-0560-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-007-0560-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033526515", 
              "https://doi.org/10.1007/s00122-007-0560-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.tplants.2008.02.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034564835"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/73392", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037746706", 
              "https://doi.org/10.1038/73392"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/73392", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037746706", 
              "https://doi.org/10.1038/73392"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fpls.2014.00598", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040952317"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-011-0368-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042363301", 
              "https://doi.org/10.1007/s11306-011-0368-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nprot.2007.96", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043516227", 
              "https://doi.org/10.1038/nprot.2007.96"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-11-158", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043854389", 
              "https://doi.org/10.1186/1471-2164-11-158"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10681-007-9640-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044972039", 
              "https://doi.org/10.1007/s10681-007-9640-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1104/pp.111.188441", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046693401"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.tibtech.2008.07.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047477678"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cbpa.2009.09.022", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048967908"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/bth445", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049700263"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-10-384", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050628645", 
              "https://doi.org/10.1186/1471-2105-10-384"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-10-384", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050628645", 
              "https://doi.org/10.1186/1471-2105-10-384"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02872013", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050778389", 
              "https://doi.org/10.1007/bf02872013"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02872013", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050778389", 
              "https://doi.org/10.1007/bf02872013"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1082563105", 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2016-06", 
        "datePublishedReg": "2016-06-01", 
        "description": "BACKGROUND: In order to find genetic and metabolic pathways related to phenotypic traits of interest, we analyzed gene expression data, metabolite data obtained with GC-MS and LC-MS, proteomics data and a selected set of tuber quality phenotypic data from a diploid segregating mapping population of potato. In this study we present an approach to integrate these\u2009~\u2009omics data sets for the purpose of predicting phenotypic traits. This gives us networks of relatively small sets of interrelated\u2009~\u2009omics variables that can predict, with higher accuracy, a quality trait of interest.\nRESULTS: We used Random Forest regression for integrating multiple\u2009~\u2009omics data for prediction of four quality traits of potato: tuber flesh colour, DSC onset, tuber shape and enzymatic discoloration. For tuber flesh colour beta-carotene hydroxylase and zeaxanthin epoxidase were ranked first and forty-fourth respectively both of which have previously been associated with flesh colour in potato tubers. Combining all the significant genes, LC-peaks, GC-peaks and proteins, the variation explained was 75\u00a0%, only slightly more than what gene expression or LC-MS data explain by themselves which indicates that there are correlations among the variables across data sets. For tuber shape regressed on the gene expression, LC-MS, GC-MS and proteomics data sets separately, only gene expression data was found to explain significant variation. For DSC onset, we found 12 significant gene expression, 5 metabolite levels (GC) and 2 proteins that are associated with the trait. Using those 19 significant variables, the variation explained was 45\u00a0%. Expression QTL (eQTL) analyses showed many associations with genomic regions in chromosome 2 with also the highest explained variation compared to other chromosomes. Transcriptomics and metabolomics analysis on enzymatic discoloration after 5\u00a0min resulted in 420 significant genes and 8 significant LC metabolites, among which two were putatively identified as caffeoylquinic acid methyl ester and tyrosine.\nCONCLUSIONS: In this study, we made a strategy for selecting and integrating multiple\u2009~\u2009omics data using random forest method and selected representative individual peaks for networks based on eQTL, mQTL or pQTL information. Network analysis was done to interpret how a particular trait is associated with gene expression, metabolite and protein data.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1186/s12859-016-1043-4", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1023786", 
            "issn": [
              "1471-2105"
            ], 
            "name": "BMC Bioinformatics", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "Suppl 5", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "17"
          }
        ], 
        "name": "Integration of multi-omics data for prediction of phenotypic traits using random forest", 
        "pagination": "180", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "7b42334acc0c5a6da64fd963c315d954a9f48735d2eb8850cd3661397afb0f72"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "27295212"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "100965194"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s12859-016-1043-4"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1025872318"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s12859-016-1043-4", 
          "https://app.dimensions.ai/details/publication/pub.1025872318"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T12:21", 
        "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/v90/0000000362_0000000362/records_87079_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1186%2Fs12859-016-1043-4"
      }
    ]
     

    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/s12859-016-1043-4'

    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/s12859-016-1043-4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12859-016-1043-4'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12859-016-1043-4'


     

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

    282 TRIPLES      21 PREDICATES      81 URIs      34 LITERALS      22 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s12859-016-1043-4 schema:about N03532aa8efc64eaf99e87727f2ecb206
    2 N1b3d3c0f06ba4adcaf7c756a4cd1900c
    3 N24a615a0a25f426688fc1bfc841d54cf
    4 N256c456c9e0947a18864b77bdd0250e0
    5 N28a7f3b5b703472491968c4322e39ba0
    6 N2a9be056b11b484e9299b4713ff04533
    7 N53f5d2b3078b42af990a7316a46f1e6b
    8 N60513db049cd490795e9cf29be4b578a
    9 N6624d3078c5b41f4965aeda56a3d1742
    10 N6691f2af8b5140fe90e7b8c58fd480e6
    11 Nbde9eef116c24dec84d592aa08e194e1
    12 Nc068407f8ad84e0b9f15a339c8b6a5aa
    13 Nd6c8c13559b24f12bccbc46e1d6cb4bc
    14 anzsrc-for:06
    15 anzsrc-for:0604
    16 schema:author Nfa05b07098424a79b11eec49bc861db5
    17 schema:citation sg:pub.10.1007/978-0-387-21606-5
    18 sg:pub.10.1007/bf00220891
    19 sg:pub.10.1007/bf02872013
    20 sg:pub.10.1007/s00122-007-0560-y
    21 sg:pub.10.1007/s10142-008-0083-x
    22 sg:pub.10.1007/s10681-007-9640-y
    23 sg:pub.10.1007/s11103-010-9647-y
    24 sg:pub.10.1007/s11306-011-0368-2
    25 sg:pub.10.1023/a:1010933404324
    26 sg:pub.10.1023/a:1013713905833
    27 sg:pub.10.1038/73392
    28 sg:pub.10.1038/nature01511
    29 sg:pub.10.1038/nature10158
    30 sg:pub.10.1038/ng1815
    31 sg:pub.10.1038/nprot.2007.96
    32 sg:pub.10.1186/1471-2105-10-384
    33 sg:pub.10.1186/1471-2164-11-158
    34 sg:pub.10.1186/1471-2229-12-17
    35 https://app.dimensions.ai/details/publication/pub.1022356842
    36 https://app.dimensions.ai/details/publication/pub.1082563105
    37 https://doi.org/10.1016/j.aca.2011.03.050
    38 https://doi.org/10.1016/j.carbpol.2005.08.004
    39 https://doi.org/10.1016/j.cbpa.2009.09.022
    40 https://doi.org/10.1016/j.copbio.2010.01.003
    41 https://doi.org/10.1016/j.tibtech.2008.07.002
    42 https://doi.org/10.1016/j.tplants.2008.02.003
    43 https://doi.org/10.1016/j.ymeth.2014.06.010
    44 https://doi.org/10.1016/s0014-5793(00)01772-5
    45 https://doi.org/10.1016/s0168-9525(01)02310-1
    46 https://doi.org/10.1039/b418288j
    47 https://doi.org/10.1046/j.1365-313x.1996.9050745.x
    48 https://doi.org/10.1046/j.1365-313x.2001.00993.x
    49 https://doi.org/10.1093/bioinformatics/bth445
    50 https://doi.org/10.1104/pp.111.188441
    51 https://doi.org/10.1111/j.1467-7652.2010.00516.x
    52 https://doi.org/10.1111/j.1744-7348.2003.tb00284.x
    53 https://doi.org/10.1146/annurev.biochem.72.121801.161511
    54 https://doi.org/10.1371/journal.pcbi.1003168
    55 https://doi.org/10.3389/fpls.2014.00598
    56 schema:datePublished 2016-06
    57 schema:datePublishedReg 2016-06-01
    58 schema:description BACKGROUND: In order to find genetic and metabolic pathways related to phenotypic traits of interest, we analyzed gene expression data, metabolite data obtained with GC-MS and LC-MS, proteomics data and a selected set of tuber quality phenotypic data from a diploid segregating mapping population of potato. In this study we present an approach to integrate these ~ omics data sets for the purpose of predicting phenotypic traits. This gives us networks of relatively small sets of interrelated ~ omics variables that can predict, with higher accuracy, a quality trait of interest. RESULTS: We used Random Forest regression for integrating multiple ~ omics data for prediction of four quality traits of potato: tuber flesh colour, DSC onset, tuber shape and enzymatic discoloration. For tuber flesh colour beta-carotene hydroxylase and zeaxanthin epoxidase were ranked first and forty-fourth respectively both of which have previously been associated with flesh colour in potato tubers. Combining all the significant genes, LC-peaks, GC-peaks and proteins, the variation explained was 75 %, only slightly more than what gene expression or LC-MS data explain by themselves which indicates that there are correlations among the variables across data sets. For tuber shape regressed on the gene expression, LC-MS, GC-MS and proteomics data sets separately, only gene expression data was found to explain significant variation. For DSC onset, we found 12 significant gene expression, 5 metabolite levels (GC) and 2 proteins that are associated with the trait. Using those 19 significant variables, the variation explained was 45 %. Expression QTL (eQTL) analyses showed many associations with genomic regions in chromosome 2 with also the highest explained variation compared to other chromosomes. Transcriptomics and metabolomics analysis on enzymatic discoloration after 5 min resulted in 420 significant genes and 8 significant LC metabolites, among which two were putatively identified as caffeoylquinic acid methyl ester and tyrosine. CONCLUSIONS: In this study, we made a strategy for selecting and integrating multiple ~ omics data using random forest method and selected representative individual peaks for networks based on eQTL, mQTL or pQTL information. Network analysis was done to interpret how a particular trait is associated with gene expression, metabolite and protein data.
    59 schema:genre research_article
    60 schema:inLanguage en
    61 schema:isAccessibleForFree true
    62 schema:isPartOf N2678632737d34a22b8f29bd4092337ab
    63 N62f4bb8c5e654e62826c289f9f225464
    64 sg:journal.1023786
    65 schema:name Integration of multi-omics data for prediction of phenotypic traits using random forest
    66 schema:pagination 180
    67 schema:productId N4e4d87d8bcbe48b882616f34fc0a9ae8
    68 N5c97cac7d25c4111bd6df4d754bf1dcf
    69 Na703c2ca823b4de2ba0345016e30f979
    70 Na92f9c9bee434bb3a2a9c03ab88bfa3c
    71 Nc612223f58514c5785f5eedab5efe157
    72 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025872318
    73 https://doi.org/10.1186/s12859-016-1043-4
    74 schema:sdDatePublished 2019-04-11T12:21
    75 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    76 schema:sdPublisher N14e2c7d0b6be44fa9c33a136573fe77d
    77 schema:url https://link.springer.com/10.1186%2Fs12859-016-1043-4
    78 sgo:license sg:explorer/license/
    79 sgo:sdDataset articles
    80 rdf:type schema:ScholarlyArticle
    81 N03532aa8efc64eaf99e87727f2ecb206 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    82 schema:name Chromatography, High Pressure Liquid
    83 rdf:type schema:DefinedTerm
    84 N14e2c7d0b6be44fa9c33a136573fe77d schema:name Springer Nature - SN SciGraph project
    85 rdf:type schema:Organization
    86 N1b3d3c0f06ba4adcaf7c756a4cd1900c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    87 schema:name Gene Expression Regulation, Plant
    88 rdf:type schema:DefinedTerm
    89 N24a615a0a25f426688fc1bfc841d54cf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    90 schema:name Phenotype
    91 rdf:type schema:DefinedTerm
    92 N256c456c9e0947a18864b77bdd0250e0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    93 schema:name Plant Proteins
    94 rdf:type schema:DefinedTerm
    95 N2678632737d34a22b8f29bd4092337ab schema:volumeNumber 17
    96 rdf:type schema:PublicationVolume
    97 N28a7f3b5b703472491968c4322e39ba0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    98 schema:name Chromosomes, Plant
    99 rdf:type schema:DefinedTerm
    100 N2a9be056b11b484e9299b4713ff04533 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    101 schema:name Plant Tubers
    102 rdf:type schema:DefinedTerm
    103 N3723cfe99f9b4fcabf9af2d92dcba800 rdf:first sg:person.0750643451.31
    104 rdf:rest Ne86c588d39fc4861a5806b7eea2cfcbc
    105 N48f21ab6c94f4ad3a9e5929af175f08d rdf:first sg:person.01247501613.36
    106 rdf:rest rdf:nil
    107 N4e4d87d8bcbe48b882616f34fc0a9ae8 schema:name doi
    108 schema:value 10.1186/s12859-016-1043-4
    109 rdf:type schema:PropertyValue
    110 N53f5d2b3078b42af990a7316a46f1e6b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    111 schema:name Solanum tuberosum
    112 rdf:type schema:DefinedTerm
    113 N5c97cac7d25c4111bd6df4d754bf1dcf schema:name nlm_unique_id
    114 schema:value 100965194
    115 rdf:type schema:PropertyValue
    116 N60513db049cd490795e9cf29be4b578a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    117 schema:name Proteomics
    118 rdf:type schema:DefinedTerm
    119 N62f4bb8c5e654e62826c289f9f225464 schema:issueNumber Suppl 5
    120 rdf:type schema:PublicationIssue
    121 N6624d3078c5b41f4965aeda56a3d1742 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    122 schema:name Quantitative Trait Loci
    123 rdf:type schema:DefinedTerm
    124 N6691f2af8b5140fe90e7b8c58fd480e6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    125 schema:name Metabolomics
    126 rdf:type schema:DefinedTerm
    127 Na703c2ca823b4de2ba0345016e30f979 schema:name dimensions_id
    128 schema:value pub.1025872318
    129 rdf:type schema:PropertyValue
    130 Na92f9c9bee434bb3a2a9c03ab88bfa3c schema:name readcube_id
    131 schema:value 7b42334acc0c5a6da64fd963c315d954a9f48735d2eb8850cd3661397afb0f72
    132 rdf:type schema:PropertyValue
    133 Nbde9eef116c24dec84d592aa08e194e1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    134 schema:name Gas Chromatography-Mass Spectrometry
    135 rdf:type schema:DefinedTerm
    136 Nc068407f8ad84e0b9f15a339c8b6a5aa schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    137 schema:name Genomics
    138 rdf:type schema:DefinedTerm
    139 Nc612223f58514c5785f5eedab5efe157 schema:name pubmed_id
    140 schema:value 27295212
    141 rdf:type schema:PropertyValue
    142 Nd6c8c13559b24f12bccbc46e1d6cb4bc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    143 schema:name Mass Spectrometry
    144 rdf:type schema:DefinedTerm
    145 Ne86c588d39fc4861a5806b7eea2cfcbc rdf:first sg:person.07374343732.28
    146 rdf:rest N48f21ab6c94f4ad3a9e5929af175f08d
    147 Nfa05b07098424a79b11eec49bc861db5 rdf:first sg:person.01201366413.30
    148 rdf:rest N3723cfe99f9b4fcabf9af2d92dcba800
    149 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    150 schema:name Biological Sciences
    151 rdf:type schema:DefinedTerm
    152 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    153 schema:name Genetics
    154 rdf:type schema:DefinedTerm
    155 sg:journal.1023786 schema:issn 1471-2105
    156 schema:name BMC Bioinformatics
    157 rdf:type schema:Periodical
    158 sg:person.01201366413.30 schema:affiliation https://www.grid.ac/institutes/grid.415055.0
    159 schema:familyName Acharjee
    160 schema:givenName Animesh
    161 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01201366413.30
    162 rdf:type schema:Person
    163 sg:person.01247501613.36 schema:affiliation https://www.grid.ac/institutes/grid.4818.5
    164 schema:familyName Maliepaard
    165 schema:givenName Chris
    166 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247501613.36
    167 rdf:type schema:Person
    168 sg:person.07374343732.28 schema:affiliation https://www.grid.ac/institutes/grid.4818.5
    169 schema:familyName Visser
    170 schema:givenName Richard G. F.
    171 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07374343732.28
    172 rdf:type schema:Person
    173 sg:person.0750643451.31 schema:affiliation https://www.grid.ac/institutes/grid.425600.5
    174 schema:familyName Kloosterman
    175 schema:givenName Bjorn
    176 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0750643451.31
    177 rdf:type schema:Person
    178 sg:pub.10.1007/978-0-387-21606-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022356842
    179 https://doi.org/10.1007/978-0-387-21606-5
    180 rdf:type schema:CreativeWork
    181 sg:pub.10.1007/bf00220891 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031677694
    182 https://doi.org/10.1007/bf00220891
    183 rdf:type schema:CreativeWork
    184 sg:pub.10.1007/bf02872013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050778389
    185 https://doi.org/10.1007/bf02872013
    186 rdf:type schema:CreativeWork
    187 sg:pub.10.1007/s00122-007-0560-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1033526515
    188 https://doi.org/10.1007/s00122-007-0560-y
    189 rdf:type schema:CreativeWork
    190 sg:pub.10.1007/s10142-008-0083-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1024797241
    191 https://doi.org/10.1007/s10142-008-0083-x
    192 rdf:type schema:CreativeWork
    193 sg:pub.10.1007/s10681-007-9640-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1044972039
    194 https://doi.org/10.1007/s10681-007-9640-y
    195 rdf:type schema:CreativeWork
    196 sg:pub.10.1007/s11103-010-9647-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1023255330
    197 https://doi.org/10.1007/s11103-010-9647-y
    198 rdf:type schema:CreativeWork
    199 sg:pub.10.1007/s11306-011-0368-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042363301
    200 https://doi.org/10.1007/s11306-011-0368-2
    201 rdf:type schema:CreativeWork
    202 sg:pub.10.1023/a:1010933404324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024739340
    203 https://doi.org/10.1023/a:1010933404324
    204 rdf:type schema:CreativeWork
    205 sg:pub.10.1023/a:1013713905833 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027843923
    206 https://doi.org/10.1023/a:1013713905833
    207 rdf:type schema:CreativeWork
    208 sg:pub.10.1038/73392 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037746706
    209 https://doi.org/10.1038/73392
    210 rdf:type schema:CreativeWork
    211 sg:pub.10.1038/nature01511 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012180132
    212 https://doi.org/10.1038/nature01511
    213 rdf:type schema:CreativeWork
    214 sg:pub.10.1038/nature10158 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002525491
    215 https://doi.org/10.1038/nature10158
    216 rdf:type schema:CreativeWork
    217 sg:pub.10.1038/ng1815 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033471111
    218 https://doi.org/10.1038/ng1815
    219 rdf:type schema:CreativeWork
    220 sg:pub.10.1038/nprot.2007.96 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043516227
    221 https://doi.org/10.1038/nprot.2007.96
    222 rdf:type schema:CreativeWork
    223 sg:pub.10.1186/1471-2105-10-384 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050628645
    224 https://doi.org/10.1186/1471-2105-10-384
    225 rdf:type schema:CreativeWork
    226 sg:pub.10.1186/1471-2164-11-158 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043854389
    227 https://doi.org/10.1186/1471-2164-11-158
    228 rdf:type schema:CreativeWork
    229 sg:pub.10.1186/1471-2229-12-17 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009026174
    230 https://doi.org/10.1186/1471-2229-12-17
    231 rdf:type schema:CreativeWork
    232 https://app.dimensions.ai/details/publication/pub.1022356842 schema:CreativeWork
    233 https://app.dimensions.ai/details/publication/pub.1082563105 schema:CreativeWork
    234 https://doi.org/10.1016/j.aca.2011.03.050 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018135175
    235 rdf:type schema:CreativeWork
    236 https://doi.org/10.1016/j.carbpol.2005.08.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016178610
    237 rdf:type schema:CreativeWork
    238 https://doi.org/10.1016/j.cbpa.2009.09.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048967908
    239 rdf:type schema:CreativeWork
    240 https://doi.org/10.1016/j.copbio.2010.01.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015304182
    241 rdf:type schema:CreativeWork
    242 https://doi.org/10.1016/j.tibtech.2008.07.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047477678
    243 rdf:type schema:CreativeWork
    244 https://doi.org/10.1016/j.tplants.2008.02.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034564835
    245 rdf:type schema:CreativeWork
    246 https://doi.org/10.1016/j.ymeth.2014.06.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022904282
    247 rdf:type schema:CreativeWork
    248 https://doi.org/10.1016/s0014-5793(00)01772-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001776486
    249 rdf:type schema:CreativeWork
    250 https://doi.org/10.1016/s0168-9525(01)02310-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026477807
    251 rdf:type schema:CreativeWork
    252 https://doi.org/10.1039/b418288j schema:sameAs https://app.dimensions.ai/details/publication/pub.1022837755
    253 rdf:type schema:CreativeWork
    254 https://doi.org/10.1046/j.1365-313x.1996.9050745.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1024275878
    255 rdf:type schema:CreativeWork
    256 https://doi.org/10.1046/j.1365-313x.2001.00993.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1026494245
    257 rdf:type schema:CreativeWork
    258 https://doi.org/10.1093/bioinformatics/bth445 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049700263
    259 rdf:type schema:CreativeWork
    260 https://doi.org/10.1104/pp.111.188441 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046693401
    261 rdf:type schema:CreativeWork
    262 https://doi.org/10.1111/j.1467-7652.2010.00516.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1025081307
    263 rdf:type schema:CreativeWork
    264 https://doi.org/10.1111/j.1744-7348.2003.tb00284.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1022546414
    265 rdf:type schema:CreativeWork
    266 https://doi.org/10.1146/annurev.biochem.72.121801.161511 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018341141
    267 rdf:type schema:CreativeWork
    268 https://doi.org/10.1371/journal.pcbi.1003168 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007788160
    269 rdf:type schema:CreativeWork
    270 https://doi.org/10.3389/fpls.2014.00598 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040952317
    271 rdf:type schema:CreativeWork
    272 https://www.grid.ac/institutes/grid.415055.0 schema:alternateName MRC Human Nutrition Research
    273 schema:name MRC Human Nutrition Research, 120 Fulbourn Road, CB1 9NL, Cambridge, UK
    274 Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands
    275 rdf:type schema:Organization
    276 https://www.grid.ac/institutes/grid.425600.5 schema:alternateName KeyGene (Netherlands)
    277 schema:name Keygene NV, PO Box 216, 6700 AE, Wageningen, The Netherlands
    278 Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands
    279 rdf:type schema:Organization
    280 https://www.grid.ac/institutes/grid.4818.5 schema:alternateName Wageningen University & Research
    281 schema:name Wageningen UR Plant Breeding, Wageningen University & Research Centre, PO Box 6700 AJ, Wageningen, The Netherlands
    282 rdf:type schema:Organization
     




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


    ...