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@ARTICLE{Bolger:860200,
      author       = {Bolger, Anthony M. and Poorter, Hendrik and Dumschott,
                      Kathryn and Bolger, Marie and Arend, Daniel and Osorio,
                      Sonia and Gundlach, Heidrun and Mayer, Klaus F. X. and
                      Lange, Matthias and Scholz, Uwe and Usadel, Björn},
      title        = {{C}omputational aspects underlying genome to phenome
                      analysis in plants},
      journal      = {The plant journal},
      volume       = {97},
      number       = {1},
      issn         = {0960-7412},
      address      = {Oxford [u.a.]},
      publisher    = {Wiley-Blackwell},
      reportid     = {FZJ-2019-00984},
      pages        = {182 - 198},
      year         = {2019},
      abstract     = {Recent advances in genomics technologies have greatly
                      accelerated the progress in both fundamental plant science
                      and applied breeding research. Concurrently,
                      high‐throughput plant phenotyping is becoming widely
                      adopted in the plant community, promising to alleviate the
                      phenotypic bottleneck. While these technological
                      breakthroughs are significantly accelerating quantitative
                      trait locus (QTL) and causal gene identification, challenges
                      to enable even more sophisticated analyses remain. In
                      particular, care needs to be taken to standardize, describe
                      and conduct experiments robustly while relying on plant
                      physiology expertise. In this article, we review the state
                      of the art regarding genome assembly and the future
                      potential of pangenomics in plant research. We also describe
                      the necessity of standardizing and describing phenotypic
                      studies using the Minimum Information About a Plant
                      Phenotyping Experiment (MIAPPE) standard to enable the reuse
                      and integration of phenotypic data. In addition, we show how
                      deep phenotypic data might yield novel trait−trait
                      correlations and review how to link phenotypic data to
                      genomic data. Finally, we provide perspectives on the golden
                      future of machine learning and their potential in linking
                      phenotypes to genomic features.},
      cin          = {IBG-2},
      ddc          = {580},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582) / 583 - Innovative
                      Synergisms (POF3-583)},
      pid          = {G:(DE-HGF)POF3-582 / G:(DE-HGF)POF3-583},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30500991},
      UT           = {WOS:000455506600013},
      doi          = {10.1111/tpj.14179},
      url          = {https://juser.fz-juelich.de/record/860200},
}