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@ARTICLE{Tiozon:1016975,
      author       = {Tiozon, Rhowell Jr. N. and Sreenivasulu, Nese and Alseekh,
                      Saleh and Sartagoda, Kristel June D. and Usadel, Björn and
                      Fernie, Alisdair R.},
      title        = {{M}etabolomics and machine learning technique revealed that
                      germination enhances the multi-nutritional properties of
                      pigmented rice},
      journal      = {Communications biology},
      volume       = {6},
      number       = {1},
      issn         = {2399-3642},
      address      = {London},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2023-03882},
      pages        = {1000},
      year         = {2023},
      abstract     = {Enhancing the dietary properties of rice is crucial to
                      contribute to alleviating hidden hunger and non-communicable
                      diseases in rice-consuming countries. Germination is a
                      bioprocessing approach to increase the bioavailability of
                      nutrients in rice. However, there is a scarce information on
                      how germination impacts the overall nutritional profile of
                      pigmented rice sprouts (PRS). Herein, we demonstrated that
                      germination resulted to increase levels of certain dietary
                      compounds, such as free phenolics and micronutrients (Ca,
                      Na, Fe, Zn, riboflavin, and biotin). Metabolomic analysis
                      revealed the preferential accumulation of dipeptides, GABA,
                      and flavonoids in the germination process. Genome-wide
                      association studies of the PRS suggested the activation of
                      specific genes such as CHS1 and UGT genes responsible for
                      increasing certain flavonoid compounds. Haplotype analyses
                      showed a significant difference (P < 0.05) between
                      alleles associated with these genes. Genetic markers
                      associated with these flavonoids were incorporated into the
                      random forest model, improving the accuracy of prediction of
                      multi-nutritional properties from $89.7\%$ to $97.7\%.$
                      Deploying this knowledge to breed rice with
                      multi-nutritional properties will be timely to address
                      double burden nutritional challenges},
      cin          = {IBG-4},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IBG-4-20200403},
      pnm          = {2171 - Biological and environmental resources for
                      sustainable use (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2171},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37783812},
      UT           = {WOS:001083931000002},
      doi          = {10.1038/s42003-023-05379-9},
      url          = {https://juser.fz-juelich.de/record/1016975},
}