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Hall-Trends Plant Sci-2022.pdf 1,43MB
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1000 Titel
  • High-throughput plant phenotyping: a role for metabolomics?
1000 Autor/in
  1. HALL, ROBERT |
  2. D'Auria, John |
  3. Silva Ferreira, Antonio Cesar |
  4. Gibon, Yves |
  5. Kruszka, Dariusz |
  6. Puneet Mishra |
  7. van de Zedde, Rick |
1000 Erscheinungsjahr 2022
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2022-03-02
1000 Erschienen in
1000 Quellenangabe
  • 27(6):549-563
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2022
1000 Lizenz
1000 Verlagsversion
  • https://dx.doi.org/10.1016/j.tplants.2022.02.001 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.
1000 Sacherschließung
lokal data integration
lokal metabolomics
lokal multimodal sensing
lokal phenomics
lokal plant phenotyping
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-5786-768X|https://orcid.org/0000-0002-4865-3938|https://orcid.org/0000-0002-1188-1021|https://orcid.org/0000-0001-8161-1089|https://orcid.org/0000-0001-5333-7588|https://frl.publisso.de/adhoc/uri/UHVuZWV0IE1pc2hyYQ==|https://orcid.org/0000-0002-8394-4538
1000 Label
1000 Förderer
  1. Horizon 2020 Framework Programme |
1000 Fördernummer
  1. 952339
1000 Förderprogramm
  1. STARGATE
1000 Dateien
  1. High-throughput plant phenotyping: a role for metabolomics?
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Horizon 2020 Framework Programme |
    1000 Förderprogramm STARGATE
    1000 Fördernummer 952339
1000 Objektart article
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1000 @id frl:6489235.rdf
1000 Erstellt am 2024-11-28T12:14:46.111+0100
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1000 Bearbeitet von 317
1000 Zuletzt bearbeitet 2025-02-18T10:59:39.796+0100
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1000 Oai Id
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1000 Sichtbarkeit Daten public
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