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  • Settles, A Mark  (2)
  • 2020-2024  (2)
  • 1
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2022
    In:  Integrative and Comparative Biology Vol. 61, No. 6 ( 2022-02-05), p. 2233-2243
    In: Integrative and Comparative Biology, Oxford University Press (OUP), Vol. 61, No. 6 ( 2022-02-05), p. 2233-2243
    Abstract: The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.
    Type of Medium: Online Resource
    ISSN: 1540-7063 , 1557-7023
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2159110-6
    SSG: 12
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  • 2
    In: Journal of the Science of Food and Agriculture, Wiley, Vol. 100, No. 8 ( 2020-06), p. 3488-3497
    Abstract: Pea ( Pisum sativum ) is a prevalent cool‐season crop that produces seeds valued for their high protein content. Modern cultivars have incorporated several traits that improved harvested yield. However, progress toward improving seed quality has received less emphasis, in part due to the lack of tools for easily and rapidly measuring seed traits. In this study we evaluated the accuracy of single‐seed near‐infrared spectroscopy (NIRS) for measuring pea‐seed weight, protein, and oil content. A total of 96 diverse pea accessions were analyzed using both single‐seed NIRS and wet chemistry methods. To demonstrate field relevance, the single‐seed NIRS protein prediction model was used to determine the impact of seed treatments and foliar fungicides on the protein content of harvested dry peas in a field trial. RESULTS External validation of partial least squares (PLS) regression models showed high prediction accuracy for protein and weight (R 2 = 0.94 for both) and less accuracy for oil (R 2 = 0.74). Single‐seed weight was weakly correlated with protein and oil content in contrast with previous reports. In the field study, the single‐seed NIRS predicted protein values were within 10 mg g −1 of an independent analytical reference measurement and were sufficiently precise to detect small treatment effects. CONCLUSION The high accuracy of protein and weight estimation show that single‐seed NIRS could be used in the dual selection of high‐protein, high‐weight peas early in the breeding cycle, allowing for faster genetic advancement toward improved pea nutritional quality. © 2020 Society of Chemical Industry
    Type of Medium: Online Resource
    ISSN: 0022-5142 , 1097-0010
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2001807-1
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