Background:Plant phenomics has made significant progress recently,with new demand to move from external characterization to internal exploration through data combination.Hyperspectral and metabolomic data,with cause-a...Background:Plant phenomics has made significant progress recently,with new demand to move from external characterization to internal exploration through data combination.Hyperspectral and metabolomic data,with cause-and-effect relationship,are given priority for integration.However,few efficient integrating methods are available.Results:Here,we showed the way to explore hyperspectral data through combining with upper-level metabolomic data and perform higher-level-data-guided dimension reduction in target-trait-oriented manner to obtain high analysis efficiency.To verify its feasibility,two-stage pipeline combining hyperspectral and metabolic data was designed to discriminate salt-tolerant phenotype for Medicago truncatula mutants.Centered on salt tolerance,data are combined through constructing metabolite-based spectral indices outlining tolerance-related metabolic changes in primary screening,and models converting hyperspectral data to metabolite content for detailed characterizing in secondary screening.Target phenotype could be discriminated after five-day salt-treatment,much earlier than phenotypic difference appearance.20 mutants with salt-tolerant phenotype were successfully identified from about 1000 mutants,almost tripled that of unintegrated analysis.Accuracy rate,confirmed with salt-tolerance analysis for experimental verification,reached 90%,which can be optimized to 100%theoretically utilizing results from hierarchical-clustering-assisted Principal Component Analysis.Conclusions:Mutant-screening pipeline provided here is a practical example for targeted data integration and data mining under the guide of upper-layer omic data.Targeted combination of phenomic and metabolomic data provides the ability for accurate phenotype discrimination and prediction from both external and internal aspects,providing a powerful tool for phenotype selection in new-generation crop breeding.展开更多
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(XDA26030102)the CAS-CSIRO Project(063GJHZ2022047MI)the CAS Special Research Assistant(SRA)Program(Y973RG1001).
文摘Background:Plant phenomics has made significant progress recently,with new demand to move from external characterization to internal exploration through data combination.Hyperspectral and metabolomic data,with cause-and-effect relationship,are given priority for integration.However,few efficient integrating methods are available.Results:Here,we showed the way to explore hyperspectral data through combining with upper-level metabolomic data and perform higher-level-data-guided dimension reduction in target-trait-oriented manner to obtain high analysis efficiency.To verify its feasibility,two-stage pipeline combining hyperspectral and metabolic data was designed to discriminate salt-tolerant phenotype for Medicago truncatula mutants.Centered on salt tolerance,data are combined through constructing metabolite-based spectral indices outlining tolerance-related metabolic changes in primary screening,and models converting hyperspectral data to metabolite content for detailed characterizing in secondary screening.Target phenotype could be discriminated after five-day salt-treatment,much earlier than phenotypic difference appearance.20 mutants with salt-tolerant phenotype were successfully identified from about 1000 mutants,almost tripled that of unintegrated analysis.Accuracy rate,confirmed with salt-tolerance analysis for experimental verification,reached 90%,which can be optimized to 100%theoretically utilizing results from hierarchical-clustering-assisted Principal Component Analysis.Conclusions:Mutant-screening pipeline provided here is a practical example for targeted data integration and data mining under the guide of upper-layer omic data.Targeted combination of phenomic and metabolomic data provides the ability for accurate phenotype discrimination and prediction from both external and internal aspects,providing a powerful tool for phenotype selection in new-generation crop breeding.
