The United Nations has predicted that the human population will increase to close to 10 billion people by 2050, which could lead to a food shortage and increased food insecurity. Therefore, increasing the rate of improvement in strategic crops is vital for sustainable food production and lifting living standards in the near future. As plant breeders mostly focus on complex traits that are controlled by intrinsic and extrinsic factors, fast-paced crop improvement may benefit from the integration of data sets covering the interplay of the so-called “omics” fields of inquiry, that is; genomics, transcriptomics, proteomics, metabolomics, phenomics, ionomics, and enviromics.
Recently, the utilization of large datasets from different omics sources has provided plant breeders with a better prediction accuracy of a genotype’s traits of interest. These efforts involve the study of diverse omics levels, from molecular biology to the agricultural environments, passing through the data analytics of DNA, RNA, proteins, metabolites, (eco)physiology, and phenotypes. The utilization of different omics data sets to accelerate plant improvement research programs has great promise. Nonetheless, the scale and complexity of these data, coupled with an increasingly high data volume, present significant challenges. Novel insights and analysis methods are required to utilize this information for accurate decision-making in plant breeding.
Researchers are invited to submit their manuscripts discussing the latest efforts in multi-omics data integration to better understand a complex trait in plant breeding programs. The outcome of this Research Topic could be used to initiate novel plant breeding pipelines to accelerate crop improvement rates in different plant species.
This Research Topic welcomes submissions of Original Research papers, Opinions, Perspectives, Reviews, and Mini-Reviews related to these themes:
• Crop breeding and genetics using big data analysis methods and data-driven approaches;
• Computational tools involving the development and implementation of data packages, statistical methods, and data-driven pipelines for processing and analyzing crop breeding datasets;
• Analyses of remote, satellite, and aerial imaging that enhance the prediction accuracy of complex traits in plant breeding;
• Omics-based selection of strategic crops, including genomics, phenomics, metabolomics, and enviromics prediction methods and the interplay between two or more omics fields;
• Comparative analysis of conventional plant breeding vs. modern plant breeding approaches involving omics or multi-omics insights;
• Novel Proteomics, transcriptomics, metabolomics, and ionomics-based prediction models for plant research improvement;
• Genetics studies relating different omics fields, such as genome-phenome, proteome-phenome, envirome-phenome, genome-ionomics, and others.
The United Nations has predicted that the human population will increase to close to 10 billion people by 2050, which could lead to a food shortage and increased food insecurity. Therefore, increasing the rate of improvement in strategic crops is vital for sustainable food production and lifting living standards in the near future. As plant breeders mostly focus on complex traits that are controlled by intrinsic and extrinsic factors, fast-paced crop improvement may benefit from the integration of data sets covering the interplay of the so-called “omics” fields of inquiry, that is; genomics, transcriptomics, proteomics, metabolomics, phenomics, ionomics, and enviromics.
Recently, the utilization of large datasets from different omics sources has provided plant breeders with a better prediction accuracy of a genotype’s traits of interest. These efforts involve the study of diverse omics levels, from molecular biology to the agricultural environments, passing through the data analytics of DNA, RNA, proteins, metabolites, (eco)physiology, and phenotypes. The utilization of different omics data sets to accelerate plant improvement research programs has great promise. Nonetheless, the scale and complexity of these data, coupled with an increasingly high data volume, present significant challenges. Novel insights and analysis methods are required to utilize this information for accurate decision-making in plant breeding.
Researchers are invited to submit their manuscripts discussing the latest efforts in multi-omics data integration to better understand a complex trait in plant breeding programs. The outcome of this Research Topic could be used to initiate novel plant breeding pipelines to accelerate crop improvement rates in different plant species.
This Research Topic welcomes submissions of Original Research papers, Opinions, Perspectives, Reviews, and Mini-Reviews related to these themes:
• Crop breeding and genetics using big data analysis methods and data-driven approaches;
• Computational tools involving the development and implementation of data packages, statistical methods, and data-driven pipelines for processing and analyzing crop breeding datasets;
• Analyses of remote, satellite, and aerial imaging that enhance the prediction accuracy of complex traits in plant breeding;
• Omics-based selection of strategic crops, including genomics, phenomics, metabolomics, and enviromics prediction methods and the interplay between two or more omics fields;
• Comparative analysis of conventional plant breeding vs. modern plant breeding approaches involving omics or multi-omics insights;
• Novel Proteomics, transcriptomics, metabolomics, and ionomics-based prediction models for plant research improvement;
• Genetics studies relating different omics fields, such as genome-phenome, proteome-phenome, envirome-phenome, genome-ionomics, and others.
