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Crop improvement using life cycle datasets acquired under field conditions.

Mochida K, Saisho D, Hirayama T - Front Plant Sci (2015)

Bottom Line: Recent advances in omics analytical techniques and information technology have enabled us to integrate data from a spectrum of physiological metrics of field crops.Here, we describe recent advances in technologies that are key components for data driven crop design, such as population genomics, chronological omics analyses, and computer-aided molecular network prediction.These elements would help us to genetically engineer "designed crops" to prevent yield shortfalls because of environmental fluctuations due to future climate change.

View Article: PubMed Central - PubMed

Affiliation: Cellulose Production Research Team, Biomass Engineering Research Division, RIKEN Center for Sustainable Resource Science , Yokohama, Japan ; Gene Discovery Research Group, RIKEN Center for Sustainable Resource Science , Yokohama, Japan ; Kihara Institute for Biological Research, Yokohama City University , Yokohama, Japan.

ABSTRACT
Crops are exposed to various environmental stresses in the field throughout their life cycle. Modern plant science has provided remarkable insights into the molecular networks of plant stress responses in laboratory conditions, but the responses of different crops to environmental stresses in the field need to be elucidated. Recent advances in omics analytical techniques and information technology have enabled us to integrate data from a spectrum of physiological metrics of field crops. The interdisciplinary efforts of plant science and data science enable us to explore factors that affect crop productivity and identify stress tolerance-related genes and alleles. Here, we describe recent advances in technologies that are key components for data driven crop design, such as population genomics, chronological omics analyses, and computer-aided molecular network prediction. Integration of the outcomes from these technologies will accelerate our understanding of crop phenology under practical field situations and identify key characteristics to represent crop stress status. These elements would help us to genetically engineer "designed crops" to prevent yield shortfalls because of environmental fluctuations due to future climate change.

No MeSH data available.


Related in: MedlinePlus

Phenology datasets during the life cycle of a crop grown under field conditions. Time series omics data such as transcriptome, epigenome, and metabolome/hormonome data can be acquired as sequential “snapshots” throughout the crop life cycle. Parameters regarding field environment including air and soil conditions can be acquired as “streamed” datasets.
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Figure 1: Phenology datasets during the life cycle of a crop grown under field conditions. Time series omics data such as transcriptome, epigenome, and metabolome/hormonome data can be acquired as sequential “snapshots” throughout the crop life cycle. Parameters regarding field environment including air and soil conditions can be acquired as “streamed” datasets.

Mentions: With the currently available methods and resources for studying plant stress responses, it is expected that interdisciplinary efforts involving plant science and data science will enable exploration of factors that affect crop productivity and will aid discovery of genes and alleles associated with quantitative traits of stress tolerance in crops. It is essential not only to examine a snapshot of the cellular network under multiple stress conditions at a particular moment but also to monitor throughout the life cycle, since changes in physiological status over time might influence the eventual phenotype. The identification and estimation of the effects of parameters, based on an understanding of the genetics and physiology of responses to environmental changes of crops throughout their life cycle, are required to design a crop with the required performance of stress tolerances in the field condition. In this mini review, we provide an overview of recent advances in technologies that are key components for data driven crop design, such as crop population genomics, chronological trans-omics analysis, and computer-aided molecular network prediction (Figure 1).


Crop improvement using life cycle datasets acquired under field conditions.

Mochida K, Saisho D, Hirayama T - Front Plant Sci (2015)

Phenology datasets during the life cycle of a crop grown under field conditions. Time series omics data such as transcriptome, epigenome, and metabolome/hormonome data can be acquired as sequential “snapshots” throughout the crop life cycle. Parameters regarding field environment including air and soil conditions can be acquired as “streamed” datasets.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585263&req=5

Figure 1: Phenology datasets during the life cycle of a crop grown under field conditions. Time series omics data such as transcriptome, epigenome, and metabolome/hormonome data can be acquired as sequential “snapshots” throughout the crop life cycle. Parameters regarding field environment including air and soil conditions can be acquired as “streamed” datasets.
Mentions: With the currently available methods and resources for studying plant stress responses, it is expected that interdisciplinary efforts involving plant science and data science will enable exploration of factors that affect crop productivity and will aid discovery of genes and alleles associated with quantitative traits of stress tolerance in crops. It is essential not only to examine a snapshot of the cellular network under multiple stress conditions at a particular moment but also to monitor throughout the life cycle, since changes in physiological status over time might influence the eventual phenotype. The identification and estimation of the effects of parameters, based on an understanding of the genetics and physiology of responses to environmental changes of crops throughout their life cycle, are required to design a crop with the required performance of stress tolerances in the field condition. In this mini review, we provide an overview of recent advances in technologies that are key components for data driven crop design, such as crop population genomics, chronological trans-omics analysis, and computer-aided molecular network prediction (Figure 1).

Bottom Line: Recent advances in omics analytical techniques and information technology have enabled us to integrate data from a spectrum of physiological metrics of field crops.Here, we describe recent advances in technologies that are key components for data driven crop design, such as population genomics, chronological omics analyses, and computer-aided molecular network prediction.These elements would help us to genetically engineer "designed crops" to prevent yield shortfalls because of environmental fluctuations due to future climate change.

View Article: PubMed Central - PubMed

Affiliation: Cellulose Production Research Team, Biomass Engineering Research Division, RIKEN Center for Sustainable Resource Science , Yokohama, Japan ; Gene Discovery Research Group, RIKEN Center for Sustainable Resource Science , Yokohama, Japan ; Kihara Institute for Biological Research, Yokohama City University , Yokohama, Japan.

ABSTRACT
Crops are exposed to various environmental stresses in the field throughout their life cycle. Modern plant science has provided remarkable insights into the molecular networks of plant stress responses in laboratory conditions, but the responses of different crops to environmental stresses in the field need to be elucidated. Recent advances in omics analytical techniques and information technology have enabled us to integrate data from a spectrum of physiological metrics of field crops. The interdisciplinary efforts of plant science and data science enable us to explore factors that affect crop productivity and identify stress tolerance-related genes and alleles. Here, we describe recent advances in technologies that are key components for data driven crop design, such as population genomics, chronological omics analyses, and computer-aided molecular network prediction. Integration of the outcomes from these technologies will accelerate our understanding of crop phenology under practical field situations and identify key characteristics to represent crop stress status. These elements would help us to genetically engineer "designed crops" to prevent yield shortfalls because of environmental fluctuations due to future climate change.

No MeSH data available.


Related in: MedlinePlus