Crop improvement using life cycle datasets acquired under field conditions

Frontiers in Plant Science

卷:6

Daisuke eSaisho¹  Takashi eHirayama¹  Keiichi eMochida³ 

[1] Okayama University;

[2] RIKEN;

[3] Yokohama City University;

关键词: Transcriptome;    machine learning;    epigenome;    Population Genomics;    Crop phenology;   

DOI  :  10.3389/fpls.2015.00740

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown