Esposito Salvatore, Carputo Domenico, Cardi Teodoro, Tripodi Pasquale
CREA Research Centre for Vegetable and Ornamental Crops, 84098 Pontecagnano Faiano, Italy.
Department of Agricultural Sciences, University of Naples Federico II, 80055 Portici, Italy.
Plants (Basel). 2019 Dec 25;9(1):34. doi: 10.3390/plants9010034.
Crops are the major source of food supply and raw materials for the processing industry. A balance between crop production and food consumption is continually threatened by plant diseases and adverse environmental conditions. This leads to serious losses every year and results in food shortages, particularly in developing countries. Presently, cutting-edge technologies for genome sequencing and phenotyping of crops combined with progress in computational sciences are leading a revolution in plant breeding, boosting the identification of the genetic basis of traits at a precision never reached before. In this frame, machine learning (ML) plays a pivotal role in data-mining and analysis, providing relevant information for decision-making towards achieving breeding targets. To this end, we summarize the recent progress in next-generation sequencing and the role of phenotyping technologies in genomics-assisted breeding toward the exploitation of the natural variation and the identification of target genes. We also explore the application of ML in managing big data and predictive models, reporting a case study using microRNAs (miRNAs) to identify genes related to stress conditions.
农作物是食物供应的主要来源以及加工业的原材料。作物生产与食物消费之间的平衡不断受到植物病害和不利环境条件的威胁。这每年都会导致严重损失，并造成粮食短缺，尤其是在发展中国家。目前，作物基因组测序和表型分析的前沿技术与计算科学的进展相结合，正在引领一场植物育种革命，以前所未有的精度推动性状遗传基础的鉴定。在此框架下，机器学习（ML）在数据挖掘和分析中发挥着关键作用，为实现育种目标的决策提供相关信息。为此，我们总结了下一代测序的最新进展以及表型分析技术在基因组辅助育种中对利用自然变异和鉴定目标基因的作用。我们还探讨了机器学习在管理大数据和预测模型中的应用，并报告了一个使用 microRNA（miRNA）鉴定与胁迫条件相关基因的案例研究。
