Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy.
Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA; The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996, USA.
Trends Biotechnol. 2019 Nov;37(11):1217-1235. doi: 10.1016/j.tibtech.2019.05.007. Epub 2019 Jun 21.
Breeding crops for high yield and superior adaptability to new and variable climates is imperative to ensure continued food security, biomass production, and ecosystem services. Advances in genomics and phenomics are delivering insights into the complex biological mechanisms that underlie plant functions in response to environmental perturbations. However, linking genotype to phenotype remains a huge challenge and is hampering the optimal application of high-throughput genomics and phenomics to advanced breeding. Critical to success is the need to assimilate large amounts of data into biologically meaningful interpretations. Here, we present the current state of genomics and field phenomics, explore emerging approaches and challenges for multiomics big data integration by means of next-generation (Next-Gen) artificial intelligence (AI), and propose a workable path to improvement.
培育高产和适应新变化气候的作物对于确保持续的粮食安全、生物质生产和生态系统服务至关重要。基因组学和表型组学的进步为深入了解植物对环境干扰的反应的复杂生物学机制提供了新的见解。然而,将基因型与表型联系起来仍然是一个巨大的挑战,这阻碍了高通量基因组学和表型组学在高级育种中的最佳应用。成功的关键是需要将大量数据整合为具有生物学意义的解释。在这里,我们展示了基因组学和田间表型组学的现状,探讨了通过下一代(Next-Gen)人工智能(AI)进行多组学大数据整合的新兴方法和挑战,并提出了改进的可行途径。