Our era has witnessed tremendous advances in plant genomics, characterized by an explosion of high-throughput techniques to identify multi-dimensional genome-wide molecular phenotypes at low costs. More importantly, genomics is not merely acquiring molecular phenotypes, but also leveraging powerful data mining tools to predict and explain them. In recent years, deep learning has been found extremely effective in these tasks. This review highlights two prominent questions at the intersection of genomics and deep learning: 1) how can the flow of information from genomic DNA sequences to molecular phenotypes be modeled; 2) how can we identify functional variants in natural populations using deep learning models? Additionally, we discuss the possibility of unleashing the power of deep learning in synthetic biology to create novel genomic elements with desirable functions. Taken together, we propose a central role of deep learning in future plant genomics research and crop genetic improvement.

中文翻译：

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用于植物基因组学和作物改良的深度学习。

我们的时代见证了植物基因组学的巨大进步，其特征是高通量技术的爆炸式增长，以低成本识别多维全基因组分子表型。更重要的是，基因组学不仅要获得分子表型，还要利用强大的数据挖掘工具来预测和解释它们。近年来，发现深度学习在这些任务中非常有效。这篇综述突出了基因组学与深度学习相交处的两个突出的问题：1）如何对从基因组DNA序列到分子表型的信息流进行建模？2）我们如何使用深度学习模型识别自然种群中的功能变异？另外，我们讨论了在合成生物学中释放深度学习功能以创建具有所需功能的新型基因组元件的可能性。总之，我们提出了深度学习在未来植物基因组学研究和作物遗传改良中的核心作用。