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使用深度学习模型识别叶子衰老的新型调节者

Chaocheng Guo¹, Zhuoran Huang¹, Jiahao Chen¹

¹Shanghai Collaborative Innovation Center of Agri-Seeds, Joint Center for Single Cell Biology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.

Plants (Basel, Switzerland)

|May 11, 2024

概括

我们开发了DEGRN,这是一种深度学习模型,可以利用表达数据绘制植物中的基因调节网络. 这种方法确定了数百万个基因相互作用和一个新的调节器MAF5,对叶子衰老至关重要.

科学领域:

植物生物学植物生物学
基因组学就是基因组学.
计算生物学是一种计算生物学.

背景情况:

基因调节对于植物的发育和反应至关重要.
目前对基因调节网络的理解是有限的,许多相互作用尚未被探索.
转录因子 (TFs) 在基因调节中起着关键作用.

研究的目的:

开发一种创新的深度学习模型,DEGRN,用于破译基因调节网络.
为了利用高维基因表达数据 (批量RNA-Seq和scRNA-Seq) 来进行网络推断.
识别植物中的新型基因相互作用和调节元素.

主要方法:

开发了DEGRN,这是一种用于基因调节网络推断的深度学习模型.
利用了来自Arabidopsis的批量和单细胞RNA测序数据.
应用该模型来识别TF-目标基因相互作用.

主要成果:

确定了3,053,363个高质量的基因相互作用,其中包括1430个TF和13,739个非TF基因.
发现了植物发育,新陈代谢和应激反应中的新调节剂.
发现了叶子衰老的复杂调节网络,包括新型TF MAF5,其突变体表现出早期叶子衰变.

结论:

从表达数据中,DEGRN有效地预测基因调节相互作用.
该模型显著扩大了植物中已知的基因调节格局.
确定MAF5是叶子衰老的新型调节者,突出了DEGRN在发现功能性基因角色方面的潜力.

关键词:

深度学习是一种深度学习.基因调节网络基因调节网络叶子衰老叶子衰老单细胞转录组分析转录因子是一种转录因子.

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