基于机器学习的PlantCdMiner预测和实验验证植物中对敏感基因的验证
Chaoqun Xu1, Ling Sun1, Lu-Dan Zhang2
1Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361104, China.
Journal of hazardous materials
|May 16, 2025
概括
这项研究使用机器学习预测植物中的反应基因,使跨物种分析和识别MYB转录因子 (TFs) 成为应激适应的关键. 一个新的工具,PlantCdMiner,有助于这个预测和 cis-regulatory 元素可视化.
科学领域:
- 植物生物学 植物生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 植物使用适应机制来应对等环境压力因素.
- 基因表达调节对于植物非生物应激反应至关重要.
- 同源基因可以表现出不同的应激反应,限制了传统的预测方法.
研究的目的:
- 开发一种机器学习方法,用于预测对敏感基因.
- 评估跨物种转录组数据对压力基因预测的有用性.
- 为了确定关键的调节元素和转录因子涉及压力.
主要方法:
- 随机森林算法用于监督对敏感基因的分类.
- 对多种植物物种 (Arabidopsis thaliana,Avicennia marina,Hordeum vulgare,Nicotiana tabacum) 的基因序列特征的分析.
- 交叉调节元素分析和实验验证 (酵母单杂交,双露西法酶记者测试).
主要成果:
- 机器学习模型准确地预测了物种内部和跨物种的敏感基因.
- 来自经过充分研究的物种的转录组数据可以预测其他物种的应激反应基因.
- MYB转录因子 (TF) 被确定为应激反应的关键调节者.
- 实验验证证了MYB TF Am06526在调节AmPCR2表达中的作用.
结论:
- 机器学习有效地预测了植物对敏感基因,克服了基于同质学的方法的局限性.
- 跨物种预测是可行的,利用模型生物的数据.
- MYB TFs是植物应激适应的关键参与者.
- 植物CdMiner工具为压力基因预测和分析提供了宝贵的资源.
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