从水果发育过程中获得的定量转录组数据中,对生长特征的跨物种预测
Chloé Beaumont1, Sylvain Prigent1,2, Kentaro Mori1
1Univ. Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, 33882 Villenave d'Ornon, France.
Journal of experimental botany
|March 18, 2025
概括
机器学习使用多种转录组准确预测水果生长特征. 基因表达数据,特别是代谢过程的基因表达数据,可以有效预测相对生长率 (RGR) 和其他发展因素.
科学领域:
- 系统生物学 系统生物学
- 基因组学就是基因组学.
- 植物科学 植物科学
背景情况:
- 将基因型与表型联系起来是一个关键的生物学挑战.
- 机器学习在系统生物学中越来越重要,用于理解复杂的特征.
- 果子的发育,包括相对生长率 (RGR),受到基因调节,新陈代谢和环境因素的影响.
研究的目的:
- 使用多种类的转录基因数据来预测水果生长特征.
- 识别与水果发育机制相关的基因表达模式.
- 评估通用线性模型 (GLMs) 在预测表型特征方面的有效性.
主要方法:
- 九种水果类型的多物种转录基因分析.
- 使用多变量方法进行比较转录基因分析.
- 使用GLMs预测生长特征 (RGR,发育进展,水果重量,蛋白质含量).
- 识别关键的基因本体学 (GO) 术语和正统组.
主要成果:
- 转录组概况显示了跨物种的类似模式.
- 代谢过程基因,特别是与细胞壁碳水化合物和蛋白质相关的基因,是最能预测生长的.
- 整合一个时间滞后改善了RGR预测,突出了蛋白质合成的作用.
- 基于多个物种的转录组,GLMs有效地预测了生长特征.
结论:
- 基因表达,特别是代谢途径,可以准确预测水果生长特征.
- 多种类的转录基因数据与机器学习相结合,为了解植物发育提供了一种强大的方法.
- 考虑到时间动态,如蛋白质生产滞后,可以提高表型特征的预测准确性.
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