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在Spartina alterniflora中通过分析365个植物物种的共同进化信号,使用遗传学概况来分析与矿盐压力相关的基因

Shang Gao^1,2, Shoukun Chen^1,2,3, Maogeng Yang^1,2,4

¹State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China.

|December 18, 2023

概括

遗传学分析 (PP) 与机器学习相结合,在Spartina alterniflora中确定了盐应激基因. 这种方法凸显了PP作为植物基因组学和发现非生物应激反应基因的宝贵工具.

科学领域:

植物基因组学植物基因组学
进化生物学进化生物学
生物信息学是一种生物信息学.

背景情况:

遗传学概况 (PP) 利用共同进化的信息来预测基因功能.
它在植物基因组学中用于与压力相关的基因发现的应用仍然未得到充分探索.

研究的目的:

为了确定与盐的压力相关的基因在植物草 *Spartina alterniflora*.
评估PP与机器学习结合在植物功能基因组学中的潜力.

主要方法:

利用机器学习和PP与365种植物的进化数据.
专注于与Spartina alterniflora*中已知的盐应激相关基因共同演变的基因.
验证的离子载体基因功能在实验中得到了验证.

主要成果:

鉴定了富含离子运输,解毒和代谢途径的基因.
验证了参与Na+吸收的五个基因 (两个,三个转运器).
发现了可能参与盐度反应的*SaCPC1*和*SaCPC2*正义体,与昼夜节律和压力路径有关.

结论:

证明了通过PP利用进化信息来挖掘与盐压力相关的基因的可行性.
强调PP是植物功能基因组学的强大工具,有助于发现无生物应激耐受性的基因.

关键词:

进化信息是一种进化信息.机器学习机器学习人类遗传学概况分析与盐的压力相关的基因与盐的压力有关.斯巴尔蒂纳 (Spartina alterniflora) 是一种植物.

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