基于深度学习的研究,对叶绿体翻译调节序列的研究
Mohammad Ali Abbasi-Vineh1, Pär K Ingvarsson2, Naser Farrokhi1
1Department of Cell & Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, Iran.
Frontiers in plant science
|December 29, 2025
概括
一个新的深度学习模型分析了植物和藻类中的质细胞转化序列. 它准确地预测了序列起源和Shine-Dalgarno图案的存在,有助于合成生物学应用.
科学领域:
- * 分子生物学 * 分子生物学
- * 生物信息学是一门学科.
- * 合成生物学 * 合成生物学
背景情况:
- * 了解叶绿体转化调节序列对于合成生物学和基因工程至关重要.
- * 多种叶绿体表现出独特的序列结构,影响基因表达.
研究的目的:
- * 开发和应用混合深度学习模型来分类和分析来自植物和藻类的叶绿体5'非翻译区域 (UTR) 序列.
- * 调查这些序列中Shine-Dalgarno (SD) 图案的存在和影响.
- * 确定在藻类塑体工程中利用异质领导序列的策略.
主要方法:
- *采用混合深度学习模型,整合了卷积神经网络 (CNN),长期短期记忆 (LSTM),注意力和残留架构.
- * 该模型分析了300个核酸领导序列,这些序列位于起始编码子的上游.
- *使用了两个数据集:植物/藻类5' UTR和带/不带SD动机的序列.
主要成果:
- * 该模型实现了对分类起源 (植物与藻类) 和SD动机存在的高预测准确度.
- * 序列的一个子集显示了跨分类学模式,表明了异构序列识别的潜力.
- *在植物和藻类之间的初始编码子上游的前30bp中观察到显著的差异,并且有/没有SD动机.
结论:
- *这项研究提供了对叶绿体转化调节序列的宝贵见解,区分了植物和藻类的模式.
- * 提出了两种在藻类塑体工程中引入异质领导序列的策略,利用植物衍生序列或混合结构.
- *这种深度学习方法为预测和利用植物和藻类中异质领导序列提供了指导.
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