一个人工智能引导的框架揭示了控制微RNA链选择的保存特征
bioRxiv : the preprint server for biology
|June 4, 2025
概括
科学家们已经发现了细胞在基因调控过程中用来选择微RNA (miRNA) 链的规则. 这种由人工智能驱动的框架预测了跨物种的链使用情况,揭示了保存的生物原理.
科学领域:
- 分子生物学分子生物学
- 遗传学 遗传学是一种遗传学.
- 生物信息学是一种生物信息学.
背景情况:
- 微RNAs (miRNAs) 是基因表达的关键调节者.
- 确定哪个miRNA链 (5p或3p) 在生物发生过程中优先使用的机制尚未完全理解.
- 了解miRNA链选择对于破译基因调节至关重要.
研究的目的:
- 开发一个全面的框架来理解miRNA链选择.
- 确定控制5p和3p miRNA链之间的选择的分子规则.
- 为不同物种的miRNA链使用创建一个预测模型.
主要方法:
- 开发了一种高通量定量PCR (qPCR) 平台,用于*Caenorhabditis elegans*中精确的miRNA链量化.
- 构建了一个机器学习模型,在实验验证的链使用数据上进行训练.
- 在AI模型中整合了77个生物信息特征,用于预测.
主要成果:
- 开发的框架准确地预测了包括人类在内的*C. elegans*和脊椎动物中的miRNA链偏好.
- 确定了影响跨物种链选择的保存的组成和结构偏差.
- 证明miRNA链的选择是一个受管制的,取决于背景的过程,而不是随机的.
结论:
- 这项研究为miRNA链选择提供了第一个统一的,可概括的模型.
- 这些发现揭示了由特定规则支配的保存,可编程的基因调节层.
- 为研究界提供开放访问资源,包括数据库和预测工具.
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