转移RM:在循环RNA中对转化增强N6-甲基氨酸 (m6A) 的弱监督学习
Lian Liu1, Xiujuan Lei1, Zheng Wang1
1School of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi 710119, China.
International journal of biological macromolecules
|March 1, 2025
概括
我们开发了TransRM,这是一种新的计算模型,用于预测N6-甲基氨酸 (m6A) 修改如何影响循环RNA (circRNA) 翻译. 这个工具有助于识别翻译的circRNAs,用于治疗开发.
科学领域:
- 分子生物学分子生物学
- 计算生物学 计算生物学
- 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.
背景情况:
- 循环RNAs (circRNAs) 通过与microRNAs和RNA结合蛋白的相互作用来调节基因表达.
- 一组circRNAs的子集表现出编码潜力,转化为功能性蛋白质.
- N6-甲基氨酸 (m6A) 修饰,特别是在起始编码子,可以作为影响circRNA转换的内部核糖体进入点 (IRES).
研究的目的:
- 开发第一个计算模型TransRM,用于预测m6A位点对circRNA转换的影响.
- 为弥合分析circRNA转化潜力的计算工具的差距,通过表皮转录组修改调制.
主要方法:
- 使用弱监督学习与两个卷积层提取RNA修饰特征.
- 利用双向封闭的循环单元来预测每个RNA修改对circRNA转换的贡献.
- 综合RNA修饰特征及其贡献使用随机森林算法来评估circRNA翻译概率.
主要成果:
- 在识别改进翻译的m6A网站时,TransRM表现出了高效率.
- 实现了0.9188.8的接收器操作特征曲线 (AUROC) 下的面积.
- 实现了0.9371.1的精度召回曲线 (AUPRC) 下的面积.
结论:
- TransRM提供了一种有价值的工具,用于理解表表转录体层的circRNA调节.
- 该模型有助于识别翻译的circRNAs,这对于开发基于circRNA的治疗方法和疫苗至关重要.
- 这项工作推进了circRNA研究领域,通过提供对翻译的表转录学控制的计算洞察力.
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