在Homo SapiensmRNA 5'UTR序列中识别潜在的 рибо开关元素,使用正无标记机器学习
William S Raymond1, Jacob DeRoo1, Brian Munsky1,2
1School of Biomedical Engineering, Colorado State University Fort Collins, CO 80523, USA.
bioRxiv : the preprint server for biology
|December 16, 2024
概括
研究人员使用机器学习来识别人类mRNA中的潜在核糖突变. 这项研究提供了5'未翻译区域 (5'UTR) 的数据库,以指导未来人类核糖突变的实验发现.
科学领域:
- *RNA生物学 *RNA生物学
- * 计算生物学 * 计算生物学
- * 基因组学 是一个学科.
背景情况:
- * рибо开关是常见于原核生物,真菌,植物和酵母中的调节性RNA分子,但尚未在人类中发现.
- *最近的发现表明,人类转基因组中的类似于核糖突变器的机制引发了关于Homo sapiens中小分子依赖核糖突变器的问题.
研究的目的:
- *以计算方式识别人类mRNA5'未翻译区域 (5'UTRs) 中潜在的小分子依赖的核糖开关.
- * 开发和应用机器学习分类器来选人类5'UTR的大型数据集,以寻找riboswitch元素.
主要方法:
- *对来自RNAcentral (67,683个示例) 的已知 рибо开关序列和来自UTRdb (48,031个示例) 的人类5'UTR进行了训练,对20个未标记的正机器学习分类器进行了训练.
- * 使用序列和二次结构特征,使用交叉验证进行准确性评估 (75%-99%).
- *分析并根据分类器协议和与已知 рибо开关的相似性对已识别的5'UTR进行排名.
主要成果:
- *通过至少一个分类器,确定了15333个人类5'UTR作为潜在的 рибо交换机.
- * 发现436个5'UTR被所有20个分类器一致标记为潜在的 рибо开关.
- *将这些436个序列映射到类似的已知核糖突变器中,并将结果编译成在线数据库.
结论:
- *这项研究提出了一种计算方法,用于在人类基因组中发现新的核突变开关.
- * 为促进实验验证,提供了一个对潜在的人类核链开关的精心策划的数据库.
- * 这项工作显著推进了在Homo sapiens中寻找功能性核突开关的研究.
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