转移学习可以通过纳米孔直接RNA测序来识别多种类型的RNA修饰
You Wu1, Wenna Shao1, Mengxiao Yan2
1Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Nature communications
|May 14, 2024
概括
深度学习框架TandemMod有效地从单个纳米孔直接RNA测序数据中识别多个RNA修饰. 这种工具通过实现跨物种和条件的全面分析来推进表观转录学.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 分子生物学分子生物学
背景情况:
- 纳米孔直接RNA测序 (DRS) 对于RNA修饰的识别至关重要.
- 在单个DRS样本中同时检测多个RNA修饰是具有挑战性的.
研究的目的:
- 开发TandemMod,一个可转移的深度学习框架,用于检测单个DRS数据中的多个RNA修改.
- 为培训和验证创建基准表体转录组数据集,并使用基准真相标签.
主要方法:
- 从cDNA库中生成的体外表转录组数据集,具有各种RNA修饰.
- 使用这些数据集开发和训练了深度学习框架TandemMod.
- 在体外转录和体内人体细胞系上验证了TandemMod,并应用了转移学习来进行新的修改.
主要成果:
- 在人体细胞系中,TandemMod精确地描述了m6A和m5C修饰位.
- 转移学习使得m7G, Ψ和 inosine的识别能够在减少数据和时间的情况下实现.
- 已证明跨物种 (大米) 的适用性以及识别三种RNA修饰类型的条件.
结论:
- TandemMod是一个高性能框架,用于从单个DRS样本中识别各种RNA修饰.
- 生成的数据集作为基于纳米孔的修饰识别方法的有价值的基准.
- TandemMod提供了一种多功能解决方案,用于在各种生物环境中进行表观转录学分析.
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