用于RNA拼接预测和设计的生成建模
Di Wu1, Natalie Maus1, Anupama Jha2
1Department of Computer and Information Science, School of Engineering, University of Pennsylvania.
bioRxiv : the preprint server for biology
|February 3, 2025
概括
我们开发了TrASPr+BOS,这是一种使用生成模型和贝叶斯优化来预测和设计用于组织特定替代拼接 (AS) 的RNA的AI工具. 这种方法增强了对基因调节的理解,并有助于治疗的发展.
科学领域:
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
背景情况:
- 替代拼接 (AS) 对于组织特异性基因调节至关重要,并与疾病有关.
- 拼接缺陷可以导致各种病理,突出需要预测和操纵工具.
- 了解和控制AS为新的治疗策略提供了潜力.
研究的目的:
- 介绍TrASPr+BOS,一个新的生成AI模型与贝叶斯优化相结合.
- 预测和设计RNA序列,以实现特定的取决于组织的替代拼接结果.
- 推进基于RNA的治疗方法的设计,并了解基因调节.
主要方法:
- 开发了TrASPr,一个多变压器生成AI模型,能够处理多种AS事件并将其推广到新的条件.
- 使用TrASPr作为一个神话来生成标记数据用于训练贝叶斯对拼接 (BOS) 算法的优化.
- 采用BOS来设计RNA序列用于特定条件的拼接调制.
主要成果:
- 在预测组织特异性拼接方面,TrASPr+BOS显著优于现有方法.
- 在组织特定的AUPRC (精度回忆曲线下的区域) 中达到2.4倍的增强.
- 使用dCas13技术识别和验证了新的组织特异性拼接变异和监管元素.
结论:
- TrASPr+BOS提供了一种强大而准确的方法,用于预测和设计用于组织特定替代拼接的RNA.
- 该模型成功地捕获了关键的组织特异性调节元素,推进了RNA治疗领域.
- 这种方法为研究基因调节和开发向治疗的研究人员提供了一种多功能工具.
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