石巴:一种多功能计算方法,用于系统地识别跨平台差异性RNA拼接
Naoto Kubota1,2, Liang Chen3, Sika Zheng1,2
1Division of Biomedical Sciences, School of Medicine, University of California, Riverside, CA 92521, United States.
Nucleic acids research
|February 25, 2025
概括
我们开发了Shiba和scShiba,这两种新的计算方法用于分析替代的mRNA前拼接 (AS). 这些工具准确地量化跨RNA-seq平台和单细胞数据的拼接事件,减少错误阳性,并使即使在有限的样本上也能够进行可靠的分析.
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.
- 基因组学就是基因组学.
背景情况:
- 替代性mRNA前拼接 (AS) 产生了转录多样性,这对细胞类型变异至关重要.
- 准确量化AS事件对于理解基因调节至关重要.
- 现有的工具往往具有低灵敏度,高假阳性,或需要大量复制.
研究的目的:
- 开发一种全面的计算方法 (Shiba) 来分析跨RNA-seq平台的替代拼接事件.
- 将Shiba的功能扩展到单细胞RNA序列数据 (scShiba).
- 提高AS分析的准确性,灵敏性和可重复性,减少错误阳性.
主要方法:
- 开发了Shiba,集成了转录组装,拼接事件识别,读数和差异拼接分析.
- 在单细胞RNA-seq.中采用scsShiba的实践,使用伪泡方法进行集群级AS分析.
- 使用模拟数据和真实n=1RNA-seq数据集的验证,并应用于单细胞数据.
主要成果:
- 西巴准确地捕获注释和未注释的AS事件,具有高灵敏度和可重现性.
- 西巴有效地解决了交叉点读取不平衡,显著减少了假阳性.
- scShiba成功地确定了多巴胺基神经元和神经元亚型中的AS调节.
结论:
- 石巴和scShiba在各种RNA-seq平台上提供了替代拼接事件的稳健和可重复量化.
- 这些工具对于机械探索RNA拼接复杂性非常有价值,即使样本大小有限或单细胞数据有限.
- 这些方法可以在容器和管道中使用,确保可访问性和可重复性.
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