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单细胞表体转录学的统计建模使得RNA甲基化轨迹和调控推断成为可能

Haozhe Wang¹, Yue Wang², Jingxian Zhou³

¹Department of Biosciences and Bioinformatics, Center for Intelligent RNA Therapeutics, Suzhou Key Laboratory of Cancer Biology and Chronic Disease, School of Science, XJTLU Entrepreneur College, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China; Department of Computer Science, University of Liverpool, L7 8TX Liverpool, UK.

|December 6, 2024

概括

我们开发了SigRM,这是一种用于分析单细胞表表谱学数据的计算框架. SigRM改善了RNA甲基化位点的检测和量化,推动了对基因表达调节的研究.

科学领域:

分子生物学分子生物学
基因组学就是基因组学.
计算生物学计算生物学

背景情况:

转录后RNA甲基化对于基因表达调节和各种生物过程至关重要.
单细胞技术允许转录组范围内的RNA甲基化概况,但数据分析仍然具有挑战性.

研究的目的:

开发一个计算框架,SigRM,用于分析大规模的单细胞表谱学数据集.
提高单细胞中RNA甲基化位点的检测和量化.

主要方法:

开发SigRM计算框架的开发.
应用SigRM来分析来自scDART-seq (SMART-seq2平台) 的单细胞表皮转录组学数据.
使用模拟和真实数据集对最先进的模型进行性能评估.

主要成果:

在RNA甲基化位点检测方面,SigRM的性能优于现有的模型.
SigRM为RNA甲基化水平提供了严格的量化指标.
该框架促进了下游分析,如轨迹推断和监管网络重建.

结论:

SigRM提供了一种有效的解决方案,用于挖掘大型单细胞表谱学数据集.
这一进步使我们能够更深入地了解RNA甲基化的动态和调节.
SigRM支持对生物过程和疾病中的RNA甲基化进行全面分析.

关键词:

通过RNA甲基化.细胞循环中的细胞循环.副本转录组 (epitranscriptome) 是一个副本转录组.基因监管网络基因监管网络 m(6) A6) 一个这就是scDART-seqq.一个单细胞的单细胞.统计建模统计建模轨迹推断的推断是指轨迹的推断.

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