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在单细胞RNA-seq数据中使用SCINPAS识别实验支持的多A) 位

Youngbin Moon^1,2, Dominik Burri^1,2, Mihaela Zavolan^1,2

¹Computational and Systems Biology, Biozentrum University of Basel, Spitalstrasse 41, CH-4056 Basel, Switzerland.

NAR genomics and bioinformatics

|September 14, 2023

概括

通过使用单细胞RNA测序数据,SCINPAS识别了多化 (多A) 位点. 这种计算方法提高了转录组多样性研究的多样性 (A) 位点恢复分辨率.

科学领域:

基因组学就是基因组学.
分子生物学分子生物学
生物信息学是一种生物信息学.

背景情况:

替代多基化 (APA) 驱动哺乳动物的转录组多样性.
特定于细胞类型的APA规则要求提供定量多元 (A) 站点使用数据.
3'端单细胞RNA测序 (scRNA-seq) 提供了实验性多元A位点数据.

研究的目的:

开发一种用于从scRNA-seq数据中识别多个A位点的计算方法.
与标准软件相比,为了提高多个站点恢复的分辨率.
为了能够识别基因和非基因地区的多基因A) 位点.

主要方法:

开发了SCINPAS,用于scRNA-seq数据分析的计算工作流.
修改了读取分复制,以优先考虑远距离读取与多个A尾.
应用了SCINPAS来识别和量化多元 (A) 位点.

主要成果:

SCINPAS 增强了多样性 (A) 部位恢复分辨率.
在实验支持下确定了已知和新型多A) 位点.
在基因和非基因区域中,SCINPAS确定了多基因A位点.

结论:

SCINPAS从3'端scrRNA-seq数据准确地识别实验支持的多A位点.
模块化工作流允许与其他单细胞分析集成.
SCINPAS促进了转录组多样性和APA调节的研究.

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相关概念视频

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...