Department of Biochemistry and Molecular Biology
Sealy Centre for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Galveston, Texas, 77555.
G3 (Bethesda). 2019 Jun 5;9(6):1825-1830. doi: 10.1534/g3.119.400273.
Poly(A)-tail targeted RNAseq approaches, such as 3'READS, PAS-Seq and Poly(A)-ClickSeq, are becoming popular alternatives to random-primed RNAseq to focus sequencing reads just to the 3' ends of polyadenylated RNAs to identify poly(A)-sites and characterize changes in their usage. Additionally, we and others have demonstrated that these approaches perform similarly to other RNAseq strategies for differential gene expression analysis, while saving on the volume of sequencing data required and providing a simpler library synthesis strategy. Here, we present DPAC ( ifferential oly( )- lustering); a streamlined pipeline for the preprocessing of poly(A)-tail targeted RNAseq data, mapping of poly(A)-sites, poly(A)-site clustering and annotation, and determination of differential poly(A)-cluster usage using DESeq2. Changes in poly(A)-cluster usage is simultaneously used to report differential gene expression, differential terminal exon usage and alternative polyadenylation (APA).
聚腺苷酸化尾巴靶向 RNA 测序方法,如 3'READS、PAS-Seq 和 Poly(A)-ClickSeq,正成为随机引物 RNA 测序的热门替代方法,可将测序reads 仅聚焦到多聚腺苷酸化 RNA 的 3' 端,以鉴定 poly(A) 位点,并分析其使用情况的变化。此外,我们和其他人已经证明,这些方法在差异基因表达分析方面与其他 RNA 测序策略表现相似,同时节省了所需测序数据的量,并提供了更简单的文库合成策略。在这里,我们提出了 DPAC(differential poly(A)-clustering);这是一个用于聚腺苷酸化尾巴靶向 RNA 测序数据预处理、poly(A) 位点映射、poly(A) 位点聚类和注释以及使用 DESeq2 确定差异 poly(A) 聚类使用的简化流水线。poly(A) 聚类使用的变化同时用于报告差异基因表达、差异末端外显子使用和选择性多聚腺苷酸化 (APA)。